def _plot_crop_mix(self, crop_mix):
crop_mix, data, years, commodities = read_crop_mix(crop_mix.id)
groups = [g.as_cropgroup() for g in crop_mix.cropmixgroup_set.all()]
if groups:
revenue_table = data.get_derived_table("Revenue", groups)
revenue_table_cpi = econ_analysis.adjust_cpi(
revenue_table, get_bls_key(), crop_mix.cpi_adjustment_year,
ConsumerPriceIndexData.as_dataframe())
niwr_table = data.get_derived_table("NIWR", groups)
labor_table = data.get_derived_table("Labor", groups)
revenue_af_plot = econ.plots.plot_revenue_af_table(
revenue_table_cpi, niwr_table)
labor_plot = econ.plots.plot_labor_table(labor_table)
return self.EconPlots(crop_mix.name, revenue_af_plot, labor_plot)
else:
# Just do the best we can with what we have and return only the
# acreages.
acre_plot = econ.plots.plot_acres(data, groups)
acre_fraction_plot = econ.plots.plot_acre_fractions(data, groups)
return self.EconPlots(crop_mix.name, acre_plot, acre_fraction_plot)
def _plot_crop_mix(self, crop_mix):
crop_mix, data, years, commodities = read_crop_mix(crop_mix.id)
groups = [g.as_cropgroup() for g in crop_mix.cropmixgroup_set.all()]
if groups:
revenue_table = data.get_derived_table("Revenue", groups)
revenue_table_cpi = econ_analysis.adjust_cpi(
revenue_table,
get_bls_key(),
crop_mix.cpi_adjustment_year,
ConsumerPriceIndexData.as_dataframe()
)
niwr_table = data.get_derived_table("NIWR", groups)
labor_table = data.get_derived_table("Labor", groups)
revenue_af_plot = econ.plots.plot_revenue_af_table(revenue_table_cpi, niwr_table)
labor_plot = econ.plots.plot_labor_table(labor_table)
return self.EconPlots(crop_mix.name, revenue_af_plot, labor_plot)
else:
# Just do the best we can with what we have and return only the
# acreages.
acre_plot = econ.plots.plot_acres(data, groups)
acre_fraction_plot = econ.plots.plot_acre_fractions(data, groups)
return self.EconPlots(crop_mix.name, acre_plot, acre_fraction_plot)
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)
