From c3642df547f31d13a460a2c9218b94e6e588ad97 Mon Sep 17 00:00:00 2001 From: Jannis Date: Wed, 17 Jun 2020 17:10:09 +0200 Subject: [PATCH 1/3] converted new function from notebook into package --- .vscode/settings.json | 2 +- conflict_model/__init__.py | 6 +- conflict_model/analysis.py | 64 ---- conflict_model/env_vars_nc.py | 83 ----- conflict_model/get_boolean_conflict.py | 49 +++ conflict_model/get_var_from_nc.py | 130 ++++++++ data/run_setting.cfg | 4 +- example/example_notebook.ipynb | 441 ++++++------------------- 8 files changed, 282 insertions(+), 497 deletions(-) delete mode 100644 conflict_model/analysis.py delete mode 100644 conflict_model/env_vars_nc.py create mode 100644 conflict_model/get_boolean_conflict.py create mode 100644 conflict_model/get_var_from_nc.py diff --git a/.vscode/settings.json b/.vscode/settings.json index ef0cd5e..6bd136b 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -1,4 +1,4 @@ { - "python.pythonPath": "C:\\Users\\hoch0001\\AppData\\Local\\Continuum\\anaconda3\\python.exe", + "python.pythonPath": "C:\\Users\\hoch0001\\AppData\\Local\\Continuum\\anaconda3\\envs\\conflict_model\\python.exe", "restructuredtext.confPath": "${workspaceFolder}\\docs" } \ No newline at end of file diff --git a/conflict_model/__init__.py b/conflict_model/__init__.py index 3770c2c..56496f9 100644 --- a/conflict_model/__init__.py +++ b/conflict_model/__init__.py @@ -2,9 +2,9 @@ from . import selection from . import utils -from . import analysis -from . import env_vars_nc +from . import get_boolean_conflict +from . import get_var_from_nc __author__ = """Jannis M. Hoch""" __email__ = 'j.m.hoch@uu.nl' -__version__ = '0.0.1-beta' +__version__ = '0.0.1' diff --git a/conflict_model/analysis.py b/conflict_model/analysis.py deleted file mode 100644 index 6817c39..0000000 --- a/conflict_model/analysis.py +++ /dev/null @@ -1,64 +0,0 @@ -import geopandas as gpd -import pandas as pd -import numpy as np -import matplotlib.pyplot as plt -import os - -def conflict_in_year_bool(conflict_gdf, extent_gdf, config, sim_year, out_dir, saving_plots=False, showing_plots=False): - """Determines whether conflict took place in a region in one year and, if so, assigns a value of 1 to this region. - - Arguments: - conflict_gdf {[type]} -- [description] - extent_gdf {[type]} -- [description] - config {[type]} -- [description] - sim_year {[type]} -- [description] - out_dir {[type]} -- [description] - - Keyword Arguments: - saving_plots (bool): whether or not to save the plot (default: False) - showing_plots (bool): whether or not to show the plot (default: False) - - Returns: - dataframe: dataframe containing column with boolean information about conflict for each year - """ - - print('determining whether a conflict took place or not') - - out_df = extent_gdf.copy() - - # each year initialize new column with default value 0 (=False) - out_df['boolean_conflict_' + str(sim_year)] = 0 - - # select the entries which occured in this year - temp_sel_year = conflict_gdf.loc[conflict_gdf.year == sim_year] - - # merge the dataframes with polygons and conflict information, creating a sub-set of polygons/regions - data_merged = gpd.sjoin(temp_sel_year, out_df) - - # determine the aggregated amount of fatalities in one region (e.g. water province) - fatalities_per_watProv = data_merged['best'].groupby(data_merged['watprovID']).sum().to_frame().rename(columns={"best": 'total_fatalities'}) - - # loop through all regions and check if exists in sub-set - # if so, this means that there was conflict and thus assign value 1 - for i in range(len(out_df)): - i_watProv = out_df.iloc[i]['watprovID'] - if i_watProv in fatalities_per_watProv.index.values: - fats = int(fatalities_per_watProv.loc[i_watProv]) - out_df.loc[i, 'boolean_conflict_' + str(sim_year)] = 1 - - print('...DONE' + os.linesep) - - # plotting - fig, ax = plt.subplots(1, 1, figsize=(20,10)) - ax.set_title('boolean_conflict_' + str(sim_year)) - out_df.plot(ax=ax, column='boolean_conflict_' + str(sim_year), legend=True, categorical=True) - plt.tight_layout() - - if saving_plots: - fn_out = os.path.join(out_dir, 'boolean_conflict_map_' + str(sim_year) + '.png') - plt.savefig(fn_out, dpi=300) - - if not showing_plots: - plt.close() - - return out_df \ No newline at end of file diff --git a/conflict_model/env_vars_nc.py b/conflict_model/env_vars_nc.py deleted file mode 100644 index fd11798..0000000 --- a/conflict_model/env_vars_nc.py +++ /dev/null @@ -1,83 +0,0 @@ -import xarray as xr -import rasterio as rio -import pandas as pd -import geopandas as gpd -import rasterstats as rstats -import numpy as np -import matplotlib.pyplot as plt -import os, sys - -def rasterstats_GDP_PPP(gdf, config, sim_year, out_dir, saving_plots=False, showing_plots=False): - - print('calculating GDP PPP mean per aggregation unit') - - nc_fo = os.path.join(config.get('general', 'input_dir'), - config.get('env_vars', 'GDP_PPP')) - - nc_ds = xr.open_dataset(nc_fo) - - nc_var = nc_ds['GDP_per_capita_PPP'] - - # years = pd.to_datetime(nc_ds.time.values).to_period(freq='Y').strftime('%Y').to_numpy(dtype=int) - # if sim_year not in years: - # raise ValueError('the simulation year {0} can not be found in file {1}'.format(sim_year, nc_fo)) - # sim_year_idx = int(np.where(years == sim_year)[0]) - - affine = rio.open(nc_fo).transform - - # gdf['zonal_stats_min_' + str(sim_year)] = np.nan - # gdf['zonal_stats_max_' + str(sim_year)] = np.nan - # gdf['GDP_PPP_mean_' + str(sim_year)] = np.nan - - nc_arr = nc_var.sel(time=sim_year) - nc_arr_vals = nc_arr.values - if nc_arr_vals.size == 0: - raise ValueError('the data was found for this year in the nc-file {}, check if all is correct'.format(nc_fo)) - - list_GDP_PPP = [] - - for i in range(len(gdf)): - prov = gdf.iloc[i] - zonal_stats = rstats.zonal_stats(prov.geometry, nc_arr_vals, affine=affine, stats="mean") - # gdf.loc[i, 'zonal_stats_min_' + str(sim_year)] = zonal_stats[0]['min'] - # gdf.loc[i, 'zonal_stats_max_' + str(sim_year)] = zonal_stats[0]['max'] - list_GDP_PPP.append(zonal_stats[0]['mean']) - - print('...DONE' + os.linesep) - - return list_GDP_PPP - -def rasterstats_totalEvap(gdf_in, config, sim_year, out_dir): - - print('calculating evaporation mean per aggregation unit') - - nc_fo = os.path.join(config.get('general', 'input_dir'), - config.get('env_vars', 'evaporation')) - - nc_ds = xr.open_dataset(nc_fo) - - nc_var = nc_ds['total_evaporation'] - - years = nc_ds['time'].values - years = years[years>=config.getint('settings', 'y_start')] - years = years[years<=config.getint('settings', 'y_end')] - - affine = rio.open(nc_fo).transform - - gdf = gdf_in.copy() - - gdf['evap_mean_' + str(sim_year)] = np.nan - - nc_arr = nc_var.sel(time=sim_year) - nc_arr_vals = nc_arr.values - if nc_arr_vals.size == 0: - raise ValueError('the data was found for this year in the nc-file {}, check if all is correct'.format(nc_fo)) - - for i in range(len(gdf)): - prov = gdf.iloc[i] - zonal_stats = rstats.zonal_stats(prov.geometry, nc_arr_vals, affine=affine, stats="mean") - gdf.loc[i, 'evap_mean_' + str(sim_year)] = zonal_stats[0]['mean'] - - print('...DONE' + os.linesep) - - return gdf \ No newline at end of file diff --git a/conflict_model/get_boolean_conflict.py b/conflict_model/get_boolean_conflict.py new file mode 100644 index 0000000..5de14c6 --- /dev/null +++ b/conflict_model/get_boolean_conflict.py @@ -0,0 +1,49 @@ +import geopandas as gpd +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +import os + +def conflict_in_year_bool(conflict_gdf, extent_gdf, config, sim_year): + """Creates a list for each timestep with boolean information whether a conflict took place in a polygon or not. + + Args: + conflict_gdf (geodataframe): geo-dataframe containing georeferenced information of conflict (tested with PRIO/UCDP data) + extent_gdf (geodataframe): geo-dataframe containing one or more polygons with geometry information for which values are extracted + config (config): parsed configuration settings of run + sim_year (int): year for which data is extracted + + Raises: + AssertionError: raised if the length of output list does not match length of input geo-dataframe + + Returns: + list: list containing 0/1 per polygon depending on conflict occurence + """ + + print('determining whether a conflict took place or not') + + # select the entries which occured in this year + temp_sel_year = conflict_gdf.loc[conflict_gdf.year == sim_year] + + # merge the dataframes with polygons and conflict information, creating a sub-set of polygons/regions + data_merged = gpd.sjoin(temp_sel_year, extent_gdf) + + # determine the aggregated amount of fatalities in one region (e.g. water province) + fatalities_per_watProv = data_merged['best'].groupby(data_merged['watprovID']).sum().to_frame().rename(columns={"best": 'total_fatalities'}) + + # loop through all regions and check if exists in sub-set + # if so, this means that there was conflict and thus assign value 1 + list_out = [] + for i in range(len(extent_gdf)): + i_watProv = extent_gdf.iloc[i]['watprovID'] + if i_watProv in fatalities_per_watProv.index.values: + list_out.append(1) + else: + list_out.append(0) + + if not len(extent_gdf) == len(list_out): + raise AssertionError('the dataframe with polygons has a lenght {0} while the lenght of the resulting list is {1}'.format(len(extent_gdf), len(list_out))) + + print('...DONE' + os.linesep) + + return list_out \ No newline at end of file diff --git a/conflict_model/get_var_from_nc.py b/conflict_model/get_var_from_nc.py new file mode 100644 index 0000000..e7fffb6 --- /dev/null +++ b/conflict_model/get_var_from_nc.py @@ -0,0 +1,130 @@ +import xarray as xr +import rasterio as rio +import pandas as pd +import geopandas as gpd +import rasterstats as rstats +import numpy as np +import matplotlib.pyplot as plt +import os, sys + +def nc_with_integer_timestamp(extent_gdf, config, var_name, sim_year, stat_func='mean'): + """This function extracts a statistical value from a netCDF-file (specified in the config-file) for each polygon specified in extent_gdf for a given year. + By default, the mean value of all cells within a polygon is computed. + The resulting list does not contain additional meta-information about the files or polygons and is mostly intended for data-driven approaches such as machine learning. + + NOTE: + The var_name must be identical to the key in the config-file. + + NOTE: + This function is specifically written for netCDF-files where the time variable contains integer (year-)values, e.g. 1995, 1996, ... + + NOTE: + Works only with nc-files with annual data. + + Args: + extent_gdf (geodataframe): geo-dataframe containing one or more polygons with geometry information for which values are extracted + config (config): parsed configuration settings of run + var_name (str): name of variable in nc-file, must also be the same under which path to nc-file is specified in cfg-file + sim_year (int): year for which data is extracted + stat_func (str, optional): Statistical function to be applied, choose from available options in rasterstats package. Defaults to 'mean'. + + Raises: + ValueError: raised if the extracted variable at a time step does not contain data + + Returns: + list: list containing statistical value per polygon, i.e. with same length as extent_gdf + """ + # get path to netCDF-file. + nc_fo = os.path.join(config.get('general', 'input_dir'), + config.get('env_vars', var_name)) + + print('calculating mean {0} per aggregation unit from file {1} for year {2}'.format(var_name, nc_fo, sim_year)) + + # open nc-file with xarray as dataset + nc_ds = xr.open_dataset(nc_fo) + # get xarray data-array for specified variable + nc_var = nc_ds[var_name] + + # open nc-file with rasterio to get affine information + affine = rio.open(nc_fo).transform + + # get values from data-array for specified year + nc_arr = nc_var.sel(time=sim_year) + nc_arr_vals = nc_arr.values + if nc_arr_vals.size == 0: + raise ValueError('the data was found for this year in the nc-file {}, check if all is correct'.format(nc_fo)) + + # initialize output list + list_out = [] + # loop through all polygons in geo-dataframe and compute statistics, then append to output file + for i in range(len(extent_gdf)): + prov = extent_gdf.iloc[i] + zonal_stats = rstats.zonal_stats(prov.geometry, nc_arr_vals, affine=affine, stats=stat_func) + list_out.append(zonal_stats[0][stat_func]) + + print('...DONE' + os.linesep) + + return list_out + +def nc_with_continous_regular_timestamp(extent_gdf, config, var_name, sim_year, stat_func='mean'): + """This function extracts a statistical value from a netCDF-file (specified in the config-file) for each polygon specified in extent_gdf for a given year. + By default, the mean value of all cells within a polygon is computed. + The resulting list does not contain additional meta-information about the files or polygons and is mostly intended for data-driven approaches such as machine learning. + + NOTE: + The var_name must be identical to the key in the config-file. + + NOTE: + Works only with nc-files with annual data. + + Args: + extent_gdf (geodataframe): geo-dataframe containing one or more polygons with geometry information for which values are extracted + config (config): parsed configuration settings of run + var_name (str): name of variable in nc-file, must also be the same under which path to nc-file is specified in cfg-file + sim_year (int): year for which data is extracted + stat_func (str, optional): Statistical function to be applied, choose from available options in rasterstats package. Defaults to 'mean'. + + Raises: + ValueError: raised if specfied year cannot be found in years in nc-file + ValueError: raised if the extracted variable at a time step does not contain data + + Returns: + list: list containing statistical value per polygon, i.e. with same length as extent_gdf + """ + # get path to netCDF-file. + nc_fo = os.path.join(config.get('general', 'input_dir'), + config.get('env_vars', var_name)) + + print('calculating mean {0} per aggregation unit from file {1} for year {2}'.format(var_name, nc_fo, sim_year)) + + # open nc-file with xarray as dataset + nc_ds = xr.open_dataset(nc_fo) + # get xarray data-array for specified variable + nc_var = nc_ds[var_name] + # get years contained in nc-file as integer array to be compatible with sim_year + years = pd.to_datetime(nc_ds.time.values).to_period(freq='Y').strftime('%Y').to_numpy(dtype=int) + if sim_year not in years: + raise ValueError('the simulation year {0} can not be found in file {1}'.format(sim_year, nc_fo)) + + # get index which corresponds with sim_year in years in nc-file + sim_year_idx = int(np.where(years == sim_year)[0]) + # get values from data-array for specified year based on index + nc_arr = nc_var.sel(time=nc_ds.time.values[sim_year_idx]) + nc_arr_vals = nc_arr.values + if nc_arr_vals.size == 0: + raise ValueError('no data was found for this year in the nc-file {}, check if all is correct'.format(nc_fo)) + + # open nc-file with rasterio to get affine information + affine = rio.open(nc_fo).transform + + # initialize output list + list_out = [] + # loop through all polygons in geo-dataframe and compute statistics, then append to output file + for i in range(len(extent_gdf)): + prov = extent_gdf.iloc[i] + zonal_stats = rstats.zonal_stats(prov.geometry, nc_arr_vals, affine=affine, stats=stat_func) + list_out.append(zonal_stats[0][stat_func]) + + print('...DONE' + os.linesep) + + return list_out \ No newline at end of file diff --git a/data/run_setting.cfg b/data/run_setting.cfg index 574c0a6..7208568 100644 --- a/data/run_setting.cfg +++ b/data/run_setting.cfg @@ -21,5 +21,5 @@ zones=BWh,BSh code2class=KoeppenGeiger/classification_codes.txt [env_vars] -GDP_PPP=GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc -evaporation=PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc \ No newline at end of file +GDP_per_capita_PPP=GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc +total_evaporation=PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc \ No newline at end of file diff --git a/example/example_notebook.ipynb b/example/example_notebook.ipynb index 30e9ff6..c45e3eb 100644 --- a/example/example_notebook.ipynb +++ b/example/example_notebook.ipynb @@ -49,7 +49,7 @@ "output_type": "stream", "text": [ "Python version: 3.7.7 (default, Apr 15 2020, 05:09:04) [MSC v.1916 64 bit (AMD64)]\n", - "conflict_model version: 0.0.1-beta\n", + "conflict_model version: 0.0.1\n", "geopandas version: 0.7.0\n", "xarray version: 0.15.1\n", "rasterio version: 1.1.0\n", @@ -209,128 +209,6 @@ "# Functions" ] }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "def conflict_in_year_bool(conflict_gdf, extent_gdf, config, sim_year): \n", - " \n", - " print('determining whether a conflict took place or not')\n", - "\n", - " # each year initialize new column with default value 0 (=False)\n", - " list_boolConflict = []\n", - " \n", - " # select the entries which occured in this year\n", - " temp_sel_year = conflict_gdf.loc[conflict_gdf.year == sim_year] \n", - " \n", - " # merge the dataframes with polygons and conflict information, creating a sub-set of polygons/regions\n", - " data_merged = gpd.sjoin(temp_sel_year, extent_gdf)\n", - " \n", - " # determine the aggregated amount of fatalities in one region (e.g. water province)\n", - " fatalities_per_watProv = data_merged['best'].groupby(data_merged['watprovID']).sum().to_frame().rename(columns={\"best\": 'total_fatalities'})\n", - " \n", - " # loop through all regions and check if exists in sub-set\n", - " # if so, this means that there was conflict and thus assign value 1\n", - " for i in range(len(extent_gdf)):\n", - " i_watProv = extent_gdf.iloc[i]['watprovID']\n", - " if i_watProv in fatalities_per_watProv.index.values:\n", - " list_boolConflict.append(1)\n", - " else:\n", - " list_boolConflict.append(0)\n", - " \n", - " if not len(extent_gdf) == len(list_boolConflict):\n", - " raise AssertionError('the dataframe with polygons has a lenght {0} while the lenght of the resulting list is {1}'.format(len(extent_gdf), len(list_boolConflict)))\n", - " \n", - " print('...DONE' + os.linesep)\n", - "\n", - " return list_boolConflict" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "def rasterstats_GDP_PPP(gdf, config, sim_year):\n", - "\n", - " print('calculating GDP PPP mean per aggregation unit')\n", - " \n", - " nc_fo = os.path.join(config.get('general', 'input_dir'), \n", - " config.get('env_vars', 'GDP_PPP'))\n", - "\n", - " nc_ds = xr.open_dataset(nc_fo)\n", - "\n", - " nc_var = nc_ds['GDP_per_capita_PPP']\n", - "\n", - " affine = rio.open(nc_fo).transform\n", - "\n", - " nc_arr = nc_var.sel(time=sim_year)\n", - " nc_arr_vals = nc_arr.values\n", - " if nc_arr_vals.size == 0:\n", - " raise ValueError('the data was found for this year in the nc-file {}, check if all is correct'.format(nc_fo))\n", - "\n", - " list_GDP_PPP = []\n", - " \n", - " for i in range(len(gdf)):\n", - " prov = gdf.iloc[i]\n", - " zonal_stats = rstats.zonal_stats(prov.geometry, nc_arr_vals, affine=affine, stats=\"mean\")\n", - " list_GDP_PPP.append(zonal_stats[0]['mean'])\n", - "\n", - " print('...DONE' + os.linesep)\n", - "\n", - " return list_GDP_PPP" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "def rasterstats_totalEvap(gdf, config, sim_year):\n", - " \n", - " nc_fo = os.path.join(config.get('general', 'input_dir'), \n", - " config.get('env_vars', 'evaporation'))\n", - " \n", - " print('calculating evaporation mean per aggregation unit from {}'.format(nc_fo))\n", - "\n", - " nc_ds = xr.open_dataset(nc_fo)\n", - "\n", - " nc_var = nc_ds.total_evaporation\n", - "\n", - " years = pd.to_datetime(nc_ds.time.values).to_period(freq='Y').strftime('%Y').to_numpy(dtype=int)\n", - "\n", - " if sim_year not in years:\n", - " raise ValueError('the simulation year {0} can not be found in file {1}'.format(sim_year, nc_fo))\n", - "\n", - " affine = rio.open(nc_fo).transform\n", - "\n", - " gdf['evap_mean_' + str(sim_year)] = np.nan\n", - " \n", - " sim_year_idx = int(np.where(years == sim_year)[0])\n", - "\n", - " nc_arr = nc_var.sel(time=nc_ds.time.values[sim_year_idx])\n", - " \n", - " nc_arr_vals = nc_arr.values\n", - " \n", - " if nc_arr_vals.size == 0:\n", - " raise ValueError('no data was found for this year in the nc-file {}, check if all is correct'.format(nc_fo))\n", - "\n", - " list_Evap = []\n", - " \n", - " for i in range(len(gdf)):\n", - " prov = gdf.iloc[i]\n", - " zonal_stats = rstats.zonal_stats(prov.geometry, nc_arr_vals, affine=affine, stats=\"mean\")\n", - " list_Evap.append(zonal_stats[0][\"mean\"])\n", - "\n", - " print('...DONE' + os.linesep)\n", - "\n", - " return list_Evap" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -342,7 +220,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -356,19 +234,7 @@ "determining whether a conflict took place or not\n", "...DONE\r\n", "\n", - "0 0\n", - "1 0\n", - "2 0\n", - "3 0\n", - "4 0\n", - " ..\n", - "381 0\n", - "382 0\n", - "383 0\n", - "384 0\n", - "385 0\n", - "Length: 386, dtype: int32\n", - "calculating GDP PPP mean per aggregation unit\n" + "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc\n" ] }, { @@ -385,7 +251,7 @@ "text": [ "...DONE\r\n", "\n", - "calculating evaporation mean per aggregation unit from C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n" + "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n" ] }, { @@ -407,19 +273,7 @@ "determining whether a conflict took place or not\n", "...DONE\r\n", "\n", - "0 0\n", - "1 0\n", - "2 0\n", - "3 0\n", - "4 0\n", - " ..\n", - "381 0\n", - "382 0\n", - "383 0\n", - "384 0\n", - "385 0\n", - "Length: 386, dtype: int32\n", - "calculating GDP PPP mean per aggregation unit\n" + "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc\n" ] }, { @@ -436,7 +290,7 @@ "text": [ "...DONE\r\n", "\n", - "calculating evaporation mean per aggregation unit from C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n" + "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n" ] }, { @@ -458,19 +312,7 @@ "determining whether a conflict took place or not\n", "...DONE\r\n", "\n", - "0 0\n", - "1 0\n", - "2 0\n", - "3 0\n", - "4 0\n", - " ..\n", - "381 0\n", - "382 0\n", - "383 0\n", - "384 0\n", - "385 0\n", - "Length: 386, dtype: int32\n", - "calculating GDP PPP mean per aggregation unit\n" + "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc\n" ] }, { @@ -487,7 +329,7 @@ "text": [ "...DONE\r\n", "\n", - "calculating evaporation mean per aggregation unit from C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n" + "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n" ] }, { @@ -509,19 +351,7 @@ "determining whether a conflict took place or not\n", "...DONE\r\n", "\n", - "0 0\n", - "1 0\n", - "2 0\n", - "3 0\n", - "4 0\n", - " ..\n", - "381 0\n", - "382 0\n", - "383 0\n", - "384 0\n", - "385 0\n", - "Length: 386, dtype: int32\n", - "calculating GDP PPP mean per aggregation unit\n" + "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc\n" ] }, { @@ -538,7 +368,7 @@ "text": [ "...DONE\r\n", "\n", - "calculating evaporation mean per aggregation unit from C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n" + "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n" ] }, { @@ -560,19 +390,7 @@ "determining whether a conflict took place or not\n", "...DONE\r\n", "\n", - "0 0\n", - "1 0\n", - "2 0\n", - "3 0\n", - "4 0\n", - " ..\n", - "381 0\n", - "382 0\n", - "383 0\n", - "384 0\n", - "385 0\n", - "Length: 386, dtype: int32\n", - "calculating GDP PPP mean per aggregation unit\n" + "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc\n" ] }, { @@ -589,7 +407,7 @@ "text": [ "...DONE\r\n", "\n", - "calculating evaporation mean per aggregation unit from C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n" + "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n" ] }, { @@ -611,19 +429,7 @@ "determining whether a conflict took place or not\n", "...DONE\r\n", "\n", - "0 0\n", - "1 0\n", - "2 0\n", - "3 0\n", - "4 0\n", - " ..\n", - "381 0\n", - "382 0\n", - "383 0\n", - "384 0\n", - "385 0\n", - "Length: 386, dtype: int32\n", - "calculating GDP PPP mean per aggregation unit\n" + "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc\n" ] }, { @@ -640,7 +446,7 @@ "text": [ "...DONE\r\n", "\n", - "calculating evaporation mean per aggregation unit from C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n" + "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n" ] }, { @@ -662,19 +468,7 @@ "determining whether a conflict took place or not\n", "...DONE\r\n", "\n", - "0 0\n", - "1 0\n", - "2 0\n", - "3 0\n", - "4 0\n", - " ..\n", - "381 0\n", - "382 0\n", - "383 0\n", - "384 0\n", - "385 0\n", - "Length: 386, dtype: int32\n", - "calculating GDP PPP mean per aggregation unit\n" + "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc\n" ] }, { @@ -691,7 +485,7 @@ "text": [ "...DONE\r\n", "\n", - "calculating evaporation mean per aggregation unit from C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n" + "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n" ] }, { @@ -713,19 +507,7 @@ "determining whether a conflict took place or not\n", "...DONE\r\n", "\n", - "0 0\n", - "1 0\n", - "2 0\n", - "3 0\n", - "4 0\n", - " ..\n", - "381 0\n", - "382 0\n", - "383 0\n", - "384 0\n", - "385 0\n", - "Length: 386, dtype: int32\n", - "calculating GDP PPP mean per aggregation unit\n" + "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc\n" ] }, { @@ -742,7 +524,7 @@ "text": [ "...DONE\r\n", "\n", - "calculating evaporation mean per aggregation unit from C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n" + "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n" ] }, { @@ -764,19 +546,7 @@ "determining whether a conflict took place or not\n", "...DONE\r\n", "\n", - "0 0\n", - "1 0\n", - "2 0\n", - "3 0\n", - "4 0\n", - " ..\n", - "381 0\n", - "382 0\n", - "383 0\n", - "384 0\n", - "385 0\n", - "Length: 386, dtype: int32\n", - "calculating GDP PPP mean per aggregation unit\n" + "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc\n" ] }, { @@ -793,7 +563,7 @@ "text": [ "...DONE\r\n", "\n", - "calculating evaporation mean per aggregation unit from C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n" + "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n" ] }, { @@ -815,19 +585,7 @@ "determining whether a conflict took place or not\n", "...DONE\r\n", "\n", - "0 0\n", - "1 0\n", - "2 0\n", - "3 0\n", - "4 0\n", - " ..\n", - "381 0\n", - "382 0\n", - "383 0\n", - "384 0\n", - "385 0\n", - "Length: 386, dtype: int32\n", - "calculating GDP PPP mean per aggregation unit\n" + "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc\n" ] }, { @@ -844,7 +602,7 @@ "text": [ "...DONE\r\n", "\n", - "calculating evaporation mean per aggregation unit from C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n" + "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n" ] }, { @@ -866,19 +624,7 @@ "determining whether a conflict took place or not\n", "...DONE\r\n", "\n", - "0 0\n", - "1 0\n", - "2 0\n", - "3 0\n", - "4 0\n", - " ..\n", - "381 0\n", - "382 0\n", - "383 0\n", - "384 0\n", - "385 0\n", - "Length: 386, dtype: int32\n", - "calculating GDP PPP mean per aggregation unit\n" + "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc\n" ] }, { @@ -895,7 +641,7 @@ "text": [ "...DONE\r\n", "\n", - "calculating evaporation mean per aggregation unit from C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n" + "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n" ] }, { @@ -929,16 +675,16 @@ " \n", " print('entering year {}'.format(sim_year) + os.linesep)\n", " \n", - " list_boolConflict = conflict_in_year_bool(conflict_gdf, extent_gdf, config, sim_year)\n", + " list_boolConflict = conflict_model.get_boolean_conflict.conflict_in_year_bool(conflict_gdf, extent_gdf, config, sim_year)\n", " Y = Y.append(pd.Series(list_boolConflict, dtype=int), ignore_index=True)\n", " \n", - " list_GDP_PPP = rasterstats_GDP_PPP(extent_gdf, config, sim_year)\n", + " list_GDP_PPP = conflict_model.get_var_from_nc.nc_with_integer_timestamp(extent_gdf, config, 'GDP_per_capita_PPP', sim_year)\n", " X1 = X1.append(pd.Series(list_GDP_PPP), ignore_index=True)\n", " \n", " if not len(list_GDP_PPP) == len(list_boolConflict):\n", " raise AssertionError('length of lists do not match, they are {0} and {1}'.format(len(list_GDP_PPP), len(list_boolConflict)))\n", " \n", - " list_Evap = rasterstats_totalEvap(extent_gdf, config, sim_year)\n", + " list_Evap = conflict_model.get_var_from_nc.nc_with_continous_regular_timestamp(extent_gdf, config, 'total_evaporation', sim_year)\n", " X2 = X2.append(pd.Series(list_Evap), ignore_index=True)\n", " \n", " if not len(list_Evap) == len(list_boolConflict):\n", @@ -960,12 +706,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "First, create the X (variables) from all columns sampled and Y (target) and concatenate/copy." + "First, create a pandas dataframe from all variables and targets and kick out rows with missing values (they do not work with ML)" ] }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -987,7 +733,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -1021,31 +767,31 @@ " 0\n", " 2361.934264\n", " 0.042316\n", - " [0]\n", + " 0\n", " \n", " \n", " 1\n", " 3104.051687\n", " 0.040520\n", - " [0]\n", + " 0\n", " \n", " \n", " 2\n", " 1192.025215\n", " 0.039277\n", - " [0]\n", + " 0\n", " \n", " \n", " 3\n", " 1275.859490\n", " 0.025305\n", - " [0]\n", + " 0\n", " \n", " \n", " 4\n", " 1182.202026\n", " 0.036308\n", - " [0]\n", + " 0\n", " \n", " \n", " ...\n", @@ -1057,31 +803,31 @@ " 4241\n", " 3277.156738\n", " 0.060997\n", - " [0]\n", + " 0\n", " \n", " \n", " 4242\n", " 3277.156738\n", " 0.068696\n", - " [0]\n", + " 0\n", " \n", " \n", " 4243\n", " 1381.966901\n", " 0.048329\n", - " [0]\n", + " 0\n", " \n", " \n", " 4244\n", " 1390.211488\n", " 0.052157\n", - " [0]\n", + " 0\n", " \n", " \n", " 4245\n", " 1391.689834\n", " 0.052872\n", - " [0]\n", + " 0\n", " \n", " \n", "\n", @@ -1089,23 +835,23 @@ "" ], "text/plain": [ - " GDP_PPP ET conflict\n", - "0 2361.934264 0.042316 [0]\n", - "1 3104.051687 0.040520 [0]\n", - "2 1192.025215 0.039277 [0]\n", - "3 1275.859490 0.025305 [0]\n", - "4 1182.202026 0.036308 [0]\n", - "... ... ... ...\n", - "4241 3277.156738 0.060997 [0]\n", - "4242 3277.156738 0.068696 [0]\n", - "4243 1381.966901 0.048329 [0]\n", - "4244 1390.211488 0.052157 [0]\n", - "4245 1391.689834 0.052872 [0]\n", + " GDP_PPP ET conflict\n", + "0 2361.934264 0.042316 0\n", + "1 3104.051687 0.040520 0\n", + "2 1192.025215 0.039277 0\n", + "3 1275.859490 0.025305 0\n", + "4 1182.202026 0.036308 0\n", + "... ... ... ...\n", + "4241 3277.156738 0.060997 0\n", + "4242 3277.156738 0.068696 0\n", + "4243 1381.966901 0.048329 0\n", + "4244 1390.211488 0.052157 0\n", + "4245 1391.689834 0.052872 0\n", "\n", "[4224 rows x 3 columns]" ] }, - "execution_count": 83, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1123,7 +869,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1138,7 +884,7 @@ " [1.39168983e+03, 5.28718745e-02]])" ] }, - "execution_count": 88, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1150,7 +896,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1159,7 +905,7 @@ "array([0, 0, 0, ..., 0, 0, 0])" ] }, - "execution_count": 86, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1169,13 +915,6 @@ "Y" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The scatterplot of the (two) variables in X looks like this. Also the sample size n is provided." - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1185,23 +924,30 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = model_selection.train_test_split(preprocessing.scale(X),\n", " Y,\n", - " test_size=0.5)" + " test_size=0.7)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The scatterplot of the (two) variables in X looks like this. Also the sample size n is provided." ] }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1225,7 +971,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -1234,7 +980,7 @@ "(array([-4.71003707e-17, 6.39219317e-17]), array([1., 1.]))" ] }, - "execution_count": 96, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1261,11 +1007,11 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ - "clf = svm.SVC(class_weight='balanced')" + "clf = svm.SVC(class_weight='balanced', C=0.9)" ] }, { @@ -1277,19 +1023,19 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "SVC(C=1.0, break_ties=False, cache_size=200, class_weight='balanced', coef0=0.0,\n", + "SVC(C=0.9, break_ties=False, cache_size=200, class_weight='balanced', coef0=0.0,\n", " decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n", " max_iter=-1, probability=False, random_state=None, shrinking=True,\n", " tol=0.001, verbose=False)" ] }, - "execution_count": 98, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -1307,16 +1053,16 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0, 0, 0, ..., 0, 0, 1])" + "array([0, 0, 0, ..., 0, 1, 0])" ] }, - "execution_count": 99, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -1326,19 +1072,26 @@ "y_pred" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "No clue right now what this does... look it up!" + ] + }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([-2.43595112, -2.90152701, -0.41222322, ..., -0.47675971,\n", - " -0.26396479, 0.03287877])" + "array([-0.60452387, -1.03416277, -0.95833466, ..., -0.6758252 ,\n", + " 1.07185603, -1.04955169])" ] }, - "execution_count": 100, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -1368,16 +1121,16 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Accuracy: 0.6875\n", - "Precision: 0.09971509971509972\n", - "Recall: 0.7142857142857143\n" + "Accuracy: 0.6604666892120392\n", + "Precision: 0.09620721554116558\n", + "Recall: 0.7938931297709924\n" ] } ], @@ -1400,14 +1153,14 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Average precision-recall score: 0.11\n" + "Average precision-recall score: 0.12\n" ] } ], @@ -1419,12 +1172,12 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] From 9ca0326ea4035b7578a26bea437cf93e83b2219b Mon Sep 17 00:00:00 2001 From: Jannis Date: Wed, 17 Jun 2020 18:02:13 +0200 Subject: [PATCH 2/3] checked notebook --- example/example_notebook.html | 278 ++++++++++------------------------ 1 file changed, 76 insertions(+), 202 deletions(-) diff --git a/example/example_notebook.html b/example/example_notebook.html index d81d928..552a3fe 100644 --- a/example/example_notebook.html +++ b/example/example_notebook.html @@ -13139,7 +13139,7 @@

Import libraries and file with 
Python version: 3.7.7 (default, Apr 15 2020, 05:09:04) [MSC v.1916 64 bit (AMD64)]
-conflict_model version: 0.0.1-beta
+conflict_model version: 0.0.1
 geopandas version: 0.7.0
 xarray version: 0.15.1
 rasterio version: 1.1.0
@@ -13364,140 +13364,6 @@ 
Applying functionsFunctions
- -
-
-
In [9]:
-
-
-
def conflict_in_year_bool(conflict_gdf, extent_gdf, config, sim_year): 
-    
-    print('determining whether a conflict took place or not')
-
-    # each year initialize new column with default value 0 (=False)
-    list_boolConflict = []
-    
-    # select the entries which occured in this year
-    temp_sel_year = conflict_gdf.loc[conflict_gdf.year == sim_year]   
-    
-    # merge the dataframes with polygons and conflict information, creating a sub-set of polygons/regions
-    data_merged = gpd.sjoin(temp_sel_year, extent_gdf)
-    
-    # determine the aggregated amount of fatalities in one region (e.g. water province)
-    fatalities_per_watProv = data_merged['best'].groupby(data_merged['watprovID']).sum().to_frame().rename(columns={"best": 'total_fatalities'})
- 
-    # loop through all regions and check if exists in sub-set
-    # if so, this means that there was conflict and thus assign value 1
-    for i in range(len(extent_gdf)):
-        i_watProv = extent_gdf.iloc[i]['watprovID']
-        if i_watProv in fatalities_per_watProv.index.values:
-            list_boolConflict.append(1)
-        else:
-            list_boolConflict.append(0)
-            
-    if not len(extent_gdf) == len(list_boolConflict):
-        raise AssertionError('the dataframe with polygons has a lenght {0} while the lenght of the resulting list is {1}'.format(len(extent_gdf), len(list_boolConflict)))
-    
-    print('...DONE' + os.linesep)
-
-    return list_boolConflict
-
- -
-
-
- -
-
-
-
In [10]:
-
-
-
def rasterstats_GDP_PPP(gdf, config, sim_year):
-
-    print('calculating GDP PPP mean per aggregation unit')
-    
-    nc_fo = os.path.join(config.get('general', 'input_dir'), 
-                         config.get('env_vars', 'GDP_PPP'))
-
-    nc_ds = xr.open_dataset(nc_fo)
-
-    nc_var = nc_ds['GDP_per_capita_PPP']
-
-    affine = rio.open(nc_fo).transform
-
-    nc_arr = nc_var.sel(time=sim_year)
-    nc_arr_vals = nc_arr.values
-    if nc_arr_vals.size == 0:
-        raise ValueError('the data was found for this year in the nc-file {}, check if all is correct'.format(nc_fo))
-
-    list_GDP_PPP = []
-    
-    for i in range(len(gdf)):
-        prov = gdf.iloc[i]
-        zonal_stats = rstats.zonal_stats(prov.geometry, nc_arr_vals, affine=affine, stats="mean")
-        list_GDP_PPP.append(zonal_stats[0]['mean'])
-
-    print('...DONE' + os.linesep)
-
-    return list_GDP_PPP
-
- -
-
-
- -
-
-
-
In [11]:
-
-
-
def rasterstats_totalEvap(gdf, config, sim_year):
-    
-    nc_fo = os.path.join(config.get('general', 'input_dir'), 
-                         config.get('env_vars', 'evaporation'))
-    
-    print('calculating evaporation mean per aggregation unit from {}'.format(nc_fo))
-
-    nc_ds = xr.open_dataset(nc_fo)
-
-    nc_var = nc_ds.total_evaporation
-
-    years = pd.to_datetime(nc_ds.time.values).to_period(freq='Y').strftime('%Y').to_numpy(dtype=int)
-
-    if sim_year not in years:
-        raise ValueError('the simulation year {0} can not be found in file {1}'.format(sim_year, nc_fo))
-
-    affine = rio.open(nc_fo).transform
-
-    gdf['evap_mean_' + str(sim_year)] = np.nan
-    
-    sim_year_idx = int(np.where(years == sim_year)[0])
-
-    nc_arr = nc_var.sel(time=nc_ds.time.values[sim_year_idx])
-    
-    nc_arr_vals = nc_arr.values
-    
-    if nc_arr_vals.size == 0:
-        raise ValueError('no data was found for this year in the nc-file {}, check if all is correct'.format(nc_fo))
-
-    list_Evap = []
-    
-    for i in range(len(gdf)):
-        prov = gdf.iloc[i]
-        zonal_stats = rstats.zonal_stats(prov.geometry, nc_arr_vals, affine=affine, stats="mean")
-        list_Evap.append(zonal_stats[0]["mean"])
-
-    print('...DONE' + os.linesep)
-
-    return list_Evap
-
- -
-
-
-
@@ -13509,7 +13375,7 @@

Analysis per year
-
In [12]:
+
In [9]:
print('simulation period from', str(config.getint('settings', 'y_start')), 'to', str(config.getint('settings', 'y_end')))
@@ -13524,16 +13390,16 @@ 
Analysis per yearprint('entering year {}'.format(sim_year) + os.linesep)
     
-    list_boolConflict = conflict_in_year_bool(conflict_gdf, extent_gdf, config, sim_year)
+    list_boolConflict = conflict_model.get_boolean_conflict.conflict_in_year_bool(conflict_gdf, extent_gdf, config, sim_year)
     Y = Y.append(pd.Series(list_boolConflict, dtype=int), ignore_index=True)
     
-    list_GDP_PPP = rasterstats_GDP_PPP(extent_gdf, config, sim_year)
+    list_GDP_PPP = conflict_model.get_var_from_nc.nc_with_integer_timestamp(extent_gdf, config, 'GDP_per_capita_PPP', sim_year)
     X1 = X1.append(pd.Series(list_GDP_PPP), ignore_index=True)
     
     if not len(list_GDP_PPP) == len(list_boolConflict):
         raise AssertionError('length of lists do not match, they are {0} and {1}'.format(len(list_GDP_PPP), len(list_boolConflict)))
     
-    list_Evap = rasterstats_totalEvap(extent_gdf, config, sim_year)
+    list_Evap = conflict_model.get_var_from_nc.nc_with_continous_regular_timestamp(extent_gdf, config, 'total_evaporation', sim_year)
     X2 = X2.append(pd.Series(list_Evap), ignore_index=True)
     
     if not len(list_Evap) == len(list_boolConflict):
@@ -13563,7 +13429,7 @@ 
Analysis per yearAnalysis per year
 
...DONE

 
-calculating evaporation mean per aggregation unit from C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc
+calculating mean total_evaporation per aggregation unit from file C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2000
@@ -13618,7 +13484,7 @@

Analysis per yearAnalysis per year 
...DONE

 
-calculating evaporation mean per aggregation unit from C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc
+calculating mean total_evaporation per aggregation unit from file C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2001

@@ -13673,7 +13539,7 @@

Analysis per yearAnalysis per year 
...DONE

 
-calculating evaporation mean per aggregation unit from C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc
+calculating mean total_evaporation per aggregation unit from file C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2002

@@ -13728,7 +13594,7 @@

Analysis per yearAnalysis per year 
...DONE

 
-calculating evaporation mean per aggregation unit from C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc
+calculating mean total_evaporation per aggregation unit from file C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2003
@@ -13783,7 +13649,7 @@

Analysis per yearAnalysis per year 
...DONE

 
-calculating evaporation mean per aggregation unit from C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc
+calculating mean total_evaporation per aggregation unit from file C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2004
@@ -13838,7 +13704,7 @@

Analysis per yearAnalysis per year 
...DONE

 
-calculating evaporation mean per aggregation unit from C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc
+calculating mean total_evaporation per aggregation unit from file C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2005
@@ -13893,7 +13759,7 @@

Analysis per yearAnalysis per year 
...DONE

 
-calculating evaporation mean per aggregation unit from C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc
+calculating mean total_evaporation per aggregation unit from file C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2006
@@ -13948,7 +13814,7 @@

Analysis per yearAnalysis per year 
...DONE

 
-calculating evaporation mean per aggregation unit from C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc
+calculating mean total_evaporation per aggregation unit from file C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2007
@@ -14003,7 +13869,7 @@

Analysis per yearAnalysis per year 
...DONE

 
-calculating evaporation mean per aggregation unit from C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc
+calculating mean total_evaporation per aggregation unit from file C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2008
@@ -14058,7 +13924,7 @@

Analysis per yearAnalysis per year 
...DONE

 
-calculating evaporation mean per aggregation unit from C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc
+calculating mean total_evaporation per aggregation unit from file C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2009
@@ -14113,7 +13979,7 @@

Analysis per yearAnalysis per year 
...DONE

 
-calculating evaporation mean per aggregation unit from C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc
+calculating mean total_evaporation per aggregation unit from file C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2010
@@ -14182,14 +14048,14 @@

Machine Learning
-

First, create the X (variables) from all columns sampled and Y (target) and concatenate/copy.

+

First, create a pandas dataframe from all variables and targets and kick out rows with missing values (they do not work with ML)

-
In [13]:
+
In [10]:
XY_data = list(zip(X1, X2, Y))
@@ -14225,7 +14091,7 @@ 
Machine Learning
 

-
In [14]:

+
In [11]:

 

     

 
XY_data
@@ -14241,7 +14107,7 @@ 
Machine Learning
 
-    
Out[14]:

+    
Out[11]:

 
 
 
@@ -14358,7 +14224,7 @@ 
Machine Learning
 

-
In [15]:

+
In [12]:

 

     

 
X = XY_data[['GDP_PPP', 'ET']].to_numpy()
@@ -14375,7 +14241,7 @@ 
Machine Learning
 
-    
Out[15]:

+    
Out[12]:

 
 
 
@@ -14398,7 +14264,7 @@ 
Machine Learning
 

-
In [16]:

+
In [13]:

 

     

 
Y = XY_data.conflict.astype(int).to_numpy()
@@ -14415,7 +14281,7 @@ 
Machine Learning
 
-    
Out[16]:

+    
Out[13]:

 
 
 
@@ -14429,14 +14295,6 @@ 
Machine Learning

-

-

-
The scatterplot of the (two) variables in X looks like this. Also the sample size n is provided.

-
-

-

 

 

 

@@ -14448,22 +14306,30 @@ 
Machine Learning
 

-
In [17]:

+
In [14]:

 

     

 
X_train, X_test, y_train, y_test = model_selection.train_test_split(preprocessing.scale(X),
                                                                     Y,
-                                                                    test_size=0.5)
+                                                                    test_size=0.7)
 

 
     

 

 

 
+

+

+

+

+
The scatterplot of the (two) variables in X looks like this. Also the sample size n is provided.

+
+

+

 

 

 

-
In [18]:

+
In [15]:

 

     

 
plt.figure(figsize=(10,10))
@@ -14492,7 +14358,7 @@ 
Machine Learning
-
 

@@ -14505,7 +14371,7 @@ 
Machine Learning
 

-
In [19]:

+
In [16]:

 

     

 
preprocessing.scale(X).mean(axis=0), preprocessing.scale(X).std(axis=0)
@@ -14521,7 +14387,7 @@ 
Machine Learning
 
-    
Out[19]:

+    
Out[16]:

 
 
 
@@ -14553,10 +14419,10 @@ 
Model

 

-
In [20]:

+
In [17]:

 

     

-
clf = svm.SVC(class_weight='balanced')
+
clf = svm.SVC(class_weight='balanced', C=0.9)
 

 
     

@@ -14574,7 +14440,7 @@ 
Model

 

-
In [21]:

+
In [18]:

 

     

 
clf.fit(X_train, y_train)
@@ -14590,13 +14456,13 @@ 
Model

 
-    
Out[21]:

+    
Out[18]:

 
 
 
 
 

-
SVC(C=1.0, break_ties=False, cache_size=200, class_weight='balanced', coef0=0.0,
+
SVC(C=0.9, break_ties=False, cache_size=200, class_weight='balanced', coef0=0.0,
     decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',
     max_iter=-1, probability=False, random_state=None, shrinking=True,
     tol=0.001, verbose=False)

@@ -14618,7 +14484,7 @@ 
Model

 

-
In [22]:

+
In [19]:

 

     

 
y_pred = clf.predict(X_test)
@@ -14635,13 +14501,13 @@ 
Model

 
-    
Out[22]:

+    
Out[19]:

 
 
 
 
 

-
array([1, 1, 0, ..., 0, 0, 0])

+
array([1, 0, 0, ..., 0, 1, 0])

 

 
 

@@ -14649,10 +14515,18 @@ 
Model

+

+

+
No clue right now what this does... look it up!

+
+

+

 

 

 

-
In [23]:

+
In [20]:

 

     

 
y_score = clf.decision_function(X_test)
@@ -14669,14 +14543,14 @@ 
Model

 
-    
Out[23]:

+    
Out[20]:

 
 
 
 
 

-
array([ 0.30562033,  0.78824461, -1.62267313, ..., -0.46900522,
-       -1.36638482, -1.35364663])

+
array([ 0.71345701, -2.26722865, -1.34963997, ..., -1.12775605,
+        0.6524443 , -0.24453507])

 

 
 

@@ -14704,7 +14578,7 @@ 
Evaluation
 

 

-
In [24]:

+
In [21]:

 

     

 
print("Accuracy:", metrics.accuracy_score(y_test, y_pred))
@@ -14726,9 +14600,9 @@ 
Evaluation
-
Accuracy: 0.599905303030303
-Precision: 0.09020902090209021
-Recall: 0.82
+
Accuracy: 0.669259384511329
+Precision: 0.09933142311365807
+Recall: 0.7482014388489209
 

 

 

@@ -14749,7 +14623,7 @@ 
Evaluation
 

 

-
In [25]:

+
In [22]:

 

     

 
average_precision = metrics.average_precision_score(y_test, y_score)
@@ -14782,7 +14656,7 @@ 
Evaluation
 

 

-
In [26]:

+
In [23]:

 

     

 
disp = metrics.plot_precision_recall_curve(clf, X_test, y_test)
@@ -14805,7 +14679,7 @@ 
Evaluation
-
 


From 350e3b230e3cd3192b4fe60bcdb8bb5305fce66b Mon Sep 17 00:00:00 2001
From: Jannis 
Date: Thu, 18 Jun 2020 10:20:24 +0200
Subject: [PATCH 3/3] aligned runner.py with notebook

---
 data/run_setting.cfg |  3 +-
 scripts/runner.py    | 95 ++++++++++++++++++++++++++++++++++----------
 2 files changed, 77 insertions(+), 21 deletions(-)

diff --git a/data/run_setting.cfg b/data/run_setting.cfg
index 7208568..e3214a0 100644
--- a/data/run_setting.cfg
+++ b/data/run_setting.cfg
@@ -4,7 +4,7 @@ output_dir=C:\Users\hoch0001\Documents\_code\conflict_model\data\OUT
 
 [settings]
 y_start=2000
-y_end=2011
+y_end=2015
 
 [extent]
 shp=waterProvinces/waterProvinces_Africa.shp
@@ -21,5 +21,6 @@ zones=BWh,BSh
 code2class=KoeppenGeiger/classification_codes.txt
 
 [env_vars]
+#variable name here needs to be identical with variable name in nc-file
 GDP_per_capita_PPP=GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc
 total_evaporation=PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc
\ No newline at end of file
diff --git a/scripts/runner.py b/scripts/runner.py
index 270a241..1e63f5d 100644
--- a/scripts/runner.py
+++ b/scripts/runner.py
@@ -6,18 +6,13 @@
 from os.path import isdir, dirname, abspath
 from os import makedirs
 import geopandas as gpd
+import pandas as pd
 import numpy as np
+import seaborn as sbs
+from sklearn import svm, preprocessing, model_selection, metrics
+import matplotlib.pyplot as plt
 import os, sys
 
-# ad-hoc functions
-def parse_dir(param, path):
-    try:
-        path = abspath(path)
-        if not isdir(path):
-            os.makedirs(path)
-    except:
-        raise click.BadParameter("Couldn't understand or create folder directory for the '{}' argument.".format(param))
-    return path
 
 @click.group()
 def cli():
@@ -25,8 +20,9 @@ def cli():
 
 @click.command()
 @click.argument('cfg',)
+@click.option('-so', '--safe-output', default=False, help='whether or not to save output', type=click.BOOL)
 
-def main(cfg, out_dir=None, safe_plots=False):
+def main(cfg, safe_output=False):
     """
     Runs the conflict_model from command line with several options and the settings cfg-file as argument.
 
@@ -41,27 +37,86 @@ def main(cfg, out_dir=None, safe_plots=False):
     config = RawConfigParser(allow_no_value=True)
     config.read(cfg)
 
-    #out_dir
-    out_dir = config.get('general','output_dir')
-    if not os.path.isdir(out_dir):
+    if safe_output:
+        out_dir = config.get('general','output_dir')
+        if not os.path.isdir(out_dir):
             os.makedirs(out_dir)
-    print('for the record, saving output to folder {}'.format(out_dir) + os.linesep)
+        print('saving output to folder {}'.format(out_dir) + os.linesep)
+    else:
+        print('not saving output' + os.linesep)
 
-    conflict_gdf = conflict_model.utils.get_geodataframe(config)
+    gdf = conflict_model.utils.get_geodataframe(config)
 
-    selected_conflict_gdf, extent_gdf = conflict_model.selection.select(conflict_gdf, config)
+    conflict_gdf, extent_gdf = conflict_model.selection.select(gdf, config)
 
-    sim_years = np.arange(config.getint('settings', 'y_start'), config.getint('settings', 'y_end'), 1)
+    print('data retrieval period from', str(config.getint('settings', 'y_start')), 'to', str(config.getint('settings', 'y_end')))
+    print('')
 
-    print('preps are all done, now entering annual analysis' + os.linesep)
+    X1 = pd.Series(dtype=float)
+    X2 = pd.Series(dtype=float)
+    Y  = pd.Series(dtype=int)
 
     for sim_year in np.arange(config.getint('settings', 'y_start'), config.getint('settings', 'y_end'), 1):
 
         print('entering year {}'.format(sim_year) + os.linesep)
 
-        conflict_gdf_perYear, extent_conflict_merged, fatalities_per_waterProvince, extent_waterProvinces_with_boolFatalities = conflict_model.analysis.conflict_in_year_bool(selected_conflict_gdf, extent_gdf, config, sim_year, out_dir, saving_plots=True)
+        list_boolConflict = conflict_model.get_boolean_conflict.conflict_in_year_bool(conflict_gdf, extent_gdf, config, sim_year)
+        Y = Y.append(pd.Series(list_boolConflict, dtype=int), ignore_index=True)
+        
+        list_GDP_PPP = conflict_model.get_var_from_nc.nc_with_integer_timestamp(extent_gdf, config, 'GDP_per_capita_PPP', sim_year)
+        X1 = X1.append(pd.Series(list_GDP_PPP), ignore_index=True)
+
+        if not len(list_GDP_PPP) == len(list_boolConflict):
+            raise AssertionError('length of lists do not match, they are {0} and {1}'.format(len(list_GDP_PPP), len(list_boolConflict)))
+
+        list_Evap = conflict_model.get_var_from_nc.nc_with_continous_regular_timestamp(extent_gdf, config, 'total_evaporation', sim_year)
+        X2 = X2.append(pd.Series(list_Evap), ignore_index=True)
+
+        if not len(list_Evap) == len(list_boolConflict):
+            raise AssertionError('length of lists do not match, they are {0} and {1}'.format(len(list_Evap), len(list_boolConflict)))
+
+    print('...all data retrieved' + os.linesep)
+
+    print('preparing data for Machine Learning model' + os.linesep)
+    XY_data = list(zip(X1, X2, Y))
+    XY_data = pd.DataFrame(XY_data, columns=['GDP_PPP', 'ET', 'conflict'])
+    XY_data = XY_data.dropna()
+    X = XY_data[['GDP_PPP', 'ET']].to_numpy()
+    Y = XY_data.conflict.astype(int).to_numpy()
+
+    print('scaling data and splitting into trainings and test samples' + os.linesep)
+    X_train, X_test, y_train, y_test = model_selection.train_test_split(preprocessing.scale(X),
+                                                                        Y,
+                                                                        test_size=0.7)
+
+    plt.figure(figsize=(10,10))
+    sbs.scatterplot(x=X_train[:,0],
+                    y=X_train[:,1],  
+                    hue=y_train)
+
+    plt.title('n=' + str(len(X_train)))
+    if safe_output:
+        plt.savefig(os.path.join(out_dir, 'scatter_plot.png'), dpi=300)
+
+    print('initializing Support Vector Classification model' + os.linesep)
+    clf = svm.SVC(class_weight='balanced')
+
+    print('fitting model with trainings data' + os.linesep)
+    clf.fit(X_train, y_train)
+
+    print('making a prediction' + os.linesep)
+    y_pred = clf.predict(X_test)
+
+    y_score = clf.decision_function(X_test)
+
+    print('Model evaluation')
+    print("...Accuracy:", metrics.accuracy_score(y_test, y_pred))
+    print("...Precision:", metrics.precision_score(y_test, y_pred))
+    print("...Recall:", metrics.recall_score(y_test, y_pred))
+    print('...Average precision-recall score: {0:0.2f}'.format(metrics.average_precision_score(y_test, y_score)))
 
-        GDP_PPP_gdf = conflict_model.env_vars_nc.rasterstats_GDP_PPP(extent_waterProvinces_with_boolFatalities, extent_gdf, config, sim_year, out_dir, saving_plots=True)
+    disp = metrics.plot_precision_recall_curve(clf, X_test, y_test)
+    disp.ax_.set_title('2-class Precision-Recall curve: AP={0:0.2f}'.format(metrics.average_precision_score(y_test, y_score)))
 
 if __name__ == '__main__':
     main()
\ No newline at end of file