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shortened cantonal summary
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@hammerdirt
hammerdirt committed Jun 10, 2024
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47 changes: 46 additions & 1 deletion _build/jupyter_execute/bern.ipynb
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"\n",
"## Municipal Results - all data\n",
"\n",
"The average pieces per meter of the most common objects for each city.\n",
"The average pieces per meter and the combined land use classification for each city.\n",
"\n",
"::::::::::{tab-set}\n",
"\n",
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83 changes: 58 additions & 25 deletions _build/jupyter_execute/combined.ipynb
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"dxlc = dxl[['location', 'canton', 'feature_type']].drop_duplicates('location')\n",
"dxlc.set_index(['location'], inplace=True, drop=True)\n",
"dxf['canton'] = dxf.location.apply(lambda x : dxlc.loc[x, 'canton'])\n",
"sumlu = {x:'sum' for x in session_config.feature_variables}\n",
"dxf = dxf.groupby(['sample_id', 'canton', *session_config.feature_variables], as_index=False).agg(session_config.unit_agg)\n",
"# sumlu = {x:'sum' for x in session_config.feature_variables}\n",
"dxf = dxf.groupby(['sample_id', 'canton'], as_index=False).agg(session_config.unit_agg)\n",
"\n",
"dxf = dxf.groupby(['canton']).agg({'quantity':'sum', 'pcs/m':'mean', 'sample_id':'nunique', **sumlu})\n",
"dxf = dxf.groupby(['canton']).agg({'quantity':'sum', 'pcs/m':'mean', 'sample_id':'nunique'})\n",
"\n",
"for alabel in session_config.feature_variables:\n",
" dxf[alabel] = dxf[alabel]/dxf.sample_id\n",
"# for alabel in session_config.feature_variables:\n",
"# dxf[alabel] = dxf[alabel]/dxf.sample_id\n",
" \n",
"dxf['check'] = dxf[session_config.feature_variables[:-1]].sum(axis=1)\n",
"dxfc = geospatial.categorize_features(dxf, feature_columns=session_config.feature_variables)\n",
"dxfc.rename(columns={'sample_id':'samples'}, inplace=True)\n",
"dxfc.drop('check', axis=1, inplace=True)\n",
"dxfc = dxfc.style.set_table_styles(userdisplay.table_css_styles)\n",
"# dxf['check'] = dxf[session_config.feature_variables[:-1]].sum(axis=1)\n",
"# dxfc = geospatial.categorize_features(dxf, feature_columns=session_config.feature_variables)\n",
"dxf.rename(columns={'sample_id':'samples'}, inplace=True)\n",
"dxfc = dxf.style.set_table_styles(userdisplay.table_css_styles)\n",
"dxfc = dxfc.apply(highlight_max, arg=lake_results['this_report'].sampling_results_summary['average'], subset=pd.IndexSlice[:, ['pcs/m']])\n",
"dxfc = dxfc.format(userdisplay.format_kwargs, precision=2)\n",
"\n",
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"dxlc = dxl[['location', 'canton', 'feature_type']].drop_duplicates('location')\n",
"dxlc.set_index(['location'], inplace=True, drop=True)\n",
"dxf['canton'] = dxf.location.apply(lambda x : dxlc.loc[x, 'canton'])\n",
"sumlu = {x:'sum' for x in session_config.feature_variables}\n",
"dxf = dxf.groupby(['sample_id', 'canton', *session_config.feature_variables], as_index=False).agg(session_config.unit_agg)\n",
"# sumlu = {x:'sum' for x in session_config.feature_variables}\n",
"dxf = dxf.groupby(['sample_id', 'canton'], as_index=False).agg(session_config.unit_agg)\n",
"\n",
"dxf = dxf.groupby(['canton']).agg({'quantity':'sum', 'pcs/m':'mean', 'sample_id':'nunique', **sumlu})\n",
"dxf = dxf.groupby(['canton']).agg({'quantity':'sum', 'pcs/m':'mean', 'sample_id':'nunique'})\n",
"\n",
"for alabel in session_config.feature_variables:\n",
" dxf[alabel] = dxf[alabel]/dxf.sample_id\n",
"# for alabel in session_config.feature_variables:\n",
"# dxf[alabel] = dxf[alabel]/dxf.sample_id\n",
" \n",
"dxf['check'] = dxf[session_config.feature_variables[:-1]].sum(axis=1)\n",
"dxfcr = geospatial.categorize_features(dxf, feature_columns=session_config.feature_variables)\n",
"dxfcr.rename(columns={'sample_id':'samples'}, inplace=True)\n",
"dxfcr.drop('check', axis=1, inplace=True)\n",
"dxfcr = dxfcr.style.set_table_styles(userdisplay.table_css_styles)\n",
"# dxf['check'] = dxf[session_config.feature_variables[:-1]].sum(axis=1)\n",
"# dxfcr = geospatial.categorize_features(dxf, feature_columns=session_config.feature_variables)\n",
"dxf.rename(columns={'sample_id':'samples'}, inplace=True)\n",
"# dxfcr.drop('check', axis=1, inplace=True)\n",
"dxfcr = dxf.style.set_table_styles(userdisplay.table_css_styles)\n",
"dxfcr = dxfcr.apply(highlight_max, arg=lake_results['this_report'].sampling_results_summary['average'], subset=pd.IndexSlice[:, ['pcs/m']])\n",
"dxfcr = dxfcr.format(userdisplay.format_kwargs, precision=2)\n",
"# glue('all-data-municipal-results', i , display=False)\n",
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":::\n",
"::::\n",
"\n",
"## Municipal Results - all data\n",
"## Cantonal Results - all data\n",
"\n",
"The average pieces per meter of the most common objects for each canton.\n",
"The average pieces per meter for each canton.\n",
"\n",
"::::::::::{tab-set}\n",
"::::::::::{grid}\n",
"\n",
":::::::::{tab-item} Lakes\n",
":::::::::{grid-item-card}\n",
"Lakes\n",
"^^^\n",
"```{glue} lake-municipal-results\n",
"```\n",
":::::::::\n",
"\n",
":::::::::{tab-item} Rivers\n",
":::::::::{grid-item-card}\n",
"Rivers\n",
"^^^\n",
"```{glue} river-municipal-results\n",
"``` \n",
":::::::::\n",
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