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6c5444fc06
@ -1,39 +1,161 @@
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name: C:\Users\z5189959\Desktop\nsw-2016-storm-impact\.venv
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channels:
|
||||
- conda-forge
|
||||
- plotly
|
||||
- defaults
|
||||
- conda-forge
|
||||
dependencies:
|
||||
- python=3.6
|
||||
- attrs
|
||||
- autopep8
|
||||
- black
|
||||
- click
|
||||
- click-plugins
|
||||
- colorlover
|
||||
- fiona
|
||||
- ipykernel
|
||||
- ipython
|
||||
- ipywidgets
|
||||
- matplotlib
|
||||
- nbformat
|
||||
- notebook
|
||||
- numpy
|
||||
- pandas
|
||||
- pandoc
|
||||
- pip
|
||||
- plotly
|
||||
- plotly-orca
|
||||
- proj4
|
||||
- pyproj
|
||||
- python-dateutil
|
||||
- pytz
|
||||
- pyyaml
|
||||
- requests
|
||||
- scikit-learn
|
||||
- scipy
|
||||
- setuptools
|
||||
- shapely
|
||||
- yaml
|
||||
- yapf
|
||||
- black=18.9b0=py_0
|
||||
- colorlover=0.2.1=py_0
|
||||
- jupyter_contrib_core=0.3.3=py_2
|
||||
- jupyter_contrib_nbextensions=0.5.0=py36_1000
|
||||
- jupyter_highlight_selected_word=0.2.0=py36_1000
|
||||
- jupyter_latex_envs=1.4.4=py36_1000
|
||||
- jupyter_nbextensions_configurator=0.4.0=py36_1000
|
||||
- matplotlib-base=3.0.2=py36h3e3dc42_1001
|
||||
- appdirs=1.4.3=py36h28b3542_0
|
||||
- asn1crypto=0.24.0=py36_0
|
||||
- attrs=18.2.0=py36h28b3542_0
|
||||
- autopep8=1.4.3=py36_0
|
||||
- backcall=0.1.0=py36_0
|
||||
- blas=1.0=mkl
|
||||
- bleach=3.0.2=py36_0
|
||||
- boost=1.67.0=py36_4
|
||||
- boost-cpp=1.67.0=hfa6e2cd_4
|
||||
- ca-certificates=2018.03.07=0
|
||||
- certifi=2018.10.15=py36_0
|
||||
- cffi=1.11.5=py36h74b6da3_1
|
||||
- chardet=3.0.4=py36_1
|
||||
- click=7.0=py36_0
|
||||
- click-plugins=1.0.4=py36_0
|
||||
- cligj=0.5.0=py36_0
|
||||
- colorama=0.4.0=py36_0
|
||||
- cryptography=2.3.1=py36h74b6da3_0
|
||||
- curl=7.62.0=h2a8f88b_0
|
||||
- cycler=0.10.0=py36h009560c_0
|
||||
- decorator=4.3.0=py36_0
|
||||
- entrypoints=0.2.3=py36_2
|
||||
- expat=2.2.5=he025d50_0
|
||||
- fiona=1.7.10=py36h5bf8d1d_0
|
||||
- freetype=2.9.1=ha9979f8_1
|
||||
- freexl=1.0.5=hfa6e2cd_0
|
||||
- gdal=2.2.2=py36hcebd033_1
|
||||
- geos=3.6.2=h9ef7328_2
|
||||
- geotiff=1.4.2=hd5bfa41_0
|
||||
- hdf4=4.2.13=h712560f_2
|
||||
- hdf5=1.10.1=h98b8871_1
|
||||
- icc_rt=2017.0.4=h97af966_0
|
||||
- icu=58.2=ha66f8fd_1
|
||||
- idna=2.7=py36_0
|
||||
- intel-openmp=2019.1=144
|
||||
- ipykernel=5.1.0=py36h39e3cac_0
|
||||
- ipython=7.2.0=py36h39e3cac_0
|
||||
- ipython_genutils=0.2.0=py36h3c5d0ee_0
|
||||
- ipywidgets=7.4.2=py36_0
|
||||
- jedi=0.13.1=py36_0
|
||||
- jinja2=2.10=py36_0
|
||||
- jpeg=9b=hb83a4c4_2
|
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- jsonschema=2.6.0=py36h7636477_0
|
||||
- jupyter_client=5.2.3=py36_0
|
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- jupyter_core=4.4.0=py36_0
|
||||
- kealib=1.4.7=ha5b336b_5
|
||||
- kiwisolver=1.0.1=py36h6538335_0
|
||||
- krb5=1.16.1=h038dc86_6
|
||||
- libboost=1.67.0=hfd51bdf_4
|
||||
- libcurl=7.62.0=h2a8f88b_0
|
||||
- libgdal=2.2.2=h2727f2b_1
|
||||
- libiconv=1.15=h1df5818_7
|
||||
- libkml=1.3.0=he5f2a48_4
|
||||
- libnetcdf=4.4.1.1=h825a56a_8
|
||||
- libpng=1.6.35=h2a8f88b_0
|
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- libpq=10.5=h5fe2233_0
|
||||
- libsodium=1.0.16=h9d3ae62_0
|
||||
- libspatialite=4.3.0a=h383548d_18
|
||||
- libssh2=1.8.0=hd619d38_4
|
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- libtiff=4.0.9=h36446d0_2
|
||||
- libxml2=2.9.8=hadb2253_1
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- libxslt=1.1.32=hf6f1972_0
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||||
- lxml=4.2.5=py36hef2cd61_0
|
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- m2w64-gcc-libgfortran=5.3.0=6
|
||||
- m2w64-gcc-libs=5.3.0=7
|
||||
- m2w64-gcc-libs-core=5.3.0=7
|
||||
- m2w64-gmp=6.1.0=2
|
||||
- m2w64-libwinpthread-git=5.0.0.4634.697f757=2
|
||||
- markupsafe=1.1.0=py36he774522_0
|
||||
- matplotlib=3.0.1=py36hc8f65d3_0
|
||||
- mistune=0.8.4=py36he774522_0
|
||||
- mkl=2018.0.3=1
|
||||
- mkl_fft=1.0.6=py36hdbbee80_0
|
||||
- mkl_random=1.0.1=py36h77b88f5_1
|
||||
- msys2-conda-epoch=20160418=1
|
||||
- munch=2.3.2=py36_0
|
||||
- nbconvert=5.3.1=py36_0
|
||||
- nbformat=4.4.0=py36h3a5bc1b_0
|
||||
- notebook=5.7.2=py36_0
|
||||
- numpy=1.15.4=py36ha559c80_0
|
||||
- numpy-base=1.15.4=py36h8128ebf_0
|
||||
- openjpeg=2.3.0=h5ec785f_1
|
||||
- openssl=1.0.2p=hfa6e2cd_0
|
||||
- pandas=0.23.4=py36h830ac7b_0
|
||||
- pandoc=2.2.3.2=0
|
||||
- pandocfilters=1.4.2=py36_1
|
||||
- parso=0.3.1=py36_0
|
||||
- pickleshare=0.7.5=py36_0
|
||||
- pip=18.1=py36_0
|
||||
- plotly=3.4.2=py36h28b3542_0
|
||||
- proj4=4.9.3=hcf24537_7
|
||||
- prometheus_client=0.4.2=py36_0
|
||||
- prompt_toolkit=2.0.7=py36_0
|
||||
- psutil=5.4.8=py36he774522_0
|
||||
- py-boost=1.67.0=py36h8300f20_4
|
||||
- pycodestyle=2.4.0=py36_0
|
||||
- pycparser=2.19=py36_0
|
||||
- pygments=2.2.0=py36hb010967_0
|
||||
- pyopenssl=18.0.0=py36_0
|
||||
- pyparsing=2.3.0=py36_0
|
||||
- pyproj=1.9.5.1=py36_0
|
||||
- pyqt=5.9.2=py36h6538335_2
|
||||
- pysocks=1.6.8=py36_0
|
||||
- python=3.6.7=h33f27b4_1
|
||||
- python-dateutil=2.7.5=py36_0
|
||||
- pytz=2018.7=py36_0
|
||||
- pywinpty=0.5.4=py36_0
|
||||
- pyyaml=3.13=py36hfa6e2cd_0
|
||||
- pyzmq=17.1.2=py36hfa6e2cd_0
|
||||
- qt=5.9.6=vc14h1e9a669_2
|
||||
- requests=2.20.1=py36_0
|
||||
- retrying=1.3.3=py36_2
|
||||
- scikit-learn=0.20.1=py36hb854c30_0
|
||||
- scipy=1.1.0=py36h4f6bf74_1
|
||||
- send2trash=1.5.0=py36_0
|
||||
- setuptools=40.6.2=py36_0
|
||||
- shapely=1.6.4=py36hc90234e_0
|
||||
- sip=4.19.8=py36h6538335_0
|
||||
- six=1.11.0=py36_1
|
||||
- sqlite=3.25.3=he774522_0
|
||||
- terminado=0.8.1=py36_1
|
||||
- testpath=0.4.2=py36_0
|
||||
- tk=8.6.8=hfa6e2cd_0
|
||||
- toml=0.10.0=py36h28b3542_0
|
||||
- tornado=5.1.1=py36hfa6e2cd_0
|
||||
- traitlets=4.3.2=py36h096827d_0
|
||||
- urllib3=1.23=py36_0
|
||||
- vc=14.1=h0510ff6_4
|
||||
- vs2015_runtime=14.15.26706=h3a45250_0
|
||||
- wcwidth=0.1.7=py36h3d5aa90_0
|
||||
- webencodings=0.5.1=py36_1
|
||||
- wheel=0.32.3=py36_0
|
||||
- widgetsnbextension=3.4.2=py36_0
|
||||
- win_inet_pton=1.0.1=py36_1
|
||||
- wincertstore=0.2=py36h7fe50ca_0
|
||||
- winpty=0.4.3=4
|
||||
- xerces-c=3.2.2=ha925a31_0
|
||||
- xz=5.2.4=h2fa13f4_4
|
||||
- yaml=0.1.7=hc54c509_2
|
||||
- yapf=0.25.0=py36_0
|
||||
- zeromq=4.2.5=he025d50_1
|
||||
- zlib=1.2.11=h62dcd97_3
|
||||
- plotly-orca=1.1.1=1
|
||||
- pip:
|
||||
- blackcellmagic
|
||||
- mat4py
|
||||
- blackcellmagic==0.0.1
|
||||
- mat4py==0.4.1
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||||
prefix: C:\Users\z5189959\Desktop\nsw-2016-storm-impact\.venv
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File diff suppressed because it is too large
Load Diff
Binary file not shown.
@ -1,103 +0,0 @@
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"""
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After generating interim data files based on raw data, we may need to overwrite some rows with manual data.
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"""
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import pandas as pd
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import numpy as np
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import click
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||||
from utils import setup_logging
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logger = setup_logging()
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||||
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def overwrite_profile_features(df_interim, df_overwrite, df_profiles, overwrite=True):
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"""
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Overwrite the interim profile features file with an excel file.
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:param interim_file: Should be './data/interim/profile_features.csv'
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:param overwrite_file: Should be './data/raw/profile_features_chris_leaman/profile_features_chris_leaman.csv'
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:param overwrite: Whether or not to overwrite the original interim_file. If false, file will not be written
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:return:
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"""
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# Merge
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df_merged = df_interim.merge(df_overwrite, left_index=True, right_index=True, suffixes=["", "_overwrite"])
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# Remove x vals if overwrite file as remove
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df_merged.loc[df_merged.dune_crest_x_overwrite == "remove", "dune_crest_x"] = np.nan
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df_merged.loc[df_merged.dune_toe_x_overwrite == "remove", "dune_toe_x"] = np.nan
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# Put in new x vals. Note that a NaN value in the overwrite column, means keep the original value.
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idx = (df_merged.dune_crest_x_overwrite.notnull()) & (df_merged.dune_crest_x_overwrite != "remove")
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df_merged.loc[idx, "dune_crest_x"] = df_merged.loc[idx, "dune_crest_x_overwrite"]
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idx = (df_merged.dune_toe_x_overwrite.notnull()) & (df_merged.dune_toe_x_overwrite != "remove")
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df_merged.loc[idx, "dune_toe_x"] = df_merged.loc[idx, "dune_toe_x_overwrite"]
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# Recalculate z values from x coordinates
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for site_id in df_merged.index.get_level_values("site_id").unique():
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logger.info("Overwriting dune crest/toes with manual values: {}".format(site_id))
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# Get profiles
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df_profile = df_profiles.query('site_id=="{}"'.format(site_id))
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for param in ["prestorm", "poststorm"]:
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for loc in ["crest", "toe"]:
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# Get x value to find corresponding z value
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x_val = df_merged.loc[(site_id, param), "dune_{}_x".format(loc)]
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if np.isnan(x_val):
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df_merged.loc[(site_id, param), "dune_{}_z".format(loc)] = np.nan
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continue
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# Get the corresponding z value for our x value
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query = 'site_id=="{}" & profile_type=="{}" & x=="{}"'.format(site_id, param, x_val)
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# Try get the value from the other profile if we return nan or empty dataframe
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if df_profile.query(query).empty:
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if param == "prestorm":
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query = 'site_id=="{}" & profile_type=="{}" & x=="{}"'.format(site_id, "poststorm", x_val)
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elif param == "poststorm":
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query = 'site_id=="{}" & profile_type=="{}" & x=="{}"'.format(site_id, "prestorm", x_val)
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z_val = df_profile.query(query).iloc[0].z
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else:
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z_val = df_profile.query(query).iloc[0].z
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# Put results back into merged dataframe
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df_merged.loc[(site_id, param), "dune_{}_z".format(loc)] = z_val
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# Drop columns
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df_merged = df_merged.drop(columns=["dune_crest_x_overwrite", "dune_toe_x_overwrite", "comment"], errors="ignore")
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# Merge back into interim data frame. Use concat/duplicates since .update will not update nan values
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df_final = pd.concat([df_merged, df_interim])
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df_final = df_final[~df_final.index.duplicated(keep="first")]
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df_final = df_final.sort_index()
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# Write to file
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return df_final
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@click.command(short_help="overwrite profile_features with manual excel sheet")
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@click.option("--interim_file", required=True, help="path of profile_features.csv")
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@click.option("--overwrite_file", required=True, help="path of excel file with overwrite data")
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@click.option("--profile_file", required=True, help="path of profiles.csv")
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@click.option("--overwrite/--no-overwrite", default=True)
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||||
def apply_profile_features_overwrite(interim_file, overwrite_file, profile_file, overwrite):
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logger.info("Overwriting profile features with manual excel file")
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||||
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||||
# Load files
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df_interim = pd.read_csv(interim_file, index_col=[0, 1])
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df_overwrite = pd.read_excel(overwrite_file)
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df_profiles = pd.read_csv(profile_file, index_col=[0, 1, 2])
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if "site_id" in df_overwrite.columns and "profile_type" in df_overwrite.columns:
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df_overwrite = df_overwrite.set_index(["site_id", "profile_type"])
|
||||
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||||
# Replace interim values with overwrite values
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||||
df_interim = overwrite_profile_features(df_interim, df_overwrite, df_profiles, overwrite)
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||||
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# Write to csv
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||||
df_interim.to_csv(interim_file, float_format="%.3f")
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||||
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||||
logger.info("Done!")
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@ -1,333 +1,34 @@
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"""
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||||
Converts .csv files to .shape files
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||||
"""
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||||
import os
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||||
import numpy.ma as ma
|
||||
import click
|
||||
import fiona
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from fiona.crs import from_epsg
|
||||
from shapely.geometry import Point, mapping, LineString
|
||||
from collections import OrderedDict
|
||||
from data.parse_shp import convert_coord_systems
|
||||
from shapely.geometry import Point, mapping
|
||||
|
||||
from utils import setup_logging
|
||||
|
||||
logger = setup_logging()
|
||||
|
||||
|
||||
|
||||
def R_high_to_geojson(sites_csv, profiles_csv, impacts_csv, output_geojson):
|
||||
|
||||
sites_csv = './data/interim/sites.csv'
|
||||
profiles_csv = './data/interim/profiles.csv'
|
||||
impacts_csv = './data/interim/impacts_forecasted_mean_slope_sto06.csv'
|
||||
output_geojson = './data/interim/R_high_forecasted_mean_slope_sto06.geojson'
|
||||
|
||||
df_sites = pd.read_csv(sites_csv, index_col=[0])
|
||||
df_profiles = pd.read_csv(profiles_csv, index_col=[0,1,2])
|
||||
df_impacts = pd.read_csv(impacts_csv, index_col=[0])
|
||||
|
||||
# Create geojson file
|
||||
schema = {
|
||||
'geometry': 'Point',
|
||||
'properties': OrderedDict([
|
||||
('beach', 'str'),
|
||||
('site_id', 'str'),
|
||||
('elevation', 'float'),
|
||||
])
|
||||
}
|
||||
|
||||
with fiona.open(output_geojson, 'w', driver='GeoJSON', crs=from_epsg(4326), schema=schema) as output:
|
||||
for index, row in df_impacts.iterrows():
|
||||
|
||||
site_id = index
|
||||
beach = index[:-4]
|
||||
|
||||
# Find lat/lon of R_high position
|
||||
R_high_z = row['R_high']
|
||||
|
||||
|
||||
# Get poststorm profile (or should this be prestorm?)
|
||||
df_profile = df_profiles.query('site_id=="{}" & profile_type=="prestorm"'.format(index))
|
||||
int_x = crossings(df_profile.index.get_level_values('x').tolist(),
|
||||
df_profile.z.tolist(),
|
||||
R_high_z)
|
||||
|
||||
# Take most landward interesection. Continue to next site if there is no intersection
|
||||
try:
|
||||
int_x = max(int_x)
|
||||
except:
|
||||
continue
|
||||
|
||||
# Get lat/lon on intercept position
|
||||
site = df_sites.loc[site_id]
|
||||
center_profile_x = site["profile_x_lat_lon"]
|
||||
orientation = site["orientation"]
|
||||
center_lat_lon = Point(site['lon'], site['lat']) # Get lat/lon of center of profile
|
||||
center_xy = convert_coord_systems(center_lat_lon)
|
||||
center_x, center_y = center_xy.xy
|
||||
|
||||
# Calculate xy position of point and convert to lat/lon
|
||||
point_x = center_x + (center_profile_x - int_x) * np.cos(np.deg2rad(orientation))
|
||||
point_y = center_y + (center_profile_x - int_x) * np.sin(np.deg2rad(orientation))
|
||||
point_xy = Point(point_x, point_y)
|
||||
point_lat_lon = convert_coord_systems(point_xy,
|
||||
in_coord_system="EPSG:28356",
|
||||
out_coord_system="EPSG:4326")
|
||||
|
||||
prop = OrderedDict([
|
||||
('beach', beach),
|
||||
('site_id', site_id),
|
||||
('elevation', R_high_z),
|
||||
])
|
||||
output.write({"geometry": mapping(point_lat_lon), "properties": prop})
|
||||
|
||||
|
||||
|
||||
def crossings(profile_x, profile_z, constant_z):
|
||||
"""
|
||||
Finds the x coordinate of a z elevation for a beach profile. Much faster than using shapely to calculate
|
||||
intersections since we are only interested in intersections of a constant value. Will return multiple
|
||||
intersections if found. Used in calculating beach slope.
|
||||
Adapted from https://stackoverflow.com/a/34745789
|
||||
:param profile_x: List of x coordinates for the beach profile section
|
||||
:param profile_z: List of z coordinates for the beach profile section
|
||||
:param constant_z: Float of the elevation to find corresponding x coordinates
|
||||
:return: List of x coordinates which correspond to the constant_z
|
||||
"""
|
||||
|
||||
# Remove nans to suppress warning messages
|
||||
valid = ~ma.masked_invalid(profile_z).mask
|
||||
profile_z = np.array(profile_z)[valid]
|
||||
profile_x = np.array(profile_x)[valid]
|
||||
|
||||
# Normalize the 'signal' to zero.
|
||||
# Use np.subtract rather than a list comprehension for performance reasons
|
||||
z = np.subtract(profile_z, constant_z)
|
||||
|
||||
# Find all indices right before any crossing.
|
||||
# TODO Sometimes this can give a runtime warning https://stackoverflow.com/a/36489085
|
||||
indicies = np.where(z[:-1] * z[1:] < 0)[0]
|
||||
|
||||
# Use linear interpolation to find intersample crossings.
|
||||
return [profile_x[i] - (profile_x[i] - profile_x[i + 1]) / (z[i] - z[i + 1]) * (z[i]) for i in indicies]
|
||||
|
||||
|
||||
|
||||
@click.command()
|
||||
@click.option("--sites-csv", required=True, help=".csv file to convert")
|
||||
@click.option("--profile-features-csv", required=True, help=".csv file to convert")
|
||||
@click.option("--output-geojson", required=True, help="where to store .geojson file")
|
||||
def profile_features_to_geojson(sites_csv, profile_features_csv, output_geojson):
|
||||
"""
|
||||
Converts profile_features containing dune toes and crest locations to a geojson we can load into QGIS
|
||||
:param sites_csv:
|
||||
:param profiles_csv:
|
||||
:param profile_features_csv:
|
||||
:param output_geojson:
|
||||
:return:
|
||||
"""
|
||||
logger.info("Creating profile features geojson")
|
||||
|
||||
# Read files from interim folder
|
||||
df_sites = pd.read_csv(sites_csv, index_col=[0])
|
||||
df_profile_features = pd.read_csv(profile_features_csv, index_col=[0])
|
||||
|
||||
# Create geojson file
|
||||
schema = {
|
||||
'geometry': 'Point',
|
||||
'properties': OrderedDict([
|
||||
('beach', 'str'),
|
||||
('site_id', 'str'),
|
||||
('point_type', 'str'), # prestorm_dune_toe, prestorm_dune_crest, poststorm_dune_toe, poststorm_dune_crest
|
||||
('profile_type', 'str'),
|
||||
('elevation', 'float'),
|
||||
])
|
||||
}
|
||||
|
||||
with fiona.open(output_geojson, 'w', driver='GeoJSON', crs=from_epsg(4326), schema=schema) as output:
|
||||
for index, row in df_profile_features.iterrows():
|
||||
beach = index[:-4]
|
||||
site_id = index
|
||||
profile_type = row['profile_type']
|
||||
|
||||
for point_type in ['crest', 'toe']:
|
||||
# point_type='crest'
|
||||
elevation = row['dune_{}_z'.format(point_type)]
|
||||
x = row['dune_{}_x'.format(point_type)]
|
||||
|
||||
if np.isnan(x):
|
||||
continue
|
||||
|
||||
# Geojsons need to use 'null' instead of 'nan'
|
||||
if np.isnan(elevation):
|
||||
elevation = None
|
||||
|
||||
# Convert x position to lat/lon
|
||||
site = df_sites.loc[site_id]
|
||||
center_profile_x = site["profile_x_lat_lon"]
|
||||
orientation = site["orientation"]
|
||||
center_lat_lon = Point(site['lon'], site['lat']) # Get lat/lon of center of profile
|
||||
center_xy = convert_coord_systems(center_lat_lon)
|
||||
center_x, center_y = center_xy.xy
|
||||
|
||||
# Calculate xy position of point and convert to lat/lon
|
||||
point_x = center_x + (center_profile_x - x) * np.cos(np.deg2rad(orientation))
|
||||
point_y = center_y + (center_profile_x - x) * np.sin(np.deg2rad(orientation))
|
||||
point_xy = Point(point_x, point_y)
|
||||
point_lat_lon = convert_coord_systems(point_xy,
|
||||
in_coord_system="EPSG:28356",
|
||||
out_coord_system="EPSG:4326")
|
||||
|
||||
prop = OrderedDict([
|
||||
('beach', beach),
|
||||
('site_id',site_id),
|
||||
('point_type', point_type),
|
||||
('profile_type', profile_type),
|
||||
('elevation', elevation),
|
||||
])
|
||||
output.write({"geometry": mapping(point_lat_lon), "properties": prop})
|
||||
|
||||
|
||||
|
||||
@click.command()
|
||||
@click.option("--input-csv", required=True, help=".csv file to convert")
|
||||
@click.option("--output-geojson", required=True, help="where to store .geojson file")
|
||||
def sites_csv_to_geojson(input_csv, output_geojson):
|
||||
@click.option("--output-shp", required=True, help="where to store .shp file")
|
||||
def sites_csv_to_shp(input_csv, output_shp):
|
||||
"""
|
||||
Converts our dataframe of sites to .geojson to load in QGis. Sites are loaded as linestrings of the profile
|
||||
cross-sections
|
||||
Converts our dataframe of sites to .shp to load in QGis
|
||||
:param input_csv:
|
||||
:param output_geojson:
|
||||
:param output_shp:
|
||||
:return:
|
||||
"""
|
||||
logger.info("Converting %s to %s", input_csv, output_geojson)
|
||||
logger.info("Converting %s to %s", input_csv, output_shp)
|
||||
df_sites = pd.read_csv(input_csv, index_col=[0])
|
||||
logger.info(os.environ.get("GDAL_DATA", None))
|
||||
|
||||
schema = {
|
||||
'geometry': 'LineString',
|
||||
'properties': OrderedDict([
|
||||
('beach','str'),
|
||||
('site_id', 'str'),
|
||||
])
|
||||
}
|
||||
|
||||
with fiona.open(output_geojson, 'w', driver='GeoJSON', crs=from_epsg(4326), schema=schema) as output:
|
||||
schema = {"geometry": "Point", "properties": {"beach": "str", "site_id": "str"}}
|
||||
with fiona.open(output_shp, "w", crs=from_epsg(4326), driver="ESRI Shapefile", schema=schema) as output:
|
||||
for index, row in df_sites.iterrows():
|
||||
# Work out where center of profile is
|
||||
orientation = row["orientation"]
|
||||
center_profile_x = row["profile_x_lat_lon"]
|
||||
center_lon = row["lon"]
|
||||
center_lat = row["lat"]
|
||||
center_lat_lon = Point(center_lon, center_lat)
|
||||
center_xy = convert_coord_systems(center_lat_lon)
|
||||
center_x, center_y = center_xy.xy
|
||||
|
||||
# Work out where landward profile limit is
|
||||
land_x = center_x + center_profile_x * np.cos(np.deg2rad(orientation))
|
||||
land_y = center_y + center_profile_x * np.sin(np.deg2rad(orientation))
|
||||
land_xy = Point(land_x, land_y)
|
||||
land_lat_lon = convert_coord_systems(land_xy, in_coord_system="EPSG:28356",
|
||||
out_coord_system="EPSG:4326")
|
||||
|
||||
# Work out where seaward profile limit is
|
||||
sea_x = center_x - center_profile_x * np.cos(np.deg2rad(orientation))
|
||||
sea_y = center_y - center_profile_x * np.sin(np.deg2rad(orientation))
|
||||
sea_xy = Point(sea_x, sea_y)
|
||||
sea_lat_lon = convert_coord_systems(sea_xy, in_coord_system="EPSG:28356", out_coord_system="EPSG:4326")
|
||||
|
||||
line_string = LineString([land_lat_lon, center_lat_lon, sea_lat_lon])
|
||||
prop = OrderedDict([("beach", row["beach"]),
|
||||
("site_id", index)])
|
||||
output.write({"geometry": mapping(line_string), "properties": prop})
|
||||
|
||||
logger.info("Done!")
|
||||
|
||||
|
||||
@click.command()
|
||||
@click.option("--sites-csv", required=True, help="sites.csv file to convert")
|
||||
@click.option("--observed-impacts-csv", required=True, help="impacts-observed.csv file to convert")
|
||||
@click.option("--forecast-impacts-csv", required=True, help="impacts-forecast.csv file to convert")
|
||||
@click.option("--output-geojson", required=True, help="where to store .geojson file")
|
||||
def impacts_to_geojson(sites_csv, observed_impacts_csv, forecast_impacts_csv, output_geojson):
|
||||
"""
|
||||
Converts impacts observed and forecasted to a geojson for visualization in QGIS
|
||||
:param sites_csv:
|
||||
:param observed_impacts_csv:
|
||||
:param forecast_impacts_csv:
|
||||
:param output_geojson:
|
||||
:return:
|
||||
"""
|
||||
|
||||
# Get information from .csv and read into pandas dataframe
|
||||
df_sites = pd.read_csv(sites_csv, index_col=[0])
|
||||
df_observed = pd.read_csv(observed_impacts_csv, index_col=[0])
|
||||
df_forecast = pd.read_csv(forecast_impacts_csv, index_col=[0]).rename({'storm_regime': 'forecast_storm_regime'})
|
||||
|
||||
# Rename columns, so we can distinguish between forecast and observed
|
||||
df_observed = df_observed.rename(columns={'storm_regime': 'observed_storm_regime'})
|
||||
df_forecast = df_forecast.rename(columns={'storm_regime': 'forecast_storm_regime'})
|
||||
|
||||
# Concat into one big dataframe
|
||||
df = pd.concat([df_sites, df_observed, df_forecast], sort=True,axis=1)
|
||||
|
||||
# Make new column for accuracy of forecast. Use underpredict/correct/overpredict classes
|
||||
df.loc[df.observed_storm_regime == df.forecast_storm_regime, 'forecast_accuray'] = 'correct'
|
||||
|
||||
# Observed/Forecasted/Class for each combination
|
||||
classes = [('swash', 'collision', 'overpredict'),
|
||||
('swash', 'swash', 'correct'),
|
||||
('swash', 'overwash', 'overpredict'),
|
||||
('collision', 'swash', 'underpredict'),
|
||||
('collision', 'collision', 'correct'),
|
||||
('collision', 'overwash', 'overpredict'),
|
||||
('overwash', 'swash', 'underpredict'),
|
||||
('overwash', 'collision', 'underpredict'),
|
||||
('overwash', 'overwash', 'correct')]
|
||||
for c in classes:
|
||||
df.loc[(df.observed_storm_regime == c[0])&(df.forecast_storm_regime == c[1]), 'forecast_accuracy'] = c[2]
|
||||
|
||||
schema = {
|
||||
'geometry': 'Point',
|
||||
'properties': OrderedDict([
|
||||
('beach','str'),
|
||||
('site_id', 'str'),
|
||||
('forecast_storm_regime', 'str'),
|
||||
('observed_storm_regime', 'str',),
|
||||
('forecast_accuracy', 'str')
|
||||
])
|
||||
}
|
||||
|
||||
# TODO Impact marker location should be at the seaward end of the profile
|
||||
with fiona.open(output_geojson, 'w', driver='GeoJSON', crs=from_epsg(4326), schema=schema) as output:
|
||||
for index, row in df.iterrows():
|
||||
|
||||
# Locate the marker at the seaward end of the profile to avoid cluttering the coastline.
|
||||
# Work out where seaward profile limit is
|
||||
orientation = row["orientation"]
|
||||
center_profile_x = row["profile_x_lat_lon"]
|
||||
center_lon = row["lon"]
|
||||
center_lat = row["lat"]
|
||||
center_lat_lon = Point(center_lon, center_lat)
|
||||
center_xy = convert_coord_systems(center_lat_lon)
|
||||
center_x, center_y = center_xy.xy
|
||||
|
||||
sea_x = center_x - center_profile_x * np.cos(np.deg2rad(orientation))
|
||||
sea_y = center_y - center_profile_x * np.sin(np.deg2rad(orientation))
|
||||
sea_xy = Point(sea_x, sea_y)
|
||||
sea_lat_lon = convert_coord_systems(sea_xy, in_coord_system="EPSG:28356", out_coord_system="EPSG:4326")
|
||||
|
||||
prop = OrderedDict([
|
||||
('beach',row["beach"]),
|
||||
('site_id', index),
|
||||
('forecast_storm_regime', row["forecast_storm_regime"]),
|
||||
('observed_storm_regime', row["observed_storm_regime"],),
|
||||
('forecast_accuracy', row["forecast_accuracy"])
|
||||
])
|
||||
|
||||
output.write({"geometry": mapping(sea_lat_lon), "properties": prop})
|
||||
|
||||
|
||||
point = Point(row["x_200_lon"], row["x_200_lat"])
|
||||
prop = {"beach": row["beach"], "site_id": index}
|
||||
output.write({"geometry": mapping(point), "properties": prop})
|
||||
logger.info("Done!")
|
||||
|
||||
Loading…
Reference in New Issue