diff --git a/wafo/__init__.py b/wafo/__init__.py
index 48d6db0..c3a88ee 100644
--- a/wafo/__init__.py
+++ b/wafo/__init__.py
@@ -2,28 +2,28 @@ from __future__ import division, print_function, absolute_import
from .info import __doc__
-from . import misc
-from . import data
-from . import demos
-from . import kdetools
-from . import objects
-from . import spectrum
-from . import transform
-from . import definitions
-from . import polynomial
-from . import stats
-from . import interpolate
-from . import dctpack
-from . import wave_theory
-try:
- from . import fig
-except ImportError:
- print('fig import only supported on Windows')
+# from . import misc
+# from . import data
+# from . import demos
+# from . import kdetools
+# from . import objects
+# from . import spectrum
+# from . import transform
+# from . import definitions
+# from . import polynomial
+# from . import stats
+# from . import interpolate
+# from . import dctpack
+# from . import wave_theory
+# try:
+# from . import fig
+# except ImportError:
+# print('fig import only supported on Windows')
-try:
- from wafo.version import version as __version__
-except ImportError:
- __version__ = 'nobuilt'
+# try:
+# from wafo.version import version as __version__
+# except ImportError:
+# __version__ = 'nobuilt'
-from numpy.testing import Tester
-test = Tester().test
+# from numpy.testing import Tester
+# test = Tester().test
diff --git a/wafo/data/__init__.py b/wafo/data/__init__.py
deleted file mode 100644
index d9eb13b..0000000
--- a/wafo/data/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from __future__ import absolute_import
-from .info import __doc__
-from .info import *
diff --git a/wafo/data/atlantic.dat b/wafo/data/atlantic.dat
deleted file mode 100644
index 7786ff1..0000000
--- a/wafo/data/atlantic.dat
+++ /dev/null
@@ -1,582 +0,0 @@
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diff --git a/wafo/data/gfaks89.dat b/wafo/data/gfaks89.dat
deleted file mode 100644
index 5c07ddb..0000000
--- a/wafo/data/gfaks89.dat
+++ /dev/null
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diff --git a/wafo/data/gfaksr89.dat b/wafo/data/gfaksr89.dat
deleted file mode 100644
index f5b1ac7..0000000
--- a/wafo/data/gfaksr89.dat
+++ /dev/null
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- 1.5599200e+04 1.0943089e+00
- 1.5599600e+04 1.0254654e+00
diff --git a/wafo/data/info.py b/wafo/data/info.py
deleted file mode 100644
index 6eb1206..0000000
--- a/wafo/data/info.py
+++ /dev/null
@@ -1,485 +0,0 @@
-"""
-Data package in WAFO Toolbox.
-
-Contents
---------
-atlantic - Significant wave-height data recorded in the Atlantic Ocean
-gfaks89 - Surface elevation measured at Gullfaks C 24.12.1989
-gfaksr89 - Reconstructed surface elevation measured at Gullfaks C 24.12.1989.
-japansea - coastline map of The Japan Sea
-northsea - coastline map of The Nortsea
-sea - Surface elevation dataset used in WAT version 1.1.
-sfa89 - Wind measurements at Statfjord A 24.12.1989
-sn - Fatigue experiment, constant-amplitude loading.
-yura87 - Surface elevation measured off the coast of Yura
-
-
-
-This module gives gives detailed information and easy access to all datasets
-included in WAFO
-
-"""
-import numpy as np
-
-import os
-__path2data = os.path.dirname(os.path.realpath(__file__))
-
-__all__ = ['atlantic', 'gfaks89', 'gfaksr89', 'japansea', 'northsea', 'sea',
- 'sfa89', 'sn', 'yura87']
-
-_NANS = set(['nan', 'NaN', '-1.#IND00+00', '1.#IND00+00', '-1.#INF00+00'])
-
-
-def _tofloat(x):
- return np.nan if x in _NANS else float(x or 0)
-
-
-_MYCONVERTER = {}
-for i in range(2):
- _MYCONVERTER[i] = _tofloat
-
-
-def _load(file): # @ReservedAssignment
- """ local load function
- """
- return np.loadtxt(os.path.join(__path2data, file))
-
-
-def _loadnan(file): # @ReservedAssignment
- """ local load function accepting nan's
- """
- return np.loadtxt(os.path.join(__path2data, file), converters=_MYCONVERTER)
-
-
-def atlantic():
- """
- Return Significant wave-height data recorded in the Atlantic Ocean
-
- Data summary
- ------------
- Size : 582 X 1
- Sampling Rate : ~ 14 times a month
- Device :
- Source :
- Format : ascii
-
- Description
- ------------
- atlantic.dat contains average significant wave-height data recorded
- approximately 14 times a month in December-February during 7 years and
- at 2 locations in the Atlantic Ocean
-
- Example
- --------
- >>> import pylab
- >>> import wafo
- >>> Hs = wafo.data.atlantic()
- >>> np.allclose(Hs[:3], [ 5.48296296, 4.3615 , 5.26023256])
- True
-
- h = pylab.plot(Hs)
-
- Acknowledgement:
- ---------------
- This dataset were made available by Dr. David Carter
- and Dr. David Cotton, Satellite Observing Systems, UK.
- """
- return _load('atlantic.dat')
-
-
-def gfaks89():
- """
- Return Surface elevation measured at Gullfaks C 24.12.1989
-
- Data summary
- ------------
- Size : 39000 X 2
- Sampling Rate : 2.5 Hz
- Device : EMI laser
- Source : STATOIL
- Format : ascii, c1: time c2: surface elevation
-
- Description
- ------------
- The wave data was measured 24th December 1989 at the Gullfaks C platform
- in the North Sea from 17.00 to 21.20. The period from 20.00 to 20.20
- is missing and contains NaNs. The water depth of 218 m is
- regarded as deep water for the most important wave components.
- There are two EMI laser sensors named 219 and 220. This data set is
- obtained from sensor 219, which is located in the Northwest
- corner approximately two platform leg diameters away from
- the closest leg.
- Thus the wave elevation is not expected to be significantly
- affected by diffraction effects for incoming waves in the western sector.
- The wind direction for this period is from the south.
- Some difficulties in calibration of the instruments have been reported
- resulting in several consecutive measured values being equal or almost
- equal in the observed data set.
-
- This dataset is for non-commercial use only.
-
- Hm0 = 6.8m, Tm02 = 8s, Tp = 10.5
-
- Example
- -------
- >>> import pylab
- >>> import wafo
- >>> x = wafo.data.gfaks89()
- >>> np.allclose(x[:3, 1], [-0.19667949, -0.46667949, -0.38667949])
- True
-
- h = pylab.plot(x[:,0],x[:,1])
-
- Acknowledgement:
- ---------------
- This dataset were prepared and made available by Dr. S. Haver,
- STATOIL, Norway
-
- See also
- --------
- gfaksr89, northsea
-
- """
- return _loadnan('gfaks89.dat')
-
-
-def gfaksr89():
- """
- Return a reconstruction of surface elevation measured at Gullfaks C
- 24.12.1989.
-
-
- Data summary
- ------------
- Size : 39000 X 2
- Sampling Rate : 2.5 Hz
- Device : EMI laser
- Source : STATOIL
- Format : ascii, c1: time c2: surface elevation
-
- Description
- -----------
- This is a reconstructed version of the data in the GFAKS89.DAT file.
- The following calls were made to reconstruct the data:
-
- inds = findoutliers(gfaks89,.02,2,1.23);
- gfaksr89 = reconstruct(gfaks89,inds,6);
-
- The wave data was measured 24th December 1989 at the Gullfaks C platform
- in the North Sea from 17.00 to 21.20. The period from 20.00 to 20.20
- is missing in the original data. The water depth of 218 m is
- regarded as deep water for the most important wave components.
- There are two EMI laser sensors named 219 and 220. This data set is
- obtained from sensor 219, which is located in the Northwest
- corner approximately two platform leg diameters away from
- the closest leg.
- Thus the wave elevation is not expected to be significantly
- affected by diffraction effects for incoming waves in the western sector.
- The wind direction for this period is from the south.
- Some difficulties in calibration of the instruments have been reported
- resulting in several consecutive measured values being equal or almost
- equal in the observed data set.
-
- Hm0 = 6.8m, Tm02 = 8s, Tp = 10.5
-
-
- Example
- -------
- >>> import pylab
- >>> import wafo
- >>> x = wafo.data.gfaksr89()
-
- h = pylab.plot(x[:,0],x[:,1])
-
- See also
- --------
- gfaks89
- """
- return _loadnan('gfaksr89.dat')
-
-
-def japansea():
- """
- Return coastline map of The Japan Sea
-
-
- Data summary
- ------------
- Size : 692 X 2
- Sampling Rate :
- Device :
- Source : http://crusty.er.usgs.gov/coast/getcoast.html
- Format : ascii, c1: longitude c2: latitude
-
- Description
- -----------
- JAPANSEA.DAT contains data for plotting a map of The Japan Sea.
- The data is obtained from USGS coastline extractor.
-
- Example:
- -------
- #the map is seen by
-
- >>> import pylab
- >>> import wafo
- >>> map1 = wafo.data.japansea()
- >>> np.allclose(map1[1:4, 0], [ 141.960057, 142.058624, 142.103214])
- True
-
- h = pylab.plot(map1[:,0],map1[:,1])
- lon_loc = [131,132,132,135,139.5,139]
- lat_loc = [46, 43, 40, 35, 38.3, 35.7]
- loc = ['China','Vladivostok','Japan Sea', 'Japan', 'Yura','Tokyo']
- algn = 'right'
- for lon, lat, name in zip(lon_loc,lat_loc,loc):
- pylab.text(lon,lat,name,horizontalalignment=algn)
-
-
- # If you have the m_map toolbox (see http://www.ocgy.ubc.ca/~rich/):
- m_proj('lambert','long',[130 148],'lat',[30 48]);
- m_line(map(:,1),map(:,2));
- m_grid('box','fancy','tickdir','out');
- m_text(131,46,'China');
- m_text(132,43,'Vladivostok');
- m_text(132,40,'Japan Sea');
- m_text(135,35,'Japan');
- m_text(139.5,38.3,'Yura');
- m_text(139,35.7,'Tokyo');
- """
- return _loadnan('japansea.dat')
-
-
-def northsea():
- """
- NORTHSEA coastline map of The Nortsea
-
- Data summary
- -------------
- Size : 60646 X 2
- Sampling Rate :
- Device :
- Source : http://crusty.er.usgs.gov/coast/getcoast.html
- Format : ascii, c1: longitude c2: latitude
-
- Description
- -----------
- NORTHSEA.DAT contains data for plotting a map of The Northsea.
- The data is obtained from USGS coastline extractor.
-
- Example
- -------
- # the map is seen by
-
- >>> import pylab
- >>> import wafo
- >>> map1 = wafo.data.northsea()
- >>> np.allclose(map1[1:4, 0], [ 1.261996, 1.264064, 1.268171])
- True
-
- h = pylab.plot(map1[:,0],map1[:,1])
- lon_pltfrm = [1.8, 2.3, 2., 1.9, 2.6]
- lat_pltfrm = [61.2, 61.2, 59.9, 58.4, 57.7]
- pltfrm = ['Statfjord A', 'Gullfaks C', 'Frigg', 'Sleipner', 'Draupner']
- h = pylab.scatter(lon_pltfrm,lat_pltfrm);
- algn = 'right'
- for lon, lat, name in zip(lon_pltfrm,lat_pltfrm,pltfrm):
- pylab.text(lon,lat,name,horizontalalignment=algn); algn = 'left'
-
-
- lon_city = [10.8, 10.8, 5.52, 5.2]
- lat_city = [59.85, 63.4, 58.9, 60.3]
- city = ['Oslo','Trondheim','Stavanger', 'Bergen']
- h = pylab.scatter(lon_city,lat_city);
- algn = 'right'
- for lon, lat, name in zip(lon_city,lat_city,city):
- pylab.text(lon,lat,name,horizontalalignment=algn)
-
- # If you have the mpl_toolkits.basemap installed
- from mpl_toolkits.basemap import Basemap
- import matplotlib.pyplot as plt
-
- # setup Lambert Conformal basemap.
- m = Basemap(width=1200000,height=900000,projection='lcc',
- resolution='f',lat_1=56.,lat_2=64,lat_0=58,lon_0=5.)
- m.drawcoastlines()
- h = m.scatter(lon_pltfrm,lat_pltfrm);
- algn = 'right'
- for lon, lat, name in zip(lon_pltfrm,lat_pltfrm,pltfrm):
- m.text(lon,lat,name,horizontalalignment=algn); algn = 'left'
- m.scatter(lon_city,lat_city)
- algn = 'right'
- for lon, lat, name in zip(lon_city,lat_city,city):
- m.text(lon,lat,name,horizontalalignment=algn)
- """
- return _loadnan('northsea.dat')
-
-
-def sea():
- """
- Return Surface elevation dataset used in WAT version 1.1.
-
- Data summary
- ------------
- Size : 9524 X 2
- Sampling Rate : 4.0 Hz
- Device : unknown
- Source : unknown
- Format : ascii, c1: time c2: surface elevation
-
- Description
- -----------
- The wave data was used in one of WAFO predecessors, i.e. the Wave
- Analysis Toolbox version 1.1 (WAT)
- Hm0 = 1.9m, Tm02 = 4.0s, Tp2 = 11.5s Tp1=5.6s
-
- Example
- -------
- >>> import pylab
- >>> import wafo
- >>> x = wafo.data.sea()
- >>> np.allclose(x[:3,1],[-1.2004945 , -1.0904945 , -0.79049454])
- True
-
- h = pylab.plot(x[:,0],x[:,1])
- """
- return _load('sea.dat')
-
-
-def sfa89():
- """
- Return Wind measurements at Statfjord A 24.12.1989
-
- Data summary
- ------------
- Size : 144 X 3
- Sampling Rate : 1/600 Hz
- Device :
- Source : DNMI (The Norwegian Meteorological Institute)
- Format : ascii, c1: time (hours)
- c2: velocity (m/s)
- c3: direction (degrees)
- Description
- -----------
- The registration of wind speeds at the Gullfaks field
- started up on Statfjord A in 1978 and continued until 1990.
- The dataregistration was transferred to Gullfaks C in Nov 1989.
- Due to some difficulties of the windregistration on Gullfaks C in
- the beginning, they continued to use the registered data from
- Statfjord A.
- The windspeed is measured in (meter/second), 110 m above mean water
- level (MWL) and the wind direction is given in degrees for the data.
- The data are a mean value of every 10 minutes.
- Wind directions are defined in the meteorological convention, i.e.,
- 0 degrees = wind approaching from North, 90 degrees = wind from East, etc.
- This dataset is for non-commercial use only.
-
- Example
- -------
- >>> import pylab
- >>> import wafo
- >>> x = wafo.data.sfa89()
-
- h = pylab.plot(x[:,0],x[:,1])
-
- Acknowledgement
- ----------------
- These data are made available by Knut A. Iden, DNMI.
-
- See also
- --------
- northsea
- """
- return _load('sfa89.dat')
-
-
-def sn():
- """
- Return SN Fatigue experiment, constant-amplitude loading.
-
-
- Data summary
- ------------
- Size : 40 X 2
- Source : unknown
- Format : ascii, c1: Amplitude MPa c2: Number of cycles
-
- Description
- -----------
- A fatigue experiment with constant amplitudes at five levels:
- 10,15,20,25 and 30 MPa. For each level is related 8 observations of
- the number of cycles to failure.
-
- The origin of the data is unknown.
-
- Example
- -------
- >>> import pylab
- >>> import wafo
- >>> x = wafo.data.sn()
-
- h = pylab.plot(x[:,0],x[:,1])
-
- See also
- --------
- The same data appear in the directory wdemos/itmkurs/
- as SN.mat.
-
- """
- return _load('sn.dat')
-
-
-def yura87():
- """
- Return Surface elevation measured off the coast of Yura.
-
-
- Data summary
- -----------
- Size : 85547 X 4
- Sampling Rate : 1 Hz
- Device : ultrasonic wave gauges
- Source : SRI, Ministry of Transport, Japan
- Format : ascii, c1: time (sec) c2-4: surface elevation (m)
-
- Description
- -----------
- The wave data was measured at the Poseidon platform
- in the Japan Sea from 24th November 1987 08.12 hours to 25th November
- 1987 07.57 hours. Poseidon was located 3 km off the coast of Yura
- in the Yamagata prefecture, in the Japan Sea during the measurements.
- The most important wave components are to some extent influenced by the
- water depth of 42 m. The data are measured with three ultrasonic wave
- gauges located at the sea floor and the relative coordinates of the
- gauges are as follows (x-axis points to the East, y-axis points to
- the North):
- X (m) Y (m)
- c2: -4.93, 25.02
- c3: 5.80, 92.12
- c4: 0.00, 0.00
-
- This dataset is for non-commercial use only.
-
- Hm0 = 5.1m, Tm02 = 7.7s, Tp = 12.8s
- Example
- -------
- >>> import pylab
- >>> import wafo
- >>> x = wafo.data.yura87()
-
- h = pylab.plot(x[:,0],x[:,1])
-
- Acknowledgement:
- -----------------
- This dataset were prepared and made available by Dr. Sc. H. Tomita,
- Ship Research Institute, Ministry of Transport, Japan.
-
- See also
- --------
- japansea
- """
- return _load('yura87.dat')
-
-
-if __name__ == '__main__':
- import doctest
- doctest.testmod()
diff --git a/wafo/data/japansea.dat b/wafo/data/japansea.dat
deleted file mode 100644
index a9fc01f..0000000
--- a/wafo/data/japansea.dat
+++ /dev/null
@@ -1,692 +0,0 @@
-nan nan
-141.960057 40.022926
-142.058624 39.818752
-142.103214 39.640392
-142.157191 39.469073
-142.136070 39.332957
-142.070358 39.229696
-142.046890 39.074805
-141.927201 38.955116
-141.805166 38.948076
-141.727720 38.765023
-141.638541 38.542073
-141.638541 38.368408
-141.629153 38.316777
-141.396817 38.333205
-141.143358 38.194742
-141.054179 37.934243
-141.087034 37.671397
-141.143358 37.453142
-141.143358 37.143360
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-140.854698 36.603588
-140.812455 36.533182
-140.756131 36.258603
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-140.887553 35.791583
-140.922756 35.683628
-140.767865 35.603836
-140.589505 35.359765
-140.479204 35.162631
-140.237480 35.007740
-nan nan
-139.984022 35.233036
-140.016877 35.369152
-140.148300 35.495881
-140.192890 35.622610
-nan nan
-139.972288 39.161638
-140.070855 39.323569
-140.138913 39.530091
-140.148300 39.750693
-140.016877 39.844567
-nan nan
-140.237480 35.007740
-139.993409 34.862236
-139.951166 35.007740
-139.984022 35.233036
-nan nan
-140.192890 35.622610
-139.951166 35.622610
-139.894842 35.451291
-139.805662 35.305788
-139.784541 35.153243
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-nan nan
-140.016877 39.844567
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-nan nan
-138.604083 38.272187
-138.580614 38.098521
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-138.514903 37.812208
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-138.449192 38.204129
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-nan nan
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diff --git a/wafo/data/northsea.dat b/wafo/data/northsea.dat
deleted file mode 100644
index a28abc2..0000000
--- a/wafo/data/northsea.dat
+++ /dev/null
@@ -1,60646 +0,0 @@
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-nan nan
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-nan nan
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-nan nan
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-nan nan
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-nan nan
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-nan nan
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-nan nan
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-nan nan
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-nan nan
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-nan nan
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-nan nan
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-nan nan
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-nan nan
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-nan nan
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-nan nan
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-nan nan
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-nan nan
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-nan nan
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-7.304524 53.444476
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-nan nan
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-nan nan
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-10.956493 53.652758
-nan nan
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-10.767866 53.777434
-nan nan
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-10.905743 53.946992
-10.905449 53.945232
-nan nan
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-10.808936 53.976621
-10.806589 53.974568
-10.805416 53.972221
-nan nan
-10.697755 53.876587
-10.699808 53.874534
-10.701862 53.872187
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-10.687781 53.875707
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-10.692768 53.878934
-10.696875 53.879814
-10.697755 53.876587
-nan nan
-10.376532 53.990409
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-10.368024 53.991876
-10.373305 53.990996
-10.376532 53.990409
-nan nan
-10.626176 54.147647
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-10.639377 54.147354
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-10.617375 54.145007
-10.617082 54.147940
-10.619429 54.150580
-10.623243 54.149994
-10.626176 54.147647
-nan nan
-10.602708 54.185196
-10.607401 54.184903
-10.612682 54.184316
-10.617962 54.184316
-10.622363 54.183729
-10.624709 54.181383
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-10.621189 54.176396
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-10.611802 54.168182
-10.610922 54.164955
-10.610922 54.162021
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-nan nan
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-nan nan
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-nan nan
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-nan nan
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-nan nan
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-nan nan
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-5.965948 52.708450
-nan nan
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--2.964053 54.414013
--2.958479 54.413133
--2.955839 54.411959
--2.952612 54.409319
--2.948505 54.405505
-nan nan
--3.140652 54.582398
--3.141532 54.577998
--3.143879 54.574478
--3.146813 54.571251
--3.149746 54.568317
--3.151506 54.564503
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--3.163240 54.588559
--3.161774 54.592665
--3.158547 54.595892
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--3.154146 54.594426
--3.151800 54.591785
--3.147106 54.589439
--3.144466 54.586798
--3.140652 54.582398
-nan nan
--4.000000 56.578571
--3.999007 56.578381
--4.000000 56.577290
-nan nan
--3.588898 57.625950
--3.594765 57.626537
--3.601512 57.627710
--3.610019 57.628297
--3.615300 57.626830
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--3.634368 57.637977
--3.627034 57.637977
--3.620580 57.639151
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--3.610606 57.642671
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--3.613246 57.649712
--3.617353 57.652352
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--3.611193 57.653232
--3.609139 57.650005
--3.605325 57.647365
--3.598578 57.646191
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--3.589191 57.642084
--3.584497 57.639738
--3.580684 57.637097
--3.577750 57.634457
--3.575697 57.631230
--3.576577 57.627417
--3.583617 57.626830
--3.588898 57.625950
-nan nan
--2.482365 56.691029
--2.486472 56.692790
--2.494686 56.693376
--2.503193 56.693963
--2.508473 56.695430
--2.511114 56.698657
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--2.507887 56.710097
--2.503193 56.711858
--2.498499 56.713911
--2.492339 56.714791
--2.486472 56.715965
--2.477965 56.715378
--2.475031 56.713618
--2.475911 56.713618
--2.471217 56.711564
--2.469457 56.708337
--2.469457 56.704230
--2.471804 56.700417
--2.468284 56.698070
--2.461243 56.698657
--2.455376 56.699537
--2.449509 56.700417
--2.445989 56.700417
--2.451269 56.698657
--2.457430 56.697777
--2.462710 56.696310
--2.467404 56.694256
--2.472684 56.692790
--2.482365 56.691029
-nan nan
-8.229471 56.178539
-8.233871 56.175606
-8.237978 56.171499
-8.245019 56.168272
-8.252059 56.165045
-8.258806 56.161818
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diff --git a/wafo/data/sea.dat b/wafo/data/sea.dat
deleted file mode 100644
index 06cc49b..0000000
--- a/wafo/data/sea.dat
+++ /dev/null
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diff --git a/wafo/data/sea.m b/wafo/data/sea.m
deleted file mode 100644
index 36c2052..0000000
--- a/wafo/data/sea.m
+++ /dev/null
@@ -1,16 +0,0 @@
-%SEA Surface elevation dataset used in WAT version 1.1.
-%
-% CALL: xn = load('sea.dat');
-%
-% Size : 9524 X 2
-% Sampling Rate : 4.0 Hz
-% Device : unknown
-% Source : unknown
-% Format : ascii, c1: time c2: surface elevation
-% Description :
-% The wave data was used in one of WAFO predecessors, i.e. the Wave
-% Analysis Toolbox version 1.1 (WAT)
-%
-% Hm0 = 1.9m, Tm02 = 4.0s, Tp2 = 11.5s Tp1=5.6s
-%
-% See also
diff --git a/wafo/data/sfa89.dat b/wafo/data/sfa89.dat
deleted file mode 100644
index 8411859..0000000
--- a/wafo/data/sfa89.dat
+++ /dev/null
@@ -1,144 +0,0 @@
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diff --git a/wafo/doc/__init__.py b/wafo/doc/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/wafo/doc/tutorial_scripts/WAFO Chapter 1.ipynb b/wafo/doc/tutorial_scripts/WAFO Chapter 1.ipynb
deleted file mode 100644
index e5aa752..0000000
--- a/wafo/doc/tutorial_scripts/WAFO Chapter 1.ipynb
+++ /dev/null
@@ -1,462 +0,0 @@
-{
- "metadata": {
- "name": "WAFO Chapter 1"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
- {
- "cells": [
- {
- "cell_type": "heading",
- "level": 1,
- "metadata": {},
- "source": [
- "CHAPTER 1 demonstrates some applications of WAFO"
- ]
- },
- {
- "cell_type": "raw",
- "metadata": {},
- "source": [
- "CHAPTER1 gives an overview through examples some of the capabilities of WAFO. WAFO is a toolbox of Matlab routines for statistical analysis and simulation of random waves and loads. The commands are edited for fast computation.\n"
- ]
- },
- {
- "cell_type": "heading",
- "level": 2,
- "metadata": {},
- "source": [
- "Section 1.4 Some applications of WAFO"
- ]
- },
- {
- "cell_type": "heading",
- "level": 3,
- "metadata": {},
- "source": [
- "Section 1.4.1 Simulation from spectrum, estimation of spectrum "
- ]
- },
- {
- "cell_type": "raw",
- "metadata": {},
- "source": [
- "Simulation of the sea surface from spectrum. The following code generates 200 seconds of data sampled with 10Hz from the Torsethaugen spectrum."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import wafo.spectrum.models as wsm\n",
- "S = wsm.Torsethaugen(Hm0=6, Tp=8);\n",
- "S1 = S.tospecdata()\n",
- "S1.plot()\n",
- "show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "display_data",
- "png": 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NmlW2MJXnDP71L+XDdvly1V7SLDNnAhERwP/8j+xKiMiWqTZnEBQUhI0bN6Ko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\niorCjRs3cOnSJak1AepPcPf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- }
- ],
- "prompt_number": 5
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import wafo.objects as wo\n",
- "xs = S1.sim(ns=2000, dt=0.1)\n",
- "ts = wo.mat2timeseries(xs)\n",
- "ts.plot_wave('-')\n",
- "show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "display_data",
- "png": 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IR6gNrGRnq7ekmzVTVuhHr0CNK8T99m2aYt+qlXmbHHF3leUu5ZaR+yARrwUx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\nfOB4/U09iIwsO9Hr6FH9HqxaxF202r2hKrbBYD1vQolLRsR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dbBwAAAAASUVORK5CYII=\n"
- }
- ],
- "prompt_number": 6
- },
- {
- "cell_type": "heading",
- "level": 4,
- "metadata": {},
- "source": [
- "Estimation of spectrum "
- ]
- },
- {
- "cell_type": "raw",
- "metadata": {},
- "source": [
- "A common situation is that one wants to estimate the spectrum for wave measurements. The following code simulate 20 minutes signal sampled at 4Hz and compare the spectral estimate with the original Torsethaugen spectum.\n"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "clf()\n",
- "Fs = 4; \n",
- "xs = S1.sim(ns=fix(20 * 60 * Fs), dt=1. / Fs) \n",
- "ts = wo.mat2timeseries(xs) \n",
- "Sest = ts.tospecdata(L=400)\n",
- "S1.plot()\n",
- "Sest.plot('--')\n",
- "axis([0, 3, 0, 5]) # This may depend on the simulation\n",
- "show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "display_data",
- "png": 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A34dw++1NHGATiYiA/fvVjkII0RwMSgYbN26sSQBarRZFUWqaiQYOHMjAgQMp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\n/Xg6ejI4ZDDXdrgW1zauLXY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- }
- ],
- "prompt_number": 7
- },
- {
- "cell_type": "heading",
- "level": 3,
- "metadata": {},
- "source": [
- "Section 1.4.2 Probability distributions of wave characteristics."
- ]
- },
- {
- "cell_type": "raw",
- "metadata": {},
- "source": [
- "Probability distribution of wave trough period: WAFO gives the possibility of computing the exact probability distributions for a number of characteristics given a spectral density. In the following example we study the trough period extracted from the time series and compared with the theoretical density computed with exact spectrum, S1, and the estimated spectrum, Sest.\n"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "clf()\n",
- "import wafo.misc as wm\n",
- "dtyex = S1.to_t_pdf(pdef='Tt', paramt=(0, 10, 51), nit=3)\n",
- "dtyest = Sest.to_t_pdf(pdef='Tt', paramt=(0, 10, 51), nit=3)\n",
- "\n",
- "T, index = ts.wave_periods(vh=0, pdef='d2u')\n",
- "bins = wm.good_bins(T, num_bins=25, odd=True)\n",
- "wm.plot_histgrm(T, bins=bins, normed=True)\n",
- "\n",
- "dtyex.plot()\n",
- "dtyest.plot('-.')\n",
- "axis([0, 10, 0, 0.35])\n",
- "show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "display_data",
- "png": 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v0QOABdELmL13NruG7cLP1a+QAUR5eKHFCzTyeZ8ePp64qB1GCAMiIyOJjIws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\ngg==\n"
- }
- ],
- "prompt_number": 8
- },
- {
- "cell_type": "heading",
- "level": 3,
- "metadata": {},
- "source": [
- "Section 1.4.3 Directional spectra"
- ]
- },
- {
- "cell_type": "raw",
- "metadata": {},
- "source": [
- "Here are a few lines of code, which produce directional spectra with frequency independent and frequency dependent spreading."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "clf()\n",
- "plotflag = 1\n",
- "Nt = 101; # number of angles\n",
- "th0 = pi / 2; # primary direction of waves\n",
- "Sp = 15; # spreading parameter\n",
- "\n",
- "D1 = wsm.Spreading(type='cos', theta0=th0, method=None) # frequency independent\n",
- "D12 = wsm.Spreading(type='cos', theta0=0, method='mitsuyasu') # frequency dependent\n",
- "\n",
- "SD1 = D1.tospecdata2d(S1)\n",
- "SD12 = D12.tospecdata2d(S1)\n",
- "SD1.plot()\n",
- "SD12.plot()#linestyle='dashdot')\n",
- "show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "display_data",
- "png": 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zcmHae7DvNAxsB96ukJIFhy5AoQH+0wPG9oEuzSp+yelcHGzcq4y3928LSydC\ngLeyby1J7Ceb9TQlg80kshoHmlHABdzpTwCvcJBC5hHNd7TAAw2LN8OuoxD+Bthqy687Pj6blSv/\nYv36Y4we3ZqXXupFQEDZaZ8lSbqzlRU7rQ74AMnJyeh0OsssnaCgoFvXwlJUZ8AHePjhb+jVK4hZ\ns7qU2B6ZDPtOQUYe1HNVhmlCAyv3JmtuASz9Fj7+CT6ZBQ90VXLvT+cSzXHgOfzQiwQ+WPM7Wzdn\nkpZqJKynP5dfbsDq4JZ0x4XtB+GJj+DQ8vJnEp09m8ry5Qf47ruzTJjQlrlze8hAL0l1QJmx05oH\nANu2bRNNmjQRjo6OIiQkRKhUKtGyZctb8WyhXFY275YJD48UoaHvC7PZXOV1/XVOCP9JQqzarnzO\nEAYxTJwRbxVGiwce+FJ06fKJ+OmnCPHZ3xEiaMFm4ea9VHz11UlxOlp5SPvXubLLPnw4Xgwdukn4\n+CwTixaFi7S0vCq/HkmSao6yYqdVETUsLEykpqaKdu3aCSGE+PXXX8XkyZNvXevKUN0B32w2i7Zt\n14itW89WS31RyUI0flSID3cqnxONOhE88lMRMOwT8Z4+TswUl0Rv8Y/4U2SJ48cThX/ASuHZ9Q/x\nv1/KKC8qQ4we/Y3w81sh3n//oMjP11fLdUiSVLOUFTutmrWt1Wrx9vbGbDZjMpno168fR44cuemf\nHVOmTMHX15ewsLCbLutWUKlUrFgxiJkzd5GaWvUpI4J94OfX4I0tsOFX2LjyCAEJghWbh2LQquiH\nG7toSXdcadi0Pp5DpmK+fBj95ZLvQBgMJpYs2U/Hjmtp3tyLCxdmMXNmFxwcKhjclySpTrFqlo6H\nhwc5OTn06tWLsWPH4uPjg7Oz801XPnnyZJ588kkmTJhw02XdKv37N+KRR8J4+OFv+L//exh3d3ur\nz9XrTWRm6jCZzNjba3B1tcPGpvzv1Ia+StDvMzuNgl//4MSx6TSy9yhxTGwqPLQEend05clnx9Gn\nz3qaNfOid+9gTp5MZsKErfj6OnH48HQaNvQooyZJkuo6qx7a5uXlYW9vj9ls5osvviA7O5uxY8fi\n5XXzuQeioqIYOnQoJ0+evL5x1fzQ9l8Gg4lnn93Nzp0XeeWV3gwfHloiJ7zBYOLixXSOHUvk6NFE\njh9P4ty5NFJT83F3t0ejUaPTGcnL0+Pr60zjxh60bFmPDh0acNddgbRo4W158A0ghOCu3hu5aG7G\ng2O68epi834SAAAgAElEQVTDEFgPrmTDxnB4cwvMfRCeG648KP7hh4tMn/49M2Z0ZPXqQyxdOoDJ\nk9uVKFOSpLqr0rN0jEYjAwcOZO/evVXSsJoY8P+1e/cl3n33L/btiyYoyA1XVzsyMnTExGQRGOhK\n27b16dixAe3b1yc0tB4BAa4l0hIYDCbi43OIiEjn9OkUjhxJ5LffonBzs+fFF+9izJgw1GoVX399\nmtde+43f/niMN75Rs+5nZS6/Tg+D2sOro6FNyNV26fUmOnT4mJiYLI4efZSmTaso6Y8kSXekm5qW\n2b9/f7799lvc3d0rOvSGVRTwi6dn7tu3L3379r3lbahIYaGRixfTyc4uxN3dnkaNPLC3r9z6gWaz\n4JdfLjN//l5UKli+fCAjRmxh69bRdOsWAIDBCFdywN0J7K9JR5+bq2fUqC1otWpycw107x7A4sV3\n3+wlSpJ0BwsPDyc8PNzyedGiRZUP+MOGDePYsWMMGjQIR0dH5USVitWrV990Q2tiD18Iwbp1x1iy\n5HfuvrshCxf2wd//1s5fN5sFH310mKef3k2/fiH88MNYy5CMwQhf7oO4K9C9OfRro5wTG5vFAw98\nRbt29fn44/vJyNDRocPHfPLJUIYMaXpL2ydJ0p2rrNhpVTd1+PDhDB8+3BKQhBC1erz4669Ps2LF\nAdatG8bOnRfp0+czfvtt0i0N+mq1CkdHW4KD3UhKyuXBBzfz8cf3Y+vkTK8XoYEndGwM096Hvq0F\nvdxP8sJzu5k7tztz5/ZApVLh4+PEpk3/YdSoLRw6NJ2gILdb1j5JkmqhKp0MWoHRo0eLBg0aCFtb\nWxEQECA+/fTTEvtvV/N6914vtmw5bfm8ZMl+0bbtGpGXd+vmtZ87lyq8vd8W//yTJAoLjeKll34R\nHl7LRMjoNPHEBwYhhBAGg0l8uuG0cBkQI3zuPiUOHYortaylS38XXbp8IgoLjbesfZIk3bnKip3l\nRtTp06dXWLA1x1TW7Qj4ycm5ws1tSYngaTabxfjx/yeGDt0kjEbTTddx9myqCAhYKdavP1Zi+9yP\n8oXf8BTh7rFUtG27Rri7vyV69FgnNn97UTSdYRZf7y+9PLPZLIYN+1I8+eSum26bJEl3vrJiZ7lj\n+PXq1WPMmDHljqP/8MMPRERE3PJfHnB7xvA3bjzB1q3n+fbbUSW2GwwmBg36nJ49g3j99X6VLn/f\nvmhGjdrCW28NYNKkdpbt5+Pgrhfg6DtgL3JJSMihQQMX6tdX3nfYfxrGroBza8DR7vpyMzN1dO78\nCa++2pvx49tWun2SJN35KjWGv2zZsgrH6nv16nVzLath9u2L4e67Q67brtXa8NVX/6FTp0/o2TOQ\nwYOb3FC5QghWrz7IG2/s54svhjNwYOMS+59eBy+PUt6+BWd8fUu+2NarlZIKecVWeOXh68t3d7fn\nu+8epl+//xEW5ku7dpVc2FaSpNqr2n5jVMLtaF6PHutEeHhkmft/+eWyCAhYKdLTrV8hKjU1Tzzw\nwJeiQ4ePxaVL6dft33NciEbThSgsekSgF2axT2SJv0WOZWUrIYSISFASp2WVkwvtyy9PikaNVskV\nrCSpDisrdlZiueva7fz5NFq08C5z/913N2T48FDGj/8Oo9Fcblm5uXqWLv2dli0/oEkTT/78cwqN\nGpVMfWA0wbOfKqtW2WohGT33coYPSWQeMTzKJQpR6mncQMnL/9/dZdc5enRrhg1rzpQp227rS2uS\nJNU8MuAXk59vIC/PgI+PU7nHLV8+EL3exJw5P5YaVFNS8li0KJxGjVbx999J/PbbJJYvH4Sd3fUj\naKu/VxZWGXkXFGDmcS4zCm++pDk7CcUZG14hBoFSz8x74aMflaUVy/LWW/2Jisrks8+O39gNkCSp\nVrvhgG8ymcjOzq6Kttx2sbFZBAS4VvjcQqu14ZtvRrFvXzTvvXcIAKPRzO7dl3jkkW9p1uw94uJy\n2LdvMps3jyA0tF6p5UQmK3lyPnxMyZHzKckEYcc0fACwQcUSgjlLAftR7nmPUNDaKA9xy2Jnp2HD\nhod4/vk9REdnVuJOSJJUG1kV8MeMGUN2djZ5eXmEhYURGhrK22+/XdVtq3axsdkEBlr3cpWrqx3f\nfz+Gt976nY4d1+Lnt4KXX/6Vbt0CuHx5Dp98MrTcoSGjCcathJdGQjN/SMHA56TyHH6ouPqFY4+a\nmdTnA5IQCFQqmDwA1v9SfvvatPHlmWe6MWPGDjm0I0kSYGXAP3PmDK6urmzdupUhQ4YQFRXFxo0b\nq7pt1S42NovAQOvfVg0JcefMmZmsWXMfBw9O4/Dh6cye3RVPz/IXBhcC5n4Kjrbw1DBl24ck8h+8\n8Of6OZeDcCcfMwfIAWBcX/juL2W5xPLMnduDpKRcvvrqlNXXJElS7WVVwDcajRgMBrZu3crQoUPR\narW1MrVCSkoevr4lx+9TMmH2Wuj0DMz4QJkvX5y7uz1duvhbnYfeZFKC/W+n4esXQK2GBPT8RCZT\n8cWMnihe4CS9ieRpTOShRsUYvNlGOgD1PaBzE9h9rPy6tFobVq8ewssv/4rBYLL6PkiSVDtZFfBn\nzJhBSEgIubm59O7dm6ioKNzcal/elvR0XYneuRDwwBtQaIB3pkKAN/R8EV7aAHrDjZd/IR4GvAon\nopRFTzyKptp/SjL/wQsPNMSzDBM5NGMTNrhwiRkIBANx5zey0RfN2HmgK2w7VHGdvXsH06iRBxs3\n/nPjDZYkqXapzBxPs9ksDAZD5SeJWqmSzau0adO2i7Vrj1g+/3hUiNAnhDAWS1GTnCHE0NeF6PKs\nENEp1pV7JkaI6e8J4fWIEMv+r2R58aJQdBMnRKrQi2xxQJwSA4VBZAkhhDALkzgrhosM8ZMQQohx\n4rz4VWQKIYSISRHC8xEhDFakz9m3L0o0bPiu0Otlrh1JqgvKip1WZcvU6XR8++23REVFYTQaAeXV\n3VdffbUKv4qq35Ur+SV6+K9vhlcfBhubq8f4uMO2l5U3XrvOhc3PQe/WJcsxm+FcHPx0DLb8oczG\neXQwnP0Q6l3zw+gjkhiFN15ouMgHNOBJNCgPjlWo8eNp4ngTN+5mMB7sJpN+uBFYT1kecf/pq+mT\ny9KrVzDBwe5s3nyaceMqOFiSpFrLqoD/wAMP4O7uTseOHbG3t36N1ztNRoYODw8l4Kdlw6kYZX78\ntVQqmPuQsgrViKXg5wkNPEBngNQsJcD7uEH/tjB/lPKylLaUOx2Jjj1ksouW5HEMA6l4MKTEMS7c\nhRoncjlMPzrw8b+zdVAxtDP88HfFAR9g7tzuvPbaPhnwJakOsyrgx8fH89NPP1V1W2673Fw9Li7K\nElMnIqFtSMne/bUGtYfo/8LJaOULwt4WvFyUnrerY/l1CQSLiWMG9XFHQxRfUo+xqNDAyR2QFglh\n96PybogbfclmP350wwYV8egJwI6+YfDceuuubfDgJkydup2IiHSaNPG07iRJkmoVqx7a9ujRg3/+\nqf0P/XJyCnFyKhbwG1Z8joMddGkG93aCu9so51QU7AG2ks4VDIylHkYyyWYfngyF87/CF49C1EH4\n4F7Q5+NKb7LZjwoVbXHkOHmAkkztbBxk5VVcn0ajZuTIVnKKpiTVYVYF/P3799OxY0eaNWtGWFgY\nYWFhtGlT+4YGSvTwo0oP+NEUcpZ8sjBWup4j5LKCBJYRggYV6WzHlT5odBrYMAXGrYPJn0NQJ/i/\n53GkFUYyKCSedjhxoijg22mhS1PYf8a6ekeNasmWLVYeLElSrWPVkM4PP/wAUGKJw9ooN1ePs7MS\n8P+Jgln3Xd2XjoHHuUwSejzREIeeRthzD+4Mwr3UF6auJRBsJZ3lJLCcYJrigEBwhS0E8Cr89T8I\n6giti8bxR74LrzZGNWwxLo53kcMftOVefuBquoR+YbD3H7i/c8XXd9ddQaSm5nHhwhWaNfO6kVsj\nSVItYFXADwkJ4fjx4+zfvx+VSkWvXr1o27b2LbKRn2/A0VELQHQKNPJVtgsEC4ilPU48RzNsUKHH\nzBFy+ZFMRnEBf2wZgBvdcaE5DtgW/XhSArqRA+SwmTSyMLGBpjRGefhdwBnMGHCmExx5Bga/eLVB\nzl7QqDuc/xXH9mEUcI6m/IfL6DAjUKOiUxNY9p1116dWqxg4sDHh4VEy4EtSHWTVkM6qVasYN24c\nqampJCcnM27cOFavXl3VbatWJpMZo9GMra0NOj3k6cDTRdkXTjYxFPIMftgU5bmxRU0PXHmNIH6j\nNU/jRypG5hNDV/6hDycZwGm6cZKhnOVHMhiNN9/RwhLsATLYhQdDUGUlQeIpaDGgZMNaDIBze3Cg\nCQVcxAUbnFCTjPLmV6sgOBVt/XX26BHAH3/E3tS9kiTpzmRVD/+///0vBw8exMlJSTvw4osv0q1b\nN2bPnl2ljatOhYUm7O01qFQqEjOU9AX/Zo/4gQweoZ6l134tDSq640J3lG8IPWYyMGJA4IwNbtiU\nSIj2L4GZDH6gMWvhxFZofR9o7dDn5qJ1clKG0FoMgP0fY89r6IhAIGiEPZfR0QBbArzBaIbEdGhg\nxeSbHj0CWbHiQKXvkyRJdy6r0yOr1epS/15b6HRG7O2V77/EdGVePSjBex/Z3I31qSRsUeOLLQHY\n4Y6m1GAPkM1vaPDCgSZwYhu0fRBdZiarGjbk3eBgLu7aBX5hkJeOJlOHCjVG0iwBH5QvpQ6N4O9L\n1rWtZct6JCfnkZpqxdQeSZJqFasi9+TJk+natSsLFy5kwYIFdOvWjSlTplR126qVTme0LFByJUdZ\nlATgNPkEYEs9tCWOFwjyOEEiH5DP2UrVmcYWfBgHJiNE7IcWAzj26ac0HjSIfq+/zu9vvaVkVwto\niyrhNHY0REcUwdgRg95STlgInI6xrk4bGzUdOjTg2LGkSrVZkqQ7l1UB/5lnnmH9+vV4eHjg5eXF\nZ599xtNPP13VbatWBoMJrVa5Hbk6cCnKsHCZQppSMt2xgStE8iRRPI+RTC4xnVQ+v6H6zBSQy2Fc\n6QspF8GtATi6c3LTJtpNnkzLESNI/Ptv9Lm54BUC6TFo8cbIFbzRcoWr2duC6kHcFevrbtzYg8jI\njBtqryRJd75yx/Czs7NxdXUlPT2dhg0bEhISAijTM9PT0/H0rD1vbBqNZrRa5bXa3AJwLorxkeho\nWGzKpcBIJHNwJJQQVqLGFl8mc5FJgIp6jLWqvmz+xJHWaHCDuF3g35aMyEiy4+II6dcPtY0Nfp06\nEb1/P009gyA9Bg1eGEnDEw3pxd4DCPCCfeWsgHWtkBB3IiPlSliSVNeUG/DHjBnDzp076dChQ6n5\n7yMjI6usYdXNYDCj0Vzt4TsXTaSJpZAhXM11n8RHqHHAn3moin4g2eJHE9YTwSQEJnyYUGF9qWzA\ni/8oH+JOQGA74g4cILhXL9RF+Rwa9u9P5J49NB0RBhf2oqErBq7gjYYrxQO+N8SlWX+tDRu6s337\nBetPkCSpVig34O/cuROAqKio6mjLbXXtkI5TUcBPwkB9lJexzOhI5XNa8J0l2P/LDn+a8j8imAaY\n8WFSmXXlcRI9iXhwr7Ih4ST0fJTEX/ZRv0MHy3Ehffvy83PPwfR7i4Z07iWf03iiKTGkE+B1Y0M6\nDRt6EBUle/iSVNdYNYbfv39/q7bdycxmgVqt/IrRG8C26KswDxMuKD3uQmLQUg9bGpRahi1+NOJD\nklmHmcIy68rjOK70UhKlAWQlgrs/OYmJuAUFWY5zadCA/LQ0cHAHXTZqHDFTgAM2FBQthAJK7p6c\nCpY7LM7Dw56sLJ31J0iSVCuUG/ALCgq4cuUKqamppKenW/5ERUURHx9/05X/+OOPtGjRgqZNm7J0\n6dKbLu9mCIEl4JvMoCnKklmAGfuiaZV64rDFv9xy7AnBgZZksLPMY3RcxJ6mVzfkpoGzNwXp6TgU\ney5i5+pKYXY22DqAPh81dpgpxBYVegQCJcWFgy0UlP39ch1HRy15eZVYskuSpDtauUM6H3/8MatW\nrSIhIYGOHTtatru4uDBr1qybqthkMjFr1iz27NmDv78/nTt3ZtiwYYSGht5UuZVVvIdvNEPRcD6F\nCOyKvhcLicOWwArLqs8MongOD+5FTcn1AwSCHA7izZirG/OugJPXdQHf1sVFCfhaB9AXoMIOgR6b\nogElI6Dlaq59g7H0vPvXcnKyJT9fBnxJqmvK7eE/9dRTREZGsnz5ciIjIy1//vnnn5sO+IcOHaJJ\nkyaEhISg1WoZPXo027Ztu6kyb4bZLCwPpo2mqz18HWYcim6TnjjsCKiwLGc64EjLUqdqFnAGFSoc\naKFsMOjAbAQ75+sCvsbeHmEyYUIDhgLU2GIumn9vi9qyvi0oaZoL9FjFyUkrA74k1UFWpVZQqVRk\nZGTg4aHMVsnIyODLL7/kiSeeqHTF8fHxBAZe7S0HBARw8ODB645buHCh5e99+/alb9++la7TWsWT\ngZqKkpQpf89Hfc2c/LLYEUwB56/bricJLX5X3741FoKNLahUmPR6bGxtLceqVCrUWi0mkxkbsxHl\n+9kERX8zFyvXRq0MRVlDo1FjMJisO1iSpBovPDyc8PDwCo+zKuB/8sknJXr0Hh4erF279qYCfmnT\nPEtTPOBXJZXqatrn4sHTHjWFRb18O/zRU/GzCyOZpPMdTUvp4bvQnWjmYSQDDR5g76r08o16HDw8\n0GVkQHAwAGajEZNej1ajAq0DAj2qoncC9Ahsi6Vs0OmVsXxr5OUZLAu9SJJ057u2M7xo0aJSj7Nq\nlo7ZbMZsvtp9NJlMGAw3NyTg7+9PbOzVrI2xsbEEBFQ8XFJV1GqVpWevsSkZ8HVFfWlbAqwK+Mn8\nF3cGY8/1K6jY4IgLXcjhL2WDSgVOnpCnDOcUpKdbji3MycHW2RmVUQe2jpjRo8YWgcCAQFsU8M1m\n0BuVBVGsUTwNtCRJdYdVAX/w4MGMHj2aX375hT179jB69Gjuueeem6q4U6dOXLx4kaioKPR6PZs3\nb2bYsGE3VebNUKlUmM1KxNfYKOP4AHZFPXxQAn4h5acWNpHPFf4PH8rONeRACwq4eHWDkxfkpV0f\n8LOzsXN1BUNBUQ+/EBW2lmD/71CTzqAEeyt/NJGXp8fJSQZ8SaprrBrSWbp0KWvXrmXNmjUADBw4\nkGnTpt1cxRoN77//PoMHD8ZkMjF16tTbNkMHlB5+8YD/7xC3AyryiwK+HYEUEo3AgIrrA6ZAkMhq\nnOlQ7sNdB5qRxtdXNzh7Q04qDl5e5KWmWjYXZmUpAb8wr6iHr0ONHYXXDOfkF4JjxQtuWeTm6mUP\nX5LqIKsCvo2NDRMnTqRfv360aNHillU+ZMgQhgwZcsvKuxkajRqjUQnsjraQmq1s90ZLKkZCAS3e\nONGWFDbgy9QS5wvMxLGYfE7TiDXl1uVKL2JZTAEXcKAZ+DaHpLPUa9WKlGKLxSccOYJP69aQlQBu\nDTCSiQYPMjHiXuyfLikD6rtbf62xsdkEBLhaf4IkSbWCVUM627dvp3379pZhnGPHjt3W4ZeqoNVe\nnbni7KCkVwDww5b4Ym/NBjCfFNaTx9XALBDE8ToFnKcJ69BSflI5NQ74MJ4UNhQV2g7ijuPXqRNx\nxWYqRf7yC40GDID0GPAMxkgaGry4ghGvYgE/Nk3Jp2OtyMgMGjb0qPhASZJqFasC/sKFCzl48KBl\nWmb79u25fPlylTasuhXv4TvbKxkzARpiT2SxgG9HIEG8xiUeI5EPKeAC0TxHPmdpzMfY4GxVfe7c\nSzbhCEwQ0BbiTuDfpQvZcXFkXL6MEILLe/YUBfxo8AzCwJWigG8oEfDj0pR8OtaKisqiYcMb+Ekg\nSVKtYFXA12q1uLuXDBC1bdUrrdYGg0EJ+C4OkFcU4xtiRyQl8864cTfN+Ro98UQwHQ2eNOFTq4M9\nKMnWNHiRxwnwbwMJp7CxUdNyxAhOfvklKadOYevsjHtISFEPPwgjV9BaevhXx+Djrtx4Dz8kRAZ8\nSaprrBrDb9WqFV988QVGo5GLFy+yevVqevToUdVtq1a2tjYUFioph92cICNX2d4SR06Sjx5ziTVt\n7QggmDduqk4P7uUK3+Ls8IayyEn0ETpMm8bajh35Z8MGQocPVw5MPgf1mqAnHi0NSECPb7GAfyEe\n7ulQeh2lOXUqhdDQG/iGkCSpVrCqm/7+++9z+vRp7OzsGDNmDK6urrz77rtV3bZq5eCgobBQGcP3\ndVcehALUQ0tj7DlI7i2v05uHyeRnTORA2P1w8nsadOjAtIMHuWf1au5+803ISYGsRMwNGqEnCXuC\niURH42I5ev6+DB0aW1dnSkoeV64UEBpa75ZfjyRJNVuFPXyj0ch9993H3r17efPNN6ujTbeFvb0G\nnU7p4ft5QmKxFQAH4MZO0ulF2TNbBIIIdBwkl/MUkIoBPQJXbAjClg440w0X7It9x2pwx4WuZLIH\nr7YPwcYpMGwx/l26XC34xK/QtA86mzjsCEKFlssU0rAo4OcWKGP4oRXndAPgwIFYunb1tySKkySp\n7qiwh6/RaFCr1WRm1u4FMzQaNWazwGg04+4EhYarKYcfwouD5HKAnBLnCASnyGc58QzmDE9wmQsU\n0AoHRuPNVHy4B3ccsGE9KQzkNJ+RgomryXo8uI8MdkFIFyjMgcQzJRt2dg+0GICOCOxpggFBLIWE\nFKVYOB0Dzf2vJnuryB9/xNKjh5XfDpIk1SpWjeE7OTkRFhbGwIEDcXJyApQ3U1evXl2ljatOKpUK\ne3sNBQUGXFzsaOChPAxt6gfuaFhMEM8RxSi88EbLZXTsIxsbVNyDO6toSAscriZFu8bj1CeCAhYR\ny1/k8DbBuKLBjT7EshCDOg1t+//Aka9g6GvKSWYznP0JBs6lgO9xoClxFOKD1pKy+WQ0tAoqtcpS\n7dsXzRtv3H2zt0uSpDuQVQF/+PDhDP/3AWIRa5Of3UmcnW3Jy1MCfstAOBWtBHyAu3BlLY3ZRQYR\n6AjEltU0ojn2ZQb5azXBgU9pyhvEMptI1tIYWxxw5x6u8H/U7/UYLO8JfWaCqy8c3gSu9aF+C3J4\niQBe5hD5tMLRUuZvp6BXK+uuLzY2i4sX0+nVK/hGb40kSbWAVQF/0qRJVdyMmsHZ2ZacnELq13em\nbUP4Jwoe6n51f0scaVks2FaGFhWvEMgcInmbeOYTiDcjieQpfOs/iqrno/DFdBgwF755Bmb/hIFU\n9CTiRFuOk0g7lF9ZQsDek/DqaOvq/uabMzzwQHNsba0c/5EkqVYpdwx/5MiRAISFhV33p02bNtXS\nwOrk4mJrWfqvbUM4EVU19dig4k2C2E0mJ8nDkVbY4EoOB+C+BYAKvpoJD7wBge3JZj8udEOFhuPk\nWQJ+RKJSXpPSl9i9zubNpxk1ysqfA5Ik1Trl9vBXrVoFwPfff18tjbndnJ1tyc1Vlo1qGwLzNpR/\nfG4BvPIFhJ+C1CywtwVPZ2hUH9o1hP5toVOT0rNYuqLhGfx4nTg20wxvRpHGl7hq74LHS678lc1+\nXOlNPiaiKSS0aBGW8JPQL8y6LJmXLqUTGZnJgAGNrLkVkiTVQuUGfD8/ZQA7JCSkOtpy27m52ZOZ\nqbxV26SBEtDPxUGLUhJfRibDQ28qvwTWzlSSl+kMkJYNl5LgyEUYv1LJZDm+Hzw6GIJ9SpbxAJ5s\nIJVfyKIfQ0nkAwqIwIEmlmMMpJDDXwTwCn+QSyscLS+A/XQM7utk3bW9//5hJkxoi0ZTu96QliTJ\neuUGfGdn5zIfzqpUKrKzs6ukUbeLp6cD6elKEh0bG5gzDBZvhs+fLXnc9oMw/X14eRQ8eX/JHnZT\nP+jeAsb1VT6fioZP90CHp2FoZ3hjPPgX5b1RoWI2DVhJAv1ogQ8TSeAdGvOBpbxEPsSL/6DFk91E\nMwg3QJky+vNxWPN4xdeVmprH//53nH/+seJgSZJqrXK7e7m5ueTk5DBnzhyWLl1KfHw88fHxvP32\n28yZM6e62lhtvLyuBnyAWffB7uNwsGhp2vgrMHkVzPkEvnsJZg+teDildTCsnMr/t3fvcVWV6QLH\nf5vLCCiCmiASZyQhhJT7gHgpTMFLg854AfFeaJ6ZMzr1KTtjfpq0kuqYOpbdzJTSUtGOSqYeHBU1\nkTERkcQLOqLgLXEUQUFu6/yxh53E3rKVy9q4nu9f7rXfvdezHvVh8a53PYt/LtMX+sA/w9d7fn7/\nKdrTFitSuUFnxlNBIdf4BoBSMilmB65MpYIadlPMIPQ9cHYe1U8bdXZq+Ljee+8A8fG9pCWyEBqn\nU5S7H9ltnL+/P0fv6tNualtT0+l0mBFek3njjT1UVtbw5psDDNuSv4cZn0JHR33B/+MweHUMtH/A\nxTrZZ2H0OzDpaXgtTr9tLzdZyAU20oMKzpLHROzxpYwT/Jp3aE8/9nKTT7nMVzwO6H/D8POAF0fc\ne39Xr96iR48POXJkOh4eZvx0EEK0eqZqp9k3Xq1evZr4+HgA1q5dS7t25neGbC06dbLnxx+v1tkW\n2w+GBkPeJfB9FOzv48lSxgR4wvfvwlOzoUNb+NNvoT+OLMWKv1NMNI/Rg83c5ih2PG54cta3/Ish\n/z67r6zSTyv9ZVTD+5s7dw/jxvWSYi+EMK952tdff01ycjKurq64urqSnJzM119/3dyxtTh39/YU\nFta/LuHooG9O9stiX11dw4wZ2wgK+pTo6FV89lmm4aLvvbg6w7a5MG8tZJ3Rz+XPwI3FXKSCGmx5\nBCeeNhT7fMpJp4Tfo5/835YJXl2hewPLMQ8cKGDjxuO88UakGUcvhHjYmVXwPT09SUlJoaioiKKi\nIjZv3vxQrtzx8GhPQUGx2eP/8ped/PjjTyxfHsP06SH83/+doVu3vzFp0kZ++OHCPT/r6Qp/mwrj\nF+n79vSnPR78imSK6o39hMtMoDPt0N8wtXInTGmgO0JZWSXPPruZJUuG0KGDvdnHJIR4eMkavbt4\neOnHURQAABWPSURBVDhRUGDeyqNlyzLZtOkEGzaMISSkK6NG+bFhQyxnzszE39+V0aPXExmZxM6d\n/zR5HWJ8JHi7wf/8r/71K7jzMVc4yc8XjrdznR8oZQL6dsZXbujvro3rf+/4Xn11F0FBbowZIzda\nCSH0zLpoq5aWvmirKAr29vO5du0V2rb9lclxqalnmDx5E/v2PYuXl/Hn11ZV1bBmTQ5vvbUPN7d2\nvP/+UPz9XeuNO38Vgl+Af7ynn6LZynUWcIFpuFJCNau4ynK86PHvm60S18M/L8PyGaaP4+9//ydT\npmzi6NE/0LGjnN0LoTWmaqec4d9Fp9Px2GMdOHPmuskxRUW3mTJlE2vWjDJZ7EHfbnnixACOHfsj\n48b1YuDAL/ngg3/U+0v4j876lTa1d/UOowNz8SCTUi5Swcq7in15BSz9Tr/235SCgmImTtzIF1/8\nToq9EKIOswr+5cuXSUhIYMiQIQDk5uby+eefN2tgavHz60xu7lWj7ymKwvTpW4iP70VkZDezvs/G\nxornnw/hwIEEkpKyiY3dYGjfUOvFEZB+AjL+vd7/KZxYiCfz+A+8+blof7IdQrrrV/oYc+dOFaNH\nr+fFF3szcKC0UBBC1GVWwZ8yZQrR0dFcvHgRAG9vbxYvXtysgaklONiNAwcKjb63aNEBzp278UD9\n5L28OrJ//3O0b9+Gfv1WcOHCz9cKHNrA/AnwX59AVbXxzxfdhPnJ8O5k0/uYOXM7Hh7tmTXr4Xre\nsBCiaZhV8IuKioiLi8PaWr9KxNbWFhsbs5bwtzpDh3qxbVteve1btpxiwYJ0NmyIxc7uwY7dzs6G\n5ctjiIt7giefTOLcuZ+fIjbpaX3jtYWbjH/2v5P0F2r9TDzsZPnyw+zde46VK0c8lM8qEEI0nlkF\nv127dly7ds3wOiMjAyenh/NGnsDALty+XcmPP/5k2Hbs2E88++xmNm8eS7duzo36fp1Ox+zZ/fnj\nH0N55pmvKSur/Pd2WPYnWLRJ3wXzbp/vgP3HIXGi8e88cuQys2fvZOPGOBwdG3lnmBDi4aWY4dCh\nQ0pERITSvn17JSIiQvHy8lKOHDlizkeNSk5OVvz8/BQrKyslMzPT5Dgzw2tyr722S5kyZZNSXV2j\n5Ob+pLi7L1RWr85u0n3U1NQosbHrlRkzttbZviNLURxjFWXtXkU5fVFR5qxSlC6TFOVEgfHvuX69\nTOnefYmyZk1Ok8YnhGi9TNVOs5dlVlVVceLECRRFwcfHh1/9yvSyxYacOHECKysrpk+fzsKFCwkO\nDjY6rqWXZdb66adbDB++hhs3yrl69TaLFkUzeXJgk+/n+vUy/P0/YeXKEXX61Kcfh1eS9M/U7e2j\nv0GrS4f6n1cUhVGjknFzc+TDD4c1eXxCiNapUb10/P39GTt2LHFxcXTv3r3RwfTo0aPR39GcXFza\nsn//c+zenU9YmDvt2zfPNEmHDvYsXx5DQkIKR4/+J05OdgD08dX322nIkiX/4Pz5YtasMaOpjhBC\n88yaw09JScHa2prY2FhCQ0N57733OH/+fHPHpipraysGDXqs2Yp9rcGDvRgyxItXXvn7fX3uwIEC\nEhP3sX79GNq0eTgvoAshmtZ932mbl5fHm2++yVdffUV1tYk1hEBUVBSXL1+utz0xMZGYmBgABgwY\n0OCUzuuvv254HRkZSWRk5P2E2yoUF5fTo8eHfPttPKGhXRscf/XqLUJClvHhh8OIifFpgQiFEJYs\nLS2NtLQ0w+t58+YZndIxu+Dn5+ezbt06kpOTsba2Ji4ujpdeeqnhD96DOQVfjTl8NaxcmcXSpT+Q\nkZGAra21yXFVVTUMG/YVISFdefvtgS0YoRCitWhUa4Xw8HB+//vfU1NTw/r16zl48GCji30trRT0\nhkyZEkjnzg688873JscoisIf/vAdOp2uzkNahBDCHGad4Z84caJJL7Ru3LiRmTNnUlRUhJOTE0FB\nQWzbtq1+cBo6wwd9H5zevT9n4cJoxo7tWee9qqoa/vSnrWRmXmLXrkmy3l4IYZKp2mn2lM6WLVvI\nzc2lrKzMcCfnX//616aN8pfBaazgA+TkXGHQoFXMmxfJ9Okh6HQ68vNvkJCQgq2tFcnJY5r9QrIQ\nonVrVMGfPn06ZWVl7Nq1i2nTprF+/XrCw8ObvYGaFgs+wIkTRYwb9w3Xr5fTqZM9p05d49VX+/Py\ny32wsZEGp0KIe2tUwe/Vqxc5OTmGB5eXlpYyZMgQvv/e9HxzU9BqwQeoqVHIy7vG1au3CQlxw97e\nVu2QhBCtRKNuvLK317fodXBw4MKFC3Tq1MnokkvRdKysdPj4PIKPrLoUQjQRswr+b3/7W65fv86s\nWbMICQkBYNq0ac0amBBCiKZ1zymdxYsX07dvX4KDgw3tkMvLyykvL8fZuXFdI80KTsNTOkII8aAe\naEqnsLCQF154gePHj9OrVy/69etHnz596NNHHrAhhBCtjVkXbe/cucOhQ4c4cOAA6enpHDhwAGdn\nZ44fP968wckZvhBC3LdGXbQtKyvj5s2bFBcXU1xcTNeuXfH392/yIIUQQjSfe57hT5s2jdzcXBwd\nHQkLCyMiIoLevXvToYOR5uzNEZyc4QshxH17oF4658+f586dO3Tp0gV3d3fc3d1b5GKtEEKIptfg\nHH5NTQ3Hjh0zzN/n5OTQqVMnevfuzRtvvNG8wckZvhBC3LdG99IpKCggPT2d/fv3s2XLFq5du0Zx\ncXGTB1onOCn4Qghx3x6o4C9ZssSwKsfGxoY+ffrQt29f+vTpQ8+ePbG2Nt23vTmDFkIIYdoDrdLJ\nz88nNjaWxYsX07Vrw09iEkIIYbnu+xGHLUnO8IUQ4v416olXQgghWj8p+EIIoRFS8IUQQiOk4Ash\nhEZIwRdCCI2Qgi+EEBohBV8IITRCCr4QQmiEFHwhhNAIKfhCCKERUvCFEEIjpOALIYRGqFLwZ82a\nha+vLwEBAYwcObLZ++oLIYRQqeBHR0dz7NgxsrOzefzxx3n77bfVCEMIITRFlYIfFRWFlZV+1+Hh\n4RQWFqoRhhBCaMo9H4DSElasWEF8fLzJ9+fOnWv4c2RkJJGRkc0flBBCtCJpaWmkpaU1OK7ZHoAS\nFRXF5cuX621PTEwkJiYGgPnz53P48GG++eYb48HJA1CEEOK+Nfoh5k0tKSmJzz77jJ07d2JnZ2d0\njBR8IYS4fw/0TNvmsn37dhYsWMCePXtMFnshhBBNS5UzfG9vbyoqKujYsSMAERERfPTRR/WDkzN8\nIYS4bxY3pWMOKfhCCHH/5CHmQgihcVLwhRBCI6TgCyGERkjBF0IIjZCCL4QQGiEFXwghNEIKvhBC\naIQUfCGE0Agp+EIIoRFS8IUQQiOk4AshhEZIwRdCCI2Qgi+EEBohBV8IITRCCr4QQmiEFHwhhNAI\nKfhCCKERUvCFEEIjpOALIYRGSMEXQgiNkIIvhBAaIQVfCCE0Qgq+EEJohBR8IYTQCCn4QgihEVLw\nhRBCI1Qp+K+99hoBAQEEBgYycOBACgoK1AhDCCE0RacoitLSOy0pKcHR0RGADz74gOzsbJYvX14/\nOJ0OFcITQohWzVTtVOUMv7bYA5SWlvLII4+oEYYQQmiKjVo7njNnDqtWrcLBwYGMjAy1whBCCM1o\ntimdqKgoLl++XG97YmIiMTExhtfvvPMOJ0+eZOXKlfWD0+l4/fXXDa8jIyOJjIxsjnCFEKLVSktL\nIy0tzfB63rx5xqfDFZWdO3dOeeKJJ4y+ZwHh3dPu3bvVDqFBEmPjWXp8imL5MVp6fIrycMVoqnaq\nMoefl5dn+PPmzZsJCgpSI4xGu/snqqWSGBvP0uMDy4/R0uMDbcSoyhz+7NmzOXnyJNbW1nTv3p2P\nP/5YjTCEEEJTVCn4GzZsUGO3QgihaaqswzeXTqdTOwQhhGiVjJV21ZZlmsOCfxYJIUSrI710hBBC\nI6TgCyGERlhEwd++fTs9evTA29ubd9991+iYmTNn4u3tTUBAAFlZWRYVX1paGk5OTgQFBREUFMRb\nb73VovE999xzuLq60qtXL5Nj1MwfNByj2jksKChgwIABPPHEE/Ts2ZP333/f6Dg182hOjGrmsby8\nnPDwcAIDA/Hz82P27NlGx6mZQ3NiVPvfIkB1dTVBQUF1blK92wPnsInuB3hgVVVVSvfu3ZWzZ88q\nFRUVSkBAgJKbm1tnzHfffacMHTpUURRFycjIUMLDwy0qvt27dysxMTEtFtMv7d27Vzl8+LDSs2dP\no++rmb9aDcWodg4vXbqkZGVlKYqiKCUlJcrjjz9uUf8OzY1R7TzeunVLURRFqaysVMLDw5V9+/bV\neV/tHJoTo9o5VBRFWbhwoTJu3DijcTQmh6qf4R88eBAvLy+6deuGra0tY8eOZfPmzXXGpKSkMHny\nZADCw8O5ceMGV65csZj4QN0LzP3796dDhw4m31czf7UaihHUzWGXLl0IDAwEoF27dvj6+nLx4sU6\nY9TOozkxgrp5dHBwAKCiooLq6mo6duxY5321c2hOjKBuDgsLC9m6dStTp041Gkdjcqh6wb9w4QIe\nHh6G148++igXLlxocExhYaHFxKfT6UhPTycgIIBhw4aRm5vbIrGZS838mcuScpifn09WVhbh4eF1\ntltSHk3FqHYea2pqCAwMxNXVlQEDBuDn51fnfUvIYUMxqp3DF198kQULFmBlZbw8NyaHqhd8c9fa\n//InXUut0TdnP8HBwRQUFJCdnc2MGTP43e9+1wKR3R+18mcuS8lhaWkpo0ePZsmSJbRr167e+5aQ\nx3vFqHYeraysOHLkCIWFhezdu9doKwC1c9hQjGrmcMuWLbi4uBAUFHTP3zIeNIeqF3x3d/c6T7wq\nKCjg0UcfveeYwsJC3N3dLSY+R0dHw6+JQ4cOpbKykn/9618tEp851MyfuSwhh5WVlYwaNYoJEyYY\n/U9uCXlsKEZLyCOAk5MTzzzzDIcOHaqz3RJyWMtUjGrmMD09nZSUFDw9PYmPj2fXrl1MmjSpzpjG\n5FD1gh8aGkpeXh75+flUVFSwbt06hg8fXmfM8OHD+fLLLwHIyMjA2dkZV1dXi4nvypUrhp+4Bw8e\nRFEUo/OCalEzf+ZSO4eKopCQkICfnx8vvPCC0TFq59GcGNXMY1FRETdu3ACgrKyMHTt21GuMqHYO\nzYlRzRwmJiZSUFDA2bNnWbt2LU8//bQhX7Uak0PV77S1sbFh6dKlDB48mOrqahISEvD19eXTTz8F\nYPr06QwbNoytW7fi5eVF27ZtjfbOVzO+DRs28PHHH2NjY4ODgwNr165tsfgA4uPj2bNnD0VFRXh4\neDBv3jwqKysN8amZP3NjVDuH+/fvZ/Xq1fj7+xsKQGJiIufPnzfEqHYezYlRzTxeunSJyZMnU1NT\nQ01NDRMnTmTgwIEW83/Z3BjV/rd4t9qpmqbKoUX30hFCCNF0VJ/SEUII0TKk4AshhEZIwRdCCI2Q\ngi+EEBohBV9YFGtra0PTqqCgIMMKlNYuKSmJzp078/zzzzfqe+bOncvChQsNrzMyMkx+Z3l5OYGB\ngbRp08ai7gsR6lF9WaYQd3NwcDDZ/a92QZml3SVsDp1OR3x8vNEOl1VVVdjYmPdf8ZfHvm3bNoYO\nHWp0rJ2dHUeOHMHT0/P+AxYPJTnDFxYtPz8fHx8fJk+eTK9evSgoKGDBggWEhYUREBDA3LlzDWPn\nz5+Pj48P/fv3Z9y4cYYz4cjISDIzMwH9jTe1BbC6uppZs2YZvmvZsmWAvj1uZGQkY8aMwdfXlwkT\nJhj28cMPP9C3b18CAwPp3bs3paWlPPXUU2RnZxvG9OvXj5ycnHrHcvcK6KSkJIYPH87AgQOJiori\n1q1bDBo0iJCQEPz9/UlJSTF6XCdPnqzznbt27WLQoEEcO3aM8PBwgoKCCAgI4PTp0w+acvEQkzN8\nYVHKysoMNxU99thjLFq0iNOnT7Nq1SrCwsJITU3l9OnTHDx4kJqaGkaMGMG+fftwcHBg3bp1ZGdn\nU1lZSXBwMKGhoYD+rNjYbwWff/45zs7OHDx4kDt37tCvXz+io6MBOHLkCLm5ubi5udG3b1/S09MJ\nDQ1l7NixJCcnExISQmlpKfb29iQkJJCUlMTixYs5deoUd+7cueezCWplZWWRk5ODs7Mz1dXVbNy4\nEUdHR4qKioiIiGD48OFkZmaaPK6ioiJsbW1xdHTkk08+4c9//jPjxo2jqqqKqqqqpvorEQ8RKfjC\notjb29eZ0snPz+fXv/41YWFhAKSmppKammr4oXDr1i3y8vIoKSlh5MiR2NnZYWdnV6/9hTGpqank\n5OSwYcMGAG7evMnp06extbUlLCyMrl27AhAYGMjZs2dxdHTEzc2NkJAQAEPjstGjR/Pmm2+yYMEC\nVqxYwbPPPtvgvnU6HdHR0Tg7OwP6Do6zZ89m3759WFlZcfHiRa5cucK+ffvqHVftbwqpqakMHjwY\ngD59+jB//nwKCwsZOXIkXl5eDSdbaI5M6QiL17Zt2zqvZ8+eTVZWFllZWZw6dYrnnnsOqDtlcvef\nbWxsqKmpAfQXMu+2dOlSw3edOXOGQYMGoSgKbdq0MYyxtramqqrK5LUDBwcHoqKi2LRpE+vXr2f8\n+PFmHVdtgy6Ar776iqKiIg4fPkxWVhYuLi6Ul5ej0+nqHVdtHNu3b2fIkCGAvnXFt99+i729PcOG\nDWP37t1mxSC0RQq+aFUGDx7MihUruHXrFqDvDX716lWefPJJNm3aRHl5OSUlJWzZssXwmW7duhk6\nItaezdd+10cffWSY/jh16hS3b982ul+dToePjw+XLl0yfFdJSQnV1dUATJ06lZkzZxIWFoaTk1OD\nx/HLjiY3b97ExcUFa2trdu/ezblz59DpdCaPS1EUjh49SkBAAABnz57F09OTGTNmMGLECKPXEISQ\nKR1hUYydRd+9LSoqiuPHjxMREQHoW9muXr2aoKAg4uLiCAgIwMXFhd/85jeGovryyy8TGxvLsmXL\neOaZZwzfN3XqVPLz8wkODkZRFFxcXNi4caPJOX9bW1vWrVvHjBkzKCsrw8HBgR07dtC2bVuCg4Nx\ncnIyazqn9pju3sf48eOJiYnB39+f0NBQfH19AeodV+3UVmZmZp0uj8nJyaxatQpbW1vc3NyYM2eO\nWXEIbZHmaeKhNG/ePNq1a8dLL73UIvu7ePEiAwYMqLeKptYXX3zBoUOH+OCDD5pkf/Pnz8fb25vY\n2NgGx3p6epKZmWlRLbuFOmRKRzy0Wmq9/pdffknv3r1JTEw0Ocbe3p5t27Y1+sarWnPmzGmw2Nfe\neFVVVWXycXlCW+QMXwghNEJ+7AshhEZIwRdCCI2Qgi+EEBohBV8IITRCCr4QQmiEFHwhhNCI/wfH\nIpf6HamIVgAAAABJRU5ErkJggg==\n"
- }
- ],
- "prompt_number": 9
- },
- {
- "cell_type": "heading",
- "level": 4,
- "metadata": {},
- "source": [
- "3D Simulation of the sea surface "
- ]
- },
- {
- "cell_type": "raw",
- "metadata": {},
- "source": [
- "The simulations show that frequency dependent spreading leads to much more irregular surface so the orientation of waves is less transparent compared to the frequency independent case."
- ]
- },
- {
- "cell_type": "heading",
- "level": 5,
- "metadata": {},
- "source": [
- "Frequency independent spreading"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "#plotflag = 1; iseed = 1;\n",
- "#\n",
- "#Nx = 2 ^ 8;Ny = Nx;Nt = 1;dx = 0.5; dy = dx; dt = 0.25; fftdim = 2;\n",
- "#randn('state', iseed)\n",
- "#Y1 = seasim(SD1, Nx, Ny, Nt, dx, dy, dt, fftdim, plotflag);\n",
- "#wafostamp('', '(ER)')\n",
- "#axis('fill')\n",
- "#disp('Block = 6'), pause(pstate)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "heading",
- "level": 5,
- "metadata": {},
- "source": [
- "Frequency dependent spreading"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "#randn('state', iseed)\n",
- "#Y12 = seasim(SD12, Nx, Ny, Nt, dx, dy, dt, fftdim, plotflag);\n",
- "#wafostamp('', '(ER)')\n",
- "#axis('fill')"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "heading",
- "level": 3,
- "metadata": {},
- "source": [
- "Estimation of directional spectrum"
- ]
- },
- {
- "cell_type": "raw",
- "metadata": {},
- "source": [
- "The figure is not shown in the Tutorial"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "# Nx = 3; Ny = 2; Nt = 2 ^ 12; dx = 10; dy = 10;dt = 0.5;\n",
- "# F = seasim(SD12, Nx, Ny, Nt, dx, dy, dt, 1, 0); \n",
- "# Z = permute(F.Z, [3 1 2]);\n",
- "# [X, Y] = meshgrid(F.x, F.y);\n",
- "# N = Nx * Ny;\n",
- "# types = repmat(sensortypeid('n'), N, 1);\n",
- "# bfs = ones(N, 1);\n",
- "# pos = [X(:), Y(:), zeros(N, 1)];\n",
- "# h = inf;\n",
- "# nfft = 128;\n",
- "# nt = 101;\n",
- "# SDe = dat2dspec([F.t Z(:, :)], [pos types, bfs], h, nfft, nt);\n",
- "#plotspec(SDe), hold on\n",
- "#plotspec(SD12, '--'), hold off\n",
- "#disp('Block = 8'), pause(pstate)\n"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "heading",
- "level": 3,
- "metadata": {},
- "source": [
- "Section 1.4.4 Fatigue, Load cycles and Markov models"
- ]
- },
- {
- "cell_type": "raw",
- "metadata": {},
- "source": [
- "Switching Markow chain of turningpoints.\n",
- "In fatigue applications the exact sample path is not important, but only the tops and bottoms of the load, called the sequence of turning points (TP). From the turning points one can extract load cycles, from which damage calculations and fatigue life predictions can be performed.\n",
- "\n",
- "The commands below computes the intensity of rainflowcycles for the Gaussian model with spectrum S1 using the Markov approximation. \n",
- "The rainflow cycles found in the simulated load signal are shown in the figure."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "#clf()\n",
- "#paramu = [-6 6 61];\n",
- "#frfc = spec2cmat(S1, [], 'rfc', [], paramu);\n",
- "#pdfplot(frfc);\n",
- "#hold on\n",
- "#tp = dat2tp(xs);\n",
- "#rfc = tp2rfc(tp);\n",
- "#plot(rfc(:, 2), rfc(:, 1), '.')\n",
- "#wafostamp('', '(ER)')\n",
- "#hold off\n",
- "#disp('Block = 9'), pause(pstate)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "heading",
- "level": 3,
- "metadata": {},
- "source": [
- "Section 1.4.5 Extreme value statistics"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "clf()\n",
- "import wafo.data as wd\n",
- "xn = wd.yura87()\n",
- "#xn = load('yura87.dat'); \n",
- "subplot(211) \n",
- "plot(xn[::30, 0] / 3600, xn[::30, 1], '.')\n",
- "title('Water level')\n",
- "ylabel('(m)')"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "pyout",
- "prompt_number": 10,
- "text": [
- ""
- ]
- },
- {
- "output_type": "display_data",
- "png": 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7IKUT6WZqTj8pruJiSxlwZua+9a4u72eqBkMyQZeuBVepQI4/DMUVk6l4gSrmIgfI5ff5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\nebtscv9w5SHzY3GxFTvgSoR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8UgDvvfceWlpaBj/H4/HBShUiQqEQFi9ejFmzZuG73/1u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\nQPH/x7b+U3K6ZM4AAAAASUVORK5CYII=\n"
- }
- ],
- "prompt_number": 10
- },
- {
- "cell_type": "raw",
- "metadata": {},
- "source": [
- "Formation of 5 min maxima"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "yura = xn[:85500, 1]\n",
- "yura = np.reshape(yura, (285, 300)).T\n",
- "maxyura = yura.max(axis=0)\n",
- "subplot(212)\n",
- "plot(xn[299:85500:300, 0] / 3600, maxyura, '.')\n",
- "xlabel('Time (h)')\n",
- "ylabel('(m)')\n",
- "title('Maximum 5 min water level')\n",
- "show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "display_data",
- "png": 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ly2js2LHyQokKoYey8aZeeH2E608etT1ad3UAlFwS7kBrJ0saqaSl560G6XyC\n5TmZTMrrJyz5KgV1qNVN9uR3ZSfXawyAZS8gf39/ioqKoqysLBo1ahR17dqVunfvTiNHjpTdCZTI\nuhDeNNHGmOH6k8fb6sWT8mh9b5UUtL171SpP8XyC9Hc47NWT0uSyM/sNObrdtz28xgA4g7gQ3vZS\nMQxjH63vrS0FbQsta03k9g8TXxMRYR59SHdRtTe57Mh+Q2pXcGtFqwHwg5cTEgJs3mz+zzBM/UDr\nexsSAvz3v8DDDwP79wOtWwMZGcCwYcClS8r3BQWZ//fqBaxcKX9NcTGQnw/s3Qv4+8vLVFwMlJUB\nt24BlZXma+PjzXlnZ5vl2rPHfO/UqUBRkfm+pCRgzRp1ZVSS+8svrdN3J4b/WQ2vwmAwwAvFYhif\nY+pUs3IMCjIrQncpqNRUs9IGzMpx82b56y5dMssYFASYTPJyDhsG5OSYla2SkrVcI0Wa99Sp5u+X\nL5u/p6cD27drryeL3CtXurZONetO5wYc+uClYjGMz+GpiC+tcwjOruOxRDf93/+ZVxmrWcdjNNZO\nENv6nXJ3olV38giAYRhF1PSe9UBrD9mVcsrlbenhnzgBlJcDRiNw9CgwfnztSAWom7/lvlOngOho\noHlzfUdSWnWnrgZg0qRJ2LlzJ8LCwnD8+HEAwAsvvIBPP/0UAQEB6NixI1avXo0WLVpYC8UGgGG8\nAr1cFa5GbznFLqmoKOD4cXM+FsOTlGRW8KtXW+cvvs9C+/ZAu3bW7iJXudq8ygUktyHcnj176Pbt\n20RENHv2bNn9gHQWi2EYH8CZ+Hq120DIuZfE+yVZfhNb/PsRcmGfrnK1adWdukYBpaSkwGg0Wh0b\nNGgQ/PzM2fbp0wc//fSTniIwDOOjWKJ/cnLMPWxn7pVGAlmQi3ayRBT98gtQUWEeMRw7Vnt/8+bm\n61q1As6fN48i/P3Nx2xFM+lBY/dlVZdVq1YhMzNT9tzChQuFz6mpqUhNTXWPUAzDNAjUhIiqvdei\n6LXcC5hdQ/v3194/dSpw5QoQEQHExACHDpmvS083GwitLqy8vDzk5eWpv0GC7pPAJpMJw4cPF+YA\nLLz22msoLCzE1q1b6wrFcwAMwziJM/MCzt47YQJgMNieE4iIMI8UXDnBrlV3emQEsGbNGuzatQv7\n9u3zRPYMw/gAWnrtrr53+3b5c+KRxZYtwAsveHaC3e0GYPfu3XjzzTeRn5+Ppk2bujt7hmEYj5Gd\nbT2ycNSGSFawAAAHc0lEQVTIuApdXUCZmZnIz89HeXk5wsPDsWjRIixevBi3bt1Cy5YtAQB9+/bF\nihUrrIViFxDDMIxmvGodgKOwAWAYhtGOVt3p9ZvBMQzDMPrABoBhGMZHYQPAMAzjo7AB8HKcWeTR\n0OC6qIXrohauC8fR1QBMmjQJ4eHhSExMFI5t2bIFCQkJaNSoEQoLC/XMvkHAjbsWrotauC5q4bpw\nHF0NwMSJE7F7926rY4mJidi2bRsGDBigZ9YMwzCMHXRdCJaSkgKTyWR17M4779QzS4ZhGEYtjm88\nqo4zZ85YbQdtITU1lb7++mvZewDwH//xH//xnwN/WvDobqBKEC8CYxiG0R2OAmIYhvFRPGoAuKfP\nMAzjOdy+GVzLli0xY8YMlJeXo0WLFkhKSkJOTo5eIjAMwzBKaJ7V1ZmcnByKi4uj2NhYWrJkiafF\n8SjR0dGUmJhIPXr0oF69enlaHLcyceJECgsLswogqKiooLS0NOrUqRMNGjSIKrX+0Gs9Ra4uFixY\nQHfccQf16NGDevToQTk5OR6U0D2UlJRQamoqdenShRISEmjp0qVE5JvtQqkutLYLrzIA1dXV1LFj\nRzpz5gzdunWLunfvTidPnvS0WB4jJiaGKioqPC2GRzhw4AAVFhZaKb0XXniB/vKXvxAR0ZIlS2j2\n7NmeEs+tyNXFwoUL6e233/agVO6ntLSUjh49SkREV69epc6dO9PJkyd9sl0o1YXWduFVk8AFBQWI\njY1FTEwM/P39MXr0aHzyySeeFsujkI/Ok6SkpMBoNFod27FjB8aPHw8AGD9+PLYr/exSA0OuLgDf\naxsRERHo0aMHAKBZs2aIj4/HuXPnfLJdKNUFoK1deJUBOHfuHNq2bSt8j4qKEgrlixgMBqSlpSE5\nORkfffSRp8XxOD///DPCw8MBAOHh4fj55589LJFnWb58Obp3747Jkyfj0qVLnhbHrZhMJhw9ehR9\n+vTx+XZhqYu7774bgLZ24VUGwGAweFoEr+LQoUM4evQocnJy8N577+HgwYOeFslrMBgMPt1epk+f\njjNnzuCbb75BZGQknnvuOU+L5DaqqqowatQoLF26FMHBwVbnfK1dVFVV4aGHHsLSpUvRrFkzze3C\nqwzAHXfcgR9//FH4/uOPPyIqKsqDEnmWyMhIAEDr1q0xYsQIFBQUeFgizxIeHo6ysjIAQGlpKcLC\nwjwskecICwsTlN0TTzzhM23jt99+w6hRozBu3DhkZGQA8N12YamLsWPHCnWhtV14lQFITk7GDz/8\nAJPJhFu3bmHTpk148MEHPS2WR7h+/TquXr0KALh27Rr27NljtauqL/Lggw9i7dq1AIC1a9cKjd4X\nKS0tFT5v27bNJ9oGEWHy5Mno0qULnnnmGeG4L7YLpbrQ3C50mKB2il27dlHnzp2pY8eO9Prrr3ta\nHI9x+vRp6t69O3Xv3p0SEhJ8ri5Gjx5NkZGR5O/vT1FRUbRq1SqqqKig3//+9z4V7kdUty6ysrJo\n3LhxlJiYSN26daP09HQqKyvztJi6c/DgQTIYDNS9e3erMEdfbBdydbFr1y7N7cIrfxSeYRiG0R+v\ncgExDMMw7oMNAMMwjI/CBoBhGMZHYQPAMAzjo7ABYBo0FRUVSEpKQlJSEiIjIxEVFYWkpCQEBwfj\nqaee0iXPd999F2vWrAEApKam4uuvv65zTVFRESZPnqxL/gyjFq/8RTCGcRWhoaE4evQoAGDRokUI\nDg7GrFmzdMuPiJCVlYUjR44AUF7d3q1bN5w6dQoXLlzwmYVLjPfBIwDGp7BEPefl5WH48OEAgIUL\nF2L8+PEYMGAAYmJi8M9//hPPP/88unXrhqFDh6K6uhoA8PXXXyM1NRXJyckYMmSIsPpUzKFDh3Dn\nnXeicePavtWWLVvQp08fxMXF4bPPPhOODx06FFu2bNGzuAxjEzYADAPgzJkzyM3NxY4dOzB27FgM\nGjQIRUVFCAwMxM6dO/Hbb79hxowZ2Lp1K7766itMnDgRc+fOrZPOZ599huTkZKtjt2/fxuHDh/HO\nO+9g0aJFwvHevXvjwIEDupeNYZRgFxDj8xgMBgwdOhSNGjVC165dUVNTg8GDBwMAEhMTYTKZUFxc\njBMnTiAtLQ2AWam3adOmTlolJSXo37+/1bGRI0cCAHr27AmTySQcj4yMtPrOMO6GDQDDAAgICAAA\n+Pn5wd/fXzju5+eH6upqEBESEhLw+eef201Luri+SZMmAIBGjRoJ7iTLdb60cyXjfbALiPF51OyG\nEhcXh4sXL+LLL78EYN6J8eTJk3Wui46Olp0bkKO0tBTR0dHahGUYF8IGgPEpLD1u8b7x0j3kpb1y\ng8EAf39/fPzxx5g9ezZ69OiBpKQkfPHFF3XS79+/P7766iu7+QPmX8AbMGCAU+VhGGfgzeAYxoUQ\nEXr27InDhw8LbiUlUlNTsXnzZg4DZTwGjwAYxoUYDAZMmTIFGzZssHldUVERYmNjWfkzHoVHAAzD\nMD4KjwAYhmF8FDYADMMwPgobAIZhGB+FDQDDMIyPwgaAYRjGR/l/GpOCVM/8oZQAAAAASUVORK5C\nYII=\n"
- }
- ],
- "prompt_number": 11
- },
- {
- "cell_type": "raw",
- "metadata": {},
- "source": [
- "Estimation of GEV for yuramax"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "clf()\n",
- "import wafo.stats as ws\n",
- "phat = ws.genextreme.fit2(maxyura, method='ml')\n",
- "phat.plotfitsummary()\n",
- "show()\n",
- "#disp('Block = 11, Last block')"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stderr",
- "text": [
- "c:\\pab\\workspace\\pywafo_svn\\pywafo\\src\\wafo\\stats\\estimation.py:1080: UserWarning: P-value is on the conservative side (i.e. too large) due to ties in the data!\n",
- " warnings.warn('P-value is on the conservative side (i.e. too large) due to ties in the data!')\n"
- ]
- },
- {
- "output_type": "display_data",
- "png": 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WL14s1vVC/+cvXrxYzpgVKfgoMDCQDbgTEx0doGPH/88q3r1j9jH++WceM4J0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\nGAwGg1EjLFAwGAwGo0ZYoGAwGAxGjfw/VL1XcDTSAWEAAAAASUVORK5CYII=\n"
- }
- ],
- "prompt_number": 12
- }
- ],
- "metadata": {}
- }
- ]
-}
\ No newline at end of file
diff --git a/wafo/doc/tutorial_scripts/WAFO Chapter 2.ipynb b/wafo/doc/tutorial_scripts/WAFO Chapter 2.ipynb
deleted file mode 100644
index 806de65..0000000
--- a/wafo/doc/tutorial_scripts/WAFO Chapter 2.ipynb
+++ /dev/null
@@ -1,706 +0,0 @@
-{
- "metadata": {
- "name": "WAFO Chapter 2"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
- {
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "CHAPTER2 Modelling random loads and stochastic waves\n",
- "====================================================\n",
- "\n",
- "Chapter2 contains the commands used in Chapter 2 of the tutorial and present some tools for analysis of random functions with respect to their correlation, spectral and distributional properties. The presentation is divided into three examples: \n",
- "\n",
- "Example1 is devoted to estimation of different parameters in the model.\n",
- "Example2 deals with spectral densities and\n",
- "Example3 presents the use of WAFO to simulate samples of a Gaussian process.\n",
- "\n",
- "Some of the commands are edited for fast computation. \n",
- "\n",
- "Section 2.1 Introduction and preliminary analysis\n",
- "=================================================\n",
- "\n",
- "Example 1: Sea data\n",
- "-------------------\n",
- "Observed crossings compared to the expected for Gaussian signals\n"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import wafo\n",
- "import wafo.objects as wo\n",
- "xx = wafo.data.sea()\n",
- "me = xx[:, 1].mean()\n",
- "sa = xx[:, 1].std()\n",
- "xx[:, 1] -= me\n",
- "ts = wo.mat2timeseries(xx)\n",
- "tp = ts.turning_points()\n",
- "\n",
- "cc = tp.cycle_pairs()\n",
- "lc = cc.level_crossings()\n",
- "lc.plot()\n",
- "show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "display_data",
- "png": 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FtVOrZelwrTbfQ5KSZEmDOXNYzoDshJ+fTBTnzhV6aJ06wK+/AoMHA0eOmCE2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\ngg==\n"
- }
- ],
- "prompt_number": 1
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Average number of upcrossings per time unit\n",
- "----------------------------------------------\n",
- "Next we compute the mean frequency as the average number of upcrossings per time unit of the mean level (= 0); this may require interpolation in the crossing intensity curve, as follows. \n"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "T = xx[:, 0].max() - xx[:, 0].min()\n",
- "f0 = np.interp(0, lc.args, lc.data, 0) / T #! zero up-crossing frequency \n",
- "print('f0 = %g' % f0)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "f0 = 0.224071\n"
- ]
- }
- ],
- "prompt_number": 3
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Turningpoints and irregularity factor\n",
- "----------------------------------------"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "fm = len(tp.data) / (2 * T) # frequency of maxima\n",
- "alfa = f0 / fm # approx Tm24/Tm02\n",
- "\n",
- "print('fm = %g, alpha = %g, ' % (fm, alfa))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "fm = 0.456159, alpha = 0.491212, \n"
- ]
- }
- ],
- "prompt_number": 4
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Visually examine data\n",
- "------------------------\n",
- "We finish this section with some remarks about the quality of the measured data. Especially sea surface measurements can be of poor quality. We shall now check the quality of the dataset {\\tt xx}. It is always good practice to visually examine the data before the analysis to get an impression of the quality, \n",
- "non-linearities and narrow-bandedness of the data.First we shall plot the data and zoom in on a specific region. A part of sea data is visualized with the following commands"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "clf()\n",
- "ts.plot_wave('k-', tp, '*', nfig=1, nsub=1)\n",
- "\n",
- "axis([0, 2, -2, 2])\n",
- "show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "display_data",
- "png": 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NzfDz84NSqURdXV2n1uVO4m9oaBAtfoHQ6/Xw9/eHTCZDfn4+goOD4ePjIxJ/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\nTkSuQmCC\n"
- }
- ],
- "prompt_number": 5
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Finding possible spurious points\n",
- "------------------------------------\n",
- "However, if the amount of data is too large for visual examinations one could use the following criteria to find possible spurious points. One must be careful using the criteria for extremevalue analysis, because\n",
- "it might remove extreme waves that are OK and not spurious."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import wafo.misc as wm\n",
- "dt = ts.sampling_period()\n",
- "# dt = np.diff(xx[:2,0])\n",
- "dcrit = 5 * dt\n",
- "ddcrit = 9.81 / 2 * dt * dt\n",
- "zcrit = 0\n",
- "inds, indg = wm.findoutliers(ts.data, zcrit, dcrit, ddcrit, verbose=True)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "Found 0 spurious positive jumps of Dx\n",
- "Found 0 spurious negative jumps of Dx\n",
- "Found 37 spurious positive jumps of D^2x\n",
- "Found 200 spurious negative jumps of D^2x\n",
- "Found 244 consecutive equal values\n",
- "Found the total of 1152 spurious points\n"
- ]
- }
- ],
- "prompt_number": 6
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Section 2.2 Frequency Modeling of Load Histories\n",
- "---------------------------------------------------\n",
- "Periodogram: Raw spectrum"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "clf()\n",
- "Lmax = 9500\n",
- "S = ts.tospecdata(L=Lmax)\n",
- "S.plot()\n",
- "axis([0, 5, 0, 0.7])\n",
- "show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "display_data",
- "png": 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dO9q2YJCcRurdu7lPJaOuCYJwX2S5uMzMTFz763XZ29sbaWlpmDlzJubOnYub\nN2861UBXRkn1kNQYgevXgT175OdhnI8xU6dyA+4c2UgtTCNRwyiC2iAIwrOQJRBJSUn6qTR+/PFH\nvPzyy5g2bRoCAgLcZiEfZ6C0/cDYIS9aBHz7rfhYSQmgYForq2WYOwaIu7kqyc8cNTiYJAiPRPZ0\n3w0bNgQApKamIikpCePGjcO4cePQtWtXpxroyihxiFIN2lJERQFXrpg/r/St396J9RgDvv9eXnpq\ngyAIz0KWi9PpdKioqAAA7N27FwMHDtSfq6ysdI5lboCSCGL+fHl5Zmfbbg9g+wC6q1e5z59/Nk3z\nwgvy8qNqJYLwLGQJxGOPPYYBAwYgMTERfn5+6NevHwAgJycHgYGBTjXQlVEiENZ6PElx8KD0MqDW\nsKWRml85bu1a03O+vvLKpTYIgvAsZFUxLVy4EIMGDUJ+fj6GDRum77nEGMPHH3/sVAPdjfPnpY/b\nUuVy6BCQl2d/PtbaIADDPErGS4wqEQiNhpuOIzpauY0EQbgesqfa6N27t8mxhx56yKHGuBtKnLXQ\nGfNv19besm1p9DWe/0nuQDneFimB8PGRvk4qv169gBo4JIYgPBLqd2IHzm6ItWU1NyW9mOSkkTp+\n/rxhTIRU2vJyaqQmCE+ABMIObHWCciMIW5Hbi0nOVBtS5154AXj4Ycv5EQTh/pBA2IHSHkOOcJy2\nRANK5mKyNQIRotGYit+JE8C5c8ryIQhCXRRP900YcORiPM5CrjDxbSTCthL+eiVlmaNHD6B1a+DC\nBfn5EQShLhRB2IEaVSmOHCgnJ4JQEvlYK5eqngjCvaAIopqwJVqw5ZqCAqBBA/ExOQ3Q5qbdEI6R\nsAZ/7YgRgL+/vGsIgnBdSCDswBWrmM6cEe/LjSAsVTG1bWtYbtQSwrLu37d8niAI14eqmOzAVauY\n5F4jJRCMAffuGdLk5wPBwcrLtGZHVRVw6pRj8iUIwjmoJhBpaWlo37492rVrhyVLlpic37hxI7p2\n7YouXbqgb9++OOWC3sQVIwhj5LYh8FNqVVWJq5Tefx9YulT6GmP7lTyPHTuAGjzPI0G4BapUMel0\nOsyePRt79+5FcHAwoqOjkZiYiIiICH2aNm3a4Mcff0T9+vWRlpaGZ555BocPH1bDXLMocYjCSfDs\nEQZnrCjHmEEg7LFFySC+khLl5REEUb2oEkFkZmYiLCwMoaGh8PX1xaRJk7B9+3ZRmt69e6N+/foA\ngJiYGFw+CkK8AAAgAElEQVSxNAe2SrhDnbqlCELY3tC4seGYrQImVc6NG9Ln3eHZEURNR5UIIi8v\nDy1atNDvh4SE4IiFVtDVq1djxIgRZs8vWrRIvx0XF4e4uDhHmGkVV5xqQwrjhmepPBMSgF9+sU8g\npLhyBWjSRLlNBEE4loyMDGRkZCi6RhWB0CjwQPv378eaNWtw8OBBs2mEAlGduMNbsNzJ+hzxdi91\nnVAIKIIgCPUwfnlevHix1WtUEYjg4GBotVr9vlarRYjEwgenTp3C008/jbS0NDQw7tzvArhDL6bS\nUvNv61IO254pQawJRF4et+/lRREEQbgDqrRBREVFIScnB7m5uSgvL0dqaioSExNFaS5fvoyxY8di\nw4YNCAsLU8NMq1RnFdOECebXmrDEokXyqpj4e6mqst15WxMIwLAGNwkEQbg+qgiEj48PVq5cifj4\neHTo0AETJ05EREQEkpOTkZycDAB44403cOvWLTz33HOIjIxEz5491TDVIjIiNLsQOtyvvgLS0pSL\n0o0b5kdCS42krqqSX4YtbRXl5eLyHEFWFjBunOPyIwiCQ7WR1AkJCUhISBAdS0pK0m9//vnn+Pzz\nz6vbLEVs3ly95Slx3jzCQXDGHD1qSOOsKqbqYPt2YOtWdcomCE+GRlKrwCuvyEtn/IZuy1u3l5f1\nuZSMR1Lb6uil7DM3VsKRYmLLynsEQViH5mJSAVurV3Q6sWPt1An47TfL13h5Gap1LGFLG4Tx4Lqb\nN61fU1UFTJoEnDwprww5VMeIdIKoidC7l4rY69jatpVXhpJGaiURhNHYRrz2mrzrUlOBP/6Ql1YO\nJBAE4RwognBhvvlGvG/svOVUrZw9C1y9aj2dI6qY5OCMvEkgCMI5UAThwvzwg+kxpQIhRxwc1Ugt\nBxIIgnAfSCDcCGPn+vXXyvOQ6i28ciWwc6ehDGdMCGhu3xGQQBCEc6AqJjdDuFaDLfj6mh7jB68B\n8toslJCeDvz3v47LTwoSCIJwDiQQbgRjQHi4fXlICYQQLy/HvuV/9hk3xYYzIYEgCOdAVUweTpcu\n4n1rAtGpk2MFgqbUIAj3hQTCjbDFcXt7i/cfeMB82iZNAH9/xwqEtUF6joAiCIJwDiQQKrFnj/Jr\nbHHcxg7aUgRx4wbwxhvVG0GcPg2UldlXBgkEQTgHEgiVsLD+kVmUOm6pBmdrVUwA0KaN6bH33lNW\nNo81gejQAVi2zLa8eUggCMI5kEB4MBoN4OcnPiZHIKQIDLRcPWUOOW0Q9vbMIoEgCOdAAuFCWIsQ\njKe2ePddy+k1GqBpU2DYMMMxWwXC29s5y50SBOG6kECoiNLZWg8fVp5/VZX4zd8egbDF2VMjNUG4\nLyQQKrJxo3hfqQO25hg1Gs5BC6fksFUgfHxsEwil19y8aX2GWmNIIAjCOZBAqMiNG+J9pc7UWpWP\nlEDY0o4A2F7FpDSCmDUL6NxZ2TW8QDz1lLLrCIKwDAmEC2HOAR86ZFt+Xl7cmg3CN2x7qphsQY7o\nCe2Ts3aFuevXrlV+LUEQ5iGBcCHMOVPBSqw25SlVxWQ8lbg1nCkQQmxZHY6qmAjCOZBAOIH/+z9g\nwgTl15lzpuaO29IGwW8rdcTVJRC2OHu512Rnq7duNkG4IyQQTsIWR2fOmWZn226DsUB89BH3qVQg\nnLnuc3U47StXgI4dgaNHnV8WQXgKJBAuhKPHDHh5cQIhFKuiIsM5JdgaQSjFGRHExYtAixbcdmmp\n8vwJoqZCAuEENBrb3oqd8SZtHEHUqsV9upJAKF0lzxhrAmFLw7eQBx8Etm2zLw+CcEdUE4i0tDS0\nb98e7dq1w5IlS0zO//HHH+jduzdq166N999/XwUL5VOnDveZmWl7Hrt2OT6CkKpiWr/ecE4J7iwQ\nQtttEeGCAuDHH5VfRxDujioCodPpMHv2bKSlpSE7OxubNm3C6dOnRWkaNWqEjz/+GPPnz1fDREWM\nGsV9Rkdzn7t2Kc9j5EjHRxBSVUxNmxrOKaE6BIIx5zZS2wNNGULURFQRiMzMTISFhSE0NBS+vr6Y\nNGkSthtNNNSkSRNERUXB19aO+05GOCgrMVF8LjfXtjxtma3V2nlXq2K6csX0GH/fISGmo8sdja0i\nTAJB1ERUWXI0Ly8PLfhWQwAhISE4cuSIzfktWrRIvx0XF4e4uDg7rJOHcFCWlPOU44jatgXOn1d2\njdIyzI2klhKIZcsA44AtJIRz6uYEwstLmfOcN8/0GH8fV6/Kz0eIHKG0F+oeS7g7GRkZyMjIUHSN\nKgKhcXCdgFAg1MDW2zF2iM7oxVRVJT2SWkog6tY1PcYLgzmB4PN+6SVAoilJFvY6X2vPTZi/rWWR\nQBDujvHL8+LFi61eo0oVU3BwMLRarX5fq9UiJCREDVOcQlCQvHQlJeL9VascawffBiE1klpKICw5\nWmsiWFmp3D4lVFSYP2fNeQvviwSCIOSjikBERUUhJycHubm5KC8vR2pqKhKNK/L/grnhf2bDhobt\njh2lV2iT4o03HGsHPxeTlEBIOXwpgZD7+C05cGtIlXH9uniA4AMPAAcOyL9eiPC+bI32qA2CqImo\nUsXk4+ODlStXIj4+HjqdDjNmzEBERASSk5MBAElJScjPz0d0dDTu3r0LLy8vfPTRR8jOzkZdqXqQ\nakaOQ+LTNGtme926vUhVMVlqbJa6rw8/BMaONX/PfN72CISU833sMWD/frFdgqBThBKBsBU3fE8h\nCLtRRSAAICEhAQkJCaJjSYJZ6Zo1ayaqhnIlrFWnGDtkZzkXa/lKVTFZ6r0k5UgfecRyGfy9Sj0T\nb2/bFwySGtxmznZrAuCIRYtIIIiaCI2ktoGyMsvnhY7MWCA2bXKOTebsMK5iUiIQwi6ptkQQPjJf\nPxgDjh+XzleIOduVtkHodIDRu4kkV68CK1aY5kEQNQUSCBswFghjZ2ZJIHr3dpwd1urTpaqYeNsY\nA86eFac3drTBwYZtYwfZtq3YBqkIQu4Qlqoq4IUXrKczvl/GuEWXlFYxFRcDaWnWy1uzBnj+eUNZ\nBFHTIIGwAePqD6EDat4c6NVL3AZh71QS5rCniokxoF07+fkZt10YTxtuTwSxYYO08zfG+Nlt2MCN\nDLf2dm/u/PXrlq+zd4oOgnB3SCBswDiCEDqPy5fF3VX56bV5HDkEhHfAAQHS55W2QViyTTCuUZSP\nI6qY7tyR103W2D6+8d+WKiYA+PRTw3GtlrNDiPBZURUTURMhgbABSxGEt7fYsfj5iR2YI2cOad6c\n+/zXv6TPS7VB8BMLSmFOIBgTVzfxeQs/pRy8EjE0bkgWPmP++Wk04me9d6/4vDmMBYLfFwpCy5bA\n5Mni64T2UwRB1ERIIGzAOIJQMpLXUQJRUABMmmQ5jXEbxLZtQL165tObi0QsIaeba5Mm1vMxXqdB\nuLAPLxaVlZwAnzjB7fMCIfX8//wTiIoyPV9VZRAjfpJgviG6oECch7UR2ImJjh/cSBCuBAmEDRhH\nEFLOw5xzkVvtYo0mTcRvuI8+aprGy4uzlU/n52c+v5wcYNo0+eXLqWLiz8mZCeX+ffPn+OfNl3H5\nsvi81PM/e9bQM0oYncTHA99/L07788/S+Qj3pUTof/8DvvjCvN0E4e6QQNiApTYIKeLjgR49uG1n\nTE7LGLB5s+lx3on/+ad4n79GSFiYshlb+bTGAiGspuGjAjkN88bTjggRRhDCTx7hvfDTm/Nl9ujB\nzRMlxPitn78XSwJBVUxETYQEwgYstUFI8Z//AMeOcduOiiCEGK+jcPs298k7yfJy4J13xF1s7W0s\n5683boN46CFDGmvjRYRYiiD4fHgRKiwUnxc+f/674Y+dOAEcOiROb7z4Dy8QVVXcmh6rV3M90Rwx\nhxNBuDMkEDagtA2C5+BB5RFEmzamPYikEDr8+vW5T96R+vgAL78srmKyVahmzOA+jauYeIEQLs3J\nV+3ImYbDkkDcvSvO57nnDGtrA2LnzadRMnkgfw9lZZyQb98OHDkC3LsnXYaQ6lqrmyDUgATCBuQI\nhJRD6dNHeVn79wP+/pbTmKvCyc+XPt6sGVelBIgnFuRp3x746Sfpa195RVym8TgIYX0/76SlprrY\nulW8byna4BuPhaJgTiD+ms7LpnWo+UUNebuFNlkbSU4QnggJhA3IaaS2B34JUx5rb+BSAmHOgQLA\ntWuGpUe1WtMeRI0bA337SpdlHHkYt0FI2SL1Nm9tjichvNC9+KLhWEWFoSzhLK9Ll3KfxcXm82vZ\n0nJ5vL1SXW2NXwbMifOKFaZtHwThbpBA2ICcRmp7RGPHDuDZZ7ltf3+xQERFAbNmidNLOSlrUQeP\nn59hGVI5dvHjIfj7M26DkBqUx58zJzrWkIqEWrY0OGvheT5a4SMdKYyfzblz4n3+eUtFEN7ewMWL\nhuPmBOK997g/Z3D3LrB7t3PyJgghJBBmOHHC8JZtjPDNsmVL6XS3btlX/qpVwM2bQKNGYoFo3x5Y\nuVKc1lovIUdFOKNGGSIGY4GQiiD4MReVlVx6fryBUqxNifHrr4btmze5z1OnzKcvLeVEkV+n4+BB\n8XmpCOLAAYOQCO0x9+wdOaWKkHPnuPEbDz/snPwJQggJhBmOHuUmgpOirAxo0IDbvnQJGDPG0HOI\n54cf7LeBL0NYRSPl7M3Vg/PdOZUKhJIGbH6Na14ghLbwjeJy14oYPFj6uLmR4rZSUsLZZq7dg3/e\nJ08ajl27BkyZwm2PHs2NGwHMC4Gz2ibateN6pBFEdUACYQZLTrW8XOwYNBpDzyEe43176NwZCA83\nf95cnTpfTeUMgeDz3L8f+P13ru5/xQqxY2zcmPvkHa41p8mLjbMpLeWqmYzbXngOH+Y+jaMQvgda\nQQGQns5tOytS4Nm3z/T7s2dxJoJQAgmEDZSVWXcM48YBgvXBrfLoo4C5Zbl37wZ++YXbNnaiJSXA\nsGGW81YqEHK6bvJ5tm4NdOjA2T9njkEMLlzg9qUw9+zktoXYS0mJZYEwh1DgjMeB6HTA3//OzTAL\niJ95Zqa404AxwkiFr47j8xg82P7qSsbE1XAEIRcSCDNYetstLwf69+fe7M2xerVhyUw5jB8PTJgg\nfa5WLaB2bcO2EP44YNp1lMcZAmFu7MeAAUC3bpxw8GMmjO0wNxbEuNw33uDycjRlZVwVU2kpMHWq\n/OuEvwnj+8/P55ZnfeIJbp9fDHHtWiAmhmuPuXDBMH8Uz9Wr3D3y03/4+nKiU1pqqAIrL+fstLR0\nbViY6RQkPIcPA126yLtHghBCAmEGqbfc0lKuaqGsjPuHs9QQakt5CQncCF5LtG9v/py5yfaUDuaS\nU8VkLs0HHwBZWdy28TM0FojISGDgQMN5466pr75qvqOAMZbmmZKiVi3urX/9evnXCLvT8tEH35At\njP6Egrxnj2F7+nRg6FBxnnyvsOHDxasN3rtneB4lJZyd334rbdf+/cD589xzf+0185HRjRumK/dZ\n4s4dy92FCc+HBMIMUk510yZuXqWffrI8bbZcHnrI0F3RywsYMsQwcZwUd+4Af/ub+fODBgG5uabH\nlY6atiYoL78snlLDEgkJwMiR3DY/qytfx3/ihPjNlu8dFBgo31aeZs2Upbd3BDS/Ip3UgDx+tT1A\n/Oz5CECq+2tlpXgeq/v3DY6er54y16g+aBD3WVoKvPkm8Mcf4vN8m8WMGYYZbqXIyRFXhbVpw81Y\nS9RcSCDMwFchCHsQ8dNB7N/PdT+1l1q1DGsjy5mCIyDActWXRgO0aiU+lpwMLFigzC5r4vfOO/IF\nYvduwxxQLVtyb9cdOxrOC0dy8+0rAwaY5jNvnuVy5NbT82MgrE2PYk1A+CqhjAzTvITjJIRRwfnz\n3OdLL3EN+8YLFAm5dcvQU4oXUanIQNhgzfe6Mx4nw0cBv/8uXZZGw0VHDz1kWGIV4LoMZ2dz20eO\ncONgli8HHnzQvN0AF5lRQ7pnQAJhhqef5j6F/5TCGUcdIRDCxmVnzPIKAM88I9+Z8wQFWU+zcCHX\n9dNeXn4ZuHKFmzdq+3Zg8WKuOiUvjzvP9wbj124wpnt37nPQIGD2bG578WLz5fFVUcJ5lnjGjjVs\nK/k+jMXE2khtAOjUyfIcW927m3Zy4KvueD7/HJg717DPdwo4dIgTrdu3uSqn337jjl+4YL68/v25\nT+Ou3fx3PGUK17338GHD1CeMSVdBPf00EBFhvizCjWAqsWfPHhYeHs7CwsLYu+++K5lmzpw5LCws\njHXp0oWdOHFCMo2zboH7+TNWWMjtl5cbjgGMHThge96DBzO2YIG4rO++s89exhjbv3+/3XmcO8dY\nSYn9tjiKP/9kLDub2/79d8Z69xZ/D4xxn2PGMLZuHbc9ezZjwH6Wnc3Ys89yx+LiuM8HH+Q+a9Uy\n5PHFF4y99x5j9+4ZjrVpIy5H6m/gQMaCgsTHoqMZGzvW+rXO/rt+nbF//5vf329ynufECfHxhATu\n+x8+3HBszhzD9oQJ3GdWFmOffcZtl5WJv7POncVlMMbY8eOM6XSMFRUxlpJi+TuvqmKsooKx4mLG\nRo5kbN48+35DQhzxP+IIKitNn1t1I8d3qiIQlZWVrG3btuzixYusvLycde3alWXzXuAvdu3axRIS\nEhhjjB0+fJjFxMRI5gVA78Rt5dIlxrp3536UjDF25IjpP9Px4+JjRuYqorKS+2fhGTGCsYIC2/Pj\nef311+3PxA345RdODBo35vYBxiZPZmz3bm67f3/GgNcZY4wdPcody89nLDiYscREU2fKU1ZmONa2\nrThNVBT3KfxtPPmkOE1FBWP//a99jp13ro79e93kWGiodNrERE74zOX1wAPSx8eP5z7XreNEEuBE\nV6fjHD7A2K5djH3zDbf9xRfc/8GePYxptYbv4PRp7uXpwQcZ+/pr8bMVEhfHfadlZVz+a9cydvUq\nd+7WLcb+7/8Yy8w0/e3w/yPXr5vmaczVq5ygWaKqyvRYSYn4eFmZ+P+dMU70+N+vNYqLpcuxF5cV\niEOHDrH4+Hj9/jvvvMPeeecdUZqkpCS2efNm/X54eDjLz883yQsAe+ABxk6eZOytt7gv9cYNxgID\nGdu4kbFt2xjbsoWx3FzGSksN1xUVcU45PV38Qw8JMf3xv/SSYfuTT7jPmzcd/1zspaYIhDHZ2Yzd\nvs39E547x9ihQ4yNHv06Y4z7x7pxg0tXXMz9s86bx1j9+lxUeP++OK+iIu77bdiQ2z97lrHffuOu\nzc3ljv38M5dm61YunVBoCgoM+2++yX0+9xz3Rv7II4x9+y1jTZuKf1/z53OfjRpxeWzYwFizZowN\nG8bY3r2MxcdzkawcMZB24K+zU6ecITzy/sLCrKd54gnGdu4UH3vjDdN0wshv0CDGvLwYa93acCw2\nVpw+IIDLG2Ds8GHGOnZ8nU2caDjfrBn3/Qwdytjq1Yx17MjYrFncM+fTrF7N2PLlnMB99x0noIcP\nM3bsmOF7/vJL7jd46xZ3bMgQxj76iLG//53b79aNi7r27uVecJo3547PnMnlO3QoY5Mmcb+BV17h\norP33+dEDmDs8ce5+0hOZmzfPi5vrZa79vRpxs6c4SLGjz5i7O5d7mVp3jxOkE+dYiw1lfut37vH\n/cZeftmFBeKrr75iM2fO1O+vX7+ezZ49W5Rm5MiR7ODBg/r9wYMHs2PHjpnkBcDkR2T8Dyj8Cwpi\nrF496z/Ypk0Zy8kRH9u8mXMqr73mvGdjDzVVIKSw9Cx0OvHLgjHTpnERiVwuXWJM8FNlFRVcdRhj\npgLEGCdgjRsztnAhY5cvc8fS0jjhsUZZGWN37nCRxrJljE2fztiLL3Ll/PEH5zD++U/OEb34Iud0\n+/d/nTHGOYszZxh7/nnGPviAczr/+Adnx6+/GsTt8GFOaEeO5Mrp359z8j//zFiDBlyaVq24qqdx\n4xjr0MHwP6LRcM4vJoZzdLw4PP64OM2jj3LO2dr/YePGtomStzcXmUuJpRoi6eVVveX5+MhJ56IC\nsWXLFlkC8dNPP+n3Bw8ezI4fP26SFycQ9Ed/9Ed/9Kf0zxpOWADTOsHBwdDyQ00BaLVahBjNM2Gc\n5sqVKwjmRxUJ4DSCIAiCcDSqdHONiopCTk4OcnNzUV5ejtTUVCQajchJTEzEunXrAACHDx9GYGAg\nHrTWAZsgCIJwGKpEED4+Pli5ciXi4+Oh0+kwY8YMREREIPmv9SKTkpIwYsQI7N69G2FhYfD398fa\ntWvVMJUgCKLGomFuWkeTlpaGuXPnQqfTYebMmXipBq/v+NRTT2HXrl1o2rQpfq3B03ZqtVpMnToV\nBQUF0Gg0eOaZZ/A3S3OTeDClpaUYMGAAysrKUF5ejtGjR+OdGr6QhE6nQ1RUFEJCQvC///1PbXNU\nIzQ0FAEBAfD29oavry8yMzPNpnVLgdDpdAgPD8fevXsRHByM6OhobNq0CRE1dPjmgQMHULduXUyd\nOrVGC0R+fj7y8/PRrVs3FBUVoUePHti2bVuN/V0UFxfDz88PlZWViI2NxbJlyxAbG6u2WaqxfPly\nHD9+HPfu3cOOHTvUNkc1WrdujePHj6OhcJ4bM7jlVBuZmZkICwtDaGgofH19MWnSJGzfvl1ts1Sj\nX79+aMAvP1eDadasGbr9NT943bp1ERERgauW5sj2cPz+mlekvLwcOp1OlkPwVK5cuYLdu3dj5syZ\n1LEF8jv3uKVA5OXloYVgIpuQkBDk8ZP3EASA3NxcZGVlISYmRm1TVKOqqgrdunXDgw8+iIEDB6JD\nhw5qm6Qaf//737F06VJ4OXsJQDdAo9FgyJAhiIqKwmeffWYxrVs+LY2zFvwlPIKioiKMHz8eH330\nEerWrau2Oarh5eWFX375BVeuXMGPP/6IjIwMtU1ShZ07d6Jp06aIjIyk6AHAwYMHkZWVhT179uDf\n//43DggXOjHCLQVCzjgKomZSUVGBcePG4fHHH8eYMWPUNsclqF+/Ph5++GEcO3ZMbVNU4dChQ9ix\nYwdat26Nxx57DPv27cNUJUsJehhBf03X3KRJEzzyyCMWG6ndUiDkjKMgah6MMcyYMQMdOnTAXOE8\n2DWQwsJC3L59GwBQUlKC7777DpGRkSpbpQ7/+te/oNVqcfHiRWzevBmDBg3Sj7GqaRQXF+PeX3Pd\n379/H+np6ehsYe1ktxQI4TiKDh06YOLEiTW2pwoAPPbYY+jTpw/Onj2LFi1a1NgxIwcPHsSGDRuw\nf/9+REZGIjIyEmn80m81jGvXrmHQoEHo1q0bYmJiMGrUKAwePFhts1yCmlxFff36dfTr10//uxg5\nciSGCRemMcItu7kSBEEQzsctIwiCIAjC+ZBAEARBEJKQQBAEQRCSkEAQBEEQkpBAEG6Ht7e3vpdS\nZGQkLl++rLZJDiElJQVNmjTBM888Y1c+ixYtwvvvv6/fP3z4sNk8S0tL0a1bN9SqVQs3b960q1zC\n81Blum+CsAc/Pz9kZWVJnuM75bljV0aNRoPHHnsMK1asMDlXWVkJHx95/67G975nzx4kJCRIpq1d\nuzZ++eUXtG7dWrnBhMdDEQTh9uTm5iI8PBzTpk1D586dodVqsXTpUvTs2RNdu3bFokWL9Gnffvtt\nhIeHo1+/fpg8ebL+TTsuLg7Hjx8HwA0y4x2mTqfDiy++qM/r008/BQBkZGQgLi4Ojz76KCIiIvD4\n44/ryzh69Cj69u2Lbt26oVevXigqKsKAAQNw8uRJfZrY2FjJmXeFvc5TUlKQmJiIwYMHY+jQobh/\n/z6GDBmCHj16oEuXLqIZSYX3debMGVGe+/btw5AhQ/D7778jJiYGkZGR6Nq1K86dO2frIydqCBRB\nEG5HSUmJflRwmzZtsHz5cpw7dw7r169Hz549kZ6ejnPnziEzMxNVVVUYPXo0Dhw4AD8/P6SmpuLk\nyZOoqKhA9+7dERUVBYB765aKOlavXo3AwEBkZmairKwMsbGx+oFFv/zyC7KzsxEUFIS+ffvi0KFD\niIqKwqRJk/Dll1+iR48eKCoqQp06dTBjxgykpKTggw8+wNmzZ1FWVmZxBCtPVlYWfv31VwQGBkKn\n0+Gbb75BvXr1UFhYiN69eyMxMRHHjx83e1+FhYXw9fVFvXr18Mknn+D555/H5MmTUVlZicrKSkd9\nJYSHQgJBuB116tQRVTHl5uaiVatW6NmzJwAgPT0d6enpehG5f/8+cnJycO/ePYwdOxa1a9dG7dq1\nZU3Pkp6ejl9//RVbtmwBANy9exfnzp2Dr68vevbsiebNmwMAunXrhosXL6JevXoICgpCjx49AEA/\nWeD48ePx5ptvYunSpVizZg2efPJJq2VrNBoMGzYMgYGBALjZWRcsWIADBw7Ay8sLV69exfXr13Hg\nwAGT++IjkfT0dMTHxwMA+vTpg7fffhtXrlzB2LFjERYWZv1hEzUaqmIiPAJ/f3/R/oIFC5CVlYWs\nrCycPXsWTz31FABxFY5w28fHB1VVVQC4hlshK1eu1Od1/vx5DBkyBIwx1KpVS5/G29sblZWVZts+\n/Pz8MHToUGzbtg1fffUVpkyZIuu++DUdAGDjxo0oLCzEiRMnkJWVhaZNm6K0tBQajcbkvng70tLS\nMHz4cADclCz/+9//UKdOHYwYMQL79++XZQNRcyGBIDyO+Ph4rFmzBvfv3wfArR9y48YN9O/fH9u2\nbUNpaSnu3buHnTt36q8JDQ3Vz3bKRwt8Xv/5z3/01TFnz55FcXGxZLkajQbh4eG4du2aPq979+5B\np9MBAGbOnIm//e1v6NmzJ+rXr2/1Poxnwbl79y6aNm0Kb29v7N+/H5cuXYJGozF7X4wxnDp1Cl27\ndgUAXLx4Ea1bt8acOXMwevToGr36ICEPqmIi3A6pt3ThsaFDh+L06dPo3bs3AKBevXrYsGEDIiMj\nMXHiRHTt2hVNmzZFdHS03gnPnz8fEyZMwKeffoqHH35Yn9/MmTORm5uL7t27gzGGpk2b4ptvvjHb\nZs4TFPIAAAEHSURBVOHr64vU1FTMmTMHJSUl8PPzw3fffQd/f390794d9evXl1W9xN+TsIwpU6Zg\n1KhR6NKlC6KiovQTVBrfF1/Vdvz4cdEMrl9++SXWr18PX19fBAUFYeHChbLsIGouNFkfUWNZvHgx\n6tatixdeeKFayrt69SoGDhxo0suI57///S+OHTuGjz/+2CHlvf3222jXrh0mTJhgNa2SdYqJmgNV\nMRE1muoaL7Fu3Tr06tUL//rXv8ymqVOnDvbs2WP3QDmehQsXWhUHfqBcZWUlLcdJmEARBEEQBCEJ\nvTIQBEEQkpBAEARBEJKQQBAEQRCSkEAQBEEQkpBAEARBEJKQQBAEQRCS/D/5ezxaVcA+sAAAAABJ\nRU5ErkJggg==\n"
- }
- ],
- "prompt_number": 7
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Calculate moments \n",
- "-------------------"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "mom, text = S.moment(nr=4)\n",
- "print('sigma = %g, m0 = %g' % (sa, sqrt(mom[0])))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "sigma = 0.472955, m0 = 0.472955\n"
- ]
- }
- ],
- "prompt_number": 8
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Section 2.2.1 Random functions in Spectral Domain - Gaussian processes\n",
- "--------------------------------------------------------------------------\n",
- "Smoothing of spectral estimate \n",
- "----------------------------------\n",
- "By decreasing Lmax the spectrum estimate becomes smoother."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "clf()\n",
- "Lmax0 = 200; Lmax1 = 50\n",
- "S1 = ts.tospecdata(L=Lmax0)\n",
- "S2 = ts.tospecdata(L=Lmax1)\n",
- "S1.plot('-.')\n",
- "S2.plot()\n",
- "show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "display_data",
- "png": 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TyKRJk1AUhaFDh1JcXMyxY8eaI64WlZ6f3uylkEoBHQM4lHmoRZ4lhBDNrd42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\nWcjL0zqKptPbuzefT/6c+76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- }
- ],
- "prompt_number": 9
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- " Estimated autocovariance\n",
- "----------------------------\n",
- "Obviously knowing the spectrum one can compute the covariance\n",
- "function. The following code will compute the covariance for the \n",
- "unimodal spectral density S1 and compare it with estimated \n",
- "covariance of the signal xx."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "clf()\n",
- "Lmax = 85\n",
- "R1 = S1.tocovdata(nr=1) \n",
- "Rest = ts.tocovdata(lag=Lmax)\n",
- "R1.plot('.')\n",
- "Rest.plot()\n",
- "axis([0, 25, -0.1, 0.25])\n",
- "show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "display_data",
- "png": 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J5KNIzA6fjQnvTMCqgauoWYs0KTQSvYHaaraFTisdvHj1QtmhECWoYBXYcXMH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\nUAIhhBAiE0oghBBCZEIJhBBCiEz+H72XH4/yYkS0AAAAAElFTkSuQmCC\n"
- }
- ],
- "prompt_number": 10
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can see in Figure below that the covariance function corresponding to the spectral density S2 significantly differs from the one estimated directly from data. It can be seen in Figure above that the covariance corresponding to S1 agrees much better with the estimated covariance function."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "clf()\n",
- "R2 = S2.tocovdata(nr=1)\n",
- "R2.plot('.')\n",
- "Rest.plot()\n",
- "show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "display_data",
- "png": 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- }
- ],
- "prompt_number": 11
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Section 2.2.2 Transformed Gaussian models\n",
- "-------------------------------------------\n",
- "We begin with computing skewness and kurtosis for the data set xx and compare it with the second order wave approximation proposed by Winterstein:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import wafo.stats as ws\n",
- "rho3 = ws.skew(xx[:, 1])\n",
- "rho4 = ws.kurtosis(xx[:, 1])\n",
- "\n",
- "sk, ku = S1.stats_nl(moments='sk')"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 13
- },
- {
- "cell_type": "raw",
- "metadata": {},
- "source": [
- "Comparisons of 3 transformations"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "clf()\n",
- "import wafo.transform.models as wtm\n",
- "gh = wtm.TrHermite(mean=me, sigma=sa, skew=sk, kurt=ku).trdata()\n",
- "g = wtm.TrLinear(mean=me, sigma=sa).trdata() # Linear transformation \n",
- "glc, gemp = lc.trdata(mean=me, sigma=sa)\n",
- "\n",
- "glc.plot('b-') #! Transf. estimated from level-crossings\n",
- "gh.plot('b-.') #! Hermite Transf. estimated from moments\n",
- "g.plot('r')\n",
- "grid('on')\n",
- "show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "display_data",
- "png": 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- }
- ],
- "prompt_number": 14
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Test Gaussianity of a stochastic process\n",
- "------------------------------------------\n",
- "TESTGAUSSIAN simulates e(g(u)-u) = int (g(u)-u)^2 du for Gaussian processes given the spectral density, S. The result is plotted if test0 is given. This is useful for testing if the process X(t) is Gaussian.\n",
- "If 95% of TEST1 is less than TEST0 then X(t) is not Gaussian at a 5% level.\n",
- "\n",
- "As we see from the figure below: none of the simulated values of test1 is above 1.00. Thus the data significantly departs from a Gaussian distribution. "
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "clf()\n",
- "test0 = glc.dist2gauss()\n",
- "# the following test takes time\n",
- "N = len(xx)\n",
- "test1 = S1.testgaussian(ns=N, cases=50, test0=test0)\n",
- "is_gaussian = sum(test1 > test0) > 5 \n",
- "print(is_gaussian)\n",
- "show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "False\n"
- ]
- },
- {
- "output_type": "display_data",
- "png": 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fP6SkpLi8XIVW2gIA9u3bh/79++PEiRPIzMxEXFycw/OdaQvFniF0Zh2D1oSG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\nY0it2QAAAABJRU5ErkJggg==\n"
- }
- ],
- "prompt_number": 15
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Normalplot of data xx\n",
- "------------------------\n",
- "indicates that the underlying distribution has a \"heavy\" upper tail and a \"light\" lower tail."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "clf()\n",
- "import pylab\n",
- "ws.probplot(ts.data.ravel(), dist='norm', plot=pylab)\n",
- "show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "display_data",
- "png": 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+fJmbN29qH7dp0+beqqsBEg6isdBpLTjlwMgT0CQftrrCRavb7pZQEHdWI+c5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\nAIWDiIgE/ABEguqfnObDqQAAAABJRU5ErkJggg==\n"
- }
- ],
- "prompt_number": 16
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Section 2.2.3 Spectral densities of sea data\n",
- "-----------------------------------------------\n",
- "Example 2: Different forms of spectra"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import wafo.spectrum.models as wsm\n",
- "clf()\n",
- "Hm0 = 7; Tp = 11;\n",
- "spec = wsm.Jonswap(Hm0=Hm0, Tp=Tp).tospecdata()\n",
- "spec.plot()\n",
- "show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "display_data",
- "png": 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RtGPHDri5ueG5555DixYt0KFDB7Rv3x4tWrTA7Nmz4enpiV27dskda4NmrE7r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\n"
- }
- ],
- "prompt_number": 17
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Directional spectrum and Encountered directional spectrum\n",
- "=========================================================\n",
- "Directional spectrum\n",
- "---------------------"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "clf()\n",
- "D = wsm.Spreading('cos2s')\n",
- "Sd = D.tospecdata2d(spec)\n",
- "Sd.plot()\n",
- "show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "display_data",
- "png": 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JrbQYPJgNr7zCrVOnsvKJJ4iaOBF++R+ERpHDAdxozkFyaIKrdZ0/HYQOTWyr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\nRU5ErkJggg==\n"
- }
- ],
- "prompt_number": 18
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Encountered directional spectrum\n",
- "--------------------------------- "
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "#clf()\n",
- "#Se = spec2spec(Sd,'encdir',0,10);\n",
- "#plotspec(Se), hold on\n",
- "#plotspec(Sd,1,'--'), hold off\n",
- "##!wafostamp('','(ER)')\n",
- "#disp('Block = 17'),pause(pstate)\n",
- "#\n",
- "##!#! Frequency spectra\n",
- "#clf\n",
- "#Sd1 =spec2spec(Sd,'freq');\n",
- "#Sd2 = spec2spec(Se,'enc');\n",
- "#plotspec(spec), hold on\n",
- "#plotspec(Sd1,1,'.'),\n",
- "#plotspec(Sd2),\n",
- "##!wafostamp('','(ER)')\n",
- "#hold off\n",
- "#disp('Block = 18'),pause(pstate)\n",
- "#\n",
- "##!#! Wave number spectrum\n",
- "#clf\n",
- "#Sk = spec2spec(spec,'k1d')\n",
- "#Skd = spec2spec(Sd,'k1d')\n",
- "#plotspec(Sk), hold on\n",
- "#plotspec(Skd,1,'--'), hold off\n",
- "##!wafostamp('','(ER)')\n",
- "#disp('Block = 19'),pause(pstate)\n",
- "#\n",
- "##!#! Effect of waterdepth on spectrum\n",
- "#clf\n",
- "#plotspec(spec,1,'--'), hold on\n",
- "#S20 = spec;\n",
- "#S20.S = S20.S.*phi1(S20.w,20);\n",
- "#S20.h = 20;\n",
- "#plotspec(S20), hold off\n",
- "##!wafostamp('','(ER)')\n",
- "#disp('Block = 20'),pause(pstate)\n",
- "#\n",
- "##!#! Section 2.3 Simulation of transformed Gaussian process\n",
- "##!#! Example 3: Simulation of random sea \n",
- "##! The reconstruct function replaces the spurious points of seasurface by\n",
- "##! simulated data on the basis of the remaining data and a transformed Gaussian\n",
- "##! process. As noted previously one must be careful using the criteria \n",
- "##! for finding spurious points when reconstructing a dataset, because\n",
- "##! these criteria might remove the highest and steepest waves as we can see\n",
- "##! in this plot where the spurious points is indicated with a '+' sign:\n",
- "##!\n",
- "#clf\n",
- "#[y, grec] = reconstruct(xx,inds);\n",
- "#waveplot(y,'-',xx(inds,:),'+',1,1)\n",
- "#axis([0 inf -inf inf])\n",
- "##!wafostamp('','(ER)')\n",
- "#disp('Block = 21'),pause(pstate)\n",
- "#\n",
- "##! Compare transformation (grec) from reconstructed (y) \n",
- "##! with original (glc) from (xx)\n",
- "#clf\n",
- "#trplot(g), hold on\n",
- "#plot(gemp(:,1),gemp(:,2))\n",
- "#plot(glc(:,1),glc(:,2),'-.')\n",
- "#plot(grec(:,1),grec(:,2)), hold off \n",
- "#disp('Block = 22'),pause(pstate)\n",
- "#\n",
- "##!#!\n",
- "#clf\n",
- "#L = 200;\n",
- "#x = dat2gaus(y,grec);\n",
- "#Sx = dat2spec(x,L);\n",
- "#disp('Block = 23'),pause(pstate)\n",
- "# \n",
- "##!#!\n",
- "#clf\n",
- "#dt = spec2dt(Sx)\n",
- "#Ny = fix(2*60/dt) #! = 2 minutes\n",
- "#Sx.tr = grec;\n",
- "#ysim = spec2sdat(Sx,Ny);\n",
- "#waveplot(ysim,'-')\n",
- "##!wafostamp('','(CR)')\n",
- "#disp('Block = 24'),pause(pstate)\n",
- "\n"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Estimated spectrum compared to Torsethaugen spectrum\n",
- "-------------------------------------------------------"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "clf()\n",
- "fp = 1.1;dw = 0.01\n",
- "H0 = S1.characteristic('Hm0')[0]\n",
- "St = wsm.Torsethaugen(Hm0=H0,Tp=2*pi/fp).tospecdata(np.arange(0,5+dw/2,dw)) \n",
- "S1.plot()\n",
- "St.plot('-.')\n",
- "axis([0, 6, 0, 0.4])\n",
- "show()\n"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "display_data",
- "png": 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WKHPmzBlGjhzJF198QVBQkEV1bVWhUsjCHQtLdFz9+ivccQc0bQpeXlCFPC6E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\ncQghhLCIJA4hhBAW+X/+nuw44UNfwAAAAABJRU5ErkJggg==\n"
- }
- ],
- "prompt_number": 19
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Transformed Gaussian model compared to Gaussian model\n",
- "-------------------------------------------------------\n"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "dt = St.sampling_period()\n",
- "va, sk, ku = St.stats_nl(moments='vsk' )\n",
- "#sa = sqrt(va)\n",
- "gh = wtm.TrHermite(mean=me, sigma=sa, skew=sk, kurt=ku, ysigma=sa)\n",
- " \n",
- "ysim_t = St.sim(ns=240, dt=0.5)\n",
- "xsim_t = ysim_t.copy()\n",
- "xsim_t[:,1] = gh.gauss2dat(ysim_t[:,1])\n",
- "\n",
- "ts_y = wo.mat2timeseries(ysim_t)\n",
- "ts_x = wo.mat2timeseries(xsim_t)\n",
- "ts_y.plot_wave(sym1='r.', ts=ts_x, sym2='b', sigma=sa, nsub=5, nfig=1)\n",
- "show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "display_data",
- "png": 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7h8Eh9TSCkn+G+/m1MDQUenAX4wzx9bMP8FvyGzCZNU6u+HvaXMU+BGKK/lIs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\ngg==\n"
- }
- ],
- "prompt_number": 20
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [],
- "language": "python",
- "metadata": {},
- "outputs": []
- }
- ],
- "metadata": {}
- }
- ]
-}
\ No newline at end of file
diff --git a/wafo/doc/tutorial_scripts/WAFO Chapter 3.ipynb b/wafo/doc/tutorial_scripts/WAFO Chapter 3.ipynb
deleted file mode 100644
index 8787457..0000000
--- a/wafo/doc/tutorial_scripts/WAFO Chapter 3.ipynb
+++ /dev/null
@@ -1,73 +0,0 @@
-{
- "metadata": {
- "name": "WAFO Chapter 3"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
- {
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "CHAPTER3 Demonstrates distributions of wave characteristics\n",
- "=============================================================\n",
- "\n",
- "Chapter3 contains the commands used in Chapter3 in the tutorial.\n",
- " \n",
- "Some of the commands are edited for fast computation. \n",
- "\n",
- "Section 3.2 Estimation of wave characteristics from data\n",
- "----------------------------------------------------------\n",
- "Example 1\n",
- "~~~~~~~~~~ "
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "speed = 'fast'\n",
- "#speed = 'slow'\n",
- "\n",
- "import wafo.data as wd\n",
- "import wafo.misc as wm\n",
- "import wafo.objects as wo\n",
- "xx = wd.sea() \n",
- "xx[:,1] = wm.detrendma(xx[:,1],len(xx))\n",
- "ts = wo.mat2timeseries(xx)\n",
- "Tcrcr, ix = ts.wave_periods(vh=0, pdef='c2c', wdef='tw', rate=8)\n",
- "Tc, ixc = ts.wave_periods(vh=0, pdef='u2d', wdef='tw', rate=8)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "ename": "AssertionError",
- "evalue": "",
- "output_type": "pyerr",
- "traceback": [
- "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)",
- "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[0mxx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdetrendma\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[0mts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmat2timeseries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mTcrcr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mix\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwave_periods\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvh\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpdef\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'c2c'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwdef\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'tw'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 11\u001b[0m \u001b[0mTc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mixc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwave_periods\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvh\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpdef\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'u2d'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwdef\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'tw'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32mc:\\pab\\workspace\\pywafo_svn\\pywafo\\src\\wafo\\objects.pyc\u001b[0m in \u001b[0;36mwave_periods\u001b[1;34m(self, vh, pdef, wdef, index, rate)\u001b[0m\n\u001b[0;32m 1980\u001b[0m \u001b[0mn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mceil\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mrate\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1981\u001b[0m \u001b[0mti\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlinspace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1982\u001b[1;33m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstineman_interp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mti\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1983\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1984\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32mC:\\Python27\\lib\\site-packages\\matplotlib\\mlab.pyc\u001b[0m in \u001b[0;36mstineman_interp\u001b[1;34m(xi, x, y, yp)\u001b[0m\n\u001b[0;32m 2932\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2933\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2934\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2935\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2936\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0myp\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;31mAssertionError\u001b[0m: "
- ]
- }
- ],
- "prompt_number": 12
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [],
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\ No newline at end of file
diff --git a/wafo/doc/tutorial_scripts/WAFO Chapter 4.ipynb b/wafo/doc/tutorial_scripts/WAFO Chapter 4.ipynb
deleted file mode 100644
index 7ab36e8..0000000
--- a/wafo/doc/tutorial_scripts/WAFO Chapter 4.ipynb
+++ /dev/null
@@ -1,468 +0,0 @@
-{
- "metadata": {
- "name": "WAFO Chapter 4"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
- {
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Chapter 4 Fatigue load analysis and rain-flow cycles\n",
- "=====================================================\n",
- "\n",
- "Section 4.3.1 Crossing intensity\n",
- "--------------------------------\n"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import wafo.data as wd\n",
- "import wafo.objects as wo\n",
- "\n",
- "printing=0\n",
- "xx_sea = wd.sea() \n",
- "ts = wo.mat2timeseries(xx_sea)\n",
- "tp = ts.turning_points()\n",
- "mM = tp.cycle_pairs(kind='min2max')\n",
- "lc = mM.level_crossings(intensity=True)\n",
- "T_sea = ts.args[-1]-ts.args[0]\n",
- "\n",
- "subplot(1,2,1)\n",
- "lc.plot()\n",
- "subplot(1,2,2)\n",
- "lc.setplotter(plotmethod='step')\n",
- "lc.plot()\n",
- "show() \n",
- " \n",
- " \n",
- "m_sea = ts.data.mean() \n",
- "f0_sea = interp(m_sea, lc.args,lc.data)\n",
- "extr_sea = len(tp.data)/(2*T_sea)\n",
- "alfa_sea = f0_sea/extr_sea\n",
- "print('alfa = %g ' % alfa_sea )"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "display_data",
- "png": 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dywboLDA8PDxw6dIl1ePt27fDy5Kd5a0QEbB/P9C/v9SRWIlOncQFMg4dwogR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\nkT+tW9R/eaN3FgAAAABJRU5ErkJggg==\n"
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "alfa = 0.491212 \n"
- ]
- }
- ],
- "prompt_number": 1
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Section 4.3.2 Extraction of rainflow cycles\n",
- "-------------------------------------------\n",
- "Min-max and rainflow cycle plots\n",
- "---------------------------------"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "mM_rfc = tp.cycle_pairs(h=0.3)\n",
- " \n",
- "clf()\n",
- "subplot(122), \n",
- "mM.plot() \n",
- "title('min-max cycle pairs')\n",
- "subplot(121), \n",
- "mM_rfc.plot()\n",
- "title('Rainflow filtered cycles')\n",
- "show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "display_data",
- "png": 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nmzs3gFWhjY1llWgB5izXOl9X33NPqQ4rlUQI46mQtrSwC+Pdd927ufSUi8DX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\nAAAASUVORK5CYII=\n"
- }
- ],
- "prompt_number": 2
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Min-max and rainflow cycle distributions\n",
- "-------------------------------------------"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import wafo.misc as wm\n",
- "ampmM_sea = mM.amplitudes()\n",
- "ampRFC_sea = mM_rfc.amplitudes()\n",
- "clf()\n",
- "subplot(121) \n",
- "wm.plot_histgrm(ampmM_sea,25)\n",
- "ylim = gca().get_ylim()\n",
- "title('min-max amplitude distribution')\n",
- "subplot(122)\n",
- "wm.plot_histgrm(ampRFC_sea,25)\n",
- "gca().set_ylim(ylim)\n",
- "title('Rainflow amplitude distribution')\n",
- "show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "display_data",
- "png": 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IAAsCERHpsCAQEREAFgQiItJhQSAiIgAsCEREpMOCQEREAFgQiIhIhwWBiIgA\nsCAQEZEOCwIREQFgQSAiIh0WBCIiAsCCQEREOiwIREQEgAWBiIh02iwIS5YsgaenJ8aMGSM/l5CQ\nAKVSidDQUISGhuLQoUPytMTERPj7+yMwMBCHDx+2Tq+JzMS8JmpJEkKI1mY4fvw4HBwcsHDhQvz0\n008AgHXr1kGhUODZZ5/VmzczMxOPPfYYvvvuOxQWFmLq1KnIysqCjY1+3ZEkCYaalSSg9d4Qtc1Y\nfjXVmXlNZCnWzrE2jxAiIiLg4uLS4nlDnUpNTUVsbCzs7e2hUqng5+eH9PR0y/SUyIKY10Qt2XX0\nhW+++SY++OADhIWFYfPmzXB2dkZRURHCw8PleZRKJQoLCw2+PiEhQb6v0Wig0Wg62hXq47RaLbRa\nrUViMa+pO7FkbptEmCAnJ0eMHj1aflxaWioaGxtFY2OjWLVqlViyZIkQQoinn35a7NixQ55v6dKl\nYs+ePS3iGWvWtN4Qtc7EtO60vCayFGvnWIdGGXl4eECSJEiShGXLlsmHz97e3igoKJDnu3TpEry9\nvc2vWkSdgHlNfV2HCkJxcbF8//PPP5dHakRHR2PXrl2ora1FTk4OsrOzMW7cOMv0lMjKmNfU17V5\nDSE2NhbHjh3D1atX4ePjg3Xr1kGr1eLMmTOQJAm+vr549913AQBqtRoxMTFQq9Wws7PD22+/DUmS\nrP4miNqLeU3UUpvDTq3SKIedkhV11fBPDjsla+vyYadERNQ3sCAQERGAblgQJKnlzdW1q3tFRNT7\ndfiLadZi6PQYr98REVlftztCICKirsGCQEREAFgQiIhIhwWBiIgAsCAQEZEOCwIREQFgQSAiIh0W\nBCIiAsCCQEREOiwIREQEgAWBiIh0WBCIiAgACwIREemwIBAREQAWBCIi0mFBICIiACwIRESkw4JA\nREQAWBCIiEiHBYGIiACwIBARkQ4LAhERAWBBICIiHRYEIiICYEJBWLJkCTw9PTFmzBj5ubKyMkRF\nRSEgIADTpk1DRUWFPC0xMRH+/v4IDAzE4cOHrdNrIjMxr4laarMgLF68GGlpaXrPJSUlISoqCllZ\nWZgyZQqSkpIAAJmZmUhJSUFmZibS0tLw61//Go2NjdbpOZEZmNdELbVZECIiIuDi4qL33L59+xAf\nHw8AiI+Px969ewEAqampiI2Nhb29PVQqFfz8/JCenm6FbhOZh3lN1JJdR15UWloKT09PAICnpydK\nS0sBAEXKToxdAAALYklEQVRFRQgPD5fnUyqVKCwsNBgjISFBvq/RaKDRaDrSFSJotVpotVqz4zCv\nqbuxVG6bqkMFoSlJkiBJUqvTDWm64RCZo/kH77p168yOybym7sAaud2aDo0y8vT0RElJCQCguLgY\nHh4eAABvb28UFBTI8126dAne3t4W6CYgSS1vrq4WCU0EoGvymqg76VBBiI6ORnJyMgAgOTkZs2fP\nlp/ftWsXamtrkZOTg+zsbIwbN84iHRWi5a283CKhiQB0TV4TdSuiDfPnzxeDBw8W9vb2QqlUivff\nf19cu3ZNTJkyRfj7+4uoqChRXl4uz79hwwYxYsQIMXLkSJGWlmYwprFmjfWmvc9T32ZCWndqXhNZ\nirVzTNI10qkkSYKhZiXp9p6/uc9T32Ysv3pru9R3WDvH+E1lIiICwIJAREQ6LAhERASABYGIiHRY\nEIiICAALAhER6bAgEBERABYEIiLSYUEgIiIALAhERKTDgkBERABYEIiISIcFgYiIALAgEBGRDgsC\nEREBYEEgIiIdFgTc/m9m/mczEfV1dl3dge6gvNz4P7IREfUVPEIgIiIAvaAg8FQPEZFl9PhTRjzV\nQ0RkGT3+CIGIiCyDBYGIiACwIBARkQ4LAhERAWBBICIiHRYEIiICwIJAREQ6Zn0PQaVSwdHREba2\ntrC3t0d6ejrKysowb9485OXlQaVS4ZNPPoGzs7Ol+msWV9fbP1NB1JqeltdElmLWEYIkSdBqtTh9\n+jTS09MBAElJSYiKikJWVhamTJmCpKQki3TUEu78ZlHzG1FTPS2viSzF7FNGotkn6r59+xAfHw8A\niI+Px969e81tgqjTMa+pLzL7CGHq1KkICwvDe++9BwAoLS2Fp6cnAMDT0xOlpaXm97IL8beS+p6+\nkNdEhph1DeHrr7/G4MGDceXKFURFRSEwMFBvuiRJkIz8sFBCQoJ8X6PRQKPRmNMVq+FvJXV/Wq0W\nWq3WYvH6Ql5Tz2Dp3G6LJJofG3fQunXr4ODggPfeew9arRZeXl4oLi7G5MmTcf78ef1GJanFIfnt\n541/APeE56l7MJZfHWGJvCayFGvnWIdPGd28eROVlZUAgJ9//hmHDx/GmDFjEB0djeTkZABAcnIy\nZs+ebZmeEnUC5jX1ZR0+QsjJycEvf/lLAEB9fT0WLFiAlStXoqysDDExMcjPzzc6PK83HCEY4uIC\nlJUZnkadx5y9KGvkNZGlWDvHLHbKqF2NdkJBMIanknq/rvpgZkEga7N2jvX4P8gxhheDiYjahz9d\nQUREAFgQiIhIhwWBiIgAsCAQ9XqurvzGPZmm115UJqLb7vyoY3McZEHN8QiBqBcxdDRg7ut5JNF3\n8AiBqBcxdDTQnqJg7PWGYvCLmL0PjxCIqE2G/kfEGn82xesdXYsFoQfhxkLdXXtOORma19ifWPGf\nDjsHTxn1ILw4SN2Nodwz9ZSTsXmp6/AIoRO0d8/e2PxElmZujpn6d7T869qegUcInaC1PXtT95zu\nzE9kSdxDp6Z4hNDFrLnnxGsOPQ+HfVJX4hFCL8ZrDj2PucNGiczBgmBh3HjJGphXpi0DfjfCPCwI\nFsY9crIGU/+QqTfnmqH32pfef2fgNQQiIgLAI4Reg3tGRGQuFoRegqeqiMhcPGVERL0Kh1p3HI8Q\niKhX4dFyx/EIgWT8IhtR38YjhD6KP5lBfQ2/x9A2FoQ+qr0f/PyDFOrp+D2GtrEgkEl45EDU+/Ea\nAhERAWBBICLqkN44CMMqBSEtLQ2BgYHw9/fHxo0brdGEQVqttkfE7E1xzfvjH22P2oC6Kq8BbY+K\na50ctEZMw301Nadb+7tPa22H1mbxgtDQ0ICnn34aaWlpyMzMxM6dO/GPf/zD0s0YxILQ+XHb8/+3\nzTegtWu1Peb/crsyr1kQgM7sq7Gcbs+/y02erO2RRw0WLwjp6enw8/ODSqWCvb095s+fj9TUVEs3\nQ91ce/8C1BJ/MWrNjY55Te35I6u1a9suKO25dVZBsfgoo8LCQvj4+MiPlUolTp48aelmqJtr76ik\n7v4Xo8xrMpcpw16NPd9pI/qEhX366adi2bJl8uMPP/xQPP3003rzAOCNN6vemNe89dabNVn8CMHb\n2xsFBQXy44KCAiiVSr15hCX/OJioEzCvqS+w+DWEsLAwZGdnIzc3F7W1tUhJSUF0dLSlmyHqVMxr\n6gssfoRgZ2eHP/3pT5g+fToaGhqwdOlSjBo1ytLNEHUq5jX1CdY8H3Xo0CExcuRI4efnJ5KSkgzO\ns3z5cuHn5yfGjh0rTp06ZXbMHTt2iLFjx4oxY8aIiRMnirNnz1qsr0IIkZ6eLmxtbcWePXssFvfo\n0aMiJCREBAUFicjISLNjXrlyRUyfPl0EBweLoKAgsW3btjZjLl68WHh4eIjRo0cbnae968qUuB1d\nX6b0V4j2ry9TWCOvTYnbnXLbGnltSlzm9r9ZI7etVhDq6+vFiBEjRE5OjqitrRXBwcEiMzNTb54D\nBw6ImTNnCiGEOHHihBg/frzZMb/55htRUVEhhLidXG3FNDXunfkmT54sHnroIfHpp59aJG55eblQ\nq9WioKBACHE74c2NuXbtWvHiiy/K8VxdXUVdXV2rcb/88ktx6tQpo0nY3nVlatyOrC9T4grR/vVl\nCmvktalxu0tuWyOvTY3L3L7NGrkthBBW++kKU8Zt79u3D/Hx8QCA8ePHo6KiAqWlpWbFnDBhApyc\nnOSYly5dskhfAeDNN9/E3Llz4e7u3mZMU+N+/PHHmDNnjnyBctCgQWbHHDx4MG7cuAEAuHHjBtzc\n3GBn1/rZwYiICLi4uBid3t51ZWrcjqwvU+IC7V9fprBGXpsat7vktjXy2tS4zO3brJHbgBV/y8jQ\nuO3CwsI252ltoZkSs6mtW7di1qxZFutramoqnnrqKQCAZMLAYFPiZmdno6ysDJMnT0ZYWBg+/PBD\ns2M+8cQTyMjIwJAhQxAcHIzXX3+9zb525L2YmuCmMnV9maIj68vUuJbOa1PjNtWVuW2NvDY1LnPb\nerkNWPHnr03tpGg2VK+117XnjR89ehTvv/8+vv766zbnNSXuihUrkJSUBEmSIG6farNI3Lq6Opw6\ndQpffPEFbt68iQkTJiA8PBz+/v4djvnqq68iJCQEWq0WFy5cQFRUFM6ePQuFQtHma1vTnnXVXu1Z\nX6boyPoyhTXyuj1xga7PbWvktalxmdvWy23AigXBlHHbzee5dOkSvL29zYoJAD/++COeeOIJpKWl\ntXnoZWrcH374AfPnzwcAXL16FYcOHYK9vX2rQw9Nievj44NBgwahf//+6N+/PyZNmoSzZ88a3XBM\nifnNN99g1apVAIARI0bA19cX//znPxEWFtbaYmhVe9dVe7R3fZmiI+vLFNbIa1PjAt0jt62R16bG\nZW5bL7cBWG+UUV1dnRg+fLjIyckRNTU1bV58+/bbb9u86GJKzLy8PDFixAjx7bffWrSvTS1atMik\nK/umxP3HP/4hpkyZIurr68XPP/8sRo8eLTIyMsyK+dvf/lYkJCQIIYQoKSkR3t7e4tq1a232Nycn\nx6QLb6asK1PjdmR9mRK3KVP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- }
- ],
- "prompt_number": 3
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "#!#! Section 4.3.3 Simulation of rainflow cycles\n",
- "#!#! Simulation of cycles in a Markov model\n",
- "n=41; param_m=[-1, 1, n]; param_D=[1, n, n];\n",
- "u_markov=levels(param_m);\n",
- "G_markov=mktestmat(param_m,[-0.2, 0.2],0.15,1);\n",
- "T_markov=5000;\n",
- "#xxD_markov=mctpsim({G_markov [,]},T_markov);\n",
- "#xx_markov=[(1:T_markov)' u_markov(xxD_markov)'];\n",
- "#clf\n",
- "#plot(xx_markov(1:50,1),xx_markov(1:50,2))\n",
- "#title('Markov chain of turning points')\n",
- "#wafostamp([],'(ER)')\n",
- "#disp('Block 5'),pause(pstate)\n",
- "#\n",
- "#\n",
- "##!#! Rainflow cycles in a transformed Gaussian model\n",
- "##!#! Hermite transformed wave data and rainflow filtered turning points, h = 0.2.\n",
- "#me = mean(xx_sea(:,2));\n",
- "#sa = std(xx_sea(:,2));\n",
- "#Hm0_sea = 4*sa;\n",
- "#Tp_sea = 1/max(lc_sea(:,2));\n",
- "#spec = jonswap([],[Hm0_sea Tp_sea]);\n",
- "#\n",
- "#[sk, ku] = spec2skew(spec);\n",
- "#spec.tr = hermitetr([],[sa sk ku me]);\n",
- "#param_h = [-1.5 2 51];\n",
- "#spec_norm = spec;\n",
- "#spec_norm.S = spec_norm.S/sa^2;\n",
- "#xx_herm = spec2sdat(spec_norm,[2^15 1],0.1);\n",
- "##! ????? PJ, JR 11-Apr-2001\n",
- "##! NOTE, in the simulation program spec2sdat\n",
- "##!the spectrum must be normalized to variance 1 \n",
- "##! ?????\n",
- "#h = 0.2;\n",
- "#[dtp,u_herm,xx_herm_1]=dat2dtp(param_h,xx_herm,h);\n",
- "#clf\n",
- "#plot(xx_herm(:,1),xx_herm(:,2),'k','LineWidth',2); hold on;\n",
- "#plot(xx_herm_1(:,1),xx_herm_1(:,2),'k--','Linewidth',2);\n",
- "#axis([0 50 -1 1]), hold off;\n",
- "#title('Rainflow filtered wave data')\n",
- "#wafostamp([],'(ER)')\n",
- "#disp('Block 6'),pause(pstate)\n",
- "#\n",
- "##!#! Rainflow cycles and rainflow filtered rainflow cycles in the transformed Gaussian process.\n",
- "#tp_herm=dat2tp(xx_herm);\n",
- "#RFC_herm=tp2rfc(tp_herm);\n",
- "#mM_herm=tp2mm(tp_herm);\n",
- "#h=0.2;\n",
- "#[dtp,u,tp_herm_1]=dat2dtp(param_h,xx_herm,h);\n",
- "#RFC_herm_1 = tp2rfc(tp_herm_1);\n",
- "#clf\n",
- "#subplot(121), ccplot(RFC_herm)\n",
- "#title('h=0')\n",
- "#subplot(122), ccplot(RFC_herm_1)\n",
- "#title('h=0.2')\n",
- "#if (printing==1), print -deps ../bilder/fatigue_8.eps \n",
- "#end\n",
- "#wafostamp([],'(ER)')\n",
- "#disp('Block 7'),pause(pstate)\n",
- "#\n",
- "##!#! Section 4.3.4 Calculating the rainflow matrix\n",
- "#\n",
- "#\n",
- "#Grfc_markov=mctp2rfm({G_markov []});\n",
- "#clf\n",
- "#subplot(121), cmatplot(u_markov,u_markov,G_markov), axis('square')\n",
- "#subplot(122), cmatplot(u_markov,u_markov,Grfc_markov), axis('square')\n",
- "#wafostamp([],'(ER)')\n",
- "#disp('Block 8'),pause(pstate)\n",
- "#\n",
- "##!#! \n",
- "#clf\n",
- "#cmatplot(u_markov,u_markov,{G_markov Grfc_markov},3) \n",
- "#wafostamp([],'(ER)')\n",
- "#disp('Block 9'),pause(pstate)\t\n",
- "#\n",
- "##!#! Min-max-matrix and theoretical rainflow matrix for test Markov sequence.\n",
- "#cmatplot(u_markov,u_markov,{G_markov Grfc_markov},4)\n",
- "#subplot(121), axis('square'), title('min2max transition matrix')\n",
- "#subplot(122), axis('square'), title('Rainflow matrix')\n",
- "#if (printing==1), print -deps ../bilder/fatigue_9.eps \n",
- "#end\n",
- "#wafostamp([],'(ER)')\n",
- "#disp('Block 10'),pause(pstate)\n",
- "#\n",
- "##!#! Observed and theoretical rainflow matrix for test Markov sequence.\n",
- "#n=length(u_markov);\n",
- "#Frfc_markov=dtp2rfm(xxD_markov,n);\n",
- "#clf\n",
- "#cmatplot(u_markov,u_markov,{Frfc_markov Grfc_markov*T_markov/2},3) \n",
- "#subplot(121), axis('square'), title('Observed rainflow matrix')\n",
- "#subplot(122), axis('square'), title('Theoretical rainflow matrix')\n",
- "#if (printing==1), print -deps ../bilder/fatigue_10.eps \n",
- "#end\n",
- "#wafostamp([],'(ER)')\n",
- "#disp('Block 11'),pause(pstate)\n",
- "#\n",
- "##!#! Smoothed observed and calculated rainflow matrix for test Markov sequence.\n",
- "#tp_markov=dat2tp(xx_markov);\n",
- "#RFC_markov=tp2rfc(tp_markov);\n",
- "#h=1;\n",
- "#Frfc_markov_smooth=cc2cmat(param_m,RFC_markov,[],1,h);\n",
- "#clf\n",
- "#cmatplot(u_markov,u_markov,{Frfc_markov_smooth Grfc_markov*T_markov/2},4)\n",
- "#subplot(121), axis('square'), title('Smoothed observed rainflow matrix')\n",
- "#subplot(122), axis('square'), title('Theoretical rainflow matrix')\n",
- "#if (printing==1), print -deps ../bilder/fatigue_11.eps \n",
- "#end\n",
- "#wafostamp([],'(ER)')\n",
- "#disp('Block 12'),pause(pstate)\n",
- "#\n",
- "##!#! Rainflow matrix from spectrum\n",
- "#clf\n",
- "##!GmM3_herm=spec2mmtpdf(spec,[],'Mm',[],[],2);\n",
- "#GmM3_herm=spec2cmat(spec,[],'Mm',[],param_h,2);\n",
- "#pdfplot(GmM3_herm)\n",
- "#wafostamp([],'(ER)')\n",
- "#disp('Block 13'),pause(pstate)\n",
- "#\n",
- "#\n",
- "##!#! Min-max matrix and theoretical rainflow matrix for Hermite-transformed Gaussian waves.\n",
- "#Grfc_herm=mctp2rfm({GmM3_herm.f []});\n",
- "#u_herm=levels(param_h);\n",
- "#clf\n",
- "#cmatplot(u_herm,u_herm,{GmM3_herm.f Grfc_herm},4)\n",
- "#subplot(121), axis('square'), title('min-max matrix')\n",
- "#subplot(122), axis('square'), title('Theoretical rainflow matrix')\n",
- "#if (printing==1), print -deps ../bilder/fatigue_12.eps \n",
- "#end\n",
- "#wafostamp([],'(ER)')\n",
- "#disp('Block 14'),pause(pstate)\n",
- "#\n",
- "##!#!\n",
- "#clf\n",
- "#Grfc_direct_herm=spec2cmat(spec,[],'rfc',[],[],2);\n",
- "#subplot(121), pdfplot(GmM3_herm), axis('square'), hold on\n",
- "#subplot(122), pdfplot(Grfc_direct_herm), axis('square'), hold off\n",
- "#if (printing==1), print -deps ../bilder/fig_mmrfcjfr.eps\n",
- "#end\n",
- "#wafostamp([],'(ER)')\n",
- "#disp('Block 15'),pause(pstate)\n",
- "#\n",
- "#\n",
- "##!#! Observed smoothed and theoretical min-max matrix, \n",
- "##!#! (and observed smoothed and theoretical rainflow matrix for Hermite-transformed Gaussian waves).\n",
- "#tp_herm=dat2tp(xx_herm);\n",
- "#RFC_herm=tp2rfc(tp_herm);\n",
- "#mM_herm=tp2mm(tp_herm);\n",
- "#h=0.2;\n",
- "#FmM_herm_smooth=cc2cmat(param_h,mM_herm,[],1,h);\n",
- "#Frfc_herm_smooth=cc2cmat(param_h,RFC_herm,[],1,h);\n",
- "#T_herm=xx_herm(end,1)-xx_herm(1,1);\n",
- "#clf\n",
- "#cmatplot(u_herm,u_herm,{FmM_herm_smooth GmM3_herm.f*length(mM_herm) ; ...\n",
- "# Frfc_herm_smooth Grfc_herm*length(RFC_herm)},4)\n",
- "#subplot(221), axis('square'), title('Observed smoothed min-max matrix')\n",
- "#subplot(222), axis('square'), title('Theoretical min-max matrix')\n",
- "#subplot(223), axis('square'), title('Observed smoothed rainflow matrix')\n",
- "#subplot(224), axis('square'), title('Theoretical rainflow matrix')\n",
- "#if (printing==1), print -deps ../bilder/fatigue_13.eps \n",
- "#end\n",
- "#wafostamp([],'(ER)')\n",
- "#disp('Block 16'),pause(pstate)\n",
- "# \n",
- "##!#! Section 4.3.5 Simulation from crossings and rainflow structure\n",
- "#\n",
- "##!#! Crossing spectrum (smooth curve) and obtained spectrum (wiggled curve)\n",
- "##!#! for simulated process with irregularity factor 0.25.\n",
- "#clf\n",
- "#cross_herm=dat2lc(xx_herm);\n",
- "#alpha1=0.25;\n",
- "#alpha2=0.75;\n",
- "#xx_herm_sim1=lc2sdat(cross_herm,500,alpha1);\n",
- "#cross_herm_sim1=dat2lc(xx_herm_sim1);\n",
- "#subplot(211)\n",
- "#plot(cross_herm(:,1),cross_herm(:,2)/max(cross_herm(:,2)))\n",
- "#hold on\n",
- "#stairs(cross_herm_sim1(:,1),...\n",
- "# cross_herm_sim1(:,2)/max(cross_herm_sim1(:,2)))\n",
- "#hold off\n",
- "#title('Crossing intensity, \\alpha = 0.25')\n",
- "#subplot(212)\n",
- "#plot(xx_herm_sim1(:,1),xx_herm_sim1(:,2))\n",
- "#title('Simulated load, \\alpha = 0.25')\n",
- "#if (printing==1), print -deps ../bilder/fatigue_14_25.eps \n",
- "#end\n",
- "#wafostamp([],'(ER)')\n",
- "#disp('Block 16'),pause(pstate)\n",
- "#\n",
- "##!#! Crossing spectrum (smooth curve) and obtained spectrum (wiggled curve)\n",
- "##!#! for simulated process with irregularity factor 0.75.\n",
- "#xx_herm_sim2=lc2sdat(cross_herm,500,alpha2);\n",
- "#cross_herm_sim2=dat2lc(xx_herm_sim2);\n",
- "#subplot(211)\n",
- "#plot(cross_herm(:,1),cross_herm(:,2)/max(cross_herm(:,2)))\n",
- "#hold on\n",
- "#stairs(cross_herm_sim2(:,1),...\n",
- "# cross_herm_sim2(:,2)/max(cross_herm_sim2(:,2)))\n",
- "#hold off\n",
- "#title('Crossing intensity, \\alpha = 0.75')\n",
- "#subplot(212)\n",
- "#plot(xx_herm_sim2(:,1),xx_herm_sim2(:,2))\n",
- "#title('Simulated load, \\alpha = 0.75')\n",
- "#if (printing==1), print -deps ../bilder/fatigue_14_75.eps \n",
- "#end\n",
- "#wafostamp([],'(ER)')\n",
- "#disp('Block 17'),pause(pstate)\n",
- "#\n",
- "##!#! Section 4.4 Fatigue damage and fatigue life distribution\n",
- "##!#! Section 4.4.1 Introduction\n",
- "#beta=3.2; gam=5.5E-10; T_sea=xx_sea(end,1)-xx_sea(1,1);\n",
- "#d_beta=cc2dam(RFC_sea,beta)/T_sea;\n",
- "#time_fail=1/gam/d_beta/3600 #!in hours of the specific storm\n",
- "#disp('Block 18'),pause(pstate)\n",
- "#\n",
- "##!#! Section 4.4.2 Level crossings\n",
- "##!#! Crossing intensity as calculated from the Markov matrix (solid curve) and from the observed rainflow matrix (dashed curve).\n",
- "#clf\n",
- "#mu_markov=cmat2lc(param_m,Grfc_markov);\n",
- "#muObs_markov=cmat2lc(param_m,Frfc_markov/(T_markov/2));\n",
- "#clf\n",
- "#plot(mu_markov(:,1),mu_markov(:,2),muObs_markov(:,1),muObs_markov(:,2),'--')\n",
- "#title('Theoretical and observed crossing intensity ')\n",
- "#if (printing==1), print -deps ../bilder/fatigue_15.eps \n",
- "#end\n",
- "#wafostamp([],'(ER)')\n",
- "#disp('Block 19'),pause(pstate)\n",
- "#\n",
- "##!#! Section 4.4.3 Damage\n",
- "##!#! Distribution of damage from different RFC cycles, from calculated theoretical and from observed rainflow matrix.\n",
- "#beta = 4;\n",
- "#Dam_markov = cmat2dam(param_m,Grfc_markov,beta)\n",
- "#DamObs1_markov = cc2dam(RFC_markov,beta)/(T_markov/2)\n",
- "#DamObs2_markov = cmat2dam(param_m,Frfc_markov,beta)/(T_markov/2)\n",
- "#disp('Block 20'),pause(pstate)\n",
- "#\n",
- "#Dmat_markov = cmat2dmat(param_m,Grfc_markov,beta);\n",
- "#DmatObs_markov = cmat2dmat(param_m,Frfc_markov,beta)/(T_markov/2); \n",
- "#clf\n",
- "#subplot(121), cmatplot(u_markov,u_markov,Dmat_markov,4)\n",
- "#title('Theoretical damage matrix') \n",
- "#subplot(122), cmatplot(u_markov,u_markov,DmatObs_markov,4)\n",
- "#title('Observed damage matrix') \n",
- "#if (printing==1), print -deps ../bilder/fatigue_16.eps \n",
- "#end\n",
- "#wafostamp([],'(ER)')\n",
- "#disp('Block 21'),pause(pstate)\n",
- "#\n",
- "#\n",
- "##!#!\n",
- "##!Damplus_markov = lc2dplus(mu_markov,beta)\n",
- "#pause(pstate)\n",
- "#\n",
- "##!#! Section 4.4.4 Estimation of S-N curve\n",
- "#\n",
- "##!#! Load SN-data and plot in log-log scale.\n",
- "#SN = load('sn.dat');\n",
- "#s = SN(:,1);\n",
- "#N = SN(:,2);\n",
- "#clf\n",
- "#loglog(N,s,'o'), axis([0 14e5 10 30])\n",
- "##!if (printing==1), print -deps ../bilder/fatigue_?.eps end\n",
- "#wafostamp([],'(ER)')\n",
- "#disp('Block 22'),pause(pstate)\n",
- "#\n",
- "#\n",
- "##!#! Check of S-N-model on normal probability paper.\n",
- "#\n",
- "#normplot(reshape(log(N),8,5))\n",
- "#if (printing==1), print -deps ../bilder/fatigue_17.eps \n",
- "#end\n",
- "#wafostamp([],'(ER)')\n",
- "#disp('Block 23'),pause(pstate)\n",
- "#\n",
- "##!#! Estimation of S-N-model on linear scale.\n",
- "#clf\n",
- "#[e0,beta0,s20] = snplot(s,N,12);\n",
- "#title('S-N-data with estimated N(s)','FontSize',20)\n",
- "#set(gca,'FontSize',20)\n",
- "#if (printing==1), print -deps ../bilder/fatigue_18a.eps \n",
- "#end\n",
- "#wafostamp([],'(ER)')\n",
- "#disp('Block 24'),pause(pstate)\n",
- "#\n",
- "##!#! Estimation of S-N-model on log-log scale.\n",
- "#clf\n",
- "#[e0,beta0,s20] = snplot(s,N,14);\n",
- "#title('S-N-data with estimated N(s)','FontSize',20)\n",
- "#set(gca,'FontSize',20)\n",
- "#if (printing==1), print -deps ../bilder/fatigue_18b.eps \n",
- "#end\n",
- "#wafostamp([],'(ER)')\n",
- "#disp('Block 25'),pause(pstate)\n",
- "#\n",
- "##!#! Section 4.4.5 From S-N curve to fatigue life distribution\n",
- "##!#! Damage intensity as function of $\\beta$\n",
- "#beta = 3:0.1:8;\n",
- "#DRFC = cc2dam(RFC_sea,beta);\n",
- "#dRFC = DRFC/T_sea;\n",
- "#plot(beta,dRFC), axis([3 8 0 0.25])\n",
- "#title('Damage intensity as function of \\beta')\n",
- "#if (printing==1), print -deps ../bilder/fatigue_19.eps \n",
- "#end\n",
- "#wafostamp([],'(ER)')\n",
- "#disp('Block 26'),pause(pstate)\n",
- "#\n",
- "##!#! Fatigue life distribution with sea load.\n",
- "#dam0 = cc2dam(RFC_sea,beta0)/T_sea;\n",
- "#[t0,F0] = ftf(e0,dam0,s20,0.5,1);\n",
- "#[t1,F1] = ftf(e0,dam0,s20,0,1);\n",
- "#[t2,F2] = ftf(e0,dam0,s20,5,1);\n",
- "#plot(t0,F0,t1,F1,t2,F2)\n",
- "#title('Fatigue life distribution function')\n",
- "#if (printing==1), print -deps ../bilder/fatigue_20.eps \n",
- "#end\n",
- "#wafostamp([],'(ER)')\n",
- "#disp('Block 27, last block')"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- }
- ],
- "metadata": {}
- }
- ]
-}
\ No newline at end of file
diff --git a/wafo/doc/tutorial_scripts/WAFO Chapter 5.ipynb b/wafo/doc/tutorial_scripts/WAFO Chapter 5.ipynb
deleted file mode 100644
index 5d0d96b..0000000
--- a/wafo/doc/tutorial_scripts/WAFO Chapter 5.ipynb
+++ /dev/null
@@ -1,11412 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "%matplotlib notebook\n",
- "import numpy as np\n",
- "import wafo.data as wd\n",
- "import wafo.stats as ws\n",
- "import matplotlib.pyplot as plt\n",
- "# import mpld3\n",
- "# mpld3.enable_notebook() # Enable interactive plots"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Chapter 5 Extreme value analysis\n",
- "=================================\n",
- "\n",
- "Of particular interest in wave analysis is how to find extreme quantiles and extreme significant values for a wave series. Often this implies going outside the range of observed data, i.e. to predict, from a limited number of observations, how large the extreme values might be. Such analysis is\n",
- "commonly known as Weibull analysis or Gumbel analysis, from the names of two familiar extreme value distributions. WAFO contains routines for fitting of such distributions, both for the Weibull and Gumbel distributions, and for two more general classes of distributions, the Generalized Pareto\n",
- "Distribution (GPD) and the Generalized Extreme Value distribution (GEV).\n",
- "\n",
- "Section 5.1 Weibull and Gumbel papers\n",
- "--------------------------------------\n",
- "\n",
- "Significant wave-height data on Weibull paper, on Gumbel paper and logarithm of data on Normal probability paper:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "application/javascript": [
- "/* Put everything inside the global mpl namespace */\n",
- "window.mpl = {};\n",
- "\n",
- "mpl.get_websocket_type = function() {\n",
- " if (typeof(WebSocket) !== 'undefined') {\n",
- " return WebSocket;\n",
- " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
- " return MozWebSocket;\n",
- " } else {\n",
- " alert('Your browser does not have WebSocket support.' +\n",
- " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
- " 'Firefox 4 and 5 are also supported but you ' +\n",
- " 'have to enable WebSockets in about:config.');\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
- " this.id = figure_id;\n",
- "\n",
- " this.ws = websocket;\n",
- "\n",
- " this.supports_binary = (this.ws.binaryType != undefined);\n",
- "\n",
- " if (!this.supports_binary) {\n",
- " var warnings = document.getElementById(\"mpl-warnings\");\n",
- " if (warnings) {\n",
- " warnings.style.display = 'block';\n",
- " warnings.textContent = (\n",
- " \"This browser does not support binary websocket messages. \" +\n",
- " \"Performance may be slow.\");\n",
- " }\n",
- " }\n",
- "\n",
- " this.imageObj = new Image();\n",
- "\n",
- " this.context = undefined;\n",
- " this.message = undefined;\n",
- " this.canvas = undefined;\n",
- " this.rubberband_canvas = undefined;\n",
- " this.rubberband_context = undefined;\n",
- " this.format_dropdown = undefined;\n",
- "\n",
- " this.image_mode = 'full';\n",
- "\n",
- " this.root = $('');\n",
- " this._root_extra_style(this.root)\n",
- " this.root.attr('style', 'display: inline-block');\n",
- "\n",
- " $(parent_element).append(this.root);\n",
- "\n",
- " this._init_header(this);\n",
- " this._init_canvas(this);\n",
- " this._init_toolbar(this);\n",
- "\n",
- " var fig = this;\n",
- "\n",
- " this.waiting = false;\n",
- "\n",
- " this.ws.onopen = function () {\n",
- " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
- " fig.send_message(\"send_image_mode\", {});\n",
- " fig.send_message(\"refresh\", {});\n",
- " }\n",
- "\n",
- " this.imageObj.onload = function() {\n",
- " if (fig.image_mode == 'full') {\n",
- " // Full images could contain transparency (where diff images\n",
- " // almost always do), so we need to clear the canvas so that\n",
- " // there is no ghosting.\n",
- " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
- " }\n",
- " fig.context.drawImage(fig.imageObj, 0, 0);\n",
- " };\n",
- "\n",
- " this.imageObj.onunload = function() {\n",
- " this.ws.close();\n",
- " }\n",
- "\n",
- " this.ws.onmessage = this._make_on_message_function(this);\n",
- "\n",
- " this.ondownload = ondownload;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_header = function() {\n",
- " var titlebar = $(\n",
- " '');\n",
- " var titletext = $(\n",
- " '');\n",
- " titlebar.append(titletext)\n",
- " this.root.append(titlebar);\n",
- " this.header = titletext[0];\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_canvas = function() {\n",
- " var fig = this;\n",
- "\n",
- " var canvas_div = $('');\n",
- "\n",
- " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
- "\n",
- " function canvas_keyboard_event(event) {\n",
- " return fig.key_event(event, event['data']);\n",
- " }\n",
- "\n",
- " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
- " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
- " this.canvas_div = canvas_div\n",
- " this._canvas_extra_style(canvas_div)\n",
- " this.root.append(canvas_div);\n",
- "\n",
- " var canvas = $('');\n",
- " canvas.addClass('mpl-canvas');\n",
- " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
- "\n",
- " this.canvas = canvas[0];\n",
- " this.context = canvas[0].getContext(\"2d\");\n",
- "\n",
- " var rubberband = $('');\n",
- " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
- "\n",
- " var pass_mouse_events = true;\n",
- "\n",
- " canvas_div.resizable({\n",
- " start: function(event, ui) {\n",
- " pass_mouse_events = false;\n",
- " },\n",
- " resize: function(event, ui) {\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " stop: function(event, ui) {\n",
- " pass_mouse_events = true;\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " });\n",
- "\n",
- " function mouse_event_fn(event) {\n",
- " if (pass_mouse_events)\n",
- " return fig.mouse_event(event, event['data']);\n",
- " }\n",
- "\n",
- " rubberband.mousedown('button_press', mouse_event_fn);\n",
- " rubberband.mouseup('button_release', mouse_event_fn);\n",
- " // Throttle sequential mouse events to 1 every 20ms.\n",
- " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
- "\n",
- " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
- " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
- "\n",
- " canvas_div.on(\"wheel\", function (event) {\n",
- " event = event.originalEvent;\n",
- " event['data'] = 'scroll'\n",
- " if (event.deltaY < 0) {\n",
- " event.step = 1;\n",
- " } else {\n",
- " event.step = -1;\n",
- " }\n",
- " mouse_event_fn(event);\n",
- " });\n",
- "\n",
- " canvas_div.append(canvas);\n",
- " canvas_div.append(rubberband);\n",
- "\n",
- " this.rubberband = rubberband;\n",
- " this.rubberband_canvas = rubberband[0];\n",
- " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
- " this.rubberband_context.strokeStyle = \"#000000\";\n",
- "\n",
- " this._resize_canvas = function(width, height) {\n",
- " // Keep the size of the canvas, canvas container, and rubber band\n",
- " // canvas in synch.\n",
- " canvas_div.css('width', width)\n",
- " canvas_div.css('height', height)\n",
- "\n",
- " canvas.attr('width', width);\n",
- " canvas.attr('height', height);\n",
- "\n",
- " rubberband.attr('width', width);\n",
- " rubberband.attr('height', height);\n",
- " }\n",
- "\n",
- " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
- " // upon first draw.\n",
- " this._resize_canvas(600, 600);\n",
- "\n",
- " // Disable right mouse context menu.\n",
- " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
- " return false;\n",
- " });\n",
- "\n",
- " function set_focus () {\n",
- " canvas.focus();\n",
- " canvas_div.focus();\n",
- " }\n",
- "\n",
- " window.setTimeout(set_focus, 100);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " // put a spacer in here.\n",
- " continue;\n",
- " }\n",
- " var button = $('');\n",
- " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
- " 'ui-button-icon-only');\n",
- " button.attr('role', 'button');\n",
- " button.attr('aria-disabled', 'false');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- "\n",
- " var icon_img = $('');\n",
- " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
- " icon_img.addClass(image);\n",
- " icon_img.addClass('ui-corner-all');\n",
- "\n",
- " var tooltip_span = $('');\n",
- " tooltip_span.addClass('ui-button-text');\n",
- " tooltip_span.html(tooltip);\n",
- "\n",
- " button.append(icon_img);\n",
- " button.append(tooltip_span);\n",
- "\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " var fmt_picker_span = $('');\n",
- "\n",
- " var fmt_picker = $('');\n",
- " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
- " fmt_picker_span.append(fmt_picker);\n",
- " nav_element.append(fmt_picker_span);\n",
- " this.format_dropdown = fmt_picker[0];\n",
- "\n",
- " for (var ind in mpl.extensions) {\n",
- " var fmt = mpl.extensions[ind];\n",
- " var option = $(\n",
- " '', {selected: fmt === mpl.default_extension}).html(fmt);\n",
- " fmt_picker.append(option)\n",
- " }\n",
- "\n",
- " // Add hover states to the ui-buttons\n",
- " $( \".ui-button\" ).hover(\n",
- " function() { $(this).addClass(\"ui-state-hover\");},\n",
- " function() { $(this).removeClass(\"ui-state-hover\");}\n",
- " );\n",
- "\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
- " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
- " // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_message = function(type, properties) {\n",
- " properties['type'] = type;\n",
- " properties['figure_id'] = this.id;\n",
- " this.ws.send(JSON.stringify(properties));\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_draw_message = function() {\n",
- " if (!this.waiting) {\n",
- " this.waiting = true;\n",
- " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
- " }\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " var format_dropdown = fig.format_dropdown;\n",
- " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
- " fig.ondownload(fig, format);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
- " var size = msg['size'];\n",
- " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1]);\n",
- " fig.send_message(\"refresh\", {});\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
- " var x0 = msg['x0'];\n",
- " var y0 = fig.canvas.height - msg['y0'];\n",
- " var x1 = msg['x1'];\n",
- " var y1 = fig.canvas.height - msg['y1'];\n",
- " x0 = Math.floor(x0) + 0.5;\n",
- " y0 = Math.floor(y0) + 0.5;\n",
- " x1 = Math.floor(x1) + 0.5;\n",
- " y1 = Math.floor(y1) + 0.5;\n",
- " var min_x = Math.min(x0, x1);\n",
- " var min_y = Math.min(y0, y1);\n",
- " var width = Math.abs(x1 - x0);\n",
- " var height = Math.abs(y1 - y0);\n",
- "\n",
- " fig.rubberband_context.clearRect(\n",
- " 0, 0, fig.canvas.width, fig.canvas.height);\n",
- "\n",
- " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
- " // Updates the figure title.\n",
- " fig.header.textContent = msg['label'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch(cursor)\n",
- " {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
- " fig.message.textContent = msg['message'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
- " // Request the server to send over a new figure.\n",
- " fig.send_draw_message();\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
- " fig.image_mode = msg['mode'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Called whenever the canvas gets updated.\n",
- " this.send_message(\"ack\", {});\n",
- "}\n",
- "\n",
- "// A function to construct a web socket function for onmessage handling.\n",
- "// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
- " return function socket_on_message(evt) {\n",
- " if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = \"image/png\";\n",
- "\n",
- " /* Free the memory for the previous frames */\n",
- " if (fig.imageObj.src) {\n",
- " (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src);\n",
- " }\n",
- "\n",
- " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data);\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
- " fig.imageObj.src = evt.data;\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- "\n",
- " var msg = JSON.parse(evt.data);\n",
- " var msg_type = msg['type'];\n",
- "\n",
- " // Call the \"handle_{type}\" callback, which takes\n",
- " // the figure and JSON message as its only arguments.\n",
- " try {\n",
- " var callback = fig[\"handle_\" + msg_type];\n",
- " } catch (e) {\n",
- " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
- " return;\n",
- " }\n",
- "\n",
- " if (callback) {\n",
- " try {\n",
- " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
- " callback(fig, msg);\n",
- " } catch (e) {\n",
- " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
- " }\n",
- " }\n",
- " };\n",
- "}\n",
- "\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function(e) {\n",
- " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
- " var targ;\n",
- " if (!e)\n",
- " e = window.event;\n",
- " if (e.target)\n",
- " targ = e.target;\n",
- " else if (e.srcElement)\n",
- " targ = e.srcElement;\n",
- " if (targ.nodeType == 3) // defeat Safari bug\n",
- " targ = targ.parentNode;\n",
- "\n",
- " // jQuery normalizes the pageX and pageY\n",
- " // pageX,Y are the mouse positions relative to the document\n",
- " // offset() returns the position of the element relative to the document\n",
- " var x = e.pageX - $(targ).offset().left;\n",
- " var y = e.pageY - $(targ).offset().top;\n",
- "\n",
- " return {\"x\": x, \"y\": y};\n",
- "};\n",
- "\n",
- "/*\n",
- " * return a copy of an object with only non-object keys\n",
- " * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
- " */\n",
- "function simpleKeys (original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object')\n",
- " obj[key] = original[key]\n",
- " return obj;\n",
- " }, {});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.mouse_event = function(event, name) {\n",
- " var canvas_pos = mpl.findpos(event)\n",
- "\n",
- " if (name === 'button_press')\n",
- " {\n",
- " this.canvas.focus();\n",
- " this.canvas_div.focus();\n",
- " }\n",
- "\n",
- " var x = canvas_pos.x;\n",
- " var y = canvas_pos.y;\n",
- "\n",
- " this.send_message(name, {x: x, y: y, button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event)});\n",
- "\n",
- " /* This prevents the web browser from automatically changing to\n",
- " * the text insertion cursor when the button is pressed. We want\n",
- " * to control all of the cursor setting manually through the\n",
- " * 'cursor' event from matplotlib */\n",
- " event.preventDefault();\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " // Handle any extra behaviour associated with a key event\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.key_event = function(event, name) {\n",
- "\n",
- " // Prevent repeat events\n",
- " if (name == 'key_press')\n",
- " {\n",
- " if (event.which === this._key)\n",
- " return;\n",
- " else\n",
- " this._key = event.which;\n",
- " }\n",
- " if (name == 'key_release')\n",
- " this._key = null;\n",
- "\n",
- " var value = '';\n",
- " if (event.ctrlKey && event.which != 17)\n",
- " value += \"ctrl+\";\n",
- " if (event.altKey && event.which != 18)\n",
- " value += \"alt+\";\n",
- " if (event.shiftKey && event.which != 16)\n",
- " value += \"shift+\";\n",
- "\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
- "\n",
- " this._key_event_extra(event, name);\n",
- "\n",
- " this.send_message(name, {key: value,\n",
- " guiEvent: simpleKeys(event)});\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
- " if (name == 'download') {\n",
- " this.handle_save(this, null);\n",
- " } else {\n",
- " this.send_message(\"toolbar_button\", {name: name});\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
- " this.message.textContent = tooltip;\n",
- "};\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
- "\n",
- "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
- "\n",
- "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
- " // Create a \"websocket\"-like object which calls the given IPython comm\n",
- " // object with the appropriate methods. Currently this is a non binary\n",
- " // socket, so there is still some room for performance tuning.\n",
- " var ws = {};\n",
- "\n",
- " ws.close = function() {\n",
- " comm.close()\n",
- " };\n",
- " ws.send = function(m) {\n",
- " //console.log('sending', m);\n",
- " comm.send(m);\n",
- " };\n",
- " // Register the callback with on_msg.\n",
- " comm.on_msg(function(msg) {\n",
- " //console.log('receiving', msg['content']['data'], msg);\n",
- " // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data'])\n",
- " });\n",
- " return ws;\n",
- "}\n",
- "\n",
- "mpl.mpl_figure_comm = function(comm, msg) {\n",
- " // This is the function which gets called when the mpl process\n",
- " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
- "\n",
- " var id = msg.content.data.id;\n",
- " // Get hold of the div created by the display call when the Comm\n",
- " // socket was opened in Python.\n",
- " var element = $(\"#\" + id);\n",
- " var ws_proxy = comm_websocket_adapter(comm)\n",
- "\n",
- " function ondownload(figure, format) {\n",
- " window.open(figure.imageObj.src);\n",
- " }\n",
- "\n",
- " var fig = new mpl.figure(id, ws_proxy,\n",
- " ondownload,\n",
- " element.get(0));\n",
- "\n",
- " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
- " // web socket which is closed, not our websocket->open comm proxy.\n",
- " ws_proxy.onopen();\n",
- "\n",
- " fig.parent_element = element.get(0);\n",
- " fig.cell_info = mpl.find_output_cell(\"\");\n",
- " if (!fig.cell_info) {\n",
- " console.error(\"Failed to find cell for figure\", id, fig);\n",
- " return;\n",
- " }\n",
- "\n",
- " var output_index = fig.cell_info[2]\n",
- " var cell = fig.cell_info[0];\n",
- "\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
- " fig.root.unbind('remove')\n",
- "\n",
- " // Update the output cell to use the data from the current canvas.\n",
- " fig.push_to_output();\n",
- " var dataURL = fig.canvas.toDataURL();\n",
- " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
- " // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable()\n",
- " $(fig.parent_element).html('');\n",
- " fig.close_ws(fig, msg);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
- " fig.send_message('closing', msg);\n",
- " // fig.ws.close()\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
- " // Turn the data on the canvas into data in the output cell.\n",
- " var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] = '';\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Tell IPython that the notebook contents must change.\n",
- " IPython.notebook.set_dirty(true);\n",
- " this.send_message(\"ack\", {});\n",
- " var fig = this;\n",
- " // Wait a second, then push the new image to the DOM so\n",
- " // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () { fig.push_to_output() }, 1000);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items){\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) { continue; };\n",
- "\n",
- " var button = $('');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " // Add the status bar.\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "\n",
- " // Add the close button to the window.\n",
- " var buttongrp = $('');\n",
- " var button = $('');\n",
- " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
- " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
- " buttongrp.append(button);\n",
- " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
- " titlebar.prepend(buttongrp);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(el){\n",
- " var fig = this\n",
- " el.on(\"remove\", function(){\n",
- "\tfig.close_ws(fig, {});\n",
- " });\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(el){\n",
- " // this is important to make the div 'focusable\n",
- " el.attr('tabindex', 0)\n",
- " // reach out to IPython and tell the keyboard manager to turn it's self\n",
- " // off when our div gets focus\n",
- "\n",
- " // location in version 3\n",
- " if (IPython.notebook.keyboard_manager) {\n",
- " IPython.notebook.keyboard_manager.register_events(el);\n",
- " }\n",
- " else {\n",
- " // location in version 2\n",
- " IPython.keyboard_manager.register_events(el);\n",
- " }\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager)\n",
- " manager = IPython.keyboard_manager;\n",
- "\n",
- " // Check for shift+enter\n",
- " if (event.shiftKey && event.which == 13) {\n",
- " this.canvas_div.blur();\n",
- " event.shiftKey = false;\n",
- " // Send a \"J\" for go to next cell\n",
- " event.which = 74;\n",
- " event.keyCode = 74;\n",
- " manager.command_mode();\n",
- " manager.handle_keydown(event);\n",
- " }\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " fig.ondownload(fig, null);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.find_output_cell = function(html_output) {\n",
- " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
- " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
- " // IPython event is triggered only after the cells have been serialised, which for\n",
- " // our purposes (turning an active figure into a static one), is too late.\n",
- " var cells = IPython.notebook.get_cells();\n",
- " var ncells = cells.length;\n",
- " for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
- " data = data.data;\n",
- " }\n",
- " if (data['text/html'] == html_output) {\n",
- " return [cell, data, j];\n",
- " }\n",
- " }\n",
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- "}\n",
- "\n",
- "// Register the function which deals with the matplotlib target/channel.\n",
- "// The kernel may be null if the page has been refreshed.\n",
- "if (IPython.notebook.kernel != null) {\n",
- " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
- "}\n"
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- "output_type": "stream",
- "text": [
- "c:\\pab\\workspace\\git_pywafo\\wafo\\stats\\estimation.py:1553: UserWarning: P-value is on the conservative side (i.e. too large) due to ties in the data!\n",
- " ' ties in the data!')\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "fig, ax = plt.subplots(2, 2, figsize=(11, 8))\n",
- "Hs = wd.atlantic()\n",
- "wei = ws.weibull_min.fit2(Hs)\n",
- "gum = ws.gumbel_r.fit2(Hs)\n",
- "axf = ax.ravel()\n",
- "tmp = ws.probplot(Hs, wei.par, dist='weibull_min', plot=axf[0])\n",
- "axf[0].set_title('Weibull Probability Plot')\n",
- "tmp = ws.probplot(Hs, gum.par, dist='gumbel_r', plot=axf[1])\n",
- "axf[1].set_title('Gumbel Probability Plot')\n",
- "tmp = ws.probplot(np.log(Hs), plot=axf[2])\n",
- "axf[2].set_title('Normal Probability Plot')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Return values in the Gumbel distribution"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
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- "/* Put everything inside the global mpl namespace */\n",
- "window.mpl = {};\n",
- "\n",
- "mpl.get_websocket_type = function() {\n",
- " if (typeof(WebSocket) !== 'undefined') {\n",
- " return WebSocket;\n",
- " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
- " return MozWebSocket;\n",
- " } else {\n",
- " alert('Your browser does not have WebSocket support.' +\n",
- " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
- " 'Firefox 4 and 5 are also supported but you ' +\n",
- " 'have to enable WebSockets in about:config.');\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
- " this.id = figure_id;\n",
- "\n",
- " this.ws = websocket;\n",
- "\n",
- " this.supports_binary = (this.ws.binaryType != undefined);\n",
- "\n",
- " if (!this.supports_binary) {\n",
- " var warnings = document.getElementById(\"mpl-warnings\");\n",
- " if (warnings) {\n",
- " warnings.style.display = 'block';\n",
- " warnings.textContent = (\n",
- " \"This browser does not support binary websocket messages. \" +\n",
- " \"Performance may be slow.\");\n",
- " }\n",
- " }\n",
- "\n",
- " this.imageObj = new Image();\n",
- "\n",
- " this.context = undefined;\n",
- " this.message = undefined;\n",
- " this.canvas = undefined;\n",
- " this.rubberband_canvas = undefined;\n",
- " this.rubberband_context = undefined;\n",
- " this.format_dropdown = undefined;\n",
- "\n",
- " this.image_mode = 'full';\n",
- "\n",
- " this.root = $('');\n",
- " this._root_extra_style(this.root)\n",
- " this.root.attr('style', 'display: inline-block');\n",
- "\n",
- " $(parent_element).append(this.root);\n",
- "\n",
- " this._init_header(this);\n",
- " this._init_canvas(this);\n",
- " this._init_toolbar(this);\n",
- "\n",
- " var fig = this;\n",
- "\n",
- " this.waiting = false;\n",
- "\n",
- " this.ws.onopen = function () {\n",
- " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
- " fig.send_message(\"send_image_mode\", {});\n",
- " fig.send_message(\"refresh\", {});\n",
- " }\n",
- "\n",
- " this.imageObj.onload = function() {\n",
- " if (fig.image_mode == 'full') {\n",
- " // Full images could contain transparency (where diff images\n",
- " // almost always do), so we need to clear the canvas so that\n",
- " // there is no ghosting.\n",
- " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
- " }\n",
- " fig.context.drawImage(fig.imageObj, 0, 0);\n",
- " };\n",
- "\n",
- " this.imageObj.onunload = function() {\n",
- " this.ws.close();\n",
- " }\n",
- "\n",
- " this.ws.onmessage = this._make_on_message_function(this);\n",
- "\n",
- " this.ondownload = ondownload;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_header = function() {\n",
- " var titlebar = $(\n",
- " '');\n",
- " var titletext = $(\n",
- " '');\n",
- " titlebar.append(titletext)\n",
- " this.root.append(titlebar);\n",
- " this.header = titletext[0];\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_canvas = function() {\n",
- " var fig = this;\n",
- "\n",
- " var canvas_div = $('');\n",
- "\n",
- " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
- "\n",
- " function canvas_keyboard_event(event) {\n",
- " return fig.key_event(event, event['data']);\n",
- " }\n",
- "\n",
- " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
- " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
- " this.canvas_div = canvas_div\n",
- " this._canvas_extra_style(canvas_div)\n",
- " this.root.append(canvas_div);\n",
- "\n",
- " var canvas = $('');\n",
- " canvas.addClass('mpl-canvas');\n",
- " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
- "\n",
- " this.canvas = canvas[0];\n",
- " this.context = canvas[0].getContext(\"2d\");\n",
- "\n",
- " var rubberband = $('');\n",
- " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
- "\n",
- " var pass_mouse_events = true;\n",
- "\n",
- " canvas_div.resizable({\n",
- " start: function(event, ui) {\n",
- " pass_mouse_events = false;\n",
- " },\n",
- " resize: function(event, ui) {\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " stop: function(event, ui) {\n",
- " pass_mouse_events = true;\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " });\n",
- "\n",
- " function mouse_event_fn(event) {\n",
- " if (pass_mouse_events)\n",
- " return fig.mouse_event(event, event['data']);\n",
- " }\n",
- "\n",
- " rubberband.mousedown('button_press', mouse_event_fn);\n",
- " rubberband.mouseup('button_release', mouse_event_fn);\n",
- " // Throttle sequential mouse events to 1 every 20ms.\n",
- " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
- "\n",
- " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
- " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
- "\n",
- " canvas_div.on(\"wheel\", function (event) {\n",
- " event = event.originalEvent;\n",
- " event['data'] = 'scroll'\n",
- " if (event.deltaY < 0) {\n",
- " event.step = 1;\n",
- " } else {\n",
- " event.step = -1;\n",
- " }\n",
- " mouse_event_fn(event);\n",
- " });\n",
- "\n",
- " canvas_div.append(canvas);\n",
- " canvas_div.append(rubberband);\n",
- "\n",
- " this.rubberband = rubberband;\n",
- " this.rubberband_canvas = rubberband[0];\n",
- " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
- " this.rubberband_context.strokeStyle = \"#000000\";\n",
- "\n",
- " this._resize_canvas = function(width, height) {\n",
- " // Keep the size of the canvas, canvas container, and rubber band\n",
- " // canvas in synch.\n",
- " canvas_div.css('width', width)\n",
- " canvas_div.css('height', height)\n",
- "\n",
- " canvas.attr('width', width);\n",
- " canvas.attr('height', height);\n",
- "\n",
- " rubberband.attr('width', width);\n",
- " rubberband.attr('height', height);\n",
- " }\n",
- "\n",
- " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
- " // upon first draw.\n",
- " this._resize_canvas(600, 600);\n",
- "\n",
- " // Disable right mouse context menu.\n",
- " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
- " return false;\n",
- " });\n",
- "\n",
- " function set_focus () {\n",
- " canvas.focus();\n",
- " canvas_div.focus();\n",
- " }\n",
- "\n",
- " window.setTimeout(set_focus, 100);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " // put a spacer in here.\n",
- " continue;\n",
- " }\n",
- " var button = $('');\n",
- " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
- " 'ui-button-icon-only');\n",
- " button.attr('role', 'button');\n",
- " button.attr('aria-disabled', 'false');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- "\n",
- " var icon_img = $('');\n",
- " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
- " icon_img.addClass(image);\n",
- " icon_img.addClass('ui-corner-all');\n",
- "\n",
- " var tooltip_span = $('');\n",
- " tooltip_span.addClass('ui-button-text');\n",
- " tooltip_span.html(tooltip);\n",
- "\n",
- " button.append(icon_img);\n",
- " button.append(tooltip_span);\n",
- "\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " var fmt_picker_span = $('');\n",
- "\n",
- " var fmt_picker = $('');\n",
- " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
- " fmt_picker_span.append(fmt_picker);\n",
- " nav_element.append(fmt_picker_span);\n",
- " this.format_dropdown = fmt_picker[0];\n",
- "\n",
- " for (var ind in mpl.extensions) {\n",
- " var fmt = mpl.extensions[ind];\n",
- " var option = $(\n",
- " '', {selected: fmt === mpl.default_extension}).html(fmt);\n",
- " fmt_picker.append(option)\n",
- " }\n",
- "\n",
- " // Add hover states to the ui-buttons\n",
- " $( \".ui-button\" ).hover(\n",
- " function() { $(this).addClass(\"ui-state-hover\");},\n",
- " function() { $(this).removeClass(\"ui-state-hover\");}\n",
- " );\n",
- "\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
- " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
- " // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_message = function(type, properties) {\n",
- " properties['type'] = type;\n",
- " properties['figure_id'] = this.id;\n",
- " this.ws.send(JSON.stringify(properties));\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_draw_message = function() {\n",
- " if (!this.waiting) {\n",
- " this.waiting = true;\n",
- " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
- " }\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " var format_dropdown = fig.format_dropdown;\n",
- " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
- " fig.ondownload(fig, format);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
- " var size = msg['size'];\n",
- " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1]);\n",
- " fig.send_message(\"refresh\", {});\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
- " var x0 = msg['x0'];\n",
- " var y0 = fig.canvas.height - msg['y0'];\n",
- " var x1 = msg['x1'];\n",
- " var y1 = fig.canvas.height - msg['y1'];\n",
- " x0 = Math.floor(x0) + 0.5;\n",
- " y0 = Math.floor(y0) + 0.5;\n",
- " x1 = Math.floor(x1) + 0.5;\n",
- " y1 = Math.floor(y1) + 0.5;\n",
- " var min_x = Math.min(x0, x1);\n",
- " var min_y = Math.min(y0, y1);\n",
- " var width = Math.abs(x1 - x0);\n",
- " var height = Math.abs(y1 - y0);\n",
- "\n",
- " fig.rubberband_context.clearRect(\n",
- " 0, 0, fig.canvas.width, fig.canvas.height);\n",
- "\n",
- " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
- " // Updates the figure title.\n",
- " fig.header.textContent = msg['label'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch(cursor)\n",
- " {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
- " fig.message.textContent = msg['message'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
- " // Request the server to send over a new figure.\n",
- " fig.send_draw_message();\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
- " fig.image_mode = msg['mode'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Called whenever the canvas gets updated.\n",
- " this.send_message(\"ack\", {});\n",
- "}\n",
- "\n",
- "// A function to construct a web socket function for onmessage handling.\n",
- "// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
- " return function socket_on_message(evt) {\n",
- " if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = \"image/png\";\n",
- "\n",
- " /* Free the memory for the previous frames */\n",
- " if (fig.imageObj.src) {\n",
- " (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src);\n",
- " }\n",
- "\n",
- " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data);\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
- " fig.imageObj.src = evt.data;\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- "\n",
- " var msg = JSON.parse(evt.data);\n",
- " var msg_type = msg['type'];\n",
- "\n",
- " // Call the \"handle_{type}\" callback, which takes\n",
- " // the figure and JSON message as its only arguments.\n",
- " try {\n",
- " var callback = fig[\"handle_\" + msg_type];\n",
- " } catch (e) {\n",
- " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
- " return;\n",
- " }\n",
- "\n",
- " if (callback) {\n",
- " try {\n",
- " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
- " callback(fig, msg);\n",
- " } catch (e) {\n",
- " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
- " }\n",
- " }\n",
- " };\n",
- "}\n",
- "\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function(e) {\n",
- " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
- " var targ;\n",
- " if (!e)\n",
- " e = window.event;\n",
- " if (e.target)\n",
- " targ = e.target;\n",
- " else if (e.srcElement)\n",
- " targ = e.srcElement;\n",
- " if (targ.nodeType == 3) // defeat Safari bug\n",
- " targ = targ.parentNode;\n",
- "\n",
- " // jQuery normalizes the pageX and pageY\n",
- " // pageX,Y are the mouse positions relative to the document\n",
- " // offset() returns the position of the element relative to the document\n",
- " var x = e.pageX - $(targ).offset().left;\n",
- " var y = e.pageY - $(targ).offset().top;\n",
- "\n",
- " return {\"x\": x, \"y\": y};\n",
- "};\n",
- "\n",
- "/*\n",
- " * return a copy of an object with only non-object keys\n",
- " * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
- " */\n",
- "function simpleKeys (original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object')\n",
- " obj[key] = original[key]\n",
- " return obj;\n",
- " }, {});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.mouse_event = function(event, name) {\n",
- " var canvas_pos = mpl.findpos(event)\n",
- "\n",
- " if (name === 'button_press')\n",
- " {\n",
- " this.canvas.focus();\n",
- " this.canvas_div.focus();\n",
- " }\n",
- "\n",
- " var x = canvas_pos.x;\n",
- " var y = canvas_pos.y;\n",
- "\n",
- " this.send_message(name, {x: x, y: y, button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event)});\n",
- "\n",
- " /* This prevents the web browser from automatically changing to\n",
- " * the text insertion cursor when the button is pressed. We want\n",
- " * to control all of the cursor setting manually through the\n",
- " * 'cursor' event from matplotlib */\n",
- " event.preventDefault();\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " // Handle any extra behaviour associated with a key event\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.key_event = function(event, name) {\n",
- "\n",
- " // Prevent repeat events\n",
- " if (name == 'key_press')\n",
- " {\n",
- " if (event.which === this._key)\n",
- " return;\n",
- " else\n",
- " this._key = event.which;\n",
- " }\n",
- " if (name == 'key_release')\n",
- " this._key = null;\n",
- "\n",
- " var value = '';\n",
- " if (event.ctrlKey && event.which != 17)\n",
- " value += \"ctrl+\";\n",
- " if (event.altKey && event.which != 18)\n",
- " value += \"alt+\";\n",
- " if (event.shiftKey && event.which != 16)\n",
- " value += \"shift+\";\n",
- "\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
- "\n",
- " this._key_event_extra(event, name);\n",
- "\n",
- " this.send_message(name, {key: value,\n",
- " guiEvent: simpleKeys(event)});\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
- " if (name == 'download') {\n",
- " this.handle_save(this, null);\n",
- " } else {\n",
- " this.send_message(\"toolbar_button\", {name: name});\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
- " this.message.textContent = tooltip;\n",
- "};\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
- "\n",
- "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
- "\n",
- "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
- " // Create a \"websocket\"-like object which calls the given IPython comm\n",
- " // object with the appropriate methods. Currently this is a non binary\n",
- " // socket, so there is still some room for performance tuning.\n",
- " var ws = {};\n",
- "\n",
- " ws.close = function() {\n",
- " comm.close()\n",
- " };\n",
- " ws.send = function(m) {\n",
- " //console.log('sending', m);\n",
- " comm.send(m);\n",
- " };\n",
- " // Register the callback with on_msg.\n",
- " comm.on_msg(function(msg) {\n",
- " //console.log('receiving', msg['content']['data'], msg);\n",
- " // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data'])\n",
- " });\n",
- " return ws;\n",
- "}\n",
- "\n",
- "mpl.mpl_figure_comm = function(comm, msg) {\n",
- " // This is the function which gets called when the mpl process\n",
- " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
- "\n",
- " var id = msg.content.data.id;\n",
- " // Get hold of the div created by the display call when the Comm\n",
- " // socket was opened in Python.\n",
- " var element = $(\"#\" + id);\n",
- " var ws_proxy = comm_websocket_adapter(comm)\n",
- "\n",
- " function ondownload(figure, format) {\n",
- " window.open(figure.imageObj.src);\n",
- " }\n",
- "\n",
- " var fig = new mpl.figure(id, ws_proxy,\n",
- " ondownload,\n",
- " element.get(0));\n",
- "\n",
- " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
- " // web socket which is closed, not our websocket->open comm proxy.\n",
- " ws_proxy.onopen();\n",
- "\n",
- " fig.parent_element = element.get(0);\n",
- " fig.cell_info = mpl.find_output_cell(\"\");\n",
- " if (!fig.cell_info) {\n",
- " console.error(\"Failed to find cell for figure\", id, fig);\n",
- " return;\n",
- " }\n",
- "\n",
- " var output_index = fig.cell_info[2]\n",
- " var cell = fig.cell_info[0];\n",
- "\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
- " fig.root.unbind('remove')\n",
- "\n",
- " // Update the output cell to use the data from the current canvas.\n",
- " fig.push_to_output();\n",
- " var dataURL = fig.canvas.toDataURL();\n",
- " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
- " // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable()\n",
- " $(fig.parent_element).html('');\n",
- " fig.close_ws(fig, msg);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
- " fig.send_message('closing', msg);\n",
- " // fig.ws.close()\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
- " // Turn the data on the canvas into data in the output cell.\n",
- " var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] = '';\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Tell IPython that the notebook contents must change.\n",
- " IPython.notebook.set_dirty(true);\n",
- " this.send_message(\"ack\", {});\n",
- " var fig = this;\n",
- " // Wait a second, then push the new image to the DOM so\n",
- " // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () { fig.push_to_output() }, 1000);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items){\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) { continue; };\n",
- "\n",
- " var button = $('');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " // Add the status bar.\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "\n",
- " // Add the close button to the window.\n",
- " var buttongrp = $('');\n",
- " var button = $('');\n",
- " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
- " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
- " buttongrp.append(button);\n",
- " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
- " titlebar.prepend(buttongrp);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(el){\n",
- " var fig = this\n",
- " el.on(\"remove\", function(){\n",
- "\tfig.close_ws(fig, {});\n",
- " });\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(el){\n",
- " // this is important to make the div 'focusable\n",
- " el.attr('tabindex', 0)\n",
- " // reach out to IPython and tell the keyboard manager to turn it's self\n",
- " // off when our div gets focus\n",
- "\n",
- " // location in version 3\n",
- " if (IPython.notebook.keyboard_manager) {\n",
- " IPython.notebook.keyboard_manager.register_events(el);\n",
- " }\n",
- " else {\n",
- " // location in version 2\n",
- " IPython.keyboard_manager.register_events(el);\n",
- " }\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager)\n",
- " manager = IPython.keyboard_manager;\n",
- "\n",
- " // Check for shift+enter\n",
- " if (event.shiftKey && event.which == 13) {\n",
- " this.canvas_div.blur();\n",
- " event.shiftKey = false;\n",
- " // Send a \"J\" for go to next cell\n",
- " event.which = 74;\n",
- " event.keyCode = 74;\n",
- " manager.command_mode();\n",
- " manager.handle_keydown(event);\n",
- " }\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " fig.ondownload(fig, null);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.find_output_cell = function(html_output) {\n",
- " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
- " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
- " // IPython event is triggered only after the cells have been serialised, which for\n",
- " // our purposes (turning an active figure into a static one), is too late.\n",
- " var cells = IPython.notebook.get_cells();\n",
- " var ncells = cells.length;\n",
- " for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
- " data = data.data;\n",
- " }\n",
- " if (data['text/html'] == html_output) {\n",
- " return [cell, data, j];\n",
- " }\n",
- " }\n",
- " }\n",
- " }\n",
- "}\n",
- "\n",
- "// Register the function which deals with the matplotlib target/channel.\n",
- "// The kernel may be null if the page has been refreshed.\n",
- "if (IPython.notebook.kernel != null) {\n",
- " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
- "}\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- ""
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "fig, axes = plt.subplots()\n",
- "T=np.r_[1:100001]\n",
- "#sT=gum.par[1] - gum.par[0]*log(-log(1-1./T));\n",
- "sT = gum.isf(1./T)\n",
- "plt.semilogx(T,sT)\n",
- "plt.hold(True)\n",
- "N=np.r_[1:len(Hs)+1]; \n",
- "Nmax=max(N);\n",
- "plt.plot(Nmax/N, sorted(Hs, reverse=True),'.')\n",
- "plt.title('Return values in the Gumbel model')\n",
- "plt.xlabel('Return period')\n",
- "plt.ylabel('Return value') \n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Section 5.2 Generalized Pareto and Extreme Value distributions\n",
- "----------------------------------------------------------\n",
- "Section 5.2.1 Generalized Extreme Value distribution\n",
- "-------------------------------------------------\n",
- "\n",
- "Empirical distribution of significant wave-height with estimated Generalized Extreme Value distribution"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "application/javascript": [
- "/* Put everything inside the global mpl namespace */\n",
- "window.mpl = {};\n",
- "\n",
- "mpl.get_websocket_type = function() {\n",
- " if (typeof(WebSocket) !== 'undefined') {\n",
- " return WebSocket;\n",
- " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
- " return MozWebSocket;\n",
- " } else {\n",
- " alert('Your browser does not have WebSocket support.' +\n",
- " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
- " 'Firefox 4 and 5 are also supported but you ' +\n",
- " 'have to enable WebSockets in about:config.');\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
- " this.id = figure_id;\n",
- "\n",
- " this.ws = websocket;\n",
- "\n",
- " this.supports_binary = (this.ws.binaryType != undefined);\n",
- "\n",
- " if (!this.supports_binary) {\n",
- " var warnings = document.getElementById(\"mpl-warnings\");\n",
- " if (warnings) {\n",
- " warnings.style.display = 'block';\n",
- " warnings.textContent = (\n",
- " \"This browser does not support binary websocket messages. \" +\n",
- " \"Performance may be slow.\");\n",
- " }\n",
- " }\n",
- "\n",
- " this.imageObj = new Image();\n",
- "\n",
- " this.context = undefined;\n",
- " this.message = undefined;\n",
- " this.canvas = undefined;\n",
- " this.rubberband_canvas = undefined;\n",
- " this.rubberband_context = undefined;\n",
- " this.format_dropdown = undefined;\n",
- "\n",
- " this.image_mode = 'full';\n",
- "\n",
- " this.root = $('');\n",
- " this._root_extra_style(this.root)\n",
- " this.root.attr('style', 'display: inline-block');\n",
- "\n",
- " $(parent_element).append(this.root);\n",
- "\n",
- " this._init_header(this);\n",
- " this._init_canvas(this);\n",
- " this._init_toolbar(this);\n",
- "\n",
- " var fig = this;\n",
- "\n",
- " this.waiting = false;\n",
- "\n",
- " this.ws.onopen = function () {\n",
- " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
- " fig.send_message(\"send_image_mode\", {});\n",
- " fig.send_message(\"refresh\", {});\n",
- " }\n",
- "\n",
- " this.imageObj.onload = function() {\n",
- " if (fig.image_mode == 'full') {\n",
- " // Full images could contain transparency (where diff images\n",
- " // almost always do), so we need to clear the canvas so that\n",
- " // there is no ghosting.\n",
- " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
- " }\n",
- " fig.context.drawImage(fig.imageObj, 0, 0);\n",
- " };\n",
- "\n",
- " this.imageObj.onunload = function() {\n",
- " this.ws.close();\n",
- " }\n",
- "\n",
- " this.ws.onmessage = this._make_on_message_function(this);\n",
- "\n",
- " this.ondownload = ondownload;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_header = function() {\n",
- " var titlebar = $(\n",
- " '');\n",
- " var titletext = $(\n",
- " '');\n",
- " titlebar.append(titletext)\n",
- " this.root.append(titlebar);\n",
- " this.header = titletext[0];\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_canvas = function() {\n",
- " var fig = this;\n",
- "\n",
- " var canvas_div = $('');\n",
- "\n",
- " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
- "\n",
- " function canvas_keyboard_event(event) {\n",
- " return fig.key_event(event, event['data']);\n",
- " }\n",
- "\n",
- " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
- " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
- " this.canvas_div = canvas_div\n",
- " this._canvas_extra_style(canvas_div)\n",
- " this.root.append(canvas_div);\n",
- "\n",
- " var canvas = $('');\n",
- " canvas.addClass('mpl-canvas');\n",
- " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
- "\n",
- " this.canvas = canvas[0];\n",
- " this.context = canvas[0].getContext(\"2d\");\n",
- "\n",
- " var rubberband = $('');\n",
- " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
- "\n",
- " var pass_mouse_events = true;\n",
- "\n",
- " canvas_div.resizable({\n",
- " start: function(event, ui) {\n",
- " pass_mouse_events = false;\n",
- " },\n",
- " resize: function(event, ui) {\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " stop: function(event, ui) {\n",
- " pass_mouse_events = true;\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " });\n",
- "\n",
- " function mouse_event_fn(event) {\n",
- " if (pass_mouse_events)\n",
- " return fig.mouse_event(event, event['data']);\n",
- " }\n",
- "\n",
- " rubberband.mousedown('button_press', mouse_event_fn);\n",
- " rubberband.mouseup('button_release', mouse_event_fn);\n",
- " // Throttle sequential mouse events to 1 every 20ms.\n",
- " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
- "\n",
- " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
- " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
- "\n",
- " canvas_div.on(\"wheel\", function (event) {\n",
- " event = event.originalEvent;\n",
- " event['data'] = 'scroll'\n",
- " if (event.deltaY < 0) {\n",
- " event.step = 1;\n",
- " } else {\n",
- " event.step = -1;\n",
- " }\n",
- " mouse_event_fn(event);\n",
- " });\n",
- "\n",
- " canvas_div.append(canvas);\n",
- " canvas_div.append(rubberband);\n",
- "\n",
- " this.rubberband = rubberband;\n",
- " this.rubberband_canvas = rubberband[0];\n",
- " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
- " this.rubberband_context.strokeStyle = \"#000000\";\n",
- "\n",
- " this._resize_canvas = function(width, height) {\n",
- " // Keep the size of the canvas, canvas container, and rubber band\n",
- " // canvas in synch.\n",
- " canvas_div.css('width', width)\n",
- " canvas_div.css('height', height)\n",
- "\n",
- " canvas.attr('width', width);\n",
- " canvas.attr('height', height);\n",
- "\n",
- " rubberband.attr('width', width);\n",
- " rubberband.attr('height', height);\n",
- " }\n",
- "\n",
- " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
- " // upon first draw.\n",
- " this._resize_canvas(600, 600);\n",
- "\n",
- " // Disable right mouse context menu.\n",
- " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
- " return false;\n",
- " });\n",
- "\n",
- " function set_focus () {\n",
- " canvas.focus();\n",
- " canvas_div.focus();\n",
- " }\n",
- "\n",
- " window.setTimeout(set_focus, 100);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " // put a spacer in here.\n",
- " continue;\n",
- " }\n",
- " var button = $('');\n",
- " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
- " 'ui-button-icon-only');\n",
- " button.attr('role', 'button');\n",
- " button.attr('aria-disabled', 'false');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- "\n",
- " var icon_img = $('');\n",
- " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
- " icon_img.addClass(image);\n",
- " icon_img.addClass('ui-corner-all');\n",
- "\n",
- " var tooltip_span = $('');\n",
- " tooltip_span.addClass('ui-button-text');\n",
- " tooltip_span.html(tooltip);\n",
- "\n",
- " button.append(icon_img);\n",
- " button.append(tooltip_span);\n",
- "\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " var fmt_picker_span = $('');\n",
- "\n",
- " var fmt_picker = $('');\n",
- " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
- " fmt_picker_span.append(fmt_picker);\n",
- " nav_element.append(fmt_picker_span);\n",
- " this.format_dropdown = fmt_picker[0];\n",
- "\n",
- " for (var ind in mpl.extensions) {\n",
- " var fmt = mpl.extensions[ind];\n",
- " var option = $(\n",
- " '', {selected: fmt === mpl.default_extension}).html(fmt);\n",
- " fmt_picker.append(option)\n",
- " }\n",
- "\n",
- " // Add hover states to the ui-buttons\n",
- " $( \".ui-button\" ).hover(\n",
- " function() { $(this).addClass(\"ui-state-hover\");},\n",
- " function() { $(this).removeClass(\"ui-state-hover\");}\n",
- " );\n",
- "\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
- " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
- " // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_message = function(type, properties) {\n",
- " properties['type'] = type;\n",
- " properties['figure_id'] = this.id;\n",
- " this.ws.send(JSON.stringify(properties));\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_draw_message = function() {\n",
- " if (!this.waiting) {\n",
- " this.waiting = true;\n",
- " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
- " }\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " var format_dropdown = fig.format_dropdown;\n",
- " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
- " fig.ondownload(fig, format);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
- " var size = msg['size'];\n",
- " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1]);\n",
- " fig.send_message(\"refresh\", {});\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
- " var x0 = msg['x0'];\n",
- " var y0 = fig.canvas.height - msg['y0'];\n",
- " var x1 = msg['x1'];\n",
- " var y1 = fig.canvas.height - msg['y1'];\n",
- " x0 = Math.floor(x0) + 0.5;\n",
- " y0 = Math.floor(y0) + 0.5;\n",
- " x1 = Math.floor(x1) + 0.5;\n",
- " y1 = Math.floor(y1) + 0.5;\n",
- " var min_x = Math.min(x0, x1);\n",
- " var min_y = Math.min(y0, y1);\n",
- " var width = Math.abs(x1 - x0);\n",
- " var height = Math.abs(y1 - y0);\n",
- "\n",
- " fig.rubberband_context.clearRect(\n",
- " 0, 0, fig.canvas.width, fig.canvas.height);\n",
- "\n",
- " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
- " // Updates the figure title.\n",
- " fig.header.textContent = msg['label'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch(cursor)\n",
- " {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
- " fig.message.textContent = msg['message'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
- " // Request the server to send over a new figure.\n",
- " fig.send_draw_message();\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
- " fig.image_mode = msg['mode'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Called whenever the canvas gets updated.\n",
- " this.send_message(\"ack\", {});\n",
- "}\n",
- "\n",
- "// A function to construct a web socket function for onmessage handling.\n",
- "// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
- " return function socket_on_message(evt) {\n",
- " if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = \"image/png\";\n",
- "\n",
- " /* Free the memory for the previous frames */\n",
- " if (fig.imageObj.src) {\n",
- " (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src);\n",
- " }\n",
- "\n",
- " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data);\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
- " fig.imageObj.src = evt.data;\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- "\n",
- " var msg = JSON.parse(evt.data);\n",
- " var msg_type = msg['type'];\n",
- "\n",
- " // Call the \"handle_{type}\" callback, which takes\n",
- " // the figure and JSON message as its only arguments.\n",
- " try {\n",
- " var callback = fig[\"handle_\" + msg_type];\n",
- " } catch (e) {\n",
- " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
- " return;\n",
- " }\n",
- "\n",
- " if (callback) {\n",
- " try {\n",
- " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
- " callback(fig, msg);\n",
- " } catch (e) {\n",
- " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
- " }\n",
- " }\n",
- " };\n",
- "}\n",
- "\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function(e) {\n",
- " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
- " var targ;\n",
- " if (!e)\n",
- " e = window.event;\n",
- " if (e.target)\n",
- " targ = e.target;\n",
- " else if (e.srcElement)\n",
- " targ = e.srcElement;\n",
- " if (targ.nodeType == 3) // defeat Safari bug\n",
- " targ = targ.parentNode;\n",
- "\n",
- " // jQuery normalizes the pageX and pageY\n",
- " // pageX,Y are the mouse positions relative to the document\n",
- " // offset() returns the position of the element relative to the document\n",
- " var x = e.pageX - $(targ).offset().left;\n",
- " var y = e.pageY - $(targ).offset().top;\n",
- "\n",
- " return {\"x\": x, \"y\": y};\n",
- "};\n",
- "\n",
- "/*\n",
- " * return a copy of an object with only non-object keys\n",
- " * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
- " */\n",
- "function simpleKeys (original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object')\n",
- " obj[key] = original[key]\n",
- " return obj;\n",
- " }, {});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.mouse_event = function(event, name) {\n",
- " var canvas_pos = mpl.findpos(event)\n",
- "\n",
- " if (name === 'button_press')\n",
- " {\n",
- " this.canvas.focus();\n",
- " this.canvas_div.focus();\n",
- " }\n",
- "\n",
- " var x = canvas_pos.x;\n",
- " var y = canvas_pos.y;\n",
- "\n",
- " this.send_message(name, {x: x, y: y, button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event)});\n",
- "\n",
- " /* This prevents the web browser from automatically changing to\n",
- " * the text insertion cursor when the button is pressed. We want\n",
- " * to control all of the cursor setting manually through the\n",
- " * 'cursor' event from matplotlib */\n",
- " event.preventDefault();\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " // Handle any extra behaviour associated with a key event\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.key_event = function(event, name) {\n",
- "\n",
- " // Prevent repeat events\n",
- " if (name == 'key_press')\n",
- " {\n",
- " if (event.which === this._key)\n",
- " return;\n",
- " else\n",
- " this._key = event.which;\n",
- " }\n",
- " if (name == 'key_release')\n",
- " this._key = null;\n",
- "\n",
- " var value = '';\n",
- " if (event.ctrlKey && event.which != 17)\n",
- " value += \"ctrl+\";\n",
- " if (event.altKey && event.which != 18)\n",
- " value += \"alt+\";\n",
- " if (event.shiftKey && event.which != 16)\n",
- " value += \"shift+\";\n",
- "\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
- "\n",
- " this._key_event_extra(event, name);\n",
- "\n",
- " this.send_message(name, {key: value,\n",
- " guiEvent: simpleKeys(event)});\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
- " if (name == 'download') {\n",
- " this.handle_save(this, null);\n",
- " } else {\n",
- " this.send_message(\"toolbar_button\", {name: name});\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
- " this.message.textContent = tooltip;\n",
- "};\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
- "\n",
- "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
- "\n",
- "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
- " // Create a \"websocket\"-like object which calls the given IPython comm\n",
- " // object with the appropriate methods. Currently this is a non binary\n",
- " // socket, so there is still some room for performance tuning.\n",
- " var ws = {};\n",
- "\n",
- " ws.close = function() {\n",
- " comm.close()\n",
- " };\n",
- " ws.send = function(m) {\n",
- " //console.log('sending', m);\n",
- " comm.send(m);\n",
- " };\n",
- " // Register the callback with on_msg.\n",
- " comm.on_msg(function(msg) {\n",
- " //console.log('receiving', msg['content']['data'], msg);\n",
- " // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data'])\n",
- " });\n",
- " return ws;\n",
- "}\n",
- "\n",
- "mpl.mpl_figure_comm = function(comm, msg) {\n",
- " // This is the function which gets called when the mpl process\n",
- " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
- "\n",
- " var id = msg.content.data.id;\n",
- " // Get hold of the div created by the display call when the Comm\n",
- " // socket was opened in Python.\n",
- " var element = $(\"#\" + id);\n",
- " var ws_proxy = comm_websocket_adapter(comm)\n",
- "\n",
- " function ondownload(figure, format) {\n",
- " window.open(figure.imageObj.src);\n",
- " }\n",
- "\n",
- " var fig = new mpl.figure(id, ws_proxy,\n",
- " ondownload,\n",
- " element.get(0));\n",
- "\n",
- " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
- " // web socket which is closed, not our websocket->open comm proxy.\n",
- " ws_proxy.onopen();\n",
- "\n",
- " fig.parent_element = element.get(0);\n",
- " fig.cell_info = mpl.find_output_cell(\"\");\n",
- " if (!fig.cell_info) {\n",
- " console.error(\"Failed to find cell for figure\", id, fig);\n",
- " return;\n",
- " }\n",
- "\n",
- " var output_index = fig.cell_info[2]\n",
- " var cell = fig.cell_info[0];\n",
- "\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
- " fig.root.unbind('remove')\n",
- "\n",
- " // Update the output cell to use the data from the current canvas.\n",
- " fig.push_to_output();\n",
- " var dataURL = fig.canvas.toDataURL();\n",
- " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
- " // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable()\n",
- " $(fig.parent_element).html('');\n",
- " fig.close_ws(fig, msg);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
- " fig.send_message('closing', msg);\n",
- " // fig.ws.close()\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
- " // Turn the data on the canvas into data in the output cell.\n",
- " var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] = '';\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Tell IPython that the notebook contents must change.\n",
- " IPython.notebook.set_dirty(true);\n",
- " this.send_message(\"ack\", {});\n",
- " var fig = this;\n",
- " // Wait a second, then push the new image to the DOM so\n",
- " // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () { fig.push_to_output() }, 1000);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items){\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) { continue; };\n",
- "\n",
- " var button = $('');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " // Add the status bar.\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "\n",
- " // Add the close button to the window.\n",
- " var buttongrp = $('');\n",
- " var button = $('');\n",
- " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
- " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
- " buttongrp.append(button);\n",
- " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
- " titlebar.prepend(buttongrp);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(el){\n",
- " var fig = this\n",
- " el.on(\"remove\", function(){\n",
- "\tfig.close_ws(fig, {});\n",
- " });\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(el){\n",
- " // this is important to make the div 'focusable\n",
- " el.attr('tabindex', 0)\n",
- " // reach out to IPython and tell the keyboard manager to turn it's self\n",
- " // off when our div gets focus\n",
- "\n",
- " // location in version 3\n",
- " if (IPython.notebook.keyboard_manager) {\n",
- " IPython.notebook.keyboard_manager.register_events(el);\n",
- " }\n",
- " else {\n",
- " // location in version 2\n",
- " IPython.keyboard_manager.register_events(el);\n",
- " }\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager)\n",
- " manager = IPython.keyboard_manager;\n",
- "\n",
- " // Check for shift+enter\n",
- " if (event.shiftKey && event.which == 13) {\n",
- " this.canvas_div.blur();\n",
- " event.shiftKey = false;\n",
- " // Send a \"J\" for go to next cell\n",
- " event.which = 74;\n",
- " event.keyCode = 74;\n",
- " manager.command_mode();\n",
- " manager.handle_keydown(event);\n",
- " }\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " fig.ondownload(fig, null);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.find_output_cell = function(html_output) {\n",
- " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
- " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
- " // IPython event is triggered only after the cells have been serialised, which for\n",
- " // our purposes (turning an active figure into a static one), is too late.\n",
- " var cells = IPython.notebook.get_cells();\n",
- " var ncells = cells.length;\n",
- " for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
- " data = data.data;\n",
- " }\n",
- " if (data['text/html'] == html_output) {\n",
- " return [cell, data, j];\n",
- " }\n",
- " }\n",
- " }\n",
- " }\n",
- "}\n",
- "\n",
- "// Register the function which deals with the matplotlib target/channel.\n",
- "// The kernel may be null if the page has been refreshed.\n",
- "if (IPython.notebook.kernel != null) {\n",
- " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
- "}\n"
- ],
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- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\pab\\workspace\\git_numdifftools\\numdifftools\\extrapolation.py:280: RuntimeWarning: invalid value encountered in absolute\n",
- " return np.maximum(np.abs(a1), np.abs(a2))\n",
- "c:\\pab\\workspace\\git_numdifftools\\numdifftools\\extrapolation.py:280: RuntimeWarning: invalid value encountered in maximum\n",
- " return np.maximum(np.abs(a1), np.abs(a2))\n",
- "c:\\pab\\workspace\\git_numdifftools\\numdifftools\\extrapolation.py:443: RuntimeWarning: invalid value encountered in less_equal\n",
- " converged = err <= tol\n",
- "c:\\pab\\workspace\\git_numdifftools\\numdifftools\\extrapolation.py:446: RuntimeWarning: invalid value encountered in absolute\n",
- " old_sequence[-m+1:]) * fact)\n",
- "c:\\pab\\workspace\\git_numdifftools\\numdifftools\\extrapolation.py:355: RuntimeWarning: invalid value encountered in absolute\n",
- " abserr = err1 + err2 + np.where(converged, tol2 * 10, np.abs(result - e2))\n",
- "c:\\pab\\workspace\\git_numdifftools\\numdifftools\\limits.py:146: RuntimeWarning: invalid value encountered in less\n",
- " outliers = (((abs(der) < (a_median / trim_fact)) +\n",
- "c:\\pab\\workspace\\git_numdifftools\\numdifftools\\limits.py:147: RuntimeWarning: invalid value encountered in greater\n",
- " (abs(der) > (a_median * trim_fact))) * (a_median > 1e-8) +\n",
- "c:\\pab\\workspace\\git_numdifftools\\numdifftools\\limits.py:148: RuntimeWarning: invalid value encountered in less\n",
- " ((der < p25-1.5*iqr) + (p75+1.5*iqr < der)))\n",
- "c:\\pab\\workspace\\git_numdifftools\\numdifftools\\extrapolation.py:441: RuntimeWarning: invalid value encountered in absolute\n",
- " err = np.abs(np.diff(new_sequence, axis=0)) * fact\n",
- "c:\\pab\\workspace\\git_numdifftools\\numdifftools\\limits.py:146: RuntimeWarning: invalid value encountered in absolute\n",
- " outliers = (((abs(der) < (a_median / trim_fact)) +\n",
- "c:\\pab\\workspace\\git_numdifftools\\numdifftools\\limits.py:147: RuntimeWarning: invalid value encountered in absolute\n",
- " (abs(der) > (a_median * trim_fact))) * (a_median > 1e-8) +\n",
- "c:\\pab\\workspace\\git_numdifftools\\numdifftools\\limits.py:149: RuntimeWarning: invalid value encountered in absolute\n",
- " errors = outliers * np.abs(der - median)\n"
- ]
- }
- ],
- "source": [
- "gev = ws.genextreme.fit2(Hs)\n",
- "fig, axes = plt.subplots()\n",
- "gev.plotesf()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "application/javascript": [
- "/* Put everything inside the global mpl namespace */\n",
- "window.mpl = {};\n",
- "\n",
- "mpl.get_websocket_type = function() {\n",
- " if (typeof(WebSocket) !== 'undefined') {\n",
- " return WebSocket;\n",
- " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
- " return MozWebSocket;\n",
- " } else {\n",
- " alert('Your browser does not have WebSocket support.' +\n",
- " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
- " 'Firefox 4 and 5 are also supported but you ' +\n",
- " 'have to enable WebSockets in about:config.');\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
- " this.id = figure_id;\n",
- "\n",
- " this.ws = websocket;\n",
- "\n",
- " this.supports_binary = (this.ws.binaryType != undefined);\n",
- "\n",
- " if (!this.supports_binary) {\n",
- " var warnings = document.getElementById(\"mpl-warnings\");\n",
- " if (warnings) {\n",
- " warnings.style.display = 'block';\n",
- " warnings.textContent = (\n",
- " \"This browser does not support binary websocket messages. \" +\n",
- " \"Performance may be slow.\");\n",
- " }\n",
- " }\n",
- "\n",
- " this.imageObj = new Image();\n",
- "\n",
- " this.context = undefined;\n",
- " this.message = undefined;\n",
- " this.canvas = undefined;\n",
- " this.rubberband_canvas = undefined;\n",
- " this.rubberband_context = undefined;\n",
- " this.format_dropdown = undefined;\n",
- "\n",
- " this.image_mode = 'full';\n",
- "\n",
- " this.root = $('');\n",
- " this._root_extra_style(this.root)\n",
- " this.root.attr('style', 'display: inline-block');\n",
- "\n",
- " $(parent_element).append(this.root);\n",
- "\n",
- " this._init_header(this);\n",
- " this._init_canvas(this);\n",
- " this._init_toolbar(this);\n",
- "\n",
- " var fig = this;\n",
- "\n",
- " this.waiting = false;\n",
- "\n",
- " this.ws.onopen = function () {\n",
- " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
- " fig.send_message(\"send_image_mode\", {});\n",
- " fig.send_message(\"refresh\", {});\n",
- " }\n",
- "\n",
- " this.imageObj.onload = function() {\n",
- " if (fig.image_mode == 'full') {\n",
- " // Full images could contain transparency (where diff images\n",
- " // almost always do), so we need to clear the canvas so that\n",
- " // there is no ghosting.\n",
- " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
- " }\n",
- " fig.context.drawImage(fig.imageObj, 0, 0);\n",
- " };\n",
- "\n",
- " this.imageObj.onunload = function() {\n",
- " this.ws.close();\n",
- " }\n",
- "\n",
- " this.ws.onmessage = this._make_on_message_function(this);\n",
- "\n",
- " this.ondownload = ondownload;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_header = function() {\n",
- " var titlebar = $(\n",
- " '');\n",
- " var titletext = $(\n",
- " '');\n",
- " titlebar.append(titletext)\n",
- " this.root.append(titlebar);\n",
- " this.header = titletext[0];\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_canvas = function() {\n",
- " var fig = this;\n",
- "\n",
- " var canvas_div = $('');\n",
- "\n",
- " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
- "\n",
- " function canvas_keyboard_event(event) {\n",
- " return fig.key_event(event, event['data']);\n",
- " }\n",
- "\n",
- " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
- " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
- " this.canvas_div = canvas_div\n",
- " this._canvas_extra_style(canvas_div)\n",
- " this.root.append(canvas_div);\n",
- "\n",
- " var canvas = $('');\n",
- " canvas.addClass('mpl-canvas');\n",
- " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
- "\n",
- " this.canvas = canvas[0];\n",
- " this.context = canvas[0].getContext(\"2d\");\n",
- "\n",
- " var rubberband = $('');\n",
- " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
- "\n",
- " var pass_mouse_events = true;\n",
- "\n",
- " canvas_div.resizable({\n",
- " start: function(event, ui) {\n",
- " pass_mouse_events = false;\n",
- " },\n",
- " resize: function(event, ui) {\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " stop: function(event, ui) {\n",
- " pass_mouse_events = true;\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " });\n",
- "\n",
- " function mouse_event_fn(event) {\n",
- " if (pass_mouse_events)\n",
- " return fig.mouse_event(event, event['data']);\n",
- " }\n",
- "\n",
- " rubberband.mousedown('button_press', mouse_event_fn);\n",
- " rubberband.mouseup('button_release', mouse_event_fn);\n",
- " // Throttle sequential mouse events to 1 every 20ms.\n",
- " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
- "\n",
- " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
- " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
- "\n",
- " canvas_div.on(\"wheel\", function (event) {\n",
- " event = event.originalEvent;\n",
- " event['data'] = 'scroll'\n",
- " if (event.deltaY < 0) {\n",
- " event.step = 1;\n",
- " } else {\n",
- " event.step = -1;\n",
- " }\n",
- " mouse_event_fn(event);\n",
- " });\n",
- "\n",
- " canvas_div.append(canvas);\n",
- " canvas_div.append(rubberband);\n",
- "\n",
- " this.rubberband = rubberband;\n",
- " this.rubberband_canvas = rubberband[0];\n",
- " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
- " this.rubberband_context.strokeStyle = \"#000000\";\n",
- "\n",
- " this._resize_canvas = function(width, height) {\n",
- " // Keep the size of the canvas, canvas container, and rubber band\n",
- " // canvas in synch.\n",
- " canvas_div.css('width', width)\n",
- " canvas_div.css('height', height)\n",
- "\n",
- " canvas.attr('width', width);\n",
- " canvas.attr('height', height);\n",
- "\n",
- " rubberband.attr('width', width);\n",
- " rubberband.attr('height', height);\n",
- " }\n",
- "\n",
- " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
- " // upon first draw.\n",
- " this._resize_canvas(600, 600);\n",
- "\n",
- " // Disable right mouse context menu.\n",
- " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
- " return false;\n",
- " });\n",
- "\n",
- " function set_focus () {\n",
- " canvas.focus();\n",
- " canvas_div.focus();\n",
- " }\n",
- "\n",
- " window.setTimeout(set_focus, 100);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " // put a spacer in here.\n",
- " continue;\n",
- " }\n",
- " var button = $('');\n",
- " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
- " 'ui-button-icon-only');\n",
- " button.attr('role', 'button');\n",
- " button.attr('aria-disabled', 'false');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- "\n",
- " var icon_img = $('');\n",
- " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
- " icon_img.addClass(image);\n",
- " icon_img.addClass('ui-corner-all');\n",
- "\n",
- " var tooltip_span = $('');\n",
- " tooltip_span.addClass('ui-button-text');\n",
- " tooltip_span.html(tooltip);\n",
- "\n",
- " button.append(icon_img);\n",
- " button.append(tooltip_span);\n",
- "\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " var fmt_picker_span = $('');\n",
- "\n",
- " var fmt_picker = $('');\n",
- " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
- " fmt_picker_span.append(fmt_picker);\n",
- " nav_element.append(fmt_picker_span);\n",
- " this.format_dropdown = fmt_picker[0];\n",
- "\n",
- " for (var ind in mpl.extensions) {\n",
- " var fmt = mpl.extensions[ind];\n",
- " var option = $(\n",
- " '', {selected: fmt === mpl.default_extension}).html(fmt);\n",
- " fmt_picker.append(option)\n",
- " }\n",
- "\n",
- " // Add hover states to the ui-buttons\n",
- " $( \".ui-button\" ).hover(\n",
- " function() { $(this).addClass(\"ui-state-hover\");},\n",
- " function() { $(this).removeClass(\"ui-state-hover\");}\n",
- " );\n",
- "\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
- " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
- " // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_message = function(type, properties) {\n",
- " properties['type'] = type;\n",
- " properties['figure_id'] = this.id;\n",
- " this.ws.send(JSON.stringify(properties));\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_draw_message = function() {\n",
- " if (!this.waiting) {\n",
- " this.waiting = true;\n",
- " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
- " }\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " var format_dropdown = fig.format_dropdown;\n",
- " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
- " fig.ondownload(fig, format);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
- " var size = msg['size'];\n",
- " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1]);\n",
- " fig.send_message(\"refresh\", {});\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
- " var x0 = msg['x0'];\n",
- " var y0 = fig.canvas.height - msg['y0'];\n",
- " var x1 = msg['x1'];\n",
- " var y1 = fig.canvas.height - msg['y1'];\n",
- " x0 = Math.floor(x0) + 0.5;\n",
- " y0 = Math.floor(y0) + 0.5;\n",
- " x1 = Math.floor(x1) + 0.5;\n",
- " y1 = Math.floor(y1) + 0.5;\n",
- " var min_x = Math.min(x0, x1);\n",
- " var min_y = Math.min(y0, y1);\n",
- " var width = Math.abs(x1 - x0);\n",
- " var height = Math.abs(y1 - y0);\n",
- "\n",
- " fig.rubberband_context.clearRect(\n",
- " 0, 0, fig.canvas.width, fig.canvas.height);\n",
- "\n",
- " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
- " // Updates the figure title.\n",
- " fig.header.textContent = msg['label'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch(cursor)\n",
- " {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
- " fig.message.textContent = msg['message'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
- " // Request the server to send over a new figure.\n",
- " fig.send_draw_message();\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
- " fig.image_mode = msg['mode'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Called whenever the canvas gets updated.\n",
- " this.send_message(\"ack\", {});\n",
- "}\n",
- "\n",
- "// A function to construct a web socket function for onmessage handling.\n",
- "// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
- " return function socket_on_message(evt) {\n",
- " if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = \"image/png\";\n",
- "\n",
- " /* Free the memory for the previous frames */\n",
- " if (fig.imageObj.src) {\n",
- " (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src);\n",
- " }\n",
- "\n",
- " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data);\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
- " fig.imageObj.src = evt.data;\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- "\n",
- " var msg = JSON.parse(evt.data);\n",
- " var msg_type = msg['type'];\n",
- "\n",
- " // Call the \"handle_{type}\" callback, which takes\n",
- " // the figure and JSON message as its only arguments.\n",
- " try {\n",
- " var callback = fig[\"handle_\" + msg_type];\n",
- " } catch (e) {\n",
- " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
- " return;\n",
- " }\n",
- "\n",
- " if (callback) {\n",
- " try {\n",
- " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
- " callback(fig, msg);\n",
- " } catch (e) {\n",
- " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
- " }\n",
- " }\n",
- " };\n",
- "}\n",
- "\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function(e) {\n",
- " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
- " var targ;\n",
- " if (!e)\n",
- " e = window.event;\n",
- " if (e.target)\n",
- " targ = e.target;\n",
- " else if (e.srcElement)\n",
- " targ = e.srcElement;\n",
- " if (targ.nodeType == 3) // defeat Safari bug\n",
- " targ = targ.parentNode;\n",
- "\n",
- " // jQuery normalizes the pageX and pageY\n",
- " // pageX,Y are the mouse positions relative to the document\n",
- " // offset() returns the position of the element relative to the document\n",
- " var x = e.pageX - $(targ).offset().left;\n",
- " var y = e.pageY - $(targ).offset().top;\n",
- "\n",
- " return {\"x\": x, \"y\": y};\n",
- "};\n",
- "\n",
- "/*\n",
- " * return a copy of an object with only non-object keys\n",
- " * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
- " */\n",
- "function simpleKeys (original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object')\n",
- " obj[key] = original[key]\n",
- " return obj;\n",
- " }, {});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.mouse_event = function(event, name) {\n",
- " var canvas_pos = mpl.findpos(event)\n",
- "\n",
- " if (name === 'button_press')\n",
- " {\n",
- " this.canvas.focus();\n",
- " this.canvas_div.focus();\n",
- " }\n",
- "\n",
- " var x = canvas_pos.x;\n",
- " var y = canvas_pos.y;\n",
- "\n",
- " this.send_message(name, {x: x, y: y, button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event)});\n",
- "\n",
- " /* This prevents the web browser from automatically changing to\n",
- " * the text insertion cursor when the button is pressed. We want\n",
- " * to control all of the cursor setting manually through the\n",
- " * 'cursor' event from matplotlib */\n",
- " event.preventDefault();\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " // Handle any extra behaviour associated with a key event\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.key_event = function(event, name) {\n",
- "\n",
- " // Prevent repeat events\n",
- " if (name == 'key_press')\n",
- " {\n",
- " if (event.which === this._key)\n",
- " return;\n",
- " else\n",
- " this._key = event.which;\n",
- " }\n",
- " if (name == 'key_release')\n",
- " this._key = null;\n",
- "\n",
- " var value = '';\n",
- " if (event.ctrlKey && event.which != 17)\n",
- " value += \"ctrl+\";\n",
- " if (event.altKey && event.which != 18)\n",
- " value += \"alt+\";\n",
- " if (event.shiftKey && event.which != 16)\n",
- " value += \"shift+\";\n",
- "\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
- "\n",
- " this._key_event_extra(event, name);\n",
- "\n",
- " this.send_message(name, {key: value,\n",
- " guiEvent: simpleKeys(event)});\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
- " if (name == 'download') {\n",
- " this.handle_save(this, null);\n",
- " } else {\n",
- " this.send_message(\"toolbar_button\", {name: name});\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
- " this.message.textContent = tooltip;\n",
- "};\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
- "\n",
- "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
- "\n",
- "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
- " // Create a \"websocket\"-like object which calls the given IPython comm\n",
- " // object with the appropriate methods. Currently this is a non binary\n",
- " // socket, so there is still some room for performance tuning.\n",
- " var ws = {};\n",
- "\n",
- " ws.close = function() {\n",
- " comm.close()\n",
- " };\n",
- " ws.send = function(m) {\n",
- " //console.log('sending', m);\n",
- " comm.send(m);\n",
- " };\n",
- " // Register the callback with on_msg.\n",
- " comm.on_msg(function(msg) {\n",
- " //console.log('receiving', msg['content']['data'], msg);\n",
- " // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data'])\n",
- " });\n",
- " return ws;\n",
- "}\n",
- "\n",
- "mpl.mpl_figure_comm = function(comm, msg) {\n",
- " // This is the function which gets called when the mpl process\n",
- " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
- "\n",
- " var id = msg.content.data.id;\n",
- " // Get hold of the div created by the display call when the Comm\n",
- " // socket was opened in Python.\n",
- " var element = $(\"#\" + id);\n",
- " var ws_proxy = comm_websocket_adapter(comm)\n",
- "\n",
- " function ondownload(figure, format) {\n",
- " window.open(figure.imageObj.src);\n",
- " }\n",
- "\n",
- " var fig = new mpl.figure(id, ws_proxy,\n",
- " ondownload,\n",
- " element.get(0));\n",
- "\n",
- " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
- " // web socket which is closed, not our websocket->open comm proxy.\n",
- " ws_proxy.onopen();\n",
- "\n",
- " fig.parent_element = element.get(0);\n",
- " fig.cell_info = mpl.find_output_cell(\"\");\n",
- " if (!fig.cell_info) {\n",
- " console.error(\"Failed to find cell for figure\", id, fig);\n",
- " return;\n",
- " }\n",
- "\n",
- " var output_index = fig.cell_info[2]\n",
- " var cell = fig.cell_info[0];\n",
- "\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
- " fig.root.unbind('remove')\n",
- "\n",
- " // Update the output cell to use the data from the current canvas.\n",
- " fig.push_to_output();\n",
- " var dataURL = fig.canvas.toDataURL();\n",
- " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
- " // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable()\n",
- " $(fig.parent_element).html('');\n",
- " fig.close_ws(fig, msg);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
- " fig.send_message('closing', msg);\n",
- " // fig.ws.close()\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
- " // Turn the data on the canvas into data in the output cell.\n",
- " var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] = '';\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Tell IPython that the notebook contents must change.\n",
- " IPython.notebook.set_dirty(true);\n",
- " this.send_message(\"ack\", {});\n",
- " var fig = this;\n",
- " // Wait a second, then push the new image to the DOM so\n",
- " // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () { fig.push_to_output() }, 1000);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items){\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) { continue; };\n",
- "\n",
- " var button = $('');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " // Add the status bar.\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "\n",
- " // Add the close button to the window.\n",
- " var buttongrp = $('');\n",
- " var button = $('');\n",
- " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
- " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
- " buttongrp.append(button);\n",
- " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
- " titlebar.prepend(buttongrp);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(el){\n",
- " var fig = this\n",
- " el.on(\"remove\", function(){\n",
- "\tfig.close_ws(fig, {});\n",
- " });\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(el){\n",
- " // this is important to make the div 'focusable\n",
- " el.attr('tabindex', 0)\n",
- " // reach out to IPython and tell the keyboard manager to turn it's self\n",
- " // off when our div gets focus\n",
- "\n",
- " // location in version 3\n",
- " if (IPython.notebook.keyboard_manager) {\n",
- " IPython.notebook.keyboard_manager.register_events(el);\n",
- " }\n",
- " else {\n",
- " // location in version 2\n",
- " IPython.keyboard_manager.register_events(el);\n",
- " }\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager)\n",
- " manager = IPython.keyboard_manager;\n",
- "\n",
- " // Check for shift+enter\n",
- " if (event.shiftKey && event.which == 13) {\n",
- " this.canvas_div.blur();\n",
- " event.shiftKey = false;\n",
- " // Send a \"J\" for go to next cell\n",
- " event.which = 74;\n",
- " event.keyCode = 74;\n",
- " manager.command_mode();\n",
- " manager.handle_keydown(event);\n",
- " }\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " fig.ondownload(fig, null);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.find_output_cell = function(html_output) {\n",
- " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
- " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
- " // IPython event is triggered only after the cells have been serialised, which for\n",
- " // our purposes (turning an active figure into a static one), is too late.\n",
- " var cells = IPython.notebook.get_cells();\n",
- " var ncells = cells.length;\n",
- " for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
- " data = data.data;\n",
- " }\n",
- " if (data['text/html'] == html_output) {\n",
- " return [cell, data, j];\n",
- " }\n",
- " }\n",
- " }\n",
- " }\n",
- "}\n",
- "\n",
- "// Register the function which deals with the matplotlib target/channel.\n",
- "// The kernel may be null if the page has been refreshed.\n",
- "if (IPython.notebook.kernel != null) {\n",
- " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
- "}\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- ""
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "import wafo.kdetools as wk\n",
- "fig, axes = plt.subplots()\n",
- "wk.TKDE(Hs, L2=0.5)(output='plot').plot('g--')\n",
- "plt.hold(True)\n",
- "gev.plotepdf() "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Analysis of yura87 wave data. \n",
- " Wave data interpolated (spline) and organized in 5-minute intervals\n",
- "Normalized to mean 0 and std = 1 to get stationary conditions. \n",
- "maximum level over each 5-minute interval analysed by GEV"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "application/javascript": [
- "/* Put everything inside the global mpl namespace */\n",
- "window.mpl = {};\n",
- "\n",
- "mpl.get_websocket_type = function() {\n",
- " if (typeof(WebSocket) !== 'undefined') {\n",
- " return WebSocket;\n",
- " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
- " return MozWebSocket;\n",
- " } else {\n",
- " alert('Your browser does not have WebSocket support.' +\n",
- " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
- " 'Firefox 4 and 5 are also supported but you ' +\n",
- " 'have to enable WebSockets in about:config.');\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
- " this.id = figure_id;\n",
- "\n",
- " this.ws = websocket;\n",
- "\n",
- " this.supports_binary = (this.ws.binaryType != undefined);\n",
- "\n",
- " if (!this.supports_binary) {\n",
- " var warnings = document.getElementById(\"mpl-warnings\");\n",
- " if (warnings) {\n",
- " warnings.style.display = 'block';\n",
- " warnings.textContent = (\n",
- " \"This browser does not support binary websocket messages. \" +\n",
- " \"Performance may be slow.\");\n",
- " }\n",
- " }\n",
- "\n",
- " this.imageObj = new Image();\n",
- "\n",
- " this.context = undefined;\n",
- " this.message = undefined;\n",
- " this.canvas = undefined;\n",
- " this.rubberband_canvas = undefined;\n",
- " this.rubberband_context = undefined;\n",
- " this.format_dropdown = undefined;\n",
- "\n",
- " this.image_mode = 'full';\n",
- "\n",
- " this.root = $('');\n",
- " this._root_extra_style(this.root)\n",
- " this.root.attr('style', 'display: inline-block');\n",
- "\n",
- " $(parent_element).append(this.root);\n",
- "\n",
- " this._init_header(this);\n",
- " this._init_canvas(this);\n",
- " this._init_toolbar(this);\n",
- "\n",
- " var fig = this;\n",
- "\n",
- " this.waiting = false;\n",
- "\n",
- " this.ws.onopen = function () {\n",
- " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
- " fig.send_message(\"send_image_mode\", {});\n",
- " fig.send_message(\"refresh\", {});\n",
- " }\n",
- "\n",
- " this.imageObj.onload = function() {\n",
- " if (fig.image_mode == 'full') {\n",
- " // Full images could contain transparency (where diff images\n",
- " // almost always do), so we need to clear the canvas so that\n",
- " // there is no ghosting.\n",
- " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
- " }\n",
- " fig.context.drawImage(fig.imageObj, 0, 0);\n",
- " };\n",
- "\n",
- " this.imageObj.onunload = function() {\n",
- " this.ws.close();\n",
- " }\n",
- "\n",
- " this.ws.onmessage = this._make_on_message_function(this);\n",
- "\n",
- " this.ondownload = ondownload;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_header = function() {\n",
- " var titlebar = $(\n",
- " '');\n",
- " var titletext = $(\n",
- " '');\n",
- " titlebar.append(titletext)\n",
- " this.root.append(titlebar);\n",
- " this.header = titletext[0];\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_canvas = function() {\n",
- " var fig = this;\n",
- "\n",
- " var canvas_div = $('');\n",
- "\n",
- " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
- "\n",
- " function canvas_keyboard_event(event) {\n",
- " return fig.key_event(event, event['data']);\n",
- " }\n",
- "\n",
- " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
- " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
- " this.canvas_div = canvas_div\n",
- " this._canvas_extra_style(canvas_div)\n",
- " this.root.append(canvas_div);\n",
- "\n",
- " var canvas = $('');\n",
- " canvas.addClass('mpl-canvas');\n",
- " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
- "\n",
- " this.canvas = canvas[0];\n",
- " this.context = canvas[0].getContext(\"2d\");\n",
- "\n",
- " var rubberband = $('');\n",
- " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
- "\n",
- " var pass_mouse_events = true;\n",
- "\n",
- " canvas_div.resizable({\n",
- " start: function(event, ui) {\n",
- " pass_mouse_events = false;\n",
- " },\n",
- " resize: function(event, ui) {\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " stop: function(event, ui) {\n",
- " pass_mouse_events = true;\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " });\n",
- "\n",
- " function mouse_event_fn(event) {\n",
- " if (pass_mouse_events)\n",
- " return fig.mouse_event(event, event['data']);\n",
- " }\n",
- "\n",
- " rubberband.mousedown('button_press', mouse_event_fn);\n",
- " rubberband.mouseup('button_release', mouse_event_fn);\n",
- " // Throttle sequential mouse events to 1 every 20ms.\n",
- " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
- "\n",
- " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
- " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
- "\n",
- " canvas_div.on(\"wheel\", function (event) {\n",
- " event = event.originalEvent;\n",
- " event['data'] = 'scroll'\n",
- " if (event.deltaY < 0) {\n",
- " event.step = 1;\n",
- " } else {\n",
- " event.step = -1;\n",
- " }\n",
- " mouse_event_fn(event);\n",
- " });\n",
- "\n",
- " canvas_div.append(canvas);\n",
- " canvas_div.append(rubberband);\n",
- "\n",
- " this.rubberband = rubberband;\n",
- " this.rubberband_canvas = rubberband[0];\n",
- " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
- " this.rubberband_context.strokeStyle = \"#000000\";\n",
- "\n",
- " this._resize_canvas = function(width, height) {\n",
- " // Keep the size of the canvas, canvas container, and rubber band\n",
- " // canvas in synch.\n",
- " canvas_div.css('width', width)\n",
- " canvas_div.css('height', height)\n",
- "\n",
- " canvas.attr('width', width);\n",
- " canvas.attr('height', height);\n",
- "\n",
- " rubberband.attr('width', width);\n",
- " rubberband.attr('height', height);\n",
- " }\n",
- "\n",
- " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
- " // upon first draw.\n",
- " this._resize_canvas(600, 600);\n",
- "\n",
- " // Disable right mouse context menu.\n",
- " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
- " return false;\n",
- " });\n",
- "\n",
- " function set_focus () {\n",
- " canvas.focus();\n",
- " canvas_div.focus();\n",
- " }\n",
- "\n",
- " window.setTimeout(set_focus, 100);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " // put a spacer in here.\n",
- " continue;\n",
- " }\n",
- " var button = $('');\n",
- " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
- " 'ui-button-icon-only');\n",
- " button.attr('role', 'button');\n",
- " button.attr('aria-disabled', 'false');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- "\n",
- " var icon_img = $('');\n",
- " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
- " icon_img.addClass(image);\n",
- " icon_img.addClass('ui-corner-all');\n",
- "\n",
- " var tooltip_span = $('');\n",
- " tooltip_span.addClass('ui-button-text');\n",
- " tooltip_span.html(tooltip);\n",
- "\n",
- " button.append(icon_img);\n",
- " button.append(tooltip_span);\n",
- "\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " var fmt_picker_span = $('');\n",
- "\n",
- " var fmt_picker = $('');\n",
- " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
- " fmt_picker_span.append(fmt_picker);\n",
- " nav_element.append(fmt_picker_span);\n",
- " this.format_dropdown = fmt_picker[0];\n",
- "\n",
- " for (var ind in mpl.extensions) {\n",
- " var fmt = mpl.extensions[ind];\n",
- " var option = $(\n",
- " '', {selected: fmt === mpl.default_extension}).html(fmt);\n",
- " fmt_picker.append(option)\n",
- " }\n",
- "\n",
- " // Add hover states to the ui-buttons\n",
- " $( \".ui-button\" ).hover(\n",
- " function() { $(this).addClass(\"ui-state-hover\");},\n",
- " function() { $(this).removeClass(\"ui-state-hover\");}\n",
- " );\n",
- "\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
- " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
- " // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_message = function(type, properties) {\n",
- " properties['type'] = type;\n",
- " properties['figure_id'] = this.id;\n",
- " this.ws.send(JSON.stringify(properties));\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_draw_message = function() {\n",
- " if (!this.waiting) {\n",
- " this.waiting = true;\n",
- " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
- " }\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " var format_dropdown = fig.format_dropdown;\n",
- " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
- " fig.ondownload(fig, format);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
- " var size = msg['size'];\n",
- " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1]);\n",
- " fig.send_message(\"refresh\", {});\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
- " var x0 = msg['x0'];\n",
- " var y0 = fig.canvas.height - msg['y0'];\n",
- " var x1 = msg['x1'];\n",
- " var y1 = fig.canvas.height - msg['y1'];\n",
- " x0 = Math.floor(x0) + 0.5;\n",
- " y0 = Math.floor(y0) + 0.5;\n",
- " x1 = Math.floor(x1) + 0.5;\n",
- " y1 = Math.floor(y1) + 0.5;\n",
- " var min_x = Math.min(x0, x1);\n",
- " var min_y = Math.min(y0, y1);\n",
- " var width = Math.abs(x1 - x0);\n",
- " var height = Math.abs(y1 - y0);\n",
- "\n",
- " fig.rubberband_context.clearRect(\n",
- " 0, 0, fig.canvas.width, fig.canvas.height);\n",
- "\n",
- " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
- " // Updates the figure title.\n",
- " fig.header.textContent = msg['label'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch(cursor)\n",
- " {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
- " fig.message.textContent = msg['message'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
- " // Request the server to send over a new figure.\n",
- " fig.send_draw_message();\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
- " fig.image_mode = msg['mode'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Called whenever the canvas gets updated.\n",
- " this.send_message(\"ack\", {});\n",
- "}\n",
- "\n",
- "// A function to construct a web socket function for onmessage handling.\n",
- "// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
- " return function socket_on_message(evt) {\n",
- " if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = \"image/png\";\n",
- "\n",
- " /* Free the memory for the previous frames */\n",
- " if (fig.imageObj.src) {\n",
- " (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src);\n",
- " }\n",
- "\n",
- " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data);\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
- " fig.imageObj.src = evt.data;\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- "\n",
- " var msg = JSON.parse(evt.data);\n",
- " var msg_type = msg['type'];\n",
- "\n",
- " // Call the \"handle_{type}\" callback, which takes\n",
- " // the figure and JSON message as its only arguments.\n",
- " try {\n",
- " var callback = fig[\"handle_\" + msg_type];\n",
- " } catch (e) {\n",
- " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
- " return;\n",
- " }\n",
- "\n",
- " if (callback) {\n",
- " try {\n",
- " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
- " callback(fig, msg);\n",
- " } catch (e) {\n",
- " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
- " }\n",
- " }\n",
- " };\n",
- "}\n",
- "\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function(e) {\n",
- " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
- " var targ;\n",
- " if (!e)\n",
- " e = window.event;\n",
- " if (e.target)\n",
- " targ = e.target;\n",
- " else if (e.srcElement)\n",
- " targ = e.srcElement;\n",
- " if (targ.nodeType == 3) // defeat Safari bug\n",
- " targ = targ.parentNode;\n",
- "\n",
- " // jQuery normalizes the pageX and pageY\n",
- " // pageX,Y are the mouse positions relative to the document\n",
- " // offset() returns the position of the element relative to the document\n",
- " var x = e.pageX - $(targ).offset().left;\n",
- " var y = e.pageY - $(targ).offset().top;\n",
- "\n",
- " return {\"x\": x, \"y\": y};\n",
- "};\n",
- "\n",
- "/*\n",
- " * return a copy of an object with only non-object keys\n",
- " * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
- " */\n",
- "function simpleKeys (original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object')\n",
- " obj[key] = original[key]\n",
- " return obj;\n",
- " }, {});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.mouse_event = function(event, name) {\n",
- " var canvas_pos = mpl.findpos(event)\n",
- "\n",
- " if (name === 'button_press')\n",
- " {\n",
- " this.canvas.focus();\n",
- " this.canvas_div.focus();\n",
- " }\n",
- "\n",
- " var x = canvas_pos.x;\n",
- " var y = canvas_pos.y;\n",
- "\n",
- " this.send_message(name, {x: x, y: y, button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event)});\n",
- "\n",
- " /* This prevents the web browser from automatically changing to\n",
- " * the text insertion cursor when the button is pressed. We want\n",
- " * to control all of the cursor setting manually through the\n",
- " * 'cursor' event from matplotlib */\n",
- " event.preventDefault();\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " // Handle any extra behaviour associated with a key event\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.key_event = function(event, name) {\n",
- "\n",
- " // Prevent repeat events\n",
- " if (name == 'key_press')\n",
- " {\n",
- " if (event.which === this._key)\n",
- " return;\n",
- " else\n",
- " this._key = event.which;\n",
- " }\n",
- " if (name == 'key_release')\n",
- " this._key = null;\n",
- "\n",
- " var value = '';\n",
- " if (event.ctrlKey && event.which != 17)\n",
- " value += \"ctrl+\";\n",
- " if (event.altKey && event.which != 18)\n",
- " value += \"alt+\";\n",
- " if (event.shiftKey && event.which != 16)\n",
- " value += \"shift+\";\n",
- "\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
- "\n",
- " this._key_event_extra(event, name);\n",
- "\n",
- " this.send_message(name, {key: value,\n",
- " guiEvent: simpleKeys(event)});\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
- " if (name == 'download') {\n",
- " this.handle_save(this, null);\n",
- " } else {\n",
- " this.send_message(\"toolbar_button\", {name: name});\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
- " this.message.textContent = tooltip;\n",
- "};\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
- "\n",
- "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
- "\n",
- "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
- " // Create a \"websocket\"-like object which calls the given IPython comm\n",
- " // object with the appropriate methods. Currently this is a non binary\n",
- " // socket, so there is still some room for performance tuning.\n",
- " var ws = {};\n",
- "\n",
- " ws.close = function() {\n",
- " comm.close()\n",
- " };\n",
- " ws.send = function(m) {\n",
- " //console.log('sending', m);\n",
- " comm.send(m);\n",
- " };\n",
- " // Register the callback with on_msg.\n",
- " comm.on_msg(function(msg) {\n",
- " //console.log('receiving', msg['content']['data'], msg);\n",
- " // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data'])\n",
- " });\n",
- " return ws;\n",
- "}\n",
- "\n",
- "mpl.mpl_figure_comm = function(comm, msg) {\n",
- " // This is the function which gets called when the mpl process\n",
- " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
- "\n",
- " var id = msg.content.data.id;\n",
- " // Get hold of the div created by the display call when the Comm\n",
- " // socket was opened in Python.\n",
- " var element = $(\"#\" + id);\n",
- " var ws_proxy = comm_websocket_adapter(comm)\n",
- "\n",
- " function ondownload(figure, format) {\n",
- " window.open(figure.imageObj.src);\n",
- " }\n",
- "\n",
- " var fig = new mpl.figure(id, ws_proxy,\n",
- " ondownload,\n",
- " element.get(0));\n",
- "\n",
- " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
- " // web socket which is closed, not our websocket->open comm proxy.\n",
- " ws_proxy.onopen();\n",
- "\n",
- " fig.parent_element = element.get(0);\n",
- " fig.cell_info = mpl.find_output_cell(\"\");\n",
- " if (!fig.cell_info) {\n",
- " console.error(\"Failed to find cell for figure\", id, fig);\n",
- " return;\n",
- " }\n",
- "\n",
- " var output_index = fig.cell_info[2]\n",
- " var cell = fig.cell_info[0];\n",
- "\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
- " fig.root.unbind('remove')\n",
- "\n",
- " // Update the output cell to use the data from the current canvas.\n",
- " fig.push_to_output();\n",
- " var dataURL = fig.canvas.toDataURL();\n",
- " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
- " // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable()\n",
- " $(fig.parent_element).html('');\n",
- " fig.close_ws(fig, msg);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
- " fig.send_message('closing', msg);\n",
- " // fig.ws.close()\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
- " // Turn the data on the canvas into data in the output cell.\n",
- " var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] = '';\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Tell IPython that the notebook contents must change.\n",
- " IPython.notebook.set_dirty(true);\n",
- " this.send_message(\"ack\", {});\n",
- " var fig = this;\n",
- " // Wait a second, then push the new image to the DOM so\n",
- " // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () { fig.push_to_output() }, 1000);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items){\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) { continue; };\n",
- "\n",
- " var button = $('');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " // Add the status bar.\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "\n",
- " // Add the close button to the window.\n",
- " var buttongrp = $('');\n",
- " var button = $('');\n",
- " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
- " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
- " buttongrp.append(button);\n",
- " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
- " titlebar.prepend(buttongrp);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(el){\n",
- " var fig = this\n",
- " el.on(\"remove\", function(){\n",
- "\tfig.close_ws(fig, {});\n",
- " });\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(el){\n",
- " // this is important to make the div 'focusable\n",
- " el.attr('tabindex', 0)\n",
- " // reach out to IPython and tell the keyboard manager to turn it's self\n",
- " // off when our div gets focus\n",
- "\n",
- " // location in version 3\n",
- " if (IPython.notebook.keyboard_manager) {\n",
- " IPython.notebook.keyboard_manager.register_events(el);\n",
- " }\n",
- " else {\n",
- " // location in version 2\n",
- " IPython.keyboard_manager.register_events(el);\n",
- " }\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager)\n",
- " manager = IPython.keyboard_manager;\n",
- "\n",
- " // Check for shift+enter\n",
- " if (event.shiftKey && event.which == 13) {\n",
- " this.canvas_div.blur();\n",
- " event.shiftKey = false;\n",
- " // Send a \"J\" for go to next cell\n",
- " event.which = 74;\n",
- " event.keyCode = 74;\n",
- " manager.command_mode();\n",
- " manager.handle_keydown(event);\n",
- " }\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " fig.ondownload(fig, null);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.find_output_cell = function(html_output) {\n",
- " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
- " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
- " // IPython event is triggered only after the cells have been serialised, which for\n",
- " // our purposes (turning an active figure into a static one), is too late.\n",
- " var cells = IPython.notebook.get_cells();\n",
- " var ncells = cells.length;\n",
- " for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
- " data = data.data;\n",
- " }\n",
- " if (data['text/html'] == html_output) {\n",
- " return [cell, data, j];\n",
- " }\n",
- " }\n",
- " }\n",
- " }\n",
- "}\n",
- "\n",
- "// Register the function which deals with the matplotlib target/channel.\n",
- "// The kernel may be null if the page has been refreshed.\n",
- "if (IPython.notebook.kernel != null) {\n",
- " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
- "}\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- ""
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\pab\\workspace\\git_pywafo\\wafo\\stats\\estimation.py:343: UserWarning: The fitted parameters does not provide the optimum fit. Something wrong with fit (par = [-0.09964304 3.71720793 -2.49446718])\n",
- " 'Something wrong with fit (par = {})'.format(str(par)))\n",
- "c:\\pab\\workspace\\git_pywafo\\wafo\\stats\\estimation.py:343: UserWarning: The fitted parameters does not provide the optimum fit. Something wrong with fit (par = [-0.09957624 3.71723125 -2.49446718])\n",
- " 'Something wrong with fit (par = {})'.format(str(par)))\n"
- ]
- }
- ],
- "source": [
- "import scipy.interpolate as si\n",
- "xn = wd.yura87()\n",
- "XI = np.r_[0:len(xn):0.25]\n",
- "N = len(XI); \n",
- "N = N-np.mod(N,4*60*5); \n",
- "YI = si.UnivariateSpline(xn[:,0].ravel(), xn[:,1].ravel(), k=3,s=0)(XI[:N])\n",
- "YI = np.reshape(YI, (4*60*5, N/(4*60*5))) # Each column holds 5 minutes of interpolated data.\n",
- "Y5 = (YI-YI.mean(axis=0))/(YI.std(axis=0))\n",
- "Y5M = Y5.max(axis=0)\n",
- "Y5gev = ws.genextreme.fit2(Y5M,method='mps')\n",
- "fig, axes = plt.subplots()\n",
- "Y5gev.plotesf()\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Section 5.2.2 Generalized Pareto distribution\n",
- "-------------------------------------------\n",
- "Exceedances of significant wave-height data over level 3."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "application/javascript": [
- "/* Put everything inside the global mpl namespace */\n",
- "window.mpl = {};\n",
- "\n",
- "mpl.get_websocket_type = function() {\n",
- " if (typeof(WebSocket) !== 'undefined') {\n",
- " return WebSocket;\n",
- " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
- " return MozWebSocket;\n",
- " } else {\n",
- " alert('Your browser does not have WebSocket support.' +\n",
- " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
- " 'Firefox 4 and 5 are also supported but you ' +\n",
- " 'have to enable WebSockets in about:config.');\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
- " this.id = figure_id;\n",
- "\n",
- " this.ws = websocket;\n",
- "\n",
- " this.supports_binary = (this.ws.binaryType != undefined);\n",
- "\n",
- " if (!this.supports_binary) {\n",
- " var warnings = document.getElementById(\"mpl-warnings\");\n",
- " if (warnings) {\n",
- " warnings.style.display = 'block';\n",
- " warnings.textContent = (\n",
- " \"This browser does not support binary websocket messages. \" +\n",
- " \"Performance may be slow.\");\n",
- " }\n",
- " }\n",
- "\n",
- " this.imageObj = new Image();\n",
- "\n",
- " this.context = undefined;\n",
- " this.message = undefined;\n",
- " this.canvas = undefined;\n",
- " this.rubberband_canvas = undefined;\n",
- " this.rubberband_context = undefined;\n",
- " this.format_dropdown = undefined;\n",
- "\n",
- " this.image_mode = 'full';\n",
- "\n",
- " this.root = $('');\n",
- " this._root_extra_style(this.root)\n",
- " this.root.attr('style', 'display: inline-block');\n",
- "\n",
- " $(parent_element).append(this.root);\n",
- "\n",
- " this._init_header(this);\n",
- " this._init_canvas(this);\n",
- " this._init_toolbar(this);\n",
- "\n",
- " var fig = this;\n",
- "\n",
- " this.waiting = false;\n",
- "\n",
- " this.ws.onopen = function () {\n",
- " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
- " fig.send_message(\"send_image_mode\", {});\n",
- " fig.send_message(\"refresh\", {});\n",
- " }\n",
- "\n",
- " this.imageObj.onload = function() {\n",
- " if (fig.image_mode == 'full') {\n",
- " // Full images could contain transparency (where diff images\n",
- " // almost always do), so we need to clear the canvas so that\n",
- " // there is no ghosting.\n",
- " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
- " }\n",
- " fig.context.drawImage(fig.imageObj, 0, 0);\n",
- " };\n",
- "\n",
- " this.imageObj.onunload = function() {\n",
- " this.ws.close();\n",
- " }\n",
- "\n",
- " this.ws.onmessage = this._make_on_message_function(this);\n",
- "\n",
- " this.ondownload = ondownload;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_header = function() {\n",
- " var titlebar = $(\n",
- " '');\n",
- " var titletext = $(\n",
- " '');\n",
- " titlebar.append(titletext)\n",
- " this.root.append(titlebar);\n",
- " this.header = titletext[0];\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_canvas = function() {\n",
- " var fig = this;\n",
- "\n",
- " var canvas_div = $('');\n",
- "\n",
- " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
- "\n",
- " function canvas_keyboard_event(event) {\n",
- " return fig.key_event(event, event['data']);\n",
- " }\n",
- "\n",
- " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
- " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
- " this.canvas_div = canvas_div\n",
- " this._canvas_extra_style(canvas_div)\n",
- " this.root.append(canvas_div);\n",
- "\n",
- " var canvas = $('');\n",
- " canvas.addClass('mpl-canvas');\n",
- " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
- "\n",
- " this.canvas = canvas[0];\n",
- " this.context = canvas[0].getContext(\"2d\");\n",
- "\n",
- " var rubberband = $('');\n",
- " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
- "\n",
- " var pass_mouse_events = true;\n",
- "\n",
- " canvas_div.resizable({\n",
- " start: function(event, ui) {\n",
- " pass_mouse_events = false;\n",
- " },\n",
- " resize: function(event, ui) {\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " stop: function(event, ui) {\n",
- " pass_mouse_events = true;\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " });\n",
- "\n",
- " function mouse_event_fn(event) {\n",
- " if (pass_mouse_events)\n",
- " return fig.mouse_event(event, event['data']);\n",
- " }\n",
- "\n",
- " rubberband.mousedown('button_press', mouse_event_fn);\n",
- " rubberband.mouseup('button_release', mouse_event_fn);\n",
- " // Throttle sequential mouse events to 1 every 20ms.\n",
- " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
- "\n",
- " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
- " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
- "\n",
- " canvas_div.on(\"wheel\", function (event) {\n",
- " event = event.originalEvent;\n",
- " event['data'] = 'scroll'\n",
- " if (event.deltaY < 0) {\n",
- " event.step = 1;\n",
- " } else {\n",
- " event.step = -1;\n",
- " }\n",
- " mouse_event_fn(event);\n",
- " });\n",
- "\n",
- " canvas_div.append(canvas);\n",
- " canvas_div.append(rubberband);\n",
- "\n",
- " this.rubberband = rubberband;\n",
- " this.rubberband_canvas = rubberband[0];\n",
- " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
- " this.rubberband_context.strokeStyle = \"#000000\";\n",
- "\n",
- " this._resize_canvas = function(width, height) {\n",
- " // Keep the size of the canvas, canvas container, and rubber band\n",
- " // canvas in synch.\n",
- " canvas_div.css('width', width)\n",
- " canvas_div.css('height', height)\n",
- "\n",
- " canvas.attr('width', width);\n",
- " canvas.attr('height', height);\n",
- "\n",
- " rubberband.attr('width', width);\n",
- " rubberband.attr('height', height);\n",
- " }\n",
- "\n",
- " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
- " // upon first draw.\n",
- " this._resize_canvas(600, 600);\n",
- "\n",
- " // Disable right mouse context menu.\n",
- " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
- " return false;\n",
- " });\n",
- "\n",
- " function set_focus () {\n",
- " canvas.focus();\n",
- " canvas_div.focus();\n",
- " }\n",
- "\n",
- " window.setTimeout(set_focus, 100);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " // put a spacer in here.\n",
- " continue;\n",
- " }\n",
- " var button = $('');\n",
- " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
- " 'ui-button-icon-only');\n",
- " button.attr('role', 'button');\n",
- " button.attr('aria-disabled', 'false');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- "\n",
- " var icon_img = $('');\n",
- " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
- " icon_img.addClass(image);\n",
- " icon_img.addClass('ui-corner-all');\n",
- "\n",
- " var tooltip_span = $('');\n",
- " tooltip_span.addClass('ui-button-text');\n",
- " tooltip_span.html(tooltip);\n",
- "\n",
- " button.append(icon_img);\n",
- " button.append(tooltip_span);\n",
- "\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " var fmt_picker_span = $('');\n",
- "\n",
- " var fmt_picker = $('');\n",
- " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
- " fmt_picker_span.append(fmt_picker);\n",
- " nav_element.append(fmt_picker_span);\n",
- " this.format_dropdown = fmt_picker[0];\n",
- "\n",
- " for (var ind in mpl.extensions) {\n",
- " var fmt = mpl.extensions[ind];\n",
- " var option = $(\n",
- " '', {selected: fmt === mpl.default_extension}).html(fmt);\n",
- " fmt_picker.append(option)\n",
- " }\n",
- "\n",
- " // Add hover states to the ui-buttons\n",
- " $( \".ui-button\" ).hover(\n",
- " function() { $(this).addClass(\"ui-state-hover\");},\n",
- " function() { $(this).removeClass(\"ui-state-hover\");}\n",
- " );\n",
- "\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
- " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
- " // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_message = function(type, properties) {\n",
- " properties['type'] = type;\n",
- " properties['figure_id'] = this.id;\n",
- " this.ws.send(JSON.stringify(properties));\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_draw_message = function() {\n",
- " if (!this.waiting) {\n",
- " this.waiting = true;\n",
- " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
- " }\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " var format_dropdown = fig.format_dropdown;\n",
- " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
- " fig.ondownload(fig, format);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
- " var size = msg['size'];\n",
- " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1]);\n",
- " fig.send_message(\"refresh\", {});\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
- " var x0 = msg['x0'];\n",
- " var y0 = fig.canvas.height - msg['y0'];\n",
- " var x1 = msg['x1'];\n",
- " var y1 = fig.canvas.height - msg['y1'];\n",
- " x0 = Math.floor(x0) + 0.5;\n",
- " y0 = Math.floor(y0) + 0.5;\n",
- " x1 = Math.floor(x1) + 0.5;\n",
- " y1 = Math.floor(y1) + 0.5;\n",
- " var min_x = Math.min(x0, x1);\n",
- " var min_y = Math.min(y0, y1);\n",
- " var width = Math.abs(x1 - x0);\n",
- " var height = Math.abs(y1 - y0);\n",
- "\n",
- " fig.rubberband_context.clearRect(\n",
- " 0, 0, fig.canvas.width, fig.canvas.height);\n",
- "\n",
- " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
- " // Updates the figure title.\n",
- " fig.header.textContent = msg['label'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch(cursor)\n",
- " {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
- " fig.message.textContent = msg['message'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
- " // Request the server to send over a new figure.\n",
- " fig.send_draw_message();\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
- " fig.image_mode = msg['mode'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Called whenever the canvas gets updated.\n",
- " this.send_message(\"ack\", {});\n",
- "}\n",
- "\n",
- "// A function to construct a web socket function for onmessage handling.\n",
- "// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
- " return function socket_on_message(evt) {\n",
- " if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = \"image/png\";\n",
- "\n",
- " /* Free the memory for the previous frames */\n",
- " if (fig.imageObj.src) {\n",
- " (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src);\n",
- " }\n",
- "\n",
- " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data);\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
- " fig.imageObj.src = evt.data;\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- "\n",
- " var msg = JSON.parse(evt.data);\n",
- " var msg_type = msg['type'];\n",
- "\n",
- " // Call the \"handle_{type}\" callback, which takes\n",
- " // the figure and JSON message as its only arguments.\n",
- " try {\n",
- " var callback = fig[\"handle_\" + msg_type];\n",
- " } catch (e) {\n",
- " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
- " return;\n",
- " }\n",
- "\n",
- " if (callback) {\n",
- " try {\n",
- " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
- " callback(fig, msg);\n",
- " } catch (e) {\n",
- " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
- " }\n",
- " }\n",
- " };\n",
- "}\n",
- "\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function(e) {\n",
- " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
- " var targ;\n",
- " if (!e)\n",
- " e = window.event;\n",
- " if (e.target)\n",
- " targ = e.target;\n",
- " else if (e.srcElement)\n",
- " targ = e.srcElement;\n",
- " if (targ.nodeType == 3) // defeat Safari bug\n",
- " targ = targ.parentNode;\n",
- "\n",
- " // jQuery normalizes the pageX and pageY\n",
- " // pageX,Y are the mouse positions relative to the document\n",
- " // offset() returns the position of the element relative to the document\n",
- " var x = e.pageX - $(targ).offset().left;\n",
- " var y = e.pageY - $(targ).offset().top;\n",
- "\n",
- " return {\"x\": x, \"y\": y};\n",
- "};\n",
- "\n",
- "/*\n",
- " * return a copy of an object with only non-object keys\n",
- " * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
- " */\n",
- "function simpleKeys (original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object')\n",
- " obj[key] = original[key]\n",
- " return obj;\n",
- " }, {});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.mouse_event = function(event, name) {\n",
- " var canvas_pos = mpl.findpos(event)\n",
- "\n",
- " if (name === 'button_press')\n",
- " {\n",
- " this.canvas.focus();\n",
- " this.canvas_div.focus();\n",
- " }\n",
- "\n",
- " var x = canvas_pos.x;\n",
- " var y = canvas_pos.y;\n",
- "\n",
- " this.send_message(name, {x: x, y: y, button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event)});\n",
- "\n",
- " /* This prevents the web browser from automatically changing to\n",
- " * the text insertion cursor when the button is pressed. We want\n",
- " * to control all of the cursor setting manually through the\n",
- " * 'cursor' event from matplotlib */\n",
- " event.preventDefault();\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " // Handle any extra behaviour associated with a key event\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.key_event = function(event, name) {\n",
- "\n",
- " // Prevent repeat events\n",
- " if (name == 'key_press')\n",
- " {\n",
- " if (event.which === this._key)\n",
- " return;\n",
- " else\n",
- " this._key = event.which;\n",
- " }\n",
- " if (name == 'key_release')\n",
- " this._key = null;\n",
- "\n",
- " var value = '';\n",
- " if (event.ctrlKey && event.which != 17)\n",
- " value += \"ctrl+\";\n",
- " if (event.altKey && event.which != 18)\n",
- " value += \"alt+\";\n",
- " if (event.shiftKey && event.which != 16)\n",
- " value += \"shift+\";\n",
- "\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
- "\n",
- " this._key_event_extra(event, name);\n",
- "\n",
- " this.send_message(name, {key: value,\n",
- " guiEvent: simpleKeys(event)});\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
- " if (name == 'download') {\n",
- " this.handle_save(this, null);\n",
- " } else {\n",
- " this.send_message(\"toolbar_button\", {name: name});\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
- " this.message.textContent = tooltip;\n",
- "};\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
- "\n",
- "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
- "\n",
- "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
- " // Create a \"websocket\"-like object which calls the given IPython comm\n",
- " // object with the appropriate methods. Currently this is a non binary\n",
- " // socket, so there is still some room for performance tuning.\n",
- " var ws = {};\n",
- "\n",
- " ws.close = function() {\n",
- " comm.close()\n",
- " };\n",
- " ws.send = function(m) {\n",
- " //console.log('sending', m);\n",
- " comm.send(m);\n",
- " };\n",
- " // Register the callback with on_msg.\n",
- " comm.on_msg(function(msg) {\n",
- " //console.log('receiving', msg['content']['data'], msg);\n",
- " // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data'])\n",
- " });\n",
- " return ws;\n",
- "}\n",
- "\n",
- "mpl.mpl_figure_comm = function(comm, msg) {\n",
- " // This is the function which gets called when the mpl process\n",
- " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
- "\n",
- " var id = msg.content.data.id;\n",
- " // Get hold of the div created by the display call when the Comm\n",
- " // socket was opened in Python.\n",
- " var element = $(\"#\" + id);\n",
- " var ws_proxy = comm_websocket_adapter(comm)\n",
- "\n",
- " function ondownload(figure, format) {\n",
- " window.open(figure.imageObj.src);\n",
- " }\n",
- "\n",
- " var fig = new mpl.figure(id, ws_proxy,\n",
- " ondownload,\n",
- " element.get(0));\n",
- "\n",
- " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
- " // web socket which is closed, not our websocket->open comm proxy.\n",
- " ws_proxy.onopen();\n",
- "\n",
- " fig.parent_element = element.get(0);\n",
- " fig.cell_info = mpl.find_output_cell(\"\");\n",
- " if (!fig.cell_info) {\n",
- " console.error(\"Failed to find cell for figure\", id, fig);\n",
- " return;\n",
- " }\n",
- "\n",
- " var output_index = fig.cell_info[2]\n",
- " var cell = fig.cell_info[0];\n",
- "\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
- " fig.root.unbind('remove')\n",
- "\n",
- " // Update the output cell to use the data from the current canvas.\n",
- " fig.push_to_output();\n",
- " var dataURL = fig.canvas.toDataURL();\n",
- " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
- " // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable()\n",
- " $(fig.parent_element).html('');\n",
- " fig.close_ws(fig, msg);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
- " fig.send_message('closing', msg);\n",
- " // fig.ws.close()\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
- " // Turn the data on the canvas into data in the output cell.\n",
- " var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] = '';\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Tell IPython that the notebook contents must change.\n",
- " IPython.notebook.set_dirty(true);\n",
- " this.send_message(\"ack\", {});\n",
- " var fig = this;\n",
- " // Wait a second, then push the new image to the DOM so\n",
- " // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () { fig.push_to_output() }, 1000);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items){\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) { continue; };\n",
- "\n",
- " var button = $('');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " // Add the status bar.\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "\n",
- " // Add the close button to the window.\n",
- " var buttongrp = $('');\n",
- " var button = $('');\n",
- " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
- " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
- " buttongrp.append(button);\n",
- " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
- " titlebar.prepend(buttongrp);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(el){\n",
- " var fig = this\n",
- " el.on(\"remove\", function(){\n",
- "\tfig.close_ws(fig, {});\n",
- " });\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(el){\n",
- " // this is important to make the div 'focusable\n",
- " el.attr('tabindex', 0)\n",
- " // reach out to IPython and tell the keyboard manager to turn it's self\n",
- " // off when our div gets focus\n",
- "\n",
- " // location in version 3\n",
- " if (IPython.notebook.keyboard_manager) {\n",
- " IPython.notebook.keyboard_manager.register_events(el);\n",
- " }\n",
- " else {\n",
- " // location in version 2\n",
- " IPython.keyboard_manager.register_events(el);\n",
- " }\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager)\n",
- " manager = IPython.keyboard_manager;\n",
- "\n",
- " // Check for shift+enter\n",
- " if (event.shiftKey && event.which == 13) {\n",
- " this.canvas_div.blur();\n",
- " event.shiftKey = false;\n",
- " // Send a \"J\" for go to next cell\n",
- " event.which = 74;\n",
- " event.keyCode = 74;\n",
- " manager.command_mode();\n",
- " manager.handle_keydown(event);\n",
- " }\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " fig.ondownload(fig, null);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.find_output_cell = function(html_output) {\n",
- " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
- " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
- " // IPython event is triggered only after the cells have been serialised, which for\n",
- " // our purposes (turning an active figure into a static one), is too late.\n",
- " var cells = IPython.notebook.get_cells();\n",
- " var ncells = cells.length;\n",
- " for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
- " data = data.data;\n",
- " }\n",
- " if (data['text/html'] == html_output) {\n",
- " return [cell, data, j];\n",
- " }\n",
- " }\n",
- " }\n",
- " }\n",
- "}\n",
- "\n",
- "// Register the function which deals with the matplotlib target/channel.\n",
- "// The kernel may be null if the page has been refreshed.\n",
- "if (IPython.notebook.kernel != null) {\n",
- " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
- "}\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- ""
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\pab\\workspace\\git_pywafo\\wafo\\stats\\estimation.py:517: UserWarning: Upper bound is larger\n",
- " warnings.warn('Upper bound is larger')\n"
- ]
- }
- ],
- "source": [
- "gpd3 = ws.genpareto.fit2(Hs[Hs>3],floc=3)\n",
- "fig, axes = plt.subplots()\n",
- "gpd3.plotesf()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Exceedances of significant wave-height data over level 7,"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "application/javascript": [
- "/* Put everything inside the global mpl namespace */\n",
- "window.mpl = {};\n",
- "\n",
- "mpl.get_websocket_type = function() {\n",
- " if (typeof(WebSocket) !== 'undefined') {\n",
- " return WebSocket;\n",
- " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
- " return MozWebSocket;\n",
- " } else {\n",
- " alert('Your browser does not have WebSocket support.' +\n",
- " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
- " 'Firefox 4 and 5 are also supported but you ' +\n",
- " 'have to enable WebSockets in about:config.');\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
- " this.id = figure_id;\n",
- "\n",
- " this.ws = websocket;\n",
- "\n",
- " this.supports_binary = (this.ws.binaryType != undefined);\n",
- "\n",
- " if (!this.supports_binary) {\n",
- " var warnings = document.getElementById(\"mpl-warnings\");\n",
- " if (warnings) {\n",
- " warnings.style.display = 'block';\n",
- " warnings.textContent = (\n",
- " \"This browser does not support binary websocket messages. \" +\n",
- " \"Performance may be slow.\");\n",
- " }\n",
- " }\n",
- "\n",
- " this.imageObj = new Image();\n",
- "\n",
- " this.context = undefined;\n",
- " this.message = undefined;\n",
- " this.canvas = undefined;\n",
- " this.rubberband_canvas = undefined;\n",
- " this.rubberband_context = undefined;\n",
- " this.format_dropdown = undefined;\n",
- "\n",
- " this.image_mode = 'full';\n",
- "\n",
- " this.root = $('');\n",
- " this._root_extra_style(this.root)\n",
- " this.root.attr('style', 'display: inline-block');\n",
- "\n",
- " $(parent_element).append(this.root);\n",
- "\n",
- " this._init_header(this);\n",
- " this._init_canvas(this);\n",
- " this._init_toolbar(this);\n",
- "\n",
- " var fig = this;\n",
- "\n",
- " this.waiting = false;\n",
- "\n",
- " this.ws.onopen = function () {\n",
- " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
- " fig.send_message(\"send_image_mode\", {});\n",
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- " }\n",
- "\n",
- " this.imageObj.onload = function() {\n",
- " if (fig.image_mode == 'full') {\n",
- " // Full images could contain transparency (where diff images\n",
- " // almost always do), so we need to clear the canvas so that\n",
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- "\n",
- " this.ws.onmessage = this._make_on_message_function(this);\n",
- "\n",
- " this.ondownload = ondownload;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_header = function() {\n",
- " var titlebar = $(\n",
- " '');\n",
- " var titletext = $(\n",
- " '');\n",
- " titlebar.append(titletext)\n",
- " this.root.append(titlebar);\n",
- " this.header = titletext[0];\n",
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- "\n",
- "\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_canvas = function() {\n",
- " var fig = this;\n",
- "\n",
- " var canvas_div = $('');\n",
- "\n",
- " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
- "\n",
- " function canvas_keyboard_event(event) {\n",
- " return fig.key_event(event, event['data']);\n",
- " }\n",
- "\n",
- " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
- " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
- " this.canvas_div = canvas_div\n",
- " this._canvas_extra_style(canvas_div)\n",
- " this.root.append(canvas_div);\n",
- "\n",
- " var canvas = $('');\n",
- " canvas.addClass('mpl-canvas');\n",
- " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
- "\n",
- " this.canvas = canvas[0];\n",
- " this.context = canvas[0].getContext(\"2d\");\n",
- "\n",
- " var rubberband = $('');\n",
- " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
- "\n",
- " var pass_mouse_events = true;\n",
- "\n",
- " canvas_div.resizable({\n",
- " start: function(event, ui) {\n",
- " pass_mouse_events = false;\n",
- " },\n",
- " resize: function(event, ui) {\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " stop: function(event, ui) {\n",
- " pass_mouse_events = true;\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " });\n",
- "\n",
- " function mouse_event_fn(event) {\n",
- " if (pass_mouse_events)\n",
- " return fig.mouse_event(event, event['data']);\n",
- " }\n",
- "\n",
- " rubberband.mousedown('button_press', mouse_event_fn);\n",
- " rubberband.mouseup('button_release', mouse_event_fn);\n",
- " // Throttle sequential mouse events to 1 every 20ms.\n",
- " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
- "\n",
- " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
- " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
- "\n",
- " canvas_div.on(\"wheel\", function (event) {\n",
- " event = event.originalEvent;\n",
- " event['data'] = 'scroll'\n",
- " if (event.deltaY < 0) {\n",
- " event.step = 1;\n",
- " } else {\n",
- " event.step = -1;\n",
- " }\n",
- " mouse_event_fn(event);\n",
- " });\n",
- "\n",
- " canvas_div.append(canvas);\n",
- " canvas_div.append(rubberband);\n",
- "\n",
- " this.rubberband = rubberband;\n",
- " this.rubberband_canvas = rubberband[0];\n",
- " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
- " this.rubberband_context.strokeStyle = \"#000000\";\n",
- "\n",
- " this._resize_canvas = function(width, height) {\n",
- " // Keep the size of the canvas, canvas container, and rubber band\n",
- " // canvas in synch.\n",
- " canvas_div.css('width', width)\n",
- " canvas_div.css('height', height)\n",
- "\n",
- " canvas.attr('width', width);\n",
- " canvas.attr('height', height);\n",
- "\n",
- " rubberband.attr('width', width);\n",
- " rubberband.attr('height', height);\n",
- " }\n",
- "\n",
- " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
- " // upon first draw.\n",
- " this._resize_canvas(600, 600);\n",
- "\n",
- " // Disable right mouse context menu.\n",
- " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
- " return false;\n",
- " });\n",
- "\n",
- " function set_focus () {\n",
- " canvas.focus();\n",
- " canvas_div.focus();\n",
- " }\n",
- "\n",
- " window.setTimeout(set_focus, 100);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " // put a spacer in here.\n",
- " continue;\n",
- " }\n",
- " var button = $('');\n",
- " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
- " 'ui-button-icon-only');\n",
- " button.attr('role', 'button');\n",
- " button.attr('aria-disabled', 'false');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- "\n",
- " var icon_img = $('');\n",
- " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
- " icon_img.addClass(image);\n",
- " icon_img.addClass('ui-corner-all');\n",
- "\n",
- " var tooltip_span = $('');\n",
- " tooltip_span.addClass('ui-button-text');\n",
- " tooltip_span.html(tooltip);\n",
- "\n",
- " button.append(icon_img);\n",
- " button.append(tooltip_span);\n",
- "\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " var fmt_picker_span = $('');\n",
- "\n",
- " var fmt_picker = $('');\n",
- " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
- " fmt_picker_span.append(fmt_picker);\n",
- " nav_element.append(fmt_picker_span);\n",
- " this.format_dropdown = fmt_picker[0];\n",
- "\n",
- " for (var ind in mpl.extensions) {\n",
- " var fmt = mpl.extensions[ind];\n",
- " var option = $(\n",
- " '', {selected: fmt === mpl.default_extension}).html(fmt);\n",
- " fmt_picker.append(option)\n",
- " }\n",
- "\n",
- " // Add hover states to the ui-buttons\n",
- " $( \".ui-button\" ).hover(\n",
- " function() { $(this).addClass(\"ui-state-hover\");},\n",
- " function() { $(this).removeClass(\"ui-state-hover\");}\n",
- " );\n",
- "\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
- " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
- " // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_message = function(type, properties) {\n",
- " properties['type'] = type;\n",
- " properties['figure_id'] = this.id;\n",
- " this.ws.send(JSON.stringify(properties));\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_draw_message = function() {\n",
- " if (!this.waiting) {\n",
- " this.waiting = true;\n",
- " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
- " }\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " var format_dropdown = fig.format_dropdown;\n",
- " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
- " fig.ondownload(fig, format);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
- " var size = msg['size'];\n",
- " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1]);\n",
- " fig.send_message(\"refresh\", {});\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
- " var x0 = msg['x0'];\n",
- " var y0 = fig.canvas.height - msg['y0'];\n",
- " var x1 = msg['x1'];\n",
- " var y1 = fig.canvas.height - msg['y1'];\n",
- " x0 = Math.floor(x0) + 0.5;\n",
- " y0 = Math.floor(y0) + 0.5;\n",
- " x1 = Math.floor(x1) + 0.5;\n",
- " y1 = Math.floor(y1) + 0.5;\n",
- " var min_x = Math.min(x0, x1);\n",
- " var min_y = Math.min(y0, y1);\n",
- " var width = Math.abs(x1 - x0);\n",
- " var height = Math.abs(y1 - y0);\n",
- "\n",
- " fig.rubberband_context.clearRect(\n",
- " 0, 0, fig.canvas.width, fig.canvas.height);\n",
- "\n",
- " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
- " // Updates the figure title.\n",
- " fig.header.textContent = msg['label'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch(cursor)\n",
- " {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
- " fig.message.textContent = msg['message'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
- " // Request the server to send over a new figure.\n",
- " fig.send_draw_message();\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
- " fig.image_mode = msg['mode'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Called whenever the canvas gets updated.\n",
- " this.send_message(\"ack\", {});\n",
- "}\n",
- "\n",
- "// A function to construct a web socket function for onmessage handling.\n",
- "// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
- " return function socket_on_message(evt) {\n",
- " if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = \"image/png\";\n",
- "\n",
- " /* Free the memory for the previous frames */\n",
- " if (fig.imageObj.src) {\n",
- " (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src);\n",
- " }\n",
- "\n",
- " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data);\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
- " fig.imageObj.src = evt.data;\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- "\n",
- " var msg = JSON.parse(evt.data);\n",
- " var msg_type = msg['type'];\n",
- "\n",
- " // Call the \"handle_{type}\" callback, which takes\n",
- " // the figure and JSON message as its only arguments.\n",
- " try {\n",
- " var callback = fig[\"handle_\" + msg_type];\n",
- " } catch (e) {\n",
- " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
- " return;\n",
- " }\n",
- "\n",
- " if (callback) {\n",
- " try {\n",
- " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
- " callback(fig, msg);\n",
- " } catch (e) {\n",
- " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
- " }\n",
- " }\n",
- " };\n",
- "}\n",
- "\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function(e) {\n",
- " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
- " var targ;\n",
- " if (!e)\n",
- " e = window.event;\n",
- " if (e.target)\n",
- " targ = e.target;\n",
- " else if (e.srcElement)\n",
- " targ = e.srcElement;\n",
- " if (targ.nodeType == 3) // defeat Safari bug\n",
- " targ = targ.parentNode;\n",
- "\n",
- " // jQuery normalizes the pageX and pageY\n",
- " // pageX,Y are the mouse positions relative to the document\n",
- " // offset() returns the position of the element relative to the document\n",
- " var x = e.pageX - $(targ).offset().left;\n",
- " var y = e.pageY - $(targ).offset().top;\n",
- "\n",
- " return {\"x\": x, \"y\": y};\n",
- "};\n",
- "\n",
- "/*\n",
- " * return a copy of an object with only non-object keys\n",
- " * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
- " */\n",
- "function simpleKeys (original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object')\n",
- " obj[key] = original[key]\n",
- " return obj;\n",
- " }, {});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.mouse_event = function(event, name) {\n",
- " var canvas_pos = mpl.findpos(event)\n",
- "\n",
- " if (name === 'button_press')\n",
- " {\n",
- " this.canvas.focus();\n",
- " this.canvas_div.focus();\n",
- " }\n",
- "\n",
- " var x = canvas_pos.x;\n",
- " var y = canvas_pos.y;\n",
- "\n",
- " this.send_message(name, {x: x, y: y, button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event)});\n",
- "\n",
- " /* This prevents the web browser from automatically changing to\n",
- " * the text insertion cursor when the button is pressed. We want\n",
- " * to control all of the cursor setting manually through the\n",
- " * 'cursor' event from matplotlib */\n",
- " event.preventDefault();\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " // Handle any extra behaviour associated with a key event\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.key_event = function(event, name) {\n",
- "\n",
- " // Prevent repeat events\n",
- " if (name == 'key_press')\n",
- " {\n",
- " if (event.which === this._key)\n",
- " return;\n",
- " else\n",
- " this._key = event.which;\n",
- " }\n",
- " if (name == 'key_release')\n",
- " this._key = null;\n",
- "\n",
- " var value = '';\n",
- " if (event.ctrlKey && event.which != 17)\n",
- " value += \"ctrl+\";\n",
- " if (event.altKey && event.which != 18)\n",
- " value += \"alt+\";\n",
- " if (event.shiftKey && event.which != 16)\n",
- " value += \"shift+\";\n",
- "\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
- "\n",
- " this._key_event_extra(event, name);\n",
- "\n",
- " this.send_message(name, {key: value,\n",
- " guiEvent: simpleKeys(event)});\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
- " if (name == 'download') {\n",
- " this.handle_save(this, null);\n",
- " } else {\n",
- " this.send_message(\"toolbar_button\", {name: name});\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
- " this.message.textContent = tooltip;\n",
- "};\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
- "\n",
- "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
- "\n",
- "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
- " // Create a \"websocket\"-like object which calls the given IPython comm\n",
- " // object with the appropriate methods. Currently this is a non binary\n",
- " // socket, so there is still some room for performance tuning.\n",
- " var ws = {};\n",
- "\n",
- " ws.close = function() {\n",
- " comm.close()\n",
- " };\n",
- " ws.send = function(m) {\n",
- " //console.log('sending', m);\n",
- " comm.send(m);\n",
- " };\n",
- " // Register the callback with on_msg.\n",
- " comm.on_msg(function(msg) {\n",
- " //console.log('receiving', msg['content']['data'], msg);\n",
- " // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data'])\n",
- " });\n",
- " return ws;\n",
- "}\n",
- "\n",
- "mpl.mpl_figure_comm = function(comm, msg) {\n",
- " // This is the function which gets called when the mpl process\n",
- " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
- "\n",
- " var id = msg.content.data.id;\n",
- " // Get hold of the div created by the display call when the Comm\n",
- " // socket was opened in Python.\n",
- " var element = $(\"#\" + id);\n",
- " var ws_proxy = comm_websocket_adapter(comm)\n",
- "\n",
- " function ondownload(figure, format) {\n",
- " window.open(figure.imageObj.src);\n",
- " }\n",
- "\n",
- " var fig = new mpl.figure(id, ws_proxy,\n",
- " ondownload,\n",
- " element.get(0));\n",
- "\n",
- " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
- " // web socket which is closed, not our websocket->open comm proxy.\n",
- " ws_proxy.onopen();\n",
- "\n",
- " fig.parent_element = element.get(0);\n",
- " fig.cell_info = mpl.find_output_cell(\"\");\n",
- " if (!fig.cell_info) {\n",
- " console.error(\"Failed to find cell for figure\", id, fig);\n",
- " return;\n",
- " }\n",
- "\n",
- " var output_index = fig.cell_info[2]\n",
- " var cell = fig.cell_info[0];\n",
- "\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
- " fig.root.unbind('remove')\n",
- "\n",
- " // Update the output cell to use the data from the current canvas.\n",
- " fig.push_to_output();\n",
- " var dataURL = fig.canvas.toDataURL();\n",
- " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
- " // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable()\n",
- " $(fig.parent_element).html('');\n",
- " fig.close_ws(fig, msg);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
- " fig.send_message('closing', msg);\n",
- " // fig.ws.close()\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
- " // Turn the data on the canvas into data in the output cell.\n",
- " var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] = '';\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Tell IPython that the notebook contents must change.\n",
- " IPython.notebook.set_dirty(true);\n",
- " this.send_message(\"ack\", {});\n",
- " var fig = this;\n",
- " // Wait a second, then push the new image to the DOM so\n",
- " // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () { fig.push_to_output() }, 1000);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items){\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) { continue; };\n",
- "\n",
- " var button = $('');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " // Add the status bar.\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "\n",
- " // Add the close button to the window.\n",
- " var buttongrp = $('');\n",
- " var button = $('');\n",
- " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
- " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
- " buttongrp.append(button);\n",
- " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
- " titlebar.prepend(buttongrp);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(el){\n",
- " var fig = this\n",
- " el.on(\"remove\", function(){\n",
- "\tfig.close_ws(fig, {});\n",
- " });\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(el){\n",
- " // this is important to make the div 'focusable\n",
- " el.attr('tabindex', 0)\n",
- " // reach out to IPython and tell the keyboard manager to turn it's self\n",
- " // off when our div gets focus\n",
- "\n",
- " // location in version 3\n",
- " if (IPython.notebook.keyboard_manager) {\n",
- " IPython.notebook.keyboard_manager.register_events(el);\n",
- " }\n",
- " else {\n",
- " // location in version 2\n",
- " IPython.keyboard_manager.register_events(el);\n",
- " }\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager)\n",
- " manager = IPython.keyboard_manager;\n",
- "\n",
- " // Check for shift+enter\n",
- " if (event.shiftKey && event.which == 13) {\n",
- " this.canvas_div.blur();\n",
- " event.shiftKey = false;\n",
- " // Send a \"J\" for go to next cell\n",
- " event.which = 74;\n",
- " event.keyCode = 74;\n",
- " manager.command_mode();\n",
- " manager.handle_keydown(event);\n",
- " }\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " fig.ondownload(fig, null);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.find_output_cell = function(html_output) {\n",
- " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
- " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
- " // IPython event is triggered only after the cells have been serialised, which for\n",
- " // our purposes (turning an active figure into a static one), is too late.\n",
- " var cells = IPython.notebook.get_cells();\n",
- " var ncells = cells.length;\n",
- " for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
- " data = data.data;\n",
- " }\n",
- " if (data['text/html'] == html_output) {\n",
- " return [cell, data, j];\n",
- " }\n",
- " }\n",
- " }\n",
- " }\n",
- "}\n",
- "\n",
- "// Register the function which deals with the matplotlib target/channel.\n",
- "// The kernel may be null if the page has been refreshed.\n",
- "if (IPython.notebook.kernel != null) {\n",
- " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
- "}\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- ""
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "gpd7 = ws.genpareto.fit2(Hs[Hs>7], floc=7)\n",
- "fig, axes = plt.subplots()\n",
- "gpd7.plotesf()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Simulates 100 values from the GEV distribution with parameters (0.3, 1, 2), then estimates the\n",
- "parameters using two different methods and plots the estimated distribution functions together\n",
- "with the empirical distribution.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "application/javascript": [
- "/* Put everything inside the global mpl namespace */\n",
- "window.mpl = {};\n",
- "\n",
- "mpl.get_websocket_type = function() {\n",
- " if (typeof(WebSocket) !== 'undefined') {\n",
- " return WebSocket;\n",
- " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
- " return MozWebSocket;\n",
- " } else {\n",
- " alert('Your browser does not have WebSocket support.' +\n",
- " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
- " 'Firefox 4 and 5 are also supported but you ' +\n",
- " 'have to enable WebSockets in about:config.');\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
- " this.id = figure_id;\n",
- "\n",
- " this.ws = websocket;\n",
- "\n",
- " this.supports_binary = (this.ws.binaryType != undefined);\n",
- "\n",
- " if (!this.supports_binary) {\n",
- " var warnings = document.getElementById(\"mpl-warnings\");\n",
- " if (warnings) {\n",
- " warnings.style.display = 'block';\n",
- " warnings.textContent = (\n",
- " \"This browser does not support binary websocket messages. \" +\n",
- " \"Performance may be slow.\");\n",
- " }\n",
- " }\n",
- "\n",
- " this.imageObj = new Image();\n",
- "\n",
- " this.context = undefined;\n",
- " this.message = undefined;\n",
- " this.canvas = undefined;\n",
- " this.rubberband_canvas = undefined;\n",
- " this.rubberband_context = undefined;\n",
- " this.format_dropdown = undefined;\n",
- "\n",
- " this.image_mode = 'full';\n",
- "\n",
- " this.root = $('');\n",
- " this._root_extra_style(this.root)\n",
- " this.root.attr('style', 'display: inline-block');\n",
- "\n",
- " $(parent_element).append(this.root);\n",
- "\n",
- " this._init_header(this);\n",
- " this._init_canvas(this);\n",
- " this._init_toolbar(this);\n",
- "\n",
- " var fig = this;\n",
- "\n",
- " this.waiting = false;\n",
- "\n",
- " this.ws.onopen = function () {\n",
- " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
- " fig.send_message(\"send_image_mode\", {});\n",
- " fig.send_message(\"refresh\", {});\n",
- " }\n",
- "\n",
- " this.imageObj.onload = function() {\n",
- " if (fig.image_mode == 'full') {\n",
- " // Full images could contain transparency (where diff images\n",
- " // almost always do), so we need to clear the canvas so that\n",
- " // there is no ghosting.\n",
- " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
- " }\n",
- " fig.context.drawImage(fig.imageObj, 0, 0);\n",
- " };\n",
- "\n",
- " this.imageObj.onunload = function() {\n",
- " this.ws.close();\n",
- " }\n",
- "\n",
- " this.ws.onmessage = this._make_on_message_function(this);\n",
- "\n",
- " this.ondownload = ondownload;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_header = function() {\n",
- " var titlebar = $(\n",
- " '');\n",
- " var titletext = $(\n",
- " '');\n",
- " titlebar.append(titletext)\n",
- " this.root.append(titlebar);\n",
- " this.header = titletext[0];\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_canvas = function() {\n",
- " var fig = this;\n",
- "\n",
- " var canvas_div = $('');\n",
- "\n",
- " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
- "\n",
- " function canvas_keyboard_event(event) {\n",
- " return fig.key_event(event, event['data']);\n",
- " }\n",
- "\n",
- " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
- " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
- " this.canvas_div = canvas_div\n",
- " this._canvas_extra_style(canvas_div)\n",
- " this.root.append(canvas_div);\n",
- "\n",
- " var canvas = $('');\n",
- " canvas.addClass('mpl-canvas');\n",
- " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
- "\n",
- " this.canvas = canvas[0];\n",
- " this.context = canvas[0].getContext(\"2d\");\n",
- "\n",
- " var rubberband = $('');\n",
- " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
- "\n",
- " var pass_mouse_events = true;\n",
- "\n",
- " canvas_div.resizable({\n",
- " start: function(event, ui) {\n",
- " pass_mouse_events = false;\n",
- " },\n",
- " resize: function(event, ui) {\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " stop: function(event, ui) {\n",
- " pass_mouse_events = true;\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " });\n",
- "\n",
- " function mouse_event_fn(event) {\n",
- " if (pass_mouse_events)\n",
- " return fig.mouse_event(event, event['data']);\n",
- " }\n",
- "\n",
- " rubberband.mousedown('button_press', mouse_event_fn);\n",
- " rubberband.mouseup('button_release', mouse_event_fn);\n",
- " // Throttle sequential mouse events to 1 every 20ms.\n",
- " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
- "\n",
- " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
- " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
- "\n",
- " canvas_div.on(\"wheel\", function (event) {\n",
- " event = event.originalEvent;\n",
- " event['data'] = 'scroll'\n",
- " if (event.deltaY < 0) {\n",
- " event.step = 1;\n",
- " } else {\n",
- " event.step = -1;\n",
- " }\n",
- " mouse_event_fn(event);\n",
- " });\n",
- "\n",
- " canvas_div.append(canvas);\n",
- " canvas_div.append(rubberband);\n",
- "\n",
- " this.rubberband = rubberband;\n",
- " this.rubberband_canvas = rubberband[0];\n",
- " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
- " this.rubberband_context.strokeStyle = \"#000000\";\n",
- "\n",
- " this._resize_canvas = function(width, height) {\n",
- " // Keep the size of the canvas, canvas container, and rubber band\n",
- " // canvas in synch.\n",
- " canvas_div.css('width', width)\n",
- " canvas_div.css('height', height)\n",
- "\n",
- " canvas.attr('width', width);\n",
- " canvas.attr('height', height);\n",
- "\n",
- " rubberband.attr('width', width);\n",
- " rubberband.attr('height', height);\n",
- " }\n",
- "\n",
- " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
- " // upon first draw.\n",
- " this._resize_canvas(600, 600);\n",
- "\n",
- " // Disable right mouse context menu.\n",
- " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
- " return false;\n",
- " });\n",
- "\n",
- " function set_focus () {\n",
- " canvas.focus();\n",
- " canvas_div.focus();\n",
- " }\n",
- "\n",
- " window.setTimeout(set_focus, 100);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " // put a spacer in here.\n",
- " continue;\n",
- " }\n",
- " var button = $('');\n",
- " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
- " 'ui-button-icon-only');\n",
- " button.attr('role', 'button');\n",
- " button.attr('aria-disabled', 'false');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- "\n",
- " var icon_img = $('');\n",
- " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
- " icon_img.addClass(image);\n",
- " icon_img.addClass('ui-corner-all');\n",
- "\n",
- " var tooltip_span = $('');\n",
- " tooltip_span.addClass('ui-button-text');\n",
- " tooltip_span.html(tooltip);\n",
- "\n",
- " button.append(icon_img);\n",
- " button.append(tooltip_span);\n",
- "\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " var fmt_picker_span = $('');\n",
- "\n",
- " var fmt_picker = $('');\n",
- " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
- " fmt_picker_span.append(fmt_picker);\n",
- " nav_element.append(fmt_picker_span);\n",
- " this.format_dropdown = fmt_picker[0];\n",
- "\n",
- " for (var ind in mpl.extensions) {\n",
- " var fmt = mpl.extensions[ind];\n",
- " var option = $(\n",
- " '', {selected: fmt === mpl.default_extension}).html(fmt);\n",
- " fmt_picker.append(option)\n",
- " }\n",
- "\n",
- " // Add hover states to the ui-buttons\n",
- " $( \".ui-button\" ).hover(\n",
- " function() { $(this).addClass(\"ui-state-hover\");},\n",
- " function() { $(this).removeClass(\"ui-state-hover\");}\n",
- " );\n",
- "\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
- " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
- " // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_message = function(type, properties) {\n",
- " properties['type'] = type;\n",
- " properties['figure_id'] = this.id;\n",
- " this.ws.send(JSON.stringify(properties));\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_draw_message = function() {\n",
- " if (!this.waiting) {\n",
- " this.waiting = true;\n",
- " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
- " }\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " var format_dropdown = fig.format_dropdown;\n",
- " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
- " fig.ondownload(fig, format);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
- " var size = msg['size'];\n",
- " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1]);\n",
- " fig.send_message(\"refresh\", {});\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
- " var x0 = msg['x0'];\n",
- " var y0 = fig.canvas.height - msg['y0'];\n",
- " var x1 = msg['x1'];\n",
- " var y1 = fig.canvas.height - msg['y1'];\n",
- " x0 = Math.floor(x0) + 0.5;\n",
- " y0 = Math.floor(y0) + 0.5;\n",
- " x1 = Math.floor(x1) + 0.5;\n",
- " y1 = Math.floor(y1) + 0.5;\n",
- " var min_x = Math.min(x0, x1);\n",
- " var min_y = Math.min(y0, y1);\n",
- " var width = Math.abs(x1 - x0);\n",
- " var height = Math.abs(y1 - y0);\n",
- "\n",
- " fig.rubberband_context.clearRect(\n",
- " 0, 0, fig.canvas.width, fig.canvas.height);\n",
- "\n",
- " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
- " // Updates the figure title.\n",
- " fig.header.textContent = msg['label'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch(cursor)\n",
- " {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
- " fig.message.textContent = msg['message'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
- " // Request the server to send over a new figure.\n",
- " fig.send_draw_message();\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
- " fig.image_mode = msg['mode'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Called whenever the canvas gets updated.\n",
- " this.send_message(\"ack\", {});\n",
- "}\n",
- "\n",
- "// A function to construct a web socket function for onmessage handling.\n",
- "// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
- " return function socket_on_message(evt) {\n",
- " if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = \"image/png\";\n",
- "\n",
- " /* Free the memory for the previous frames */\n",
- " if (fig.imageObj.src) {\n",
- " (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src);\n",
- " }\n",
- "\n",
- " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data);\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
- " fig.imageObj.src = evt.data;\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- "\n",
- " var msg = JSON.parse(evt.data);\n",
- " var msg_type = msg['type'];\n",
- "\n",
- " // Call the \"handle_{type}\" callback, which takes\n",
- " // the figure and JSON message as its only arguments.\n",
- " try {\n",
- " var callback = fig[\"handle_\" + msg_type];\n",
- " } catch (e) {\n",
- " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
- " return;\n",
- " }\n",
- "\n",
- " if (callback) {\n",
- " try {\n",
- " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
- " callback(fig, msg);\n",
- " } catch (e) {\n",
- " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
- " }\n",
- " }\n",
- " };\n",
- "}\n",
- "\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function(e) {\n",
- " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
- " var targ;\n",
- " if (!e)\n",
- " e = window.event;\n",
- " if (e.target)\n",
- " targ = e.target;\n",
- " else if (e.srcElement)\n",
- " targ = e.srcElement;\n",
- " if (targ.nodeType == 3) // defeat Safari bug\n",
- " targ = targ.parentNode;\n",
- "\n",
- " // jQuery normalizes the pageX and pageY\n",
- " // pageX,Y are the mouse positions relative to the document\n",
- " // offset() returns the position of the element relative to the document\n",
- " var x = e.pageX - $(targ).offset().left;\n",
- " var y = e.pageY - $(targ).offset().top;\n",
- "\n",
- " return {\"x\": x, \"y\": y};\n",
- "};\n",
- "\n",
- "/*\n",
- " * return a copy of an object with only non-object keys\n",
- " * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
- " */\n",
- "function simpleKeys (original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object')\n",
- " obj[key] = original[key]\n",
- " return obj;\n",
- " }, {});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.mouse_event = function(event, name) {\n",
- " var canvas_pos = mpl.findpos(event)\n",
- "\n",
- " if (name === 'button_press')\n",
- " {\n",
- " this.canvas.focus();\n",
- " this.canvas_div.focus();\n",
- " }\n",
- "\n",
- " var x = canvas_pos.x;\n",
- " var y = canvas_pos.y;\n",
- "\n",
- " this.send_message(name, {x: x, y: y, button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event)});\n",
- "\n",
- " /* This prevents the web browser from automatically changing to\n",
- " * the text insertion cursor when the button is pressed. We want\n",
- " * to control all of the cursor setting manually through the\n",
- " * 'cursor' event from matplotlib */\n",
- " event.preventDefault();\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " // Handle any extra behaviour associated with a key event\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.key_event = function(event, name) {\n",
- "\n",
- " // Prevent repeat events\n",
- " if (name == 'key_press')\n",
- " {\n",
- " if (event.which === this._key)\n",
- " return;\n",
- " else\n",
- " this._key = event.which;\n",
- " }\n",
- " if (name == 'key_release')\n",
- " this._key = null;\n",
- "\n",
- " var value = '';\n",
- " if (event.ctrlKey && event.which != 17)\n",
- " value += \"ctrl+\";\n",
- " if (event.altKey && event.which != 18)\n",
- " value += \"alt+\";\n",
- " if (event.shiftKey && event.which != 16)\n",
- " value += \"shift+\";\n",
- "\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
- "\n",
- " this._key_event_extra(event, name);\n",
- "\n",
- " this.send_message(name, {key: value,\n",
- " guiEvent: simpleKeys(event)});\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
- " if (name == 'download') {\n",
- " this.handle_save(this, null);\n",
- " } else {\n",
- " this.send_message(\"toolbar_button\", {name: name});\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
- " this.message.textContent = tooltip;\n",
- "};\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
- "\n",
- "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
- "\n",
- "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
- " // Create a \"websocket\"-like object which calls the given IPython comm\n",
- " // object with the appropriate methods. Currently this is a non binary\n",
- " // socket, so there is still some room for performance tuning.\n",
- " var ws = {};\n",
- "\n",
- " ws.close = function() {\n",
- " comm.close()\n",
- " };\n",
- " ws.send = function(m) {\n",
- " //console.log('sending', m);\n",
- " comm.send(m);\n",
- " };\n",
- " // Register the callback with on_msg.\n",
- " comm.on_msg(function(msg) {\n",
- " //console.log('receiving', msg['content']['data'], msg);\n",
- " // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data'])\n",
- " });\n",
- " return ws;\n",
- "}\n",
- "\n",
- "mpl.mpl_figure_comm = function(comm, msg) {\n",
- " // This is the function which gets called when the mpl process\n",
- " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
- "\n",
- " var id = msg.content.data.id;\n",
- " // Get hold of the div created by the display call when the Comm\n",
- " // socket was opened in Python.\n",
- " var element = $(\"#\" + id);\n",
- " var ws_proxy = comm_websocket_adapter(comm)\n",
- "\n",
- " function ondownload(figure, format) {\n",
- " window.open(figure.imageObj.src);\n",
- " }\n",
- "\n",
- " var fig = new mpl.figure(id, ws_proxy,\n",
- " ondownload,\n",
- " element.get(0));\n",
- "\n",
- " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
- " // web socket which is closed, not our websocket->open comm proxy.\n",
- " ws_proxy.onopen();\n",
- "\n",
- " fig.parent_element = element.get(0);\n",
- " fig.cell_info = mpl.find_output_cell(\"\");\n",
- " if (!fig.cell_info) {\n",
- " console.error(\"Failed to find cell for figure\", id, fig);\n",
- " return;\n",
- " }\n",
- "\n",
- " var output_index = fig.cell_info[2]\n",
- " var cell = fig.cell_info[0];\n",
- "\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
- " fig.root.unbind('remove')\n",
- "\n",
- " // Update the output cell to use the data from the current canvas.\n",
- " fig.push_to_output();\n",
- " var dataURL = fig.canvas.toDataURL();\n",
- " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
- " // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable()\n",
- " $(fig.parent_element).html('');\n",
- " fig.close_ws(fig, msg);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
- " fig.send_message('closing', msg);\n",
- " // fig.ws.close()\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
- " // Turn the data on the canvas into data in the output cell.\n",
- " var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] = '';\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Tell IPython that the notebook contents must change.\n",
- " IPython.notebook.set_dirty(true);\n",
- " this.send_message(\"ack\", {});\n",
- " var fig = this;\n",
- " // Wait a second, then push the new image to the DOM so\n",
- " // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () { fig.push_to_output() }, 1000);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items){\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) { continue; };\n",
- "\n",
- " var button = $('');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " // Add the status bar.\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "\n",
- " // Add the close button to the window.\n",
- " var buttongrp = $('');\n",
- " var button = $('');\n",
- " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
- " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
- " buttongrp.append(button);\n",
- " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
- " titlebar.prepend(buttongrp);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(el){\n",
- " var fig = this\n",
- " el.on(\"remove\", function(){\n",
- "\tfig.close_ws(fig, {});\n",
- " });\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(el){\n",
- " // this is important to make the div 'focusable\n",
- " el.attr('tabindex', 0)\n",
- " // reach out to IPython and tell the keyboard manager to turn it's self\n",
- " // off when our div gets focus\n",
- "\n",
- " // location in version 3\n",
- " if (IPython.notebook.keyboard_manager) {\n",
- " IPython.notebook.keyboard_manager.register_events(el);\n",
- " }\n",
- " else {\n",
- " // location in version 2\n",
- " IPython.keyboard_manager.register_events(el);\n",
- " }\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager)\n",
- " manager = IPython.keyboard_manager;\n",
- "\n",
- " // Check for shift+enter\n",
- " if (event.shiftKey && event.which == 13) {\n",
- " this.canvas_div.blur();\n",
- " event.shiftKey = false;\n",
- " // Send a \"J\" for go to next cell\n",
- " event.which = 74;\n",
- " event.keyCode = 74;\n",
- " manager.command_mode();\n",
- " manager.handle_keydown(event);\n",
- " }\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " fig.ondownload(fig, null);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.find_output_cell = function(html_output) {\n",
- " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
- " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
- " // IPython event is triggered only after the cells have been serialised, which for\n",
- " // our purposes (turning an active figure into a static one), is too late.\n",
- " var cells = IPython.notebook.get_cells();\n",
- " var ncells = cells.length;\n",
- " for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
- " data = data.data;\n",
- " }\n",
- " if (data['text/html'] == html_output) {\n",
- " return [cell, data, j];\n",
- " }\n",
- " }\n",
- " }\n",
- " }\n",
- "}\n",
- "\n",
- "// Register the function which deals with the matplotlib target/channel.\n",
- "// The kernel may be null if the page has been refreshed.\n",
- "if (IPython.notebook.kernel != null) {\n",
- " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
- "}\n"
- ],
- "text/plain": [
- ""
- ]
- },
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- "output_type": "display_data"
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- ""
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- ""
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- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\pab\\workspace\\git_pywafo\\wafo\\stats\\estimation.py:343: UserWarning: The fitted parameters does not provide the optimum fit. Something wrong with fit (par = [ 0.34847708 1.19360282 -5.94834482])\n",
- " 'Something wrong with fit (par = {})'.format(str(par)))\n",
- "c:\\pab\\workspace\\git_pywafo\\wafo\\stats\\estimation.py:117: RuntimeWarning: divide by zero encountered in log\n",
- " loglogP = log(-log(-expm1(logsf)))\n",
- "c:\\pab\\workspace\\git_pywafo\\wafo\\stats\\estimation.py:343: UserWarning: The fitted parameters does not provide the optimum fit. Something wrong with fit (par = [ 0.34847789 1.19366352 -6.90775528])\n",
- " 'Something wrong with fit (par = {})'.format(str(par)))\n",
- "c:\\pab\\workspace\\git_pywafo\\wafo\\stats\\estimation.py:343: UserWarning: The fitted parameters does not provide the optimum fit. Something wrong with fit (par = [ 0.34847474 1.19359554 -6.90775528])\n",
- " 'Something wrong with fit (par = {})'.format(str(par)))\n",
- "c:\\pab\\workspace\\git_pywafo\\wafo\\stats\\estimation.py:343: UserWarning: The fitted parameters does not provide the optimum fit. Something wrong with fit (par = [ 0.35499767 1.23279902 -1.15129255])\n",
- " 'Something wrong with fit (par = {})'.format(str(par)))\n",
- "c:\\pab\\workspace\\git_pywafo\\wafo\\stats\\estimation.py:343: UserWarning: The fitted parameters does not provide the optimum fit. Something wrong with fit (par = [ 0.35494524 1.23277614 -2.110703 ])\n",
- " 'Something wrong with fit (par = {})'.format(str(par)))\n",
- "c:\\pab\\workspace\\git_pywafo\\wafo\\stats\\estimation.py:343: UserWarning: The fitted parameters does not provide the optimum fit. Something wrong with fit (par = [ 0.35495493 1.23276739 -3.07011346])\n",
- " 'Something wrong with fit (par = {})'.format(str(par)))\n",
- "c:\\pab\\workspace\\git_pywafo\\wafo\\stats\\estimation.py:343: UserWarning: The fitted parameters does not provide the optimum fit. Something wrong with fit (par = [ 0.35497638 1.23281073 -3.07011346])\n",
- " 'Something wrong with fit (par = {})'.format(str(par)))\n",
- "c:\\pab\\workspace\\git_pywafo\\wafo\\stats\\estimation.py:343: UserWarning: The fitted parameters does not provide the optimum fit. Something wrong with fit (par = [ 0.35499767 1.23279902 -4.98893437])\n",
- " 'Something wrong with fit (par = {})'.format(str(par)))\n",
- "c:\\pab\\workspace\\git_pywafo\\wafo\\stats\\estimation.py:343: UserWarning: The fitted parameters does not provide the optimum fit. Something wrong with fit (par = [ 0.35499767 1.23279902 -5.94834482])\n",
- " 'Something wrong with fit (par = {})'.format(str(par)))\n",
- "c:\\pab\\workspace\\git_pywafo\\wafo\\stats\\estimation.py:343: UserWarning: The fitted parameters does not provide the optimum fit. Something wrong with fit (par = [ 0.35496599 1.23285957 -6.9060821 ])\n",
- " 'Something wrong with fit (par = {})'.format(str(par)))\n"
- ]
- }
- ],
- "source": [
- "Rgev = ws.genextreme.rvs(0.3,1,2,size=100)\n",
- "gp = ws.genextreme.fit2(Rgev,method='mps')\n",
- "gm = ws.genextreme.fit2(Rgev,method='ml')\n",
- "fig, axes = plt.subplots()\n",
- "gp.plotesf()\n",
- "plt.hold(True)\n",
- "gm.plotesf('r--')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Similarly for the GPD distribution"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "application/javascript": [
- "/* Put everything inside the global mpl namespace */\n",
- "window.mpl = {};\n",
- "\n",
- "mpl.get_websocket_type = function() {\n",
- " if (typeof(WebSocket) !== 'undefined') {\n",
- " return WebSocket;\n",
- " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
- " return MozWebSocket;\n",
- " } else {\n",
- " alert('Your browser does not have WebSocket support.' +\n",
- " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
- " 'Firefox 4 and 5 are also supported but you ' +\n",
- " 'have to enable WebSockets in about:config.');\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
- " this.id = figure_id;\n",
- "\n",
- " this.ws = websocket;\n",
- "\n",
- " this.supports_binary = (this.ws.binaryType != undefined);\n",
- "\n",
- " if (!this.supports_binary) {\n",
- " var warnings = document.getElementById(\"mpl-warnings\");\n",
- " if (warnings) {\n",
- " warnings.style.display = 'block';\n",
- " warnings.textContent = (\n",
- " \"This browser does not support binary websocket messages. \" +\n",
- " \"Performance may be slow.\");\n",
- " }\n",
- " }\n",
- "\n",
- " this.imageObj = new Image();\n",
- "\n",
- " this.context = undefined;\n",
- " this.message = undefined;\n",
- " this.canvas = undefined;\n",
- " this.rubberband_canvas = undefined;\n",
- " this.rubberband_context = undefined;\n",
- " this.format_dropdown = undefined;\n",
- "\n",
- " this.image_mode = 'full';\n",
- "\n",
- " this.root = $('');\n",
- " this._root_extra_style(this.root)\n",
- " this.root.attr('style', 'display: inline-block');\n",
- "\n",
- " $(parent_element).append(this.root);\n",
- "\n",
- " this._init_header(this);\n",
- " this._init_canvas(this);\n",
- " this._init_toolbar(this);\n",
- "\n",
- " var fig = this;\n",
- "\n",
- " this.waiting = false;\n",
- "\n",
- " this.ws.onopen = function () {\n",
- " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
- " fig.send_message(\"send_image_mode\", {});\n",
- " fig.send_message(\"refresh\", {});\n",
- " }\n",
- "\n",
- " this.imageObj.onload = function() {\n",
- " if (fig.image_mode == 'full') {\n",
- " // Full images could contain transparency (where diff images\n",
- " // almost always do), so we need to clear the canvas so that\n",
- " // there is no ghosting.\n",
- " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
- " }\n",
- " fig.context.drawImage(fig.imageObj, 0, 0);\n",
- " };\n",
- "\n",
- " this.imageObj.onunload = function() {\n",
- " this.ws.close();\n",
- " }\n",
- "\n",
- " this.ws.onmessage = this._make_on_message_function(this);\n",
- "\n",
- " this.ondownload = ondownload;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_header = function() {\n",
- " var titlebar = $(\n",
- " '');\n",
- " var titletext = $(\n",
- " '');\n",
- " titlebar.append(titletext)\n",
- " this.root.append(titlebar);\n",
- " this.header = titletext[0];\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_canvas = function() {\n",
- " var fig = this;\n",
- "\n",
- " var canvas_div = $('');\n",
- "\n",
- " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
- "\n",
- " function canvas_keyboard_event(event) {\n",
- " return fig.key_event(event, event['data']);\n",
- " }\n",
- "\n",
- " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
- " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
- " this.canvas_div = canvas_div\n",
- " this._canvas_extra_style(canvas_div)\n",
- " this.root.append(canvas_div);\n",
- "\n",
- " var canvas = $('');\n",
- " canvas.addClass('mpl-canvas');\n",
- " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
- "\n",
- " this.canvas = canvas[0];\n",
- " this.context = canvas[0].getContext(\"2d\");\n",
- "\n",
- " var rubberband = $('');\n",
- " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
- "\n",
- " var pass_mouse_events = true;\n",
- "\n",
- " canvas_div.resizable({\n",
- " start: function(event, ui) {\n",
- " pass_mouse_events = false;\n",
- " },\n",
- " resize: function(event, ui) {\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " stop: function(event, ui) {\n",
- " pass_mouse_events = true;\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " });\n",
- "\n",
- " function mouse_event_fn(event) {\n",
- " if (pass_mouse_events)\n",
- " return fig.mouse_event(event, event['data']);\n",
- " }\n",
- "\n",
- " rubberband.mousedown('button_press', mouse_event_fn);\n",
- " rubberband.mouseup('button_release', mouse_event_fn);\n",
- " // Throttle sequential mouse events to 1 every 20ms.\n",
- " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
- "\n",
- " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
- " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
- "\n",
- " canvas_div.on(\"wheel\", function (event) {\n",
- " event = event.originalEvent;\n",
- " event['data'] = 'scroll'\n",
- " if (event.deltaY < 0) {\n",
- " event.step = 1;\n",
- " } else {\n",
- " event.step = -1;\n",
- " }\n",
- " mouse_event_fn(event);\n",
- " });\n",
- "\n",
- " canvas_div.append(canvas);\n",
- " canvas_div.append(rubberband);\n",
- "\n",
- " this.rubberband = rubberband;\n",
- " this.rubberband_canvas = rubberband[0];\n",
- " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
- " this.rubberband_context.strokeStyle = \"#000000\";\n",
- "\n",
- " this._resize_canvas = function(width, height) {\n",
- " // Keep the size of the canvas, canvas container, and rubber band\n",
- " // canvas in synch.\n",
- " canvas_div.css('width', width)\n",
- " canvas_div.css('height', height)\n",
- "\n",
- " canvas.attr('width', width);\n",
- " canvas.attr('height', height);\n",
- "\n",
- " rubberband.attr('width', width);\n",
- " rubberband.attr('height', height);\n",
- " }\n",
- "\n",
- " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
- " // upon first draw.\n",
- " this._resize_canvas(600, 600);\n",
- "\n",
- " // Disable right mouse context menu.\n",
- " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
- " return false;\n",
- " });\n",
- "\n",
- " function set_focus () {\n",
- " canvas.focus();\n",
- " canvas_div.focus();\n",
- " }\n",
- "\n",
- " window.setTimeout(set_focus, 100);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " // put a spacer in here.\n",
- " continue;\n",
- " }\n",
- " var button = $('');\n",
- " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
- " 'ui-button-icon-only');\n",
- " button.attr('role', 'button');\n",
- " button.attr('aria-disabled', 'false');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- "\n",
- " var icon_img = $('');\n",
- " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
- " icon_img.addClass(image);\n",
- " icon_img.addClass('ui-corner-all');\n",
- "\n",
- " var tooltip_span = $('');\n",
- " tooltip_span.addClass('ui-button-text');\n",
- " tooltip_span.html(tooltip);\n",
- "\n",
- " button.append(icon_img);\n",
- " button.append(tooltip_span);\n",
- "\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " var fmt_picker_span = $('');\n",
- "\n",
- " var fmt_picker = $('');\n",
- " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
- " fmt_picker_span.append(fmt_picker);\n",
- " nav_element.append(fmt_picker_span);\n",
- " this.format_dropdown = fmt_picker[0];\n",
- "\n",
- " for (var ind in mpl.extensions) {\n",
- " var fmt = mpl.extensions[ind];\n",
- " var option = $(\n",
- " '', {selected: fmt === mpl.default_extension}).html(fmt);\n",
- " fmt_picker.append(option)\n",
- " }\n",
- "\n",
- " // Add hover states to the ui-buttons\n",
- " $( \".ui-button\" ).hover(\n",
- " function() { $(this).addClass(\"ui-state-hover\");},\n",
- " function() { $(this).removeClass(\"ui-state-hover\");}\n",
- " );\n",
- "\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
- " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
- " // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_message = function(type, properties) {\n",
- " properties['type'] = type;\n",
- " properties['figure_id'] = this.id;\n",
- " this.ws.send(JSON.stringify(properties));\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_draw_message = function() {\n",
- " if (!this.waiting) {\n",
- " this.waiting = true;\n",
- " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
- " }\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " var format_dropdown = fig.format_dropdown;\n",
- " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
- " fig.ondownload(fig, format);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
- " var size = msg['size'];\n",
- " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1]);\n",
- " fig.send_message(\"refresh\", {});\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
- " var x0 = msg['x0'];\n",
- " var y0 = fig.canvas.height - msg['y0'];\n",
- " var x1 = msg['x1'];\n",
- " var y1 = fig.canvas.height - msg['y1'];\n",
- " x0 = Math.floor(x0) + 0.5;\n",
- " y0 = Math.floor(y0) + 0.5;\n",
- " x1 = Math.floor(x1) + 0.5;\n",
- " y1 = Math.floor(y1) + 0.5;\n",
- " var min_x = Math.min(x0, x1);\n",
- " var min_y = Math.min(y0, y1);\n",
- " var width = Math.abs(x1 - x0);\n",
- " var height = Math.abs(y1 - y0);\n",
- "\n",
- " fig.rubberband_context.clearRect(\n",
- " 0, 0, fig.canvas.width, fig.canvas.height);\n",
- "\n",
- " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
- " // Updates the figure title.\n",
- " fig.header.textContent = msg['label'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch(cursor)\n",
- " {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
- " fig.message.textContent = msg['message'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
- " // Request the server to send over a new figure.\n",
- " fig.send_draw_message();\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
- " fig.image_mode = msg['mode'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Called whenever the canvas gets updated.\n",
- " this.send_message(\"ack\", {});\n",
- "}\n",
- "\n",
- "// A function to construct a web socket function for onmessage handling.\n",
- "// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
- " return function socket_on_message(evt) {\n",
- " if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = \"image/png\";\n",
- "\n",
- " /* Free the memory for the previous frames */\n",
- " if (fig.imageObj.src) {\n",
- " (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src);\n",
- " }\n",
- "\n",
- " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data);\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
- " fig.imageObj.src = evt.data;\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- "\n",
- " var msg = JSON.parse(evt.data);\n",
- " var msg_type = msg['type'];\n",
- "\n",
- " // Call the \"handle_{type}\" callback, which takes\n",
- " // the figure and JSON message as its only arguments.\n",
- " try {\n",
- " var callback = fig[\"handle_\" + msg_type];\n",
- " } catch (e) {\n",
- " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
- " return;\n",
- " }\n",
- "\n",
- " if (callback) {\n",
- " try {\n",
- " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
- " callback(fig, msg);\n",
- " } catch (e) {\n",
- " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
- " }\n",
- " }\n",
- " };\n",
- "}\n",
- "\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function(e) {\n",
- " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
- " var targ;\n",
- " if (!e)\n",
- " e = window.event;\n",
- " if (e.target)\n",
- " targ = e.target;\n",
- " else if (e.srcElement)\n",
- " targ = e.srcElement;\n",
- " if (targ.nodeType == 3) // defeat Safari bug\n",
- " targ = targ.parentNode;\n",
- "\n",
- " // jQuery normalizes the pageX and pageY\n",
- " // pageX,Y are the mouse positions relative to the document\n",
- " // offset() returns the position of the element relative to the document\n",
- " var x = e.pageX - $(targ).offset().left;\n",
- " var y = e.pageY - $(targ).offset().top;\n",
- "\n",
- " return {\"x\": x, \"y\": y};\n",
- "};\n",
- "\n",
- "/*\n",
- " * return a copy of an object with only non-object keys\n",
- " * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
- " */\n",
- "function simpleKeys (original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object')\n",
- " obj[key] = original[key]\n",
- " return obj;\n",
- " }, {});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.mouse_event = function(event, name) {\n",
- " var canvas_pos = mpl.findpos(event)\n",
- "\n",
- " if (name === 'button_press')\n",
- " {\n",
- " this.canvas.focus();\n",
- " this.canvas_div.focus();\n",
- " }\n",
- "\n",
- " var x = canvas_pos.x;\n",
- " var y = canvas_pos.y;\n",
- "\n",
- " this.send_message(name, {x: x, y: y, button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event)});\n",
- "\n",
- " /* This prevents the web browser from automatically changing to\n",
- " * the text insertion cursor when the button is pressed. We want\n",
- " * to control all of the cursor setting manually through the\n",
- " * 'cursor' event from matplotlib */\n",
- " event.preventDefault();\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " // Handle any extra behaviour associated with a key event\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.key_event = function(event, name) {\n",
- "\n",
- " // Prevent repeat events\n",
- " if (name == 'key_press')\n",
- " {\n",
- " if (event.which === this._key)\n",
- " return;\n",
- " else\n",
- " this._key = event.which;\n",
- " }\n",
- " if (name == 'key_release')\n",
- " this._key = null;\n",
- "\n",
- " var value = '';\n",
- " if (event.ctrlKey && event.which != 17)\n",
- " value += \"ctrl+\";\n",
- " if (event.altKey && event.which != 18)\n",
- " value += \"alt+\";\n",
- " if (event.shiftKey && event.which != 16)\n",
- " value += \"shift+\";\n",
- "\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
- "\n",
- " this._key_event_extra(event, name);\n",
- "\n",
- " this.send_message(name, {key: value,\n",
- " guiEvent: simpleKeys(event)});\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
- " if (name == 'download') {\n",
- " this.handle_save(this, null);\n",
- " } else {\n",
- " this.send_message(\"toolbar_button\", {name: name});\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
- " this.message.textContent = tooltip;\n",
- "};\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
- "\n",
- "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
- "\n",
- "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
- " // Create a \"websocket\"-like object which calls the given IPython comm\n",
- " // object with the appropriate methods. Currently this is a non binary\n",
- " // socket, so there is still some room for performance tuning.\n",
- " var ws = {};\n",
- "\n",
- " ws.close = function() {\n",
- " comm.close()\n",
- " };\n",
- " ws.send = function(m) {\n",
- " //console.log('sending', m);\n",
- " comm.send(m);\n",
- " };\n",
- " // Register the callback with on_msg.\n",
- " comm.on_msg(function(msg) {\n",
- " //console.log('receiving', msg['content']['data'], msg);\n",
- " // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data'])\n",
- " });\n",
- " return ws;\n",
- "}\n",
- "\n",
- "mpl.mpl_figure_comm = function(comm, msg) {\n",
- " // This is the function which gets called when the mpl process\n",
- " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
- "\n",
- " var id = msg.content.data.id;\n",
- " // Get hold of the div created by the display call when the Comm\n",
- " // socket was opened in Python.\n",
- " var element = $(\"#\" + id);\n",
- " var ws_proxy = comm_websocket_adapter(comm)\n",
- "\n",
- " function ondownload(figure, format) {\n",
- " window.open(figure.imageObj.src);\n",
- " }\n",
- "\n",
- " var fig = new mpl.figure(id, ws_proxy,\n",
- " ondownload,\n",
- " element.get(0));\n",
- "\n",
- " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
- " // web socket which is closed, not our websocket->open comm proxy.\n",
- " ws_proxy.onopen();\n",
- "\n",
- " fig.parent_element = element.get(0);\n",
- " fig.cell_info = mpl.find_output_cell(\"\");\n",
- " if (!fig.cell_info) {\n",
- " console.error(\"Failed to find cell for figure\", id, fig);\n",
- " return;\n",
- " }\n",
- "\n",
- " var output_index = fig.cell_info[2]\n",
- " var cell = fig.cell_info[0];\n",
- "\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
- " fig.root.unbind('remove')\n",
- "\n",
- " // Update the output cell to use the data from the current canvas.\n",
- " fig.push_to_output();\n",
- " var dataURL = fig.canvas.toDataURL();\n",
- " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
- " // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable()\n",
- " $(fig.parent_element).html('');\n",
- " fig.close_ws(fig, msg);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
- " fig.send_message('closing', msg);\n",
- " // fig.ws.close()\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
- " // Turn the data on the canvas into data in the output cell.\n",
- " var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] = '';\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Tell IPython that the notebook contents must change.\n",
- " IPython.notebook.set_dirty(true);\n",
- " this.send_message(\"ack\", {});\n",
- " var fig = this;\n",
- " // Wait a second, then push the new image to the DOM so\n",
- " // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () { fig.push_to_output() }, 1000);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items){\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) { continue; };\n",
- "\n",
- " var button = $('');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " // Add the status bar.\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "\n",
- " // Add the close button to the window.\n",
- " var buttongrp = $('');\n",
- " var button = $('');\n",
- " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
- " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
- " buttongrp.append(button);\n",
- " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
- " titlebar.prepend(buttongrp);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(el){\n",
- " var fig = this\n",
- " el.on(\"remove\", function(){\n",
- "\tfig.close_ws(fig, {});\n",
- " });\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(el){\n",
- " // this is important to make the div 'focusable\n",
- " el.attr('tabindex', 0)\n",
- " // reach out to IPython and tell the keyboard manager to turn it's self\n",
- " // off when our div gets focus\n",
- "\n",
- " // location in version 3\n",
- " if (IPython.notebook.keyboard_manager) {\n",
- " IPython.notebook.keyboard_manager.register_events(el);\n",
- " }\n",
- " else {\n",
- " // location in version 2\n",
- " IPython.keyboard_manager.register_events(el);\n",
- " }\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager)\n",
- " manager = IPython.keyboard_manager;\n",
- "\n",
- " // Check for shift+enter\n",
- " if (event.shiftKey && event.which == 13) {\n",
- " this.canvas_div.blur();\n",
- " event.shiftKey = false;\n",
- " // Send a \"J\" for go to next cell\n",
- " event.which = 74;\n",
- " event.keyCode = 74;\n",
- " manager.command_mode();\n",
- " manager.handle_keydown(event);\n",
- " }\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " fig.ondownload(fig, null);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.find_output_cell = function(html_output) {\n",
- " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
- " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
- " // IPython event is triggered only after the cells have been serialised, which for\n",
- " // our purposes (turning an active figure into a static one), is too late.\n",
- " var cells = IPython.notebook.get_cells();\n",
- " var ncells = cells.length;\n",
- " for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
- " data = data.data;\n",
- " }\n",
- " if (data['text/html'] == html_output) {\n",
- " return [cell, data, j];\n",
- " }\n",
- " }\n",
- " }\n",
- " }\n",
- "}\n",
- "\n",
- "// Register the function which deals with the matplotlib target/channel.\n",
- "// The kernel may be null if the page has been refreshed.\n",
- "if (IPython.notebook.kernel != null) {\n",
- " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
- "}\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- ""
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "Rgpd = ws.genpareto.rvs(0.4,size=100);\n",
- "gmps = ws.genpareto.fit2(Rgpd, method='mps')\n",
- "gml = ws.genpareto.fit2(Rgpd, method='ml')\n",
- "fig, axes = plt.subplots()\n",
- "gmps.plotesf()\n",
- "plt.hold(True)\n",
- "gml.plotesf('r--')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Return values for the GEV distribution"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\pab\\workspace\\git_pywafo\\wafo\\stats\\estimation.py:343: UserWarning: The fitted parameters does not provide the optimum fit. Something wrong with fit (par = [-0.09964304 3.71720793 4.9932272 ])\n",
- " 'Something wrong with fit (par = {})'.format(str(par)))\n",
- "c:\\pab\\workspace\\git_pywafo\\wafo\\stats\\estimation.py:343: UserWarning: The fitted parameters does not provide the optimum fit. Something wrong with fit (par = [-0.09957624 3.71723125 4.9932272 ])\n",
- " 'Something wrong with fit (par = {})'.format(str(par)))\n",
- "c:\\pab\\workspace\\git_pywafo\\wafo\\stats\\estimation.py:343: UserWarning: The fitted parameters does not provide the optimum fit. Something wrong with fit (par = [ -0.09961102 3.7171997 10.41365265])\n",
- " 'Something wrong with fit (par = {})'.format(str(par)))\n"
- ]
- },
- {
- "data": {
- "application/javascript": [
- "/* Put everything inside the global mpl namespace */\n",
- "window.mpl = {};\n",
- "\n",
- "mpl.get_websocket_type = function() {\n",
- " if (typeof(WebSocket) !== 'undefined') {\n",
- " return WebSocket;\n",
- " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
- " return MozWebSocket;\n",
- " } else {\n",
- " alert('Your browser does not have WebSocket support.' +\n",
- " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
- " 'Firefox 4 and 5 are also supported but you ' +\n",
- " 'have to enable WebSockets in about:config.');\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
- " this.id = figure_id;\n",
- "\n",
- " this.ws = websocket;\n",
- "\n",
- " this.supports_binary = (this.ws.binaryType != undefined);\n",
- "\n",
- " if (!this.supports_binary) {\n",
- " var warnings = document.getElementById(\"mpl-warnings\");\n",
- " if (warnings) {\n",
- " warnings.style.display = 'block';\n",
- " warnings.textContent = (\n",
- " \"This browser does not support binary websocket messages. \" +\n",
- " \"Performance may be slow.\");\n",
- " }\n",
- " }\n",
- "\n",
- " this.imageObj = new Image();\n",
- "\n",
- " this.context = undefined;\n",
- " this.message = undefined;\n",
- " this.canvas = undefined;\n",
- " this.rubberband_canvas = undefined;\n",
- " this.rubberband_context = undefined;\n",
- " this.format_dropdown = undefined;\n",
- "\n",
- " this.image_mode = 'full';\n",
- "\n",
- " this.root = $('');\n",
- " this._root_extra_style(this.root)\n",
- " this.root.attr('style', 'display: inline-block');\n",
- "\n",
- " $(parent_element).append(this.root);\n",
- "\n",
- " this._init_header(this);\n",
- " this._init_canvas(this);\n",
- " this._init_toolbar(this);\n",
- "\n",
- " var fig = this;\n",
- "\n",
- " this.waiting = false;\n",
- "\n",
- " this.ws.onopen = function () {\n",
- " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
- " fig.send_message(\"send_image_mode\", {});\n",
- " fig.send_message(\"refresh\", {});\n",
- " }\n",
- "\n",
- " this.imageObj.onload = function() {\n",
- " if (fig.image_mode == 'full') {\n",
- " // Full images could contain transparency (where diff images\n",
- " // almost always do), so we need to clear the canvas so that\n",
- " // there is no ghosting.\n",
- " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
- " }\n",
- " fig.context.drawImage(fig.imageObj, 0, 0);\n",
- " };\n",
- "\n",
- " this.imageObj.onunload = function() {\n",
- " this.ws.close();\n",
- " }\n",
- "\n",
- " this.ws.onmessage = this._make_on_message_function(this);\n",
- "\n",
- " this.ondownload = ondownload;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_header = function() {\n",
- " var titlebar = $(\n",
- " '');\n",
- " var titletext = $(\n",
- " '');\n",
- " titlebar.append(titletext)\n",
- " this.root.append(titlebar);\n",
- " this.header = titletext[0];\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_canvas = function() {\n",
- " var fig = this;\n",
- "\n",
- " var canvas_div = $('');\n",
- "\n",
- " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
- "\n",
- " function canvas_keyboard_event(event) {\n",
- " return fig.key_event(event, event['data']);\n",
- " }\n",
- "\n",
- " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
- " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
- " this.canvas_div = canvas_div\n",
- " this._canvas_extra_style(canvas_div)\n",
- " this.root.append(canvas_div);\n",
- "\n",
- " var canvas = $('');\n",
- " canvas.addClass('mpl-canvas');\n",
- " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
- "\n",
- " this.canvas = canvas[0];\n",
- " this.context = canvas[0].getContext(\"2d\");\n",
- "\n",
- " var rubberband = $('');\n",
- " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
- "\n",
- " var pass_mouse_events = true;\n",
- "\n",
- " canvas_div.resizable({\n",
- " start: function(event, ui) {\n",
- " pass_mouse_events = false;\n",
- " },\n",
- " resize: function(event, ui) {\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " stop: function(event, ui) {\n",
- " pass_mouse_events = true;\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " });\n",
- "\n",
- " function mouse_event_fn(event) {\n",
- " if (pass_mouse_events)\n",
- " return fig.mouse_event(event, event['data']);\n",
- " }\n",
- "\n",
- " rubberband.mousedown('button_press', mouse_event_fn);\n",
- " rubberband.mouseup('button_release', mouse_event_fn);\n",
- " // Throttle sequential mouse events to 1 every 20ms.\n",
- " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
- "\n",
- " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
- " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
- "\n",
- " canvas_div.on(\"wheel\", function (event) {\n",
- " event = event.originalEvent;\n",
- " event['data'] = 'scroll'\n",
- " if (event.deltaY < 0) {\n",
- " event.step = 1;\n",
- " } else {\n",
- " event.step = -1;\n",
- " }\n",
- " mouse_event_fn(event);\n",
- " });\n",
- "\n",
- " canvas_div.append(canvas);\n",
- " canvas_div.append(rubberband);\n",
- "\n",
- " this.rubberband = rubberband;\n",
- " this.rubberband_canvas = rubberband[0];\n",
- " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
- " this.rubberband_context.strokeStyle = \"#000000\";\n",
- "\n",
- " this._resize_canvas = function(width, height) {\n",
- " // Keep the size of the canvas, canvas container, and rubber band\n",
- " // canvas in synch.\n",
- " canvas_div.css('width', width)\n",
- " canvas_div.css('height', height)\n",
- "\n",
- " canvas.attr('width', width);\n",
- " canvas.attr('height', height);\n",
- "\n",
- " rubberband.attr('width', width);\n",
- " rubberband.attr('height', height);\n",
- " }\n",
- "\n",
- " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
- " // upon first draw.\n",
- " this._resize_canvas(600, 600);\n",
- "\n",
- " // Disable right mouse context menu.\n",
- " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
- " return false;\n",
- " });\n",
- "\n",
- " function set_focus () {\n",
- " canvas.focus();\n",
- " canvas_div.focus();\n",
- " }\n",
- "\n",
- " window.setTimeout(set_focus, 100);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " // put a spacer in here.\n",
- " continue;\n",
- " }\n",
- " var button = $('');\n",
- " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
- " 'ui-button-icon-only');\n",
- " button.attr('role', 'button');\n",
- " button.attr('aria-disabled', 'false');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- "\n",
- " var icon_img = $('');\n",
- " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
- " icon_img.addClass(image);\n",
- " icon_img.addClass('ui-corner-all');\n",
- "\n",
- " var tooltip_span = $('');\n",
- " tooltip_span.addClass('ui-button-text');\n",
- " tooltip_span.html(tooltip);\n",
- "\n",
- " button.append(icon_img);\n",
- " button.append(tooltip_span);\n",
- "\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " var fmt_picker_span = $('');\n",
- "\n",
- " var fmt_picker = $('');\n",
- " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
- " fmt_picker_span.append(fmt_picker);\n",
- " nav_element.append(fmt_picker_span);\n",
- " this.format_dropdown = fmt_picker[0];\n",
- "\n",
- " for (var ind in mpl.extensions) {\n",
- " var fmt = mpl.extensions[ind];\n",
- " var option = $(\n",
- " '', {selected: fmt === mpl.default_extension}).html(fmt);\n",
- " fmt_picker.append(option)\n",
- " }\n",
- "\n",
- " // Add hover states to the ui-buttons\n",
- " $( \".ui-button\" ).hover(\n",
- " function() { $(this).addClass(\"ui-state-hover\");},\n",
- " function() { $(this).removeClass(\"ui-state-hover\");}\n",
- " );\n",
- "\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
- " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
- " // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_message = function(type, properties) {\n",
- " properties['type'] = type;\n",
- " properties['figure_id'] = this.id;\n",
- " this.ws.send(JSON.stringify(properties));\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_draw_message = function() {\n",
- " if (!this.waiting) {\n",
- " this.waiting = true;\n",
- " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
- " }\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " var format_dropdown = fig.format_dropdown;\n",
- " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
- " fig.ondownload(fig, format);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
- " var size = msg['size'];\n",
- " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1]);\n",
- " fig.send_message(\"refresh\", {});\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
- " var x0 = msg['x0'];\n",
- " var y0 = fig.canvas.height - msg['y0'];\n",
- " var x1 = msg['x1'];\n",
- " var y1 = fig.canvas.height - msg['y1'];\n",
- " x0 = Math.floor(x0) + 0.5;\n",
- " y0 = Math.floor(y0) + 0.5;\n",
- " x1 = Math.floor(x1) + 0.5;\n",
- " y1 = Math.floor(y1) + 0.5;\n",
- " var min_x = Math.min(x0, x1);\n",
- " var min_y = Math.min(y0, y1);\n",
- " var width = Math.abs(x1 - x0);\n",
- " var height = Math.abs(y1 - y0);\n",
- "\n",
- " fig.rubberband_context.clearRect(\n",
- " 0, 0, fig.canvas.width, fig.canvas.height);\n",
- "\n",
- " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
- " // Updates the figure title.\n",
- " fig.header.textContent = msg['label'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch(cursor)\n",
- " {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
- " fig.message.textContent = msg['message'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
- " // Request the server to send over a new figure.\n",
- " fig.send_draw_message();\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
- " fig.image_mode = msg['mode'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Called whenever the canvas gets updated.\n",
- " this.send_message(\"ack\", {});\n",
- "}\n",
- "\n",
- "// A function to construct a web socket function for onmessage handling.\n",
- "// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
- " return function socket_on_message(evt) {\n",
- " if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = \"image/png\";\n",
- "\n",
- " /* Free the memory for the previous frames */\n",
- " if (fig.imageObj.src) {\n",
- " (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src);\n",
- " }\n",
- "\n",
- " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data);\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
- " fig.imageObj.src = evt.data;\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- "\n",
- " var msg = JSON.parse(evt.data);\n",
- " var msg_type = msg['type'];\n",
- "\n",
- " // Call the \"handle_{type}\" callback, which takes\n",
- " // the figure and JSON message as its only arguments.\n",
- " try {\n",
- " var callback = fig[\"handle_\" + msg_type];\n",
- " } catch (e) {\n",
- " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
- " return;\n",
- " }\n",
- "\n",
- " if (callback) {\n",
- " try {\n",
- " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
- " callback(fig, msg);\n",
- " } catch (e) {\n",
- " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
- " }\n",
- " }\n",
- " };\n",
- "}\n",
- "\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function(e) {\n",
- " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
- " var targ;\n",
- " if (!e)\n",
- " e = window.event;\n",
- " if (e.target)\n",
- " targ = e.target;\n",
- " else if (e.srcElement)\n",
- " targ = e.srcElement;\n",
- " if (targ.nodeType == 3) // defeat Safari bug\n",
- " targ = targ.parentNode;\n",
- "\n",
- " // jQuery normalizes the pageX and pageY\n",
- " // pageX,Y are the mouse positions relative to the document\n",
- " // offset() returns the position of the element relative to the document\n",
- " var x = e.pageX - $(targ).offset().left;\n",
- " var y = e.pageY - $(targ).offset().top;\n",
- "\n",
- " return {\"x\": x, \"y\": y};\n",
- "};\n",
- "\n",
- "/*\n",
- " * return a copy of an object with only non-object keys\n",
- " * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
- " */\n",
- "function simpleKeys (original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object')\n",
- " obj[key] = original[key]\n",
- " return obj;\n",
- " }, {});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.mouse_event = function(event, name) {\n",
- " var canvas_pos = mpl.findpos(event)\n",
- "\n",
- " if (name === 'button_press')\n",
- " {\n",
- " this.canvas.focus();\n",
- " this.canvas_div.focus();\n",
- " }\n",
- "\n",
- " var x = canvas_pos.x;\n",
- " var y = canvas_pos.y;\n",
- "\n",
- " this.send_message(name, {x: x, y: y, button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event)});\n",
- "\n",
- " /* This prevents the web browser from automatically changing to\n",
- " * the text insertion cursor when the button is pressed. We want\n",
- " * to control all of the cursor setting manually through the\n",
- " * 'cursor' event from matplotlib */\n",
- " event.preventDefault();\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " // Handle any extra behaviour associated with a key event\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.key_event = function(event, name) {\n",
- "\n",
- " // Prevent repeat events\n",
- " if (name == 'key_press')\n",
- " {\n",
- " if (event.which === this._key)\n",
- " return;\n",
- " else\n",
- " this._key = event.which;\n",
- " }\n",
- " if (name == 'key_release')\n",
- " this._key = null;\n",
- "\n",
- " var value = '';\n",
- " if (event.ctrlKey && event.which != 17)\n",
- " value += \"ctrl+\";\n",
- " if (event.altKey && event.which != 18)\n",
- " value += \"alt+\";\n",
- " if (event.shiftKey && event.which != 16)\n",
- " value += \"shift+\";\n",
- "\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
- "\n",
- " this._key_event_extra(event, name);\n",
- "\n",
- " this.send_message(name, {key: value,\n",
- " guiEvent: simpleKeys(event)});\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
- " if (name == 'download') {\n",
- " this.handle_save(this, null);\n",
- " } else {\n",
- " this.send_message(\"toolbar_button\", {name: name});\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
- " this.message.textContent = tooltip;\n",
- "};\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
- "\n",
- "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
- "\n",
- "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
- " // Create a \"websocket\"-like object which calls the given IPython comm\n",
- " // object with the appropriate methods. Currently this is a non binary\n",
- " // socket, so there is still some room for performance tuning.\n",
- " var ws = {};\n",
- "\n",
- " ws.close = function() {\n",
- " comm.close()\n",
- " };\n",
- " ws.send = function(m) {\n",
- " //console.log('sending', m);\n",
- " comm.send(m);\n",
- " };\n",
- " // Register the callback with on_msg.\n",
- " comm.on_msg(function(msg) {\n",
- " //console.log('receiving', msg['content']['data'], msg);\n",
- " // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data'])\n",
- " });\n",
- " return ws;\n",
- "}\n",
- "\n",
- "mpl.mpl_figure_comm = function(comm, msg) {\n",
- " // This is the function which gets called when the mpl process\n",
- " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
- "\n",
- " var id = msg.content.data.id;\n",
- " // Get hold of the div created by the display call when the Comm\n",
- " // socket was opened in Python.\n",
- " var element = $(\"#\" + id);\n",
- " var ws_proxy = comm_websocket_adapter(comm)\n",
- "\n",
- " function ondownload(figure, format) {\n",
- " window.open(figure.imageObj.src);\n",
- " }\n",
- "\n",
- " var fig = new mpl.figure(id, ws_proxy,\n",
- " ondownload,\n",
- " element.get(0));\n",
- "\n",
- " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
- " // web socket which is closed, not our websocket->open comm proxy.\n",
- " ws_proxy.onopen();\n",
- "\n",
- " fig.parent_element = element.get(0);\n",
- " fig.cell_info = mpl.find_output_cell(\"\");\n",
- " if (!fig.cell_info) {\n",
- " console.error(\"Failed to find cell for figure\", id, fig);\n",
- " return;\n",
- " }\n",
- "\n",
- " var output_index = fig.cell_info[2]\n",
- " var cell = fig.cell_info[0];\n",
- "\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
- " fig.root.unbind('remove')\n",
- "\n",
- " // Update the output cell to use the data from the current canvas.\n",
- " fig.push_to_output();\n",
- " var dataURL = fig.canvas.toDataURL();\n",
- " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
- " // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable()\n",
- " $(fig.parent_element).html('');\n",
- " fig.close_ws(fig, msg);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
- " fig.send_message('closing', msg);\n",
- " // fig.ws.close()\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
- " // Turn the data on the canvas into data in the output cell.\n",
- " var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] = '';\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Tell IPython that the notebook contents must change.\n",
- " IPython.notebook.set_dirty(true);\n",
- " this.send_message(\"ack\", {});\n",
- " var fig = this;\n",
- " // Wait a second, then push the new image to the DOM so\n",
- " // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () { fig.push_to_output() }, 1000);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items){\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) { continue; };\n",
- "\n",
- " var button = $('');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " // Add the status bar.\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "\n",
- " // Add the close button to the window.\n",
- " var buttongrp = $('');\n",
- " var button = $('');\n",
- " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
- " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
- " buttongrp.append(button);\n",
- " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
- " titlebar.prepend(buttongrp);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(el){\n",
- " var fig = this\n",
- " el.on(\"remove\", function(){\n",
- "\tfig.close_ws(fig, {});\n",
- " });\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(el){\n",
- " // this is important to make the div 'focusable\n",
- " el.attr('tabindex', 0)\n",
- " // reach out to IPython and tell the keyboard manager to turn it's self\n",
- " // off when our div gets focus\n",
- "\n",
- " // location in version 3\n",
- " if (IPython.notebook.keyboard_manager) {\n",
- " IPython.notebook.keyboard_manager.register_events(el);\n",
- " }\n",
- " else {\n",
- " // location in version 2\n",
- " IPython.keyboard_manager.register_events(el);\n",
- " }\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager)\n",
- " manager = IPython.keyboard_manager;\n",
- "\n",
- " // Check for shift+enter\n",
- " if (event.shiftKey && event.which == 13) {\n",
- " this.canvas_div.blur();\n",
- " event.shiftKey = false;\n",
- " // Send a \"J\" for go to next cell\n",
- " event.which = 74;\n",
- " event.keyCode = 74;\n",
- " manager.command_mode();\n",
- " manager.handle_keydown(event);\n",
- " }\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " fig.ondownload(fig, null);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.find_output_cell = function(html_output) {\n",
- " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
- " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
- " // IPython event is triggered only after the cells have been serialised, which for\n",
- " // our purposes (turning an active figure into a static one), is too late.\n",
- " var cells = IPython.notebook.get_cells();\n",
- " var ncells = cells.length;\n",
- " for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
- " data = data.data;\n",
- " }\n",
- " if (data['text/html'] == html_output) {\n",
- " return [cell, data, j];\n",
- " }\n",
- " }\n",
- " }\n",
- " }\n",
- "}\n",
- "\n",
- "// Register the function which deals with the matplotlib target/channel.\n",
- "// The kernel may be null if the page has been refreshed.\n",
- "if (IPython.notebook.kernel != null) {\n",
- " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
- "}\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- ""
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "T = np.logspace(1, 5, 10)\n",
- "sT = Y5gev.isf(1./T)\n",
- "ci = []\n",
- "t = []\n",
- "for Ti, sTi in zip(T, sT):\n",
- " try:\n",
- " Lx = Y5gev.profile_quantile(sTi, i=2)\n",
- " ci.append(Lx.get_bounds(alpha=0.05))\n",
- " t.append(Ti)\n",
- " except Exception:\n",
- " pass\n",
- "fig, axes = plt.subplots()\n",
- "plt.semilogx(T,sT, t, ci,'r')\n",
- "plt.hold(True)\n",
- "N = np.r_[1:len(Y5M)+1]\n",
- "Nmax = max(N)\n",
- "plt.plot(Nmax/N, sorted(Y5M, reverse=True), '.')\n",
- "plt.title('Return values in the GEV model')\n",
- "plt.xlabel('Return period')\n",
- "plt.ylabel('Return value') \n",
- "plt.grid(True) "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "application/javascript": [
- "/* Put everything inside the global mpl namespace */\n",
- "window.mpl = {};\n",
- "\n",
- "mpl.get_websocket_type = function() {\n",
- " if (typeof(WebSocket) !== 'undefined') {\n",
- " return WebSocket;\n",
- " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
- " return MozWebSocket;\n",
- " } else {\n",
- " alert('Your browser does not have WebSocket support.' +\n",
- " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
- " 'Firefox 4 and 5 are also supported but you ' +\n",
- " 'have to enable WebSockets in about:config.');\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
- " this.id = figure_id;\n",
- "\n",
- " this.ws = websocket;\n",
- "\n",
- " this.supports_binary = (this.ws.binaryType != undefined);\n",
- "\n",
- " if (!this.supports_binary) {\n",
- " var warnings = document.getElementById(\"mpl-warnings\");\n",
- " if (warnings) {\n",
- " warnings.style.display = 'block';\n",
- " warnings.textContent = (\n",
- " \"This browser does not support binary websocket messages. \" +\n",
- " \"Performance may be slow.\");\n",
- " }\n",
- " }\n",
- "\n",
- " this.imageObj = new Image();\n",
- "\n",
- " this.context = undefined;\n",
- " this.message = undefined;\n",
- " this.canvas = undefined;\n",
- " this.rubberband_canvas = undefined;\n",
- " this.rubberband_context = undefined;\n",
- " this.format_dropdown = undefined;\n",
- "\n",
- " this.image_mode = 'full';\n",
- "\n",
- " this.root = $('');\n",
- " this._root_extra_style(this.root)\n",
- " this.root.attr('style', 'display: inline-block');\n",
- "\n",
- " $(parent_element).append(this.root);\n",
- "\n",
- " this._init_header(this);\n",
- " this._init_canvas(this);\n",
- " this._init_toolbar(this);\n",
- "\n",
- " var fig = this;\n",
- "\n",
- " this.waiting = false;\n",
- "\n",
- " this.ws.onopen = function () {\n",
- " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
- " fig.send_message(\"send_image_mode\", {});\n",
- " fig.send_message(\"refresh\", {});\n",
- " }\n",
- "\n",
- " this.imageObj.onload = function() {\n",
- " if (fig.image_mode == 'full') {\n",
- " // Full images could contain transparency (where diff images\n",
- " // almost always do), so we need to clear the canvas so that\n",
- " // there is no ghosting.\n",
- " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
- " }\n",
- " fig.context.drawImage(fig.imageObj, 0, 0);\n",
- " };\n",
- "\n",
- " this.imageObj.onunload = function() {\n",
- " this.ws.close();\n",
- " }\n",
- "\n",
- " this.ws.onmessage = this._make_on_message_function(this);\n",
- "\n",
- " this.ondownload = ondownload;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_header = function() {\n",
- " var titlebar = $(\n",
- " '');\n",
- " var titletext = $(\n",
- " '');\n",
- " titlebar.append(titletext)\n",
- " this.root.append(titlebar);\n",
- " this.header = titletext[0];\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_canvas = function() {\n",
- " var fig = this;\n",
- "\n",
- " var canvas_div = $('');\n",
- "\n",
- " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
- "\n",
- " function canvas_keyboard_event(event) {\n",
- " return fig.key_event(event, event['data']);\n",
- " }\n",
- "\n",
- " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
- " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
- " this.canvas_div = canvas_div\n",
- " this._canvas_extra_style(canvas_div)\n",
- " this.root.append(canvas_div);\n",
- "\n",
- " var canvas = $('');\n",
- " canvas.addClass('mpl-canvas');\n",
- " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
- "\n",
- " this.canvas = canvas[0];\n",
- " this.context = canvas[0].getContext(\"2d\");\n",
- "\n",
- " var rubberband = $('');\n",
- " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
- "\n",
- " var pass_mouse_events = true;\n",
- "\n",
- " canvas_div.resizable({\n",
- " start: function(event, ui) {\n",
- " pass_mouse_events = false;\n",
- " },\n",
- " resize: function(event, ui) {\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " stop: function(event, ui) {\n",
- " pass_mouse_events = true;\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " });\n",
- "\n",
- " function mouse_event_fn(event) {\n",
- " if (pass_mouse_events)\n",
- " return fig.mouse_event(event, event['data']);\n",
- " }\n",
- "\n",
- " rubberband.mousedown('button_press', mouse_event_fn);\n",
- " rubberband.mouseup('button_release', mouse_event_fn);\n",
- " // Throttle sequential mouse events to 1 every 20ms.\n",
- " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
- "\n",
- " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
- " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
- "\n",
- " canvas_div.on(\"wheel\", function (event) {\n",
- " event = event.originalEvent;\n",
- " event['data'] = 'scroll'\n",
- " if (event.deltaY < 0) {\n",
- " event.step = 1;\n",
- " } else {\n",
- " event.step = -1;\n",
- " }\n",
- " mouse_event_fn(event);\n",
- " });\n",
- "\n",
- " canvas_div.append(canvas);\n",
- " canvas_div.append(rubberband);\n",
- "\n",
- " this.rubberband = rubberband;\n",
- " this.rubberband_canvas = rubberband[0];\n",
- " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
- " this.rubberband_context.strokeStyle = \"#000000\";\n",
- "\n",
- " this._resize_canvas = function(width, height) {\n",
- " // Keep the size of the canvas, canvas container, and rubber band\n",
- " // canvas in synch.\n",
- " canvas_div.css('width', width)\n",
- " canvas_div.css('height', height)\n",
- "\n",
- " canvas.attr('width', width);\n",
- " canvas.attr('height', height);\n",
- "\n",
- " rubberband.attr('width', width);\n",
- " rubberband.attr('height', height);\n",
- " }\n",
- "\n",
- " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
- " // upon first draw.\n",
- " this._resize_canvas(600, 600);\n",
- "\n",
- " // Disable right mouse context menu.\n",
- " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
- " return false;\n",
- " });\n",
- "\n",
- " function set_focus () {\n",
- " canvas.focus();\n",
- " canvas_div.focus();\n",
- " }\n",
- "\n",
- " window.setTimeout(set_focus, 100);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " // put a spacer in here.\n",
- " continue;\n",
- " }\n",
- " var button = $('');\n",
- " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
- " 'ui-button-icon-only');\n",
- " button.attr('role', 'button');\n",
- " button.attr('aria-disabled', 'false');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- "\n",
- " var icon_img = $('');\n",
- " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
- " icon_img.addClass(image);\n",
- " icon_img.addClass('ui-corner-all');\n",
- "\n",
- " var tooltip_span = $('');\n",
- " tooltip_span.addClass('ui-button-text');\n",
- " tooltip_span.html(tooltip);\n",
- "\n",
- " button.append(icon_img);\n",
- " button.append(tooltip_span);\n",
- "\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " var fmt_picker_span = $('');\n",
- "\n",
- " var fmt_picker = $('');\n",
- " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
- " fmt_picker_span.append(fmt_picker);\n",
- " nav_element.append(fmt_picker_span);\n",
- " this.format_dropdown = fmt_picker[0];\n",
- "\n",
- " for (var ind in mpl.extensions) {\n",
- " var fmt = mpl.extensions[ind];\n",
- " var option = $(\n",
- " '', {selected: fmt === mpl.default_extension}).html(fmt);\n",
- " fmt_picker.append(option)\n",
- " }\n",
- "\n",
- " // Add hover states to the ui-buttons\n",
- " $( \".ui-button\" ).hover(\n",
- " function() { $(this).addClass(\"ui-state-hover\");},\n",
- " function() { $(this).removeClass(\"ui-state-hover\");}\n",
- " );\n",
- "\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
- " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
- " // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_message = function(type, properties) {\n",
- " properties['type'] = type;\n",
- " properties['figure_id'] = this.id;\n",
- " this.ws.send(JSON.stringify(properties));\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_draw_message = function() {\n",
- " if (!this.waiting) {\n",
- " this.waiting = true;\n",
- " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
- " }\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " var format_dropdown = fig.format_dropdown;\n",
- " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
- " fig.ondownload(fig, format);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
- " var size = msg['size'];\n",
- " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1]);\n",
- " fig.send_message(\"refresh\", {});\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
- " var x0 = msg['x0'];\n",
- " var y0 = fig.canvas.height - msg['y0'];\n",
- " var x1 = msg['x1'];\n",
- " var y1 = fig.canvas.height - msg['y1'];\n",
- " x0 = Math.floor(x0) + 0.5;\n",
- " y0 = Math.floor(y0) + 0.5;\n",
- " x1 = Math.floor(x1) + 0.5;\n",
- " y1 = Math.floor(y1) + 0.5;\n",
- " var min_x = Math.min(x0, x1);\n",
- " var min_y = Math.min(y0, y1);\n",
- " var width = Math.abs(x1 - x0);\n",
- " var height = Math.abs(y1 - y0);\n",
- "\n",
- " fig.rubberband_context.clearRect(\n",
- " 0, 0, fig.canvas.width, fig.canvas.height);\n",
- "\n",
- " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
- " // Updates the figure title.\n",
- " fig.header.textContent = msg['label'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch(cursor)\n",
- " {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
- " fig.message.textContent = msg['message'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
- " // Request the server to send over a new figure.\n",
- " fig.send_draw_message();\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
- " fig.image_mode = msg['mode'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Called whenever the canvas gets updated.\n",
- " this.send_message(\"ack\", {});\n",
- "}\n",
- "\n",
- "// A function to construct a web socket function for onmessage handling.\n",
- "// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
- " return function socket_on_message(evt) {\n",
- " if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = \"image/png\";\n",
- "\n",
- " /* Free the memory for the previous frames */\n",
- " if (fig.imageObj.src) {\n",
- " (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src);\n",
- " }\n",
- "\n",
- " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data);\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
- " fig.imageObj.src = evt.data;\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- "\n",
- " var msg = JSON.parse(evt.data);\n",
- " var msg_type = msg['type'];\n",
- "\n",
- " // Call the \"handle_{type}\" callback, which takes\n",
- " // the figure and JSON message as its only arguments.\n",
- " try {\n",
- " var callback = fig[\"handle_\" + msg_type];\n",
- " } catch (e) {\n",
- " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
- " return;\n",
- " }\n",
- "\n",
- " if (callback) {\n",
- " try {\n",
- " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
- " callback(fig, msg);\n",
- " } catch (e) {\n",
- " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
- " }\n",
- " }\n",
- " };\n",
- "}\n",
- "\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function(e) {\n",
- " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
- " var targ;\n",
- " if (!e)\n",
- " e = window.event;\n",
- " if (e.target)\n",
- " targ = e.target;\n",
- " else if (e.srcElement)\n",
- " targ = e.srcElement;\n",
- " if (targ.nodeType == 3) // defeat Safari bug\n",
- " targ = targ.parentNode;\n",
- "\n",
- " // jQuery normalizes the pageX and pageY\n",
- " // pageX,Y are the mouse positions relative to the document\n",
- " // offset() returns the position of the element relative to the document\n",
- " var x = e.pageX - $(targ).offset().left;\n",
- " var y = e.pageY - $(targ).offset().top;\n",
- "\n",
- " return {\"x\": x, \"y\": y};\n",
- "};\n",
- "\n",
- "/*\n",
- " * return a copy of an object with only non-object keys\n",
- " * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
- " */\n",
- "function simpleKeys (original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object')\n",
- " obj[key] = original[key]\n",
- " return obj;\n",
- " }, {});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.mouse_event = function(event, name) {\n",
- " var canvas_pos = mpl.findpos(event)\n",
- "\n",
- " if (name === 'button_press')\n",
- " {\n",
- " this.canvas.focus();\n",
- " this.canvas_div.focus();\n",
- " }\n",
- "\n",
- " var x = canvas_pos.x;\n",
- " var y = canvas_pos.y;\n",
- "\n",
- " this.send_message(name, {x: x, y: y, button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event)});\n",
- "\n",
- " /* This prevents the web browser from automatically changing to\n",
- " * the text insertion cursor when the button is pressed. We want\n",
- " * to control all of the cursor setting manually through the\n",
- " * 'cursor' event from matplotlib */\n",
- " event.preventDefault();\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " // Handle any extra behaviour associated with a key event\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.key_event = function(event, name) {\n",
- "\n",
- " // Prevent repeat events\n",
- " if (name == 'key_press')\n",
- " {\n",
- " if (event.which === this._key)\n",
- " return;\n",
- " else\n",
- " this._key = event.which;\n",
- " }\n",
- " if (name == 'key_release')\n",
- " this._key = null;\n",
- "\n",
- " var value = '';\n",
- " if (event.ctrlKey && event.which != 17)\n",
- " value += \"ctrl+\";\n",
- " if (event.altKey && event.which != 18)\n",
- " value += \"alt+\";\n",
- " if (event.shiftKey && event.which != 16)\n",
- " value += \"shift+\";\n",
- "\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
- "\n",
- " this._key_event_extra(event, name);\n",
- "\n",
- " this.send_message(name, {key: value,\n",
- " guiEvent: simpleKeys(event)});\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
- " if (name == 'download') {\n",
- " this.handle_save(this, null);\n",
- " } else {\n",
- " this.send_message(\"toolbar_button\", {name: name});\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
- " this.message.textContent = tooltip;\n",
- "};\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
- "\n",
- "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
- "\n",
- "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
- " // Create a \"websocket\"-like object which calls the given IPython comm\n",
- " // object with the appropriate methods. Currently this is a non binary\n",
- " // socket, so there is still some room for performance tuning.\n",
- " var ws = {};\n",
- "\n",
- " ws.close = function() {\n",
- " comm.close()\n",
- " };\n",
- " ws.send = function(m) {\n",
- " //console.log('sending', m);\n",
- " comm.send(m);\n",
- " };\n",
- " // Register the callback with on_msg.\n",
- " comm.on_msg(function(msg) {\n",
- " //console.log('receiving', msg['content']['data'], msg);\n",
- " // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data'])\n",
- " });\n",
- " return ws;\n",
- "}\n",
- "\n",
- "mpl.mpl_figure_comm = function(comm, msg) {\n",
- " // This is the function which gets called when the mpl process\n",
- " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
- "\n",
- " var id = msg.content.data.id;\n",
- " // Get hold of the div created by the display call when the Comm\n",
- " // socket was opened in Python.\n",
- " var element = $(\"#\" + id);\n",
- " var ws_proxy = comm_websocket_adapter(comm)\n",
- "\n",
- " function ondownload(figure, format) {\n",
- " window.open(figure.imageObj.src);\n",
- " }\n",
- "\n",
- " var fig = new mpl.figure(id, ws_proxy,\n",
- " ondownload,\n",
- " element.get(0));\n",
- "\n",
- " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
- " // web socket which is closed, not our websocket->open comm proxy.\n",
- " ws_proxy.onopen();\n",
- "\n",
- " fig.parent_element = element.get(0);\n",
- " fig.cell_info = mpl.find_output_cell(\"\");\n",
- " if (!fig.cell_info) {\n",
- " console.error(\"Failed to find cell for figure\", id, fig);\n",
- " return;\n",
- " }\n",
- "\n",
- " var output_index = fig.cell_info[2]\n",
- " var cell = fig.cell_info[0];\n",
- "\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
- " fig.root.unbind('remove')\n",
- "\n",
- " // Update the output cell to use the data from the current canvas.\n",
- " fig.push_to_output();\n",
- " var dataURL = fig.canvas.toDataURL();\n",
- " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
- " // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable()\n",
- " $(fig.parent_element).html('');\n",
- " fig.close_ws(fig, msg);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
- " fig.send_message('closing', msg);\n",
- " // fig.ws.close()\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
- " // Turn the data on the canvas into data in the output cell.\n",
- " var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] = '';\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Tell IPython that the notebook contents must change.\n",
- " IPython.notebook.set_dirty(true);\n",
- " this.send_message(\"ack\", {});\n",
- " var fig = this;\n",
- " // Wait a second, then push the new image to the DOM so\n",
- " // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () { fig.push_to_output() }, 1000);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items){\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) { continue; };\n",
- "\n",
- " var button = $('');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " // Add the status bar.\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "\n",
- " // Add the close button to the window.\n",
- " var buttongrp = $('');\n",
- " var button = $('');\n",
- " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
- " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
- " buttongrp.append(button);\n",
- " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
- " titlebar.prepend(buttongrp);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(el){\n",
- " var fig = this\n",
- " el.on(\"remove\", function(){\n",
- "\tfig.close_ws(fig, {});\n",
- " });\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(el){\n",
- " // this is important to make the div 'focusable\n",
- " el.attr('tabindex', 0)\n",
- " // reach out to IPython and tell the keyboard manager to turn it's self\n",
- " // off when our div gets focus\n",
- "\n",
- " // location in version 3\n",
- " if (IPython.notebook.keyboard_manager) {\n",
- " IPython.notebook.keyboard_manager.register_events(el);\n",
- " }\n",
- " else {\n",
- " // location in version 2\n",
- " IPython.keyboard_manager.register_events(el);\n",
- " }\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager)\n",
- " manager = IPython.keyboard_manager;\n",
- "\n",
- " // Check for shift+enter\n",
- " if (event.shiftKey && event.which == 13) {\n",
- " this.canvas_div.blur();\n",
- " event.shiftKey = false;\n",
- " // Send a \"J\" for go to next cell\n",
- " event.which = 74;\n",
- " event.keyCode = 74;\n",
- " manager.command_mode();\n",
- " manager.handle_keydown(event);\n",
- " }\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " fig.ondownload(fig, null);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.find_output_cell = function(html_output) {\n",
- " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
- " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
- " // IPython event is triggered only after the cells have been serialised, which for\n",
- " // our purposes (turning an active figure into a static one), is too late.\n",
- " var cells = IPython.notebook.get_cells();\n",
- " var ncells = cells.length;\n",
- " for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
- " data = data.data;\n",
- " }\n",
- " if (data['text/html'] == html_output) {\n",
- " return [cell, data, j];\n",
- " }\n",
- " }\n",
- " }\n",
- " }\n",
- "}\n",
- "\n",
- "// Register the function which deals with the matplotlib target/channel.\n",
- "// The kernel may be null if the page has been refreshed.\n",
- "if (IPython.notebook.kernel != null) {\n",
- " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
- "}\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- ""
- ],
- "text/plain": [
- ""
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- "metadata": {},
- "output_type": "display_data"
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- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\pab\\workspace\\git_pywafo\\wafo\\stats\\estimation.py:343: UserWarning: The fitted parameters does not provide the optimum fit. Something wrong with fit (par = [-0.63422275 0.5 -2.110703 ])\n",
- " 'Something wrong with fit (par = {})'.format(str(par)))\n",
- "c:\\pab\\workspace\\git_pywafo\\wafo\\stats\\estimation.py:343: UserWarning: The fitted parameters does not provide the optimum fit. Something wrong with fit (par = [-0.63409889 0.5 -5.94834482])\n",
- " 'Something wrong with fit (par = {})'.format(str(par)))\n",
- "c:\\pab\\workspace\\git_pywafo\\wafo\\stats\\estimation.py:343: UserWarning: The fitted parameters does not provide the optimum fit. Something wrong with fit (par = [-0.63409889 0.5 -6.90775528])\n",
- " 'Something wrong with fit (par = {})'.format(str(par)))\n"
- ]
- }
- ],
- "source": [
- "import wafo.stats as ws\n",
- "R = ws.genpareto.rvs(-0.5,size=100);\n",
- "phat = ws.genpareto.fit2(R[R>.5], -.5, scale=1, floc=0.5)\n",
- "\n",
- "phat.plotfitsummary()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "application/javascript": [
- "/* Put everything inside the global mpl namespace */\n",
- "window.mpl = {};\n",
- "\n",
- "mpl.get_websocket_type = function() {\n",
- " if (typeof(WebSocket) !== 'undefined') {\n",
- " return WebSocket;\n",
- " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
- " return MozWebSocket;\n",
- " } else {\n",
- " alert('Your browser does not have WebSocket support.' +\n",
- " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
- " 'Firefox 4 and 5 are also supported but you ' +\n",
- " 'have to enable WebSockets in about:config.');\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
- " this.id = figure_id;\n",
- "\n",
- " this.ws = websocket;\n",
- "\n",
- " this.supports_binary = (this.ws.binaryType != undefined);\n",
- "\n",
- " if (!this.supports_binary) {\n",
- " var warnings = document.getElementById(\"mpl-warnings\");\n",
- " if (warnings) {\n",
- " warnings.style.display = 'block';\n",
- " warnings.textContent = (\n",
- " \"This browser does not support binary websocket messages. \" +\n",
- " \"Performance may be slow.\");\n",
- " }\n",
- " }\n",
- "\n",
- " this.imageObj = new Image();\n",
- "\n",
- " this.context = undefined;\n",
- " this.message = undefined;\n",
- " this.canvas = undefined;\n",
- " this.rubberband_canvas = undefined;\n",
- " this.rubberband_context = undefined;\n",
- " this.format_dropdown = undefined;\n",
- "\n",
- " this.image_mode = 'full';\n",
- "\n",
- " this.root = $('');\n",
- " this._root_extra_style(this.root)\n",
- " this.root.attr('style', 'display: inline-block');\n",
- "\n",
- " $(parent_element).append(this.root);\n",
- "\n",
- " this._init_header(this);\n",
- " this._init_canvas(this);\n",
- " this._init_toolbar(this);\n",
- "\n",
- " var fig = this;\n",
- "\n",
- " this.waiting = false;\n",
- "\n",
- " this.ws.onopen = function () {\n",
- " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
- " fig.send_message(\"send_image_mode\", {});\n",
- " fig.send_message(\"refresh\", {});\n",
- " }\n",
- "\n",
- " this.imageObj.onload = function() {\n",
- " if (fig.image_mode == 'full') {\n",
- " // Full images could contain transparency (where diff images\n",
- " // almost always do), so we need to clear the canvas so that\n",
- " // there is no ghosting.\n",
- " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
- " }\n",
- " fig.context.drawImage(fig.imageObj, 0, 0);\n",
- " };\n",
- "\n",
- " this.imageObj.onunload = function() {\n",
- " this.ws.close();\n",
- " }\n",
- "\n",
- " this.ws.onmessage = this._make_on_message_function(this);\n",
- "\n",
- " this.ondownload = ondownload;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_header = function() {\n",
- " var titlebar = $(\n",
- " '');\n",
- " var titletext = $(\n",
- " '');\n",
- " titlebar.append(titletext)\n",
- " this.root.append(titlebar);\n",
- " this.header = titletext[0];\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_canvas = function() {\n",
- " var fig = this;\n",
- "\n",
- " var canvas_div = $('');\n",
- "\n",
- " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
- "\n",
- " function canvas_keyboard_event(event) {\n",
- " return fig.key_event(event, event['data']);\n",
- " }\n",
- "\n",
- " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
- " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
- " this.canvas_div = canvas_div\n",
- " this._canvas_extra_style(canvas_div)\n",
- " this.root.append(canvas_div);\n",
- "\n",
- " var canvas = $('');\n",
- " canvas.addClass('mpl-canvas');\n",
- " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
- "\n",
- " this.canvas = canvas[0];\n",
- " this.context = canvas[0].getContext(\"2d\");\n",
- "\n",
- " var rubberband = $('');\n",
- " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
- "\n",
- " var pass_mouse_events = true;\n",
- "\n",
- " canvas_div.resizable({\n",
- " start: function(event, ui) {\n",
- " pass_mouse_events = false;\n",
- " },\n",
- " resize: function(event, ui) {\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " stop: function(event, ui) {\n",
- " pass_mouse_events = true;\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " });\n",
- "\n",
- " function mouse_event_fn(event) {\n",
- " if (pass_mouse_events)\n",
- " return fig.mouse_event(event, event['data']);\n",
- " }\n",
- "\n",
- " rubberband.mousedown('button_press', mouse_event_fn);\n",
- " rubberband.mouseup('button_release', mouse_event_fn);\n",
- " // Throttle sequential mouse events to 1 every 20ms.\n",
- " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
- "\n",
- " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
- " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
- "\n",
- " canvas_div.on(\"wheel\", function (event) {\n",
- " event = event.originalEvent;\n",
- " event['data'] = 'scroll'\n",
- " if (event.deltaY < 0) {\n",
- " event.step = 1;\n",
- " } else {\n",
- " event.step = -1;\n",
- " }\n",
- " mouse_event_fn(event);\n",
- " });\n",
- "\n",
- " canvas_div.append(canvas);\n",
- " canvas_div.append(rubberband);\n",
- "\n",
- " this.rubberband = rubberband;\n",
- " this.rubberband_canvas = rubberband[0];\n",
- " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
- " this.rubberband_context.strokeStyle = \"#000000\";\n",
- "\n",
- " this._resize_canvas = function(width, height) {\n",
- " // Keep the size of the canvas, canvas container, and rubber band\n",
- " // canvas in synch.\n",
- " canvas_div.css('width', width)\n",
- " canvas_div.css('height', height)\n",
- "\n",
- " canvas.attr('width', width);\n",
- " canvas.attr('height', height);\n",
- "\n",
- " rubberband.attr('width', width);\n",
- " rubberband.attr('height', height);\n",
- " }\n",
- "\n",
- " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
- " // upon first draw.\n",
- " this._resize_canvas(600, 600);\n",
- "\n",
- " // Disable right mouse context menu.\n",
- " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
- " return false;\n",
- " });\n",
- "\n",
- " function set_focus () {\n",
- " canvas.focus();\n",
- " canvas_div.focus();\n",
- " }\n",
- "\n",
- " window.setTimeout(set_focus, 100);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " // put a spacer in here.\n",
- " continue;\n",
- " }\n",
- " var button = $('');\n",
- " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
- " 'ui-button-icon-only');\n",
- " button.attr('role', 'button');\n",
- " button.attr('aria-disabled', 'false');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- "\n",
- " var icon_img = $('');\n",
- " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
- " icon_img.addClass(image);\n",
- " icon_img.addClass('ui-corner-all');\n",
- "\n",
- " var tooltip_span = $('');\n",
- " tooltip_span.addClass('ui-button-text');\n",
- " tooltip_span.html(tooltip);\n",
- "\n",
- " button.append(icon_img);\n",
- " button.append(tooltip_span);\n",
- "\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " var fmt_picker_span = $('');\n",
- "\n",
- " var fmt_picker = $('');\n",
- " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
- " fmt_picker_span.append(fmt_picker);\n",
- " nav_element.append(fmt_picker_span);\n",
- " this.format_dropdown = fmt_picker[0];\n",
- "\n",
- " for (var ind in mpl.extensions) {\n",
- " var fmt = mpl.extensions[ind];\n",
- " var option = $(\n",
- " '', {selected: fmt === mpl.default_extension}).html(fmt);\n",
- " fmt_picker.append(option)\n",
- " }\n",
- "\n",
- " // Add hover states to the ui-buttons\n",
- " $( \".ui-button\" ).hover(\n",
- " function() { $(this).addClass(\"ui-state-hover\");},\n",
- " function() { $(this).removeClass(\"ui-state-hover\");}\n",
- " );\n",
- "\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
- " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
- " // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_message = function(type, properties) {\n",
- " properties['type'] = type;\n",
- " properties['figure_id'] = this.id;\n",
- " this.ws.send(JSON.stringify(properties));\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_draw_message = function() {\n",
- " if (!this.waiting) {\n",
- " this.waiting = true;\n",
- " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
- " }\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " var format_dropdown = fig.format_dropdown;\n",
- " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
- " fig.ondownload(fig, format);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
- " var size = msg['size'];\n",
- " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1]);\n",
- " fig.send_message(\"refresh\", {});\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
- " var x0 = msg['x0'];\n",
- " var y0 = fig.canvas.height - msg['y0'];\n",
- " var x1 = msg['x1'];\n",
- " var y1 = fig.canvas.height - msg['y1'];\n",
- " x0 = Math.floor(x0) + 0.5;\n",
- " y0 = Math.floor(y0) + 0.5;\n",
- " x1 = Math.floor(x1) + 0.5;\n",
- " y1 = Math.floor(y1) + 0.5;\n",
- " var min_x = Math.min(x0, x1);\n",
- " var min_y = Math.min(y0, y1);\n",
- " var width = Math.abs(x1 - x0);\n",
- " var height = Math.abs(y1 - y0);\n",
- "\n",
- " fig.rubberband_context.clearRect(\n",
- " 0, 0, fig.canvas.width, fig.canvas.height);\n",
- "\n",
- " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
- " // Updates the figure title.\n",
- " fig.header.textContent = msg['label'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch(cursor)\n",
- " {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
- " fig.message.textContent = msg['message'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
- " // Request the server to send over a new figure.\n",
- " fig.send_draw_message();\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
- " fig.image_mode = msg['mode'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Called whenever the canvas gets updated.\n",
- " this.send_message(\"ack\", {});\n",
- "}\n",
- "\n",
- "// A function to construct a web socket function for onmessage handling.\n",
- "// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
- " return function socket_on_message(evt) {\n",
- " if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = \"image/png\";\n",
- "\n",
- " /* Free the memory for the previous frames */\n",
- " if (fig.imageObj.src) {\n",
- " (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src);\n",
- " }\n",
- "\n",
- " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data);\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
- " fig.imageObj.src = evt.data;\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- "\n",
- " var msg = JSON.parse(evt.data);\n",
- " var msg_type = msg['type'];\n",
- "\n",
- " // Call the \"handle_{type}\" callback, which takes\n",
- " // the figure and JSON message as its only arguments.\n",
- " try {\n",
- " var callback = fig[\"handle_\" + msg_type];\n",
- " } catch (e) {\n",
- " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
- " return;\n",
- " }\n",
- "\n",
- " if (callback) {\n",
- " try {\n",
- " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
- " callback(fig, msg);\n",
- " } catch (e) {\n",
- " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
- " }\n",
- " }\n",
- " };\n",
- "}\n",
- "\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function(e) {\n",
- " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
- " var targ;\n",
- " if (!e)\n",
- " e = window.event;\n",
- " if (e.target)\n",
- " targ = e.target;\n",
- " else if (e.srcElement)\n",
- " targ = e.srcElement;\n",
- " if (targ.nodeType == 3) // defeat Safari bug\n",
- " targ = targ.parentNode;\n",
- "\n",
- " // jQuery normalizes the pageX and pageY\n",
- " // pageX,Y are the mouse positions relative to the document\n",
- " // offset() returns the position of the element relative to the document\n",
- " var x = e.pageX - $(targ).offset().left;\n",
- " var y = e.pageY - $(targ).offset().top;\n",
- "\n",
- " return {\"x\": x, \"y\": y};\n",
- "};\n",
- "\n",
- "/*\n",
- " * return a copy of an object with only non-object keys\n",
- " * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
- " */\n",
- "function simpleKeys (original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object')\n",
- " obj[key] = original[key]\n",
- " return obj;\n",
- " }, {});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.mouse_event = function(event, name) {\n",
- " var canvas_pos = mpl.findpos(event)\n",
- "\n",
- " if (name === 'button_press')\n",
- " {\n",
- " this.canvas.focus();\n",
- " this.canvas_div.focus();\n",
- " }\n",
- "\n",
- " var x = canvas_pos.x;\n",
- " var y = canvas_pos.y;\n",
- "\n",
- " this.send_message(name, {x: x, y: y, button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event)});\n",
- "\n",
- " /* This prevents the web browser from automatically changing to\n",
- " * the text insertion cursor when the button is pressed. We want\n",
- " * to control all of the cursor setting manually through the\n",
- " * 'cursor' event from matplotlib */\n",
- " event.preventDefault();\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " // Handle any extra behaviour associated with a key event\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.key_event = function(event, name) {\n",
- "\n",
- " // Prevent repeat events\n",
- " if (name == 'key_press')\n",
- " {\n",
- " if (event.which === this._key)\n",
- " return;\n",
- " else\n",
- " this._key = event.which;\n",
- " }\n",
- " if (name == 'key_release')\n",
- " this._key = null;\n",
- "\n",
- " var value = '';\n",
- " if (event.ctrlKey && event.which != 17)\n",
- " value += \"ctrl+\";\n",
- " if (event.altKey && event.which != 18)\n",
- " value += \"alt+\";\n",
- " if (event.shiftKey && event.which != 16)\n",
- " value += \"shift+\";\n",
- "\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
- "\n",
- " this._key_event_extra(event, name);\n",
- "\n",
- " this.send_message(name, {key: value,\n",
- " guiEvent: simpleKeys(event)});\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
- " if (name == 'download') {\n",
- " this.handle_save(this, null);\n",
- " } else {\n",
- " this.send_message(\"toolbar_button\", {name: name});\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
- " this.message.textContent = tooltip;\n",
- "};\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
- "\n",
- "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
- "\n",
- "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
- " // Create a \"websocket\"-like object which calls the given IPython comm\n",
- " // object with the appropriate methods. Currently this is a non binary\n",
- " // socket, so there is still some room for performance tuning.\n",
- " var ws = {};\n",
- "\n",
- " ws.close = function() {\n",
- " comm.close()\n",
- " };\n",
- " ws.send = function(m) {\n",
- " //console.log('sending', m);\n",
- " comm.send(m);\n",
- " };\n",
- " // Register the callback with on_msg.\n",
- " comm.on_msg(function(msg) {\n",
- " //console.log('receiving', msg['content']['data'], msg);\n",
- " // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data'])\n",
- " });\n",
- " return ws;\n",
- "}\n",
- "\n",
- "mpl.mpl_figure_comm = function(comm, msg) {\n",
- " // This is the function which gets called when the mpl process\n",
- " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
- "\n",
- " var id = msg.content.data.id;\n",
- " // Get hold of the div created by the display call when the Comm\n",
- " // socket was opened in Python.\n",
- " var element = $(\"#\" + id);\n",
- " var ws_proxy = comm_websocket_adapter(comm)\n",
- "\n",
- " function ondownload(figure, format) {\n",
- " window.open(figure.imageObj.src);\n",
- " }\n",
- "\n",
- " var fig = new mpl.figure(id, ws_proxy,\n",
- " ondownload,\n",
- " element.get(0));\n",
- "\n",
- " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
- " // web socket which is closed, not our websocket->open comm proxy.\n",
- " ws_proxy.onopen();\n",
- "\n",
- " fig.parent_element = element.get(0);\n",
- " fig.cell_info = mpl.find_output_cell(\"\");\n",
- " if (!fig.cell_info) {\n",
- " console.error(\"Failed to find cell for figure\", id, fig);\n",
- " return;\n",
- " }\n",
- "\n",
- " var output_index = fig.cell_info[2]\n",
- " var cell = fig.cell_info[0];\n",
- "\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
- " fig.root.unbind('remove')\n",
- "\n",
- " // Update the output cell to use the data from the current canvas.\n",
- " fig.push_to_output();\n",
- " var dataURL = fig.canvas.toDataURL();\n",
- " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
- " // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable()\n",
- " $(fig.parent_element).html('');\n",
- " fig.close_ws(fig, msg);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
- " fig.send_message('closing', msg);\n",
- " // fig.ws.close()\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
- " // Turn the data on the canvas into data in the output cell.\n",
- " var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] = '';\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Tell IPython that the notebook contents must change.\n",
- " IPython.notebook.set_dirty(true);\n",
- " this.send_message(\"ack\", {});\n",
- " var fig = this;\n",
- " // Wait a second, then push the new image to the DOM so\n",
- " // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () { fig.push_to_output() }, 1000);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items){\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) { continue; };\n",
- "\n",
- " var button = $('');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " // Add the status bar.\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "\n",
- " // Add the close button to the window.\n",
- " var buttongrp = $('');\n",
- " var button = $('');\n",
- " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
- " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
- " buttongrp.append(button);\n",
- " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
- " titlebar.prepend(buttongrp);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(el){\n",
- " var fig = this\n",
- " el.on(\"remove\", function(){\n",
- "\tfig.close_ws(fig, {});\n",
- " });\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(el){\n",
- " // this is important to make the div 'focusable\n",
- " el.attr('tabindex', 0)\n",
- " // reach out to IPython and tell the keyboard manager to turn it's self\n",
- " // off when our div gets focus\n",
- "\n",
- " // location in version 3\n",
- " if (IPython.notebook.keyboard_manager) {\n",
- " IPython.notebook.keyboard_manager.register_events(el);\n",
- " }\n",
- " else {\n",
- " // location in version 2\n",
- " IPython.keyboard_manager.register_events(el);\n",
- " }\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager)\n",
- " manager = IPython.keyboard_manager;\n",
- "\n",
- " // Check for shift+enter\n",
- " if (event.shiftKey && event.which == 13) {\n",
- " this.canvas_div.blur();\n",
- " event.shiftKey = false;\n",
- " // Send a \"J\" for go to next cell\n",
- " event.which = 74;\n",
- " event.keyCode = 74;\n",
- " manager.command_mode();\n",
- " manager.handle_keydown(event);\n",
- " }\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " fig.ondownload(fig, null);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.find_output_cell = function(html_output) {\n",
- " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
- " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
- " // IPython event is triggered only after the cells have been serialised, which for\n",
- " // our purposes (turning an active figure into a static one), is too late.\n",
- " var cells = IPython.notebook.get_cells();\n",
- " var ncells = cells.length;\n",
- " for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
- " data = data.data;\n",
- " }\n",
- " if (data['text/html'] == html_output) {\n",
- " return [cell, data, j];\n",
- " }\n",
- " }\n",
- " }\n",
- " }\n",
- "}\n",
- "\n",
- "// Register the function which deals with the matplotlib target/channel.\n",
- "// The kernel may be null if the page has been refreshed.\n",
- "if (IPython.notebook.kernel != null) {\n",
- " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
- "}\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- ""
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "phat0_ci = [-0.87319166 -0.40162685]\n"
- ]
- }
- ],
- "source": [
- "# Better CI for phat.par[i=0] shape parameter\n",
- "Lp0 = phat.profile(i=0, pmin=-1,pmax=1)\n",
- "fig, axes = plt.subplots()\n",
- "Lp0.plot()\n",
- "phat0_ci = Lp0.get_bounds(alpha=0.1)\n",
- "print('phat0_ci = {}'.format(phat0_ci))\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "application/javascript": [
- "/* Put everything inside the global mpl namespace */\n",
- "window.mpl = {};\n",
- "\n",
- "mpl.get_websocket_type = function() {\n",
- " if (typeof(WebSocket) !== 'undefined') {\n",
- " return WebSocket;\n",
- " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
- " return MozWebSocket;\n",
- " } else {\n",
- " alert('Your browser does not have WebSocket support.' +\n",
- " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
- " 'Firefox 4 and 5 are also supported but you ' +\n",
- " 'have to enable WebSockets in about:config.');\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
- " this.id = figure_id;\n",
- "\n",
- " this.ws = websocket;\n",
- "\n",
- " this.supports_binary = (this.ws.binaryType != undefined);\n",
- "\n",
- " if (!this.supports_binary) {\n",
- " var warnings = document.getElementById(\"mpl-warnings\");\n",
- " if (warnings) {\n",
- " warnings.style.display = 'block';\n",
- " warnings.textContent = (\n",
- " \"This browser does not support binary websocket messages. \" +\n",
- " \"Performance may be slow.\");\n",
- " }\n",
- " }\n",
- "\n",
- " this.imageObj = new Image();\n",
- "\n",
- " this.context = undefined;\n",
- " this.message = undefined;\n",
- " this.canvas = undefined;\n",
- " this.rubberband_canvas = undefined;\n",
- " this.rubberband_context = undefined;\n",
- " this.format_dropdown = undefined;\n",
- "\n",
- " this.image_mode = 'full';\n",
- "\n",
- " this.root = $('');\n",
- " this._root_extra_style(this.root)\n",
- " this.root.attr('style', 'display: inline-block');\n",
- "\n",
- " $(parent_element).append(this.root);\n",
- "\n",
- " this._init_header(this);\n",
- " this._init_canvas(this);\n",
- " this._init_toolbar(this);\n",
- "\n",
- " var fig = this;\n",
- "\n",
- " this.waiting = false;\n",
- "\n",
- " this.ws.onopen = function () {\n",
- " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
- " fig.send_message(\"send_image_mode\", {});\n",
- " fig.send_message(\"refresh\", {});\n",
- " }\n",
- "\n",
- " this.imageObj.onload = function() {\n",
- " if (fig.image_mode == 'full') {\n",
- " // Full images could contain transparency (where diff images\n",
- " // almost always do), so we need to clear the canvas so that\n",
- " // there is no ghosting.\n",
- " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
- " }\n",
- " fig.context.drawImage(fig.imageObj, 0, 0);\n",
- " };\n",
- "\n",
- " this.imageObj.onunload = function() {\n",
- " this.ws.close();\n",
- " }\n",
- "\n",
- " this.ws.onmessage = this._make_on_message_function(this);\n",
- "\n",
- " this.ondownload = ondownload;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_header = function() {\n",
- " var titlebar = $(\n",
- " '');\n",
- " var titletext = $(\n",
- " '');\n",
- " titlebar.append(titletext)\n",
- " this.root.append(titlebar);\n",
- " this.header = titletext[0];\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_canvas = function() {\n",
- " var fig = this;\n",
- "\n",
- " var canvas_div = $('');\n",
- "\n",
- " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
- "\n",
- " function canvas_keyboard_event(event) {\n",
- " return fig.key_event(event, event['data']);\n",
- " }\n",
- "\n",
- " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
- " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
- " this.canvas_div = canvas_div\n",
- " this._canvas_extra_style(canvas_div)\n",
- " this.root.append(canvas_div);\n",
- "\n",
- " var canvas = $('');\n",
- " canvas.addClass('mpl-canvas');\n",
- " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
- "\n",
- " this.canvas = canvas[0];\n",
- " this.context = canvas[0].getContext(\"2d\");\n",
- "\n",
- " var rubberband = $('');\n",
- " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
- "\n",
- " var pass_mouse_events = true;\n",
- "\n",
- " canvas_div.resizable({\n",
- " start: function(event, ui) {\n",
- " pass_mouse_events = false;\n",
- " },\n",
- " resize: function(event, ui) {\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " stop: function(event, ui) {\n",
- " pass_mouse_events = true;\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " });\n",
- "\n",
- " function mouse_event_fn(event) {\n",
- " if (pass_mouse_events)\n",
- " return fig.mouse_event(event, event['data']);\n",
- " }\n",
- "\n",
- " rubberband.mousedown('button_press', mouse_event_fn);\n",
- " rubberband.mouseup('button_release', mouse_event_fn);\n",
- " // Throttle sequential mouse events to 1 every 20ms.\n",
- " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
- "\n",
- " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
- " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
- "\n",
- " canvas_div.on(\"wheel\", function (event) {\n",
- " event = event.originalEvent;\n",
- " event['data'] = 'scroll'\n",
- " if (event.deltaY < 0) {\n",
- " event.step = 1;\n",
- " } else {\n",
- " event.step = -1;\n",
- " }\n",
- " mouse_event_fn(event);\n",
- " });\n",
- "\n",
- " canvas_div.append(canvas);\n",
- " canvas_div.append(rubberband);\n",
- "\n",
- " this.rubberband = rubberband;\n",
- " this.rubberband_canvas = rubberband[0];\n",
- " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
- " this.rubberband_context.strokeStyle = \"#000000\";\n",
- "\n",
- " this._resize_canvas = function(width, height) {\n",
- " // Keep the size of the canvas, canvas container, and rubber band\n",
- " // canvas in synch.\n",
- " canvas_div.css('width', width)\n",
- " canvas_div.css('height', height)\n",
- "\n",
- " canvas.attr('width', width);\n",
- " canvas.attr('height', height);\n",
- "\n",
- " rubberband.attr('width', width);\n",
- " rubberband.attr('height', height);\n",
- " }\n",
- "\n",
- " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
- " // upon first draw.\n",
- " this._resize_canvas(600, 600);\n",
- "\n",
- " // Disable right mouse context menu.\n",
- " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
- " return false;\n",
- " });\n",
- "\n",
- " function set_focus () {\n",
- " canvas.focus();\n",
- " canvas_div.focus();\n",
- " }\n",
- "\n",
- " window.setTimeout(set_focus, 100);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " // put a spacer in here.\n",
- " continue;\n",
- " }\n",
- " var button = $('');\n",
- " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
- " 'ui-button-icon-only');\n",
- " button.attr('role', 'button');\n",
- " button.attr('aria-disabled', 'false');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- "\n",
- " var icon_img = $('');\n",
- " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
- " icon_img.addClass(image);\n",
- " icon_img.addClass('ui-corner-all');\n",
- "\n",
- " var tooltip_span = $('');\n",
- " tooltip_span.addClass('ui-button-text');\n",
- " tooltip_span.html(tooltip);\n",
- "\n",
- " button.append(icon_img);\n",
- " button.append(tooltip_span);\n",
- "\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " var fmt_picker_span = $('');\n",
- "\n",
- " var fmt_picker = $('');\n",
- " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
- " fmt_picker_span.append(fmt_picker);\n",
- " nav_element.append(fmt_picker_span);\n",
- " this.format_dropdown = fmt_picker[0];\n",
- "\n",
- " for (var ind in mpl.extensions) {\n",
- " var fmt = mpl.extensions[ind];\n",
- " var option = $(\n",
- " '', {selected: fmt === mpl.default_extension}).html(fmt);\n",
- " fmt_picker.append(option)\n",
- " }\n",
- "\n",
- " // Add hover states to the ui-buttons\n",
- " $( \".ui-button\" ).hover(\n",
- " function() { $(this).addClass(\"ui-state-hover\");},\n",
- " function() { $(this).removeClass(\"ui-state-hover\");}\n",
- " );\n",
- "\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
- " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
- " // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_message = function(type, properties) {\n",
- " properties['type'] = type;\n",
- " properties['figure_id'] = this.id;\n",
- " this.ws.send(JSON.stringify(properties));\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_draw_message = function() {\n",
- " if (!this.waiting) {\n",
- " this.waiting = true;\n",
- " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
- " }\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " var format_dropdown = fig.format_dropdown;\n",
- " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
- " fig.ondownload(fig, format);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
- " var size = msg['size'];\n",
- " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1]);\n",
- " fig.send_message(\"refresh\", {});\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
- " var x0 = msg['x0'];\n",
- " var y0 = fig.canvas.height - msg['y0'];\n",
- " var x1 = msg['x1'];\n",
- " var y1 = fig.canvas.height - msg['y1'];\n",
- " x0 = Math.floor(x0) + 0.5;\n",
- " y0 = Math.floor(y0) + 0.5;\n",
- " x1 = Math.floor(x1) + 0.5;\n",
- " y1 = Math.floor(y1) + 0.5;\n",
- " var min_x = Math.min(x0, x1);\n",
- " var min_y = Math.min(y0, y1);\n",
- " var width = Math.abs(x1 - x0);\n",
- " var height = Math.abs(y1 - y0);\n",
- "\n",
- " fig.rubberband_context.clearRect(\n",
- " 0, 0, fig.canvas.width, fig.canvas.height);\n",
- "\n",
- " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
- " // Updates the figure title.\n",
- " fig.header.textContent = msg['label'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch(cursor)\n",
- " {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
- " fig.message.textContent = msg['message'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
- " // Request the server to send over a new figure.\n",
- " fig.send_draw_message();\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
- " fig.image_mode = msg['mode'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Called whenever the canvas gets updated.\n",
- " this.send_message(\"ack\", {});\n",
- "}\n",
- "\n",
- "// A function to construct a web socket function for onmessage handling.\n",
- "// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
- " return function socket_on_message(evt) {\n",
- " if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = \"image/png\";\n",
- "\n",
- " /* Free the memory for the previous frames */\n",
- " if (fig.imageObj.src) {\n",
- " (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src);\n",
- " }\n",
- "\n",
- " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data);\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
- " fig.imageObj.src = evt.data;\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- "\n",
- " var msg = JSON.parse(evt.data);\n",
- " var msg_type = msg['type'];\n",
- "\n",
- " // Call the \"handle_{type}\" callback, which takes\n",
- " // the figure and JSON message as its only arguments.\n",
- " try {\n",
- " var callback = fig[\"handle_\" + msg_type];\n",
- " } catch (e) {\n",
- " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
- " return;\n",
- " }\n",
- "\n",
- " if (callback) {\n",
- " try {\n",
- " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
- " callback(fig, msg);\n",
- " } catch (e) {\n",
- " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
- " }\n",
- " }\n",
- " };\n",
- "}\n",
- "\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function(e) {\n",
- " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
- " var targ;\n",
- " if (!e)\n",
- " e = window.event;\n",
- " if (e.target)\n",
- " targ = e.target;\n",
- " else if (e.srcElement)\n",
- " targ = e.srcElement;\n",
- " if (targ.nodeType == 3) // defeat Safari bug\n",
- " targ = targ.parentNode;\n",
- "\n",
- " // jQuery normalizes the pageX and pageY\n",
- " // pageX,Y are the mouse positions relative to the document\n",
- " // offset() returns the position of the element relative to the document\n",
- " var x = e.pageX - $(targ).offset().left;\n",
- " var y = e.pageY - $(targ).offset().top;\n",
- "\n",
- " return {\"x\": x, \"y\": y};\n",
- "};\n",
- "\n",
- "/*\n",
- " * return a copy of an object with only non-object keys\n",
- " * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
- " */\n",
- "function simpleKeys (original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object')\n",
- " obj[key] = original[key]\n",
- " return obj;\n",
- " }, {});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.mouse_event = function(event, name) {\n",
- " var canvas_pos = mpl.findpos(event)\n",
- "\n",
- " if (name === 'button_press')\n",
- " {\n",
- " this.canvas.focus();\n",
- " this.canvas_div.focus();\n",
- " }\n",
- "\n",
- " var x = canvas_pos.x;\n",
- " var y = canvas_pos.y;\n",
- "\n",
- " this.send_message(name, {x: x, y: y, button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event)});\n",
- "\n",
- " /* This prevents the web browser from automatically changing to\n",
- " * the text insertion cursor when the button is pressed. We want\n",
- " * to control all of the cursor setting manually through the\n",
- " * 'cursor' event from matplotlib */\n",
- " event.preventDefault();\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " // Handle any extra behaviour associated with a key event\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.key_event = function(event, name) {\n",
- "\n",
- " // Prevent repeat events\n",
- " if (name == 'key_press')\n",
- " {\n",
- " if (event.which === this._key)\n",
- " return;\n",
- " else\n",
- " this._key = event.which;\n",
- " }\n",
- " if (name == 'key_release')\n",
- " this._key = null;\n",
- "\n",
- " var value = '';\n",
- " if (event.ctrlKey && event.which != 17)\n",
- " value += \"ctrl+\";\n",
- " if (event.altKey && event.which != 18)\n",
- " value += \"alt+\";\n",
- " if (event.shiftKey && event.which != 16)\n",
- " value += \"shift+\";\n",
- "\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
- "\n",
- " this._key_event_extra(event, name);\n",
- "\n",
- " this.send_message(name, {key: value,\n",
- " guiEvent: simpleKeys(event)});\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
- " if (name == 'download') {\n",
- " this.handle_save(this, null);\n",
- " } else {\n",
- " this.send_message(\"toolbar_button\", {name: name});\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
- " this.message.textContent = tooltip;\n",
- "};\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
- "\n",
- "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
- "\n",
- "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
- " // Create a \"websocket\"-like object which calls the given IPython comm\n",
- " // object with the appropriate methods. Currently this is a non binary\n",
- " // socket, so there is still some room for performance tuning.\n",
- " var ws = {};\n",
- "\n",
- " ws.close = function() {\n",
- " comm.close()\n",
- " };\n",
- " ws.send = function(m) {\n",
- " //console.log('sending', m);\n",
- " comm.send(m);\n",
- " };\n",
- " // Register the callback with on_msg.\n",
- " comm.on_msg(function(msg) {\n",
- " //console.log('receiving', msg['content']['data'], msg);\n",
- " // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data'])\n",
- " });\n",
- " return ws;\n",
- "}\n",
- "\n",
- "mpl.mpl_figure_comm = function(comm, msg) {\n",
- " // This is the function which gets called when the mpl process\n",
- " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
- "\n",
- " var id = msg.content.data.id;\n",
- " // Get hold of the div created by the display call when the Comm\n",
- " // socket was opened in Python.\n",
- " var element = $(\"#\" + id);\n",
- " var ws_proxy = comm_websocket_adapter(comm)\n",
- "\n",
- " function ondownload(figure, format) {\n",
- " window.open(figure.imageObj.src);\n",
- " }\n",
- "\n",
- " var fig = new mpl.figure(id, ws_proxy,\n",
- " ondownload,\n",
- " element.get(0));\n",
- "\n",
- " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
- " // web socket which is closed, not our websocket->open comm proxy.\n",
- " ws_proxy.onopen();\n",
- "\n",
- " fig.parent_element = element.get(0);\n",
- " fig.cell_info = mpl.find_output_cell(\"\");\n",
- " if (!fig.cell_info) {\n",
- " console.error(\"Failed to find cell for figure\", id, fig);\n",
- " return;\n",
- " }\n",
- "\n",
- " var output_index = fig.cell_info[2]\n",
- " var cell = fig.cell_info[0];\n",
- "\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
- " fig.root.unbind('remove')\n",
- "\n",
- " // Update the output cell to use the data from the current canvas.\n",
- " fig.push_to_output();\n",
- " var dataURL = fig.canvas.toDataURL();\n",
- " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
- " // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable()\n",
- " $(fig.parent_element).html('');\n",
- " fig.close_ws(fig, msg);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
- " fig.send_message('closing', msg);\n",
- " // fig.ws.close()\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
- " // Turn the data on the canvas into data in the output cell.\n",
- " var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] = '';\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Tell IPython that the notebook contents must change.\n",
- " IPython.notebook.set_dirty(true);\n",
- " this.send_message(\"ack\", {});\n",
- " var fig = this;\n",
- " // Wait a second, then push the new image to the DOM so\n",
- " // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () { fig.push_to_output() }, 1000);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items){\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) { continue; };\n",
- "\n",
- " var button = $('');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " // Add the status bar.\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "\n",
- " // Add the close button to the window.\n",
- " var buttongrp = $('');\n",
- " var button = $('');\n",
- " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
- " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
- " buttongrp.append(button);\n",
- " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
- " titlebar.prepend(buttongrp);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(el){\n",
- " var fig = this\n",
- " el.on(\"remove\", function(){\n",
- "\tfig.close_ws(fig, {});\n",
- " });\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(el){\n",
- " // this is important to make the div 'focusable\n",
- " el.attr('tabindex', 0)\n",
- " // reach out to IPython and tell the keyboard manager to turn it's self\n",
- " // off when our div gets focus\n",
- "\n",
- " // location in version 3\n",
- " if (IPython.notebook.keyboard_manager) {\n",
- " IPython.notebook.keyboard_manager.register_events(el);\n",
- " }\n",
- " else {\n",
- " // location in version 2\n",
- " IPython.keyboard_manager.register_events(el);\n",
- " }\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager)\n",
- " manager = IPython.keyboard_manager;\n",
- "\n",
- " // Check for shift+enter\n",
- " if (event.shiftKey && event.which == 13) {\n",
- " this.canvas_div.blur();\n",
- " event.shiftKey = false;\n",
- " // Send a \"J\" for go to next cell\n",
- " event.which = 74;\n",
- " event.keyCode = 74;\n",
- " manager.command_mode();\n",
- " manager.handle_keydown(event);\n",
- " }\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " fig.ondownload(fig, null);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.find_output_cell = function(html_output) {\n",
- " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
- " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
- " // IPython event is triggered only after the cells have been serialised, which for\n",
- " // our purposes (turning an active figure into a static one), is too late.\n",
- " var cells = IPython.notebook.get_cells();\n",
- " var ncells = cells.length;\n",
- " for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
- " data = data.data;\n",
- " }\n",
- " if (data['text/html'] == html_output) {\n",
- " return [cell, data, j];\n",
- " }\n",
- " }\n",
- " }\n",
- " }\n",
- "}\n",
- "\n",
- "// Register the function which deals with the matplotlib target/channel.\n",
- "// The kernel may be null if the page has been refreshed.\n",
- "if (IPython.notebook.kernel != null) {\n",
- " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
- "}\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- ""
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "phat2_ci = [ 0.66621166 1.17244546]\n"
- ]
- }
- ],
- "source": [
- "# Better CI for phat.par[i=2] scale \n",
- "Lp2 = phat.profile(i=2,pmin=0.1,pmax=2)\n",
- "fig, axes = plt.subplots()\n",
- "Lp2.plot()\n",
- "phat2_ci = Lp2.get_bounds(alpha=0.1)\n",
- "print('phat2_ci = {}'.format(phat2_ci))\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\pab\\workspace\\git_pywafo\\wafo\\stats\\estimation.py:343: UserWarning: The fitted parameters does not provide the optimum fit. Something wrong with fit (par = [-0.63409889 0.5 1.87567842])\n",
- " 'Something wrong with fit (par = {})'.format(str(par)))\n"
- ]
- },
- {
- "data": {
- "application/javascript": [
- "/* Put everything inside the global mpl namespace */\n",
- "window.mpl = {};\n",
- "\n",
- "mpl.get_websocket_type = function() {\n",
- " if (typeof(WebSocket) !== 'undefined') {\n",
- " return WebSocket;\n",
- " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
- " return MozWebSocket;\n",
- " } else {\n",
- " alert('Your browser does not have WebSocket support.' +\n",
- " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
- " 'Firefox 4 and 5 are also supported but you ' +\n",
- " 'have to enable WebSockets in about:config.');\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
- " this.id = figure_id;\n",
- "\n",
- " this.ws = websocket;\n",
- "\n",
- " this.supports_binary = (this.ws.binaryType != undefined);\n",
- "\n",
- " if (!this.supports_binary) {\n",
- " var warnings = document.getElementById(\"mpl-warnings\");\n",
- " if (warnings) {\n",
- " warnings.style.display = 'block';\n",
- " warnings.textContent = (\n",
- " \"This browser does not support binary websocket messages. \" +\n",
- " \"Performance may be slow.\");\n",
- " }\n",
- " }\n",
- "\n",
- " this.imageObj = new Image();\n",
- "\n",
- " this.context = undefined;\n",
- " this.message = undefined;\n",
- " this.canvas = undefined;\n",
- " this.rubberband_canvas = undefined;\n",
- " this.rubberband_context = undefined;\n",
- " this.format_dropdown = undefined;\n",
- "\n",
- " this.image_mode = 'full';\n",
- "\n",
- " this.root = $('');\n",
- " this._root_extra_style(this.root)\n",
- " this.root.attr('style', 'display: inline-block');\n",
- "\n",
- " $(parent_element).append(this.root);\n",
- "\n",
- " this._init_header(this);\n",
- " this._init_canvas(this);\n",
- " this._init_toolbar(this);\n",
- "\n",
- " var fig = this;\n",
- "\n",
- " this.waiting = false;\n",
- "\n",
- " this.ws.onopen = function () {\n",
- " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
- " fig.send_message(\"send_image_mode\", {});\n",
- " fig.send_message(\"refresh\", {});\n",
- " }\n",
- "\n",
- " this.imageObj.onload = function() {\n",
- " if (fig.image_mode == 'full') {\n",
- " // Full images could contain transparency (where diff images\n",
- " // almost always do), so we need to clear the canvas so that\n",
- " // there is no ghosting.\n",
- " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
- " }\n",
- " fig.context.drawImage(fig.imageObj, 0, 0);\n",
- " };\n",
- "\n",
- " this.imageObj.onunload = function() {\n",
- " this.ws.close();\n",
- " }\n",
- "\n",
- " this.ws.onmessage = this._make_on_message_function(this);\n",
- "\n",
- " this.ondownload = ondownload;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_header = function() {\n",
- " var titlebar = $(\n",
- " '');\n",
- " var titletext = $(\n",
- " '');\n",
- " titlebar.append(titletext)\n",
- " this.root.append(titlebar);\n",
- " this.header = titletext[0];\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_canvas = function() {\n",
- " var fig = this;\n",
- "\n",
- " var canvas_div = $('');\n",
- "\n",
- " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
- "\n",
- " function canvas_keyboard_event(event) {\n",
- " return fig.key_event(event, event['data']);\n",
- " }\n",
- "\n",
- " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
- " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
- " this.canvas_div = canvas_div\n",
- " this._canvas_extra_style(canvas_div)\n",
- " this.root.append(canvas_div);\n",
- "\n",
- " var canvas = $('');\n",
- " canvas.addClass('mpl-canvas');\n",
- " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
- "\n",
- " this.canvas = canvas[0];\n",
- " this.context = canvas[0].getContext(\"2d\");\n",
- "\n",
- " var rubberband = $('');\n",
- " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
- "\n",
- " var pass_mouse_events = true;\n",
- "\n",
- " canvas_div.resizable({\n",
- " start: function(event, ui) {\n",
- " pass_mouse_events = false;\n",
- " },\n",
- " resize: function(event, ui) {\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " stop: function(event, ui) {\n",
- " pass_mouse_events = true;\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " });\n",
- "\n",
- " function mouse_event_fn(event) {\n",
- " if (pass_mouse_events)\n",
- " return fig.mouse_event(event, event['data']);\n",
- " }\n",
- "\n",
- " rubberband.mousedown('button_press', mouse_event_fn);\n",
- " rubberband.mouseup('button_release', mouse_event_fn);\n",
- " // Throttle sequential mouse events to 1 every 20ms.\n",
- " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
- "\n",
- " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
- " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
- "\n",
- " canvas_div.on(\"wheel\", function (event) {\n",
- " event = event.originalEvent;\n",
- " event['data'] = 'scroll'\n",
- " if (event.deltaY < 0) {\n",
- " event.step = 1;\n",
- " } else {\n",
- " event.step = -1;\n",
- " }\n",
- " mouse_event_fn(event);\n",
- " });\n",
- "\n",
- " canvas_div.append(canvas);\n",
- " canvas_div.append(rubberband);\n",
- "\n",
- " this.rubberband = rubberband;\n",
- " this.rubberband_canvas = rubberband[0];\n",
- " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
- " this.rubberband_context.strokeStyle = \"#000000\";\n",
- "\n",
- " this._resize_canvas = function(width, height) {\n",
- " // Keep the size of the canvas, canvas container, and rubber band\n",
- " // canvas in synch.\n",
- " canvas_div.css('width', width)\n",
- " canvas_div.css('height', height)\n",
- "\n",
- " canvas.attr('width', width);\n",
- " canvas.attr('height', height);\n",
- "\n",
- " rubberband.attr('width', width);\n",
- " rubberband.attr('height', height);\n",
- " }\n",
- "\n",
- " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
- " // upon first draw.\n",
- " this._resize_canvas(600, 600);\n",
- "\n",
- " // Disable right mouse context menu.\n",
- " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
- " return false;\n",
- " });\n",
- "\n",
- " function set_focus () {\n",
- " canvas.focus();\n",
- " canvas_div.focus();\n",
- " }\n",
- "\n",
- " window.setTimeout(set_focus, 100);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " // put a spacer in here.\n",
- " continue;\n",
- " }\n",
- " var button = $('');\n",
- " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
- " 'ui-button-icon-only');\n",
- " button.attr('role', 'button');\n",
- " button.attr('aria-disabled', 'false');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- "\n",
- " var icon_img = $('');\n",
- " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
- " icon_img.addClass(image);\n",
- " icon_img.addClass('ui-corner-all');\n",
- "\n",
- " var tooltip_span = $('');\n",
- " tooltip_span.addClass('ui-button-text');\n",
- " tooltip_span.html(tooltip);\n",
- "\n",
- " button.append(icon_img);\n",
- " button.append(tooltip_span);\n",
- "\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " var fmt_picker_span = $('');\n",
- "\n",
- " var fmt_picker = $('');\n",
- " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
- " fmt_picker_span.append(fmt_picker);\n",
- " nav_element.append(fmt_picker_span);\n",
- " this.format_dropdown = fmt_picker[0];\n",
- "\n",
- " for (var ind in mpl.extensions) {\n",
- " var fmt = mpl.extensions[ind];\n",
- " var option = $(\n",
- " '', {selected: fmt === mpl.default_extension}).html(fmt);\n",
- " fmt_picker.append(option)\n",
- " }\n",
- "\n",
- " // Add hover states to the ui-buttons\n",
- " $( \".ui-button\" ).hover(\n",
- " function() { $(this).addClass(\"ui-state-hover\");},\n",
- " function() { $(this).removeClass(\"ui-state-hover\");}\n",
- " );\n",
- "\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
- " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
- " // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_message = function(type, properties) {\n",
- " properties['type'] = type;\n",
- " properties['figure_id'] = this.id;\n",
- " this.ws.send(JSON.stringify(properties));\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_draw_message = function() {\n",
- " if (!this.waiting) {\n",
- " this.waiting = true;\n",
- " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
- " }\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " var format_dropdown = fig.format_dropdown;\n",
- " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
- " fig.ondownload(fig, format);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
- " var size = msg['size'];\n",
- " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1]);\n",
- " fig.send_message(\"refresh\", {});\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
- " var x0 = msg['x0'];\n",
- " var y0 = fig.canvas.height - msg['y0'];\n",
- " var x1 = msg['x1'];\n",
- " var y1 = fig.canvas.height - msg['y1'];\n",
- " x0 = Math.floor(x0) + 0.5;\n",
- " y0 = Math.floor(y0) + 0.5;\n",
- " x1 = Math.floor(x1) + 0.5;\n",
- " y1 = Math.floor(y1) + 0.5;\n",
- " var min_x = Math.min(x0, x1);\n",
- " var min_y = Math.min(y0, y1);\n",
- " var width = Math.abs(x1 - x0);\n",
- " var height = Math.abs(y1 - y0);\n",
- "\n",
- " fig.rubberband_context.clearRect(\n",
- " 0, 0, fig.canvas.width, fig.canvas.height);\n",
- "\n",
- " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
- " // Updates the figure title.\n",
- " fig.header.textContent = msg['label'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch(cursor)\n",
- " {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
- " fig.message.textContent = msg['message'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
- " // Request the server to send over a new figure.\n",
- " fig.send_draw_message();\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
- " fig.image_mode = msg['mode'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Called whenever the canvas gets updated.\n",
- " this.send_message(\"ack\", {});\n",
- "}\n",
- "\n",
- "// A function to construct a web socket function for onmessage handling.\n",
- "// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
- " return function socket_on_message(evt) {\n",
- " if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = \"image/png\";\n",
- "\n",
- " /* Free the memory for the previous frames */\n",
- " if (fig.imageObj.src) {\n",
- " (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src);\n",
- " }\n",
- "\n",
- " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data);\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
- " fig.imageObj.src = evt.data;\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- "\n",
- " var msg = JSON.parse(evt.data);\n",
- " var msg_type = msg['type'];\n",
- "\n",
- " // Call the \"handle_{type}\" callback, which takes\n",
- " // the figure and JSON message as its only arguments.\n",
- " try {\n",
- " var callback = fig[\"handle_\" + msg_type];\n",
- " } catch (e) {\n",
- " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
- " return;\n",
- " }\n",
- "\n",
- " if (callback) {\n",
- " try {\n",
- " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
- " callback(fig, msg);\n",
- " } catch (e) {\n",
- " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
- " }\n",
- " }\n",
- " };\n",
- "}\n",
- "\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function(e) {\n",
- " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
- " var targ;\n",
- " if (!e)\n",
- " e = window.event;\n",
- " if (e.target)\n",
- " targ = e.target;\n",
- " else if (e.srcElement)\n",
- " targ = e.srcElement;\n",
- " if (targ.nodeType == 3) // defeat Safari bug\n",
- " targ = targ.parentNode;\n",
- "\n",
- " // jQuery normalizes the pageX and pageY\n",
- " // pageX,Y are the mouse positions relative to the document\n",
- " // offset() returns the position of the element relative to the document\n",
- " var x = e.pageX - $(targ).offset().left;\n",
- " var y = e.pageY - $(targ).offset().top;\n",
- "\n",
- " return {\"x\": x, \"y\": y};\n",
- "};\n",
- "\n",
- "/*\n",
- " * return a copy of an object with only non-object keys\n",
- " * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
- " */\n",
- "function simpleKeys (original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object')\n",
- " obj[key] = original[key]\n",
- " return obj;\n",
- " }, {});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.mouse_event = function(event, name) {\n",
- " var canvas_pos = mpl.findpos(event)\n",
- "\n",
- " if (name === 'button_press')\n",
- " {\n",
- " this.canvas.focus();\n",
- " this.canvas_div.focus();\n",
- " }\n",
- "\n",
- " var x = canvas_pos.x;\n",
- " var y = canvas_pos.y;\n",
- "\n",
- " this.send_message(name, {x: x, y: y, button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event)});\n",
- "\n",
- " /* This prevents the web browser from automatically changing to\n",
- " * the text insertion cursor when the button is pressed. We want\n",
- " * to control all of the cursor setting manually through the\n",
- " * 'cursor' event from matplotlib */\n",
- " event.preventDefault();\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " // Handle any extra behaviour associated with a key event\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.key_event = function(event, name) {\n",
- "\n",
- " // Prevent repeat events\n",
- " if (name == 'key_press')\n",
- " {\n",
- " if (event.which === this._key)\n",
- " return;\n",
- " else\n",
- " this._key = event.which;\n",
- " }\n",
- " if (name == 'key_release')\n",
- " this._key = null;\n",
- "\n",
- " var value = '';\n",
- " if (event.ctrlKey && event.which != 17)\n",
- " value += \"ctrl+\";\n",
- " if (event.altKey && event.which != 18)\n",
- " value += \"alt+\";\n",
- " if (event.shiftKey && event.which != 16)\n",
- " value += \"shift+\";\n",
- "\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
- "\n",
- " this._key_event_extra(event, name);\n",
- "\n",
- " this.send_message(name, {key: value,\n",
- " guiEvent: simpleKeys(event)});\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
- " if (name == 'download') {\n",
- " this.handle_save(this, null);\n",
- " } else {\n",
- " this.send_message(\"toolbar_button\", {name: name});\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
- " this.message.textContent = tooltip;\n",
- "};\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
- "\n",
- "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
- "\n",
- "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
- " // Create a \"websocket\"-like object which calls the given IPython comm\n",
- " // object with the appropriate methods. Currently this is a non binary\n",
- " // socket, so there is still some room for performance tuning.\n",
- " var ws = {};\n",
- "\n",
- " ws.close = function() {\n",
- " comm.close()\n",
- " };\n",
- " ws.send = function(m) {\n",
- " //console.log('sending', m);\n",
- " comm.send(m);\n",
- " };\n",
- " // Register the callback with on_msg.\n",
- " comm.on_msg(function(msg) {\n",
- " //console.log('receiving', msg['content']['data'], msg);\n",
- " // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data'])\n",
- " });\n",
- " return ws;\n",
- "}\n",
- "\n",
- "mpl.mpl_figure_comm = function(comm, msg) {\n",
- " // This is the function which gets called when the mpl process\n",
- " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
- "\n",
- " var id = msg.content.data.id;\n",
- " // Get hold of the div created by the display call when the Comm\n",
- " // socket was opened in Python.\n",
- " var element = $(\"#\" + id);\n",
- " var ws_proxy = comm_websocket_adapter(comm)\n",
- "\n",
- " function ondownload(figure, format) {\n",
- " window.open(figure.imageObj.src);\n",
- " }\n",
- "\n",
- " var fig = new mpl.figure(id, ws_proxy,\n",
- " ondownload,\n",
- " element.get(0));\n",
- "\n",
- " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
- " // web socket which is closed, not our websocket->open comm proxy.\n",
- " ws_proxy.onopen();\n",
- "\n",
- " fig.parent_element = element.get(0);\n",
- " fig.cell_info = mpl.find_output_cell(\"\");\n",
- " if (!fig.cell_info) {\n",
- " console.error(\"Failed to find cell for figure\", id, fig);\n",
- " return;\n",
- " }\n",
- "\n",
- " var output_index = fig.cell_info[2]\n",
- " var cell = fig.cell_info[0];\n",
- "\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
- " fig.root.unbind('remove')\n",
- "\n",
- " // Update the output cell to use the data from the current canvas.\n",
- " fig.push_to_output();\n",
- " var dataURL = fig.canvas.toDataURL();\n",
- " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
- " // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable()\n",
- " $(fig.parent_element).html('');\n",
- " fig.close_ws(fig, msg);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
- " fig.send_message('closing', msg);\n",
- " // fig.ws.close()\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
- " // Turn the data on the canvas into data in the output cell.\n",
- " var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] = '';\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Tell IPython that the notebook contents must change.\n",
- " IPython.notebook.set_dirty(true);\n",
- " this.send_message(\"ack\", {});\n",
- " var fig = this;\n",
- " // Wait a second, then push the new image to the DOM so\n",
- " // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () { fig.push_to_output() }, 1000);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items){\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) { continue; };\n",
- "\n",
- " var button = $('');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " // Add the status bar.\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "\n",
- " // Add the close button to the window.\n",
- " var buttongrp = $('');\n",
- " var button = $('');\n",
- " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
- " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
- " buttongrp.append(button);\n",
- " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
- " titlebar.prepend(buttongrp);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(el){\n",
- " var fig = this\n",
- " el.on(\"remove\", function(){\n",
- "\tfig.close_ws(fig, {});\n",
- " });\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(el){\n",
- " // this is important to make the div 'focusable\n",
- " el.attr('tabindex', 0)\n",
- " // reach out to IPython and tell the keyboard manager to turn it's self\n",
- " // off when our div gets focus\n",
- "\n",
- " // location in version 3\n",
- " if (IPython.notebook.keyboard_manager) {\n",
- " IPython.notebook.keyboard_manager.register_events(el);\n",
- " }\n",
- " else {\n",
- " // location in version 2\n",
- " IPython.keyboard_manager.register_events(el);\n",
- " }\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager)\n",
- " manager = IPython.keyboard_manager;\n",
- "\n",
- " // Check for shift+enter\n",
- " if (event.shiftKey && event.which == 13) {\n",
- " this.canvas_div.blur();\n",
- " event.shiftKey = false;\n",
- " // Send a \"J\" for go to next cell\n",
- " event.which = 74;\n",
- " event.keyCode = 74;\n",
- " manager.command_mode();\n",
- " manager.handle_keydown(event);\n",
- " }\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " fig.ondownload(fig, null);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.find_output_cell = function(html_output) {\n",
- " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
- " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
- " // IPython event is triggered only after the cells have been serialised, which for\n",
- " // our purposes (turning an active figure into a static one), is too late.\n",
- " var cells = IPython.notebook.get_cells();\n",
- " var ncells = cells.length;\n",
- " for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
- " data = data.data;\n",
- " }\n",
- " if (data['text/html'] == html_output) {\n",
- " return [cell, data, j];\n",
- " }\n",
- " }\n",
- " }\n",
- " }\n",
- "}\n",
- "\n",
- "// Register the function which deals with the matplotlib target/channel.\n",
- "// The kernel may be null if the page has been refreshed.\n",
- "if (IPython.notebook.kernel != null) {\n",
- " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
- "}\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- ""
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "X_ci = [ 1.82924693 2.05375594]\n"
- ]
- }
- ],
- "source": [
- "SF = 1./990\n",
- "x = phat.isf(SF)\n",
- "\n",
- "# CI for x\n",
- "Lx = phat.profile_quantile(x, i=2)\n",
- "fig, axes = plt.subplots()\n",
- "Lx.plot()\n",
- "x_ci = Lx.get_bounds(alpha=0.2)\n",
- "print('X_ci = {}'.format(x_ci))"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 2",
- "language": "python",
- "name": "python2"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 2
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython2",
- "version": "2.7.11"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/wafo/doc/tutorial_scripts/chapter1.py b/wafo/doc/tutorial_scripts/chapter1.py
deleted file mode 100644
index a67dd73..0000000
--- a/wafo/doc/tutorial_scripts/chapter1.py
+++ /dev/null
@@ -1,192 +0,0 @@
-import wafo.plotbackend.plotbackend as plt
-import numpy as np
-# pyreport -o chapter1.html chapter1.py
-
-#! CHAPTER1 demonstrates some applications of WAFO
-#!================================================
-#!
-#! CHAPTER1 gives an overview through examples some of the capabilities of
-#! WAFO. WAFO is a toolbox of Matlab routines for statistical analysis and
-#! simulation of random waves and loads.
-#!
-#! The commands are edited for fast computation.
-
-
-#! Section 1.4 Some applications of WAFO
-#!---------------------------------------
-#! Section 1.4.1 Simulation from spectrum, estimation of spectrum
-#!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-#! Simulation of the sea surface from spectrum
-#! The following code generates 200 seconds of data sampled with 10Hz from
-#! the Torsethaugen spectrum
-import wafo.spectrum.models as wsm
-S = wsm.Torsethaugen(Hm0=6, Tp=8)
-S1 = S.tospecdata()
-S1.plot()
-plt.show()
-
-
-##
-import wafo.objects as wo
-xs = S1.sim(ns=2000, dt=0.1)
-ts = wo.mat2timeseries(xs)
-ts.plot_wave('-')
-plt.show()
-
-
-#! Estimation of spectrum
-#!~~~~~~~~~~~~~~~~~~~~~~~
-#! A common situation is that one wants to estimate the spectrum for wave
-#! measurements. The following code simulate 20 minutes signal sampled at 4Hz
-#! and compare the spectral estimate with the original Torsethaugen spectum.
-plt.clf()
-Fs = 4
-xs = S1.sim(ns=np.fix(20 * 60 * Fs), dt=1. / Fs)
-ts = wo.mat2timeseries(xs)
-Sest = ts.tospecdata(L=400)
-S1.plot()
-Sest.plot('--')
-plt.axis([0, 3, 0, 5])
-plt.show()
-
-#! Section 1.4.2 Probability distributions of wave characteristics.
-#!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-#! Probability distribution of wave trough period:
-#! WAFO gives the possibility of computing the exact probability
-#! distributions for a number of characteristics given a spectral density.
-#! In the following example we study the trough period extracted from the
-#! time series and compared with the theoretical density computed with exact
-#! spectrum, S1, and the estimated spectrum, Sest.
-plt.clf()
-import wafo.misc as wm
-dtyex = S1.to_t_pdf(pdef='Tt', paramt=(0, 10, 51), nit=3)
-dtyest = Sest.to_t_pdf(pdef='Tt', paramt=(0, 10, 51), nit=3)
-
-T, index = ts.wave_periods(vh=0, pdef='d2u')
-bins = wm.good_bins(T, num_bins=25, odd=True)
-wm.plot_histgrm(T, bins=bins, normed=True)
-
-dtyex.plot()
-dtyest.plot('-.')
-plt.axis([0, 10, 0, 0.35])
-plt.show()
-
-#! Section 1.4.3 Directional spectra
-#!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-#! Here are a few lines of code, which produce directional spectra
-#! with frequency independent and frequency dependent spreading.
-plt.clf()
-plotflag = 1
-Nt = 101 # number of angles
-th0 = np.pi / 2 # primary direction of waves
-Sp = 15 # spreading parameter
-
-D1 = wsm.Spreading(type='cos', theta0=th0, method=None)
-D12 = wsm.Spreading(type='cos', theta0=0, method='mitsuyasu')
-
-SD1 = D1.tospecdata2d(S1)
-SD12 = D12.tospecdata2d(S1)
-SD1.plot()
-SD12.plot() # linestyle='dashdot')
-plt.show()
-
-#! 3D Simulation of the sea surface
-#!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-#! The simulations show that frequency dependent spreading leads to
-#! much more irregular surface so the orientation of waves is less
-#! transparent compared to the frequency independent case.
-#
-#! Frequency independent spreading
-#plotflag = 1; iseed = 1;
-#
-#Nx = 2 ^ 8;Ny = Nx;Nt = 1;dx = 0.5; dy = dx; dt = 0.25; fftdim = 2;
-#randn('state', iseed)
-#Y1 = seasim(SD1, Nx, Ny, Nt, dx, dy, dt, fftdim, plotflag);
-#wafostamp('', '(ER)')
-#axis('fill')
-#disp('Block = 6'), pause(pstate)
-#
-###
-## Frequency dependent spreading
-#randn('state', iseed)
-#Y12 = seasim(SD12, Nx, Ny, Nt, dx, dy, dt, fftdim, plotflag);
-#wafostamp('', '(ER)')
-#axis('fill')
-#disp('Block = 7'), pause(pstate)
-#
-#! Estimation of directional spectrum
-#!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-#! The figure is not shown in the Tutorial
-#
-# Nx = 3; Ny = 2; Nt = 2 ^ 12; dx = 10; dy = 10;dt = 0.5;
-# F = seasim(SD12, Nx, Ny, Nt, dx, dy, dt, 1, 0);
-# Z = permute(F.Z, [3 1 2]);
-# [X, Y] = meshgrid(F.x, F.y);
-# N = Nx * Ny;
-# types = repmat(sensortypeid('n'), N, 1);
-# bfs = ones(N, 1);
-# pos = [X(:), Y(:), zeros(N, 1)];
-# h = inf;
-# nfft = 128;
-# nt = 101;
-# SDe = dat2dspec([F.t Z(:, :)], [pos types, bfs], h, nfft, nt);
-#plotspec(SDe), hold on
-#plotspec(SD12, '--'), hold off
-#disp('Block = 8'), pause(pstate)
-
-#! Section 1.4.4 Fatigue, Load cycles and Markov models.
-#! ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-#! Switching Markow chain of turningpoints
-#! In fatigue applications the exact sample path is not important, but
-#! only the tops and bottoms of the load, called the sequence of turning
-#! points (TP). From the turning points one can extract load cycles, from
-#! which damage calculations and fatigue life predictions can be
-#! performed.
-#!
-#! The commands below computes the intensity of rainflowcycles for
-#! the Gaussian model with spectrum S1 using the Markov approximation.
-#! The rainflow cycles found in the simulated load signal are shown in the
-#! figure.
-
-#clf()
-#paramu = [-6 6 61];
-#frfc = spec2cmat(S1, [], 'rfc', [], paramu);
-#pdfplot(frfc);
-#hold on
-#tp = dat2tp(xs);
-#rfc = tp2rfc(tp);
-#plot(rfc(:, 2), rfc(:, 1), '.')
-#wafostamp('', '(ER)')
-#hold off
-#disp('Block = 9'), pause(pstate)
-
-#! Section 1.4.5 Extreme value statistics
-#!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-# Plot of yura87 data
-plt.clf()
-import wafo.data as wd
-xn = wd.yura87()
-#xn = load('yura87.dat');
-plt.subplot(211)
-plt.plot(xn[::30, 0] / 3600, xn[::30, 1], '.')
-plt.title('Water level')
-plt.ylabel('(m)')
-
-#! Formation of 5 min maxima
-yura = xn[:85500, 1]
-yura = np.reshape(yura, (285, 300)).T
-maxyura = yura.max(axis=0)
-plt.subplot(212)
-plt.plot(xn[299:85500:300, 0] / 3600, maxyura, '.')
-plt.xlabel('Time (h)')
-plt.ylabel('(m)')
-plt.title('Maximum 5 min water level')
-plt.show()
-
-#! Estimation of GEV for yuramax
-plt.clf()
-import wafo.stats as ws
-phat = ws.genextreme.fit2(maxyura, method='ml')
-phat.plotfitsummary()
-plt.show()
-#disp('Block = 11, Last block')
diff --git a/wafo/doc/tutorial_scripts/chapter2.py b/wafo/doc/tutorial_scripts/chapter2.py
deleted file mode 100644
index 03603c8..0000000
--- a/wafo/doc/tutorial_scripts/chapter2.py
+++ /dev/null
@@ -1,327 +0,0 @@
-import wafo.plotbackend.plotbackend as plt
-import numpy as np
-
-# pyreport -o chapter2.html chapter2.py
-
-#! CHAPTER2 Modelling random loads and stochastic waves
-#!=======================================================
-#!
-#! Chapter2 contains the commands used in Chapter 2 of the tutorial and
-#! present some tools for analysis of random functions with
-#! respect to their correlation, spectral and distributional properties.
-#! The presentation is divided into three examples:
-#!
-#! Example1 is devoted to estimation of different parameters in the model.
-#! Example2 deals with spectral densities and
-#! Example3 presents the use of WAFO to simulate samples of a Gaussian
-#! process.
-#!
-#! Some of the commands are edited for fast computation.
-#!
-#! Section 2.1 Introduction and preliminary analysis
-#!====================================================
-#! Example 1: Sea data
-#!----------------------
-#! Observed crossings compared to the expected for Gaussian signals
-
-import wafo
-import wafo.objects as wo
-xx = wafo.data.sea()
-me = xx[:, 1].mean()
-sa = xx[:, 1].std()
-xx[:, 1] -= me
-ts = wo.mat2timeseries(xx)
-tp = ts.turning_points()
-
-cc = tp.cycle_pairs()
-lc = cc.level_crossings()
-lc.plot()
-plt.show()
-
-#! Average number of upcrossings per time unit
-#!----------------------------------------------
-#! Next we compute the mean frequency as the average number of upcrossings
-#! per time unit of the mean level (= 0); this may require interpolation in the
-#! crossing intensity curve, as follows.
-T = xx[:, 0].max() - xx[:, 0].min()
-f0 = np.interp(0, lc.args, lc.data, 0) / T # zero up-crossing frequency
-print('f0 = %g' % f0)
-
-#! Turningpoints and irregularity factor
-#!----------------------------------------
-
-fm = len(tp.data) / (2 * T) # frequency of maxima
-alfa = f0 / fm # approx Tm24/Tm02
-
-print('fm = %g, alpha = %g, ' % (fm, alfa))
-
-#! Visually examine data
-#!------------------------
-#! We finish this section with some remarks about the quality
-#! of the measured data. Especially sea surface measurements can be
-#! of poor quality. We shall now check the quality of the dataset {\tt xx}.
-#! It is always good practice to visually examine the data
-#! before the analysis to get an impression of the quality,
-#! non-linearities and narrow-bandedness of the data.
-#! First we shall plot the data and zoom in on a specific region.
-#! A part of sea data is visualized with the following commands
-plt.clf()
-ts.plot_wave('k-', tp, '*', nfig=1, nsub=1)
-
-plt.axis([0, 2, -2, 2])
-plt.show()
-
-#! Finding possible spurious points
-#!------------------------------------
-#! However, if the amount of data is too large for visual examinations one
-#! could use the following criteria to find possible spurious points. One
-#! must be careful using the criteria for extremevalue analysis, because
-#! it might remove extreme waves that are OK and not spurious.
-
-import wafo.misc as wm
-dt = ts.sampling_period()
-# dt = np.diff(xx[:2,0])
-dcrit = 5 * dt
-ddcrit = 9.81 / 2 * dt * dt
-zcrit = 0
-inds, indg = wm.findoutliers(ts.data, zcrit, dcrit, ddcrit, verbose=True)
-
-#! Section 2.2 Frequency Modeling of Load Histories
-#!----------------------------------------------------
-#! Periodogram: Raw spectrum
-#!
-plt.clf()
-Lmax = 9500
-S = ts.tospecdata(L=Lmax)
-S.plot()
-plt.axis([0, 5, 0, 0.7])
-plt.show()
-
-#! Calculate moments
-#!-------------------
-mom, text = S.moment(nr=4)
-print('sigma = %g, m0 = %g' % (sa, np.sqrt(mom[0])))
-
-#! Section 2.2.1 Random functions in Spectral Domain - Gaussian processes
-#!--------------------------------------------------------------------------
-#! Smoothing of spectral estimate
-#!----------------------------------
-#! By decreasing Lmax the spectrum estimate becomes smoother.
-
-plt.clf()
-Lmax0 = 200
-Lmax1 = 50
-S1 = ts.tospecdata(L=Lmax0)
-S2 = ts.tospecdata(L=Lmax1)
-S1.plot('-.')
-S2.plot()
-plt.show()
-
-#! Estimated autocovariance
-#!----------------------------
-#! Obviously knowing the spectrum one can compute the covariance
-#! function. The following code will compute the covariance for the
-#! unimodal spectral density S1 and compare it with estimated
-#! covariance of the signal xx.
-plt.clf()
-Lmax = 85
-R1 = S1.tocovdata(nr=1)
-Rest = ts.tocovdata(lag=Lmax)
-R1.plot('.')
-Rest.plot()
-plt.axis([0, 25, -0.1, 0.25])
-plt.show()
-
-#! We can see in Figure below that the covariance function corresponding to
-#! the spectral density S2 significantly differs from the one estimated
-#! directly from data.
-#! It can be seen in Figure above that the covariance corresponding to S1
-#! agrees much better with the estimated covariance function
-
-plt.clf()
-R2 = S2.tocovdata(nr=1)
-R2.plot('.')
-Rest.plot()
-plt.show()
-
-#! Section 2.2.2 Transformed Gaussian models
-#!-------------------------------------------
-#! We begin with computing skewness and kurtosis
-#! for the data set xx and compare it with the second order wave approximation
-#! proposed by Winterstein:
-import wafo.stats as ws
-rho3 = ws.skew(xx[:, 1])
-rho4 = ws.kurtosis(xx[:, 1])
-
-sk, ku = S1.stats_nl(moments='sk')
-
-#! Comparisons of 3 transformations
-plt.clf()
-import wafo.transform.models as wtm
-gh = wtm.TrHermite(mean=me, sigma=sa, skew=sk, kurt=ku).trdata()
-g = wtm.TrLinear(mean=me, sigma=sa).trdata() # Linear transformation
-glc, gemp = lc.trdata(mean=me, sigma=sa)
-
-glc.plot('b-') # Transf. estimated from level-crossings
-gh.plot('b-.') # Hermite Transf. estimated from moments
-g.plot('r')
-plt.grid('on')
-plt.show()
-
-#! Test Gaussianity of a stochastic process
-#!------------------------------------------
-#! TESTGAUSSIAN simulates e(g(u)-u) = int (g(u)-u)^2 du for Gaussian processes
-#! given the spectral density, S. The result is plotted if test0 is given.
-#! This is useful for testing if the process X(t) is Gaussian.
-#! If 95% of TEST1 is less than TEST0 then X(t) is not Gaussian at a 5% level.
-#!
-#! As we see from the figure below: none of the simulated values of test1 is
-#! above 1.00. Thus the data significantly departs from a Gaussian distribution.
-plt.clf()
-test0 = glc.dist2gauss()
-#! the following test takes time
-N = len(xx)
-test1 = S1.testgaussian(ns=N, cases=50, test0=test0)
-is_gaussian = sum(test1 > test0) > 5
-print(is_gaussian)
-plt.show()
-
-#! Normalplot of data xx
-#!------------------------
-#! indicates that the underlying distribution has a "heavy" upper tail and a
-#! "light" lower tail.
-plt.clf()
-import pylab
-ws.probplot(ts.data.ravel(), dist='norm', plot=pylab)
-plt.show()
-#! Section 2.2.3 Spectral densities of sea data
-#!-----------------------------------------------
-#! Example 2: Different forms of spectra
-#!
-import wafo.spectrum.models as wsm
-plt.clf()
-Hm0 = 7
-Tp = 11
-spec = wsm.Jonswap(Hm0=Hm0, Tp=Tp).tospecdata()
-spec.plot()
-plt.show()
-
-#! Directional spectrum and Encountered directional spectrum
-#! Directional spectrum
-plt.clf()
-D = wsm.Spreading('cos2s')
-Sd = D.tospecdata2d(spec)
-Sd.plot()
-plt.show()
-
-
-##!Encountered directional spectrum
-##!---------------------------------
-#clf()
-#Se = spec2spec(Sd,'encdir',0,10);
-#plotspec(Se), hold on
-#plotspec(Sd,1,'--'), hold off
-##!wafostamp('','(ER)')
-#disp('Block = 17'),pause(pstate)
-#
-##!#! Frequency spectra
-#clf
-#Sd1 =spec2spec(Sd,'freq');
-#Sd2 = spec2spec(Se,'enc');
-#plotspec(spec), hold on
-#plotspec(Sd1,1,'.'),
-#plotspec(Sd2),
-##!wafostamp('','(ER)')
-#hold off
-#disp('Block = 18'),pause(pstate)
-#
-##!#! Wave number spectrum
-#clf
-#Sk = spec2spec(spec,'k1d')
-#Skd = spec2spec(Sd,'k1d')
-#plotspec(Sk), hold on
-#plotspec(Skd,1,'--'), hold off
-##!wafostamp('','(ER)')
-#disp('Block = 19'),pause(pstate)
-#
-##!#! Effect of waterdepth on spectrum
-#clf
-#plotspec(spec,1,'--'), hold on
-#S20 = spec;
-#S20.S = S20.S.*phi1(S20.w,20);
-#S20.h = 20;
-#plotspec(S20), hold off
-##!wafostamp('','(ER)')
-#disp('Block = 20'),pause(pstate)
-#
-##!#! Section 2.3 Simulation of transformed Gaussian process
-##!#! Example 3: Simulation of random sea
-##! The reconstruct function replaces the spurious points of seasurface by
-##! simulated data on the basis of the remaining data and a transformed Gaussian
-##! process. As noted previously one must be careful using the criteria
-##! for finding spurious points when reconstructing a dataset, because
-##! these criteria might remove the highest and steepest waves as we can see
-##! in this plot where the spurious points is indicated with a '+' sign:
-##!
-#clf
-#[y, grec] = reconstruct(xx,inds);
-#waveplot(y,'-',xx(inds,:),'+',1,1)
-#axis([0 inf -inf inf])
-##!wafostamp('','(ER)')
-#disp('Block = 21'),pause(pstate)
-#
-##! Compare transformation (grec) from reconstructed (y)
-##! with original (glc) from (xx)
-#clf
-#trplot(g), hold on
-#plot(gemp(:,1),gemp(:,2))
-#plot(glc(:,1),glc(:,2),'-.')
-#plot(grec(:,1),grec(:,2)), hold off
-#disp('Block = 22'),pause(pstate)
-#
-##!#!
-#clf
-#L = 200;
-#x = dat2gaus(y,grec);
-#Sx = dat2spec(x,L);
-#disp('Block = 23'),pause(pstate)
-#
-##!#!
-#clf
-#dt = spec2dt(Sx)
-#Ny = fix(2*60/dt) #! = 2 minutes
-#Sx.tr = grec;
-#ysim = spec2sdat(Sx,Ny);
-#waveplot(ysim,'-')
-##!wafostamp('','(CR)')
-#disp('Block = 24'),pause(pstate)
-#
-#! Estimated spectrum compared to Torsethaugen spectrum
-#!-------------------------------------------------------
-
-plt.clf()
-fp = 1.1
-dw = 0.01
-H0 = S1.characteristic('Hm0')[0]
-St = wsm.Torsethaugen(Hm0=H0,Tp=2*np.pi/fp).tospecdata(np.arange(0,5+dw/2,dw))
-S1.plot()
-St.plot('-.')
-plt.axis([0, 6, 0, 0.4])
-plt.show()
-
-
-#! Transformed Gaussian model compared to Gaussian model
-#!--------------------------------------------------------
-dt = St.sampling_period()
-va, sk, ku = St.stats_nl(moments='vsk')
-#sa = sqrt(va)
-gh = wtm.TrHermite(mean=me, sigma=sa, skew=sk, kurt=ku, ysigma=sa)
-
-ysim_t = St.sim(ns=240, dt=0.5)
-xsim_t = ysim_t.copy()
-xsim_t[:, 1] = gh.gauss2dat(ysim_t[:, 1])
-
-ts_y = wo.mat2timeseries(ysim_t)
-ts_x = wo.mat2timeseries(xsim_t)
-ts_y.plot_wave(sym1='r.', ts=ts_x, sym2='b', sigma=sa, nsub=5, nfig=1)
-plt.show()
diff --git a/wafo/doc/tutorial_scripts/chapter3.py b/wafo/doc/tutorial_scripts/chapter3.py
deleted file mode 100644
index 8b159c2..0000000
--- a/wafo/doc/tutorial_scripts/chapter3.py
+++ /dev/null
@@ -1,615 +0,0 @@
-from wafo.plotbackend import plotbackend as plt
-import numpy as np
-
-#! CHAPTER3 Demonstrates distributions of wave characteristics
-#!=============================================================
-#!
-#! Chapter3 contains the commands used in Chapter3 in the tutorial.
-#!
-#! Some of the commands are edited for fast computation.
-#!
-#! Section 3.2 Estimation of wave characteristics from data
-#!----------------------------------------------------------
-#! Example 1
-#!~~~~~~~~~~
-
-speed = 'fast'
-#speed = 'slow'
-import scipy.signal as ss
-import wafo.data as wd
-import wafo.misc as wm
-import wafo.objects as wo
-import wafo.stats as ws
-import wafo.spectrum.models as wsm
-xx = wd.sea()
-xx[:, 1] = ss.detrend(xx[:, 1])
-ts = wo.mat2timeseries(xx)
-Tcrcr, ix = ts.wave_periods(vh=0, pdef='c2c', wdef='tw', rate=8)
-Tc, ixc = ts.wave_periods(vh=0, pdef='u2d', wdef='tw', rate=8)
-
-#! Histogram of crestperiod compared to the kernel density estimate
-#!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-import wafo.kdetools as wk
-plt.clf()
-print(Tc.mean())
-print(Tc.max())
-
-t = np.linspace(0.01,8,200);
-ftc = wk.TKDE(Tc, L2=0, inc=128)
-
-plt.plot(t,ftc.eval_grid(t), t, ftc.eval_grid_fast(t),'-.')
-wm.plot_histgrm(Tc, normed=True)
-plt.title('Kernel Density Estimates')
-plt.xlabel('Tc [s]')
-plt.axis([0, 8, 0, 0.5])
-plt.show()
-
-#! Extreme waves - model check: the highest and steepest wave
-#!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-plt.clf()
-S, H = ts.wave_height_steepness(kind=0)
-indS = S.argmax()
-indH = H.argmax()
-ts.plot_sp_wave([indH, indS],'k.')
-plt.show()
-
-#! Does the highest wave contradict a transformed Gaussian model?
-#!----------------------------------------------------------------
-
-# TODO: Fix this
-
-#clf
-#inds1 = (5965:5974)'; #! points to remove
-#Nsim = 10;
-#[y1, grec1, g2, test, tobs, mu1o, mu1oStd] = ...
-# reconstruct(xx,inds1,Nsim);
-#spwaveplot(y1,indA-10)
-#hold on
-#plot(xx(inds1,1),xx(inds1,2),'+')
-#lamb = 2.;
-#muLstd = tranproc(mu1o-lamb*mu1oStd,fliplr(grec1));
-#muUstd = tranproc(mu1o+lamb*mu1oStd,fliplr(grec1));
-#plot (y1(inds1,1), [muLstd muUstd],'b-')
-#axis([1482 1498 -1 3]),
-#wafostamp([],'(ER)')
-#disp('Block = 6'),
-#pause(pstate)
-#
-##!#! Expected value (solid) compared to data removed
-#clf
-#plot(xx(inds1,1),xx(inds1,2),'+'), hold on
-#mu = tranproc(mu1o,fliplr(grec1));
-#plot(y1(inds1,1), mu), hold off
-#disp('Block = 7'), pause(pstate)
-
-#! Crest height PDF
-#!------------------
-#! Transform data so that kde works better
-plt.clf()
-wave_data = ts.wave_parameters()
-Ac = wave_data['Ac']
-L2 = 0.6
-
-ws.probplot(Ac**L2, dist='norm', plot=plt)
-plt.show()
-
-#!#!
-plt.clf()#
-fac = wk.TKDE(Ac,L2=L2)(np.linspace(0.01,3,200), output='plot')
-fac.plot()
-# wafostamp([],'(ER)')
-print(fac.integrate(a=0.01, b=3))
-print(fac.integrate())
-print('Block = 8'),
-# pause(pstate)
-
-#!#! Empirical crest height CDF
-plt.clf()
-Fac = fac.to_cdf()
-Femp = ws.edf(Ac)
-Fac.plot()
-Femp.plot()
-plt.axis([0, 2, 0, 1])
-
-#wafostamp([],'(ER)')
-#disp('Block = 9'), pause(pstate)
-
-#!#! Empirical crest height CDF compared to a Transformed Rayleigh approximation
-
-# facr = trraylpdf(fac.x{1},'Ac',grec1);
-# Facr = cumtrapz(facr.x{1},facr.f);
-# hold on
-# plot(facr.x{1},Facr,'.')
-# axis([1.25 2.25 0.95 1])
-# wafostamp([],'(ER)')
-# disp('Block = 10'), pause(pstate)
-
-#!#! Joint pdf of crest period and crest amplitude
-plt.clf()
-Tcf = wave_data['Tcf']
-Tcb = wave_data['Tcb']
-Tc = Tcf + Tcb
-fTcAc = wk.TKDE([Tc, Ac],L2=0.5, inc=256).eval_grid_fast(output='plot')
-fTcAc.labels.labx = 'Tc [s]'
-fTcAc.labels.laby = 'Ac [m]'
-fTcAc.plot()
-plt.hold(True)
-plt.plot(Tc, Ac,'k.')
-plt.hold(False)
-plt.show()
-#wafostamp([],'(ER)')
-#disp('Block = 11'), pause(pstate)
-
-#!#! Example 4: Simple wave characteristics obtained from Jonswap spectrum
-plt.clf()
-S = wsm.Jonswap(Hm0=5, Tp=10).tospecdata()
-m, mt = S.moment(nr=4, even=False)
-print(m)
-print(mt)
-# disp('Block = 12'), pause(pstate)
-
-plt.clf()
-S.bandwidth(['alpha'])
-ch, Sa2, chtxt = S.characteristic(['Hm0', 'Tm02'])
-
-# disp('Block = 13'), pause(pstate)
-
-#!#! Section 3.3.2 Explicit form approximations of wave characteristic densities
-#!#! Longuett-Higgins model for Tc and Ac
-# plt.clf()
-# t = np.linspace(0,15,100)
-# h = np.linspace(0,6,100)
-# flh = lh83pdf(t, h, [m[0],m[1], m[2])
-# #disp('Block = 14'), pause(pstate)
-#
-# #!#! Transformed Longuett-Higgins model for Tc and Ac
-# clf
-# [sk, ku ]=spec2skew(S);
-# sa = sqrt(m(1));
-# gh = hermitetr([],[sa sk ku 0]);
-# flhg = lh83pdf(t,h,[m(1),m(2),m(3)],gh);
-# disp('Block = 15'), pause(pstate)
-
-#!#! Cavanie model for Tc and Ac
-# clf
-# t = np.linspace(0,10,100);
-# h = np.linspace(0,7,100);
-# fcav = cav76pdf(t,h,[m(1) m(2) m(3) m(5)],[]);
-# disp('Block = 16'), pause(pstate)
-#
-# #!#! Example 5 Transformed Rayleigh approximation of crest- vs trough- amplitude
-# clf
-# xx = load('sea.dat');
-# x = xx;
-# x(:,2) = detrend(x(:,2));
-# SS = dat2spec2(x);
-# [sk, ku, me, si ] = spec2skew(SS);
-# gh = hermitetr([],[si sk ku me]);
-# Hs = 4*si;
-# r = (0:0.05:1.1*Hs)';
-# fac_h = trraylpdf(r,'Ac',gh);
-# fat_h = trraylpdf(r,'At',gh);
-# h = (0:0.05:1.7*Hs)';
-# facat_h = trraylpdf(h,'AcAt',gh);
-# pdfplot(fac_h)
-# hold on
-# pdfplot(fat_h,'--')
-# hold off
-# wafostamp([],'(ER)')
-# disp('Block = 17'), pause(pstate)
-#
-# #!#!
-# clf
-# TC = dat2tc(xx, me);
-# tc = tp2mm(TC);
-# Ac = tc(:,2);
-# At = -tc(:,1);
-# AcAt = Ac+At;
-# disp('Block = 18'), pause(pstate)
-#
-# #!#!
-# clf
-# Fac_h = [fac_h.x{1} cumtrapz(fac_h.x{1},fac_h.f)];
-# subplot(3,1,1)
-# Fac = plotedf(Ac,Fac_h);
-# hold on
-# plot(r,1-exp(-8*r.^2/Hs^2),'.')
-# axis([1. 2. 0.9 1])
-# title('Ac CDF')
-#
-# Fat_h = [fat_h.x{1} cumtrapz(fat_h.x{1},fat_h.f)];
-# subplot(3,1,2)
-# Fat = plotedf(At,Fat_h);
-# hold on
-# plot(r,1-exp(-8*r.^2/Hs^2),'.')
-# axis([1. 2. 0.9 1])
-# title('At CDF')
-#
-# Facat_h = [facat_h.x{1} cumtrapz(facat_h.x{1},facat_h.f)];
-# subplot(3,1,3)
-# Facat = plotedf(AcAt,Facat_h);
-# hold on
-# plot(r,1-exp(-2*r.^2/Hs^2),'.')
-# axis([1.5 3.5 0.9 1])
-# title('At+Ac CDF')
-#
-# wafostamp([],'(ER)')
-# disp('Block = 19'), pause(pstate)
-#
-# #!#! Section 3.4 Exact wave distributions in transformed Gaussian Sea
-# #!#! Section 3.4.1 Density of crest period, crest length or encountered crest period
-# clf
-# S1 = torsethaugen([],[6 8],1);
-# D1 = spreading(101,'cos',pi/2,[15],[],0);
-# D12 = spreading(101,'cos',0,[15],S1.w,1);
-# SD1 = mkdspec(S1,D1);
-# SD12 = mkdspec(S1,D12);
-# disp('Block = 20'), pause(pstate)
-#
-# #!#! Crest period
-# clf
-# tic
-# f_tc = spec2tpdf(S1,[],'Tc',[0 11 56],[],4);
-# toc
-# pdfplot(f_tc)
-# wafostamp([],'(ER)')
-# simpson(f_tc.x{1},f_tc.f)
-# disp('Block = 21'), pause(pstate)
-#
-# #!#! Crest length
-#
-# if strncmpi(speed,'slow',1)
-# opt1 = rindoptset('speed',5,'method',3);
-# opt2 = rindoptset('speed',5,'nit',2,'method',0);
-# else
-# #! fast
-# opt1 = rindoptset('speed',7,'method',3);
-# opt2 = rindoptset('speed',7,'nit',2,'method',0);
-# end
-#
-#
-# clf
-# if strncmpi(speed,'slow',1)
-# NITa = 5;
-# else
-# disp('NIT=5 may take time, running with NIT=3 in the following')
-# NITa = 3;
-# end
-# #!f_Lc = spec2tpdf2(S1,[],'Lc',[0 200 81],opt1); #! Faster and more accurate
-# f_Lc = spec2tpdf(S1,[],'Lc',[0 200 81],[],NITa);
-# pdfplot(f_Lc,'-.')
-# wafostamp([],'(ER)')
-# disp('Block = 22'), pause(pstate)
-#
-#
-# f_Lc_1 = spec2tpdf(S1,[],'Lc',[0 200 81],1.5,NITa);
-# #!f_Lc_1 = spec2tpdf2(S1,[],'Lc',[0 200 81],1.5,opt1);
-#
-# hold on
-# pdfplot(f_Lc_1)
-# wafostamp([],'(ER)')
-#
-# disp('Block = 23'), pause(pstate)
-# #!#!
-# clf
-# simpson(f_Lc.x{1},f_Lc.f)
-# simpson(f_Lc_1.x{1},f_Lc_1.f)
-#
-# disp('Block = 24'), pause(pstate)
-# #!#!
-# clf
-# tic
-#
-# f_Lc_d1 = spec2tpdf(rotspec(SD1,pi/2),[],'Lc',[0 300 121],[],NITa);
-# f_Lc_d12 = spec2tpdf(SD12,[],'Lc',[0 200 81],[],NITa);
-# #! f_Lc_d1 = spec2tpdf2(rotspec(SD1,pi/2),[],'Lc',[0 300 121],opt1);
-# #! f_Lc_d12 = spec2tpdf2(SD12,[],'Lc',[0 200 81],opt1);
-# toc
-# pdfplot(f_Lc_d1,'-.'), hold on
-# pdfplot(f_Lc_d12), hold off
-# wafostamp([],'(ER)')
-#
-# disp('Block = 25'), pause(pstate)
-#
-# #!#!
-#
-#
-# clf
-# opt1 = rindoptset('speed',5,'method',3);
-# SD1r = rotspec(SD1,pi/2);
-# if strncmpi(speed,'slow',1)
-# f_Lc_d1_5 = spec2tpdf(SD1r,[], 'Lc',[0 300 121],[],5);
-# pdfplot(f_Lc_d1_5), hold on
-# else
-# #! fast
-# disp('Run the following example only if you want a check on computing time')
-# disp('Edit the command file and remove #!')
-# end
-# f_Lc_d1_3 = spec2tpdf(SD1r,[],'Lc',[0 300 121],[],3);
-# f_Lc_d1_2 = spec2tpdf(SD1r,[],'Lc',[0 300 121],[],2);
-# f_Lc_d1_0 = spec2tpdf(SD1r,[],'Lc',[0 300 121],[],0);
-# #!f_Lc_d1_n4 = spec2tpdf2(SD1r,[],'Lc',[0 400 161],opt1);
-#
-# pdfplot(f_Lc_d1_3), hold on
-# pdfplot(f_Lc_d1_2)
-# pdfplot(f_Lc_d1_0)
-# #!pdfplot(f_Lc_d1_n4)
-#
-# #!simpson(f_Lc_d1_n4.x{1},f_Lc_d1_n4.f)
-#
-# disp('Block = 26'), pause(pstate)
-#
-# #!#! Section 3.4.2 Density of wave period, wave length or encountered wave period
-# #!#! Example 7: Crest period and high crest waves
-# clf
-# tic
-# xx = load('sea.dat');
-# x = xx;
-# x(:,2) = detrend(x(:,2));
-# SS = dat2spec(x);
-# si = sqrt(spec2mom(SS,1));
-# SS.tr = dat2tr(x);
-# Hs = 4*si
-# method = 0;
-# rate = 2;
-# [S, H, Ac, At, Tcf, Tcb, z_ind, yn] = dat2steep(x,rate,method);
-# Tc = Tcf+Tcb;
-# t = linspace(0.01,8,200);
-# ftc1 = kde(Tc,{'L2',0},t);
-# pdfplot(ftc1)
-# hold on
-# #! f_t = spec2tpdf(SS,[],'Tc',[0 8 81],0,4);
-# f_t = spec2tpdf(SS,[],'Tc',[0 8 81],0,2);
-# simpson(f_t.x{1},f_t.f)
-# pdfplot(f_t,'-.')
-# hold off
-# wafostamp([],'(ER)')
-# toc
-# disp('Block = 27'), pause(pstate)
-#
-# #!#!
-# clf
-# tic
-#
-# if strncmpi(speed,'slow',1)
-# NIT = 4;
-# else
-# NIT = 2;
-# end
-# #! f_t2 = spec2tpdf(SS,[],'Tc',[0 8 81],[Hs/2],4);
-# tic
-# f_t2 = spec2tpdf(SS,[],'Tc',[0 8 81],Hs/2,NIT);
-# toc
-#
-# Pemp = sum(Ac>Hs/2)/sum(Ac>0)
-# simpson(f_t2.x{1},f_t2.f)
-# index = find(Ac>Hs/2);
-# ftc1 = kde(Tc(index),{'L2',0},t);
-# ftc1.f = Pemp*ftc1.f;
-# pdfplot(ftc1)
-# hold on
-# pdfplot(f_t2,'-.')
-# hold off
-# wafostamp([],'(ER)')
-# toc
-# disp('Block = 28'), pause(pstate)
-#
-# #!#! Example 8: Wave period for high crest waves
-# #! clf
-# tic
-# f_tcc2 = spec2tccpdf(SS,[],'t>',[0 12 61],[Hs/2],[0],-1);
-# toc
-# simpson(f_tcc2.x{1},f_tcc2.f)
-# f_tcc3 = spec2tccpdf(SS,[],'t>',[0 12 61],[Hs/2],[0],3,5);
-# #! f_tcc3 = spec2tccpdf(SS,[],'t>',[0 12 61],[Hs/2],[0],1,5);
-# simpson(f_tcc3.x{1},f_tcc3.f)
-# pdfplot(f_tcc2,'-.')
-# hold on
-# pdfplot(f_tcc3)
-# hold off
-# toc
-# disp('Block = 29'), pause(pstate)
-#
-# #!#!
-# clf
-# [TC tc_ind v_ind] = dat2tc(yn,[],'dw');
-# N = length(tc_ind);
-# t_ind = tc_ind(1:2:N);
-# c_ind = tc_ind(2:2:N);
-# Pemp = sum(yn(t_ind,2)<-Hs/2 & yn(c_ind,2)>Hs/2)/length(t_ind)
-# ind = find(yn(t_ind,2)<-Hs/2 & yn(c_ind,2)>Hs/2);
-# spwaveplot(yn,ind(2:4))
-# wafostamp([],'(ER)')
-# disp('Block = 30'), pause(pstate)
-#
-# #!#!
-# clf
-# Tcc = yn(v_ind(1+2*ind),1)-yn(v_ind(1+2*(ind-1)),1);
-# t = linspace(0.01,14,200);
-# ftcc1 = kde(Tcc,{'kernel' 'epan','L2',0},t);
-# ftcc1.f = Pemp*ftcc1.f;
-# pdfplot(ftcc1,'-.')
-# wafostamp([],'(ER)')
-# disp('Block = 31'), pause(pstate)
-#
-# tic
-# f_tcc22_1 = spec2tccpdf(SS,[],'t>',[0 12 61],[Hs/2],[Hs/2],-1);
-# toc
-# simpson(f_tcc22_1.x{1},f_tcc22_1.f)
-# hold on
-# pdfplot(f_tcc22_1)
-# hold off
-# wafostamp([],'(ER)')
-# disp('Block = 32'), pause(pstate)
-#
-# disp('The rest of this chapter deals with joint densities.')
-# disp('Some calculations may take some time.')
-# disp('You could experiment with other NIT.')
-# #!return
-#
-# #!#! Section 3.4.3 Joint density of crest period and crest height
-# #!#! Example 9. Some preliminary analysis of the data
-# clf
-# tic
-# yy = load('gfaksr89.dat');
-# SS = dat2spec(yy);
-# si = sqrt(spec2mom(SS,1));
-# SS.tr = dat2tr(yy);
-# Hs = 4*si
-# v = gaus2dat([0 0],SS.tr);
-# v = v(2)
-# toc
-# disp('Block = 33'), pause(pstate)
-#
-# #!#!
-# clf
-# tic
-# [TC, tc_ind, v_ind] = dat2tc(yy,v,'dw');
-# N = length(tc_ind);
-# t_ind = tc_ind(1:2:N);
-# c_ind = tc_ind(2:2:N);
-# v_ind_d = v_ind(1:2:N+1);
-# v_ind_u = v_ind(2:2:N+1);
-# T_d = ecross(yy(:,1),yy(:,2),v_ind_d,v);
-# T_u = ecross(yy(:,1),yy(:,2),v_ind_u,v);
-#
-# Tc = T_d(2:end)-T_u(1:end);
-# Tt = T_u(1:end)-T_d(1:end-1);
-# Tcf = yy(c_ind,1)-T_u;
-# Ac = yy(c_ind,2)-v;
-# At = v-yy(t_ind,2);
-# toc
-# disp('Block = 34'), pause(pstate)
-#
-# #!#!
-# clf
-# tic
-# t = linspace(0.01,15,200);
-# kopt3 = kdeoptset('hs',0.25,'L2',0);
-# ftc1 = kde(Tc,kopt3,t);
-# ftt1 = kde(Tt,kopt3,t);
-# pdfplot(ftt1,'k')
-# hold on
-# pdfplot(ftc1,'k-.')
-# f_tc4 = spec2tpdf(SS,[],'Tc',[0 12 81],0,4,5);
-# f_tc2 = spec2tpdf(SS,[],'Tc',[0 12 81],0,2,5);
-# f_tc = spec2tpdf(SS,[],'Tc',[0 12 81],0,-1);
-# pdfplot(f_tc,'b')
-# hold off
-# legend('kde(Tt)','kde(Tc)','f_{tc}')
-# wafostamp([],'(ER)')
-# toc
-# disp('Block = 35'), pause(pstate)
-#
-# #!#! Example 10: Joint characteristics of a half wave:
-# #!#! position and height of a crest for a wave with given period
-# clf
-# tic
-# ind = find(4.4Hs/2);
-# plot(Tc(ind), Ac(ind),'.');
-# hold on
-# pdfplot(flh_g,'k-.')
-# pdfplot(f_tcac_s)
-# toc
-# wafostamp([],'(ER)')
-# disp('Block = 39'), pause(pstate)
-#
-# #!#!
-# clf
-# #! f_tcac = spec2thpdf(SS,[],'TcAc',[0 12 81],[0:0.2:8],opt1);
-# #! pdfplot(f_tcac)
-# disp('Block = 40'), pause(pstate)
-#
-# #!#! Section 3.4.4 Joint density of crest and trough height
-# #!#! Section 3.4.5 Min-to-max distributions Markov method
-# #!#! Example 11. (min-max problems with Gullfaks data)
-# #!#! Joint density of maximum and the following minimum
-# clf
-# tic
-# tp = dat2tp(yy);
-# Mm = fliplr(tp2mm(tp));
-# fmm = kde(Mm);
-# f_mM = spec2mmtpdf(SS,[],'mm',[],[-7 7 51],opt2);
-#
-# pdfplot(f_mM,'-.')
-# hold on
-# pdfplot(fmm,'k-')
-# hold off
-# wafostamp([],'(ER)')
-# toc
-# disp('Block = 41'), pause(pstate)
-#
-# #!#! The joint density of still water separated maxima and minima.
-# clf
-# tic
-# ind = find(Mm(:,1)>v & Mm(:,2) easier to evaluate using cellmode evaluation.
-#! revised pab Feb2004
-#! updated call to lc2sdat
-#! Created by GL July 13, 2000
-#! from commands used in Chapter 4
-#!
-
-#! Chapter 4 Fatigue load analysis and rain-flow cycles
-#!------------------------------------------------------
-
-printing = 0
-
-
-#! Section 4.3.1 Crossing intensity
-#!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-import numpy as np
-from wafo.plotbackend import plotbackend as plt
-import wafo.data as wd
-import wafo.objects as wo
-
-xx_sea = wd.sea()
-ts = wo.mat2timeseries(xx_sea)
-tp = ts.turning_points()
-mM = tp.cycle_pairs(kind='min2max')
-lc = mM.level_crossings(intensity=True)
-T_sea = ts.args[-1]-ts.args[0]
-
-plt.subplot(1,2,1)
-lc.plot()
-plt.subplot(1,2,2)
-lc.setplotter(plotmethod='step')
-lc.plot()
-plt.show()
-
-
-m_sea = ts.data.mean()
-f0_sea = np.interp(m_sea, lc.args,lc.data)
-extr_sea = len(tp.data)/(2*T_sea)
-alfa_sea = f0_sea/extr_sea
-print('alfa = %g ' % alfa_sea)
-
-#! Section 4.3.2 Extraction of rainflow cycles
-#!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-#! Min-max and rainflow cycle plots
-#!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-mM_rfc = tp.cycle_pairs(h=0.3)
-
-plt.clf()
-plt.subplot(122),
-mM.plot()
-plt.title('min-max cycle pairs')
-plt.subplot(121),
-mM_rfc.plot()
-plt.title('Rainflow filtered cycles')
-plt.show()
-
-#! Min-max and rainflow cycle distributions
-#!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-import wafo.misc as wm
-ampmM_sea = mM.amplitudes()
-ampRFC_sea = mM_rfc.amplitudes()
-plt.clf()
-plt.subplot(121)
-wm.plot_histgrm(ampmM_sea,25)
-ylim = plt.gca().get_ylim()
-plt.title('min-max amplitude distribution')
-plt.subplot(122)
-wm.plot_histgrm(ampRFC_sea,25)
-plt.gca().set_ylim(ylim)
-plt.title('Rainflow amplitude distribution')
-plt.show()
-
-#!#! Section 4.3.3 Simulation of rainflow cycles
-#!#! Simulation of cycles in a Markov model
-# n = 41
-# param_m = [-1, 1, n]
-# param_D = [1, n, n]
-# u_markov=levels(param_m);
-# G_markov=mktestmat(param_m,[-0.2, 0.2],0.15,1);
-# T_markov=5000;
-#xxD_markov=mctpsim({G_markov [,]},T_markov);
-#xx_markov=[(1:T_markov)' u_markov(xxD_markov)'];
-#clf
-#plot(xx_markov(1:50,1),xx_markov(1:50,2))
-#title('Markov chain of turning points')
-#wafostamp([],'(ER)')
-#disp('Block 5'),pause(pstate)
-#
-#
-##!#! Rainflow cycles in a transformed Gaussian model
-##!#! Hermite transformed wave data and rainflow filtered turning points, h = 0.2.
-#me = mean(xx_sea(:,2));
-#sa = std(xx_sea(:,2));
-#Hm0_sea = 4*sa;
-#Tp_sea = 1/max(lc_sea(:,2));
-#spec = jonswap([],[Hm0_sea Tp_sea]);
-#
-#[sk, ku] = spec2skew(spec);
-#spec.tr = hermitetr([],[sa sk ku me]);
-#param_h = [-1.5 2 51];
-#spec_norm = spec;
-#spec_norm.S = spec_norm.S/sa^2;
-#xx_herm = spec2sdat(spec_norm,[2^15 1],0.1);
-##! ????? PJ, JR 11-Apr-2001
-##! NOTE, in the simulation program spec2sdat
-##!the spectrum must be normalized to variance 1
-##! ?????
-#h = 0.2;
-#[dtp,u_herm,xx_herm_1]=dat2dtp(param_h,xx_herm,h);
-#clf
-#plot(xx_herm(:,1),xx_herm(:,2),'k','LineWidth',2); hold on;
-#plot(xx_herm_1(:,1),xx_herm_1(:,2),'k--','Linewidth',2);
-#axis([0 50 -1 1]), hold off;
-#title('Rainflow filtered wave data')
-#wafostamp([],'(ER)')
-#disp('Block 6'),pause(pstate)
-#
-##!#! Rainflow cycles and rainflow filtered rainflow cycles in the transformed Gaussian process.
-#tp_herm=dat2tp(xx_herm);
-#RFC_herm=tp2rfc(tp_herm);
-#mM_herm=tp2mm(tp_herm);
-#h=0.2;
-#[dtp,u,tp_herm_1]=dat2dtp(param_h,xx_herm,h);
-#RFC_herm_1 = tp2rfc(tp_herm_1);
-#clf
-#subplot(121), ccplot(RFC_herm)
-#title('h=0')
-#subplot(122), ccplot(RFC_herm_1)
-#title('h=0.2')
-#if (printing==1), print -deps ../bilder/fatigue_8.eps
-#end
-#wafostamp([],'(ER)')
-#disp('Block 7'),pause(pstate)
-#
-##!#! Section 4.3.4 Calculating the rainflow matrix
-#
-#
-#Grfc_markov=mctp2rfm({G_markov []});
-#clf
-#subplot(121), cmatplot(u_markov,u_markov,G_markov), axis('square')
-#subplot(122), cmatplot(u_markov,u_markov,Grfc_markov), axis('square')
-#wafostamp([],'(ER)')
-#disp('Block 8'),pause(pstate)
-#
-##!#!
-#clf
-#cmatplot(u_markov,u_markov,{G_markov Grfc_markov},3)
-#wafostamp([],'(ER)')
-#disp('Block 9'),pause(pstate)
-#
-##!#! Min-max-matrix and theoretical rainflow matrix for test Markov sequence.
-#cmatplot(u_markov,u_markov,{G_markov Grfc_markov},4)
-#subplot(121), axis('square'), title('min2max transition matrix')
-#subplot(122), axis('square'), title('Rainflow matrix')
-#if (printing==1), print -deps ../bilder/fatigue_9.eps
-#end
-#wafostamp([],'(ER)')
-#disp('Block 10'),pause(pstate)
-#
-##!#! Observed and theoretical rainflow matrix for test Markov sequence.
-#n=length(u_markov);
-#Frfc_markov=dtp2rfm(xxD_markov,n);
-#clf
-#cmatplot(u_markov,u_markov,{Frfc_markov Grfc_markov*T_markov/2},3)
-#subplot(121), axis('square'), title('Observed rainflow matrix')
-#subplot(122), axis('square'), title('Theoretical rainflow matrix')
-#if (printing==1), print -deps ../bilder/fatigue_10.eps
-#end
-#wafostamp([],'(ER)')
-#disp('Block 11'),pause(pstate)
-#
-##!#! Smoothed observed and calculated rainflow matrix for test Markov sequence.
-#tp_markov=dat2tp(xx_markov);
-#RFC_markov=tp2rfc(tp_markov);
-#h=1;
-#Frfc_markov_smooth=cc2cmat(param_m,RFC_markov,[],1,h);
-#clf
-#cmatplot(u_markov,u_markov,{Frfc_markov_smooth Grfc_markov*T_markov/2},4)
-#subplot(121), axis('square'), title('Smoothed observed rainflow matrix')
-#subplot(122), axis('square'), title('Theoretical rainflow matrix')
-#if (printing==1), print -deps ../bilder/fatigue_11.eps
-#end
-#wafostamp([],'(ER)')
-#disp('Block 12'),pause(pstate)
-#
-##!#! Rainflow matrix from spectrum
-#clf
-##!GmM3_herm=spec2mmtpdf(spec,[],'Mm',[],[],2);
-#GmM3_herm=spec2cmat(spec,[],'Mm',[],param_h,2);
-#pdfplot(GmM3_herm)
-#wafostamp([],'(ER)')
-#disp('Block 13'),pause(pstate)
-#
-#
-##!#! Min-max matrix and theoretical rainflow matrix for Hermite-transformed Gaussian waves.
-#Grfc_herm=mctp2rfm({GmM3_herm.f []});
-#u_herm=levels(param_h);
-#clf
-#cmatplot(u_herm,u_herm,{GmM3_herm.f Grfc_herm},4)
-#subplot(121), axis('square'), title('min-max matrix')
-#subplot(122), axis('square'), title('Theoretical rainflow matrix')
-#if (printing==1), print -deps ../bilder/fatigue_12.eps
-#end
-#wafostamp([],'(ER)')
-#disp('Block 14'),pause(pstate)
-#
-##!#!
-#clf
-#Grfc_direct_herm=spec2cmat(spec,[],'rfc',[],[],2);
-#subplot(121), pdfplot(GmM3_herm), axis('square'), hold on
-#subplot(122), pdfplot(Grfc_direct_herm), axis('square'), hold off
-#if (printing==1), print -deps ../bilder/fig_mmrfcjfr.eps
-#end
-#wafostamp([],'(ER)')
-#disp('Block 15'),pause(pstate)
-#
-#
-##!#! Observed smoothed and theoretical min-max matrix,
-##!#! (and observed smoothed and theoretical rainflow matrix for Hermite-transformed Gaussian waves).
-#tp_herm=dat2tp(xx_herm);
-#RFC_herm=tp2rfc(tp_herm);
-#mM_herm=tp2mm(tp_herm);
-#h=0.2;
-#FmM_herm_smooth=cc2cmat(param_h,mM_herm,[],1,h);
-#Frfc_herm_smooth=cc2cmat(param_h,RFC_herm,[],1,h);
-#T_herm=xx_herm(end,1)-xx_herm(1,1);
-#clf
-#cmatplot(u_herm,u_herm,{FmM_herm_smooth GmM3_herm.f*length(mM_herm) ; ...
-# Frfc_herm_smooth Grfc_herm*length(RFC_herm)},4)
-#subplot(221), axis('square'), title('Observed smoothed min-max matrix')
-#subplot(222), axis('square'), title('Theoretical min-max matrix')
-#subplot(223), axis('square'), title('Observed smoothed rainflow matrix')
-#subplot(224), axis('square'), title('Theoretical rainflow matrix')
-#if (printing==1), print -deps ../bilder/fatigue_13.eps
-#end
-#wafostamp([],'(ER)')
-#disp('Block 16'),pause(pstate)
-#
-##!#! Section 4.3.5 Simulation from crossings and rainflow structure
-#
-##!#! Crossing spectrum (smooth curve) and obtained spectrum (wiggled curve)
-##!#! for simulated process with irregularity factor 0.25.
-#clf
-#cross_herm=dat2lc(xx_herm);
-#alpha1=0.25;
-#alpha2=0.75;
-#xx_herm_sim1=lc2sdat(cross_herm,500,alpha1);
-#cross_herm_sim1=dat2lc(xx_herm_sim1);
-#subplot(211)
-#plot(cross_herm(:,1),cross_herm(:,2)/max(cross_herm(:,2)))
-#hold on
-#stairs(cross_herm_sim1(:,1),...
-# cross_herm_sim1(:,2)/max(cross_herm_sim1(:,2)))
-#hold off
-#title('Crossing intensity, \alpha = 0.25')
-#subplot(212)
-#plot(xx_herm_sim1(:,1),xx_herm_sim1(:,2))
-#title('Simulated load, \alpha = 0.25')
-#if (printing==1), print -deps ../bilder/fatigue_14_25.eps
-#end
-#wafostamp([],'(ER)')
-#disp('Block 16'),pause(pstate)
-#
-##!#! Crossing spectrum (smooth curve) and obtained spectrum (wiggled curve)
-##!#! for simulated process with irregularity factor 0.75.
-#xx_herm_sim2=lc2sdat(cross_herm,500,alpha2);
-#cross_herm_sim2=dat2lc(xx_herm_sim2);
-#subplot(211)
-#plot(cross_herm(:,1),cross_herm(:,2)/max(cross_herm(:,2)))
-#hold on
-#stairs(cross_herm_sim2(:,1),...
-# cross_herm_sim2(:,2)/max(cross_herm_sim2(:,2)))
-#hold off
-#title('Crossing intensity, \alpha = 0.75')
-#subplot(212)
-#plot(xx_herm_sim2(:,1),xx_herm_sim2(:,2))
-#title('Simulated load, \alpha = 0.75')
-#if (printing==1), print -deps ../bilder/fatigue_14_75.eps
-#end
-#wafostamp([],'(ER)')
-#disp('Block 17'),pause(pstate)
-#
-##!#! Section 4.4 Fatigue damage and fatigue life distribution
-##!#! Section 4.4.1 Introduction
-#beta=3.2; gam=5.5E-10; T_sea=xx_sea(end,1)-xx_sea(1,1);
-#d_beta=cc2dam(RFC_sea,beta)/T_sea;
-#time_fail=1/gam/d_beta/3600 #!in hours of the specific storm
-#disp('Block 18'),pause(pstate)
-#
-##!#! Section 4.4.2 Level crossings
-##!#! Crossing intensity as calculated from the Markov matrix (solid curve) and from the observed rainflow matrix (dashed curve).
-#clf
-#mu_markov=cmat2lc(param_m,Grfc_markov);
-#muObs_markov=cmat2lc(param_m,Frfc_markov/(T_markov/2));
-#clf
-#plot(mu_markov(:,1),mu_markov(:,2),muObs_markov(:,1),muObs_markov(:,2),'--')
-#title('Theoretical and observed crossing intensity ')
-#if (printing==1), print -deps ../bilder/fatigue_15.eps
-#end
-#wafostamp([],'(ER)')
-#disp('Block 19'),pause(pstate)
-#
-##!#! Section 4.4.3 Damage
-##!#! Distribution of damage from different RFC cycles, from calculated theoretical and from observed rainflow matrix.
-#beta = 4;
-#Dam_markov = cmat2dam(param_m,Grfc_markov,beta)
-#DamObs1_markov = cc2dam(RFC_markov,beta)/(T_markov/2)
-#DamObs2_markov = cmat2dam(param_m,Frfc_markov,beta)/(T_markov/2)
-#disp('Block 20'),pause(pstate)
-#
-#Dmat_markov = cmat2dmat(param_m,Grfc_markov,beta);
-#DmatObs_markov = cmat2dmat(param_m,Frfc_markov,beta)/(T_markov/2);
-#clf
-#subplot(121), cmatplot(u_markov,u_markov,Dmat_markov,4)
-#title('Theoretical damage matrix')
-#subplot(122), cmatplot(u_markov,u_markov,DmatObs_markov,4)
-#title('Observed damage matrix')
-#if (printing==1), print -deps ../bilder/fatigue_16.eps
-#end
-#wafostamp([],'(ER)')
-#disp('Block 21'),pause(pstate)
-#
-#
-##!#!
-##!Damplus_markov = lc2dplus(mu_markov,beta)
-#pause(pstate)
-#
-##!#! Section 4.4.4 Estimation of S-N curve
-#
-##!#! Load SN-data and plot in log-log scale.
-#SN = load('sn.dat');
-#s = SN(:,1);
-#N = SN(:,2);
-#clf
-#loglog(N,s,'o'), axis([0 14e5 10 30])
-##!if (printing==1), print -deps ../bilder/fatigue_?.eps end
-#wafostamp([],'(ER)')
-#disp('Block 22'),pause(pstate)
-#
-#
-##!#! Check of S-N-model on normal probability paper.
-#
-#normplot(reshape(log(N),8,5))
-#if (printing==1), print -deps ../bilder/fatigue_17.eps
-#end
-#wafostamp([],'(ER)')
-#disp('Block 23'),pause(pstate)
-#
-##!#! Estimation of S-N-model on linear scale.
-#clf
-#[e0,beta0,s20] = snplot(s,N,12);
-#title('S-N-data with estimated N(s)','FontSize',20)
-#set(gca,'FontSize',20)
-#if (printing==1), print -deps ../bilder/fatigue_18a.eps
-#end
-#wafostamp([],'(ER)')
-#disp('Block 24'),pause(pstate)
-#
-##!#! Estimation of S-N-model on log-log scale.
-#clf
-#[e0,beta0,s20] = snplot(s,N,14);
-#title('S-N-data with estimated N(s)','FontSize',20)
-#set(gca,'FontSize',20)
-#if (printing==1), print -deps ../bilder/fatigue_18b.eps
-#end
-#wafostamp([],'(ER)')
-#disp('Block 25'),pause(pstate)
-#
-##!#! Section 4.4.5 From S-N curve to fatigue life distribution
-##!#! Damage intensity as function of $\beta$
-#beta = 3:0.1:8;
-#DRFC = cc2dam(RFC_sea,beta);
-#dRFC = DRFC/T_sea;
-#plot(beta,dRFC), axis([3 8 0 0.25])
-#title('Damage intensity as function of \beta')
-#if (printing==1), print -deps ../bilder/fatigue_19.eps
-#end
-#wafostamp([],'(ER)')
-#disp('Block 26'),pause(pstate)
-#
-##!#! Fatigue life distribution with sea load.
-#dam0 = cc2dam(RFC_sea,beta0)/T_sea;
-#[t0,F0] = ftf(e0,dam0,s20,0.5,1);
-#[t1,F1] = ftf(e0,dam0,s20,0,1);
-#[t2,F2] = ftf(e0,dam0,s20,5,1);
-#plot(t0,F0,t1,F1,t2,F2)
-#title('Fatigue life distribution function')
-#if (printing==1), print -deps ../bilder/fatigue_20.eps
-#end
-#wafostamp([],'(ER)')
-#disp('Block 27, last block')
diff --git a/wafo/doc/tutorial_scripts/chapter5.py b/wafo/doc/tutorial_scripts/chapter5.py
deleted file mode 100644
index 669ea4c..0000000
--- a/wafo/doc/tutorial_scripts/chapter5.py
+++ /dev/null
@@ -1,238 +0,0 @@
-## CHAPTER5 contains the commands used in Chapter 5 of the tutorial
-#
-# CALL: Chapter5
-#
-# Some of the commands are edited for fast computation.
-# Each set of commands is followed by a 'pause' command.
-#
-
-# Tested on Matlab 5.3
-# History
-# Added Return values by GL August 2008
-# Revised pab sept2005
-# Added sections -> easier to evaluate using cellmode evaluation.
-# Created by GL July 13, 2000
-# from commands used in Chapter 5
-#
-
-## Chapter 5 Extreme value analysis
-
-## Section 5.1 Weibull and Gumbel papers
-from __future__ import division
-import numpy as np
-import scipy.interpolate as si
-from wafo.plotbackend import plotbackend as plt
-import wafo.data as wd
-import wafo.objects as wo
-import wafo.stats as ws
-import wafo.kdetools as wk
-pstate = 'off'
-
-# Significant wave-height data on Weibull paper,
-
-fig = plt.figure()
-ax = fig.add_subplot(111)
-Hs = wd.atlantic()
-wei = ws.weibull_min.fit(Hs)
-tmp = ws.probplot(Hs, wei, ws.weibull_min, plot=ax)
-plt.show()
-#wafostamp([],'(ER)')
-#disp('Block = 1'),pause(pstate)
-
-##
-# Significant wave-height data on Gumbel paper,
-plt.clf()
-ax = fig.add_subplot(111)
-gum = ws.gumbel_r.fit(Hs)
-tmp1 = ws.probplot(Hs, gum, ws.gumbel_r, plot=ax)
-#wafostamp([],'(ER)')
-plt.show()
-#disp('Block = 2'),pause(pstate)
-
-##
-# Significant wave-height data on Normal probability paper,
-plt.clf()
-ax = fig.add_subplot(111)
-phat = ws.norm.fit2(np.log(Hs))
-phat.plotresq()
-#tmp2 = ws.probplot(np.log(Hs), phat, ws.norm, plot=ax)
-
-#wafostamp([],'(ER)')
-plt.show()
-#disp('Block = 3'),pause(pstate)
-
-##
-# Return values in the Gumbel distribution
-plt.clf()
-T = np.r_[1:100000]
-sT = gum[0] - gum[1] * np.log(-np.log1p(-1./T))
-plt.semilogx(T, sT)
-plt.hold(True)
-# ws.edf(Hs).plot()
-Nmax = len(Hs)
-N = np.r_[1:Nmax + 1]
-
-plt.plot(Nmax/N, sorted(Hs, reverse=True), '.')
-plt.title('Return values in the Gumbel model')
-plt.xlabel('Return period')
-plt.ylabel('Return value')
-#wafostamp([],'(ER)')
-plt.show()
-#disp('Block = 4'),pause(pstate)
-
-## Section 5.2 Generalized Pareto and Extreme Value distributions
-## Section 5.2.1 Generalized Extreme Value distribution
-
-# Empirical distribution of significant wave-height with estimated
-# Generalized Extreme Value distribution,
-gev = ws.genextreme.fit2(Hs)
-gev.plotfitsummary()
-# wafostamp([],'(ER)')
-# disp('Block = 5a'),pause(pstate)
-
-plt.clf()
-x = np.linspace(0,14,200)
-kde = wk.TKDE(Hs, L2=0.5)(x, output='plot')
-kde.plot()
-plt.hold(True)
-plt.plot(x, gev.pdf(x),'--')
-# disp('Block = 5b'),pause(pstate)
-
-# Analysis of yura87 wave data.
-# Wave data interpolated (spline) and organized in 5-minute intervals
-# Normalized to mean 0 and std = 1 to get stationary conditions.
-# maximum level over each 5-minute interval analysed by GEV
-xn = wd.yura87()
-XI = np.r_[1:len(xn):0.25] - .99
-N = len(XI)
-N = N - np.mod(N, 4*60*5)
-
-YI = si.interp1d(xn[:, 0], xn[:, 1], kind='linear')(XI)
-YI = YI.reshape(4*60*5, N/(4*60*5)) # Each column holds 5 minutes of
- # interpolated data.
-Y5 = (YI - YI.mean(axis=0)) / YI.std(axis=0)
-Y5M = Y5.maximum(axis=0)
-Y5gev = ws.genextreme.fit2(Y5M,method='mps')
-Y5gev.plotfitsummary()
-#wafostamp([],'(ER)')
-#disp('Block = 6'),pause(pstate)
-
-## Section 5.2.2 Generalized Pareto distribution
-
-# Exceedances of significant wave-height data over level 3,
-gpd3 = ws.genpareto.fit2(Hs[Hs>3]-3, floc=0)
-gpd3.plotfitsummary()
-#wafostamp([],'(ER)')
-
-##
-plt.figure()
-# Exceedances of significant wave-height data over level 7,
-gpd7 = ws.genpareto.fit2(Hs(Hs>7), floc=7)
-gpd7.plotfitsummary()
-# wafostamp([],'(ER)')
-# disp('Block = 6'),pause(pstate)
-
-##
-#Simulates 100 values from the GEV distribution with parameters (0.3, 1, 2),
-# then estimates the parameters using two different methods and plots the
-# estimated distribution functions together with the empirical distribution.
-Rgev = ws.genextreme.rvs(0.3,1,2,size=100)
-gp = ws.genextreme.fit2(Rgev, method='mps');
-gm = ws.genextreme.fit2(Rgev, *gp.par.tolist(), method='ml')
-gm.plotfitsummary()
-
-gp.plotecdf()
-plt.hold(True)
-plt.plot(x, gm.cdf(x), '--')
-plt.hold(False)
-#wafostamp([],'(ER)')
-#disp('Block =7'),pause(pstate)
-
-##
-# ;
-Rgpd = ws.genpareto.rvs(0.4,0, 1,size=100)
-gp = ws.genpareto.fit2(Rgpd, method='mps')
-gml = ws.genpareto.fit2(Rgpd, method='ml')
-
-gp.plotecdf()
-x = sorted(Rgpd)
-plt.hold(True)
-plt.plot(x, gml.cdf(x))
-# gm = fitgenpar(Rgpd,'method','mom','plotflag',0);
-# plot(x,cdfgenpar(x,gm),'g--')
-#gw = fitgenpar(Rgpd,'method','pwm','plotflag',0);
-#plot(x,cdfgenpar(x,gw),'g:')
-#gml = fitgenpar(Rgpd,'method','ml','plotflag',0);
-#plot(x,cdfgenpar(x,gml),'--')
-#gmps = fitgenpar(Rgpd,'method','mps','plotflag',0);
-#plot(x,cdfgenpar(x,gmps),'r-.')
-plt.hold(False)
-#wafostamp([],'(ER)')
-#disp('Block = 8'),pause(pstate)
-
-##
-# Return values for the GEV distribution
-T = np.logspace(1, 5, 10);
-#[sT, sTlo, sTup] = invgev(1./T,Y5gev,'lowertail',false,'proflog',true);
-
-#T = 2:100000;
-#k=Y5gev.params(1); mu=Y5gev.params(3); sigma=Y5gev.params(2);
-#sT1 = invgev(1./T,Y5gev,'lowertail',false);
-#sT=mu + sigma/k*(1-(-log(1-1./T)).^k);
-plt.clf()
-#plt.semilogx(T,sT,T,sTlo,'r',T,sTup,'r')
-#plt.hold(True)
-#N = np.r_[1:len(Y5M)]
-#Nmax = max(N);
-#plot(Nmax./N, sorted(Y5M,reverse=True), '.')
-#plt.title('Return values in the GEV model')
-#plt.xlabel('Return priod')
-#plt.ylabel('Return value')
-#plt.grid(True)
-#disp('Block = 9'),pause(pstate)
-
-## Section 5.3 POT-analysis
-
-# Estimated expected exceedance over level u as function of u.
-plt.clf()
-
-mrl = ws.reslife(Hs,'umin',2,'umax',10,'Nu',200);
-mrl.plot()
-#wafostamp([],'(ER)')
-#disp('Block = 10'),pause(pstate)
-
-##
-# Estimated distribution functions of monthly maxima
-#with the POT method (solid),
-# fitting a GEV (dashed) and the empirical distribution.
-
-# POT- method
-gpd7 = ws.genpareto.fit2(Hs(Hs>7)-7, method='mps', floc=0)
-khat, loc, sigmahat = gpd7.par
-
-muhat = len(Hs[Hs>7])/(7*3*2)
-bhat = sigmahat/muhat**khat
-ahat = 7-(bhat-sigmahat)/khat
-x = np.linspace(5,15,200);
-plt.plot(x,ws.genextreme.cdf(x, khat,bhat,ahat))
-# disp('Block = 11'),pause(pstate)
-
-##
-# Since we have data to compute the monthly maxima mm over
-#42 months we can also try to fit a
-# GEV distribution directly:
-mm = np.zeros((1,41))
-for i in range(41):
- mm[i] = max(Hs[((i-1)*14+1):i*14])
-
-
-gev = ws.genextreme.fit2(mm)
-
-
-plt.hold(True)
-gev.plotecdf()
-
-plt.hold(False)
-#wafostamp([],'(ER)')
-#disp('Block = 12, Last block'),pause(pstate)
-
diff --git a/wafo/doc/tutorial_scripts/rainflow_example.py b/wafo/doc/tutorial_scripts/rainflow_example.py
deleted file mode 100644
index 75cac99..0000000
--- a/wafo/doc/tutorial_scripts/rainflow_example.py
+++ /dev/null
@@ -1,176 +0,0 @@
-from tutor_init import *
-import itertools
-# import sys
-log = logging.getLogger(__name__)
-log.setLevel(logging.DEBUG)
-
-MARKERS = ('o', 'x', '+', '.', '<', '>', '^', 'v')
-
-
-def plot_varying_symbols(x, y, color='red', size=5):
- """
- Create a plot with varying symbols
- Parameters
- ----------
- x : numpy array with x data of the points
- y : numpy array with y data of the points
- color : color of the symbols
-
- Returns
- -------
-
- """
- markers = itertools.cycle(MARKERS)
- for q, p in zip(x, y):
- plt.plot(q, p, marker=markers.next(), linestyle='', color=color,
- markersize=size)
-
-
-def damage_vs_S(S, beta, K):
- """
- Calculate the damage 1/N for a given stress S
-
- Parameters
- ----------
- S : Stress [Pa]
- beta : coefficient, typically 3
- K : constant
-
- Returns
- -------
-
- """
- return K * np.power(S, beta)
-
-# Section 4.3.1 Crossing intensity
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-import wafo.data as wd
-import wafo.objects as wo
-import wafo.misc as wm
-
-xx_sea = wd.sea()
-
-Tlength = xx_sea[-1, 0] - xx_sea[0, 0]
-beta = 3
-K1 = 6.5e-31
-Np = 200
-Tp = Tlength / Np
-A = 100e6
-log.info("setting sin wave with Tp={} and T={}".format(Tp, Tlength))
-Nc = 1.0 / damage_vs_S(A, beta, K1)
-damage = float(Np) / float(Nc)
-log.info("budget at S={} N={}: damage = {} ".format(A, Nc, damage))
-#xx_sea[:, 1] = A * np.cos(2 * np.pi * xx_sea[:, 0]/Tp)
-xx_sea[:, 1] *= 500e6
-
-log.info("loaded sea time series {}".format(xx_sea.shape))
-ts = wo.mat2timeseries(xx_sea)
-
-tp = ts.turning_points()
-mM = tp.cycle_pairs(kind='min2max')
-Mm = tp.cycle_pairs(kind='max2min')
-lc = mM.level_crossings(intensity=True)
-T_sea = ts.args[-1] - ts.args[0]
-
-# for i in dir(mM):
-# print(i)
-
-
-ts1 = wo.mat2timeseries(xx_sea[:, :])
-tp1 = ts1.turning_points()
-sig_tp = ts.turning_points(h=0, wavetype='astm')
-try:
- sig_cp = sig_tp.cycle_astm()
- log.info("Successfully used cycle_astm")
-except AttributeError:
- log.warning("Could use cycle_astm")
- sig_cp = None
-tp1 = ts1.turning_points()
-tp2 = ts1.turning_points(wavetype='Mw')
-mM1 = tp1.cycle_pairs(kind='min2max')
-Mm1 = tp1.cycle_pairs(kind='max2min')
-
-tp_rfc = tp1.rainflow_filter(h=100e6)
-mM_rfc = tp_rfc.cycle_pairs()
-try:
- mM_rfc_a = tp1.cycle_astm()
-except AttributeError:
- mM_rfc_a = None
-tc1 = ts1.trough_crest()
-min_to_max = True
-rfc_plot = True
-if min_to_max:
- m1, M1 = mM1.get_minima_and_maxima()
- i_min_start = 0
-else:
- m1, M1 = Mm1.get_minima_and_maxima()
- i_min_start = 2
-
-m_rfc, M_rfc = mM_rfc.get_minima_and_maxima()
-# m_rfc_a, M_rfc_a = mM_rfc_a.get_minima_and_maxima()
-ts1.plot('b-')
-if rfc_plot:
- plot_varying_symbols(tp_rfc.args[0::2], m_rfc, color='red', size=10)
- plot_varying_symbols(tp_rfc.args[1::2], M_rfc, color='green', size=10)
-else:
- plot_varying_symbols(tp.args[i_min_start::2], m1, color='red', size=10)
- plot_varying_symbols(tp.args[1::2], M1, color='green', size=10)
-
-set_windows_title("Sea time series", log)
-
-plt.figure()
-plt.subplot(122),
-mM.plot()
-plt.title('min-max cycle pairs')
-plt.subplot(121),
-mM_rfc.plot()
-
-title = 'Rainflow filtered cycles'
-plt.title(title)
-set_windows_title(title)
-
-
-# Min-max and rainflow cycle distributions
-#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-# import wafo.misc as wm
-ampmM_sea = mM.amplitudes()
-ampRFC_sea = mM_rfc.amplitudes()
-plt.figure()
-title = "s_n_curve"
-set_windows_title(title)
-S = np.linspace(1e6, 1000e6)
-plt.loglog(S, damage_vs_S(S, beta, K1))
-plt.figure()
-plt.subplot(121)
-stress_range = (1, 1e9)
-n_bins = 100
-wm.plot_histgrm(ampmM_sea, bins=n_bins, range=stress_range)
-plt.xlim(stress_range)
-ylim = plt.gca().get_ylim()
-plt.title('min-max amplitude distribution')
-plt.subplot(122)
-if sig_cp is not None:
- wm.plot_histgrm(sig_cp[:, 0], bins=n_bins, range=stress_range)
- plt.gca().set_ylim(ylim)
- title = 'Rainflow amplitude distribution'
- plt.title(title)
- plt.semilogy
- set_windows_title(title)
-
- hist, bin_edges = np.histogram(
- sig_cp[
- :, 0], bins=n_bins, range=stress_range)
-
- plt.figure()
- title = "my_bins"
- plt.title(title)
- plt.title(title)
- set_windows_title(title)
- plt.semilogy
- plt.bar(bin_edges[:-1], hist, width=stress_range[1] / n_bins)
-
- print("damage min/max : {}".format(mM_rfc.damage([beta], K1)))
-
- damage_rfc = K1 * np.sum(sig_cp[:, 0] ** beta)
- print("damage rfc : {}".format(damage_rfc))
-plt.show('hold')
diff --git a/wafo/doc/tutorial_scripts/tutor_init.py b/wafo/doc/tutorial_scripts/tutor_init.py
deleted file mode 100644
index 849f589..0000000
--- a/wafo/doc/tutorial_scripts/tutor_init.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import logging
-import scipy as sp
-import numpy as np
-from numpy import pi, reshape
-import matplotlib.pyplot as plt
-import matplotlib
-matplotlib.use('Qt4Agg')
-from matplotlib import rcParams
-rcParams.update({"font.size": 10})
-
-
-try:
- from win32api import LoadResource
-except ImportError:
- pass
-
-log = logging.basicConfig(
- format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
- level=logging.DEBUG)
-
-
-def set_windows_title(title, log=None):
- if log is not None:
- log.info("Set windows title {}".format(title))
- fig = plt.gcf()
- fig.canvas.set_window_title(title)
- plt.show()
diff --git a/wafo/sg_filter/__init__.py b/wafo/sg_filter/__init__.py
deleted file mode 100644
index eebc78c..0000000
--- a/wafo/sg_filter/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from wafo.sg_filter._core import * # pylint: disable=wildcard-import
-from wafo.sg_filter import demos
diff --git a/wafo/sg_filter/_core.py b/wafo/sg_filter/_core.py
deleted file mode 100644
index e1cb42c..0000000
--- a/wafo/sg_filter/_core.py
+++ /dev/null
@@ -1,1223 +0,0 @@
-from __future__ import absolute_import, division
-import numpy as np
-from numpy import (pi, linalg, concatenate, sqrt)
-from scipy.sparse import spdiags
-from scipy.sparse.linalg import spsolve
-import scipy.optimize as optimize
-from scipy.signal import _savitzky_golay
-from scipy.ndimage import convolve1d
-from scipy.ndimage.morphology import distance_transform_edt
-import warnings
-from wafo.dctpack import dctn, idctn
-
-
-__all__ = ['SavitzkyGolay', 'Kalman', 'HodrickPrescott', 'smoothn',
- 'HampelFilter', 'SmoothNd', 'noise']
-
-# noise = np.random.randn(2**8)/10
-noise = [-0.0490483773397234, 0.07101522794824691, 0.043129450693516064, 0.07858516767729644, -0.04489848540755172, -0.012710090966021995, 0.022967442347004003, -0.1593564930543959, 0.14752458454255937, -0.1220055819473534, -0.030151822649201642, 0.009880871420067841, 0.0401050562035102, -0.10931262882008379, -0.14550620919429919, -0.06632845063372966, 0.07773893951749064, -0.009527784302072342, 0.06002486046176557, 0.11972670522904964, -0.14436696992162384, 0.06009486605688445, -0.05802790838575894, 0.16964239368289297, 0.09088881573238144, -0.003398259264109856, 0.059830811447018004, -0.08189024981767952, -0.05455483548325317, 0.056651518536760745, -0.05211609539593189, -0.07848323826083178, -0.03921692262168154, -0.04755275276447492, -0.05855172473750038, 0.06480280696345982, -0.05237889271019207, -0.05891912551792037, -0.04045907452295067, -0.09058522124919187, 0.1406515441218336, 0.15557979603588584, -0.09096515320242772, 0.1724190189462715, -0.04978942687488187, -0.0855435866249914, 0.09439718859306868, -0.14758639479507882, -0.07225230856508442, 0.008364508824556314, 0.06704423745152435, -0.01718113731784587, 0.07473943576290255, 0.028133087670974395, 0.026270590730899095, 0.13175770484080895, -0.01821821552644416, 0.11325945472394446, 0.04694754851273185, -0.23899404962137366, -0.1528175431702195, 0.151870532421663, -0.07353204927616248, 0.11604199430172217, -0.09111623325687843, -0.11887366073405607, -0.029872397510562025, 0.047672685028458936, -0.18340065977268627, 0.06896217941210328, 0.042997912112300564, 0.15416998299846174, -0.0386283794526545, 0.14070600624229804, 0.020984623041646142, -0.1892741373898864, 0.03253519397457513, -0.06182705494266229, -0.1326495728975159, 0.026234150321195537, 0.0550541170409239, 0.029275813927566702, 0.042742104678489906, -0.2170004668366198, -0.00035991761313413197, -0.0638872684868346, -0.11769436550364845, -0.017792813824766808, -0.022786402363044914, -0.10668279890162544, 0.05979507681729831, -0.1008100479486818, 0.0703474638610785, 0.1630534776572414, 0.06682406484481357, -0.0527228810042394, -0.046515310355062636, 0.04609515154732255, 0.11503753838360875, 0.11517599661346192, -0.05596425736274815, -0.06149119758833357, 0.10599964719188917, -0.012076380140185552, 0.0436828262270732, -0.03910174791470852, -0.03263251315745414, -0.012513843545007558, 0.004590611827089213, 0.0762719171282112, 0.06497715695411535, -0.003280826953794463, 0.13524154885565484, -0.020441364843140027, -0.09488214173137496, 0.1385755359902911, -0.23883052310744746, -0.10110537386421652, -0.1588981058869149, 0.06645444828058467, -0.2103306051703948, 0.15215327561190056, -0.03582175680076989, 0.013593833383013293, -0.11542058494732854, -0.05613268116816099, 0.012711037661355899, 0.04242805633100794, -0.011799315325220794, 0.12141794601099387, 0.054285270560662645, 0.07549385527022169, -0.04549437694653443, 0.11009856942530691, 0.05233482224379645, -0.042246830306136955, -0.1737197924666796, -0.10589427330127077, 0.04895472597843757, 0.06756519832636187, 0.083376600742245, -0.07502859751328732, -0.09493802498812245, -0.01058967186080922, -0.23759763247649018, 0.08439637862616411, -0.2021754550870607, 0.07365816800912013, 0.07435401663661081, 0.047992791325423556, -0.005250092450514997, 0.1693610927865244, 0.030338113772413154, -0.18010537945928004, 0.01744129379023785, 0.1902505975745975, -0.004598733688659104, 0.13663542585715657, -0.04100719174496187, -0.15406303185009937, -0.05297118247908407, 0.04435144348234146, 0.022377061632995063, 0.05491057192661079, -0.08473062163887303, -0.03907641665824873, 0.008686833182075315, -0.06053451866471732, -0.051735892949367854, -0.1902071038920444, 0.11508817132666356, 0.08903045262390544, -0.028537865059606825, -0.07160660523436188, 0.05994760363400714, 0.03637820115278829, 0.027604828657436364, 0.04168122074675033, -0.021707671111253164, 0.06770739385070886, -0.04848505599153394, -0.14377853380839264, 0.17448368721141166, -0.05972663746675887, -0.1615729579782888, -0.09508063624538736, -0.05501964872264433, -0.14370852991216054, -0.1025241548369181, -0.14751000180775747, -0.05402976681470177, -0.05847606145915367, 0.015603559358987138, 0.040327317968149784, 0.015596571983936361, 0.08721780106901023, 0.13669912032986667, -0.07070030973798198, 0.04821782065785363, 0.05266507025196321, -0.013775127999269254, 0.07032239356769251, 0.04685048562398681, 0.004648720572365418, -0.19364418622487742, 0.013662994215276983, 0.04703494294810789, 0.04863794676207257, -0.09883919097676001, -0.004798538894290822, -0.22183503742087135, 0.062096556899520906, 0.07098373434409047, -0.05335639719762188, -0.09150459514627822, -0.1329311651202703, -0.037376442133682145, 0.1238732233009325, -0.01232052797514208, 0.007151238520555889, -0.04772828461473576, -0.029830395387364726, -0.03277336781995001, 0.09964048194066656, 0.09306408040020697, -0.03761782769337173, 0.07059549032551317, -0.15490333414875848, 0.12599077783991805, 0.23520519946427365, 0.021640305946603107, 0.014851729969403227, -0.039035437601777224, -0.12087588583684257, -0.07207855860199022, -0.002800081649022032, 0.2543907308881692, -0.07966223382328289, -0.1014419766425384, -0.11243061225437859, -0.08744845956375621, -0.05540140267769189, -0.04995531421885231, -0.13274847220288336, 0.06435474943034288, 0.015640361472736924, -0.11210644205346465, -0.04080648821849449, -0.011452694652695428, 0.22044736923317904, 0.024322228245949113, 0.09622705616884256, 0.05793212184654495, -0.10620553812614748, 0.06762504431789758, 0.19135075519983785] # nopep8
-
-
-def _assert(cond, msg):
- if not cond:
- raise ValueError(msg)
-
-
-class SavitzkyGolay(object):
- r"""Smooth and optionally differentiate data with a Savitzky-Golay filter.
-
- The Savitzky-Golay filter removes high frequency noise from data.
- It has the advantage of preserving the original shape and
- features of the signal better than other types of filtering
- approaches, such as moving averages techniques.
-
- Parameters
- ----------
- n : int
- the size of the smoothing window is 2*n+1.
- degree : int
- the degree of the polynomial used in the filtering.
- Must be less than `window_size` - 1, i.e, less than 2*n.
- diff_order : int
- order of the derivative to compute (default = 0 means only smoothing)
- 0 means that filter results in smoothing of function
- 1 means that filter results in smoothing the first derivative of the
- function and so on ...
- delta : float, optional
- The spacing of the samples to which the filter will be applied.
- This is only used if deriv > 0. Default is 1.0.
- axis : int, optional
- The axis of the array `x` along which the filter is to be applied.
- Default is -1.
- mode : str, optional
- Must be 'mirror', 'constant', 'nearest', 'wrap' or 'interp'. This
- determines the type of extension to use for the padded signal to
- which the filter is applied. When `mode` is 'constant', the padding
- value is given by `cval`. See the Notes for more details on 'mirror',
- 'constant', 'wrap', and 'nearest'.
- When the 'interp' mode is selected (the default), no extension
- is used. Instead, a degree `polyorder` polynomial is fit to the
- last `window_length` values of the edges, and this polynomial is
- used to evaluate the last `window_length // 2` output values.
- cval : scalar, optional
- Value to fill past the edges of the input if `mode` is 'constant'.
- Default is 0.0.
-
- Notes
- -----
- The Savitzky-Golay is a type of low-pass filter, particularly suited for
- smoothing noisy data. The main idea behind this approach is to make for
- each point a least-square fit with a polynomial of high order over a
- odd-sized window centered at the point.
-
- Details on the `mode` options:
-
- 'mirror':
- Repeats the values at the edges in reverse order. The value
- closest to the edge is not included.
- 'nearest':
- The extension contains the nearest input value.
- 'constant':
- The extension contains the value given by the `cval` argument.
- 'wrap':
- The extension contains the values from the other end of the array.
-
- For example, if the input is [1, 2, 3, 4, 5, 6, 7, 8], and
- `window_length` is 7, the following shows the extended data for
- the various `mode` options (assuming `cval` is 0)::
-
- mode | Ext | Input | Ext
- -----------+---------+------------------------+---------
- 'mirror' | 4 3 2 | 1 2 3 4 5 6 7 8 | 7 6 5
- 'nearest' | 1 1 1 | 1 2 3 4 5 6 7 8 | 8 8 8
- 'constant' | 0 0 0 | 1 2 3 4 5 6 7 8 | 0 0 0
- 'wrap' | 6 7 8 | 1 2 3 4 5 6 7 8 | 1 2 3
-
- Examples
- --------
- >>> import wafo.sg_filter as ws
- >>> t = np.linspace(-4, 4, 500)
- >>> noise = np.random.normal(0, 0.05, t.shape)
- >>> noise = np.sqrt(0.05)*np.sin(100*t)
- >>> y = np.exp( -t**2 ) + noise
- >>> ysg = SavitzkyGolay(n=20, degree=2).smooth(y)
- >>> np.allclose(ysg[:3], [ 0.01345312, 0.01164172, 0.00992839])
- True
- >>> ysm = SavitzkyGolay(n=20, degree=2, mode='mirror').smooth(y)
- >>> np.allclose(ysm[:3], [-0.01604804, -0.00592883, 0.0035858 ])
- True
- >>> ysc = SavitzkyGolay(n=20, degree=2, mode='constant').smooth(y)
- >>> np.allclose(ysc[:3], [-0.00279797, 0.00519541, 0.00666146])
- True
- >>> ysn = SavitzkyGolay(n=20, degree=2, mode='nearest').smooth(y)
- >>> np.allclose(ysn[:3], [ 0.08711171, 0.0846945 , 0.07587448])
- True
- >>> ysw = SavitzkyGolay(n=20, degree=2, mode='wrap').smooth(y)
- >>> np.allclose(ysw[:3], [-0.00208422, -0.00201491, 0.00201772])
- True
- >>> np.allclose(SavitzkyGolay(n=20, degree=2).smooth_last(y),
- ... 0.004921382626100505)
- True
-
- import matplotlib.pyplot as plt
- h = plt.plot(t, y, label='Noisy signal')
- h1 = plt.plot(t, np.exp(-t**2), 'k', lw=1.5, label='Original signal')
- h2 = plt.plot(t, ysg, 'r', label='Filtered signal')
- h3 = plt.legend()
- h4 = plt.title('Savitzky-Golay')
- plt.show()
-
- References
- ----------
- .. [1] A. Savitzky, M. J. E. Golay, Smoothing and Differentiation of
- Data by Simplified Least Squares Procedures. Analytical
- Chemistry, 1964, 36 (8), pp 1627-1639.
- .. [2] Numerical Recipes 3rd Edition: The Art of Scientific Computing
- W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery
- Cambridge University Press ISBN-13: 9780521880688
- """
-
- def __init__(self, n, degree=1, diff_order=0, delta=1.0, axis=-1,
- mode='interp', cval=0.0):
- self.n = n
- self.degree = degree
- self.diff_order = diff_order
- self.mode = mode
- self.cval = cval
- self.axis = axis
- self.delta = delta
- window_length = 2 * n + 1
- self._coeff = _savitzky_golay.savgol_coeffs(window_length,
- degree, deriv=diff_order,
- delta=delta)
-
- def smooth_last(self, signal, k=1):
- coeff = self._coeff
- n = (np.size(coeff) - 1) // 2
- y = np.squeeze(signal)
- if n == 0:
- return y
- if y.ndim > 1:
- coeff.shape = (-1, 1)
- first_vals = y[0] - np.abs(y[n:0:-1] - y[0])
- last_vals = y[-1] + np.abs(y[-2:-n - 2:-1] - y[-1])
- y = concatenate((first_vals, y, last_vals))
- return (y[-2 * n - 1 - k:-k] * coeff).sum(axis=0)
-
- def __call__(self, signal):
- return self.smooth(signal)
-
- def smooth(self, signal):
- dtype = np.result_type(signal, np.float)
- x = np.asarray(signal, dtype=dtype)
-
- coeffs = self._coeff
- mode, axis = self.mode, self.axis
- if mode == "interp":
- window_length, polyorder = self.n * 2 + 1, self.degree
- deriv, delta = self.diff_order, self.delta
- y = convolve1d(x, coeffs, axis=axis, mode="constant")
- _savitzky_golay._fit_edges_polyfit(x, window_length, polyorder,
- deriv, delta, axis, y)
- else:
- y = convolve1d(x, coeffs, axis=axis, mode=mode, cval=self.cval)
- return y
-
-
-def evar(y):
- """Noise variance estimation. Assuming that the deterministic function Y
- has additive Gaussian noise, EVAR(Y) returns an estimated variance of this
- noise.
-
- Note:
- ----
- A thin-plate smoothing spline model is used to smooth Y. It is assumed
- that the model whose generalized cross-validation score is minimum can
- provide the variance of the additive noise. A few tests showed that
- EVAR works very well with "not too irregular" functions.
-
- Examples:
- --------
- 1D signal
- >>> n = 1e6
- >>> x = np.linspace(0,100,n);
- >>> y = np.cos(x/10)+(x/50)
- >>> var0 = 0.02 # noise variance
- >>> yn = y + sqrt(var0)*np.random.randn(*y.shape)
- >>> s = evar(yn) # estimated variance
- >>> np.abs(s-var0)/var0 < 3.5/np.sqrt(n)
- True
-
- 2D function
- >>> xp = np.linspace(0,1,50)
- >>> x, y = np.meshgrid(xp,xp)
- >>> f = np.exp(x+y) + np.sin((x-2*y)*3)
- >>> var0 = 0.04 # noise variance
- >>> fn = f + sqrt(var0)*np.random.randn(*f.shape)
- >>> s = evar(fn) # estimated variance
- >>> np.abs(s-var0)/var0 < 3.5/np.sqrt(50)
- True
-
- 3D function
- >>> yp = np.linspace(-2,2,50)
- >>> [x,y,z] = np.meshgrid(yp, yp, yp, sparse=True)
- >>> f = x*np.exp(-x**2-y**2-z**2)
- >>> var0 = 0.5 # noise variance
- >>> fn = f + np.sqrt(var0)*np.random.randn(*f.shape)
- >>> s = evar(fn) # estimated variance
- >>> np.abs(s-var0)/var0 < 3.5/np.sqrt(50)
- True
-
- Other example
- -------------
- http://www.biomecardio.com/matlab/evar.html
-
- Note:
- ----
- EVAR is only adapted to evenly-gridded 1-D to N-D data.
-
- See also
- --------
- VAR, STD, SMOOTHN
-
- """
-
- # Damien Garcia -- 2008/04, revised 2009/10
- y = np.atleast_1d(y)
- d = y.ndim
- sh0 = y.shape
-
- S = np.zeros(sh0)
- sh1 = np.ones((d,), dtype=np.int64)
- cos = np.cos
- for i in range(d):
- ni = sh0[i]
- sh1[i] = ni
- t = np.arange(ni).reshape(sh1) / ni
- S += cos(pi * t)
- sh1[i] = 1
-
- S2 = 2 * (d - S).ravel()
- # N-D Discrete Cosine Transform of Y
- dcty2 = dctn(y).ravel() ** 2
-
- def score_fun(L, S2, dcty2):
- # Generalized cross validation score
- M = 1 - 1. / (1 + 10 ** L * S2)
- noisevar = (dcty2 * M ** 2).mean()
- return noisevar / M.mean() ** 2
- # fun = lambda x : score_fun(x, S2, dcty2)
- Lopt = optimize.fminbound(score_fun, -38, 38, args=(S2, dcty2))
- M = 1.0 - 1.0 / (1 + 10 ** Lopt * S2)
- noisevar = (dcty2 * M ** 2).mean()
- return noisevar
-
-
-class _Filter(object):
- def __init__(self, y, z0, weightstr, weights, s, robust, maxiter, tolz):
- self.y = y
- self.z0 = z0
- self.weightstr = weightstr
- self.s = s
- self.robust = robust
- self.maxiter = maxiter
- self.tolz = tolz
-
- self.auto_smooth = s is None
- self.is_finite = np.isfinite(y)
- self.nof = self.is_finite.sum() # number of finite elements
- self.W = self._normalized_weights(weights, self.is_finite)
-
- self.gamma = self._gamma_fun(y)
-
- self.N = self._tensor_rank(y)
- self.s_min, self.s_max = self._smoothness_limits(self.N)
-
- # Initialize before iterating
- self.Wtot = self.W
- self.is_weighted = (self.W < 1).any() # Weighted or missing data?
-
- self.z0 = self._get_start_condition(y, z0)
-
- self.y[~self.is_finite] = 0 # arbitrary values for missing y-data
-
- # Error on p. Smoothness parameter s = 10^p
- self.errp = 0.1
-
- # Relaxation factor RF: to speedup convergence
- self.RF = 1.75 if self.is_weighted else 1.0
-
- @staticmethod
- def _tensor_rank(y):
- """tensor rank of the y-array"""
- return (np.array(y.shape) != 1).sum()
-
- @staticmethod
- def _smoothness_par(h):
- return (((1 + sqrt(1 + 8 * h)) / 4. / h) ** 2 - 1) / 16
-
- def _smoothness_limits(self, n):
- """
- Return upper and lower bound for the smoothness parameter
-
- The average leverage (h) is by definition in [0 1]. Weak smoothing
- occurs if h is close to 1, while over-smoothing appears when h is
- near 0. Upper and lower bounds for h are given to avoid under- or
- over-smoothing. See equation relating h to the smoothness parameter
- (Equation #12 in the referenced CSDA paper).
- """
- h_min = 1e-6 ** (2. / n)
- h_max = 0.99 ** (2. / n)
-
- s_min = self._smoothness_par(h_max)
- s_max = self._smoothness_par(h_min)
- return s_min, s_max
-
- @staticmethod
- def _lambda_tensor(y):
- """
- Return the Lambda tensor
-
- Lambda contains the eigenvalues of the difference matrix used in this
- penalized least squares process.
- """
- d = y.ndim
- Lambda = np.zeros(y.shape)
- shape0 = [1, ] * d
- for i in range(d):
- shape0[i] = y.shape[i]
- Lambda = Lambda + \
- np.cos(pi * np.arange(y.shape[i]) / y.shape[i]).reshape(shape0)
- shape0[i] = 1
- Lambda = -2 * (d - Lambda)
- return Lambda
-
- def _gamma_fun(self, y):
- Lambda = self._lambda_tensor(y)
-
- def gamma(s):
- if s is None:
- return 1.0
- return 1. / (1 + s * Lambda ** 2)
- return gamma
-
- @staticmethod
- def _initial_guess(y, I):
- # Initial Guess with weighted/missing data
- # nearest neighbor interpolation (in case of missing values)
- z = y
- if (1 - I).any():
- notI = ~I
- z, L = distance_transform_edt(notI, return_indices=True)
- z[notI] = y[L.flat[notI]]
-
- # coarse fast smoothing using one-tenth of the DCT coefficients
- shape = z.shape
- d = z.ndim
- z = dctn(z)
- for k in range(d):
- z[int((shape[k] + 0.5) / 10) + 1::, ...] = 0
- z = z.reshape(np.roll(shape, -k))
- z = z.transpose(np.roll(range(d), -1))
- # z = shiftdim(z,1);
- return idctn(z)
-
- def _get_start_condition(self, y, z0):
- # Initial conditions for z
- if self.is_weighted:
- # With weighted/missing data
- # An initial guess is provided to ensure faster convergence. For
- # that purpose, a nearest neighbor interpolation followed by a
- # coarse smoothing are performed.
- if z0 is None:
- z = self._initial_guess(y, self.is_finite)
- else:
- z = z0 # an initial guess (z0) has been provided
- else:
- z = np.zeros(y.shape)
- return z
-
- @staticmethod
- def _normalized_weights(weight, is_finite):
- """ Return normalized weights.
-
- Zero weights are assigned to not finite values (Inf or NaN),
- (Inf/NaN values = missing data).
- """
- weights = weight * is_finite
- _assert(np.all(0 <= weights), 'Weights must all be >=0')
- return weights / weights.max()
-
- @staticmethod
- def _studentized_residuals(r, I, h):
- median_abs_deviation = np.median(np.abs(r[I] - np.median(r[I])))
- return np.abs(r / (1.4826 * median_abs_deviation) / sqrt(1 - h))
-
- def robust_weights(self, r, I, h):
- """Return weights for robust smoothing."""
- def bisquare(u):
- c = 4.685
- return (1 - (u / c) ** 2) ** 2 * ((u / c) < 1)
-
- def talworth(u):
- c = 2.795
- return u < c
-
- def cauchy(u):
- c = 2.385
- return 1. / (1 + (u / c) ** 2)
-
- u = self._studentized_residuals(r, I, h)
-
- wfun = {'cauchy': cauchy, 'talworth': talworth}.get(self.weightstr,
- bisquare)
- weights = wfun(u)
-
- weights[np.isnan(weights)] = 0
- return weights
-
- @staticmethod
- def _average_leverage(s, N):
- h = sqrt(1 + 16 * s)
- h = sqrt(1 + h) / sqrt(2) / h
- return h ** N
-
- def check_smooth_parameter(self, s):
- if self.auto_smooth:
- if np.abs(np.log10(s) - np.log10(self.s_min)) < self.errp:
- warnings.warn("""s = %g: the lower bound for s has been reached.
- Put s as an input variable if required.""" % s)
- elif np.abs(np.log10(s) - np.log10(self.s_max)) < self.errp:
- warnings.warn("""s = %g: the Upper bound for s has been reached.
- Put s as an input variable if required.""" % s)
-
- def gcv(self, p, aow, DCTy, y, Wtot):
- # Search the smoothing parameter s that minimizes the GCV score
- s = 10.0 ** p
- gamma_s = self.gamma(s)
- if aow > 0.9:
- # aow = 1 means that all of the data are equally weighted
- # very much faster: does not require any inverse DCT
- residual = DCTy.ravel() * (gamma_s.ravel() - 1)
- else:
- # take account of the weights to calculate RSS:
- is_finite = self.is_finite
- yhat = idctn(gamma_s * DCTy)
- residual = sqrt(Wtot[is_finite]) * (y[is_finite] - yhat[is_finite])
-
- TrH = gamma_s.sum()
- RSS = linalg.norm(residual)**2 # Residual sum-of-squares
- GCVscore = RSS / self.nof / (1.0 - TrH / y.size) ** 2
- return GCVscore
-
- def _smooth(self, z, s):
- auto_smooth = self.auto_smooth
- norm = linalg.norm
- y = self.y
- Wtot = self.Wtot
- gamma_s = self.gamma(s)
- # "amount" of weights (see the function GCVscore)
- aow = Wtot.sum() / y.size # 0 < aow <= 1
- for nit in range(self.maxiter):
- DCTy = dctn(Wtot * (y - z) + z)
- if auto_smooth and not np.remainder(np.log2(nit + 1), 1):
- # The generalized cross-validation (GCV) method is used.
- # We seek the smoothing parameter s that minimizes the GCV
- # score i.e. s = Argmin(GCVscore).
- # Because this process is time-consuming, it is performed from
- # time to time (when nit is a power of 2)
- log10s = optimize.fminbound(
- self.gcv, np.log10(self.s_min), np.log10(self.s_max),
- args=(aow, DCTy, y, Wtot),
- xtol=self.errp, full_output=False, disp=False)
- s = 10 ** log10s
- gamma_s = self.gamma(s)
- z0 = z
- z = self.RF * idctn(gamma_s * DCTy) + (1 - self.RF) * z
- # if no weighted/missing data => tol=0 (no iteration)
- tol = norm(z0.ravel() - z.ravel()) / norm(z.ravel())
- converged = tol <= self.tolz or not self.is_weighted
- if converged:
- break
- return z, s, converged
-
- def __call__(self, z, s):
- z, s, converged = self._smooth(z, s)
- if self.robust:
- # -- Robust Smoothing: iteratively re-weighted process
- h = self._average_leverage(s, self.N)
- self.Wtot = self.W * self.robust_weights(self.y - z,
- self.is_finite, h)
- # re-initialize for another iterative weighted process
- self.is_weighted = True
- return z, s, converged
-
-
-class SmoothNd(object):
- def __init__(self, s=None, weight=None, robust=False, z0=None, tolz=1e-3,
- maxiter=100, fulloutput=False):
- self.s = s
- self.weight = weight
- self.robust = robust
- self.z0 = z0
- self.tolz = tolz
- self.maxiter = maxiter
- self.fulloutput = fulloutput
-
- @property
- def weightstr(self):
- if isinstance(self._weight, str):
- return self._weight.lower()
- return 'bisquare'
-
- @property
- def weight(self):
- if self._weight is None or isinstance(self._weight, str):
- return 1.0
- return self._weight
-
- @weight.setter
- def weight(self, weight):
- self._weight = weight
-
- def _init_filter(self, y):
- return _Filter(y, self.z0, self.weightstr, self.weight, self.s,
- self.robust, self.maxiter, self.tolz)
-
- @property
- def num_steps(self):
- return 3 if self.robust else 1
-
- def __call__(self, data):
-
- y = np.atleast_1d(data)
- if y.size < 2:
- return data
-
- _filter = self._init_filter(y)
- z = _filter.z0
- s = _filter.s
- converged = False
- for _i in range(self.num_steps):
- z, s, converged = _filter(z, s)
-
- if not converged:
- msg = """Maximum number of iterations (%d) has been exceeded.
- Increase MaxIter option or decrease TolZ value.""" % (self.maxiter)
- warnings.warn(msg)
-
- _filter.check_smooth_parameter(s)
-
- if self.fulloutput:
- return z, s
- return z
-
-
-def smoothn(data, s=None, weight=None, robust=False, z0=None, tolz=1e-3,
- maxiter=100, fulloutput=False):
- """
- SMOOTHN fast and robust spline smoothing for 1-D to N-D data.
-
- Parameters
- ----------
- data : array like
- uniformly-sampled data array to smooth. Non finite values (NaN or Inf)
- are treated as missing values.
- s : real positive scalar
- smooting parameter. The larger S is, the smoother the output will be.
- Default value is automatically determined using the generalized
- cross-validation (GCV) method.
- weight : string or array weights
- weighting array of real positive values, that must have the same size
- as DATA. Note that a zero weight corresponds to a missing value.
- robust : bool
- If true carry out a robust smoothing that minimizes the influence of
- outlying data.
- tolz : real positive scalar
- Termination tolerance on Z (default = 1e-3)
- maxiter : scalar integer
- Maximum number of iterations allowed (default = 100)
- z0 : array-like
- Initial value for the iterative process (default = original data)
-
- Returns
- -------
- z : array like
- smoothed data
-
- To be made
- ----------
- Estimate the confidence bands (see Wahba 1983, Nychka 1988).
-
- Reference
- ---------
- Garcia D, Robust smoothing of gridded data in one and higher dimensions
- with missing values. Computational Statistics & Data Analysis, 2010.
- http://www.biomecardio.com/pageshtm/publi/csda10.pdf
-
- Examples:
- --------
-
- 1-D example
- >>> import matplotlib.pyplot as plt
- >>> import wafo.sg_filter as ws
- >>> x = np.linspace(0, 100, 2**8)
- >>> noise = np.random.randn(2**8)/10
- >>> noise = ws.noise
- >>> y = np.cos(x/10)+(x/50)**2 + noise
- >>> y[np.r_[70, 75, 80]] = np.array([5.5, 5, 6])
- >>> y[181] = np.nan
- >>> z = ws.smoothn(y) # Regular smoothing
- >>> np.allclose(z[:3], [ 0.99517904, 0.99372346, 0.99079798])
- True
- >>> zr = ws.smoothn(y,robust=True) # Robust smoothing
- >>> np.allclose(zr[:3], [ 1.01190564, 1.00976197, 1.00513244])
- True
-
- h=plt.subplot(121),
- h = plt.plot(x,y,'r.',x,z,'k',linewidth=2)
- h=plt.title('Regular smoothing')
- h=plt.subplot(122)
- h=plt.plot(x,y,'r.',x,zr,'k',linewidth=2)
- h=plt.title('Robust smoothing')
-
- 2-D example
- >>> xp = np.r_[0:1:.02]
- >>> [x,y] = np.meshgrid(xp,xp)
- >>> f = np.exp(x+y) + np.sin((x-2*y)*3)
- >>> fn = f + np.random.randn(*f.shape)*0.5
- >>> fs = smoothn(fn)
-
- h=plt.subplot(121),
- h=plt.contourf(xp, xp, fn)
- h=plt.subplot(122)
- h=plt.contourf(xp, xp, fs)
-
- 2-D example with missing data
- >>> import wafo.demos as wd
- >>> n = 256
- >>> x0, y0, z0 = wd.peaks(n)
-
- z = z0 + rand(size(y0))*2
- I = randperm(n**2)
- z[I(1:n^2*0.5)] = np.NaN; # lose 1/2 of data
- z[40:90, 140:190] = np.NaN; # create a hole
- zs = smoothn(z)
-
- plt.subplot(2,2,1)
- plt.imagesc(y) # , axis equal off
- plt.title('Noisy corrupt data')
- plt.subplot(223)
- plt.imagesc(z) # , axis equal off
- plt.title('Recovered data ...')
- plt.subplot(224)
- plt.imagesc(y0) # , axis equal off
- plt.title('... compared with original data')
-
- 3-D example
- [x,y,z] = meshgrid(-2:.2:2);
- xslice = [-0.8,1]; yslice = 2; zslice = [-2,0];
- vn = x.*exp(-x.^2-y.^2-z.^2) + randn(size(x))*0.06;
- subplot(121), slice(x,y,z,vn,xslice,yslice,zslice,'cubic')
- title('Noisy data')
- v = smoothn(vn);
- subplot(122), slice(x,y,z,v,xslice,yslice,zslice,'cubic')
- title('Smoothed data')
-
-
- Cellular vortical flow
- [x,y] = meshgrid(linspace(0,1,24));
- Vx = cos(2*pi*x+pi/2).*cos(2*pi*y);
- Vy = sin(2*pi*x+pi/2).*sin(2*pi*y);
- Vx = Vx + sqrt(0.05)*randn(24,24); adding Gaussian noise
- Vy = Vy + sqrt(0.05)*randn(24,24); adding Gaussian noise
- I = randperm(numel(Vx));
- Vx(I(1:30)) = (rand(30,1)-0.5)*5; adding outliers
- Vy(I(1:30)) = (rand(30,1)-0.5)*5; adding outliers
- Vx(I(31:60)) = NaN; missing values
- Vy(I(31:60)) = NaN; missing values
- Vs = smoothn(complex(Vx,Vy),'robust'); automatic smoothing
- subplot(121), quiver(x,y,Vx,Vy,2.5), axis square
- title('Noisy velocity field')
- subplot(122), quiver(x,y,real(Vs),imag(Vs)), axis square
- title('Smoothed velocity field')
-
- See also
- -------
- SmoothNd
-
- -- Damien Garcia -- 2009/03, revised 2010/11
- Visit
- http://www.biomecardio.com/matlab/smoothn.html
- for more details about SMOOTHN
- """
- return SmoothNd(s, weight, robust, z0, tolz, maxiter, fulloutput)(data)
-
-
-class HodrickPrescott(object):
-
- """Smooth data with a Hodrick-Prescott filter.
-
- The Hodrick-Prescott filter removes high frequency noise from data.
- It has the advantage of preserving the original shape and
- features of the signal better than other types of filtering
- approaches, such as moving averages techniques.
-
- Parameter
- ---------
- w : real scalar
- smooting parameter. Larger w means more smoothing. Values usually
- in the [100, 20000] interval. As w approach infinity H-P will approach
- a line.
-
- Examples
- --------
- >>> import wafo.sg_filter as ws
- >>> t = np.linspace(-4, 4, 500)
- >>> y = np.exp( -t**2 ) + np.random.normal(0, 0.05, t.shape)
- >>> ysg = ws.HodrickPrescott(w=10000)(y)
-
- import matplotlib.pyplot as plt
- h = plt.plot(t, y, label='Noisy signal')
- h1 = plt.plot(t, np.exp(-t**2), 'k', lw=1.5, label='Original signal')
- h2 = plt.plot(t, ysg, 'r', label='Filtered signal')
- h3 = plt.legend()
- h4 = plt.title('Hodrick-Prescott')
- plt.show()
-
- References
- ----------
- .. [1] E. T. Whittaker, On a new method of graduation. In proceedings of
- the Edinburgh Mathematical association., 1923, 78, pp 88-89.
- .. [2] R. Hodrick and E. Prescott, Postwar U.S. business cycles: an
- empirical investigation,
- Journal of money, credit and banking, 1997, 29 (1), pp 1-16.
- .. [3] Kim Hyeongwoo, Hodrick-Prescott filter,
- 2004, www.auburn.edu/~hzk0001/hpfilter.pdf
- """
-
- def __init__(self, w=100):
- self.w = w
-
- def _get_matrix(self, n):
- w = self.w
- diag_matrix = np.repeat(
- np.atleast_2d([w, -4 * w, 6 * w + 1, -4 * w, w]).T, n, axis=1)
- A = spdiags(diag_matrix, np.arange(-2, 2 + 1), n, n).tocsr()
- A[0, 0] = A[-1, -1] = 1 + w
- A[1, 1] = A[-2, -2] = 1 + 5 * w
- A[0, 1] = A[1, 0] = A[-2, -1] = A[-1, -2] = -2 * w
- return A
-
- def __call__(self, x):
- x = np.atleast_1d(x).flatten()
- n = len(x)
- if n < 4:
- return x.copy()
-
- A = self._get_matrix(n)
- return spsolve(A, x)
-
-
-class Kalman(object):
-
- """
- Kalman filter object - updates a system state vector estimate based upon an
- observation, using a discrete Kalman filter.
-
- The Kalman filter is "optimal" under a variety of
- circumstances. An excellent paper on Kalman filtering at
- the introductory level, without detailing the mathematical
- underpinnings, is:
-
- "An Introduction to the Kalman Filter"
- Greg Welch and Gary Bishop, University of North Carolina
- http://www.cs.unc.edu/~welch/kalman/kalmanIntro.html
-
- PURPOSE:
- The purpose of each iteration of a Kalman filter is to update
- the estimate of the state vector of a system (and the covariance
- of that vector) based upon the information in a new observation.
- The version of the Kalman filter in this function assumes that
- observations occur at fixed discrete time intervals. Also, this
- function assumes a linear system, meaning that the time evolution
- of the state vector can be calculated by means of a state transition
- matrix.
-
- USAGE:
- filt = Kalman(R, x, P, A, B=0, Q, H)
- x = filt(z, u=0)
-
- filt is a "system" object containing various fields used as input
- and output. The state estimate "x" and its covariance "P" are
- updated by the function. The other fields describe the mechanics
- of the system and are left unchanged. A calling routine may change
- these other fields as needed if state dynamics are time-dependent;
- otherwise, they should be left alone after initial values are set.
- The exceptions are the observation vector "z" and the input control
- (or forcing function) "u." If there is an input function, then
- "u" should be set to some nonzero value by the calling routine.
-
- System dynamics
- ---------------
-
- The system evolves according to the following difference equations,
- where quantities are further defined below:
-
- x = Ax + Bu + w meaning the state vector x evolves during one time
- step by premultiplying by the "state transition
- matrix" A. There is optionally (if nonzero) an input
- vector u which affects the state linearly, and this
- linear effect on the state is represented by
- premultiplying by the "input matrix" B. There is also
- gaussian process noise w.
- z = Hx + v meaning the observation vector z is a linear function
- of the state vector, and this linear relationship is
- represented by premultiplication by "observation
- matrix" H. There is also gaussian measurement
- noise v.
- where w ~ N(0,Q) meaning w is gaussian noise with covariance Q
- v ~ N(0,R) meaning v is gaussian noise with covariance R
-
- VECTOR VARIABLES:
-
- s.x = state vector estimate. In the input struct, this is the
- "a priori" state estimate (prior to the addition of the
- information from the new observation). In the output struct,
- this is the "a posteriori" state estimate (after the new
- measurement information is included).
- z = observation vector
- u = input control vector, optional (defaults to zero).
-
- MATRIX VARIABLES:
-
- s.A = state transition matrix (defaults to identity).
- s.P = covariance of the state vector estimate. In the input struct,
- this is "a priori," and in the output it is "a posteriori."
- (required unless autoinitializing as described below).
- s.B = input matrix, optional (defaults to zero).
- s.Q = process noise covariance (defaults to zero).
- s.R = measurement noise covariance (required).
- s.H = observation matrix (defaults to identity).
-
- NORMAL OPERATION:
-
- (1) define all state definition fields: A,B,H,Q,R
- (2) define intial state estimate: x,P
- (3) obtain observation and control vectors: z,u
- (4) call the filter to obtain updated state estimate: x,P
- (5) return to step (3) and repeat
-
- INITIALIZATION:
-
- If an initial state estimate is unavailable, it can be obtained
- from the first observation as follows, provided that there are the
- same number of observable variables as state variables. This "auto-
- intitialization" is done automatically if s.x is absent or NaN.
-
- x = inv(H)*z
- P = inv(H)*R*inv(H')
-
- This is mathematically equivalent to setting the initial state estimate
- covariance to infinity.
-
- Example (Automobile Voltimeter):
- -------
- >>> import wafo.sg_filter as ws
- >>> V0 = 12 # Define the system as a constant of 12 volts
- >>> h = 1 # voltimeter measure the voltage itself
- >>> q = 1e-5 # variance of process noise s the car operates
- >>> r = 0.1**2 # variance of measurement error
- >>> b = 0 # no system input
- >>> u = 0 # no system input
- >>> filt = ws.Kalman(R=r, A=1, Q=q, H=h, B=b)
-
- # Generate random voltages and watch the filter operate.
- >>> n = 50
- >>> truth = np.random.randn(n)*np.sqrt(q) + V0
- >>> z = truth + np.random.randn(n)*np.sqrt(r) # measurement
- >>> x = np.zeros(n)
-
- >>> for i, zi in enumerate(z):
- ... x[i] = filt(zi, u) # perform a Kalman filter iteration
-
- import matplotlib.pyplot as plt
- hz = plt.plot(z,'r.', label='observations')
-
- # a-posteriori state estimates:
- hx = plt.plot(x,'b-', label='Kalman output')
- ht = plt.plot(truth,'g-', label='true voltage')
- h = plt.legend()
- h1 = plt.title('Automobile Voltimeter Example')
- plt.show()
-
- """
-
- def __init__(self, R, x=None, P=None, A=None, B=0, Q=None, H=None):
- self.R = R # Estimated error in measurements.
- self.x = x # Initial state estimate.
- self.P = P # Initial covariance estimate.
- self.A = A # State transition matrix.
- self.B = B # Control matrix.
- self.Q = Q # Estimated error in process.
- self.H = H # Observation matrix.
- self.reset()
-
- def reset(self):
- self._filter = self._filter_first
-
- @staticmethod
- def _none_or_atleast_2d(a):
- if a is not None:
- return np.atleast_2d(a)
- return a
-
- @property
- def A(self):
- return self._a
-
- @A.setter
- def A(self, a):
- self._a = self._none_or_atleast_2d(a)
-
- def _set_A(self, n):
- if self.A is None:
- self.A = np.eye(n)
-
- @property
- def Q(self):
- return self._q
-
- @Q.setter
- def Q(self, q):
- self._q = self._none_or_atleast_2d(q)
-
- def _set_Q(self, n):
- if self.Q is None:
- self.Q = np.zeros((n, n))
-
- @property
- def H(self):
- return self._h
-
- @H.setter
- def H(self, h):
- self._h = self._none_or_atleast_2d(h)
-
- def _set_H(self, n):
- if self.H is None:
- self.H = np.eye(n)
-
- @property
- def P(self):
- return self._p
-
- @P.setter
- def P(self, p):
- self._p = self._none_or_atleast_2d(p)
-
- def _set_P(self, HI):
- if self.P is None:
- self.P = np.dot(np.dot(HI, self.R), HI.T)
-
- def _init_first(self, n):
- self._set_A(n)
- self._set_Q(n)
- self._set_H(n)
- try:
- HI = np.linalg.inv(self.H)
- except Exception:
- HI = np.eye(n)
- self._set_P(HI)
- return HI
-
- def _first_state(self, z):
- n = np.size(z)
- HI = self._init_first(n)
- # initialize state estimate from first observation
- x = np.dot(HI, z)
- return x
-
- def _filter_first(self, z, u):
-
- self._filter = self._filter_main
-
- if self.x is None:
- self.x = self._first_state(z)
- return self.x
-
- n = np.size(self.x)
- self._init_first(n)
- return self._filter_main(z, u)
-
- def _predict_state(self, x, u):
- return np.dot(self.A, x) + np.dot(self.B, u)
-
- def _predict_covariance(self, P):
- A = self.A
- return np.dot(np.dot(A, P), A.T) + self.Q
-
- def _compute_gain(self, P):
- """Kalman gain factor."""
- H = self.H
- PHT = np.dot(P, H.T)
- innovation_covariance = np.dot(H, PHT) + self.R
- # return np.linalg.solve(PHT, innovation_covariance)
- return np.dot(PHT, np.linalg.inv(innovation_covariance))
-
- def _update_state_from_observation(self, x, z, K):
- innovation = z - np.dot(self.H, x)
- return x + np.dot(K, innovation)
-
- def _update_covariance(self, P, K):
- return P - np.dot(K, np.dot(self.H, P))
- # return np.dot(np.eye(len(P)) - K * self.H, P)
-
- def _filter_main(self, z, u):
- """ This is the code which implements the discrete Kalman filter:
- """
- P = self._predict_covariance(self.P)
- x = self._predict_state(self.x, u)
-
- K = self._compute_gain(P)
-
- self.P = self._update_covariance(P, K)
- self.x = self._update_state_from_observation(x, z, K)
-
- return self.x
-
- def __call__(self, z, u=0):
- return self._filter(z, u)
-
-
-class HampelFilter(object):
- """Hampel Filter.
-
- HAMPEL(X,Y,DX,T,varargin) returns the Hampel filtered values of the
- elements in Y. It was developed to detect outliers in a time series,
- but it can also be used as an alternative to the standard median
- filter.
-
- X,Y are row or column vectors with an equal number of elements.
- The elements in Y should be Gaussian distributed.
-
- Parameters
- ----------
- dx : positive scalar (default 3 * median(diff(X))
- which defines the half width of the filter window. Dx should be
- dimensionally equivalent to the values in X.
- t : positive scalar (default 3)
- which defines the threshold value used in the equation
- |Y - Y0| > T * S0.
- adaptive: real scalar
- if greater than 0 it uses an experimental adaptive Hampel filter.
- If none it uses a standard Hampel filter
- fulloutput: bool
- if True also the vectors: outliers, Y0,LB,UB,ADX, which corresponds to
- the mask of the replaced values, nominal data, lower and upper bounds
- on the Hampel filter and the relative half size of the local window,
- respectively. outliers.sum() gives the number of outliers detected.
-
- Examples
- ---------
- Hampel filter removal of outliers
- >>> import numpy as np
- >>> randint = np.random.randint
- >>> Y = 5000 + np.random.randn(1000)
- >>> outliers = randint(0,1000, size=(10,))
- >>> Y[outliers] = Y[outliers] + randint(1000, size=(10,))
- >>> YY, res = HampelFilter(fulloutput=True)(Y)
- >>> YY1, res1 = HampelFilter(dx=1, t=3, adaptive=0.1, fulloutput=True)(Y)
- >>> YY2, res2 = HampelFilter(dx=3, t=0, fulloutput=True)(Y) # Y0 = median
-
- X = np.arange(len(YY))
- plt.plot(X, Y, 'b.') # Original Data
- plt.plot(X, YY, 'r') # Hampel Filtered Data
- plt.plot(X, res['Y0'], 'b--') # Nominal Data
- plt.plot(X, res['LB'], 'r--') # Lower Bounds on Hampel Filter
- plt.plot(X, res['UB'], 'r--') # Upper Bounds on Hampel Filter
- i = res['outliers']
- plt.plot(X[i], Y[i], 'ks') # Identified Outliers
- plt.show('hold')
-
- References
- ----------
- Chapters 1.4.2, 3.2.2 and 4.3.4 in Mining Imperfect Data: Dealing with
- Contamination and Incomplete Records by Ronald K. Pearson.
-
- Acknowledgements
- I would like to thank Ronald K. Pearson for the introduction to moving
- window filters. Please visit his blog at:
- http://exploringdatablog.blogspot.com/2012/01/moving-window-filters-and
- -pracma.html
-
- """
-
- def __init__(self, dx=None, t=3, adaptive=None, fulloutput=False):
- self.dx = dx
- self.t = t
- self.adaptive = adaptive
- self.fulloutput = fulloutput
-
- @staticmethod
- def _check(dx):
- _assert(np.isscalar(dx), 'DX must be a scalar.')
- _assert(0 < dx, 'DX must be larger than zero.')
-
- @staticmethod
- def localwindow(X, Y, DX, i):
- mask = (X[i] - DX <= X) & (X <= X[i] + DX)
- Y0 = np.median(Y[mask])
- # Calculate Local Scale of Natural Variation
- S0 = 1.4826 * np.median(np.abs(Y[mask] - Y0))
- return Y0, S0
-
- @staticmethod
- def smgauss(X, V, DX):
- Xj = X
- Xk = np.atleast_2d(X).T
- Wjk = np.exp(-((Xj - Xk) / (2 * DX)) ** 2)
- G = np.dot(Wjk, V) / np.sum(Wjk, axis=0)
- return G
-
- def _adaptive(self, Y, X, dx):
- localwindow = self.localwindow
- Y0, S0, ADX = self._init(Y, dx)
- Y0Tmp = np.nan * np.zeros(Y.shape)
- S0Tmp = np.nan * np.zeros(Y.shape)
- DXTmp = np.arange(1, len(S0) + 1) * dx
- # Integer variation of Window Half Size
- # Calculate Initial Guess of Optimal Parameters Y0, S0, ADX
- for i in range(len(Y)):
- j = 0
- S0Rel = np.inf
- while S0Rel > self.adaptive:
- Y0Tmp[j], S0Tmp[j] = localwindow(X, Y, DXTmp[j], i)
- if j > 0:
- S0Rel = np.abs((S0Tmp[j - 1] - S0Tmp[j]) /
- (S0Tmp[j - 1] + S0Tmp[j]) / 2)
- j += 1
-
- Y0[i] = Y0Tmp[j - 2]
- S0[i] = S0Tmp[j - 2]
- ADX[i] = DXTmp[j - 2] / dx
-
- # Gaussian smoothing of relevant parameters
- DX = 2 * np.median(np.diff(X))
- ADX = self.smgauss(X, ADX, DX)
- S0 = self.smgauss(X, S0, DX)
- Y0 = self.smgauss(X, Y0, DX)
- return Y0, S0, ADX
-
- def _init(self, Y, dx):
- S0 = np.nan * np.zeros(Y.shape)
- Y0 = np.nan * np.zeros(Y.shape)
- ADX = dx * np.ones(Y.shape)
- return Y0, S0, ADX
-
- def _fixed(self, Y, X, dx):
- localwindow = self.localwindow
- Y0, S0, ADX = self._init(Y, dx)
- for i in range(len(Y)):
- Y0[i], S0[i] = localwindow(X, Y, dx, i)
- return Y0, S0, ADX
-
- def _filter(self, Y, X, dx):
- if len(X) <= 1:
- Y0, S0, ADX = self._init(Y, dx)
- elif self.adaptive is None:
- Y0, S0, ADX = self._fixed(Y, X, dx)
- else:
- Y0, S0, ADX = self._adaptive(Y, X, dx) # 'adaptive'
- return Y0, S0, ADX
-
- def __call__(self, y, x=None):
- Y = np.atleast_1d(y).ravel()
- if x is None:
- x = range(len(Y))
- X = np.atleast_1d(x).ravel()
-
- dx = 3 * np.median(np.diff(X)) if self.dx is None else self.dx
- self._check(dx)
-
- Y0, S0, ADX = self._filter(Y, X, dx)
- YY = Y.copy()
- T = self.t
- # Prepare Output
- self.UB = Y0 + T * S0
- self.LB = Y0 - T * S0
- outliers = np.abs(Y - Y0) > T * S0 # possible outliers
- np.putmask(YY, outliers, Y0) # YY[outliers] = Y0[outliers]
- self.outliers = outliers
- self.num_outliers = outliers.sum()
- self.ADX = ADX
- self.Y0 = Y0
- if self.fulloutput:
- return YY, dict(outliers=outliers, Y0=Y0,
- LB=self.LB, UB=self.UB, ADX=ADX)
- return YY
-
-
-if __name__ == '__main__':
- from wafo.testing import test_docstrings
- test_docstrings(__file__)
diff --git a/wafo/sg_filter/demos.py b/wafo/sg_filter/demos.py
deleted file mode 100644
index dfffda9..0000000
--- a/wafo/sg_filter/demos.py
+++ /dev/null
@@ -1,488 +0,0 @@
-import numpy as np
-from scipy.sparse.linalg import expm
-from scipy.signal import medfilt
-from wafo.plotbackend import plotbackend as plt
-from wafo.sg_filter._core import (SavitzkyGolay, smoothn, Kalman,
- HodrickPrescott, HampelFilter)
-
-
-def demo_savitzky_on_noisy_chirp():
- """
- Example
- -------
- >>> demo_savitzky_on_noisy_chirp()
-
- >>> plt.close()
- """
- plt.figure(figsize=(7, 12))
-
- # generate chirp signal
- tvec = np.arange(0, 6.28, .02)
- true_signal = np.sin(tvec * (2.0 + tvec))
- true_d_signal = (2 + tvec) * np.cos(tvec * (2.0 + tvec))
-
- # add noise to signal
- noise = np.random.normal(size=true_signal.shape)
- signal = true_signal + .15 * noise
-
- # plot signal
- plt.subplot(311)
- plt.plot(signal)
- plt.title('signal')
-
- # smooth and plot signal
- plt.subplot(312)
- savgol = SavitzkyGolay(n=8, degree=4)
- s_signal = savgol.smooth(signal)
- s2 = smoothn(signal, robust=True)
- plt.plot(s_signal)
- plt.plot(s2)
- plt.plot(true_signal, 'r--')
- plt.title('smoothed signal')
-
- # smooth derivative of signal and plot it
- plt.subplot(313)
- savgol1 = SavitzkyGolay(n=8, degree=1, diff_order=1)
-
- dt = tvec[1] - tvec[0]
- d_signal = savgol1.smooth(signal) / dt
-
- plt.plot(d_signal)
- plt.plot(true_d_signal, 'r--')
- plt.title('smoothed derivative of signal')
-
-
-def demo_kalman_voltimeter():
- """
- Example
- -------
- >>> demo_kalman_voltimeter()
-
- >>> plt.close()
- """
- V0 = 12
- h = np.atleast_2d(1) # voltimeter measure the voltage itself
- q = 1e-9 # variance of process noise as the car operates
- r = 0.05 ** 2 # variance of measurement error
- b = 0 # no system input
- u = 0 # no system input
- filt = Kalman(R=r, A=1, Q=q, H=h, B=b)
-
- # Generate random voltages and watch the filter operate.
- n = 50
- truth = np.random.randn(n) * np.sqrt(q) + V0
- z = truth + np.random.randn(n) * np.sqrt(r) # measurement
- x = np.zeros(n)
-
- for i, zi in enumerate(z):
- x[i] = filt(zi, u) # perform a Kalman filter iteration
-
- _hz = plt.plot(z, 'r.', label='observations')
- # a-posteriori state estimates:
- _hx = plt.plot(x, 'b-', label='Kalman output')
- _ht = plt.plot(truth, 'g-', label='true voltage')
- plt.legend()
- plt.title('Automobile Voltimeter Example')
-
-
-def lti_disc(F, L=None, Q=None, dt=1):
- """LTI_DISC Discretize LTI ODE with Gaussian Noise.
-
- Syntax:
- [A,Q] = lti_disc(F,L,Qc,dt)
-
- In:
- F - NxN Feedback matrix
- L - NxL Noise effect matrix (optional, default identity)
- Qc - LxL Diagonal Spectral Density (optional, default zeros)
- dt - Time Step (optional, default 1)
-
- Out:
- A - Transition matrix
- Q - Discrete Process Covariance
-
- Description:
- Discretize LTI ODE with Gaussian Noise. The original
- ODE model is in form
-
- dx/dt = F x + L w, w ~ N(0,Qc)
-
- Result of discretization is the model
-
- x[k] = A x[k-1] + q, q ~ N(0,Q)
-
- Which can be used for integrating the model
- exactly over time steps, which are multiples
- of dt.
-
- """
- n = np.shape(F)[0]
- if L is None:
- L = np.eye(n)
-
- if Q is None:
- Q = np.zeros((n, n))
- # Closed form integration of transition matrix
- A = expm(F * dt)
-
- # Closed form integration of covariance
- # by matrix fraction decomposition
-
- Phi = np.vstack((np.hstack((F, np.dot(np.dot(L, Q), L.T))),
- np.hstack((np.zeros((n, n)), -F.T))))
- AB = np.dot(expm(Phi * dt), np.vstack((np.zeros((n, n)), np.eye(n))))
- # Q = AB[:n, :] / AB[n:(2 * n), :]
- Q = np.linalg.solve(AB[n:(2 * n), :].T, AB[:n, :].T)
- return A, Q
-
-
-def demo_kalman_sine():
- """Kalman Filter demonstration with sine signal.
-
- Example
- -------
- >>> demo_kalman_sine()
-
- >>> plt.close()
- """
- sd = 0.5
- dt = 0.1
- w = 1
- T = np.arange(0, 30 + dt / 2, dt)
- n = len(T)
- X = 3 * np.sin(w * T)
- Y = X + sd * np.random.randn(n)
-
- ''' Initialize KF to values
- x = 0
- dx/dt = 0
- with great uncertainty in derivative
- '''
- M = np.zeros((2, 1))
- P = np.diag([0.1, 2])
- R = sd ** 2
- H = np.atleast_2d([1, 0])
- q = 0.1
- F = np.atleast_2d([[0, 1],
- [0, 0]])
- A, Q = lti_disc(F, L=None, Q=np.diag([0, q]), dt=dt)
-
- # Track and animate
- m = M.shape[0]
- _MM = np.zeros((m, n))
- _PP = np.zeros((m, m, n))
- '''In this demonstration we estimate a stationary sine signal from noisy
- measurements by using the classical Kalman filter.'
- '''
- filt = Kalman(R=R, x=M, P=P, A=A, Q=Q, H=H, B=0)
-
- # Generate random voltages and watch the filter operate.
- # n = 50
- # truth = np.random.randn(n) * np.sqrt(q) + V0
- # z = truth + np.random.randn(n) * np.sqrt(r) # measurement
- truth = X
- z = Y
- x = np.zeros((n, m))
-
- for i, zi in enumerate(z):
- x[i] = np.ravel(filt(zi, u=0))
-
- _hz = plt.plot(z, 'r.', label='observations')
- # a-posteriori state estimates:
- _hx = plt.plot(x[:, 0], 'b-', label='Kalman output')
- _ht = plt.plot(truth, 'g-', label='true voltage')
- plt.legend()
- plt.title('Automobile Voltimeter Example')
-
-
-# for k in range(m):
-# [M,P] = kf_predict(M,P,A,Q);
-# [M,P] = kf_update(M,P,Y(k),H,R);
-#
-# MM(:,k) = M;
-# PP(:,:,k) = P;
-#
-# %
-# % Animate
-# %
-# if rem(k,10)==1
-# plot(T,X,'b--',...
-# T,Y,'ro',...
-# T(k),M(1),'k*',...
-# T(1:k),MM(1,1:k),'k-');
-# legend('Real signal','Measurements','Latest estimate',
-# 'Filtered estimate')
-# title('Estimating a noisy sine signal with Kalman filter.');
-# drawnow;
-#
-# pause;
-# end
-# end
-#
-# clc;
-# disp('In this demonstration we estimate a stationary sine signal '
-# 'from noisy measurements by using the classical Kalman filter.');
-# disp(' ');
-# disp('The filtering results are now displayed sequantially for 10 time '
-# 'step at a time.');
-# disp(' ');
-# disp('')
-# pause;
-# %
-# % Apply Kalman smoother
-# %
-# SM = rts_smooth(MM,PP,A,Q);
-# plot(T,X,'b--',...
-# T,MM(1,:),'k-',...
-# T,SM(1,:),'r-');
-# legend('Real signal','Filtered estimate','Smoothed estimate')
-# title('Filtered and smoothed estimate of the original signal');
-#
-# clc;
-# disp('The filtered and smoothed estimates of the signal are now '
-# 'displayed.')
-# disp(' ');
-# disp('RMS errors:');
-# %
-# % Errors
-# %
-# fprintf('KF = %.3f\nRTS = %.3f\n',...
-# sqrt(mean((MM(1,:)-X(1,:)).^2)),...
-# sqrt(mean((SM(1,:)-X(1,:)).^2)));
-
-
-def demo_hampel():
- """
- Example
- -------
- >>> demo_hampel()
-
- >>> plt.close()
- """
- randint = np.random.randint
- Y = 5000 + np.random.randn(1000)
- outliers = randint(0, 1000, size=(10,))
- Y[outliers] = Y[outliers] + randint(1000, size=(10,))
- YY, res = HampelFilter(dx=3, t=3, fulloutput=True)(Y)
- YY1, res1 = HampelFilter(dx=1, t=3, adaptive=0.1, fulloutput=True)(Y)
- YY2, res2 = HampelFilter(dx=3, t=0, fulloutput=True)(Y) # median
- plt.figure(1)
- plot_hampel(Y, YY, res)
- plt.title('Standard HampelFilter')
- plt.figure(2)
- plot_hampel(Y, YY1, res1)
- plt.title('Adaptive HampelFilter')
- plt.figure(3)
- plot_hampel(Y, YY2, res2)
- plt.title('Median filter')
-
-
-def plot_hampel(Y, YY, res):
- X = np.arange(len(YY))
- plt.plot(X, Y, 'b.') # Original Data
- plt.plot(X, YY, 'r') # Hampel Filtered Data
- plt.plot(X, res['Y0'], 'b--') # Nominal Data
- plt.plot(X, res['LB'], 'r--') # Lower Bounds on Hampel Filter
- plt.plot(X, res['UB'], 'r--') # Upper Bounds on Hampel Filter
- i = res['outliers']
- plt.plot(X[i], Y[i], 'ks') # Identified Outliers
-
-
-def demo_tide_filter():
- """
- Example
- -------
- >>> demo_tide_filter()
-
- >>> plt.close()
- """
- # import statsmodels.api as sa
- import wafo.spectrum.models as sm
- sd = 10
- Sj = sm.Jonswap(Hm0=4. * sd)
- S = Sj.tospecdata()
-
- q = (0.1 * sd) ** 2 # variance of process noise s the car operates
- r = (100 * sd) ** 2 # variance of measurement error
- b = 0 # no system input
- u = 0 # no system input
-
- from scipy.signal import butter, filtfilt, lfilter_zi # lfilter,
- freq_tide = 1. / (12 * 60 * 60)
- freq_wave = 1. / 10
- freq_filt = freq_wave / 10
- dt = 1.
- freq = 1. / dt
- fn = (freq / 2)
-
- P = 10 * np.diag([1, 0.01])
- R = r
- H = np.atleast_2d([1, 0])
-
- F = np.atleast_2d([[0, 1],
- [0, 0]])
- A, Q = lti_disc(F, L=None, Q=np.diag([0, q]), dt=dt)
-
- t = np.arange(0, 60 * 12, 1. / freq)
- w = 2 * np.pi * freq # 1 Hz
- tide = 100 * np.sin(freq_tide * w * t + 2 * np.pi / 4) + 100
- y = tide + S.sim(len(t), dt=1. / freq)[:, 1].ravel()
-# lowess = sa.nonparametric.lowess
-# y2 = lowess(y, t, frac=0.5)[:,1]
-
- filt = Kalman(R=R, x=np.array([[tide[0]], [0]]), P=P, A=A, Q=Q, H=H, B=b)
- filt2 = Kalman(R=R, x=np.array([[tide[0]], [0]]), P=P, A=A, Q=Q, H=H, B=b)
- # y = tide + 0.5 * np.sin(freq_wave * w * t)
- # Butterworth filter
- b, a = butter(9, (freq_filt / fn), btype='low')
- # y2 = [lowess(y[max(i-60,0):i + 1], t[max(i-60,0):i + 1], frac=.3)[-1,1]
- # for i in range(len(y))]
- # y2 = [lfilter(b, a, y[:i + 1])[i] for i in range(len(y))]
- # y3 = filtfilt(b, a, y[:16]).tolist() + [filtfilt(b, a, y[:i + 1])[i]
- # for i in range(16, len(y))]
- # y0 = medfilt(y, 41)
- _zi = lfilter_zi(b, a)
- # y2 = lfilter(b, a, y)#, zi=y[0]*zi) # standard filter
- y3 = filtfilt(b, a, y) # filter with phase shift correction
- y4 = []
- y5 = []
- for _i, j in enumerate(y):
- tmp = np.ravel(filt(j, u=u))
- tmp = np.ravel(filt2(tmp[0], u=u))
-# if i==0:
-# print(filt.x)
-# print(filt2.x)
- y4.append(tmp[0])
- y5.append(tmp[1])
- _y0 = medfilt(y4, 41)
- # print(filt.P)
- # plot
-
- plt.plot(t, y, 'r.-', linewidth=2, label='raw data')
- # plt.plot(t, y2, 'b.-', linewidth=2, label='lowess @ %g Hz' % freq_filt)
- # plt.plot(t, y2, 'b.-', linewidth=2, label='filter @ %g Hz' % freq_filt)
- plt.plot(t, y3, 'g.-', linewidth=2, label='filtfilt @ %g Hz' % freq_filt)
- plt.plot(t, y4, 'k.-', linewidth=2, label='kalman')
- # plt.plot(t, y5, 'k.', linewidth=2, label='kalman2')
- plt.plot(t, tide, 'y-', linewidth=2, label='True tide')
- plt.legend(frameon=False, fontsize=14)
- plt.xlabel("Time [s]")
- plt.ylabel("Amplitude")
-
-
-def demo_savitzky_on_exponential():
- """
- Example
- -------
- >>> demo_savitzky_on_exponential()
-
- >>> plt.close()
- """
- t = np.linspace(-4, 4, 500)
- y = np.exp(-t ** 2) + np.random.normal(0, 0.05, np.shape(t))
- n = 11
- ysg = SavitzkyGolay(n, degree=1, diff_order=0)(y)
- plt.plot(t, y, t, ysg, '--')
-
-
-def demo_smoothn_on_1d_cos():
- """
- Example
- -------
- >>> demo_smoothn_on_1d_cos()
-
- >>> plt.close()
- """
- x = np.linspace(0, 100, 2 ** 8)
- y = np.cos(x / 10) + (x / 50) ** 2 + np.random.randn(np.size(x)) / 10
- y[np.r_[70, 75, 80]] = np.array([5.5, 5, 6])
- z = smoothn(y) # Regular smoothing
- zr = smoothn(y, robust=True) # Robust smoothing
- _h0 = plt.subplot(121),
- _h = plt.plot(x, y, 'r.', x, z, 'k', linewidth=2)
- plt.title('Regular smoothing')
- plt.subplot(122)
- plt.plot(x, y, 'r.', x, zr, 'k', linewidth=2)
- plt.title('Robust smoothing')
-
-
-def demo_smoothn_on_2d_exp_sin():
- """
- Example
- -------
- >>> demo_smoothn_on_2d_exp_sin()
-
- >>> plt.close()
- """
- xp = np.arange(0, 1, 0.02) # np.r_[0:1:0.02]
- [x, y] = np.meshgrid(xp, xp)
- f = np.exp(x + y) + np.sin((x - 2 * y) * 3)
- fn = f + np.random.randn(*f.shape) * 0.5
- _fs, s = smoothn(fn, fulloutput=True)
- fs2 = smoothn(fn, s=2 * s)
- _h = plt.subplot(131),
- _h = plt.contourf(xp, xp, fn)
- _h = plt.subplot(132),
- _h = plt.contourf(xp, xp, fs2)
- _h = plt.subplot(133),
- _h = plt.contourf(xp, xp, f)
-
-
-def _cardioid(n=1000):
- t = np.linspace(0, 2 * np.pi, n)
- x0 = 2 * np.cos(t) * (1 - np.cos(t))
- y0 = 2 * np.sin(t) * (1 - np.cos(t))
- x = x0 + np.random.randn(x0.size) * 0.1
- y = y0 + np.random.randn(y0.size) * 0.1
- return x, y, x0, y0
-
-
-def demo_smoothn_on_cardioid():
- """
- Example
- -------
- >>> demo_smoothn_on_cardioid()
-
- >>> plt.close()
- """
- x, y, x0, y0 = _cardioid()
- z = smoothn(x + 1j * y, robust=False)
- plt.plot(x0, y0, 'y',
- x, y, 'r.',
- np.real(z), np.imag(z), 'k', linewidth=2)
-
-
-def demo_hodrick_on_cardioid():
- """
- Example
- -------
- >>> demo_hodrick_on_cardioid()
-
- >>> plt.close()
- """
- x, y, x0, y0 = _cardioid()
-
- smooth = HodrickPrescott(w=20000)
- # smooth = HampelFilter(adaptive=50)
- xs, ys = smooth(x), smooth(y)
- plt.plot(x0, y0, 'y',
- x, y, 'r.',
- xs, ys, 'k', linewidth=2)
-
-
-if __name__ == '__main__':
- from wafo.testing import test_docstrings
- test_docstrings(__file__)
- # demo_savitzky_on_noisy_chirp()
- # plt.show('hold') # show plot
- # demo_kalman_sine()
- # demo_tide_filter()
- # demo_hampel()
- # demo_kalman_voltimeter()
- # demo_savitzky_on_exponential()
-# plt.figure(1)
-# demo_hodrick_on_cardioid()
-# plt.figure(2)
-# # demo_smoothn_on_1d_cos()
-# demo_smoothn_on_cardioid()
-# plt.show('hold')
diff --git a/wafo/sg_filter/tests/__init__.py b/wafo/sg_filter/tests/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/wafo/sg_filter/tests/conftest.py b/wafo/sg_filter/tests/conftest.py
deleted file mode 100644
index b267c86..0000000
--- a/wafo/sg_filter/tests/conftest.py
+++ /dev/null
@@ -1,11 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
- Dummy conftest.py for wafo.
-
- If you don't know what this is for, just leave it empty.
- Read more about conftest.py under:
- https://pytest.org/latest/plugins.html
-"""
-from __future__ import print_function, absolute_import, division
-import pytest # @UnusedImport
diff --git a/wafo/sg_filter/tests/test_sg_filter.py b/wafo/sg_filter/tests/test_sg_filter.py
deleted file mode 100644
index 3aa9797..0000000
--- a/wafo/sg_filter/tests/test_sg_filter.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import unittest
-import numpy as np
-from wafo.sg_filter._core import HampelFilter, SavitzkyGolay, Kalman
-from numpy.ma.testutils import assert_array_almost_equal
-
-
-class Test(unittest.TestCase):
- def test_kalman(self):
- # V0 = 12
- h = np.atleast_2d(1) # voltimeter measure the voltage itself
- q = 1e-9 # variance of process noise as the car operates
- r = 0.05 ** 2 # variance of measurement error
- b = 0 # no system input
- u = 0 # no system input
- filt = Kalman(R=r, A=1, Q=q, H=h, B=b)
-
- # Generate random voltages and watch the filter operate.
- n = 50
-
- # truth = np.random.randn(n) * np.sqrt(q) + V0
- truth = [12.0000146639, 12.0000065349, 12.0000032619, 11.9999884321, 11.9999738705, 11.9999717554, 12.000012208, 12.0000269596, 12.0000235391, 12.0000420788, 11.9999763588, 12.0000272251, 11.9999278487, 11.9999775611, 12.0000280208, 11.9999788802, 11.9999531533, 11.9999963402, 11.9999318055, 11.9999537065, 12.0000011098, 11.9999750291, 12.000012755, 11.9999355349, 11.9999960117, 12.0000033276, 12.0000100317, 11.9999962376, 12.0000527124, 12.000019087, 12.0000273276, 11.9999461331, 12.0000527385, 11.9999990413, 11.9999850764, 12.0000031025, 11.9999868222, 11.9999830042, 11.9999881952, 12.0000064122, 12.0000343455, 11.9999779108, 12.0000134648, 12.0000329604, 12.0000536949, 11.999990415, 11.9999935299, 11.9999850717, 12.0000048284, 12.000060395] # nopep8
- # print(', '.join(['{0}'.format(y) for y in truth]))
- # noise = np.random.randn(n) * np.sqrt(r)
- noise = [0.0621366489443, 0.0247330994998, 0.109271521369, -0.0567657373852, -0.0315244553453, -0.0025701462034, 0.00181288937042, 0.109022849782, 0.0241169949603, -0.0543790728855, 0.0380117839575, 0.0582078684346, -0.0734606815139, -0.0665977120387, 0.0270631615171, 0.0369150024436, 0.0788445252483, 0.0111006834355, 0.0169567513801, 0.057081645789, -0.0293106512335, 0.0120282068494, 0.0211769283138, -0.0703228625119, -0.0222740803259, 0.0636500853308, 0.0302268639638, 0.0823316446844, 0.0504871636818, -0.0295402185417, 0.0780534199429, -0.0275453184976, -0.0613567940452, -0.0416930336494, -0.0972202720552, 0.0367572628693, 0.0150242119742, -0.0269063072041, 0.0654834111234, 0.0281867030757, -0.0978716452136, -0.0209143055692, -0.0236015624104, 0.0215804707908, -0.0525669442916, -0.00999304544556, -0.120029281573, -0.0255283929591, -0.00426938028504, -0.0487170747831] # nopep8
- # print('noise')
- # print(', '.join(['{0}'.format(y) for y in noise]))
- z = np.array(truth) + np.array(noise) # measurement
- x = np.zeros(n)
-
- for i, zi in enumerate(z):
- x[i] = filt(zi, u) # perform a Kalman filter iteration
-
- # print(', '.join(['{0}'.format(y) for y in x]))
- assert_array_almost_equal(x, [12.0621513128, 12.0434454699, 12.065388589, 12.0348470726, 12.0215675093, 12.0175398445, 12.0152948689, 12.0270143185, 12.0266950063, 12.0185917138, 12.0203550503, 12.0235117738, 12.0160466547, 12.0101417447, 12.0112717378, 12.0128731715, 12.0167512202, 12.0164370857, 12.0164608484, 12.0184896738, 12.0162133963, 12.0160220138, 12.0162467099, 12.0126366982, 12.0112399981, 12.01325607, 12.0138850472, 12.0163296762, 12.0175094638, 12.0159415984, 12.0179463317, 12.0165228489, 12.0141641309, 12.0125210028, 12.0093846209, 12.0101451843, 12.0102767167, 12.0092975878, 12.0107382282, 12.01117469, 12.0085152867, 12.0078138971, 12.0070834443, 12.0074137537, 12.0060816948, 12.0057319401, 12.0030552687, 12.0024592874, 12.0023220236, 12.0013021198]) # nopep8
-
- def test_savitzky_golay(self):
- t = np.linspace(-4, 4, 100)
- # noise = np.random.normal(0, 0.05, t.shape)
- noise = np.array([-0.0100738410948, 0.0434144660993, -0.00378151879776, 0.0113356386249, 0.00435077505515, -0.0477602072563, 0.0373494077292, -0.0133865881648, -0.058259106898, 0.0661995082581, -0.0302004598725, 0.0251073570423, 0.0504480752083, 0.00591197638959, -0.0936100824835, -0.0475801028574, 0.164260739963, 0.000309393717298, 0.0145265378376, 0.0451974209359, -0.0296464887607, 0.00113264461646, 0.0203243951278, 0.0250086489045, 0.0286663554031, 0.0162585459204, -0.0285237485127, 0.0841528003829, -0.0449728982279, 0.0549796633406, -0.0968901623365, 0.0306727572312, -0.06950957371, 0.00598396964893, 0.0583904237732, 0.0271856795988, 0.0503237614252, 0.179978222833, 0.0909965842309, -0.037723727536, 0.0766788834896, -0.135152061379, -0.0275445486266, 0.00437867535899, -0.0272885999582, -0.0167170993005, 0.0522077667657, -0.044618733434, -0.0323362657615, 0.0122726298648, 0.0311304227245, -0.106361849439, 0.0526578516171, 0.0600674983294, 0.0745276212563, -0.0488453979797, -0.000833273665862, 0.0232768529385, 0.0138869047145, -0.0581069396022, 0.0321001340163, -0.023975724861, -0.0281919023447, 0.0341550408589, -0.0128104984676, -0.0214928514613, -0.0930630211028, -0.086094936458, 0.0169055851217, -0.0106085935018, 0.0114499301052, 0.120823119533, 0.0401375561218, 0.0979979215035, -0.015930504967, 0.0730359376807, -0.0369380623571, 0.0628171477772, -0.0271592027456, -0.0805267873905, -0.047314352792, -0.00693573205681, -0.0276294819462, -0.00896110691771, 0.0248482543453, 0.0877066047642, -0.0325533337143, -0.0638923240986, 0.0512523528302, 0.0720952473332, 0.00555629231342, 0.0562912258368, 0.0518574742355, -0.0599288505845, 0.0129657160415, -0.0025435076237, -0.0136966498082, 0.0260013064028, 0.0315561663839, -0.0293194006299]) # nopep8
- # print(', '.join(['{0}'.format(y) for y in noise]))
- y = np.exp(-t**2) + noise
- ysg = SavitzkyGolay(n=20, degree=2).smooth(y)
- # print(', '.join(['{0}'.format(y) for y in ysg]))
- assert_array_almost_equal(ysg, [0.08136940307, 0.0628995512206, 0.0459066440291, 0.0303906814954, 0.0163516636196, 0.00378959040169, -0.00729553815838, -0.0169037220606, -0.0250349613049, -0.0316892558914, -0.03686660582, -0.0405670110908, -0.0427904717036, -0.0435369876586, -0.0428065589558, -0.0405991855951, -0.0369148675765, -0.0317536049001, -0.0251153975657, -0.0170002455736, -0.00740814892354, 0.00138883298422, 0.00903982318392, 0.0156981288342, 0.0262571138525, 0.038712235996, 0.0497401536595, 0.0708145615737, 0.0930139468092, 0.115399487253, 0.145859174356, 0.181612636791, 0.217133614374, 0.257717904903, 0.300137304571, 0.345750694351, 0.392370647909, 0.447698806473, 0.498669071812, 0.552242388661, 0.602481962016, 0.649961345551, 0.695325299394, 0.735374047294, 0.773010811455, 0.805919165272, 0.834865852047, 0.854796400219, 0.86842923705, 0.871717227059, 0.870973756546, 0.854091989437, 0.838398655214, 0.810673923439, 0.783634317013, 0.749908334726, 0.714315301461, 0.67175727743, 0.634614314389, 0.593943523363, 0.54497174327, 0.497539947485, 0.442464744684, 0.39039262486, 0.339641566641, 0.287962531039, 0.243889096129, 0.20650960823, 0.165038163827, 0.127423305227, 0.098217909507, 0.0723906956301, 0.0457840790019, 0.0325691800506, 0.0207065223098, 0.0137400799931, 0.00547944733578, -0.000268557810876, -0.00301934919753, -0.00213643706217, -0.0101846106787, -0.0170002602175, -0.0225833856784, -0.0269339870616, -0.030052064367, -0.0319376175946, -0.0325906467444, -0.0320111518164, -0.0301991328106, -0.0271545897271, -0.0228775225657, -0.0173679313266, -0.0106258160097, -0.00265117661497, 0.00655598685753, 0.0169956744078, 0.0286678860359, 0.0415726217417, 0.0557098815254, 0.0710796653868]) # nopep8
-
- def test_hampelfilter(self):
-
- y = np.array([4979, 5002, 5004, 5000, 4987, 4989, 5008, 5013, 5000, 4996, 5026, 5000, 5003, 4988, 4991, 5017, 5013, 5004, 4990, 4997, 5010, 5007, 5001, 4970, 5002, 4986, 5032, 5003, 4994, 5001, 4988, 4998, 5002, 4996, 4988, 5009, 5003, 4986, 4996, 4994, 4997, 5003, 5012, 5002, 5001, 5006, 5006, 4986, 4998, 4978, 4995, 4999, 4987, 5015, 5009, 5016, 4995, 5007, 5013, 4996, 5018, 4992, 4989, 4994, 4991, 4995, 5018, 5007, 5008, 4990, 5010, 4986, 5009, 4999, 5008, 5000, 5001, 5000, 5004, 5032, 5009, 5005, 5014, 5000, 4992, 4994, 4991, 4993, 5000, 5005, 4993, 5003, 4995, 4980, 4995, 4999, 5007, 5000, 4988, 4982, 5030, 4997, 4993, 5009, 4993, 5020, 5010, 4997, 4987, 4999, 5006, 4984, 5000, 4987, 4986, 5006, 5002, 5001, 5003, 4996, 4994, 5002, 4989, 4998, 4996, 5007, 4993, 4998, 5001, 4989, 5012, 5001, 5010, 4996, 5004, 5008, 5002, 5024, 4997, 5001, 5010, 5023, 4996, 5018, 5002, 4990, 5009, 4997, 5016, 5000, 5013, 5008, 4990, 5011, 4996, 5010, 5004, 5023, 4999, 5004, 4996, 5011, 4998, 5000, 4988, 5000, 4993, 4998, 5000, 5006, 5010, 4988, 5000, 4997, 4995, 4996, 5005, 5003, 5009, 5009, 4997, 5010, 5014, 4990, 5013, 5015, 5011, 4987, 5004, 4999, 5030, 5000, 5000, 4999, 5013, 5012, 5001, 5012, 4995, 4997, 4995, 4985, 4989, 5007, 5010, 4997, 5011, 4995, 5000, 5011, 4998, 4991, 5021, 5003, 4985, 4991, 4995, 5018, 4979, 5003, 5011, 5009, 5001, 4998, 5008, 4994, 5016, 4992, 5008, 4993, 4983, 5010, 5023, 4975, 4987, 4994, 5002, 4984, 5005, 5006, 5005, 5000, 4996, 5000, 4999, 5004, 4994, 5000, 4981, 5001, 4994, 4993, 5000, 5004, 5001, 4992, 5002, 5006, 4997, 4987, 5001, 5008, 5011, 4995, 5006, 4976, 4999, 4989, 5008, 4996, 4997, 5006, 5005, 5007, 4990, 5010, 4996, 4998, 5000, 5014, 5002, 5001, 4996, 4997, 5000, 5003, 5000, 4986, 5013, 5001, 4990, 4990, 5010, 5000, 5021, 5000, 5006, 4995, 5012, 5019, 5000, 5006, 4999, 5007, 5014, 5000, 4994, 4991, 5010, 4985, 4988, 5007, 5005, 5000, 5005, 4997, 4995, 4987, 5000, 5000, 5009, 5000, 4999, 5011, 5018, 4994, 5000, 4995, 5000, 5008, 4994, 4992, 5000, 4998, 4992, 5006, 4996, 5000, 5018, 5000, 5022, 5002, 5007, 5000, 5012, 5014, 5010, 4997, 4996, 5014, 4995, 4991, 5006, 4992, 5008, 5004, 4995, 5016, 4974, 5005, 5000, 4985, 5002, 5000, 5009, 4991, 4995, 5004, 4987, 5014, 4986, 5008, 4985, 4995, 5010, 5000, 5001, 5013, 4988, 5001, 5002, 4986, 4985, 5001, 4995, 5004, 4998, 4998, 4992, 5003, 5000, 4991, 4998, 5002, 5011, 4986, 5013, 4997, 5000, 4997, 5003, 5014, 4998, 5002, 5006, 5015, 4992, 4992, 4995, 4998, 4994, 5007, 4977, 5004, 5005, 4990, 4991, 5001, 5010, 4991, 4990, 5006, 5004, 5004, 4997, 5001, 4992, 5000, 5000, 4996, 4987, 4993, 5001, 5006, 4999, 4993, 4999, 5001, 5001, 5014, 4985, 5010, 5006, 4989, 5002, 5011, 4999, 5010, 5005, 5002, 5010, 4990, 4994, 5000, 4985, 5010, 5000, 4999, 5000, 4977, 5007, 5005, 4990, 5000, 4985, 5003, 5009, 5012, 5012, 5000, 5018, 4997, 5002, 4992, 4999, 4997, 5005, 5009, 4989, 5005, 4988, 5001, 5000, 5018, 4999, 5005, 4997, 5015, 5001, 4988, 4983, 5013, 4992, 5002, 5012, 4999, 5013, 5000, 4988, 4989, 4996, 5000, 5002, 4999, 5021, 4991, 5009, 5005, 4994, 5000, 4994, 4985, 4999, 4980, 5013, 5014, 5000, 4996, 5008, 5028, 5002, 5004, 4999, 5012, 4992, 5002, 4987, 5004, 5009, 5004, 5013, 4989, 5027, 4992, 5016, 5007, 5003, 5013, 5003, 4983, 5000, 4987, 5001, 4998, 4994, 4998, 4997, 5023, 4971, 5001, 4990, 5025, 5003, 4996, 5006, 5005, 4995, 4994, 5023, 5003, 5005, 4991, 5001, 5028, 4996, 5001, 5005, 5001, 5001, 5028, 4983, 5000, 5005, 5001, 4999, 5003, 4982, 5000, 5012, 5011, 5008, 5000, 4998, 4994, 4988, 5013, 4998, 5017, 4991, 5007, 5016, 5007, 4997, 4974, 4997, 5006, 5003, 5010, 5003, 4997, 4997, 4994, 4998, 4987, 5004, 4974, 5009, 5006, 5009, 4984, 5010, 5023, 5000, 5008, 4992, 5008, 4993, 5010, 5012, 4989, 5002, 4993, 5007, 4985, 4990, 4988, 4997, 5000, 4994, 5004, 4981, 5000, 4999, 5003, 4988, 5006, 5019, 5003, 4992, 4977, 4998, 4987, 4995, 4987, 5000, 4986, 4998, 4996, 5008, 5011, 5009, 5000, 4999, 5010, 4999, 5009, 5010, 4992, 4998, 4994, 5009, 4998, 5013, 5011, 4997, 5016, 4995, 5003, 4986, 5010, 4995, 4986, 5025, 4990, 5010, 4990, 4996, 5000, 5002, 4994, 5002, 5000, 5020, 4995, 5003, 5006, 5000, 4997, 4996, 5010, 4992, 5013, 4998, 4988, 4999, 5004, 5004, 5002, 5002, 5001, 4986, 5011, 5012, 4988, 5000, 5000, 5012, 5004, 5004, 4984, 5012, 4998, 5005, 5005, 5005, 4995, 4971, 5006, 5000, 5008, 5015, 4999, 4999, 4996, 5000, 4992, 5019, 5020, 4999, 4991, 5020, 5000, 5002, 5000, 5003, 4985, 4990, 5011, 5012, 4997, 4989, 4999, 5006, 4997, 4992, 4999, 4985, 4999, 5012, 5003, 4989, 5010, 5010, 5012, 4990, 4986, 5014, 5000, 4994, 5017, 4995, 5005, 5003, 5000, 5000, 4988, 5018, 5007, 5009, 5002, 5004, 4999, 5026, 4987, 4986, 5000, 4994, 5012, 5022, 4996, 5002, 4986, 4990, 4989, 5030, 4997, 5002, 5004, 4999, 5000, 4992, 4977, 4997, 4998, 5000, 4987, 5032, 4998, 5014, 4978, 5001, 4988, 5006, 5003, 5005, 5021, 5005, 5000, 5003, 4990, 4991, 4990, 5016, 5006, 4981, 5002, 4998, 4989, 4995, 5007, 5004, 5019, 5001, 5004, 5000, 4996, 4988, 5022, 5003, 5009, 4998, 5003, 4991, 5000, 4988, 5004, 5002, 4991, 4998, 4996, 4991, 5013, 5007, 5010, 5004, 5006, 5007, 4993, 5000, 5002, 4992, 4999, 4992, 5001, 4989, 4992, 5015, 5011, 5008, 5004, 4990, 4995, 4993, 5004, 4995, 4982, 5008, 5000, 4993, 5000, 5000, 4991, 5005, 5008, 5003, 4994, 4997, 5012, 4995, 5009, 5020, 5010, 5019, 4979, 5013, 4990, 5002, 5000, 5015, 4989, 5009, 4998, 5004, 5010, 4988, 4994, 5004, 4985, 4998, 4991, 4985, 4994, 4986, 5010, 4990, 5002, 5005, 5008, 5004, 4988, 4980, 5005, 4994, 5004, 4998, 4990, 5010, 5003, 5011, 4992, 5009, 5003, 5013, 4996, 4997, 4989, 4992, 5002, 4992, 4996, 4999, 5000, 5009, 4990, 4992, 5020, 4981, 5011, 5000, 5008, 4997, 4994, 4982, 5014, 5013, 4996, 5002, 5011, 5003, 5000, 5015, 4999, 5001, 5002, 5018, 4984, 5022, 5009, 5006, 5009, 5000, 4974, 4991, 5020, 5000, 4987, 5003, 4987, 5003, 5002, 5016, 4997, 5022, 5005, 4998, 4999, 5001, 5000, 5008, 5000, 5001, 4994, 4991, 4997, 4990, 4991, 4995, 5008, 5007, 4994, 5000, 5007, 4988, 5014, 5003, 4998, 4995, 5000, 5000, 5004], dtype=float) # nopep8
- # randint = np.random.randint
- # y = 5000 + np.int_(10*np.random.randn(1000))
- # outliers = randint(0, 1000, size=(10,))
- outliers = np.array([470, 795, 399, 614, 864, 37, 815, 112, 69, 16])
- # noise = randint(1000, size=(10,))
- noise = np.array([932, 286, 736, 427, 858, 63, 243, 264, 668, 844])
- y[outliers] = y[outliers] + noise
- yy, res = HampelFilter(fulloutput=True)(y)
- yy1 = HampelFilter(dx=1, t=3, adaptive=0.1, fulloutput=False)(y)
- yy2 = HampelFilter(dx=3, t=0, fulloutput=False)(y)
- # print(', '.join(['{0:d}'.format(yj) for yj in y]))
- # print(', '.join(['{0:d}'.format(int(yj)) for yj in yy2]))
-
- self.assert_(sum(res['outliers'] == True) == 76)
- valid_names = set(('Y0', 'outliers', 'LB', 'ADX', 'UB'))
- for name in res:
- self.assert_(name in valid_names)
- assert_array_almost_equal(yy[outliers], [5002, 5000, 5003, 5008, 5000,
- 4997, 4998, 4999, 5009, 4997])
-
- true_yy = [5001, 5002, 5004, 5000, 4987, 4989, 5008, 5013, 5000, 4996, 5000, 5000, 5003, 4988, 4991, 5017, 4997, 5004, 4990, 4997, 5010, 5007, 5001, 4970, 5002, 4986, 5032, 5003, 4994, 5001, 4988, 4998, 5002, 4996, 4988, 5009, 5003, 4997, 4996, 4994, 4997, 5003, 5012, 5002, 5001, 5006, 5006, 4986, 4998, 4978, 4995, 4999, 4987, 5015, 5009, 5016, 4995, 5007, 5013, 4996, 5018, 4992, 4989, 4994, 4991, 4995, 5018, 5007, 5008, 5009, 5010, 5008, 5009, 4999, 5000, 5000, 5001, 5000, 5004, 5005, 5009, 5005, 5014, 5000, 4992, 4994, 4991, 4993, 5000, 5005, 4993, 5003, 4995, 4980, 4995, 4999, 5007, 5000, 4988, 4982, 5030, 4997, 4993, 5009, 4993, 5020, 5010, 4997, 4987, 4999, 5006, 4984, 4999, 4987, 4986, 5006, 5002, 5001, 5003, 4996, 4994, 5002, 4989, 4998, 4996, 5007, 4993, 4998, 5001, 4989, 5012, 5001, 5010, 4996, 5004, 5008, 5002, 5004, 4997, 5001, 5010, 5023, 4996, 5018, 5002, 4990, 5009, 4997, 5016, 5000, 5013, 5008, 4990, 5011, 4996, 5010, 5004, 5023, 4999, 5004, 4996, 5011, 4998, 5000, 4998, 5000, 4993, 4998, 5000, 5006, 5010, 4988, 5000, 4997, 4995, 4996, 5005, 5003, 5009, 5009, 4997, 5010, 5014, 5011, 5013, 5015, 5011, 4987, 5004, 4999, 5000, 5000, 5000, 4999, 5001, 5012, 5001, 5012, 4995, 4997, 4995, 4985, 4989, 5007, 5010, 4997, 5011, 4995, 5000, 5011, 4998, 4991, 5021, 5003, 4985, 4991, 4995, 5018, 4979, 5003, 5011, 5009, 5001, 4998, 5008, 4994, 5016, 4992, 5008, 4993, 4983, 5010, 5023, 4975, 4987, 4994, 5002, 5002, 5005, 5006, 5005, 5000, 4996, 5000, 4999, 5004, 5000, 5000, 4981, 5001, 4994, 4993, 5000, 5004, 5001, 4992, 5002, 5006, 4997, 4987, 5001, 5008, 5011, 4995, 5006, 4976, 4999, 4989, 5008, 4996, 4997, 5006, 5005, 5007, 4990, 5010, 4996, 4998, 5000, 5000, 5002, 5001, 4996, 4997, 5000, 5003, 5000, 5000, 5013, 5001, 4990, 4990, 5010, 5000, 5021, 5000, 5006, 4995, 5012, 5019, 5000, 5006, 4999, 5007, 5014, 5000, 4994, 4991, 5010, 4985, 4988, 5007, 5005, 5000, 5005, 4997, 4995, 4987, 5000, 5000, 5000, 5000, 4999, 5011, 5018, 4994, 5000, 4995, 5000, 5008, 4994, 4992, 5000, 4998, 4992, 5006, 4996, 5000, 5018, 5000, 5002, 5002, 5007, 5000, 5012, 5014, 5010, 4997, 4996, 5014, 4995, 4991, 5006, 4992, 5008, 5004, 4995, 5016, 5000, 5005, 5000, 4985, 5002, 5000, 5009, 4991, 4995, 5004, 4987, 5014, 4986, 5008, 4985, 4995, 5010, 5000, 5001, 5001, 5001, 5001, 5002, 4986, 5001, 5001, 4995, 5004, 4998, 4998, 4992, 5003, 5000, 4991, 4998, 5002, 5011, 5000, 5013, 4997, 5000, 5003, 5003, 5014, 4998, 5002, 5006, 5015, 4992, 4992, 4995, 4998, 4994, 5007, 4977, 5004, 5005, 4990, 4991, 4991, 4991, 4991, 5004, 5006, 5004, 5004, 4997, 5001, 4992, 5000, 5000, 4996, 4987, 4993, 5001, 5006, 4999, 4993, 4999, 5001, 5001, 5014, 4985, 5010, 5006, 4989, 5002, 5011, 4999, 5010, 5005, 5002, 5010, 4990, 4994, 5000, 4985, 5000, 5000, 4999, 5000, 5000, 5007, 5005, 4990, 5000, 4985, 5003, 5009, 5012, 5012, 5000, 5002, 4997, 5002, 4992, 4999, 4997, 5005, 5009, 4989, 5005, 4988, 5001, 5000, 5000, 4999, 5005, 4997, 5015, 5001, 4988, 4983, 5013, 4992, 5002, 5012, 4999, 5013, 5000, 4988, 4989, 4996, 5000, 5002, 4999, 5002, 4991, 5009, 5005, 4994, 5000, 4994, 4985, 4999, 4980, 5013, 5014, 5000, 4996, 5008, 5002, 5002, 5004, 4999, 5012, 4992, 5002, 4987, 5004, 5009, 5004, 5013, 4989, 5027, 4992, 5016, 5007, 5003, 5013, 5003, 5001, 5000, 4987, 5001, 4998, 4994, 4998, 4997, 4997, 4971, 5001, 4990, 5001, 5003, 4996, 5006, 5005, 4995, 5005, 5023, 5003, 5005, 4991, 5001, 5001, 4996, 5001, 5005, 5001, 5001, 5001, 5001, 5000, 5005, 5001, 4999, 5003, 5001, 5000, 5012, 5011, 5008, 5000, 4998, 4994, 4988, 5013, 4998, 5017, 4991, 5007, 5016, 5007, 4997, 5003, 4997, 5006, 5003, 5010, 5003, 4997, 4997, 4994, 4998, 4987, 5004, 5004, 5009, 5006, 5009, 5009, 5010, 5023, 5000, 5008, 5008, 5008, 4993, 5010, 5012, 4989, 5002, 4993, 4990, 4985, 4990, 4988, 4997, 5000, 4994, 5004, 5000, 5000, 4999, 5003, 5003, 5006, 5019, 5003, 4992, 4977, 4998, 4987, 4995, 4987, 5000, 4986, 4998, 4996, 5008, 5011, 5009, 5000, 5009, 5010, 4999, 5009, 5010, 4992, 4998, 4994, 5009, 4998, 5013, 5011, 4997, 5016, 4995, 5003, 4986, 5010, 4995, 4986, 5025, 4990, 5010, 4990, 4996, 5000, 5002, 4994, 5002, 5000, 5002, 4995, 5003, 5006, 5000, 4997, 4996, 4998, 4992, 5013, 4998, 4988, 4999, 5004, 5004, 5002, 5002, 5001, 4986, 5011, 5012, 4988, 5000, 5000, 5012, 5004, 5004, 4984, 5004, 5005, 5005, 5005, 5005, 5005, 5005, 5006, 5000, 5008, 5015, 4999, 4999, 4996, 5000, 4992, 5019, 5020, 4999, 4991, 5000, 5000, 5002, 5000, 5003, 4985, 4990, 5011, 5012, 4997, 4989, 4999, 4997, 4997, 4992, 4999, 4985, 4999, 5012, 5003, 5010, 5010, 5010, 5012, 5010, 4986, 5014, 5000, 4994, 5017, 4995, 5005, 5003, 5000, 5000, 4988, 5018, 5007, 5009, 5002, 5004, 4999, 5000, 4987, 4986, 5000, 4994, 5012, 5022, 4996, 5002, 4986, 4990, 4989, 4997, 4997, 5002, 5004, 4999, 5000, 4992, 4998, 4997, 4998, 5000, 5000, 5032, 4998, 5014, 4978, 5001, 5003, 5006, 5003, 5005, 5005, 5005, 5000, 5003, 4990, 4991, 4990, 5016, 5006, 4981, 4998, 4998, 4989, 4995, 5007, 5004, 5004, 5001, 5004, 5000, 4996, 4988, 5000, 5003, 5009, 4998, 5003, 4991, 5000, 4988, 5004, 5002, 4991, 4998, 4996, 4991, 5013, 5007, 5010, 5004, 5006, 5007, 4993, 5000, 5002, 4992, 4999, 4992, 5001, 4989, 4992, 5015, 5011, 5008, 5004, 4990, 4995, 4993, 5004, 5000, 4982, 5008, 5000, 4993, 5000, 5000, 4991, 5005, 5008, 5003, 4994, 4997, 5012, 4995, 5009, 5020, 5010, 5019, 4979, 5013, 4990, 5002, 5000, 5015, 4989, 5009, 4998, 5004, 5010, 4988, 4994, 5004, 4985, 4998, 4991, 4985, 4994, 4986, 5010, 4990, 5002, 5005, 5008, 5004, 4988, 5004, 5005, 4994, 5004, 4998, 4990, 5010, 5003, 5011, 4992, 5009, 5003, 5013, 4996, 4997, 4989, 4992, 5002, 4992, 4996, 4999, 5000, 5009, 4990, 4992, 5020, 4981, 5011, 5000, 5008, 4997, 4994, 4982, 5014, 5013, 4996, 5002, 5011, 5003, 5000, 5002, 4999, 5001, 5002, 5018, 4984, 5022, 5009, 5006, 5009, 5000, 4974, 4991, 5020, 5000, 4987, 5003, 5002, 5003, 5002, 5016, 4997, 5002, 5005, 4998, 4999, 5001, 5000, 5000, 5000, 5001, 4994, 4991, 4997, 4990, 4991, 4995, 5008, 5007, 4994, 5000, 5007, 4988, 5014, 5003, 4998, 4995, 5000, 5000, 5004] # nopep8
- true_yy1 = [4999, 5002, 5004, 5000, 4987, 4989, 5008, 5013, 5000, 4996, 5026, 5000, 5003, 4988, 4991, 5017, 5003, 5004, 4990, 4997, 5010, 5007, 5001, 4970, 5002, 4986, 5032, 5003, 4994, 5001, 4988, 4998, 5002, 4996, 4988, 5009, 5003, 5001, 4996, 4994, 4997, 5003, 5012, 5002, 5001, 5006, 5006, 4986, 4998, 4978, 4995, 4999, 4987, 5015, 5009, 5016, 4995, 5007, 5013, 4996, 5018, 4992, 4989, 4994, 4991, 4995, 5018, 5007, 5008, 5006, 5010, 4986, 5009, 4999, 5008, 5000, 5001, 5000, 5004, 5004, 5009, 5005, 5014, 5000, 4992, 4994, 4991, 4993, 5000, 5005, 4993, 5003, 4995, 4980, 4995, 4999, 5007, 5000, 4988, 4982, 5030, 4997, 4993, 5009, 4993, 5020, 5010, 4997, 4987, 4999, 5006, 4984, 4998, 4987, 4986, 5006, 5002, 5001, 5003, 4996, 4994, 5002, 4989, 4998, 4996, 5007, 4993, 4998, 5001, 4989, 5012, 5001, 5010, 4996, 5004, 5008, 5002, 5024, 4997, 5001, 5010, 5023, 4996, 5018, 5002, 4990, 5009, 4997, 5016, 5000, 5013, 5008, 4990, 5011, 4996, 5010, 5004, 5023, 4999, 5004, 4996, 5011, 4998, 5000, 4988, 5000, 4993, 4998, 5000, 5006, 5010, 4988, 5000, 4997, 4995, 4996, 5005, 5003, 5009, 5009, 4997, 5010, 5014, 5009, 5013, 5015, 5011, 5005, 5004, 4999, 5002, 5000, 5000, 4999, 5013, 5012, 5001, 5012, 4995, 4997, 4995, 4985, 4989, 5007, 5010, 4997, 5011, 4995, 5000, 5011, 4998, 4991, 5021, 5003, 4985, 4991, 4995, 5018, 4979, 5003, 5011, 5009, 5001, 4998, 5008, 4994, 5016, 4992, 5008, 4993, 4983, 5010, 5023, 4975, 4987, 4994, 5002, 4984, 5005, 5006, 5005, 5000, 4996, 5000, 4999, 5004, 4994, 5000, 4981, 5001, 4994, 4993, 5000, 5004, 5001, 4992, 5002, 5006, 4997, 4987, 5001, 5008, 5011, 4995, 5006, 4976, 4999, 4989, 5008, 4996, 4997, 5006, 5005, 5007, 4990, 5010, 4996, 4998, 5000, 5000, 5002, 5001, 4996, 4997, 5000, 5003, 5000, 4986, 5013, 5001, 4990, 4990, 5010, 5000, 5021, 5000, 5006, 4995, 5012, 5019, 5000, 5006, 4999, 5007, 5014, 5000, 4994, 4991, 5010, 4985, 4988, 5007, 5005, 5000, 5005, 4997, 4995, 4999, 5000, 5000, 5009, 5000, 4999, 5011, 5018, 4994, 5000, 4995, 5000, 5008, 4994, 4992, 5000, 4998, 4992, 5006, 4996, 5000, 5018, 5000, 5022, 5002, 5007, 5000, 5012, 5014, 5010, 4997, 4996, 5014, 4995, 4991, 5006, 4992, 5008, 5004, 4995, 5016, 5000, 5005, 5000, 4985, 5002, 5000, 5009, 4991, 4995, 5004, 4987, 5014, 4986, 5008, 4985, 4995, 5010, 5000, 5001, 5013, 4988, 5001, 5002, 4986, 4985, 5001, 4995, 5004, 4998, 4998, 4992, 5003, 5000, 4991, 4998, 5002, 5011, 4986, 5013, 4997, 5000, 5002, 5003, 5014, 4998, 5002, 5006, 5015, 4992, 4992, 4995, 4998, 4994, 5007, 4977, 5004, 5005, 4990, 4991, 5001, 5010, 4991, 4990, 5006, 5004, 5004, 4997, 5001, 4992, 5000, 5000, 4996, 4987, 4993, 5001, 5006, 4999, 4993, 4999, 5001, 5001, 5014, 4985, 5010, 5006, 4989, 5002, 5011, 4999, 5010, 5005, 5002, 5010, 4990, 4994, 5000, 4985, 5010, 5000, 4999, 5000, 4977, 5007, 5005, 4990, 5000, 4985, 5003, 5009, 5012, 5012, 5000, 5003, 4997, 5002, 4992, 4999, 4997, 5005, 5009, 4989, 5005, 4988, 5001, 5000, 5000, 4999, 5005, 4997, 5015, 5001, 4988, 4983, 5013, 4992, 5002, 5012, 4999, 5013, 5000, 4988, 4989, 4996, 5000, 5002, 4999, 5021, 4991, 5009, 5005, 4994, 5000, 4994, 4985, 4999, 4980, 5013, 5014, 5000, 4996, 5008, 5028, 5002, 5004, 4999, 5012, 4992, 5002, 4987, 5004, 5009, 5004, 5013, 4989, 5027, 4992, 5016, 5007, 5003, 5013, 5003, 4999, 5000, 4987, 5001, 4998, 4994, 4998, 4997, 4998, 4998, 5001, 4990, 5025, 5003, 4996, 5006, 5005, 4995, 4994, 5023, 5003, 5005, 4991, 5001, 5001, 4996, 5001, 5005, 5001, 5001, 5000, 5000, 5000, 5005, 5001, 4999, 5003, 4982, 5000, 5012, 5011, 5008, 5000, 4998, 4994, 4988, 5013, 4998, 5017, 4991, 5007, 5016, 5007, 4997, 4974, 4997, 5006, 5003, 5010, 5003, 4997, 4997, 4994, 4998, 4987, 5004, 5001, 5009, 5006, 5009, 4984, 5010, 5023, 5000, 5008, 5007, 5008, 4993, 5010, 5012, 4989, 5002, 4993, 5007, 4985, 4990, 4988, 4997, 5000, 4994, 5004, 4981, 5000, 4999, 5003, 4988, 5006, 5019, 5003, 4992, 4977, 4998, 4987, 4995, 4987, 5000, 4986, 4998, 4996, 5008, 5011, 5009, 5000, 4999, 5010, 4999, 5009, 5010, 4992, 4998, 4994, 5009, 4998, 5013, 5011, 4997, 5016, 4995, 5003, 4986, 5010, 4995, 4986, 5025, 4990, 5010, 4990, 4996, 5000, 5002, 4994, 5002, 5000, 5001, 4995, 5003, 5006, 5000, 4997, 4996, 5010, 4992, 5013, 4998, 4988, 4999, 5004, 5004, 5002, 5002, 5001, 4986, 5011, 5012, 4988, 5000, 5000, 5012, 5004, 5004, 4984, 5012, 4998, 5005, 5005, 5005, 4995, 5004, 5006, 5000, 5008, 5015, 4999, 4999, 4996, 5000, 4992, 5019, 5020, 4999, 4991, 5020, 5000, 5002, 5000, 5003, 4985, 4990, 5011, 5012, 4997, 4989, 4999, 5006, 4997, 4992, 4999, 4985, 4999, 5012, 5003, 4989, 5010, 5010, 5012, 4990, 4986, 5014, 5000, 4994, 5017, 4995, 5005, 5003, 5000, 5000, 4988, 5018, 5007, 5009, 5002, 5004, 4999, 5026, 4987, 4986, 5000, 4994, 5012, 5022, 4996, 5002, 4986, 4990, 4989, 4997, 4997, 5002, 5004, 4999, 5000, 4992, 4999, 4997, 4998, 5000, 5002, 5032, 4998, 5014, 4978, 5001, 4988, 5006, 5003, 5005, 5002, 5005, 5000, 5003, 4990, 4991, 4990, 5016, 5006, 4981, 4999, 4998, 4989, 4995, 5007, 5004, 5019, 5001, 5004, 5000, 4996, 4988, 5001, 5003, 5009, 4998, 5003, 4991, 5000, 4988, 5004, 5002, 4991, 4998, 4996, 4991, 5013, 5007, 5010, 5004, 5006, 5007, 4993, 5000, 5002, 4992, 4999, 4992, 5001, 4989, 4992, 5015, 5011, 5008, 5004, 4990, 4995, 4993, 5004, 5000, 4982, 5008, 5000, 4993, 5000, 5000, 4991, 5005, 5008, 5003, 4994, 4997, 5012, 4995, 5009, 5020, 5010, 5019, 4979, 5013, 4990, 5002, 5000, 5015, 4989, 5009, 4998, 5004, 5010, 4988, 4994, 5004, 4985, 4998, 4991, 4985, 4994, 4986, 5010, 4990, 5002, 5005, 5008, 5004, 4988, 4980, 5005, 4994, 5004, 4998, 4990, 5010, 5003, 5011, 4992, 5009, 5003, 5013, 4996, 4997, 4989, 4992, 5002, 4992, 4996, 4999, 5000, 5009, 4990, 4992, 5020, 4981, 5011, 5000, 5008, 4997, 4994, 4982, 5014, 5013, 4996, 5002, 5011, 5003, 5000, 5015, 4999, 5001, 5002, 5018, 4984, 5022, 5009, 5006, 5009, 5000, 4974, 4991, 5020, 5000, 4987, 5003, 4987, 5003, 5002, 5016, 4997, 5022, 5005, 4998, 4999, 5001, 5000, 4999, 5000, 5001, 4994, 4991, 4997, 4990, 4991, 4995, 5008, 5007, 4994, 5000, 5007, 4988, 5014, 5003, 4998, 4995, 5000, 5000, 5004] # nopep8
- true_yy2 = [5001, 5000, 4994, 5000, 5002, 5000, 5000, 5000, 5000, 5003, 5000, 5000, 5000, 5003, 5003, 5003, 4997, 5004, 5007, 5004, 5001, 5001, 5001, 5002, 5002, 5001, 5001, 5001, 4998, 5001, 4998, 4996, 4998, 4998, 5002, 5002, 4996, 4997, 5003, 5003, 5002, 5001, 5002, 5003, 5003, 5002, 5001, 4998, 4998, 4995, 4995, 4998, 4999, 4999, 5007, 5009, 5009, 5009, 5007, 4996, 4996, 4994, 4994, 4994, 4994, 4995, 5007, 5008, 5008, 5009, 5008, 5008, 5008, 5001, 5000, 5001, 5001, 5004, 5004, 5005, 5005, 5005, 5005, 5000, 4994, 4994, 4994, 4993, 4994, 4995, 4995, 4995, 4995, 4995, 4999, 4995, 4995, 4999, 4999, 4997, 4997, 4993, 4997, 5009, 4997, 4997, 4999, 4999, 4999, 4999, 4997, 4987, 4999, 5002, 5001, 5002, 5001, 5001, 5002, 5001, 4998, 4996, 4996, 4996, 4998, 4998, 4998, 4998, 5001, 5001, 5001, 5001, 5004, 5004, 5004, 5004, 5002, 5004, 5008, 5002, 5010, 5002, 5002, 5009, 5002, 5002, 5002, 5002, 5008, 5008, 5008, 5008, 5008, 5008, 5008, 5004, 5004, 5004, 5004, 5004, 5000, 4999, 5000, 4998, 4998, 4998, 5000, 5000, 5000, 5000, 5000, 5000, 4997, 4997, 4997, 5000, 5003, 5003, 5005, 5009, 5009, 5009, 5010, 5011, 5011, 5011, 5004, 5011, 5004, 5000, 5000, 5000, 5000, 5001, 5001, 5001, 5001, 5001, 4997, 4995, 4995, 4995, 4997, 4997, 4997, 5000, 5007, 5000, 4998, 5000, 5000, 5000, 4998, 4995, 4995, 4995, 4995, 4995, 5003, 5003, 5003, 5003, 5003, 5008, 5001, 5001, 4998, 4994, 4994, 5008, 4993, 4993, 4993, 4994, 4994, 4994, 4994, 5002, 5002, 5002, 5000, 5000, 5000, 5000, 5000, 4999, 5000, 4999, 4994, 4994, 5000, 5000, 5000, 5000, 5001, 5001, 5001, 5001, 5001, 5002, 5001, 5001, 5001, 5001, 4999, 4999, 4996, 4997, 4997, 4999, 5005, 5005, 5005, 5005, 5005, 5000, 5000, 5000, 5001, 5000, 5000, 5000, 5001, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5001, 5000, 5000, 5000, 5006, 5006, 5006, 5006, 5006, 5006, 5007, 5006, 5000, 5000, 5000, 5000, 4994, 4994, 4994, 5000, 5005, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 4995, 5000, 4998, 4998, 4998, 4996, 4998, 5000, 5000, 5000, 5002, 5002, 5002, 5007, 5007, 5010, 5007, 5007, 5010, 5010, 4997, 4997, 4996, 4996, 5004, 4995, 5004, 5004, 5004, 5004, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 4995, 4995, 4995, 4995, 4995, 5000, 5000, 5001, 5000, 5001, 5001, 5001, 5001, 5001, 4995, 5001, 4998, 4998, 4998, 4998, 4998, 4998, 4998, 4998, 5000, 5000, 5000, 4998, 5000, 5002, 5003, 5003, 5003, 5002, 5003, 5006, 5003, 5002, 4998, 4998, 4995, 4995, 4994, 4995, 4998, 4998, 4994, 5001, 5001, 5001, 4991, 4991, 5001, 5004, 5004, 5001, 5001, 5001, 5000, 5000, 4997, 4996, 4996, 5000, 4999, 4996, 4999, 4999, 5001, 5001, 4999, 5001, 5001, 5001, 5002, 5006, 5002, 5006, 5005, 5002, 5005, 5005, 5002, 5002, 5000, 5000, 5000, 4999, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5003, 5003, 5003, 5003, 5009, 5009, 5009, 5002, 5000, 4999, 4999, 4999, 4999, 4999, 4999, 5001, 5001, 5001, 5000, 5001, 5000, 5001, 5001, 5001, 4999, 5001, 4997, 5001, 5001, 4999, 5002, 5002, 5000, 5000, 4999, 4999, 5000, 4999, 4999, 4999, 5000, 5002, 5002, 5000, 5000, 4994, 4999, 4994, 4994, 4999, 4999, 4999, 5000, 5008, 5008, 5004, 5002, 5004, 5004, 5002, 5002, 5002, 5002, 5004, 5004, 5004, 5004, 5004, 5009, 5007, 5007, 5007, 5007, 5003, 5003, 5003, 5001, 5000, 4998, 4998, 4998, 4998, 4998, 4998, 4997, 4998, 5001, 5001, 5001, 5003, 5003, 5003, 5003, 5003, 5005, 5003, 5001, 5003, 5003, 5001, 5001, 5001, 5001, 5001, 5001, 5001, 5001, 5001, 5001, 5001, 5000, 5000, 5001, 5001, 5003, 5003, 5000, 5000, 5000, 5000, 4998, 4998, 4998, 4998, 5007, 5007, 5007, 5007, 4997, 5006, 5003, 5003, 5003, 5003, 5003, 5003, 4998, 4997, 4997, 4997, 4997, 4998, 5004, 5004, 5006, 5009, 5009, 5008, 5009, 5008, 5008, 5008, 5008, 5008, 5008, 5002, 5002, 5002, 4993, 4990, 4993, 4993, 4994, 4994, 4994, 4997, 4999, 5000, 4999, 5000, 5000, 5003, 5003, 5003, 4998, 4998, 4995, 4992, 4992, 4987, 4995, 4995, 4996, 4998, 5000, 5000, 5000, 5008, 5008, 5009, 5009, 5000, 4999, 4999, 4999, 4998, 4998, 4998, 4998, 5009, 5009, 5003, 5003, 5003, 4997, 4995, 4995, 4995, 4995, 4995, 4995, 4996, 5000, 4996, 5000, 5000, 5000, 5000, 5002, 5002, 5002, 5000, 5000, 5000, 5000, 5000, 4998, 4997, 4998, 4999, 4999, 5002, 5002, 5002, 5002, 5002, 5002, 5002, 5001, 5000, 5000, 5004, 5004, 5000, 5004, 5004, 5004, 5004, 5005, 5005, 5005, 5005, 5005, 5005, 5005, 5000, 5000, 5000, 5000, 4999, 4999, 4999, 4999, 4999, 5000, 5000, 5002, 5000, 5000, 5000, 5000, 5000, 5002, 5000, 4997, 4997, 4999, 4999, 4997, 4997, 4997, 4999, 4999, 4999, 4999, 4999, 5003, 5010, 5010, 5003, 5010, 5010, 5000, 5000, 4995, 5000, 5003, 5000, 5000, 5000, 5000, 5003, 5003, 5002, 5004, 5004, 5007, 5004, 5002, 5000, 4999, 4999, 5000, 4996, 5000, 5000, 4996, 4996, 4996, 4996, 4997, 4997, 4999, 5000, 5000, 4999, 4999, 4998, 4998, 4998, 4998, 4998, 5000, 5000, 5001, 5001, 5001, 5001, 5003, 5003, 5005, 5005, 5005, 5003, 5003, 5000, 5000, 5000, 4991, 4991, 4998, 4998, 4998, 4998, 4998, 5004, 5001, 5004, 5004, 5004, 5001, 5001, 5001, 5003, 5000, 5003, 5003, 5003, 5000, 5000, 5000, 5000, 4998, 4998, 4996, 4998, 4998, 4998, 5004, 5006, 5007, 5007, 5006, 5004, 5002, 5000, 4999, 4999, 4999, 4992, 4992, 4999, 5001, 5004, 5004, 5004, 5004, 5004, 5004, 4995, 4995, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5003, 5003, 5003, 5003, 5009, 5010, 5010, 5010, 5010, 5010, 5002, 5002, 5000, 5002, 5000, 5002, 5004, 5004, 4998, 5004, 4998, 4998, 4994, 4991, 4994, 4991, 4991, 4991, 4991, 4994, 5002, 5004, 5004, 5002, 5004, 5004, 5004, 4998, 4994, 4998, 5003, 5003, 5003, 5003, 5003, 5009, 5003, 5003, 4997, 4997, 4997, 4996, 4996, 4996, 4996, 4999, 4999, 4996, 4999, 4999, 5000, 5000, 5000, 5000, 5000, 4997, 5000, 5000, 4997, 4997, 5002, 5003, 5003, 5003, 5002, 5002, 5002, 5002, 5001, 5002, 5002, 5006, 5009, 5009, 5006, 5006, 5006, 5000, 5000, 5000, 4991, 5000, 5002, 5002, 5002, 5003, 5003, 5003, 5002, 5001, 5000, 5001, 5000, 5000, 5000, 5000, 5000, 4997, 4994, 4994, 4994, 4995, 4995, 4995, 5000, 5000, 5007, 5003, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000] # nopep8
-
- assert_array_almost_equal(np.int_(yy), true_yy)
- assert_array_almost_equal(np.int_(yy1), true_yy1)
- assert_array_almost_equal(np.int_(yy2), true_yy2)
-
-
-if __name__ == "__main__":
- # import sys;sys.argv = ['', 'Test.test_hampelfilter']
- unittest.main()
diff --git a/wafo/source/c_library/build_all.py b/wafo/source/c_library/build_all.py
deleted file mode 100644
index 49ee1eb..0000000
--- a/wafo/source/c_library/build_all.py
+++ /dev/null
@@ -1,25 +0,0 @@
-"""
-f2py c_library.pyf c_functions.c -c
-
-See also http://www.scipy.org/Cookbook/CompilingExtensionsOnWindowsWithMinGW
-"""
-import os
-import sys
-from wafo.f2py_tools import f2py_call_str
-
-def compile_all():
- f2py_call = f2py_call_str()
- print '=' * 75
- print 'compiling c_codes'
- print '=' * 75
-
- compile_format = f2py_call + ' %s %s -c'
-
- pyfs = ('c_library.pyf',)
- files = ('c_functions.c',)
-
- for pyf, file_ in zip(pyfs, files):
- os.system(compile_format % (pyf, file_))
-
-if __name__ == '__main__':
- compile_all()
diff --git a/wafo/source/c_library/c_functions.c b/wafo/source/c_library/c_functions.c
deleted file mode 100644
index 8f2c2a2..0000000
--- a/wafo/source/c_library/c_functions.c
+++ /dev/null
@@ -1,779 +0,0 @@
-#include "math.h"
-/*
-* Install gfortran and run the following to build the module on windows:
- * f2py c_library.pyf c_functions.c -c --fcompiler=gnu95 --compiler=mingw32 -lmsvcr71
- */
-
-/*
- * findrfc.c -
- *
- * Returns indices to RFC turningpoints of a vector
- * of turningpoints
- *
- * 1998 by Per Andreas Brodtkorb.
- */
-
-void findrfc(double *y1,double hmin, int *ind, int n,int *info) {
- double xminus, xplus, Tpl, Tmi, *y, Tstart;
- int i, j, ix=0, NC, iy;
- info[0] = 0;
- if (*(y1+0)> *(y1+1)){
- /* if first is a max , ignore the first max*/
- y=&(*(y1+1));
- NC=floor((n-1)/2);
- Tstart=1;
- }
- else {
- y=y1;
- NC=floor(n/2);
- Tstart=0;
- }
-
- if (NC<1){
- return; /* No RFC cycles*/
- }
-
-
- if (( *(y+0) > *(y+1)) && ( *(y+1) > *(y+2)) ){
- info[0] = -1;
- return; /*This is not a sequence of turningpoints, exit */
- }
- if ((*(y+0) < *(y+1)) && (*(y+1)< *(y+2))){
- info[0]=-1;
- return; /*This is not a sequence of turningpoints, exit */
- }
-
-
- for (i=0; i=0) && (*(y+2*j+1)<=*(y+2*i+1))){
- if( (*(y+2*j)= xplus){
- if ( (*(y+2*i+1)-xminus) >= hmin){
- *(ind+ix)=Tmi;
- ix++;
- *(ind+ix)=(Tstart+2*i+1);
- ix++;
- } /*if*/
- goto L180;
- }
-
- j=i+1;
- while((j= *(y+2*i+1)) goto L170;
- if( (*(y+2*j+2) <= xplus) ){
- xplus=*(y+2*j+2);
- Tpl=(Tstart+2*j+2);
- }/*if*/
- j++;
- } /*while*/
-
-
- if ( (*(y+2*i+1)-xminus) >= hmin) {
- *(ind+ix)=Tmi;
- ix++;
- *(ind+ix)=(Tstart+2*i+1);
- ix++;
-
- } /*if*/
- goto L180;
- L170:
- if (xplus <= xminus ) {
- if ( (*(y+2*i+1)-xminus) >= hmin){
- *(ind+ix)=Tmi;
- ix++;
- *(ind+ix)=(Tstart+2*i+1);
- ix++;
- } /*if*/
- /*goto L180;*/
- }
- else{
- if ( (*(y+2*i+1)-xplus) >= hmin) {
- *(ind+ix)=(Tstart+2*i+1);
- ix++;
- *(ind+ix)=Tpl;
- ix++;
- } /*if*/
- } /*elseif*/
- L180:
- iy=i;
- } /* for i */
- info[0] = ix;
- return ;
-}
-
-
-
-/*
- * findcross.c -
- *
- * Returns indices to level v crossings of argument vector
- *
- * 1998 by Per Andreas Brodtkorb. last modified 23.06-98
- */
-
-
-void findcross(double *y, double v, int *ind, int n, int *info)
-{ int i,start, ix=0,dcross=0;
- start=0;
- if ( y[0]< v){
- dcross=-1; /* first is a up-crossing*/
- }
- else if ( y[0]> v){
- dcross=1; /* first is a down-crossing*/
- }
- else if ( y[0]== v){
- /* Find out what type of crossing we have next time.. */
- for (i=1; i v){
- ind[ix] = i-1; /* first crossing is a up-crossing*/
- ix++;
- dcross=1; /*The next crossing is a down-crossing*/
- goto L120;
- }
- }
- }
- L120:
- for (i=start; i v) ) || ((dcross==1 ) && (y[i]>=v) && (y[i+1] < v) ) ) {
-
- ind[ix] = i;
- ix++;
- dcross=-dcross;
- }
- }
- info[0] = ix;
- return;
-}
-
-
-/*
- * DISUFQ Is an internal function to spec2nlsdat
- *
- * CALL: disufq(rvec,ivec,rA,iA, w,kw,h,g,nmin,nmax,m,n)
- *
- * rvec, ivec = real and imaginary parts of the resultant (size m X n).
- * rA, iA = real and imaginary parts of the amplitudes (size m X n).
- * w = vector with angular frequencies (w>=0)
- * kw = vector with wavenumbers (kw>=0)
- * h = water depth (h >=0)
- * g = constant acceleration of gravity
- * nmin = minimum index where rA(:,nmin) and iA(:,nmin) is
- * greater than zero.
- * nmax = maximum index where rA(:,nmax) and iA(:,nmax) is
- * greater than zero.
- * m = size(rA,1),size(iA,1)
- * n = size(rA,2),size(iA,2), or size(rvec,2),size(ivec,2)
- *
- * DISUFQ returns the summation of difference frequency and sum
- * frequency effects in the vector vec = rvec +sqrt(-1)*ivec.
- * The 2'nd order contribution to the Stokes wave is then calculated by
- * a simple 1D Fourier transform, real(FFT(vec)).
- *
- * Install gfortran and run the following to build the module:
- * f2py diffsumfunq.pyf disufq1.c -c --fcompiler=gnu95 --compiler=mingw32 -lmsvcr71
- *
- * by Per Andreas Brodtkorb 15.08.2001
- * revised pab 14.03.2002, 01.05.2002 22.07.2002, oct 2008
- */
-
-void disufq(double *rvec, double *ivec,
- double *rA, double *iA,
- double *w, double *kw,
- double h, double g,
- int nmin, int nmax,
- int m, int n)
-{
- double Epij, Edij;
- double tmp1, tmp2, tmp3, tmp4, kfact;
- double w1, w2, kw1, kw2, Cg;
- double rrA, iiA, riA, irA;
- int i,jy,ix,iz1,iv1,ixi,jyi;
- //int iz2, iv2;
- //Initialize rvec and ivec to zero
- for (ix=0;ix10000){ /* deep water /Inifinite water depth */
- for (ix = nmin-1;ix=0)
- * kw = vector with wavenumbers (kw>=0)
- * h = water depth (h >=0)
- * g = constant acceleration of gravity
- * nmin = minimum index where rA(:,nmin) and iA(:,nmin) is
- * greater than zero.
- * nmax = maximum index where rA(:,nmax) and iA(:,nmax) is
- * greater than zero.
- * m = size(rA,1),size(iA,1)
- * n = size(rA,2),size(iA,2), or size(rvec,2),size(ivec,2)
- *
- * DISUFQ2 returns the summation of sum and difference frequency
- * frequency effects in the vectors svec = rsvec +sqrt(-1)*isvec and
- * dvec = rdvec +sqrt(-1)*idvec.
- * The 2'nd order contribution to the Stokes wave is then calculated by
- * a simple 1D Fourier transform, real(FFT(svec+dvec)).
- *
- *
- * This is a MEX-file for MATLAB.
- * by Per Andreas Brodtkorb 15.08.2001
- * revised pab 14.03.2002, 01.05.2002
- */
-
-void disufq2(double *rsvec, double *isvec,
- double *rdvec, double *idvec,
- double *rA, double *iA,
- double *w, double *kw,
- double h, double g,
- int nmin, int nmax,
- int m, int n)
-{
- double Epij, Edij;
- double tmp1, tmp2, tmp3, tmp4, kfact;
- double w1, w2, kw1, kw2, Cg;
- double rrA, iiA, riA, irA;
- int i,jy,ix,iz1,iv1,ixi,jyi;
- //int iz2,iv2
-
- //Initialize rvec and ivec to zero
- for (ix=0;ix10000){ /* deep water /Inifinite water depth */
- for (ix = nmin-1;ix= 2) && (fabs(a[j-1]-a[j-2]) <= fabs(a[j]-a[j-1])) ) {
- ampl=fabs( (a[j-1]-a[j-2])/2 );
- switch(j) {
- case 0: { break; }
- case 1: { break; }
- case 2: {
- mean=(a[0]+a[1])/2;
- a[0]=a[1];
- a[1]=a[2];
- j=1;
- if (ampl > 0) {
- *po++=ampl;
- *po++=mean;
- *po++=0.50;
- }
- break;
- }
- default: {
- mean=(a[j-1]+a[j-2])/2;
- a[j-2]=a[j];
- j=j-2;
- if (ampl > 0) {
- *po++=ampl;
- *po++=mean;
- *po++=1.00;
- cNr1++;
- }
- break;
- }
- }
- }
- }
- cNr2 = 1;
- for (index=0; index 0){
- *po++=ampl;
- *po++=mean;
- *po++=0.50;
- cNr2++;
- }
- }
- // array of ints nout is outputted
- nout[0] = cNr1;
- nout[1] = cNr2;
-}
-/* ++++++++++ END RF3 */
-
-
-// ++ BEGIN RF5 [ampl ampl_mean nr_of_cycle cycle_begin_time cycle_period_time]
-/* ++++++++++ Rain flow with time analysis */
-//By Adam Nieslony
-//Visit the MATLAB Central File Exchange for latest version
-//http://www.mathworks.com/matlabcentral/fileexchange/3026
-void
-findrfc5_astm(double *array_ext, double *array_t, double *array_out, int n, int *nout) {
- double *pr, *pt, *po, a[16384], t[16384], ampl, mean, period, atime;
- int tot_num, index, j, cNr1, cNr2;
-
-
-// tot_num = mxGetM(array_ext) * mxGetN(array_ext);
- tot_num = n;
-
- // pointers to the first element of the arrays
- pr = &array_ext[0];
- pt = &array_t[0];
- po = &array_out[0];
-
-// array_out = mxCreateDoubleMatrix(5, tot_num-1, mxREAL);
-
- // The original rainflow counting by Nieslony, unchanged
- j = -1;
- cNr1 = 1;
- for (index=0; index= 2) && (fabs(a[j-1]-a[j-2]) <= fabs(a[j]-a[j-1])) ) {
- ampl=fabs( (a[j-1]-a[j-2])/2 );
- switch(j)
-{
- case 0: { break; }
- case 1: { break; }
- case 2: {
- mean=(a[0]+a[1])/2;
- period=(t[1]-t[0])*2;
- atime=t[0];
- a[0]=a[1];
- a[1]=a[2];
- t[0]=t[1];
- t[1]=t[2];
- j=1;
- if (ampl > 0) {
- *po++=ampl;
- *po++=mean;
- *po++=0.50;
- *po++=atime;
- *po++=period;
- }
- break;
- }
- default: {
- mean=(a[j-1]+a[j-2])/2;
- period=(t[j-1]-t[j-2])*2;
- atime=t[j-2];
- a[j-2]=a[j];
- t[j-2]=t[j];
- j=j-2;
- if (ampl > 0) {
- *po++=ampl;
- *po++=mean;
- *po++=1.00;
- *po++=atime;
- *po++=period;
- cNr1++;
- }
- break;
- }
- }
- }
- }
- cNr2 = 1;
- for (index=0; index 0){
- *po++=ampl;
- *po++=mean;
- *po++=0.50;
- *po++=atime;
- *po++=period;
- cNr2++;
- }
- }
-// /* free the memeory !!!*/
-// mxSetN(array_out, tot_num - cNr);
- nout[0] = cNr1;
- nout[1] = cNr2;
-}
-/* ++++++++++ END RF5 */
diff --git a/wafo/source/c_library/c_library rf3 rf5 license.txt b/wafo/source/c_library/c_library rf3 rf5 license.txt
deleted file mode 100644
index 420c520..0000000
--- a/wafo/source/c_library/c_library rf3 rf5 license.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Copyright (c) 2003, Adam Niesłony
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are
-met:
-
- * Redistributions of source code must retain the above copyright
- notice, this list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright
- notice, this list of conditions and the following disclaimer in
- the documentation and/or other materials provided with the distribution
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-POSSIBILITY OF SUCH DAMAGE.
diff --git a/wafo/source/c_library/c_library.pyf b/wafo/source/c_library/c_library.pyf
deleted file mode 100644
index 5e58404..0000000
--- a/wafo/source/c_library/c_library.pyf
+++ /dev/null
@@ -1,86 +0,0 @@
-! File c_code.pyf
-python module c_library
-interface
- subroutine findrfc(y1, hmin, ind, n, info)
- intent(c) findrfc ! findrfc is a C function
- intent(c) ! all findrfc arguments are considered as C based
- integer intent(hide), depend(y1) :: n=len(y1)
- double precision dimension(n), intent(in) :: y1 ! input array
- double precision intent(in) :: hmin
- integer dimension(n), intent(out) :: ind ! output array,
- integer dimension(1), intent(out) :: info
- end subroutine findrfc
- subroutine findcross(y, v, ind, n, info)
- intent(c) findcross ! findcross is a C function
- intent(c) ! all findcross arguments are considered as C based
- integer intent(hide), depend(y) :: n=len(y)
- double precision dimension(n), intent(in) :: y ! input array
- double precision intent(in) :: v
- integer dimension(n), intent(out) :: ind ! output array,
- integer dimension(1),intent(out) :: info
- end subroutine findcross
- subroutine disufq(rvec, ivec, rA, iA, w, kw, h, g,nmin,nmax, m, n)
- intent(c) disufq ! disufq is a C function
- intent(c) ! all disufq arguments are considered as C based
- !integer intent(hide), depend(rA),check(n*m==len(iA)) :: n=len(rA)/m
- !integer intent(hide), depend(rA), check(m==shape(iA,1)) :: m=shape(rA,1)
- double precision dimension(n*m), intent(in) :: rA, iA ! input array
- double precision dimension(n/2+1), intent(in) :: w, kw ! input array
- double precision intent(in) :: h, g
- integer intent(in) :: nmin, nmax
- double precision dimension(n*m), intent(out) :: rvec, ivec ! output array,
- end subroutine disufq
- subroutine disufq2(rsvec, isvec,rdvec, idvec, rA, iA, w, kw, h, g,nmin,nmax, m, n)
- intent(c) disufq2 ! disufq2 is a C function
- intent(c) ! all disufq2 arguments are considered as C based
- !integer intent(hide), depend(rA),check(n*m==len(iA)) :: n=len(rA)/m
- !integer intent(hide), depend(rA), check(m==shape(iA,1)) :: m=shape(rA,1)
- double precision dimension(n*m), intent(in) :: rA, iA ! input array
- double precision dimension(n/2+1), intent(in) :: w, kw ! input array
- double precision intent(in) :: h, g
- integer intent(in) :: nmin, nmax
- double precision dimension(n*m), intent(out) :: rsvec, isvec, rdvec, idvec ! output array,
- end subroutine disufq2
-
- ! ===== START NIESLONY RAINFLOW FUNCTIONS
- ! RAINFLOW Revision: 1.1
- ! by Adam Nieslony, 2009
- subroutine findrfc3_astm(array_ext, array_out, n, nout)
- intent(c) findrfc3_astm ! rf3 is a C function
- intent(c) ! all rf3 arguments are
- ! considered as C based
-
- ! n is the length of the input array array_ext
- integer intent(hide), depend(array_ext) :: n=len(array_ext)
-
- ! of input array x
- double precision intent(in) :: array_ext(n)
-
- ! the output array
- double precision intent(out) :: array_out(n,3)
-
- ! nout array, to output additional ints
- integer dimension(2), intent(out) :: nout
- end subroutine findrfc3_astm
-
- subroutine findrfc5_astm(array_ext, array_t, array_out, n, nout)
- intent(c) findrfc5_astm ! rf5 is a C function
- intent(c) ! all rf5 arguments are
- ! considered as C based
-
- ! n is the length of the input array array_ext
- integer intent(hide), depend(array_ext) :: n=len(array_ext)
-
- ! of input array x
- double precision intent(in) :: array_ext(n), array_t(n)
-
- ! the output array
- double precision intent(out) :: array_out(n,5)
-
- ! nout array, to output additional ints
- integer dimension(2), intent(out) :: nout
- end subroutine findrfc5_astm
- ! ===== END NIESLONY RAINFLOW FUNCTIONS
-
-end interface
-end python module c_library
\ No newline at end of file
diff --git a/wafo/source/c_library/c_librarymodule.c b/wafo/source/c_library/c_librarymodule.c
deleted file mode 100644
index efacace..0000000
--- a/wafo/source/c_library/c_librarymodule.c
+++ /dev/null
@@ -1,1359 +0,0 @@
-/* File: c_librarymodule.c
- * This file is auto-generated with f2py (version:2).
- * f2py is a Fortran to Python Interface Generator (FPIG), Second Edition,
- * written by Pearu Peterson .
- * See http://cens.ioc.ee/projects/f2py2e/
- * Generation date: Thu Jan 29 22:06:57 2015
- * $Revision:$
- * $Date:$
- * Do not edit this file directly unless you know what you are doing!!!
- */
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-/*********************** See f2py2e/cfuncs.py: includes ***********************/
-#include "Python.h"
-#include
-#include "fortranobject.h"
-#include
-
-/**************** See f2py2e/rules.py: mod_rules['modulebody'] ****************/
-static PyObject *c_library_error;
-static PyObject *c_library_module;
-
-/*********************** See f2py2e/cfuncs.py: typedefs ***********************/
-/*need_typedefs*/
-
-/****************** See f2py2e/cfuncs.py: typedefs_generated ******************/
-/*need_typedefs_generated*/
-
-/********************** See f2py2e/cfuncs.py: cppmacros **********************/
-#define rank(var) var ## _Rank
-#define shape(var,dim) var ## _Dims[dim]
-#define old_rank(var) (((PyArrayObject *)(capi_ ## var ## _tmp))->nd)
-#define old_shape(var,dim) (((PyArrayObject *)(capi_ ## var ## _tmp))->dimensions[dim])
-#define fshape(var,dim) shape(var,rank(var)-dim-1)
-#define len(var) shape(var,0)
-#define flen(var) fshape(var,0)
-#define old_size(var) PyArray_SIZE((PyArrayObject *)(capi_ ## var ## _tmp))
-/* #define index(i) capi_i ## i */
-#define slen(var) capi_ ## var ## _len
-#define size(var, ...) f2py_size((PyArrayObject *)(capi_ ## var ## _tmp), ## __VA_ARGS__, -1)
-
-#ifdef DEBUGCFUNCS
-#define CFUNCSMESS(mess) fprintf(stderr,"debug-capi:"mess);
-#define CFUNCSMESSPY(mess,obj) CFUNCSMESS(mess) \
- PyObject_Print((PyObject *)obj,stderr,Py_PRINT_RAW);\
- fprintf(stderr,"\n");
-#else
-#define CFUNCSMESS(mess)
-#define CFUNCSMESSPY(mess,obj)
-#endif
-
-#ifndef max
-#define max(a,b) ((a > b) ? (a) : (b))
-#endif
-#ifndef min
-#define min(a,b) ((a < b) ? (a) : (b))
-#endif
-#ifndef MAX
-#define MAX(a,b) ((a > b) ? (a) : (b))
-#endif
-#ifndef MIN
-#define MIN(a,b) ((a < b) ? (a) : (b))
-#endif
-
-#define CHECKSCALAR(check,tcheck,name,show,var)\
- if (!(check)) {\
- char errstring[256];\
- sprintf(errstring, "%s: "show, "("tcheck") failed for "name, var);\
- PyErr_SetString(c_library_error,errstring);\
- /*goto capi_fail;*/\
- } else
-#if defined(PREPEND_FORTRAN)
-#if defined(NO_APPEND_FORTRAN)
-#if defined(UPPERCASE_FORTRAN)
-#define F_FUNC(f,F) _##F
-#else
-#define F_FUNC(f,F) _##f
-#endif
-#else
-#if defined(UPPERCASE_FORTRAN)
-#define F_FUNC(f,F) _##F##_
-#else
-#define F_FUNC(f,F) _##f##_
-#endif
-#endif
-#else
-#if defined(NO_APPEND_FORTRAN)
-#if defined(UPPERCASE_FORTRAN)
-#define F_FUNC(f,F) F
-#else
-#define F_FUNC(f,F) f
-#endif
-#else
-#if defined(UPPERCASE_FORTRAN)
-#define F_FUNC(f,F) F##_
-#else
-#define F_FUNC(f,F) f##_
-#endif
-#endif
-#endif
-#if defined(UNDERSCORE_G77)
-#define F_FUNC_US(f,F) F_FUNC(f##_,F##_)
-#else
-#define F_FUNC_US(f,F) F_FUNC(f,F)
-#endif
-
-
-/************************ See f2py2e/cfuncs.py: cfuncs ************************/
-static int f2py_size(PyArrayObject* var, ...)
-{
- npy_int sz = 0;
- npy_int dim;
- npy_int rank;
- va_list argp;
- va_start(argp, var);
- dim = va_arg(argp, npy_int);
- if (dim==-1)
- {
- sz = PyArray_SIZE(var);
- }
- else
- {
- rank = PyArray_NDIM(var);
- if (dim>=1 && dim<=rank)
- sz = PyArray_DIM(var, dim-1);
- else
- fprintf(stderr, "f2py_size: 2nd argument value=%d fails to satisfy 1<=value<=%d. Result will be 0.\n", dim, rank);
- }
- va_end(argp);
- return sz;
-}
-
-static int double_from_pyobj(double* v,PyObject *obj,const char *errmess) {
- PyObject* tmp = NULL;
- if (PyFloat_Check(obj)) {
-#ifdef __sgi
- *v = PyFloat_AsDouble(obj);
-#else
- *v = PyFloat_AS_DOUBLE(obj);
-#endif
- return 1;
- }
- tmp = PyNumber_Float(obj);
- if (tmp) {
-#ifdef __sgi
- *v = PyFloat_AsDouble(tmp);
-#else
- *v = PyFloat_AS_DOUBLE(tmp);
-#endif
- Py_DECREF(tmp);
- return 1;
- }
- if (PyComplex_Check(obj))
- tmp = PyObject_GetAttrString(obj,"real");
- else if (PyString_Check(obj) || PyUnicode_Check(obj))
- /*pass*/;
- else if (PySequence_Check(obj))
- tmp = PySequence_GetItem(obj,0);
- if (tmp) {
- PyErr_Clear();
- if (double_from_pyobj(v,tmp,errmess)) {Py_DECREF(tmp); return 1;}
- Py_DECREF(tmp);
- }
- {
- PyObject* err = PyErr_Occurred();
- if (err==NULL) err = c_library_error;
- PyErr_SetString(err,errmess);
- }
- return 0;
-}
-
-static int int_from_pyobj(int* v,PyObject *obj,const char *errmess) {
- PyObject* tmp = NULL;
- if (PyInt_Check(obj)) {
- *v = (int)PyInt_AS_LONG(obj);
- return 1;
- }
- tmp = PyNumber_Int(obj);
- if (tmp) {
- *v = PyInt_AS_LONG(tmp);
- Py_DECREF(tmp);
- return 1;
- }
- if (PyComplex_Check(obj))
- tmp = PyObject_GetAttrString(obj,"real");
- else if (PyString_Check(obj) || PyUnicode_Check(obj))
- /*pass*/;
- else if (PySequence_Check(obj))
- tmp = PySequence_GetItem(obj,0);
- if (tmp) {
- PyErr_Clear();
- if (int_from_pyobj(v,tmp,errmess)) {Py_DECREF(tmp); return 1;}
- Py_DECREF(tmp);
- }
- {
- PyObject* err = PyErr_Occurred();
- if (err==NULL) err = c_library_error;
- PyErr_SetString(err,errmess);
- }
- return 0;
-}
-
-
-/********************* See f2py2e/cfuncs.py: userincludes *********************/
-/*need_userincludes*/
-
-/********************* See f2py2e/capi_rules.py: usercode *********************/
-
-
-/* See f2py2e/rules.py */
-extern void findrfc(double*,double,int*,int,int*);
-extern void findcross(double*,double,int*,int,int*);
-extern void disufq(double*,double*,double*,double*,double*,double*,double,double,int,int,int,int);
-extern void disufq2(double*,double*,double*,double*,double*,double*,double*,double*,double,double,int,int,int,int);
-extern void findrfc3_astm(double*,double*,int,int*);
-extern void findrfc5_astm(double*,double*,double*,int,int*);
-/*eof externroutines*/
-
-/******************** See f2py2e/capi_rules.py: usercode1 ********************/
-
-
-/******************* See f2py2e/cb_rules.py: buildcallback *******************/
-/*need_callbacks*/
-
-/*********************** See f2py2e/rules.py: buildapi ***********************/
-
-/********************************** findrfc **********************************/
-static char doc_f2py_rout_c_library_findrfc[] = "\
-ind,info = findrfc(y1,hmin)\n\nWrapper for ``findrfc``.\
-\n\nParameters\n----------\n"
-"y1 : input rank-1 array('d') with bounds (n)\n"
-"hmin : input float\n"
-"\nReturns\n-------\n"
-"ind : rank-1 array('i') with bounds (n)\n"
-"info : rank-1 array('i') with bounds (1)";
-/* extern void findrfc(double*,double,int*,int,int*); */
-static PyObject *f2py_rout_c_library_findrfc(const PyObject *capi_self,
- PyObject *capi_args,
- PyObject *capi_keywds,
- void (*f2py_func)(double*,double,int*,int,int*)) {
- PyObject * volatile capi_buildvalue = NULL;
- volatile int f2py_success = 1;
-/*decl*/
-
- double *y1 = NULL;
- npy_intp y1_Dims[1] = {-1};
- const int y1_Rank = 1;
- PyArrayObject *capi_y1_tmp = NULL;
- int capi_y1_intent = 0;
- PyObject *y1_capi = Py_None;
- double hmin = 0;
- PyObject *hmin_capi = Py_None;
- int *ind = NULL;
- npy_intp ind_Dims[1] = {-1};
- const int ind_Rank = 1;
- PyArrayObject *capi_ind_tmp = NULL;
- int capi_ind_intent = 0;
- int n = 0;
- int *info = NULL;
- npy_intp info_Dims[1] = {-1};
- const int info_Rank = 1;
- PyArrayObject *capi_info_tmp = NULL;
- int capi_info_intent = 0;
- static char *capi_kwlist[] = {"y1","hmin",NULL};
-
-/*routdebugenter*/
-#ifdef F2PY_REPORT_ATEXIT
-f2py_start_clock();
-#endif
- if (!PyArg_ParseTupleAndKeywords(capi_args,capi_keywds,\
- "OO:c_library.findrfc",\
- capi_kwlist,&y1_capi,&hmin_capi))
- return NULL;
-/*frompyobj*/
- /* Processing variable info */
- info_Dims[0]=1;
- capi_info_intent |= F2PY_INTENT_HIDE|F2PY_INTENT_C|F2PY_INTENT_OUT;
- capi_info_tmp = array_from_pyobj(NPY_INT,info_Dims,info_Rank,capi_info_intent,Py_None);
- if (capi_info_tmp == NULL) {
- if (!PyErr_Occurred())
- PyErr_SetString(c_library_error,"failed in converting hidden `info' of c_library.findrfc to C/Fortran array" );
- } else {
- info = (int *)(capi_info_tmp->data);
-
- /* Processing variable y1 */
- ;
- capi_y1_intent |= F2PY_INTENT_C|F2PY_INTENT_IN;
- capi_y1_tmp = array_from_pyobj(NPY_DOUBLE,y1_Dims,y1_Rank,capi_y1_intent,y1_capi);
- if (capi_y1_tmp == NULL) {
- if (!PyErr_Occurred())
- PyErr_SetString(c_library_error,"failed in converting 1st argument `y1' of c_library.findrfc to C/Fortran array" );
- } else {
- y1 = (double *)(capi_y1_tmp->data);
-
- /* Processing variable hmin */
- f2py_success = double_from_pyobj(&hmin,hmin_capi,"c_library.findrfc() 2nd argument (hmin) can't be converted to double");
- if (f2py_success) {
- /* Processing variable n */
- n = len(y1);
- /* Processing variable ind */
- ind_Dims[0]=n;
- capi_ind_intent |= F2PY_INTENT_HIDE|F2PY_INTENT_C|F2PY_INTENT_OUT;
- capi_ind_tmp = array_from_pyobj(NPY_INT,ind_Dims,ind_Rank,capi_ind_intent,Py_None);
- if (capi_ind_tmp == NULL) {
- if (!PyErr_Occurred())
- PyErr_SetString(c_library_error,"failed in converting hidden `ind' of c_library.findrfc to C/Fortran array" );
- } else {
- ind = (int *)(capi_ind_tmp->data);
-
-/*end of frompyobj*/
-#ifdef F2PY_REPORT_ATEXIT
-f2py_start_call_clock();
-#endif
-/*callfortranroutine*/
- (*f2py_func)(y1,hmin,ind,n,info);
-if (PyErr_Occurred())
- f2py_success = 0;
-#ifdef F2PY_REPORT_ATEXIT
-f2py_stop_call_clock();
-#endif
-/*end of callfortranroutine*/
- if (f2py_success) {
-/*pyobjfrom*/
-/*end of pyobjfrom*/
- CFUNCSMESS("Building return value.\n");
- capi_buildvalue = Py_BuildValue("NN",capi_ind_tmp,capi_info_tmp);
-/*closepyobjfrom*/
-/*end of closepyobjfrom*/
- } /*if (f2py_success) after callfortranroutine*/
-/*cleanupfrompyobj*/
- } /*if (capi_ind_tmp == NULL) ... else of ind*/
- /* End of cleaning variable ind */
- /* End of cleaning variable n */
- } /*if (f2py_success) of hmin*/
- /* End of cleaning variable hmin */
- if((PyObject *)capi_y1_tmp!=y1_capi) {
- Py_XDECREF(capi_y1_tmp); }
- } /*if (capi_y1_tmp == NULL) ... else of y1*/
- /* End of cleaning variable y1 */
- } /*if (capi_info_tmp == NULL) ... else of info*/
- /* End of cleaning variable info */
-/*end of cleanupfrompyobj*/
- if (capi_buildvalue == NULL) {
-/*routdebugfailure*/
- } else {
-/*routdebugleave*/
- }
- CFUNCSMESS("Freeing memory.\n");
-/*freemem*/
-#ifdef F2PY_REPORT_ATEXIT
-f2py_stop_clock();
-#endif
- return capi_buildvalue;
-}
-/******************************* end of findrfc *******************************/
-
-/********************************* findcross *********************************/
-static char doc_f2py_rout_c_library_findcross[] = "\
-ind,info = findcross(y,v)\n\nWrapper for ``findcross``.\
-\n\nParameters\n----------\n"
-"y : input rank-1 array('d') with bounds (n)\n"
-"v : input float\n"
-"\nReturns\n-------\n"
-"ind : rank-1 array('i') with bounds (n)\n"
-"info : rank-1 array('i') with bounds (1)";
-/* extern void findcross(double*,double,int*,int,int*); */
-static PyObject *f2py_rout_c_library_findcross(const PyObject *capi_self,
- PyObject *capi_args,
- PyObject *capi_keywds,
- void (*f2py_func)(double*,double,int*,int,int*)) {
- PyObject * volatile capi_buildvalue = NULL;
- volatile int f2py_success = 1;
-/*decl*/
-
- double *y = NULL;
- npy_intp y_Dims[1] = {-1};
- const int y_Rank = 1;
- PyArrayObject *capi_y_tmp = NULL;
- int capi_y_intent = 0;
- PyObject *y_capi = Py_None;
- double v = 0;
- PyObject *v_capi = Py_None;
- int *ind = NULL;
- npy_intp ind_Dims[1] = {-1};
- const int ind_Rank = 1;
- PyArrayObject *capi_ind_tmp = NULL;
- int capi_ind_intent = 0;
- int n = 0;
- int *info = NULL;
- npy_intp info_Dims[1] = {-1};
- const int info_Rank = 1;
- PyArrayObject *capi_info_tmp = NULL;
- int capi_info_intent = 0;
- static char *capi_kwlist[] = {"y","v",NULL};
-
-/*routdebugenter*/
-#ifdef F2PY_REPORT_ATEXIT
-f2py_start_clock();
-#endif
- if (!PyArg_ParseTupleAndKeywords(capi_args,capi_keywds,\
- "OO:c_library.findcross",\
- capi_kwlist,&y_capi,&v_capi))
- return NULL;
-/*frompyobj*/
- /* Processing variable info */
- info_Dims[0]=1;
- capi_info_intent |= F2PY_INTENT_HIDE|F2PY_INTENT_C|F2PY_INTENT_OUT;
- capi_info_tmp = array_from_pyobj(NPY_INT,info_Dims,info_Rank,capi_info_intent,Py_None);
- if (capi_info_tmp == NULL) {
- if (!PyErr_Occurred())
- PyErr_SetString(c_library_error,"failed in converting hidden `info' of c_library.findcross to C/Fortran array" );
- } else {
- info = (int *)(capi_info_tmp->data);
-
- /* Processing variable y */
- ;
- capi_y_intent |= F2PY_INTENT_C|F2PY_INTENT_IN;
- capi_y_tmp = array_from_pyobj(NPY_DOUBLE,y_Dims,y_Rank,capi_y_intent,y_capi);
- if (capi_y_tmp == NULL) {
- if (!PyErr_Occurred())
- PyErr_SetString(c_library_error,"failed in converting 1st argument `y' of c_library.findcross to C/Fortran array" );
- } else {
- y = (double *)(capi_y_tmp->data);
-
- /* Processing variable v */
- f2py_success = double_from_pyobj(&v,v_capi,"c_library.findcross() 2nd argument (v) can't be converted to double");
- if (f2py_success) {
- /* Processing variable n */
- n = len(y);
- /* Processing variable ind */
- ind_Dims[0]=n;
- capi_ind_intent |= F2PY_INTENT_HIDE|F2PY_INTENT_C|F2PY_INTENT_OUT;
- capi_ind_tmp = array_from_pyobj(NPY_INT,ind_Dims,ind_Rank,capi_ind_intent,Py_None);
- if (capi_ind_tmp == NULL) {
- if (!PyErr_Occurred())
- PyErr_SetString(c_library_error,"failed in converting hidden `ind' of c_library.findcross to C/Fortran array" );
- } else {
- ind = (int *)(capi_ind_tmp->data);
-
-/*end of frompyobj*/
-#ifdef F2PY_REPORT_ATEXIT
-f2py_start_call_clock();
-#endif
-/*callfortranroutine*/
- (*f2py_func)(y,v,ind,n,info);
-if (PyErr_Occurred())
- f2py_success = 0;
-#ifdef F2PY_REPORT_ATEXIT
-f2py_stop_call_clock();
-#endif
-/*end of callfortranroutine*/
- if (f2py_success) {
-/*pyobjfrom*/
-/*end of pyobjfrom*/
- CFUNCSMESS("Building return value.\n");
- capi_buildvalue = Py_BuildValue("NN",capi_ind_tmp,capi_info_tmp);
-/*closepyobjfrom*/
-/*end of closepyobjfrom*/
- } /*if (f2py_success) after callfortranroutine*/
-/*cleanupfrompyobj*/
- } /*if (capi_ind_tmp == NULL) ... else of ind*/
- /* End of cleaning variable ind */
- /* End of cleaning variable n */
- } /*if (f2py_success) of v*/
- /* End of cleaning variable v */
- if((PyObject *)capi_y_tmp!=y_capi) {
- Py_XDECREF(capi_y_tmp); }
- } /*if (capi_y_tmp == NULL) ... else of y*/
- /* End of cleaning variable y */
- } /*if (capi_info_tmp == NULL) ... else of info*/
- /* End of cleaning variable info */
-/*end of cleanupfrompyobj*/
- if (capi_buildvalue == NULL) {
-/*routdebugfailure*/
- } else {
-/*routdebugleave*/
- }
- CFUNCSMESS("Freeing memory.\n");
-/*freemem*/
-#ifdef F2PY_REPORT_ATEXIT
-f2py_stop_clock();
-#endif
- return capi_buildvalue;
-}
-/****************************** end of findcross ******************************/
-
-/*********************************** disufq ***********************************/
-static char doc_f2py_rout_c_library_disufq[] = "\
-rvec,ivec = disufq(rA,iA,w,kw,h,g,nmin,nmax,m,[n])\n\nWrapper for ``disufq``.\
-\n\nParameters\n----------\n"
-"rA : input rank-1 array('d') with bounds (n*m)\n"
-"iA : input rank-1 array('d') with bounds (n*m)\n"
-"w : input rank-1 array('d') with bounds (0.5 * n + 1.0)\n"
-"kw : input rank-1 array('d') with bounds (0.5 * n + 1.0)\n"
-"h : input float\n"
-"g : input float\n"
-"nmin : input int\n"
-"nmax : input int\n"
-"m : input int\n"
-"\nOther Parameters\n----------------\n"
-"n : input int, optional\n Default: (len(w)-1.0)/(0.5)\n"
-"\nReturns\n-------\n"
-"rvec : rank-1 array('d') with bounds (n*m)\n"
-"ivec : rank-1 array('d') with bounds (n*m)";
-/* extern void disufq(double*,double*,double*,double*,double*,double*,double,double,int,int,int,int); */
-static PyObject *f2py_rout_c_library_disufq(const PyObject *capi_self,
- PyObject *capi_args,
- PyObject *capi_keywds,
- void (*f2py_func)(double*,double*,double*,double*,double*,double*,double,double,int,int,int,int)) {
- PyObject * volatile capi_buildvalue = NULL;
- volatile int f2py_success = 1;
-/*decl*/
-
- double *rvec = NULL;
- npy_intp rvec_Dims[1] = {-1};
- const int rvec_Rank = 1;
- PyArrayObject *capi_rvec_tmp = NULL;
- int capi_rvec_intent = 0;
- double *ivec = NULL;
- npy_intp ivec_Dims[1] = {-1};
- const int ivec_Rank = 1;
- PyArrayObject *capi_ivec_tmp = NULL;
- int capi_ivec_intent = 0;
- double *rA = NULL;
- npy_intp rA_Dims[1] = {-1};
- const int rA_Rank = 1;
- PyArrayObject *capi_rA_tmp = NULL;
- int capi_rA_intent = 0;
- PyObject *rA_capi = Py_None;
- double *iA = NULL;
- npy_intp iA_Dims[1] = {-1};
- const int iA_Rank = 1;
- PyArrayObject *capi_iA_tmp = NULL;
- int capi_iA_intent = 0;
- PyObject *iA_capi = Py_None;
- double *w = NULL;
- npy_intp w_Dims[1] = {-1};
- const int w_Rank = 1;
- PyArrayObject *capi_w_tmp = NULL;
- int capi_w_intent = 0;
- PyObject *w_capi = Py_None;
- double *kw = NULL;
- npy_intp kw_Dims[1] = {-1};
- const int kw_Rank = 1;
- PyArrayObject *capi_kw_tmp = NULL;
- int capi_kw_intent = 0;
- PyObject *kw_capi = Py_None;
- double h = 0;
- PyObject *h_capi = Py_None;
- double g = 0;
- PyObject *g_capi = Py_None;
- int nmin = 0;
- PyObject *nmin_capi = Py_None;
- int nmax = 0;
- PyObject *nmax_capi = Py_None;
- int m = 0;
- PyObject *m_capi = Py_None;
- int n = 0;
- PyObject *n_capi = Py_None;
- static char *capi_kwlist[] = {"rA","iA","w","kw","h","g","nmin","nmax","m","n",NULL};
-
-/*routdebugenter*/
-#ifdef F2PY_REPORT_ATEXIT
-f2py_start_clock();
-#endif
- if (!PyArg_ParseTupleAndKeywords(capi_args,capi_keywds,\
- "OOOOOOOOO|O:c_library.disufq",\
- capi_kwlist,&rA_capi,&iA_capi,&w_capi,&kw_capi,&h_capi,&g_capi,&nmin_capi,&nmax_capi,&m_capi,&n_capi))
- return NULL;
-/*frompyobj*/
- /* Processing variable nmax */
- f2py_success = int_from_pyobj(&nmax,nmax_capi,"c_library.disufq() 8th argument (nmax) can't be converted to int");
- if (f2py_success) {
- /* Processing variable g */
- f2py_success = double_from_pyobj(&g,g_capi,"c_library.disufq() 6th argument (g) can't be converted to double");
- if (f2py_success) {
- /* Processing variable h */
- f2py_success = double_from_pyobj(&h,h_capi,"c_library.disufq() 5th argument (h) can't be converted to double");
- if (f2py_success) {
- /* Processing variable m */
- f2py_success = int_from_pyobj(&m,m_capi,"c_library.disufq() 9th argument (m) can't be converted to int");
- if (f2py_success) {
- /* Processing variable w */
- ;
- capi_w_intent |= F2PY_INTENT_C|F2PY_INTENT_IN;
- capi_w_tmp = array_from_pyobj(NPY_DOUBLE,w_Dims,w_Rank,capi_w_intent,w_capi);
- if (capi_w_tmp == NULL) {
- if (!PyErr_Occurred())
- PyErr_SetString(c_library_error,"failed in converting 3rd argument `w' of c_library.disufq to C/Fortran array" );
- } else {
- w = (double *)(capi_w_tmp->data);
-
- /* Processing variable nmin */
- f2py_success = int_from_pyobj(&nmin,nmin_capi,"c_library.disufq() 7th argument (nmin) can't be converted to int");
- if (f2py_success) {
- /* Processing variable n */
- if (n_capi == Py_None) n = (len(w)-1.0)/(0.5); else
- f2py_success = int_from_pyobj(&n,n_capi,"c_library.disufq() 1st keyword (n) can't be converted to int");
- if (f2py_success) {
- CHECKSCALAR((len(w)-1.0)/(0.5)>=n,"(len(w)-1.0)/(0.5)>=n","1st keyword n","disufq:n=%d",n) {
- /* Processing variable kw */
- kw_Dims[0]=0.5 * n + 1.0;
- capi_kw_intent |= F2PY_INTENT_C|F2PY_INTENT_IN;
- capi_kw_tmp = array_from_pyobj(NPY_DOUBLE,kw_Dims,kw_Rank,capi_kw_intent,kw_capi);
- if (capi_kw_tmp == NULL) {
- if (!PyErr_Occurred())
- PyErr_SetString(c_library_error,"failed in converting 4th argument `kw' of c_library.disufq to C/Fortran array" );
- } else {
- kw = (double *)(capi_kw_tmp->data);
-
- /* Processing variable rA */
- rA_Dims[0]=n*m;
- capi_rA_intent |= F2PY_INTENT_C|F2PY_INTENT_IN;
- capi_rA_tmp = array_from_pyobj(NPY_DOUBLE,rA_Dims,rA_Rank,capi_rA_intent,rA_capi);
- if (capi_rA_tmp == NULL) {
- if (!PyErr_Occurred())
- PyErr_SetString(c_library_error,"failed in converting 1st argument `rA' of c_library.disufq to C/Fortran array" );
- } else {
- rA = (double *)(capi_rA_tmp->data);
-
- /* Processing variable iA */
- iA_Dims[0]=n*m;
- capi_iA_intent |= F2PY_INTENT_C|F2PY_INTENT_IN;
- capi_iA_tmp = array_from_pyobj(NPY_DOUBLE,iA_Dims,iA_Rank,capi_iA_intent,iA_capi);
- if (capi_iA_tmp == NULL) {
- if (!PyErr_Occurred())
- PyErr_SetString(c_library_error,"failed in converting 2nd argument `iA' of c_library.disufq to C/Fortran array" );
- } else {
- iA = (double *)(capi_iA_tmp->data);
-
- /* Processing variable ivec */
- ivec_Dims[0]=n*m;
- capi_ivec_intent |= F2PY_INTENT_HIDE|F2PY_INTENT_C|F2PY_INTENT_OUT;
- capi_ivec_tmp = array_from_pyobj(NPY_DOUBLE,ivec_Dims,ivec_Rank,capi_ivec_intent,Py_None);
- if (capi_ivec_tmp == NULL) {
- if (!PyErr_Occurred())
- PyErr_SetString(c_library_error,"failed in converting hidden `ivec' of c_library.disufq to C/Fortran array" );
- } else {
- ivec = (double *)(capi_ivec_tmp->data);
-
- /* Processing variable rvec */
- rvec_Dims[0]=n*m;
- capi_rvec_intent |= F2PY_INTENT_HIDE|F2PY_INTENT_C|F2PY_INTENT_OUT;
- capi_rvec_tmp = array_from_pyobj(NPY_DOUBLE,rvec_Dims,rvec_Rank,capi_rvec_intent,Py_None);
- if (capi_rvec_tmp == NULL) {
- if (!PyErr_Occurred())
- PyErr_SetString(c_library_error,"failed in converting hidden `rvec' of c_library.disufq to C/Fortran array" );
- } else {
- rvec = (double *)(capi_rvec_tmp->data);
-
-/*end of frompyobj*/
-#ifdef F2PY_REPORT_ATEXIT
-f2py_start_call_clock();
-#endif
-/*callfortranroutine*/
- (*f2py_func)(rvec,ivec,rA,iA,w,kw,h,g,nmin,nmax,m,n);
-if (PyErr_Occurred())
- f2py_success = 0;
-#ifdef F2PY_REPORT_ATEXIT
-f2py_stop_call_clock();
-#endif
-/*end of callfortranroutine*/
- if (f2py_success) {
-/*pyobjfrom*/
-/*end of pyobjfrom*/
- CFUNCSMESS("Building return value.\n");
- capi_buildvalue = Py_BuildValue("NN",capi_rvec_tmp,capi_ivec_tmp);
-/*closepyobjfrom*/
-/*end of closepyobjfrom*/
- } /*if (f2py_success) after callfortranroutine*/
-/*cleanupfrompyobj*/
- } /*if (capi_rvec_tmp == NULL) ... else of rvec*/
- /* End of cleaning variable rvec */
- } /*if (capi_ivec_tmp == NULL) ... else of ivec*/
- /* End of cleaning variable ivec */
- if((PyObject *)capi_iA_tmp!=iA_capi) {
- Py_XDECREF(capi_iA_tmp); }
- } /*if (capi_iA_tmp == NULL) ... else of iA*/
- /* End of cleaning variable iA */
- if((PyObject *)capi_rA_tmp!=rA_capi) {
- Py_XDECREF(capi_rA_tmp); }
- } /*if (capi_rA_tmp == NULL) ... else of rA*/
- /* End of cleaning variable rA */
- if((PyObject *)capi_kw_tmp!=kw_capi) {
- Py_XDECREF(capi_kw_tmp); }
- } /*if (capi_kw_tmp == NULL) ... else of kw*/
- /* End of cleaning variable kw */
- } /*CHECKSCALAR((len(w)-1.0)/(0.5)>=n)*/
- } /*if (f2py_success) of n*/
- /* End of cleaning variable n */
- } /*if (f2py_success) of nmin*/
- /* End of cleaning variable nmin */
- if((PyObject *)capi_w_tmp!=w_capi) {
- Py_XDECREF(capi_w_tmp); }
- } /*if (capi_w_tmp == NULL) ... else of w*/
- /* End of cleaning variable w */
- } /*if (f2py_success) of m*/
- /* End of cleaning variable m */
- } /*if (f2py_success) of h*/
- /* End of cleaning variable h */
- } /*if (f2py_success) of g*/
- /* End of cleaning variable g */
- } /*if (f2py_success) of nmax*/
- /* End of cleaning variable nmax */
-/*end of cleanupfrompyobj*/
- if (capi_buildvalue == NULL) {
-/*routdebugfailure*/
- } else {
-/*routdebugleave*/
- }
- CFUNCSMESS("Freeing memory.\n");
-/*freemem*/
-#ifdef F2PY_REPORT_ATEXIT
-f2py_stop_clock();
-#endif
- return capi_buildvalue;
-}
-/******************************* end of disufq *******************************/
-
-/********************************** disufq2 **********************************/
-static char doc_f2py_rout_c_library_disufq2[] = "\
-rsvec,isvec,rdvec,idvec = disufq2(rA,iA,w,kw,h,g,nmin,nmax,m,[n])\n\nWrapper for ``disufq2``.\
-\n\nParameters\n----------\n"
-"rA : input rank-1 array('d') with bounds (n*m)\n"
-"iA : input rank-1 array('d') with bounds (n*m)\n"
-"w : input rank-1 array('d') with bounds (0.5 * n + 1.0)\n"
-"kw : input rank-1 array('d') with bounds (0.5 * n + 1.0)\n"
-"h : input float\n"
-"g : input float\n"
-"nmin : input int\n"
-"nmax : input int\n"
-"m : input int\n"
-"\nOther Parameters\n----------------\n"
-"n : input int, optional\n Default: (len(w)-1.0)/(0.5)\n"
-"\nReturns\n-------\n"
-"rsvec : rank-1 array('d') with bounds (n*m)\n"
-"isvec : rank-1 array('d') with bounds (n*m)\n"
-"rdvec : rank-1 array('d') with bounds (n*m)\n"
-"idvec : rank-1 array('d') with bounds (n*m)";
-/* extern void disufq2(double*,double*,double*,double*,double*,double*,double*,double*,double,double,int,int,int,int); */
-static PyObject *f2py_rout_c_library_disufq2(const PyObject *capi_self,
- PyObject *capi_args,
- PyObject *capi_keywds,
- void (*f2py_func)(double*,double*,double*,double*,double*,double*,double*,double*,double,double,int,int,int,int)) {
- PyObject * volatile capi_buildvalue = NULL;
- volatile int f2py_success = 1;
-/*decl*/
-
- double *rsvec = NULL;
- npy_intp rsvec_Dims[1] = {-1};
- const int rsvec_Rank = 1;
- PyArrayObject *capi_rsvec_tmp = NULL;
- int capi_rsvec_intent = 0;
- double *isvec = NULL;
- npy_intp isvec_Dims[1] = {-1};
- const int isvec_Rank = 1;
- PyArrayObject *capi_isvec_tmp = NULL;
- int capi_isvec_intent = 0;
- double *rdvec = NULL;
- npy_intp rdvec_Dims[1] = {-1};
- const int rdvec_Rank = 1;
- PyArrayObject *capi_rdvec_tmp = NULL;
- int capi_rdvec_intent = 0;
- double *idvec = NULL;
- npy_intp idvec_Dims[1] = {-1};
- const int idvec_Rank = 1;
- PyArrayObject *capi_idvec_tmp = NULL;
- int capi_idvec_intent = 0;
- double *rA = NULL;
- npy_intp rA_Dims[1] = {-1};
- const int rA_Rank = 1;
- PyArrayObject *capi_rA_tmp = NULL;
- int capi_rA_intent = 0;
- PyObject *rA_capi = Py_None;
- double *iA = NULL;
- npy_intp iA_Dims[1] = {-1};
- const int iA_Rank = 1;
- PyArrayObject *capi_iA_tmp = NULL;
- int capi_iA_intent = 0;
- PyObject *iA_capi = Py_None;
- double *w = NULL;
- npy_intp w_Dims[1] = {-1};
- const int w_Rank = 1;
- PyArrayObject *capi_w_tmp = NULL;
- int capi_w_intent = 0;
- PyObject *w_capi = Py_None;
- double *kw = NULL;
- npy_intp kw_Dims[1] = {-1};
- const int kw_Rank = 1;
- PyArrayObject *capi_kw_tmp = NULL;
- int capi_kw_intent = 0;
- PyObject *kw_capi = Py_None;
- double h = 0;
- PyObject *h_capi = Py_None;
- double g = 0;
- PyObject *g_capi = Py_None;
- int nmin = 0;
- PyObject *nmin_capi = Py_None;
- int nmax = 0;
- PyObject *nmax_capi = Py_None;
- int m = 0;
- PyObject *m_capi = Py_None;
- int n = 0;
- PyObject *n_capi = Py_None;
- static char *capi_kwlist[] = {"rA","iA","w","kw","h","g","nmin","nmax","m","n",NULL};
-
-/*routdebugenter*/
-#ifdef F2PY_REPORT_ATEXIT
-f2py_start_clock();
-#endif
- if (!PyArg_ParseTupleAndKeywords(capi_args,capi_keywds,\
- "OOOOOOOOO|O:c_library.disufq2",\
- capi_kwlist,&rA_capi,&iA_capi,&w_capi,&kw_capi,&h_capi,&g_capi,&nmin_capi,&nmax_capi,&m_capi,&n_capi))
- return NULL;
-/*frompyobj*/
- /* Processing variable nmax */
- f2py_success = int_from_pyobj(&nmax,nmax_capi,"c_library.disufq2() 8th argument (nmax) can't be converted to int");
- if (f2py_success) {
- /* Processing variable g */
- f2py_success = double_from_pyobj(&g,g_capi,"c_library.disufq2() 6th argument (g) can't be converted to double");
- if (f2py_success) {
- /* Processing variable m */
- f2py_success = int_from_pyobj(&m,m_capi,"c_library.disufq2() 9th argument (m) can't be converted to int");
- if (f2py_success) {
- /* Processing variable w */
- ;
- capi_w_intent |= F2PY_INTENT_C|F2PY_INTENT_IN;
- capi_w_tmp = array_from_pyobj(NPY_DOUBLE,w_Dims,w_Rank,capi_w_intent,w_capi);
- if (capi_w_tmp == NULL) {
- if (!PyErr_Occurred())
- PyErr_SetString(c_library_error,"failed in converting 3rd argument `w' of c_library.disufq2 to C/Fortran array" );
- } else {
- w = (double *)(capi_w_tmp->data);
-
- /* Processing variable h */
- f2py_success = double_from_pyobj(&h,h_capi,"c_library.disufq2() 5th argument (h) can't be converted to double");
- if (f2py_success) {
- /* Processing variable nmin */
- f2py_success = int_from_pyobj(&nmin,nmin_capi,"c_library.disufq2() 7th argument (nmin) can't be converted to int");
- if (f2py_success) {
- /* Processing variable n */
- if (n_capi == Py_None) n = (len(w)-1.0)/(0.5); else
- f2py_success = int_from_pyobj(&n,n_capi,"c_library.disufq2() 1st keyword (n) can't be converted to int");
- if (f2py_success) {
- CHECKSCALAR((len(w)-1.0)/(0.5)>=n,"(len(w)-1.0)/(0.5)>=n","1st keyword n","disufq2:n=%d",n) {
- /* Processing variable rdvec */
- rdvec_Dims[0]=n*m;
- capi_rdvec_intent |= F2PY_INTENT_HIDE|F2PY_INTENT_C|F2PY_INTENT_OUT;
- capi_rdvec_tmp = array_from_pyobj(NPY_DOUBLE,rdvec_Dims,rdvec_Rank,capi_rdvec_intent,Py_None);
- if (capi_rdvec_tmp == NULL) {
- if (!PyErr_Occurred())
- PyErr_SetString(c_library_error,"failed in converting hidden `rdvec' of c_library.disufq2 to C/Fortran array" );
- } else {
- rdvec = (double *)(capi_rdvec_tmp->data);
-
- /* Processing variable idvec */
- idvec_Dims[0]=n*m;
- capi_idvec_intent |= F2PY_INTENT_HIDE|F2PY_INTENT_C|F2PY_INTENT_OUT;
- capi_idvec_tmp = array_from_pyobj(NPY_DOUBLE,idvec_Dims,idvec_Rank,capi_idvec_intent,Py_None);
- if (capi_idvec_tmp == NULL) {
- if (!PyErr_Occurred())
- PyErr_SetString(c_library_error,"failed in converting hidden `idvec' of c_library.disufq2 to C/Fortran array" );
- } else {
- idvec = (double *)(capi_idvec_tmp->data);
-
- /* Processing variable isvec */
- isvec_Dims[0]=n*m;
- capi_isvec_intent |= F2PY_INTENT_HIDE|F2PY_INTENT_C|F2PY_INTENT_OUT;
- capi_isvec_tmp = array_from_pyobj(NPY_DOUBLE,isvec_Dims,isvec_Rank,capi_isvec_intent,Py_None);
- if (capi_isvec_tmp == NULL) {
- if (!PyErr_Occurred())
- PyErr_SetString(c_library_error,"failed in converting hidden `isvec' of c_library.disufq2 to C/Fortran array" );
- } else {
- isvec = (double *)(capi_isvec_tmp->data);
-
- /* Processing variable rsvec */
- rsvec_Dims[0]=n*m;
- capi_rsvec_intent |= F2PY_INTENT_HIDE|F2PY_INTENT_C|F2PY_INTENT_OUT;
- capi_rsvec_tmp = array_from_pyobj(NPY_DOUBLE,rsvec_Dims,rsvec_Rank,capi_rsvec_intent,Py_None);
- if (capi_rsvec_tmp == NULL) {
- if (!PyErr_Occurred())
- PyErr_SetString(c_library_error,"failed in converting hidden `rsvec' of c_library.disufq2 to C/Fortran array" );
- } else {
- rsvec = (double *)(capi_rsvec_tmp->data);
-
- /* Processing variable kw */
- kw_Dims[0]=0.5 * n + 1.0;
- capi_kw_intent |= F2PY_INTENT_C|F2PY_INTENT_IN;
- capi_kw_tmp = array_from_pyobj(NPY_DOUBLE,kw_Dims,kw_Rank,capi_kw_intent,kw_capi);
- if (capi_kw_tmp == NULL) {
- if (!PyErr_Occurred())
- PyErr_SetString(c_library_error,"failed in converting 4th argument `kw' of c_library.disufq2 to C/Fortran array" );
- } else {
- kw = (double *)(capi_kw_tmp->data);
-
- /* Processing variable rA */
- rA_Dims[0]=n*m;
- capi_rA_intent |= F2PY_INTENT_C|F2PY_INTENT_IN;
- capi_rA_tmp = array_from_pyobj(NPY_DOUBLE,rA_Dims,rA_Rank,capi_rA_intent,rA_capi);
- if (capi_rA_tmp == NULL) {
- if (!PyErr_Occurred())
- PyErr_SetString(c_library_error,"failed in converting 1st argument `rA' of c_library.disufq2 to C/Fortran array" );
- } else {
- rA = (double *)(capi_rA_tmp->data);
-
- /* Processing variable iA */
- iA_Dims[0]=n*m;
- capi_iA_intent |= F2PY_INTENT_C|F2PY_INTENT_IN;
- capi_iA_tmp = array_from_pyobj(NPY_DOUBLE,iA_Dims,iA_Rank,capi_iA_intent,iA_capi);
- if (capi_iA_tmp == NULL) {
- if (!PyErr_Occurred())
- PyErr_SetString(c_library_error,"failed in converting 2nd argument `iA' of c_library.disufq2 to C/Fortran array" );
- } else {
- iA = (double *)(capi_iA_tmp->data);
-
-/*end of frompyobj*/
-#ifdef F2PY_REPORT_ATEXIT
-f2py_start_call_clock();
-#endif
-/*callfortranroutine*/
- (*f2py_func)(rsvec,isvec,rdvec,idvec,rA,iA,w,kw,h,g,nmin,nmax,m,n);
-if (PyErr_Occurred())
- f2py_success = 0;
-#ifdef F2PY_REPORT_ATEXIT
-f2py_stop_call_clock();
-#endif
-/*end of callfortranroutine*/
- if (f2py_success) {
-/*pyobjfrom*/
-/*end of pyobjfrom*/
- CFUNCSMESS("Building return value.\n");
- capi_buildvalue = Py_BuildValue("NNNN",capi_rsvec_tmp,capi_isvec_tmp,capi_rdvec_tmp,capi_idvec_tmp);
-/*closepyobjfrom*/
-/*end of closepyobjfrom*/
- } /*if (f2py_success) after callfortranroutine*/
-/*cleanupfrompyobj*/
- if((PyObject *)capi_iA_tmp!=iA_capi) {
- Py_XDECREF(capi_iA_tmp); }
- } /*if (capi_iA_tmp == NULL) ... else of iA*/
- /* End of cleaning variable iA */
- if((PyObject *)capi_rA_tmp!=rA_capi) {
- Py_XDECREF(capi_rA_tmp); }
- } /*if (capi_rA_tmp == NULL) ... else of rA*/
- /* End of cleaning variable rA */
- if((PyObject *)capi_kw_tmp!=kw_capi) {
- Py_XDECREF(capi_kw_tmp); }
- } /*if (capi_kw_tmp == NULL) ... else of kw*/
- /* End of cleaning variable kw */
- } /*if (capi_rsvec_tmp == NULL) ... else of rsvec*/
- /* End of cleaning variable rsvec */
- } /*if (capi_isvec_tmp == NULL) ... else of isvec*/
- /* End of cleaning variable isvec */
- } /*if (capi_idvec_tmp == NULL) ... else of idvec*/
- /* End of cleaning variable idvec */
- } /*if (capi_rdvec_tmp == NULL) ... else of rdvec*/
- /* End of cleaning variable rdvec */
- } /*CHECKSCALAR((len(w)-1.0)/(0.5)>=n)*/
- } /*if (f2py_success) of n*/
- /* End of cleaning variable n */
- } /*if (f2py_success) of nmin*/
- /* End of cleaning variable nmin */
- } /*if (f2py_success) of h*/
- /* End of cleaning variable h */
- if((PyObject *)capi_w_tmp!=w_capi) {
- Py_XDECREF(capi_w_tmp); }
- } /*if (capi_w_tmp == NULL) ... else of w*/
- /* End of cleaning variable w */
- } /*if (f2py_success) of m*/
- /* End of cleaning variable m */
- } /*if (f2py_success) of g*/
- /* End of cleaning variable g */
- } /*if (f2py_success) of nmax*/
- /* End of cleaning variable nmax */
-/*end of cleanupfrompyobj*/
- if (capi_buildvalue == NULL) {
-/*routdebugfailure*/
- } else {
-/*routdebugleave*/
- }
- CFUNCSMESS("Freeing memory.\n");
-/*freemem*/
-#ifdef F2PY_REPORT_ATEXIT
-f2py_stop_clock();
-#endif
- return capi_buildvalue;
-}
-/******************************* end of disufq2 *******************************/
-
-/******************************* findrfc3_astm *******************************/
-static char doc_f2py_rout_c_library_findrfc3_astm[] = "\
-array_out,nout = findrfc3_astm(array_ext)\n\nWrapper for ``findrfc3_astm``.\
-\n\nParameters\n----------\n"
-"array_ext : input rank-1 array('d') with bounds (n)\n"
-"\nReturns\n-------\n"
-"array_out : rank-2 array('d') with bounds (n,3)\n"
-"nout : rank-1 array('i') with bounds (2)";
-/* extern void findrfc3_astm(double*,double*,int,int*); */
-static PyObject *f2py_rout_c_library_findrfc3_astm(const PyObject *capi_self,
- PyObject *capi_args,
- PyObject *capi_keywds,
- void (*f2py_func)(double*,double*,int,int*)) {
- PyObject * volatile capi_buildvalue = NULL;
- volatile int f2py_success = 1;
-/*decl*/
-
- double *array_ext = NULL;
- npy_intp array_ext_Dims[1] = {-1};
- const int array_ext_Rank = 1;
- PyArrayObject *capi_array_ext_tmp = NULL;
- int capi_array_ext_intent = 0;
- PyObject *array_ext_capi = Py_None;
- double *array_out = NULL;
- npy_intp array_out_Dims[2] = {-1, -1};
- const int array_out_Rank = 2;
- PyArrayObject *capi_array_out_tmp = NULL;
- int capi_array_out_intent = 0;
- int n = 0;
- int *nout = NULL;
- npy_intp nout_Dims[1] = {-1};
- const int nout_Rank = 1;
- PyArrayObject *capi_nout_tmp = NULL;
- int capi_nout_intent = 0;
- static char *capi_kwlist[] = {"array_ext",NULL};
-
-/*routdebugenter*/
-#ifdef F2PY_REPORT_ATEXIT
-f2py_start_clock();
-#endif
- if (!PyArg_ParseTupleAndKeywords(capi_args,capi_keywds,\
- "O:c_library.findrfc3_astm",\
- capi_kwlist,&array_ext_capi))
- return NULL;
-/*frompyobj*/
- /* Processing variable array_ext */
- ;
- capi_array_ext_intent |= F2PY_INTENT_C|F2PY_INTENT_IN;
- capi_array_ext_tmp = array_from_pyobj(NPY_DOUBLE,array_ext_Dims,array_ext_Rank,capi_array_ext_intent,array_ext_capi);
- if (capi_array_ext_tmp == NULL) {
- if (!PyErr_Occurred())
- PyErr_SetString(c_library_error,"failed in converting 1st argument `array_ext' of c_library.findrfc3_astm to C/Fortran array" );
- } else {
- array_ext = (double *)(capi_array_ext_tmp->data);
-
- /* Processing variable nout */
- nout_Dims[0]=2;
- capi_nout_intent |= F2PY_INTENT_HIDE|F2PY_INTENT_C|F2PY_INTENT_OUT;
- capi_nout_tmp = array_from_pyobj(NPY_INT,nout_Dims,nout_Rank,capi_nout_intent,Py_None);
- if (capi_nout_tmp == NULL) {
- if (!PyErr_Occurred())
- PyErr_SetString(c_library_error,"failed in converting hidden `nout' of c_library.findrfc3_astm to C/Fortran array" );
- } else {
- nout = (int *)(capi_nout_tmp->data);
-
- /* Processing variable n */
- n = len(array_ext);
- /* Processing variable array_out */
- array_out_Dims[0]=n,array_out_Dims[1]=3;
- capi_array_out_intent |= F2PY_INTENT_HIDE|F2PY_INTENT_C|F2PY_INTENT_OUT;
- capi_array_out_tmp = array_from_pyobj(NPY_DOUBLE,array_out_Dims,array_out_Rank,capi_array_out_intent,Py_None);
- if (capi_array_out_tmp == NULL) {
- if (!PyErr_Occurred())
- PyErr_SetString(c_library_error,"failed in converting hidden `array_out' of c_library.findrfc3_astm to C/Fortran array" );
- } else {
- array_out = (double *)(capi_array_out_tmp->data);
-
-/*end of frompyobj*/
-#ifdef F2PY_REPORT_ATEXIT
-f2py_start_call_clock();
-#endif
-/*callfortranroutine*/
- (*f2py_func)(array_ext,array_out,n,nout);
-if (PyErr_Occurred())
- f2py_success = 0;
-#ifdef F2PY_REPORT_ATEXIT
-f2py_stop_call_clock();
-#endif
-/*end of callfortranroutine*/
- if (f2py_success) {
-/*pyobjfrom*/
-/*end of pyobjfrom*/
- CFUNCSMESS("Building return value.\n");
- capi_buildvalue = Py_BuildValue("NN",capi_array_out_tmp,capi_nout_tmp);
-/*closepyobjfrom*/
-/*end of closepyobjfrom*/
- } /*if (f2py_success) after callfortranroutine*/
-/*cleanupfrompyobj*/
- } /*if (capi_array_out_tmp == NULL) ... else of array_out*/
- /* End of cleaning variable array_out */
- /* End of cleaning variable n */
- } /*if (capi_nout_tmp == NULL) ... else of nout*/
- /* End of cleaning variable nout */
- if((PyObject *)capi_array_ext_tmp!=array_ext_capi) {
- Py_XDECREF(capi_array_ext_tmp); }
- } /*if (capi_array_ext_tmp == NULL) ... else of array_ext*/
- /* End of cleaning variable array_ext */
-/*end of cleanupfrompyobj*/
- if (capi_buildvalue == NULL) {
-/*routdebugfailure*/
- } else {
-/*routdebugleave*/
- }
- CFUNCSMESS("Freeing memory.\n");
-/*freemem*/
-#ifdef F2PY_REPORT_ATEXIT
-f2py_stop_clock();
-#endif
- return capi_buildvalue;
-}
-/**************************** end of findrfc3_astm ****************************/
-
-/******************************* findrfc5_astm *******************************/
-static char doc_f2py_rout_c_library_findrfc5_astm[] = "\
-array_out,nout = findrfc5_astm(array_ext,array_t)\n\nWrapper for ``findrfc5_astm``.\
-\n\nParameters\n----------\n"
-"array_ext : input rank-1 array('d') with bounds (n)\n"
-"array_t : input rank-1 array('d') with bounds (n)\n"
-"\nReturns\n-------\n"
-"array_out : rank-2 array('d') with bounds (n,5)\n"
-"nout : rank-1 array('i') with bounds (2)";
-/* extern void findrfc5_astm(double*,double*,double*,int,int*); */
-static PyObject *f2py_rout_c_library_findrfc5_astm(const PyObject *capi_self,
- PyObject *capi_args,
- PyObject *capi_keywds,
- void (*f2py_func)(double*,double*,double*,int,int*)) {
- PyObject * volatile capi_buildvalue = NULL;
- volatile int f2py_success = 1;
-/*decl*/
-
- double *array_ext = NULL;
- npy_intp array_ext_Dims[1] = {-1};
- const int array_ext_Rank = 1;
- PyArrayObject *capi_array_ext_tmp = NULL;
- int capi_array_ext_intent = 0;
- PyObject *array_ext_capi = Py_None;
- double *array_t = NULL;
- npy_intp array_t_Dims[1] = {-1};
- const int array_t_Rank = 1;
- PyArrayObject *capi_array_t_tmp = NULL;
- int capi_array_t_intent = 0;
- PyObject *array_t_capi = Py_None;
- double *array_out = NULL;
- npy_intp array_out_Dims[2] = {-1, -1};
- const int array_out_Rank = 2;
- PyArrayObject *capi_array_out_tmp = NULL;
- int capi_array_out_intent = 0;
- int n = 0;
- int *nout = NULL;
- npy_intp nout_Dims[1] = {-1};
- const int nout_Rank = 1;
- PyArrayObject *capi_nout_tmp = NULL;
- int capi_nout_intent = 0;
- static char *capi_kwlist[] = {"array_ext","array_t",NULL};
-
-/*routdebugenter*/
-#ifdef F2PY_REPORT_ATEXIT
-f2py_start_clock();
-#endif
- if (!PyArg_ParseTupleAndKeywords(capi_args,capi_keywds,\
- "OO:c_library.findrfc5_astm",\
- capi_kwlist,&array_ext_capi,&array_t_capi))
- return NULL;
-/*frompyobj*/
- /* Processing variable nout */
- nout_Dims[0]=2;
- capi_nout_intent |= F2PY_INTENT_HIDE|F2PY_INTENT_C|F2PY_INTENT_OUT;
- capi_nout_tmp = array_from_pyobj(NPY_INT,nout_Dims,nout_Rank,capi_nout_intent,Py_None);
- if (capi_nout_tmp == NULL) {
- if (!PyErr_Occurred())
- PyErr_SetString(c_library_error,"failed in converting hidden `nout' of c_library.findrfc5_astm to C/Fortran array" );
- } else {
- nout = (int *)(capi_nout_tmp->data);
-
- /* Processing variable array_ext */
- ;
- capi_array_ext_intent |= F2PY_INTENT_C|F2PY_INTENT_IN;
- capi_array_ext_tmp = array_from_pyobj(NPY_DOUBLE,array_ext_Dims,array_ext_Rank,capi_array_ext_intent,array_ext_capi);
- if (capi_array_ext_tmp == NULL) {
- if (!PyErr_Occurred())
- PyErr_SetString(c_library_error,"failed in converting 1st argument `array_ext' of c_library.findrfc5_astm to C/Fortran array" );
- } else {
- array_ext = (double *)(capi_array_ext_tmp->data);
-
- /* Processing variable n */
- n = len(array_ext);
- /* Processing variable array_t */
- array_t_Dims[0]=n;
- capi_array_t_intent |= F2PY_INTENT_C|F2PY_INTENT_IN;
- capi_array_t_tmp = array_from_pyobj(NPY_DOUBLE,array_t_Dims,array_t_Rank,capi_array_t_intent,array_t_capi);
- if (capi_array_t_tmp == NULL) {
- if (!PyErr_Occurred())
- PyErr_SetString(c_library_error,"failed in converting 2nd argument `array_t' of c_library.findrfc5_astm to C/Fortran array" );
- } else {
- array_t = (double *)(capi_array_t_tmp->data);
-
- /* Processing variable array_out */
- array_out_Dims[0]=n,array_out_Dims[1]=5;
- capi_array_out_intent |= F2PY_INTENT_HIDE|F2PY_INTENT_C|F2PY_INTENT_OUT;
- capi_array_out_tmp = array_from_pyobj(NPY_DOUBLE,array_out_Dims,array_out_Rank,capi_array_out_intent,Py_None);
- if (capi_array_out_tmp == NULL) {
- if (!PyErr_Occurred())
- PyErr_SetString(c_library_error,"failed in converting hidden `array_out' of c_library.findrfc5_astm to C/Fortran array" );
- } else {
- array_out = (double *)(capi_array_out_tmp->data);
-
-/*end of frompyobj*/
-#ifdef F2PY_REPORT_ATEXIT
-f2py_start_call_clock();
-#endif
-/*callfortranroutine*/
- (*f2py_func)(array_ext,array_t,array_out,n,nout);
-if (PyErr_Occurred())
- f2py_success = 0;
-#ifdef F2PY_REPORT_ATEXIT
-f2py_stop_call_clock();
-#endif
-/*end of callfortranroutine*/
- if (f2py_success) {
-/*pyobjfrom*/
-/*end of pyobjfrom*/
- CFUNCSMESS("Building return value.\n");
- capi_buildvalue = Py_BuildValue("NN",capi_array_out_tmp,capi_nout_tmp);
-/*closepyobjfrom*/
-/*end of closepyobjfrom*/
- } /*if (f2py_success) after callfortranroutine*/
-/*cleanupfrompyobj*/
- } /*if (capi_array_out_tmp == NULL) ... else of array_out*/
- /* End of cleaning variable array_out */
- if((PyObject *)capi_array_t_tmp!=array_t_capi) {
- Py_XDECREF(capi_array_t_tmp); }
- } /*if (capi_array_t_tmp == NULL) ... else of array_t*/
- /* End of cleaning variable array_t */
- /* End of cleaning variable n */
- if((PyObject *)capi_array_ext_tmp!=array_ext_capi) {
- Py_XDECREF(capi_array_ext_tmp); }
- } /*if (capi_array_ext_tmp == NULL) ... else of array_ext*/
- /* End of cleaning variable array_ext */
- } /*if (capi_nout_tmp == NULL) ... else of nout*/
- /* End of cleaning variable nout */
-/*end of cleanupfrompyobj*/
- if (capi_buildvalue == NULL) {
-/*routdebugfailure*/
- } else {
-/*routdebugleave*/
- }
- CFUNCSMESS("Freeing memory.\n");
-/*freemem*/
-#ifdef F2PY_REPORT_ATEXIT
-f2py_stop_clock();
-#endif
- return capi_buildvalue;
-}
-/**************************** end of findrfc5_astm ****************************/
-/*eof body*/
-
-/******************* See f2py2e/f90mod_rules.py: buildhooks *******************/
-/*need_f90modhooks*/
-
-/************** See f2py2e/rules.py: module_rules['modulebody'] **************/
-
-/******************* See f2py2e/common_rules.py: buildhooks *******************/
-
-/*need_commonhooks*/
-
-/**************************** See f2py2e/rules.py ****************************/
-
-static FortranDataDef f2py_routine_defs[] = {
- {"findrfc",-1,{{-1}},0,(char *)findrfc,(f2py_init_func)f2py_rout_c_library_findrfc,doc_f2py_rout_c_library_findrfc},
- {"findcross",-1,{{-1}},0,(char *)findcross,(f2py_init_func)f2py_rout_c_library_findcross,doc_f2py_rout_c_library_findcross},
- {"disufq",-1,{{-1}},0,(char *)disufq,(f2py_init_func)f2py_rout_c_library_disufq,doc_f2py_rout_c_library_disufq},
- {"disufq2",-1,{{-1}},0,(char *)disufq2,(f2py_init_func)f2py_rout_c_library_disufq2,doc_f2py_rout_c_library_disufq2},
- {"findrfc3_astm",-1,{{-1}},0,(char *)findrfc3_astm,(f2py_init_func)f2py_rout_c_library_findrfc3_astm,doc_f2py_rout_c_library_findrfc3_astm},
- {"findrfc5_astm",-1,{{-1}},0,(char *)findrfc5_astm,(f2py_init_func)f2py_rout_c_library_findrfc5_astm,doc_f2py_rout_c_library_findrfc5_astm},
-
-/*eof routine_defs*/
- {NULL}
-};
-
-static PyMethodDef f2py_module_methods[] = {
-
- {NULL,NULL}
-};
-
-#if PY_VERSION_HEX >= 0x03000000
-static struct PyModuleDef moduledef = {
- PyModuleDef_HEAD_INIT,
- "c_library",
- NULL,
- -1,
- f2py_module_methods,
- NULL,
- NULL,
- NULL,
- NULL
-};
-#endif
-
-#if PY_VERSION_HEX >= 0x03000000
-#define RETVAL m
-PyMODINIT_FUNC PyInit_c_library(void) {
-#else
-#define RETVAL
-PyMODINIT_FUNC initc_library(void) {
-#endif
- int i;
- PyObject *m,*d, *s;
-#if PY_VERSION_HEX >= 0x03000000
- m = c_library_module = PyModule_Create(&moduledef);
-#else
- m = c_library_module = Py_InitModule("c_library", f2py_module_methods);
-#endif
- Py_TYPE(&PyFortran_Type) = &PyType_Type;
- import_array();
- if (PyErr_Occurred())
- {PyErr_SetString(PyExc_ImportError, "can't initialize module c_library (failed to import numpy)"); return RETVAL;}
- d = PyModule_GetDict(m);
- s = PyString_FromString("$Revision: $");
- PyDict_SetItemString(d, "__version__", s);
-#if PY_VERSION_HEX >= 0x03000000
- s = PyUnicode_FromString(
-#else
- s = PyString_FromString(
-#endif
- "This module 'c_library' is auto-generated with f2py (version:2).\nFunctions:\n"
-" ind,info = findrfc(y1,hmin)\n"
-" ind,info = findcross(y,v)\n"
-" rvec,ivec = disufq(rA,iA,w,kw,h,g,nmin,nmax,m,n=(len(w)-1.0)/(0.5))\n"
-" rsvec,isvec,rdvec,idvec = disufq2(rA,iA,w,kw,h,g,nmin,nmax,m,n=(len(w)-1.0)/(0.5))\n"
-" array_out,nout = findrfc3_astm(array_ext)\n"
-" array_out,nout = findrfc5_astm(array_ext,array_t)\n"
-".");
- PyDict_SetItemString(d, "__doc__", s);
- c_library_error = PyErr_NewException ("c_library.error", NULL, NULL);
- Py_DECREF(s);
- for(i=0;f2py_routine_defs[i].name!=NULL;i++)
- PyDict_SetItemString(d, f2py_routine_defs[i].name,PyFortranObject_NewAsAttr(&f2py_routine_defs[i]));
-
-
-
-
-
-
-/*eof initf2pywraphooks*/
-/*eof initf90modhooks*/
-
-/*eof initcommonhooks*/
-
-
-#ifdef F2PY_REPORT_ATEXIT
- if (! PyErr_Occurred())
- on_exit(f2py_report_on_exit,(void*)"c_library");
-#endif
-
- return RETVAL;
-}
-#ifdef __cplusplus
-}
-#endif
diff --git a/wafo/source/c_library/setup.py b/wafo/source/c_library/setup.py
deleted file mode 100644
index d6d9242..0000000
--- a/wafo/source/c_library/setup.py
+++ /dev/null
@@ -1,18 +0,0 @@
-'''
-python setup.py build_src build_ext --inplace
-
-See also http://www.scipy.org/Cookbook/CompilingExtensionsOnWindowsWithMinGW
-'''
-# File setup.py
-
-
-def configuration(parent_package='', top_path=None):
- from numpy.distutils.misc_util import Configuration
- config = Configuration('', parent_package, top_path)
-
- config.add_extension('c_library',
- sources=['c_library.pyf', 'c_functions.c'])
- return config
-if __name__ == "__main__":
- from numpy.distutils.core import setup
- setup(**configuration(top_path='').todict())
diff --git a/wafo/source/cov2XXXpdf/bounds/cov2acdfb.f b/wafo/source/cov2XXXpdf/bounds/cov2acdfb.f
deleted file mode 100644
index b6a85b4..0000000
--- a/wafo/source/cov2XXXpdf/bounds/cov2acdfb.f
+++ /dev/null
@@ -1,450 +0,0 @@
- PROGRAM sp2Acdf1
-C***********************************************************************
-C This program computes upper and lower bounds for: *
-C *
-C density of T_i, for Ac <=h, in a gaussian process i.e. *
-C *
-C half wavelength (up-crossing to downcrossing) for crests h *
-C I.R. 27 Dec. 1999 *
-C***********************************************************************
- use GLOBALDATA, only : Nt,Nj,Nd,Nc,Ntd,Ntdc,NI,Mb,
- &NIT,Nx,TWOPI,XSPLT,SCIS,NSIMmax,COV
- use rind
- IMPLICIT NONE
- double precision, dimension(:,:),allocatable :: BIG
- double precision, dimension(:,:),allocatable :: ansrup
- double precision, dimension(:,:),allocatable :: ansrlo
- double precision, dimension(: ),allocatable :: ex,CY
- double precision, dimension(:,:),allocatable :: xc,fxind
- double precision, dimension(: ),allocatable :: h
- double precision, dimension(: ),allocatable :: R0,R1,R2,R3,R4
- double precision ::CC,U,XddInf,XdInf,XtInf
- double precision, dimension(:,:),allocatable :: a_up,a_lo
- integer , dimension(: ),allocatable :: seed
- integer ,dimension(7) :: indI
- integer :: Nstart,Ntime,tn,ts,speed,ph,def,seed1,seed_size,icy
- integer ::it1,it2,status
- double precision :: ds,dT ! lag spacing for covariances
-! f90 sp2Acdf1.f rind50.f
-
- CALL INIT_LEVELS(U,def,Ntime,Nstart,NIT,speed,Nx,dT)
- !print *,'U,def,Ntime,Nstart,NIT,speed,SCIS,seed1,Nx,dT'
- !print *,U,def,Ntime,Nstart,NIT,speed,SCIS,seed1,Nx,dT
- !XSPLT=1.5d0
- if (SCIS.GT.0) then
- allocate(COV(1:Nx))
- call random_seed(SIZE=seed_size)
- allocate(seed(seed_size))
- call random_seed(GET=seed(1:seed_size)) ! get current seed
- seed(1)=seed1 ! change seed
- call random_seed(PUT=seed(1:seed_size))
- deallocate(seed)
- endif
- CALL INITDATA(speed)
- !print *,ntime,speed,u,NIT
- allocate(R0(1:Ntime+1))
- allocate(R1(1:Ntime+1))
- allocate(R2(1:Ntime+1))
- if (abs(def).GT.1) THEN
- allocate(R3(1:Ntime+1))
- allocate(R4(1:Ntime+1))
- !CALL INIT_AMPLITUDES(h,def,Nx)
- endif
- allocate(h(1:Nx))
- CALL INIT_AMPLITUDES(h,def,Nx)
- CALL INIT_COVARIANCES(Ntime,def,R0,R1,R2,R3,R4)
-
- NI=4; Nd=2
- Nc=3; Mb=2
-
- Nj=0
- indI(1)=0
-C ***** The bound 'infinity' is set to 10*sigma *****
- XdInf=10.d0*SQRT(-R2(1))
- XtInf=10.d0*SQRT(R0(1))
- ! normalizing constant
- CC=TWOPI*SQRT(-R0(1)/R2(1))*exp(u*u/(2.d0*R0(1)) )
-
- allocate(CY(1:Nx))
- do icy=1,Nx
- CY(icy)=exp(-0.5*h(icy)*h(icy)/100)/(10*sqrt(twopi))
- enddo
- allocate(BIG(1:Ntime+Nc,1:Ntime+Nc),stat=status)
- if (status.ne.0) then
- print *,'can not allocate BIG'
- end if
- allocate(ex(1:Ntime+Nc),stat=status)
- if (status.ne.0) then
- print *,'can not allocate ex'
- end if
- allocate(ansrup(1:Ntime,1:Nx))
- allocate(ansrlo(1:Ntime,1:Nx))
- ansrup=0.d0
- ansrlo=0.d0
- allocate(fxind(1:Nx,1:2))
- fxind=0.d0 !this is not needed
- allocate(xc(1:Nc,1:Nx))
-
-
- allocate(a_up(Mb,NI-1))
- allocate(a_lo(Mb,NI-1))
- a_up=0.d0
- a_lo=0.d0
- xc(1,1:Nx)=h(1:Nx)
- xc(2,1:Nx)=u
- xc(3,1:Nx)=u
-
- if (def.GT.0) then
- a_up(1,1)=0.d0
- a_lo(1,1)=u
- a_up(1,2)=XdInf
- a_lo(1,3)=-XdInf
- a_up(2,1)=1.d0
- else
- a_up(1,1)=u
- a_lo(1,1)=0.d0
- a_lo(1,2)=-XdInf
- a_up(1,3)= XdInf
- a_lo(2,1)=1.d0
- endif
- !print *,'Nstart',Nstart
- Nstart=MAX(3,Nstart)
-
-
- if (SCIS.GT.0) then
- open (unit=11, file='COV.out', STATUS='unknown')
- write(11,*) 0.d0
- endif
-
- !print *,'loop starts'
- do Ntd=Nstart,Ntime
-
- Ntdc=Ntd+Nc
- ex=0.d0
- BIG=0.d0
- CALL COV_INPUT(BIG(1:Ntdc,1:Ntdc),Ntd,-1,R0,R1,R2,R3,R4) ! positive wave period
-
- Nt=Ntd-Nd;
- indI(2)=Nt;
- indI(3)=Nt+1;
- indI(4)=Ntd;
-
- CALL RINDD(fxind,Big(1:Ntdc,1:Ntdc),ex,xc,indI,a_lo,a_up)
- !print *,'test',fxind/CY(1:Nx)
-
- do icy=1,Nx
- ansrup(Ntd,icy)=fxind(icy,1)*CC/CY(icy)
- ansrlo(Ntd,icy)=fxind(icy,2)*CC/CY(icy)
- enddo
- if (SCIS.GT.0) then
- write(11,*) COV(1) ! save coefficient of variation
- endif
- if((Nx.gt.4).or.NIT.gt.4) print *,'Ready: ',Ntd,' of ',Ntime
- enddo
- goto 300
- 300 open (unit=11, file='dens.out', STATUS='unknown')
-
- do ts=1,Ntime
- do ph=1,Nx
- write(11,*) ansrup(ts,ph),ansrlo(ts,ph)
- enddo
- enddo
- !111 FORMAT(2x,F12.8)
- close(11)
- 900 continue
- deallocate(BIG)
- deallocate(ex)
- deallocate(fxind)
- deallocate(ansrup)
- deallocate(ansrlo)
- deallocate(xc)
- deallocate(R0)
- deallocate(R1)
- deallocate(R2)
- if (allocated(COV) ) then
- deallocate(COV)
- endif
-
- if (allocated(R3)) then
- deallocate(R3)
- deallocate(R4)
- deallocate(h)
- ENDIF
- stop
- !return
-
- CONTAINS
-
-
-
- SUBROUTINE INIT_LEVELS
- & (U,def,Ntime,Nstart,NIT,speed,Nx,dT)
- IMPLICIT NONE
- integer, intent(out):: def,Ntime,Nstart,NIT,speed,Nx
- double precision ,intent(out) :: U,dT
-
-
- OPEN(UNIT=14,FILE='reflev.in',STATUS= 'UNKNOWN')
- READ (14,*) U
- READ (14,*) def
- READ (14,*) Ntime
- READ (14,*) Nstart
- READ (14,*) NIT
- READ (14,*) speed
- READ (14,*) SCIS
- READ (14,*) seed1
- READ (14,*) Nx
-
- if (abs(def).GT.1) then
- READ (14,*) dT
- if (Ntime.lt.3) then
- print *,'The number of wavelength points is too small, stop'
- stop
- end if
- else
- if (Ntime.lt.2) then
- print *,'The number of wavelength points is too small, stop'
- stop
- end if
- endif
- CLOSE(UNIT=14)
-
- RETURN
- END SUBROUTINE INIT_LEVELS
-
-C******************************************************
- SUBROUTINE INIT_AMPLITUDES(h,def,Nx)
- IMPLICIT NONE
- double precision, dimension(:), intent(out) :: h
- integer, intent(in) :: def
- integer, intent(in) :: Nx
- integer :: ix
-
-
- OPEN(UNIT=4,FILE='h.in',STATUS= 'UNKNOWN')
-
-C
-C Reading in amplitudes
-C
- do ix=1,Nx
- READ (4,*) H(ix)
- enddo
- CLOSE(UNIT=4)
- !if (def.LT.0) THEN
- ! H=-H
- !endif
-
- RETURN
- END SUBROUTINE INIT_AMPLITUDES
-
-C**************************************************
-
-C***********************************************************************
-C***********************************************************************
-
- SUBROUTINE INIT_COVARIANCES(Ntime,def,R0,R1,R2,R3,R4)
- IMPLICIT NONE
- double precision, dimension(:),intent(out) :: R0,R1,R2
- double precision, dimension(:),intent(out) :: R3,R4
- integer,intent(in) :: Ntime,def
- integer :: i
- open (unit=1, file='Cd0.in',STATUS='unknown')
- open (unit=2, file='Cd1.in',STATUS='unknown')
- open (unit=3, file='Cd2.in',STATUS='unknown')
-
- do i=1,Ntime
- read(1,*) R0(i)
- read(2,*) R1(i)
- read(3,*) R2(i)
- enddo
- close(1)
- close(2)
- close(3)
-
- if (abs(def).GT.1) then
- open (unit=4, file='Cd3.in',STATUS='unknown')
- open (unit=5, file='Cd4.in',STATUS='unknown')
-
- do i=1,Ntime
- read(4,*) R3(i)
- read(5,*) R4(i)
- enddo
-
- close(4)
- close(5)
- endif
- return
- END SUBROUTINE INIT_COVARIANCES
-
-C***********************************************************************
-C***********************************************************************
-
-C**********************************************************************
-
- SUBROUTINE COV_INPUT(BIG,tn,ts, R0,R1,R2,R3,R4)
- IMPLICIT NONE
- double precision, dimension(:,:),intent(inout) :: BIG
- double precision, dimension(:),intent(in) :: R0,R1,R2
- double precision, dimension(:),intent(in) :: R3,R4
- integer ,intent(in) :: tn,ts
- integer :: i,j,shft,Ntd1,N !=Ntdc
- double precision :: tmp
-! the order of the variables in the covariance matrix
-! are organized as follows:
-! For ts>1:
-! X(t2)..X(ts),..X(tn-1) X''(ts) X'(t1) X'(tn) X(ts) X(t1) X(tn) X'(ts)
-! = [Xt Xd Xc]
-!
-! For ts<=1:
-! X(t2)..,..X(tn-1) X'(t1) X'(tn) Y X(t1) X(tn)
-! = [Xt Xd Xc]
-!Add Y Condition : Y=h
-
-! where
-!
-! Xt= time points in the indicator function
-! Xd= derivatives
-! Xc=variables to condition on
-
- if (ts.LE.1) THEN
- Ntd1=tn
- N=Ntd1+Nc;
- shft=0 ! def=1 want only crest period Tc
- else
- Ntd1=tn+1
- N=Ntd1+4
- shft=1 ! def=2 or 3 want Tc Ac or Tcf, Ac
- endif
-
- do i=1,tn-2
- !cov(Xt)
- do j=i,tn-2
- BIG(i,j) = R0(j-i+1) ! cov(X(ti+1),X(tj+1))
- enddo
- !cov(Xt,Xc)
- BIG(i ,Ntd1+1+shft) = 0.d0 !cov(X(ti+1),Y)
- BIG(i ,Ntd1+2+shft) = R0(i+1) !cov(X(ti+1),X(t1))
- BIG(tn-1-i ,Ntd1+3+shft) = R0(i+1) !cov(X(t.. ),X(tn))
- !Cov(Xt,Xd)=cov(X(ti+1),x(tj)
- BIG(i,Ntd1-1) =-R1(i+1) !cov(X(ti+1),X' (t1))
- BIG(tn-1-i,Ntd1)= R1(i+1) !cov(X(ti+1),X' (tn))
- enddo
- !call echo(big(1:tn,1:tn),tn)
-!cov(Xd)
- BIG(Ntd1 ,Ntd1 ) = -R2(1)
- BIG(Ntd1-1,Ntd1 ) = -R2(tn) !cov(X'(t1),X'(tn))
- BIG(Ntd1-1,Ntd1-1) = -R2(1)
-
-!cov(Xc)
- !print *,'t'
- BIG(Ntd1+1+shft,Ntd1+1+shft) = 100.d0!100.d0 ! cov(Y,Y)
- BIG(Ntd1+1+shft,Ntd1+2+shft) = 0.d0
- BIG(Ntd1+1+shft,Ntd1+3+shft) = 0.d0
- BIG(Ntd1+2+shft,Ntd1+2+shft) = R0(1) ! cov(X(t1),X (t1))
- BIG(Ntd1+2+shft,Ntd1+3+shft) = R0(tn) ! cov(X(t1),X (tn))
- BIG(Ntd1+3+shft,Ntd1+3+shft) = R0(1) ! cov(X(tn),X (tn))
-!cov(Xd,Xc)
- BIG(Ntd1 ,Ntd1+1+shft) = 0.d0 !cov(X'(tn),Y)
- BIG(Ntd1 ,Ntd1+2+shft) = R1(tn) !cov(X'(tn),X(t1))
- BIG(Ntd1 ,Ntd1+3+shft) = 0.d0 !cov(X'(tn),X(tn))
- BIG(Ntd1-1,Ntd1+1+shft) = 0.d0 !cov(X'(t1),Y)
- BIG(Ntd1-1,Ntd1+2+shft) = 0.d0 !cov(X'(t1),X(t1))
- BIG(Ntd1-1,Ntd1+3+shft) =-R1(tn) !cov(X'(t1),X(tn))
-
-
- !call echo(big(1:N,1:N),N)
- ! make lower triangular part equal to upper
- do j=1,N-1
- do i=j+1,N
- tmp =BIG(j,i)
- BIG(i,j)=tmp
- enddo
- !call echo(big(1:N,1:N),N)
- enddo
- !if (tn.eq.3) then
- !do j=1,N
- ! do i=j,N
- ! print *,'test',j,i,BIG(j,i)
- ! enddo
- !call echo(big(1:N,1:N),N)
- !enddo
- !endif
- !call echo(big(1:N,1:N),N)
-
-C write (*,10) ((BIG(j,i),i=N+1,N+6),j=N+1,N+6)
-C 10 format(6F8.4)
- RETURN
- END SUBROUTINE COV_INPUT
-
- SUBROUTINE COV_INPUT2(BIG,pt, R0,R1,R2)
- IMPLICIT NONE
- double precision, dimension(:,:), intent(out) :: BIG
- double precision, dimension(:), intent(in) :: R0,R1,R2
- integer :: pt,i,j
-! the order of the variables in the covariance matrix
-! are organized as follows;
-! X(t2)...X(tn-1) X'(t1) X'(tn) X(t1) X(tn) = [Xt Xd Xc]
-!
-! where Xd is the derivatives
-!
-! Xt= time points in the indicator function
-! Xd= derivatives
-! Xc=variables to condition on
-
-!cov(Xc)
- BIG(pt+2,pt+2) = R0(1)
- BIG(pt+1,pt+1) = R0(1)
- BIG(pt+1,pt+2) = R0(pt)
-!cov(Xd)
- BIG(pt,pt) = -R2(1)
- BIG(pt-1,pt-1) = -R2(1)
- BIG(pt-1,pt) = -R2(pt)
-!cov(Xd,Xc)
- BIG(pt,pt+2) = 0.d0
- BIG(pt,pt+1) = R1(pt)
- BIG(pt-1,pt+2) = -R1(pt)
- BIG(pt-1,pt+1) = 0.d0
-
- if (pt.GT.2) then
-!cov(Xt)
- do i=1,pt-2
- do j=i,pt-2
- BIG(i,j) = R0(j-i+1)
- enddo
- enddo
-!cov(Xt,Xc)
- do i=1,pt-2
- BIG(i,pt+1) = R0(i+1)
- BIG(pt-1-i,pt+2) = R0(i+1)
- enddo
-!Cov(Xt,Xd)=cov(X(ti+1),x(tj))
- do i=1,pt-2
- BIG(i,pt-1) = -R1(i+1)
- BIG(pt-1-i,pt)= R1(i+1)
- enddo
- endif
-
-
- ! make lower triangular part equal to upper
- do j=1,pt+1
- do i=j+1,pt+2
- BIG(i,j)=BIG(j,i)
- enddo
- enddo
-C write (*,10) ((BIG(j,i),i=N+1,N+6),j=N+1,N+6)
-C 10 format(6F8.4)
- RETURN
- END SUBROUTINE COV_INPUT2
-
-
- END PROGRAM sp2Acdf1
-
-
-
-
-
-
-
-
-
diff --git a/wafo/source/cov2XXXpdf/bounds/cov2mmpdfb.f b/wafo/source/cov2XXXpdf/bounds/cov2mmpdfb.f
deleted file mode 100644
index 2d9aee7..0000000
--- a/wafo/source/cov2XXXpdf/bounds/cov2mmpdfb.f
+++ /dev/null
@@ -1,356 +0,0 @@
- PROGRAM sp2mM1
-C***********************************************************************************
-C Computes upper lower bounds for density of maximum and the following minimum *
-C***********************************************************************************
- use GLOBALDATA, only : Nt,Nj,Nd,Nc,Ntd,Ntdc,NI,Mb,
- &NIT,Nx,TWOPI,XSPLT,SCIS,NSIMmax,COV
- use rind
- IMPLICIT NONE
- double precision, dimension(:,:),allocatable :: BIG
- double precision, dimension(:,:),allocatable :: ansrup
- double precision, dimension(:,:),allocatable :: ansrlo
- double precision, dimension(: ),allocatable :: ex,h
- double precision, dimension(:,:),allocatable :: xc
- double precision, dimension(:,:),allocatable :: fxind
- double precision, dimension(: ),allocatable :: R0,R1,R2,R3,R4
- double precision ::CC,U,XddInf,XdInf,XtInf
- double precision, dimension(:,:),allocatable :: a_up,a_lo
- integer , dimension(: ),allocatable :: seed
- integer ,dimension(7) :: indI
- integer :: Nstart,Ntime,tn,ts,speed,seed1,seed_size
- integer :: status,i,j,ij,Nx1
- double precision :: ds,dT ! lag spacing for covariances
-
-! f90 sp2AmM1.f rind52.f
-
- CALL INIT_LEVELS(Ntime,Nstart,NIT,speed,Nx1,dT)
- Nx=Nx1*(Nx1-1)/2
- if (SCIS.GT.0) then
- allocate(COV(1:Nx))
- call random_seed(SIZE=seed_size)
- allocate(seed(seed_size))
- call random_seed(GET=seed(1:seed_size)) ! get current seed
- seed(1)=seed1 ! change seed
- call random_seed(PUT=seed(1:seed_size))
- deallocate(seed)
- endif
-
- CALL INITDATA(speed)
-
- allocate(R0(1:Ntime+1))
- allocate(R1(1:Ntime+1))
- allocate(R2(1:Ntime+1))
- allocate(R3(1:Ntime+1))
- allocate(R4(1:Ntime+1))
- allocate(h(1:Nx1))
-
- CALL INIT_AMPLITUDES(h,Nx1)
- CALL INIT_COVARIANCES(Ntime,R0,R1,R2,R3,R4)
-!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
-! Y= X'(t2)...X'(tn-1)||X''(t1) X''(tn)||X(t1) X(tn) X'(t1) X'(tn) !!
-! = [ Xt Xd Xc ] !!
-! !!
-! Nt=tn-2, Nd=2, Nc=4 !!
-! !!
-! Xt= contains Nt time points in the indicator function !!
-! Xd= " Nd derivatives !!
-! Xc= " Nc variables to condition on !!
-! !!
-! There are 3 ( NI=4) regions with constant bariers: !!
-! (indI(1)=0); for i\in (indI(1),indI(2)] Y(i)<0. !!
-! (indI(2)=Nt) ; for i\in (indI(2)+1,indI(3)], Y(i)<0 (deriv. X''(t1)) !!
-! (indI(3)=Nt+1); for i\in (indI(3)+1,indI(4)], Y(i)>0 (deriv. X''(tn)) !!
-!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
-
-
- NI=4; Nd=2
- Nc=4; Mb=1
-
- Nj=0
- indI(1)=0
-C ***** The bound 'infinity' is set to 10*sigma *****
- XdInf=10.d0*SQRT(R4(1))
- XtInf=10.d0*SQRT(-R2(1))
- ! normalizing constant
- CC=TWOPI*SQRT(-R2(1)/R4(1))
-
- allocate(BIG(1:Ntime+Nc,1:Ntime+Nc),stat=status)
- if (status.ne.0) then
- print *,'can not allocate BIG'
- end if
- allocate(ex(1:Ntime+Nc),stat=status)
- if (status.ne.0) then
- print *,'can not allocate ex'
- end if
- allocate(ansrup(1:Nx1,1:Nx1))
- ansrup=0.d0
- allocate(ansrlo(1:Nx1,1:Nx1))
- ansrlo=0.d0
- allocate(fxind(1:Nx,1:2))
- fxind=0.d0 !this is not needed
- allocate(xc(1:Nc,1:Nx))
-
-
- allocate(a_up(Mb,NI-1))
- allocate(a_lo(Mb,NI-1))
-
- a_up=0.d0
- a_lo=0.d0
-
- ij=0
- do i=2,Nx1
- do j=1,i-1
- ij=ij+1
- xc(1,ij)=h(i)
- xc(2,ij)=h(j)
- enddo
- enddo
- xc(3,1:Nx)=0.d0
- xc(4,1:Nx)=0.d0
-
- a_lo(1,1)=-Xtinf
- a_lo(1,2)=-XdInf
- a_up(1,3)=+XdInf
-
-
- Nstart=MAX(2,Nstart)
-
-
- if (SCIS.GT.0) then
- open (unit=11, file='COV.out', STATUS='unknown')
- write(11,*) 0.d0
- endif
-
- do Ntd=Nstart,Ntime
-
- Ntdc=Ntd+Nc
- ex=0.d0
- BIG=0.d0
- CALL COV_INPUT(BIG(1:Ntdc,1:Ntdc),Ntd,R0,R1,R2,R3,R4) ! positive wave period
-
- Nt=Ntd-Nd;
- indI(2)=Nt;
- indI(3)=Nt+1;
- indI(4)=Ntd;
-
- CALL RINDD(fxind,Big(1:Ntdc,1:Ntdc),ex,xc,indI,a_lo,a_up)
- ij=0
- do i=2,Nx1
- do j=1,i-1
- ij=ij+1
- ansrup(i,j)=ansrup(i,j)+fxind(ij,1)*CC*dt
- ansrlo(i,j)=ansrlo(i,j)+fxind(ij,2)*CC*dt
- enddo
- enddo
-
- if (SCIS.GT.0) then
- write(11,*) COV(1) ! save coefficient of variation
- endif
- print *,'Ready: ',Ntd,' of ',Ntime
- enddo
- goto 300
- 300 open (unit=11, file='dens.out', STATUS='unknown')
- do i=1,Nx1
- do j=1,Nx1
- write(11,*) ansrup(i,j),ansrlo(i,j)
- enddo
- enddo
- close(11)
- 900 continue
- deallocate(BIG)
- deallocate(ex)
- deallocate(fxind)
- deallocate(ansrup)
- deallocate(ansrlo)
- deallocate(xc)
- deallocate(R0)
- deallocate(R1)
- deallocate(R2)
- deallocate(R3)
- deallocate(R4)
- deallocate(h)
-
- if (allocated(COV) ) then
- deallocate(COV)
- endif
- stop
- !return
-
- CONTAINS
-
-
-
- SUBROUTINE INIT_LEVELS
- & (Ntime,Nstart,NIT,speed,Nx,dT)
- IMPLICIT NONE
- integer, intent(out):: Ntime,Nstart,NIT,speed,Nx
- double precision ,intent(out) :: dT
-
-
- OPEN(UNIT=14,FILE='reflev.in',STATUS= 'UNKNOWN')
- READ (14,*) Ntime
- READ (14,*) Nstart
- READ (14,*) NIT
- READ (14,*) speed
- READ (14,*) SCIS
- READ (14,*) seed1
- READ (14,*) Nx
- READ (14,*) dT
-
- if (Ntime.lt.2) then
- print *,'The number of wavelength points is too small, stop'
- stop
- end if
- CLOSE(UNIT=14)
-
- RETURN
- END SUBROUTINE INIT_LEVELS
-
-C******************************************************
- SUBROUTINE INIT_AMPLITUDES(h,Nx)
- IMPLICIT NONE
- double precision, dimension(:), intent(out) :: h
- integer, intent(in) :: Nx
- integer :: ix
-
-
- OPEN(UNIT=4,FILE='h.in',STATUS= 'UNKNOWN')
-
-C
-C Reading in amplitudes
-C
- do ix=1,Nx
- READ (4,*) H(ix)
- enddo
- CLOSE(UNIT=4)
-
- RETURN
- END SUBROUTINE INIT_AMPLITUDES
-
-C**************************************************
-
-C***********************************************************************
-C***********************************************************************
-
- SUBROUTINE INIT_COVARIANCES(Ntime,R0,R1,R2,R3,R4)
- IMPLICIT NONE
- double precision, dimension(:),intent(out) :: R0,R1,R2
- double precision, dimension(:),intent(out) :: R3,R4
- integer,intent(in) :: Ntime
- integer :: i
- open (unit=1, file='Cd0.in',STATUS='unknown')
- open (unit=2, file='Cd1.in',STATUS='unknown')
- open (unit=3, file='Cd2.in',STATUS='unknown')
- open (unit=4, file='Cd3.in',STATUS='unknown')
- open (unit=5, file='Cd4.in',STATUS='unknown')
-
- do i=1,Ntime
- read(1,*) R0(i)
- read(2,*) R1(i)
- read(3,*) R2(i)
- read(4,*) R3(i)
- read(5,*) R4(i)
- enddo
- close(1)
- close(2)
- close(3)
- close(3)
- close(5)
- return
- END SUBROUTINE INIT_COVARIANCES
-
-C**********************************************************************
-
- SUBROUTINE COV_INPUT(BIG,tn,R0,R1,R2,R3,R4)
- IMPLICIT NONE
- double precision, dimension(:,:),intent(inout) :: BIG
- double precision, dimension(:),intent(in) :: R0,R1,R2
- double precision, dimension(:),intent(in) :: R3,R4
- integer ,intent(in) :: tn
- integer :: i,j,N
- double precision :: tmp
-! the order of the variables in the covariance matrix
-! are organized as follows:
-! X'(t2)..X'(ts),...,X'(tn-1) X''(t1),X''(tn) X(t1),X(tn),X'(t1),X'(tn)
-! = [ Xt | Xd | Xc ]
-!
-! where
-!
-! Xt= time points in the indicator function
-! Xd= derivatives
-! Xc=variables to condition on
-
-! Computations of all covariances follows simple rules: Cov(X(t),X(s))=r(t,s),
-! then Cov(X'(t),X(s))=dr(t,s)/dt. Now for stationary X(t) we have
-! a function r(tau) such that Cov(X(t),X(s))=r(s-t) (or r(t-s) will give the same result).
-!
-! Consequently Cov(X'(t),X(s)) = -r'(s-t) = -sign(s-t)*r'(|s-t|)
-! Cov(X'(t),X'(s)) = -r''(s-t) = -r''(|s-t|)
-! Cov(X''(t),X'(s)) = r'''(s-t) = sign(s-t)*r'''(|s-t|)
-! Cov(X''(t),X(s)) = r''(s-t) = r''(|s-t|)
-! Cov(X''(t),X''(s)) = r''''(s-t) = r''''(|s-t|)
-
- N=tn+4
- do i=1,tn-2
- !cov(Xt)
- do j=i,tn-2
- BIG(i,j) = -R2(j-i+1) ! cov(X'(ti+1),X'(tj+1))
- enddo
- !cov(Xt,Xc)
- BIG(i ,tn+1) = R1(i+1) !cov(X'(ti+1),X(t1))
- BIG(tn-1-i ,tn+2) = -R1(i+1) !cov(X'(ti+1),X(tn))
- BIG(i ,tn+3) = -R2(i+1) !cov(X'(ti+1),X'(t1))
- BIG(tn-1-i ,tn+4) = -R2(i+1) !cov(X'(ti+1),X'(tn))
- !Cov(Xt,Xd)
- BIG(i,tn-1) = R3(i+1) !cov(X'(ti+1),X''(t1))
- BIG(tn-1-i,tn) =-R3(i+1) !cov(X'(ti+1),X''(tn))
- enddo
-
-!cov(Xd)
- BIG(tn-1 ,tn-1 ) = R4(1)
- BIG(tn-1,tn ) = R4(tn) !cov(X''(t1),X''(tn))
- BIG(tn ,tn ) = R4(1)
-
-!cov(Xc)
- BIG(tn+1,tn+1) = R0(1) ! cov(X(t1),X(t1))
- BIG(tn+1,tn+2) = R0(tn) ! cov(X(t1),X(tn))
- BIG(tn+1,tn+3) = 0.d0 ! cov(X(t1),X'(t1))
- BIG(tn+1,tn+4) = R1(tn) ! cov(X(t1),X'(tn))
- BIG(tn+2,tn+2) = R0(1) ! cov(X(tn),X(tn))
- BIG(tn+2,tn+3) =-R1(tn) ! cov(X(tn),X'(t1))
- BIG(tn+2,tn+4) = 0.d0 ! cov(X(tn),X'(tn))
- BIG(tn+3,tn+3) =-R2(1) ! cov(X'(t1),X'(t1))
- BIG(tn+3,tn+4) =-R2(tn) ! cov(X'(t1),X'(tn))
- BIG(tn+4,tn+4) =-R2(1) ! cov(X'(tn),X'(tn))
-!Xc=X(t1),X(tn),X'(t1),X'(tn)
-!Xd=X''(t1),X''(tn)
-!cov(Xd,Xc)
- BIG(tn-1 ,tn+1) = R2(1) !cov(X''(t1),X(t1))
- BIG(tn-1 ,tn+2) = R2(tn) !cov(X''(t1),X(tn))
- BIG(tn-1 ,tn+3) = 0.d0 !cov(X''(t1),X'(t1))
- BIG(tn-1 ,tn+4) = R3(tn) !cov(X''(t1),X'(tn))
- BIG(tn ,tn+1) = R2(tn) !cov(X''(tn),X(t1))
- BIG(tn ,tn+2) = R2(1) !cov(X''(tn),X(tn))
- BIG(tn ,tn+3) =-R3(tn) !cov(X''(tn),X'(t1))
- BIG(tn ,tn+4) = 0.d0 !cov(X''(tn),X'(tn))
- ! make lower triangular part equal to upper
- do j=1,N-1
- do i=j+1,N
- tmp =BIG(j,i)
- BIG(i,j)=tmp
- enddo
- enddo
- RETURN
- END SUBROUTINE COV_INPUT
-
-
- END PROGRAM sp2mM1
-
-
-
-
-
-
-
-
-
diff --git a/wafo/source/cov2XXXpdf/bounds/cov2tccpdfb.f b/wafo/source/cov2XXXpdf/bounds/cov2tccpdfb.f
deleted file mode 100644
index 9f77d29..0000000
--- a/wafo/source/cov2XXXpdf/bounds/cov2tccpdfb.f
+++ /dev/null
@@ -1,504 +0,0 @@
- PROGRAM sp2tccpdf1
-C***********************************************************************
-C This program computes upper and lower bounds for the: *
-C *
-C density of T= T_1+T_2 in a gaussian process i.e. *
-C *
-C wavelengthes for crests