def _get_land_solution_analytics(land_survey):
charts = {}
for col, title in [
("% tla", "% of land allocation reached"),
("% world alloc", "% of World land allocated to solution"),
("avg abatement cost", "Average abatement cost ($/tCO2)"),
]:
charts[col] = make_comparison_chart(land_survey[col], col, "Solution",
title)
has_regions = (land_survey["has regional data"].map({
True: "Yes",
False: "No"
}).value_counts().reset_index())
has_regions.columns = ["has_region", "count"]
charts["has_regions"] = make_pie_chart(
has_regions,
"has_region",
"count",
"Has regional adoption data? (Land solutions)",
)
issues_with_regional_data = get_issues_with_regional_data(land_survey)
charts["issues_with_regional_data"] = make_pie_chart(
issues_with_regional_data,
"type",
"count",
"Issues with regional data in Custom Adoption scenarios",
)
return charts
def _get_summary_charts(all_solutions, py_solutions):
excel_python_count = get_excel_python_count(all_solutions, py_solutions)
pds_adoption_basis_counts = get_pds_adoption_basis_counts(py_solutions)
ref_adoption_basis_counts = get_ref_adoption_basis_counts(py_solutions)
custom_pds_data_basis_counts = get_custom_pds_data_basis_counts()
scenarios_per_solution = get_scenarios_per_solution(py_solutions)
charts = {}
charts["solution_implementation"] = make_pie_chart(
excel_python_count, "type", "count", "Solution Implementation")
charts["pds_adoption_basis"] = make_pie_chart(pds_adoption_basis_counts,
"type", "count",
"PDS Adoption Basis")
charts["ref_adoption_basis_counts"] = make_pie_chart(
ref_adoption_basis_counts, "type", "count", "REF Adoption Basis")
charts["custom_pds_data_basis_counts"] = make_pie_chart(
custom_pds_data_basis_counts, "type", "count",
"PDS Custom Adoption Basis")
charts["num_scenario_per_solution"] = make_hist_chart(
scenarios_per_solution.scenario,
"Num scenarios per solution",
"scenario",
"solution",
bins=15,
)
return charts
def test_make_pie_chart():
data = pd.DataFrame({"type": ["A", "B", "C"], "count": [1, 2, 3]})
result = make_pie_chart(data, "type", "count", "Title", as_html=False)
assert result.title.text == "Title"
data_source = result.renderers[0].data_source
assert data_source.properties_with_values()["data"]["type"] == ["A", "B", "C"]
assert data_source.properties_with_values()["data"]["count"] == [1, 2, 3]
result = make_pie_chart(data, "type", "count", "Title", as_html=True)
assert '<script type="text/javascript">' in result["script"]
assert "data-root-id" in result["body"]
def _get_regional_charts(survey_data):
charts = {}
for type_ in ["TAM", "Adoption"]:
col = f"RegionalFraction{type_}"
regional_non_zero = get_regional_nonzero(survey_data, col)
regional_percent = get_regional_as_percent(survey_data, col)
charts[f"regional_nonzero_{type_}"] = make_pie_chart(
regional_non_zero, "type", "count", f"Has Regional {type_} Data?")
charts[f"regional_{type_}_percent"] = make_hist_chart(
regional_percent,
f"Regional {type_} as % of the World",
"percentage",
"number of scenarios",
bins=20,
)
charts[f"pds_adoption_r_squared"] = make_hist_chart(
survey_data["Rvalue"].dropna()**2,
f"Linearity of PDS Adoption",
"R squared value",
"number of scenarios",
bins=30,
)
return charts