[35, 138, 10, 60],
[85, 175, -20, 27],
[-130, -25, -50, 25],
]
t0 = time.time()
i = 0
map, mapTitle = draw_map(region_groups[i], extents[i], geojson, country_shapes,
df, labels[i])
cr = (dfid_topics[dfid_topics["DFID region"] == labels[i]].sort_values(
'ldev').reset_index())
bar = draw_bar(cr)
header = dbc.Row(
[
dbc.Col(html.Div(), ),
dbc.Col(html.Div(html.H1("Climate and Health", className="mt-4")), ),
dbc.Col(html.Div(), )
],
id="header",
)
bar_buttons = []
for j, meta_topic in enumerate(meta_topics):
bar_buttons.append(
html.Button(meta_topic,
id=f"barfilter-{j}",
def region_interaction(i, selectedData, clickData, relThreshold, bf_0, bf_1,
bf_2, bf_3, bf_4, relayoutData, clearTopics, storeData):
rel_ids = doc_df.loc[doc_df['prediction'] >= 1 - relThreshold, "id"]
ctx = dash.callback_context
if "region-select" in ctx.triggered[0]['prop_id']:
relayoutData = None
selectedData = None
topic_selection = "No topics selected"
place_ids = []
rel_df = None
# did we just, or have we cleared topics without since clicking on them?
if "clear-topics" in ctx.triggered[0]['prop_id']:
storeData["topics"] = []
storeData["cleared"] = True
# have we just clicked on a topic bar
if "bar.clickData" in ctx.triggered[0]['prop_id']:
storeData["cleared"] = False
if clickData is not None:
cd = clickData["points"][0]["label"]
if cd in storeData["topics"]:
storeData["topics"] = [
x for x in storeData["topics"] if x != cd
]
else:
storeData["topics"] += [cd]
# Filter topics to just include those in the metacategories selected
sub_topics = dfid_topics[dfid_topics["DFID region"] == labels[i]]
topicids = set([])
for j, bf in enumerate([bf_0, bf_1, bf_2, bf_3, bf_4]):
if bf is None:
continue
elif bf % 2 == 1:
topicids = topicids | set(topic_df.loc[
topic_df['Aggregated meta-topic'] == meta_topics[j], "id"])
if len(topicids) > 0:
sub_topics = sub_topics[sub_topics.topic_id.isin(topicids)]
cr = (sub_topics.sort_values('ldev').reset_index())
if selectedData is not None:
place_ids = [x["id"] for x in selectedData["points"] if "id" in x]
if len(place_ids) == 1:
place = df.loc[df['place_doc_id'] == place_ids[0]]
place_ids = df.loc[(df['lat'] == place.lat.values[0]) &
(df['lon'] == place.lon.values[0]),
"place_doc_id"]
docids = df.loc[df['place_doc_id'].isin(place_ids), "doc_id"]
rel_df = table_df[(table_df['id'].isin(docids))
& (table_df['id'].isin(rel_ids))]
else:
docids = df.loc[df['DFID region'] == labels[i], "doc_id"]
rel_df = table_df[(table_df["id"].isin(docids))
& (table_df['id'].isin(rel_ids))]
sel_df = df
sel_df["subset"] = 0
sel_df.loc[sel_df["id"].isin(rel_df["id"]), "subset"] = 1
gdt = (sel_df[["id", "subset"]].merge(dts, left_on="id",
right_on="doc_id").groupby([
'topic_id', 'subset'
])['score'].sum().reset_index())
# How many documents are there in each topic
tns = (rel_df[["id"]].merge(dts, left_on="id", right_on="doc_id").query(
'score>0.01').groupby('topic_id')['score'].count().to_frame(
name="count").sort_values('count').reset_index())
gdt['share'] = gdt['score'] / gdt.groupby('subset')['score'].transform(
'sum')
gdt = gdt.merge(dt_sum)
gdt['deviation'] = gdt['share'] / gdt['total_share']
gdt['ldev'] = np.log(gdt['deviation'])
gdt = gdt[gdt['subset'] == 1]
if len(topicids) > 0:
gdt = gdt[gdt.topic_id.isin(topicids)]
cr = (gdt.merge(topic_df, left_on="topic_id",
right_on="id").merge(tns, how="left").sort_values('ldev'))
if len(storeData["topics"]) > 0:
topics = topic_df[topic_df["short_title"].isin(
storeData["topics"])]["id"].values
topic_selection = ", ".join(storeData["topics"])
for top in topics:
rel_df = rel_df[(rel_df[top] > 0.01)]
rel_df["topics"] = rel_df[topics].sum()
rel_df = rel_df.sort_values("topics", ascending=False)
if len(place_ids) > 0:
place_ids = set(place_ids) & set(
df.loc[df["doc_id"].isin(rel_df["id"]), "place_doc_id"])
else:
place_ids = set(df.loc[df["doc_id"].isin(rel_df["id"]),
"place_doc_id"])
cr = cr.reset_index(drop=True)
sbar = cr.loc[cr["short_title"].isin(storeData["topics"])].index
bar = draw_bar(cr, sbar)
if "barfilter" not in ctx.triggered[0]['prop_id']:
if relayoutData is not None:
if "autosize" not in relayoutData:
if "xaxis.range[0]" in relayoutData:
bar.update_xaxes(range=[
relayoutData["xaxis.range[0]"],
relayoutData["xaxis.range[1]"]
])
if "yaxis.range[0]" in relayoutData:
bar.update_yaxes(range=[
relayoutData["yaxis.range[0]"],
relayoutData["yaxis.range[1]"]
])
map, mapTitle = draw_map(region_groups[i], extents[i], geojson,
country_shapes, df[df['id'].isin(rel_ids)],
labels[i], place_ids)
if rel_df is not None:
rel_df = rel_df.to_dict('records')
return bar, map, rel_df, topic_selection, storeData
'''How to install matplotlib ?'''
from graphs import draw_multiline, draw_bar
from CsvReader import CsvReader
# draw_multiline()
# csv_reader = CsvReader("movies.csv")
# csv_reader.read_csv()
#csv_reader.draw_films_by_years()
# csv_reader.draw_films_by_profits()
draw_bar()