Small bugfixes

pywafo/.project

@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <projectDescription>
-	<name>PYWAFO</name>
+	<name>google_pywafo</name>
 	<comment></comment>
 	<projects>
 	</projects>

pywafo/.pydevproject

@@ -3,7 +3,7 @@
 
 <pydev_project>
 <pydev_pathproperty name="org.python.pydev.PROJECT_SOURCE_PATH">
-<path>/PYWAFO/src</path>
+<path>/google_pywafo/src</path>
 </pydev_pathproperty>
 <pydev_property name="org.python.pydev.PYTHON_PROJECT_VERSION">python 2.6</pydev_property>
 <pydev_property name="org.python.pydev.PYTHON_PROJECT_INTERPRETER">Default</pydev_property>

pywafo/setup.py

@@ -17,6 +17,7 @@ python setup.py sdist bdist_wininst upload --show-response
 import os, sys
 
 sys.argv.append("develop")
+#sys.argv.append("install")
 DISTUTILS_DEBUG = True
 pkg_name = 'wafo'
 root_dir = os.path.join('src',pkg_name)
@@ -44,7 +45,9 @@ testscripts = [os.path.join(subtst, f) for subtst in subtests
                        f.endswith('.old') or f.endswith('.bak'))]
 datadir = 'data'
 datafiles = [os.path.join(datadir, f)   for f in os.listdir(os.path.join(root_dir, datadir))
-				if  not (f.endswith('.py') or f.endswith('test') )]
+				if  not (f.startswith('.') or f.endswith('~') or
+                       f.endswith('.old') or f.endswith('.bak') or 
+                       f.endswith('.py') or f.endswith('test') )]
 libs = [f   for f in os.listdir(os.path.join(root_dir)) if  f.endswith('.pyd') ]
 packagedata = testscripts + datafiles + libs #['c_library.pyd'] #,'disufq1.c','diffsumfunq.pyd','diffsumfunq.pyf','findrfc.c','rfc.pyd','rfc.pyf']
 

pywafo/src/Wafo.egg-info/PKG-INFO

@@ -1,92 +1,92 @@
 Metadata-Version: 1.0
-Name: WAFO
+Name: wafo
 Version: 0.11
-Summary: 
+Summary: UNKNOWN
-WAFO
+Home-page: http://www.maths.lth.se/matstat/wafo/
-=====
+Author: WAFO-group
-   WAFO is a toolbox Python routines for statistical analysis and simulation of random waves and random loads. 
+Author-email: wafo@maths.lth.se
-   WAFO is freely redistributable software, see WAFO licence, cf. the GNU General Public License (GPL) and
+License: GPL
-   contain tools for:
+Description: 
+        WAFO
+        =====
+        WAFO is a toolbox Python routines for statistical analysis and simulation of random waves and random loads.
+        WAFO is freely redistributable software, see WAFO licence, cf. the GNU General Public License (GPL) and
+        contain tools for:
         
-Fatigue Analysis
+        Fatigue Analysis
-----------------
+        ----------------
--Fatigue life prediction for random loads
+        -Fatigue life prediction for random loads
--Theoretical density of rainflow cycles
+        -Theoretical density of rainflow cycles
         
-Sea modelling
+        Sea modelling
--------------
+        -------------
--Simulation of linear and non-linear Gaussian waves
+        -Simulation of linear and non-linear Gaussian waves
--Estimation of seamodels (spectrums)
+        -Estimation of seamodels (spectrums)
--Joint wave height, wave steepness, wave period distributions
+        -Joint wave height, wave steepness, wave period distributions
         
-Statistics
+        Statistics
-------------
+        ------------
--Extreme value analysis
+        -Extreme value analysis
--Kernel density estimation
+        -Kernel density estimation
--Hidden markov models
+        -Hidden markov models
         
- WAFO consists of several modules with short descriptions below.
+        WAFO consists of several modules with short descriptions below.
- The modules SPECTRUM, COVARIANCE, TRANSFORM, WAVEMODELS, and MULTIDIM are 
+        The modules SPECTRUM, COVARIANCE, TRANSFORM, WAVEMODELS, and MULTIDIM are
- mainly for oceanographic applications.
+        mainly for oceanographic applications.
- The modules CYCLES, MARKOV, and DAMAGE are mainly for fatigue problems. 
+        The modules CYCLES, MARKOV, and DAMAGE are mainly for fatigue problems.
- The contents file for each module is shown by typing 'help module-name' 
+        The contents file for each module is shown by typing 'help module-name'
- Type 'help fatigue' for a presentation of all routines related to fatigue.
+        Type 'help fatigue' for a presentation of all routines related to fatigue.
         
- The paths to the modules are initiated by the function 'initwafo'.
+        The paths to the modules are initiated by the function 'initwafo'.
         
- ONEDIM     - Data analysis of time series. Example: extraction of 
+        ONEDIM     - Data analysis of time series. Example: extraction of
-              turning points, estimation of spectrum and covariance function. 
+        turning points, estimation of spectrum and covariance function.
-              Estimation transformation used in transformed Gaussian model.
+        Estimation transformation used in transformed Gaussian model.
- COVARIANCE - Computation of spectral functions, linear 
+        COVARIANCE - Computation of spectral functions, linear
-              and non-linear time series simulation. 
+        and non-linear time series simulation.
- SPECTRUM   - Computation of spectral moments and covariance functions, linear 
+        SPECTRUM   - Computation of spectral moments and covariance functions, linear
-              and non-linear time series simulation. 
+        and non-linear time series simulation.
-              Ex: common spectra implemented, directional spectra, 
+        Ex: common spectra implemented, directional spectra,
-              bandwidth measures, exact distributions for wave characteristics.
+        bandwidth measures, exact distributions for wave characteristics.
- TRANSFORM  - Modelling with linear or transformed Gaussian waves. Ex: 
+        TRANSFORM  - Modelling with linear or transformed Gaussian waves. Ex:
         
- WAVEMODELS - Models for distributions of wave characteristics found in 
+        WAVEMODELS - Models for distributions of wave characteristics found in
-              the literature. Ex: parametric models for breaking 
+        the literature. Ex: parametric models for breaking
-              limited wave heights.
+        limited wave heights.
- MULTIDIM   - Multi-dimensional time series analysis.  (Under construction)
+        MULTIDIM   - Multi-dimensional time series analysis.  (Under construction)
- CYCLES     - Cycle counting, discretization, and crossings, calculation of 
+        CYCLES     - Cycle counting, discretization, and crossings, calculation of
-              damage. Simulation of discrete Markov chains, switching Markov
+        damage. Simulation of discrete Markov chains, switching Markov
-              chains, harmonic oscillator. Ex:  Rainflow cycles and matrix, 
+        chains, harmonic oscillator. Ex:  Rainflow cycles and matrix,
-              discretization of loads. Damage of a rainflow count or 
+        discretization of loads. Damage of a rainflow count or
-              matrix, damage matrix, S-N plot.
+        matrix, damage matrix, S-N plot.
- MARKOV     - Routines for Markov loads, switching Markov loads, and 
+        MARKOV     - Routines for Markov loads, switching Markov loads, and
-              their connection to rainflow cycles.
+        their connection to rainflow cycles.
- DAMAGE     - Calculation of damage. Ex: Damage of a rainflow count or 
+        DAMAGE     - Calculation of damage. Ex: Damage of a rainflow count or
-              matrix, damage matrix, S-N plot.
+        matrix, damage matrix, S-N plot.
- SIMTOOLS   - Simulation of random processes. Ex: spectral simulation, 
+        SIMTOOLS   - Simulation of random processes. Ex: spectral simulation,
-              simulation of discrete Markov chains, switching Markov
+        simulation of discrete Markov chains, switching Markov
-              chains, harmonic oscillator
+        chains, harmonic oscillator
- STATISTICS - Statistical tools and extreme-value distributions.
+        STATISTICS - Statistical tools and extreme-value distributions.
-              Ex: generation of random numbers, estimation of parameters,
+        Ex: generation of random numbers, estimation of parameters,
-              evaluation of pdf and cdf
+        evaluation of pdf and cdf
- KDETOOLS   - Kernel-density estimation.
+        KDETOOLS   - Kernel-density estimation.
- MISC       - Miscellaneous routines. Ex: numerical integration, smoothing 
+        MISC       - Miscellaneous routines. Ex: numerical integration, smoothing
-              spline, binomial coefficient, water density.
+        spline, binomial coefficient, water density.
- WDEMOS     - WAFO demos. 
+        WDEMOS     - WAFO demos.
- DOCS       - Documentation of toolbox, definitions. An overview is given 
+        DOCS       - Documentation of toolbox, definitions. An overview is given
-              in the routine wafomenu. 
+        in the routine wafomenu.
- DATA       - Measurements from marine applications.
+        DATA       - Measurements from marine applications.
- PAPERS     - Commands that generate figures in selected scientific 
+        PAPERS     - Commands that generate figures in selected scientific
-              publications.
+        publications.
- SOURCE     - Fortran and C files. Information on compilation.
+        SOURCE     - Fortran and C files. Information on compilation.
- EXEC       - Executable files (cf. SOURCE), pre-compiled for Solaris, 
+        EXEC       - Executable files (cf. SOURCE), pre-compiled for Solaris,
-              Alpha-Dec or Windows. 
+        Alpha-Dec or Windows.
         
- WAFO homepage: <http://www.maths.lth.se/matstat/wafo/>
+        WAFO homepage: <http://www.maths.lth.se/matstat/wafo/>
- On the WAFO home page you will find:
+        On the WAFO home page you will find:
-  - The WAFO Tutorial
+        - The WAFO Tutorial
-  - New versions of WAFO to download.
+        - New versions of WAFO to download.
-  - Reported bugs.
+        - Reported bugs.
-  - List of publications related to WAFO.
+        - List of publications related to WAFO.
         
-Home-page: http://www.maths.lth.se/matstat/wafo/
-Author: WAFO-group
-Author-email: wafo@maths.lth.se
-License: GPL
-Description: UNKNOWN
 Platform: UNKNOWN

pywafo/src/Wafo.egg-info/top_level.txt

@@ -1,5 +1 @@
-wafo\spectrum
-wafo\covariance
 wafo
-wafo\data
-wafo\transform

pywafo/src/wafo/integrate.py

@@ -1300,7 +1300,7 @@ def quadgr(fun,a,b,abseps=1e-5):
     err = err + 2*np.finfo(Q).eps
     # Reverse direction
     if reverse:
-    	Q = -Q
+        Q = -Q
 
     return Q, err
 

pywafo/src/wafo/interpolate.py

@@ -19,7 +19,7 @@ from numpy.lib.shape_base import vstack
 from numpy.lib.function_base import linspace
 import polynomial as pl
 
-class PPform1(object):
+class PPform(object):
     """The ppform of the piecewise polynomials is given in terms of coefficients
     and breaks.  The polynomial in the ith interval is
     x_{i} <= x < x_{i+1}

pywafo/src/wafo/kdetools.py

@@ -9,7 +9,7 @@
 # Licence:     LGPL
 #-------------------------------------------------------------------------------
 #!/usr/bin/env python
-#import numpy as np
+import numpy as np
 from scipy.special import gamma
 from numpy import pi, atleast_2d #@UnresolvedImport
 from misc import tranproc, trangood
@@ -112,29 +112,29 @@ class kde(object):
         if d != self.d:
             if d == 1 and m == self.d:
                 # points was passed in as a row vector
-                points = reshape(points, (self.d, 1))
+                points = np.reshape(points, (self.d, 1))
                 m = 1
             else:
                 msg = "points have dimension %s, dataset has dimension %s" % (d,
                     self.d)
                 raise ValueError(msg)
 
-        result = zeros((m,), points.dtype)
+        result = np.zeros((m,), points.dtype)
 
         if m >= self.n:
             # there are more points than data, so loop over data
             for i in range(self.n):
-                diff = self.dataset[:,i,newaxis] - points
+                diff = self.dataset[:,i, np.newaxis] - points
-                tdiff = dot(self.inv_cov, diff)
+                tdiff = np.dot(self.inv_cov, diff)
-                energy = sum(diff*tdiff,axis=0)/2.0
+                energy = np.sum(diff*tdiff,axis=0)/2.0
-                result += exp(-energy)
+                result += np.exp(-energy)
         else:
             # loop over points
             for i in range(m):
-                diff = self.dataset - points[:,i,newaxis]
+                diff = self.dataset - points[:,i,np.newaxis]
-                tdiff = dot(self.inv_cov, diff)
+                tdiff = np.dot(self.inv_cov, diff)
                 energy = sum(diff*tdiff,axis=0)/2.0
-                result[i] = sum(exp(-energy),axis=0)
+                result[i] = np.sum(np.exp(-energy),axis=0)
 
         result /= self._norm_factor
 

pywafo/src/wafo/misc.py

@@ -1563,7 +1563,7 @@ def meshgrid(*xi, ** kwargs):
             return broadcast_arrays(*output)
 
 
-def ndgrid(*args, ** kwargs):
+def ndgrid(*args, **kwargs):
     """
     Same as calling meshgrid with indexing='ij' (see meshgrid for
     documentation).
@@ -1666,7 +1666,7 @@ def trangood(x, f, min_n=None, min_x=None, max_x=None, max_n=inf):
 
     return xo, fo
 
-def tranproc(x, f, x0, * xi):
+def tranproc(x, f, x0, *xi):
     """
     Transforms process X and up to four derivatives
           using the transformation f.
@@ -1779,7 +1779,7 @@ def tranproc(x, f, x0, * xi):
                     y.append(y4)
                     if N > 4:
                         warnings.warn('Transformation of derivatives of order>4 not supported.')
-    return y #0,y1,y2,y3,y4
+    return y #y0,y1,y2,y3,y4
 
 
 def test_common_shape():

pywafo/src/wafo/source/c_codes/build_all.py

@@ -5,10 +5,10 @@ import os
 
 def compile_all():
     # Install gfortran and run the following to build the module:
-    #compile_format = 'f2py %s %s -c --fcompiler=gnu95 --compiler=mingw32 -lmsvcr71'
+    compile_format = 'f2py.py %s %s -c --fcompiler=gnu95 --compiler=mingw32 -lmsvcr71'
 
     # Install microsoft visual c++ .NET 2003 and run the following to build the module:
-    compile_format = 'f2py.py %s %s -c'
+    #compile_format = 'f2py.py %s %s -c'
     pyfs = ('c_library.pyf',)
     files =('c_functions.c',)
 

pywafo/src/wafo/spectrum/core.py

@@ -16,16 +16,15 @@ from pylab import stineman_interp
 
 from dispersion_relation import w2k #, k2w
 from wafo.wafodata import WafoData, now
-from wafo.misc import sub_dict_select, nextpow2, discretize, JITImport
+from wafo.misc import sub_dict_select, nextpow2, discretize, JITImport, tranproc
 try:
     from wafo.gaussian import Rind
 except ImportError:
-    
     Rind = None
 try:
     from wafo import c_library
 except ImportError:
-    warnings.warn('Compile the c_libraray.pyd again!')
+    warnings.warn('Compile the c_library.pyd again!')
     c_library = None
     
 from wafo.transform import TrData
@@ -751,8 +750,7 @@ class SpecData1D(WafoData):
         tn = paramt[1]
         Ntime = paramt[2]
         t = linspace(0, tn / A, Ntime) #normalized times
-        Nstart = max(round(t0 / tn * (Ntime - 1)), 1)  #% index to starting point to
+        Nstart = max(round(t0 / tn * (Ntime - 1)), 1)  #% index to starting point to evaluate
-                                             #% evaluate
                                     
         dt = t[1] - t[0]
         nr = 2
@@ -1055,16 +1053,22 @@ class SpecData1D(WafoData):
         if spec.tr is not None:
             print('   Transforming data.')
             g = spec.tr
-            G = fliplr(g) #% the invers of g
             if derivative:
                 for i in range(cases):
-                    tmp = tranproc(hstack((x[:, i + 1], xder[:, i + 1])), G)
+                    x[:, i + 1], xder[:, i + 1] = g.gauss2dat(x[:, i + 1], xder[:, i + 1])
-                    x[:, i + 1] = tmp[:, 0]
-                    xder[:, i + 1] = tmp[:, 1]
-
             else:
                 for i in range(cases):
-                    x[:, i + 1] = tranproc(x[:, i + 1], G)
+                    x[:, i + 1] = g.gauss2dat(x[:, i + 1])
+#            G = fliplr(g) #% the invers of g
+#            if derivative:
+#                for i in range(cases):
+#                    tmp = tranproc(hstack((x[:, i + 1], xder[:, i + 1])), G)
+#                    x[:, i + 1] = tmp[:, 0]
+#                    xder[:, i + 1] = tmp[:, 1]
+#
+#            else:
+#                for i in range(cases):
+#                    x[:, i + 1] = tranproc(x[:, i + 1], G)
 
         if derivative:
             return x, xder

pywafo/src/wafo/transform/core.py

@@ -82,7 +82,7 @@ class TrCommon(object):
         t0 = trapz(xn,(xn-yn)**2.)
         return t0
 
-    def gauss2dat(self,y, *yi):
+    def gauss2dat(self, y, *yi):
         """
         Transforms Gaussian data, y, to non-linear scale.
 
@@ -153,12 +153,19 @@ class TrData(WafoData, TrCommon):
     >>> g.sigma
     array([ 5.])
     
+    >>> g = TrData(y,x,mean=1,sigma=5)
+    >>> g.mean
+    1
+    >>> g.sigma
+    5
+    
+    
     Check that the departure from a Gaussian model is zero
     >>> g.dist2gauss() < 1e-16
     True
     """
     def __init__(self, *args, **kwds):
-        WafoData.__init__(self, *args, **kwds)
+        super(TrData, self).__init__(*args, **kwds)
         self.labels.title = 'Transform'
         self.labels.ylab = 'g(x)'
         self.labels.xlab = 'x'

pywafo/src/wafo/transform/models.py

@@ -463,7 +463,7 @@ if __name__=='__main__':
         import doctest
         doctest.testmod()
     else:
-       main()
+        main()