def __dataframe_barplot_helper(request,
project_id,
title,
analysis_f,
units=None,
formatter=None,
xlim=None,
ylim=None):
project = get_object_or_404(Project, pk=project_id)
data = __get_deficit_stats_comparison(project, analysis_f, units)
plt.style.use(DEFAULT_PLOT_STYLE)
fig, ax = new_figure()
data.plot(kind='bar', ax=ax, table=False)
ax.set_title(title)
if formatter:
ax.yaxis.set_major_formatter(formatter)
ylabel = "Volume"
ax.set_ylabel("Volume (%s)" % units)
if xlim:
ax.set_xlim(xlim)
if ylim:
ax.set_ylim(ylim)
return plot_to_response(fig)
def deficit_stats_plot_annual(request, scenario_id):
scenario, (fig, ax) = __setup_scenario_plot(scenario_id)
ax.set_ylabel(__label_volume_attribute(scenario))
ax.set_xlabel("Year")
title = None
plotting.volume_deficit_annual(scenario.get_data(), scenario.get_gap_attribute_name(), title, fig, ax)
return plot_to_response(fig)
def deficit_stats_plot_annual(request, scenario_id):
scenario, (fig, ax) = __setup_scenario_plot(scenario_id)
ax.set_ylabel(__label_volume_attribute(scenario))
title = "Annual Volume Deficit"
plotting.volume_deficit_annual(scenario.get_data(),
scenario.get_gap_attribute_name(), title,
fig, ax)
return plot_to_response(fig)
def volume_deficit_drought_plot(request, scenario_id):
scenario = get_object_or_404(Scenario, pk=scenario_id)
quantile = 1.0 - (scenario.drought_exceedance / 100.0)
plt.style.use(DEFAULT_PLOT_STYLE)
fig, ax = plots.plot_drought_volume_deficit_mpl(scenario, quantile)
ax.set_xlabel("Year")
ax.set_ylabel("Volume (af)")
return plot_to_response(fig)
def volume_deficit_drought_plot(request, scenario_id):
scenario = get_object_or_404(Scenario, pk=scenario_id)
quantile = 1.0 - (scenario.drought_exceedance / 100.0)
plt.style.use(DEFAULT_PLOT_STYLE)
fig, ax = plots.plot_drought_volume_deficit_mpl(scenario, quantile)
ax.set_xlabel("Year")
ax.set_ylabel("Volume (af)")
return plot_to_response(fig)
def temporal_deficit_drought_plot(request, scenario_id):
scenario = get_object_or_404(Scenario, pk=scenario_id)
quantile = 1.0 - (scenario.drought_exceedance / 100.0)
plt.style.use(DEFAULT_PLOT_STYLE)
fig, ax = plots.plot_drought_temporal_deficit_mpl(scenario, quantile)
ax.yaxis.set_major_formatter(FuncFormatter(to_percent))
ax.set_xlabel("Year")
ax.set_ylabel("Percent of days")
return plot_to_response(fig)
def deficit_stats_pct_plot_annual(request, scenario_id):
scenario, (fig, ax) = __setup_scenario_plot(scenario_id)
ax.yaxis.set_major_formatter(FuncFormatter(to_percent))
ax.set_ylim([0.0, 1.0])
plotting.volume_deficit_pct_annual(
scenario.get_data(), scenario.get_gap_attribute_name(),
scenario.get_target_attribute_name(),
"Annual Volume Deficit Relative to Target", fig, ax)
return plot_to_response(fig)
def temporal_deficit_drought_plot(request, scenario_id):
scenario = get_object_or_404(Scenario, pk=scenario_id)
quantile = 1.0 - (scenario.drought_exceedance / 100.0)
plt.style.use(DEFAULT_PLOT_STYLE)
fig, ax = plots.plot_drought_temporal_deficit_mpl(scenario, quantile)
ax.yaxis.set_major_formatter(FuncFormatter(to_percent))
ax.set_xlabel("Year")
ax.set_ylabel("Percent of days")
return plot_to_response(fig)
def right_plot(request, scenario_id):
scenario = get_object_or_404(Scenario, pk=scenario_id)
plotdata = target_comparison_data(scenario)
plt.style.use(DEFAULT_PLOT_STYLE)
fig, ax = new_figure()
plotdata.plot(ax=ax)
ax.set_xlabel("Month")
ax.set_ylabel(__label_scenario_attribute(scenario))
plotting.label_months(ax)
return plot_to_response(fig)
def dynamic_raster(request, scenario_id):
scenario = get_object_or_404(Scenario, pk=scenario_id)
data = scenario.get_data()
# Get visualization parameters
attribute = request.GET.get('attribute', None)
if not attribute:
return HttpResponseBadRequest()
cmap = request.GET.get('cmap', None)
title = request.GET.get('title', None)
zero = request.GET.get('zero', 'False')
logscale = request.GET.get('logscale', 'False')
zero = zero == "True"
logscale = logscale == "True"
plt.style.use(DEFAULT_PLOT_STYLE)
fig = Figure()
ax = fig.add_subplot(111)
if not zero:
min_value = data[attribute].min()
max_value = data[attribute].max()
else:
min_value = data[attribute].min()
max_value = abs(data[attribute].min())
if cmap:
colormap = cm.get_cmap(cmap)
if zero:
colormap = plotting.create_colormap(data,
attribute,
colormap,
vmin=min_value,
vmax=max_value)
colormap.set_bad('black')
else:
colormap = None
if logscale:
if min_value <= 0:
min_value = 0.001
norm = matplotlib.colors.LogNorm(vmin=min_value, vmax=max_value)
else:
norm = None
plotting.rasterplot(data,
attribute,
title,
show_colorbar=True,
norm=norm,
colormap=colormap,
vmin=min_value,
vmax=max_value,
fig=fig,
ax=ax)
return plot_to_response(fig)
def project_low_flow_plot(request, project_id):
low_flows = get_low_flows(project_id)
plt.style.use(DEFAULT_PLOT_STYLE)
fig, ax = new_figure()
low_flows.plot(kind='bar', fig=fig, ax=ax)
#ax.yaxis.set_major_formatter(FuncFormatter(to_percent))
ax.set_title("7-day Minimum Flow Trend")
labels = ax.get_xticklabels()
for label in labels:
label.set_rotation(0)
return plot_to_response(fig)
def deficit_days_plot(request, scenario_id):
scenario = get_object_or_404(Scenario, pk=scenario_id)
data = scenario.get_data()
plt.style.use(DEFAULT_PLOT_STYLE)
fig, ax = new_figure()
title = "Monthly Temporal Deficit"
ax = plotting.deficit_days_plot(data, scenario.get_gap_attribute_name(),
title, fig, ax)
ax.yaxis.set_major_formatter(FuncFormatter(to_percent))
ax.set_ylim([0.0, 1.0])
return plot_to_response(fig)
def project_deficit_days_plot(request, project_id):
project = get_object_or_404(Project, pk=project_id)
data = __get_deficit_days_comparison(
project, lambda d, g, t: analysis.monthly_deficit_pct(d, g), "Month")
plt.style.use(DEFAULT_PLOT_STYLE)
fig, ax = new_figure()
data.plot(kind='bar', ax=ax, table=False)
ax.set_title("Deficit days comparison")
ax.yaxis.set_major_formatter(FuncFormatter(to_percent))
ax.set_ylim([0.0, 1.0])
return plot_to_response(fig)
def project_low_flow_plot(request, project_id):
low_flows = get_low_flows(project_id)
plt.style.use(DEFAULT_PLOT_STYLE)
fig, ax = new_figure()
low_flows.plot(kind='bar', fig=fig, ax=ax)
#ax.yaxis.set_major_formatter(FuncFormatter(to_percent))
ax.set_title("7-day Minimum Flow Trend")
labels = ax.get_xticklabels()
for label in labels:
label.set_rotation(0)
return plot_to_response(fig)
def deficit_days_plot(request, scenario_id):
scenario = get_object_or_404(Scenario, pk=scenario_id)
data = scenario.get_data()
plt.style.use(DEFAULT_PLOT_STYLE)
fig, ax = new_figure()
title = None
ax = plotting.deficit_days_plot(data, scenario.get_gap_attribute_name(), title, fig, ax)
ax.yaxis.set_major_formatter(FuncFormatter(to_percent))
ax.set_ylim([0.0, 1.0])
ax.set_ylabel("Percent of Days")
ax.set_xlabel("Month")
return plot_to_response(fig)
def project_deficit_days_plot(request, project_id):
project = get_object_or_404(Project, pk=project_id)
data = __get_deficit_days_comparison(
project,
lambda d, g, t: analysis.monthly_deficit_pct(d, g),
"Month"
)
plt.style.use(DEFAULT_PLOT_STYLE)
fig, ax = new_figure()
data.plot(kind='bar', ax=ax, table=False)
ax.set_title("Deficit days comparison")
ax.yaxis.set_major_formatter(FuncFormatter(to_percent))
ax.set_ylim([0.0, 1.0])
return plot_to_response(fig)
def deficit_stats_pct_plot_annual(request, scenario_id):
scenario, (fig, ax) = __setup_scenario_plot(scenario_id)
ax.yaxis.set_major_formatter(FuncFormatter(to_percent))
ax.set_ylim([0.0, 1.0])
ax.set_ylabel("Percent of Target")
ax.set_xlabel("Year")
plotting.volume_deficit_pct_annual(
scenario.get_data(),
scenario.get_gap_attribute_name(),
scenario.get_target_attribute_name(),
None,
fig, ax
)
return plot_to_response(fig)
def long_term_minimum_plot(request, scenario_id):
scenario = get_object_or_404(Scenario, pk=scenario_id)
data = scenario.get_data()
column = scenario.get_attribute_name()
min_data = analysis.annual_minimum(data[column], 7, True)
plt.style.use(DEFAULT_PLOT_STYLE)
fig, ax = new_figure()
plotting.plot_with_trendline_ols(
min_data,
title=None,
fig=fig, ax=ax)
ax.set_xlabel("Year")
ax.set_ylabel(__label_scenario_attribute(scenario))
return plot_to_response(fig)
def long_term_minimum_plot(request, scenario_id):
scenario = get_object_or_404(Scenario, pk=scenario_id)
data = scenario.get_data()
column = scenario.get_attribute_name()
min_data = analysis.annual_minimum(data[column], 7, True)
plt.style.use(DEFAULT_PLOT_STYLE)
fig, ax = new_figure()
plotting.plot_with_trendline_ols(min_data,
title="7-day Minimum Flow",
fig=fig,
ax=ax)
ax.set_xlabel("Year")
ax.set_ylabel(__label_scenario_attribute(scenario))
return plot_to_response(fig)
def get(self, request, scenario_id):
scenario = get_object_or_404(Scenario, pk=scenario_id)
if scenario.instream_flow_rights:
targetdata = scenario.get_data()[scenario.get_target_attribute_name()]
flowdata = scenario.get_data()[scenario.get_attribute_name()]
ratedata = self.get_rate_data(
scenario.instream_flow_rights,
targetdata.index.min(),
targetdata.index.max()
)
plt.style.use(DEFAULT_PLOT_STYLE)
fig, ax = new_figure()
self.plot(ax, targetdata, flowdata, ratedata)
return plot_to_response(fig)
else:
raise Http404("Scenario has no instream flow rights.")
def get(self, request, scenario_id):
scenario = get_object_or_404(Scenario, pk=scenario_id)
plt.style.use(DEFAULT_PLOT_STYLE)
# Crop data.
if scenario.crop_mix:
crop_mix, data, years, commodities = read_crop_mix(
scenario.crop_mix.id)
groups = [
g.as_cropgroup() for g in crop_mix.cropmixgroup_set.all()
]
dataframe = self.data_function(data, groups)
fig, ax = new_figure()
self.plot_function(dataframe, ax)
ax.set_xlabel(self.xlabel)
ax.set_ylabel(self.ylabel)
return plot_to_response(fig)
else:
raise Http404("Scenario contains no agriculture data.")
def __dataframe_barplot_helper(request, project_id, title, analysis_f,
units=None, formatter=None, xlim=None, ylim=None):
project = get_object_or_404(Project, pk=project_id)
data = __get_deficit_stats_comparison(project, analysis_f, units)
plt.style.use(DEFAULT_PLOT_STYLE)
fig, ax = new_figure()
data.plot(kind='bar', ax=ax, table=False)
ax.set_title(title)
if formatter:
ax.yaxis.set_major_formatter(formatter)
ylabel = "Volume"
ax.set_ylabel("Volume (%s)" % units)
if xlim:
ax.set_xlim(xlim)
if ylim:
ax.set_ylim(ylim)
return plot_to_response(fig)
def get(self, request, scenario_id):
scenario = get_object_or_404(Scenario, pk=scenario_id)
plt.style.use(DEFAULT_PLOT_STYLE)
# Crop data.
if scenario.crop_mix:
crop_mix, data, years, commodities = read_crop_mix(scenario.crop_mix.id)
groups = [g.as_cropgroup() for g in crop_mix.cropmixgroup_set.all()]
dataframe = self.data_function(crop_mix, data, groups)
if isinstance(dataframe, pd.DataFrame):
dataframe_reduced = waterkit.econ.analysis.select_top_n_columns(dataframe, 6)
else:
dataframe_reduced = dataframe
fig, ax = new_figure()
ax.set_xlabel(self.xlabel)
ax.set_ylabel(self.ylabel)
self.plot_function(dataframe_reduced, ax)
return plot_to_response(fig)
else:
raise Http404("Scenario contains no agriculture data.")
def dynamic_raster(request, scenario_id):
scenario = get_object_or_404(Scenario, pk=scenario_id)
data = scenario.get_data()
# Get visualization parameters
attribute = request.GET.get('attribute', None)
if not attribute:
return HttpResponseBadRequest()
cmap = request.GET.get('cmap', None)
zero = request.GET.get('zero', 'False')
logscale = request.GET.get('logscale', 'False')
zero = zero == "True"
logscale = logscale == "True"
plt.style.use(DEFAULT_PLOT_STYLE)
fig = Figure()
ax = fig.add_subplot(111)
if not zero:
min_value = data[attribute].min()
max_value = data[attribute].max()
else:
min_value = data[attribute].min()
max_value = abs(data[attribute].min())
if cmap:
colormap = cm.get_cmap(cmap)
if zero:
colormap = plotting.create_colormap(data, attribute, colormap, vmin=min_value, vmax=max_value)
colormap.set_bad('black')
else:
colormap = None
if logscale:
if min_value <= 0:
min_value = 0.001
norm = matplotlib.colors.LogNorm(vmin=min_value, vmax=max_value)
else:
norm = None
plotting.rasterplot(data, attribute, None, show_colorbar=True, norm=norm,
colormap=colormap, vmin=min_value, vmax=max_value, fig=fig, ax=ax)
return plot_to_response(fig)
def right_plot(request, scenario_id):
scenario = get_object_or_404(Scenario, pk=scenario_id)
data = scenario.get_data()
daygroups = data.groupby(lambda x: x.dayofyear)
low = daygroups[scenario.get_attribute_name()].quantile(0.2)
median = daygroups[scenario.get_attribute_name()].quantile(0.5)
high = daygroups[scenario.get_attribute_name()].quantile(0.8)
target = daygroups[scenario.get_target_attribute_name()].mean()
plotdata = pd.concat([low, median, high, target], axis=1)
plotdata.columns = ["80% Exceedance", "Median", "20% Exceedance", "Target"]
plt.style.use(DEFAULT_PLOT_STYLE)
fig, ax = new_figure()
plotdata.plot(ax=ax)
ax.set_xlabel("Month")
ax.set_ylabel(__label_scenario_attribute(scenario))
plotting.label_months(ax)
return plot_to_response(fig)
