def generate_report(state_code: str):
print(f"Received request for {state_code}.")
state = state_code_lookup[state_code]
normalized_state = state.replace(" and ", " And ").replace(" & ", " And ")
blobs = {
f"pipeline/est/{state_code}_state_Rt.csv":
f"/tmp/state_Rt_{state_code}.csv",
f"pipeline/est/{state_code}_district_Rt.csv":
f"/tmp/district_Rt_{state_code}.csv",
f"pipeline/commons/maps/{state_code}.json":
f"/tmp/state_{state_code}.geojson"
} if normalized_state not in dissolved_states else {
f"pipeline/est/{state_code}_state_Rt.csv":
f"/tmp/state_Rt_{state_code}.csv",
}
for (blob_name, filename) in blobs.items():
bucket.blob(blob_name).download_to_filename(filename)
print(f"Downloaded estimates for {state_code}.")
state_Rt = pd.read_csv(f"/tmp/state_Rt_{state_code}.csv",
parse_dates=["dates"],
index_col=0)
plt.close("all")
dates = [pd.Timestamp(date).to_pydatetime() for date in state_Rt.dates]
plt.Rt(dates, state_Rt.Rt_pred, state_Rt.Rt_CI_lower, state_Rt.Rt_CI_upper, CI)\
.axis_labels("date", "$R_t$")\
.title(f"{state}: $R_t$ over time", ha = "center", x = 0.5)\
.adjust(left = 0.11, bottom = 0.16)
plt.gcf().set_size_inches(3840 / 300, 1986 / 300)
plt.savefig(f"/tmp/{state_code}_Rt_timeseries.png")
plt.close()
print(f"Generated timeseries plot for {state_code}.")
# check output is at least 50 KB
timeseries_size_kb = os.stat(
f"/tmp/{state_code}_Rt_timeseries.png").st_size / 1000
print(f"Timeseries artifact size: {timeseries_size_kb} kb")
assert timeseries_size_kb > 50
bucket.blob(
f"pipeline/rpt/{state_code}_Rt_timeseries.png").upload_from_filename(
f"/tmp/{state_code}_Rt_timeseries.png", content_type="image/png")
if normalized_state not in (island_states + dissolved_states):
district_Rt = pd.read_csv(f"/tmp/district_Rt_{state_code}.csv",
parse_dates=["dates"],
index_col=0)
latest_Rt = district_Rt[district_Rt.dates == district_Rt.dates.max(
)].set_index("district")["Rt_pred"].to_dict()
top10 = [(k, "> 3.0" if v > 3 else f"{v:.2f}") for (k, v) in sorted(
latest_Rt.items(), key=lambda t: t[1], reverse=True)[:10]]
gdf = gpd.read_file(f"/tmp/state_{state_code}.geojson")
gdf["Rt"] = gdf.district.map(latest_Rt)
fig, ax = plt.subplots()
fig.set_size_inches(3840 / 300, 1986 / 300)
plt.choropleth(gdf, title = None, mappable = plt.get_cmap(0.75, 2.5), fig = fig, ax = ax)\
.adjust(left = 0)
plt.sca(fig.get_axes()[0])
plt.PlotDevice(fig).title(f"{state}: $R_t$ by district",
ha="center",
x=0.5)
plt.axis('off')
plt.savefig(f"/tmp/{state_code}_Rt_choropleth.png", dpi=300)
plt.close()
print(f"Generated choropleth for {state_code}.")
# check output is at least 100 KB
choropleth_size_kb = os.stat(
f"/tmp/{state_code}_Rt_choropleth.png").st_size / 1000
print(f"Choropleth artifact size: {choropleth_size_kb} kb")
assert choropleth_size_kb > 100
bucket.blob(f"pipeline/rpt/{state_code}_Rt_choropleth.png"
).upload_from_filename(
f"/tmp/{state_code}_Rt_choropleth.png",
content_type="image/png")
else:
print(f"Skipped choropleth for {state_code}.")
if normalized_state not in dissolved_states:
fig, ax = plt.subplots(1, 1)
ax.axis('tight')
ax.axis('off')
table = ax.table(cellText=top10,
colLabels=["district", "$R_t$"],
loc='center',
cellLoc="center")
table.scale(1, 2)
for (row, col), cell in table.get_celld().items():
if (row == 0):
cell.set_text_props(fontfamily=plt.theme.label["family"],
fontsize=plt.theme.label["size"],
fontweight="semibold")
else:
cell.set_text_props(fontfamily=plt.theme.label["family"],
fontsize=plt.theme.label["size"],
fontweight="light")
plt.PlotDevice().title(f"{state}: top districts by $R_t$",
ha="center",
x=0.5)
plt.savefig(f"/tmp/{state_code}_Rt_top10.png", dpi=600)
plt.close()
print(f"Generated top 10 district listing for {state_code}.")
# check output is at least 50 KB
top10_size_kb = os.stat(
f"/tmp/{state_code}_Rt_top10.png").st_size / 1000
print(f"Top 10 listing artifact size: {top10_size_kb} kb")
assert top10_size_kb > 50
bucket.blob(
f"pipeline/rpt/{state_code}_Rt_top10.png").upload_from_filename(
f"/tmp/{state_code}_Rt_top10.png", content_type="image/png")
else:
print(f"Skipped top 10 district listing for {state_code}.")
# sleep for 15 seconds to ensure the images finish saving
time.sleep(15)
print(f"Uploaded artifacts for {state_code}.")
return "OK!"
[_.set_alpha(0.3) for _ in bars]
# plt.hlines(Rt_TTn, xmin = -3, xmax = len(md) + 1, linestyles = "dotted", colors = plt.BLK)
# plt.vlines(-1, ymin = 0, ymax = 6, linewidth = 3, colors = "black")
plt.ylim(bottom=0, top=6)
# plt.xlim(left = -3, right = len(md))
plt.xlim(left=-1, right=len(md))
plt.PlotDevice().ylabel("reproductive rate ($R_t$)\n").xlabel("\nstate")
# plt.xticks(ticks = [-2] + list(range(len(md))), labels = ["India"] + [state_name_lookup[_][:2] for _ in Rt_dist.keys()], fontsize = "20")
plt.xticks(list(range(len(md))),
labels=[state_name_lookup[_][:2] for _ in Rt_dist.keys()],
fontsize="20")
plt.yticks(fontsize="20")
plt.subplots_adjust(left=0.06, right=0.94)
plt.gca().grid(False, axis="y")
plt.legend(fontsize="20")
plt.gcf().set_size_inches((16.8 * 2, 9.92))
plt.show()
# supplement: Rt distribution (district)
sic = simulation_initial_conditions.sort_values("Rt", ascending=False)
*_, bars = plt.errorbar(x=range(len(sic)),
y=sic.Rt,
yerr=[sic.Rt - sic.Rt_lower, sic.Rt_upper - sic.Rt],
fmt="s",
color=plt.BLK,
ms=2)
for _ in bars:
_.set_alpha(0.3)
plt.xlim(0, len(sic))
plt.ylim(0, 8)
plt.subplots_adjust(left=0.02, bottom=0.02, right=0.98, top=0.98)
"/Users/satej/Downloads/pipeline_est_MH_state_Rt (1).csv",
parse_dates=["dates"],
index_col=0)
district_Rt = pd.read_csv(
"/Users/satej/Downloads/pipeline_est_MH_district_Rt (2).csv",
parse_dates=["dates"],
index_col=0)
latest_Rt = district_Rt[district_Rt.dates == district_Rt.dates.max(
)].set_index("district")["Rt_pred"].to_dict()
plt.Rt(list(state_Rt.dates), state_Rt.Rt_pred, state_Rt.Rt_CI_lower, state_Rt.Rt_CI_upper, CI)\
.axis_labels("date", "$R_t$")\
.title("Maharashtra: $R_t$ over time", ha = "center", x = 0.5)\
.adjust(left = 0.11, bottom = 0.16)
plt.gcf().set_size_inches(3840 / 300, 1986 / 300)
plt.savefig("./MH_Rt_timeseries.png")
plt.clf()
gdf = gpd.read_file("data/maharashtra.json", dpi=600)
gdf["Rt"] = gdf.district.map(latest_Rt)
fig, ax = plt.subplots()
fig.set_size_inches(3840 / 300, 1986 / 300)
plt.choropleth(gdf, title = None, mappable = plt.get_cmap(0.75, 2.5), fig = fig, ax = ax)\
.adjust(left = 0)
plt.sca(fig.get_axes()[0])
plt.PlotDevice(fig).title(f"{state}: $R_t$ by district", ha="center", x=0.5)
plt.axis('off')
plt.savefig(f"./{state_code}_Rt_choropleth.png", dpi=300)
plt.clf()