def plot_labor_table(labor_table): return plotting.bar_plot_table( labor_table.div(2080), title='Labor (FTEs 2,080 hours/year)', palette=Spectral9, legend='bottom_right', xlabel='Year', ylabel='FTEs 2,080 hours/year', tools=DEFAULT_TOOLS, logo=None, responsive=True, number_of_categories=8, yaxis_formatter=NumeralTickFormatter(format='0,0') )
def plot_acre_fractions(econ_data, groups): return plotting.bar_plot_table( econ_data.get_ratio_table('ACRES', groups), xlabel='Year', ylabel='', title='% Acres by crop type', palette=Spectral9, legend='bottom_right', tools=DEFAULT_TOOLS, logo=None, responsive=True, number_of_categories=8, yaxis_formatter=NumeralTickFormatter(format='00%'), y_range=Range1d(0.0, 1.0) )
def crop_mix_detail(request, crop_mix_id): crop_mix, data, years, commodities = read_crop_mix(crop_mix_id) bls_key = get_bls_key() groups = map(lambda g: g.as_cropgroup(), crop_mix.cropmixgroup_set.all()) context = { 'id': crop_mix_id, 'crop_mix': crop_mix, 'title': crop_mix.name, 'description': crop_mix.description, 'state': crop_mix.state, 'county': crop_mix.county, 'years': ', '.join(map(str, years)), 'commodities': ', '.join(commodities), 'year': datetime.now().year, 'source': crop_mix.source, } # We take the top 8 columns of all charts below because we're going to use # a 9 color palette to display the data. acre_plot = plots.plot_acres(data, groups) acre_script, acre_div = components(acre_plot, CDN) context.update({ 'acre_script': acre_script, 'acre_div': acre_div, }) acre_pct_plot = plots.plot_acre_fractions(data, groups) acre_pct_script, acre_pct_div = components(acre_pct_plot, CDN) context.update({ 'acre_pct_script': acre_pct_script, 'acre_pct_div': acre_pct_div, }) if groups: group_map, uncategorized = data.get_group_map(groups) group_values = {g.title: g for g in groups} context.update({ 'group_map': { key: (group_values[key], ', '.join(value)) for key, value in group_map.items() }, 'uncategorized': ", ".join(uncategorized), }) revenue_table = data.get_derived_table("Revenue", groups) niwr_table = data.get_derived_table("NIWR", groups) niwr_plot = plotting.bar_plot_table( niwr_table, title='Water Use (acre-feet)', palette=Spectral9, legend='bottom_right', xlabel='Year', ylabel='Acre-Feet', tools=DEFAULT_TOOLS, logo=None, responsive=True, number_of_categories=8, yaxis_formatter=NumeralTickFormatter(format='0,0') ) niwr_script, niwr_div = components(niwr_plot, CDN) context.update({ 'niwr_script': niwr_script, 'niwr_div': niwr_div, }) labor_table = data.get_derived_table("Labor", groups) labor_plot = plots.plot_labor_table(labor_table) labor_script, labor_div = components(labor_plot, CDN) context.update({ 'labor_script': labor_script, 'labor_div': labor_div, }) revenue_af_plot = plots.plot_revenue_af_table(revenue_table, niwr_table) revenue_af_script, revenue_af_div = components(revenue_af_plot, CDN) context.update({ 'revenue_af_script': revenue_af_script, 'revenue_af_div': revenue_af_div, }) else: revenue_table = data.get_table("$") revenue_plot = plotting.bar_plot_table( revenue_table, title='Gross Revenue ($)', palette=Spectral9, legend='bottom_right', xlabel='Year', ylabel='', tools=DEFAULT_TOOLS, logo=None, responsive=True, number_of_categories=8, yaxis_formatter=NumeralTickFormatter(format='$0,0') ) revenue_script, revenue_div = components(revenue_plot, CDN) context.update({ 'revenue_script': revenue_script, 'revenue_div': revenue_div, }) cpi_data = ConsumerPriceIndexData.as_dataframe() revenue_table_cpi = analysis.adjust_cpi( revenue_table, bls_key, crop_mix.cpi_adjustment_year, cpi_data ) revenue_cpi_plot = plotting.bar_plot_table( revenue_table_cpi, title='Gross Revenue (%s $)' % crop_mix.cpi_adjustment_year, palette=Spectral9, legend='bottom_right', xlabel='Year', ylabel='', tools=DEFAULT_TOOLS, logo=None, responsive=True, number_of_categories=8, yaxis_formatter=NumeralTickFormatter(format='$0,0') ) revenue_cpi_script, revenue_cpi_div = components(revenue_cpi_plot, CDN) context.update({ 'revenue_cpi_script': revenue_cpi_script, 'revenue_cpi_div': revenue_cpi_div, }) return render(request, 'econ/crop_mix_detail.django.html', context)
def crop_mix_detail(request, crop_mix_id): crop_mix, data, years, commodities = read_crop_mix(crop_mix_id) bls_key = get_bls_key() groups = map(lambda g: g.as_cropgroup(), crop_mix.cropmixgroup_set.all()) context = { 'id': crop_mix_id, 'crop_mix': crop_mix, 'title': crop_mix.name, 'description': crop_mix.description, 'state': crop_mix.state, 'county': crop_mix.county, 'years': ', '.join(map(str, years)), 'commodities': ', '.join(commodities), 'year': datetime.now().year, 'source': crop_mix.source, } # We take the top 8 columns of all charts below because we're going to use # a 9 color palette to display the data. acre_plot = plots.plot_acres(data, groups) acre_script, acre_div = components(acre_plot, CDN) context.update({ 'acre_script': acre_script, 'acre_div': acre_div, }) acre_pct_plot = plots.plot_acre_fractions(data, groups) acre_pct_script, acre_pct_div = components(acre_pct_plot, CDN) context.update({ 'acre_pct_script': acre_pct_script, 'acre_pct_div': acre_pct_div, }) if groups: group_map, uncategorized = data.get_group_map(groups) group_values = {g.title: g for g in groups} context.update({ 'group_map': { key: (group_values[key], ', '.join(value)) for key, value in group_map.items() }, 'uncategorized': ", ".join(uncategorized), }) revenue_table = data.get_derived_table("Revenue", groups) niwr_table = data.get_derived_table("NIWR", groups) niwr_plot = plotting.bar_plot_table( niwr_table, title='Water Use (acre-feet)', palette=Spectral9, legend='bottom_right', xlabel='Year', ylabel='Acre-Feet', tools=DEFAULT_TOOLS, logo=None, responsive=True, number_of_categories=8, yaxis_formatter=NumeralTickFormatter(format='0,0')) niwr_script, niwr_div = components(niwr_plot, CDN) context.update({ 'niwr_script': niwr_script, 'niwr_div': niwr_div, }) labor_table = data.get_derived_table("Labor", groups) labor_plot = plots.plot_labor_table(labor_table) labor_script, labor_div = components(labor_plot, CDN) context.update({ 'labor_script': labor_script, 'labor_div': labor_div, }) revenue_af_plot = plots.plot_revenue_af_table(revenue_table, niwr_table) revenue_af_script, revenue_af_div = components(revenue_af_plot, CDN) context.update({ 'revenue_af_script': revenue_af_script, 'revenue_af_div': revenue_af_div, }) else: revenue_table = data.get_table("$") revenue_plot = plotting.bar_plot_table( revenue_table, title='Gross Revenue ($)', palette=Spectral9, legend='bottom_right', xlabel='Year', ylabel='', tools=DEFAULT_TOOLS, logo=None, responsive=True, number_of_categories=8, yaxis_formatter=NumeralTickFormatter(format='$0,0')) revenue_script, revenue_div = components(revenue_plot, CDN) context.update({ 'revenue_script': revenue_script, 'revenue_div': revenue_div, }) cpi_data = ConsumerPriceIndexData.as_dataframe() revenue_table_cpi = analysis.adjust_cpi(revenue_table, bls_key, crop_mix.cpi_adjustment_year, cpi_data) revenue_cpi_plot = plotting.bar_plot_table( revenue_table_cpi, title='Gross Revenue (%s $)' % crop_mix.cpi_adjustment_year, palette=Spectral9, legend='bottom_right', xlabel='Year', ylabel='', tools=DEFAULT_TOOLS, logo=None, responsive=True, number_of_categories=8, yaxis_formatter=NumeralTickFormatter(format='$0,0')) revenue_cpi_script, revenue_cpi_div = components(revenue_cpi_plot, CDN) context.update({ 'revenue_cpi_script': revenue_cpi_script, 'revenue_cpi_div': revenue_cpi_div, }) return render(request, 'econ/crop_mix_detail.django.html', context)