def test_plumeplot_non_unique_lines(plumeplot_scmrun):
quantile_over = "climate_model"
quantile = 0.05
scenario = "a_scenario"
variable = "Surface Air Temperature Change"
summary_stats = ScmRun(
plumeplot_scmrun.quantiles_over(quantile_over, quantiles=(0.05, 0.5, 0.95))
)
error_msg = re.escape(
"More than one timeseries for "
"quantile: {}, "
"scenario: {}, "
"variable: {}.\n"
"Please process your data to create unique quantile timeseries "
"before calling :meth:`plumeplot`.\n"
"Found: {}".format(
quantile,
scenario,
variable,
summary_stats.filter(
quantile=quantile, scenario=scenario, variable=variable
),
)
)
with pytest.raises(ValueError, match=error_msg):
summary_stats.plumeplot(pre_calculated=True)
def test_plumeplot_pre_calculated(plumeplot_scmrun, quantiles_plumes):
quantiles = [v for qv in quantiles_plumes for v in qv[0]]
summary_stats = ScmRun(
plumeplot_scmrun.quantiles_over("ensemble_member", quantiles=quantiles)
)
summary_stats.plumeplot(
quantiles_plumes=quantiles_plumes, pre_calculated=True,
)
def test_plumeplot_values(plumeplot_scmrun, quantiles_plumes, time_axis, linewidth):
mock_ax = MagicMock()
palette = {"a_model": "tab:blue", "a_model_2": "tab:red"}
dashes = {"a_scenario": "-", "a_scenario_2": "--"}
quantiles = [v for qv in quantiles_plumes for v in qv[0]]
summary_stats = ScmRun(
plumeplot_scmrun.quantiles_over("ensemble_member", quantiles=quantiles)
)
summary_stats.plumeplot(
ax=mock_ax,
quantiles_plumes=quantiles_plumes,
pre_calculated=True,
hue_var="climate_model",
palette=palette,
style_var="scenario",
dashes=dashes,
time_axis=time_axis,
linewidth=linewidth,
)
xaxis = summary_stats.timeseries(time_axis=time_axis).columns.tolist()
def _is_in_calls(call_to_check, call_args_list):
pargs_to_check = call_to_check[1]
kargs_to_check = call_to_check[2]
in_call = False
for ca in call_args_list:
pargs = ca[0]
pargs_match = True
for i, p in enumerate(pargs):
if isinstance(p, np.ndarray):
if not np.allclose(p, pargs_to_check[i]):
pargs_match = False
else:
if not p == pargs_to_check[i]:
pargs_match = False
if not pargs_match:
print(p)
print(pargs_to_check[i])
kargs = ca[1]
if pargs_match and kargs == kargs_to_check:
in_call = True
return in_call
def _get_with_empty_check(idf_filtered):
if idf_filtered.empty:
raise ValueError("Empty")
return idf_filtered.values.squeeze()
def _make_fill_between_call(idf, cm, scen, quant_alpha):
quantiles = quant_alpha[0]
alpha = quant_alpha[1]
return call(
xaxis,
_get_with_empty_check(
idf.filter(climate_model=cm, scenario=scen, quantile=quantiles[0])
),
_get_with_empty_check(
idf.filter(climate_model=cm, scenario=scen, quantile=quantiles[1])
),
alpha=alpha,
color=palette[cm],
label="{:.0f}th - {:.0f}th".format(quantiles[0] * 100, quantiles[1] * 100),
)
def _make_plot_call(idf, cm, scen, quant_alpha):
quantiles = quant_alpha[0]
alpha = quant_alpha[1]
return call(
xaxis,
_get_with_empty_check(
idf.filter(climate_model=cm, scenario=scen, quantile=quantiles[0])
),
color=palette[cm],
linestyle=dashes[scen],
linewidth=linewidth,
label="{:.0f}th".format(quantiles[0] * 100),
alpha=alpha,
)
cm_scen_combos = summary_stats.meta[["climate_model", "scenario"]].drop_duplicates()
cm_scen_combos = [v[1].values.tolist() for v in cm_scen_combos.iterrows()]
plume_qa = [q for q in quantiles_plumes if len(q[0]) == 2]
fill_between_calls = [
_make_fill_between_call(summary_stats, cm, scen, qa)
for cm, scen in cm_scen_combos
for qa in plume_qa
]
# debug by looking at mock_ax.fill_between.call_args_list
assert all(
[
_is_in_calls(c, mock_ax.fill_between.call_args_list)
for c in fill_between_calls
]
)
line_qa = [q for q in quantiles_plumes if len(q[0]) == 1]
plot_calls = [
_make_plot_call(summary_stats, cm, scen, qa)
for cm, scen in cm_scen_combos
for qa in line_qa
]
# debug by looking at mock_ax.plot.call_args_list
assert all([_is_in_calls(c, mock_ax.plot.call_args_list) for c in plot_calls])