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reverse colorbar #65

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reverse colorbar #65

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f8c38bc
reverse colorbar
Makhsuda Jun 24, 2020
30f787c
add map with confirmed cases per capita
Makhsuda Jun 26, 2020
30f3723
add lineplot to see the rise of confirmed case numbers
Makhsuda Jun 26, 2020
c065f06
add noninteractive graphs
Makhsuda Jun 26, 2020
c086b33
add datasets' original links
Makhsuda Jul 2, 2020
d1909ca
change structure of the code
Makhsuda Jul 3, 2020
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70 changes: 9 additions & 61 deletions analysis/db/us_map/choroplethMap.py
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Original file line number Diff line number Diff line change
@@ -1,13 +1,13 @@
import plotly.figure_factory as ff
import numpy as np
import plotly.io as pio
from pyprojroot import here
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
import pandas as pd
from urllib.request import urlopen
import json
import pathlib as pl

import json
with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
counties = json.load(response)

Expand All @@ -34,7 +34,10 @@
molten_df['date_iso'] = pd.to_datetime(molten_df['date'], format="%m/%d/%y") # change date to ISO8601 standard format

fips = molten_df['fips_str'].tolist()
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Because of the below changes, you don't need this line anymore since you're passing in the column of values into the plotting function

# max_val = molten_df['value'].max()

# plt.show()
# color_map = plt.cm.get_cmap('viridis')
# reversed_viridis = color_map.reversed()


fig = px.choropleth(molten_df,
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I created a dataframe that was just a subset of a particular date, and then used that subseted dataframe to plot the figure

plot_data = molten_df[molten_df.date_iso == '2020-02-01']
fig = px.choropleth(plot_data,
                    geojson=counties,
                    locations=plot_data.fips_str,
                    color='value',
                    #animation_frame='date',
                    hover_data=['State', 'value'],
                    color_continuous_scale='viridis_r',
                    range_color=(0, 300),
                    scope="usa",
                    title='Confirmed cases',
                    labels={'value': 'confirmed cases'}
                    )

Expand All @@ -43,73 +46,18 @@
color='value',
animation_frame='date',
hover_data=['State', 'value'],
color_continuous_scale="Viridis",
color_continuous_scale='viridis_r',
range_color=(0, 300),
scope="usa",
title='Confirmed cases',
labels={'value': 'confirmed cases'}
)
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
# fig.show()

# save out figure
# save out working data
pl.Path(here("./output/maps", warn=False)).mkdir(parents=True, exist_ok=True)
pio.write_html(fig,
file=str(here("./output/maps/choropleth_us_cases.html", warn=False)),
auto_open=False)


# ChoroplethMap using FIPS from merged data
'''
confirmed_df = pd.read_csv('https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/'
'csse_covid_19_time_series/time_series_covid19_confirmed_US.csv')

loc_df = pd.read_excel(here('./data/db/original/maps/State_FIPS.xlsx'))
merged_df = pd.merge(loc_df, confirmed_df, right_on='Admin2', left_on='Name')

merged_df['fips_str'] = merged_df['FIPS_x'].apply(lambda x: f'{x:05.0f}') # left pad with 0 for 5 digits

colorscale = ["#f7fbff", "#ebf3fb", "#deebf7", "#d2e3f3", "#c6dbef", "#b3d2e9", "#9ecae1",
"#85bcdb", "#6baed6", "#57a0ce", "#4292c6", "#3082be", "#2171b5", "#1361a9",
"#08519c", "#0b4083", "#08306b"]

molten_df = merged_df.melt(
id_vars=['FIPS_x', 'Name', 'State', 'UID', 'iso2', 'iso3', 'code3', 'FIPS_y', 'Admin2',
'Province_State', 'Country_Region', 'Lat', 'Long_', 'Combined_Key', 'fips_str'],
var_name=['date']
)

molten_df['date_iso'] = pd.to_datetime(molten_df['date'], format="%m/%d/%y") # change date to ISO8601 standard format

endpts = list(np.linspace(0, 3000, len(colorscale) - 1))
fips = molten_df['fips_str'].tolist()
values = molten_df['date_iso'].tolist()

fig = ff.create_choropleth(
fips=fips,
# values=values,
scope=['usa',
# 'Alaska',
# 'Puerto Rico',
# 'American Samoa',
# 'Commonwealth of the Northern Mariana Islands', 'Guam',
# 'United States Virgin Islands'
],
binning_endpoints=endpts,
colorscale=colorscale,
show_state_data=True,
show_hover=True,
centroid_marker={'opacity': 0},
asp=2.9,
title='Confirmed cases on April 22',
legend_title='# confirmed cases',
text=molten_df['fips']
)

fig.layout.template = None
fig.show()
'''


auto_open=True)