def test_timedynamic_geo_json():
"""
tests folium.plugins.TimeSliderChoropleth
"""
import geopandas as gpd
assert 'naturalearth_lowres' in gpd.datasets.available
datapath = gpd.datasets.get_path('naturalearth_lowres')
gdf = gpd.read_file(datapath)
n_periods = 3
dt_index = pd.date_range('2016-1-1', periods=n_periods, freq='M').strftime('%s')
styledata = {}
for country in gdf.index:
pdf = pd.DataFrame(
{'color': np.random.normal(size=n_periods),
'opacity': np.random.normal(size=n_periods)},
index=dt_index)
styledata[country] = pdf.cumsum()
max_color, min_color = 0, 0
for country, data in styledata.items():
max_color = max(max_color, data['color'].max())
min_color = min(max_color, data['color'].min())
cmap = linear.PuRd_09.scale(min_color, max_color)
# Define function to normalize column into range [0,1]
def norm(col):
return (col - col.min())/(col.max()-col.min())
for country, data in styledata.items():
data['color'] = data['color'].apply(cmap)
data['opacity'] = norm(data['opacity'])
styledict = {str(country): data.to_dict(orient='index') for
country, data in styledata.items()}
m = folium.Map((0, 0), tiles='Stamen Watercolor', zoom_start=2)
time_slider_choropleth = TimeSliderChoropleth(
gdf.to_json(),
styledict
)
time_slider_choropleth.add_to(m)
rendered = time_slider_choropleth._template.module.script(time_slider_choropleth)
m._repr_html_()
out = m._parent.render()
assert '<script src="https://d3js.org/d3.v4.min.js"></script>' in out
# We verify that data has been inserted correctly
expected_timestamps = """var timestamps = ["1454198400", "1456704000", "1459382400"];""" # noqa
assert expected_timestamps.split(';')[0].strip() == rendered.split(';')[0].strip()
expected_styledict = json.dumps(styledict, sort_keys=True, indent=2)
assert expected_styledict in rendered
def test_timedynamic_geo_json():
"""
tests folium.plugins.TimeSliderChoropleth
"""
import geopandas as gpd
assert 'naturalearth_lowres' in gpd.datasets.available
datapath = gpd.datasets.get_path('naturalearth_lowres')
gdf = gpd.read_file(datapath)
n_periods = 3
dt_index = pd.date_range('2016-1-1', periods=n_periods, freq='M').strftime('%s')
styledata = {}
for country in gdf.index:
pdf = pd.DataFrame(
{'color': np.random.normal(size=n_periods),
'opacity': np.random.normal(size=n_periods)},
index=dt_index)
styledata[country] = pdf.cumsum()
max_color, min_color = 0, 0
for country, data in styledata.items():
max_color = max(max_color, data['color'].max())
min_color = min(max_color, data['color'].min())
cmap = linear.PuRd_09.scale(min_color, max_color)
# Define function to normalize column into range [0,1]
def norm(col):
return (col - col.min())/(col.max()-col.min())
for country, data in styledata.items():
data['color'] = data['color'].apply(cmap)
data['opacity'] = norm(data['opacity'])
styledict = {str(country): data.to_dict(orient='index') for
country, data in styledata.items()}
m = folium.Map((0, 0), tiles='Stamen Watercolor', zoom_start=2)
time_slider_choropleth = TimeSliderChoropleth(
gdf.to_json(),
styledict
)
time_slider_choropleth.add_to(m)
rendered = time_slider_choropleth._template.module.script(time_slider_choropleth)
m._repr_html_()
out = m._parent.render()
assert '<script src="https://d3js.org/d3.v4.min.js"></script>' in out
# We verify that data has been inserted correctly
expected_timestamps = """var timestamps = ["1454198400", "1456704000", "1459382400"];""" # noqa
assert expected_timestamps.split(';')[0].strip() == rendered.split(';')[0].strip()
expected_styledict = json.dumps(styledict, sort_keys=True, indent=2)
assert expected_styledict in rendered
def create_gdp_viz():
'''
Load and pre-process the geojson file
'''
geojson_path = os.path.normpath(
os.path.join(script_dir_path, '..', 'data', 'borders_geo.json'))
world_geojson = gpd.read_file(geojson_path)
world_geojson.drop(columns=['ISO_A2', 'ADMIN'], inplace=True)
world_geojson.drop(world_geojson[world_geojson['ISO_A3'] == '-99'].index,
inplace=True)
country_list = world_geojson['ISO_A3'].tolist()
'''
Load and pre-process the GDP data
'''
# Load the GDP data
df_GDP_path = os.path.normpath(
os.path.join(script_dir_path, '..', 'data',
'GDP_per_capita_world_data.csv'))
df_GDP = pd.read_csv(df_GDP_path, index_col='Country Code', skiprows=4)
# Drop unnecessary data
df_GDP.drop(labels='2020', axis=1, inplace=True)
csv_country_list = df_GDP.index.tolist()
country_list = list(set(country_list).intersection(csv_country_list))
df_GDP.drop(df_GDP[~df_GDP.index.isin(country_list)].index, inplace=True)
world_geojson.drop(
world_geojson[~world_geojson['ISO_A3'].isin(country_list)].index,
inplace=True)
country_list.sort()
# Create an enumerated country dict for id mapping
country_dict = {k: v for v, k in enumerate(country_list)}
world_geojson['country_id'] = world_geojson['ISO_A3'].map(country_dict)
# Count min and max GDP values
min_GDP_val, max_GDP_val = df_GDP[df_GDP.columns[4:]].min().min(), df_GDP[
df_GDP.columns[4:]].max().max()
# Create a color list
color_list = [
'#808080', '#A50026', '#D73027', '#F46D43', '#FDAE61', '#FEE08B',
'#FFFFBF', '#D9EF8B', '#A6D96A', '#66BD63', '#1A9850', '#006837'
]
# Create a list of geometrically spaced numbers over a min-max interval
bins = np.geomspace(min_GDP_val, max_GDP_val, 12)
# Replace NaNs (records with no data available) with '-1'
df_GDP.fillna(-1, inplace=True)
# Add NaN category to the bins
bins = np.insert(bins, 0, -1.)
bins = bins.tolist()
# Append 'color_[year]' columns to the GDP DataFrame
year = 1960
while year <= 2019:
pasted_col_id = df_GDP.columns.get_loc(str(year)) + 1
col_value = pd.cut(df_GDP[str(year)],
bins,
include_lowest=True,
labels=[
'#808080', '#A50026', '#D73027', '#F46D43',
'#FDAE61', '#FEE08B', '#FFFFBF', '#D9EF8B',
'#A6D96A', '#66BD63', '#1A9850', '#006837'
])
df_GDP.insert(loc=pasted_col_id,
column='color_' + str(year),
value=col_value)
year += 1
print(df_GDP)
'''
Create appropriately formatted dictionary that the TimeSliderChoropleth will receive as an input
'''
gdp_dict = {}
for country_code in df_GDP.index.tolist():
country_id = str(country_dict[country_code])
gdp_dict[country_id] = {}
year = 1960
while year <= 2019:
dt_obj = datetime(year=year, month=12, day=31)
year_in_ms = str(time.mktime(dt_obj.timetuple()))
color_hex = df_GDP.at[country_code, 'color_' + str(year)]
gdp_dict[country_id][year_in_ms] = {
'color': color_hex,
'opacity': 0.7
}
year += 1
'''
Initialize the map
'''
map_GDP = folium.Map(location=[0, 0],
zoom_start=4,
max_bounds=True,
min_zoom=3)
'''
Create the map content and add it to the map object
'''
# Create the choropleth
choropleth = TimeSliderChoropleth(
world_geojson.set_index('country_id').to_json(), styledict=gdp_dict)
choropleth.add_to(map_GDP)
# Create the map legend
legend_labels_dict = {}
i = 0
for color in color_list:
if i == 0:
legend_labels_dict[color_list[i]] = 'No data'
elif i == len(color_list) - 1:
legend_labels_dict[color_list[i]] = '> ' + str(round(bins[i],
2)) + '$'
break
else:
legend_labels_dict[color] = str(round(
bins[i], 2)) + '$' + ' - ' + str(round(bins[i + 1], 2)) + '$'
i += 1
template = utils.create_legend(caption='GDP per capita in USD',
legend_labels=legend_labels_dict)
macro = MacroElement()
macro._template = Template(template)
map_GDP.get_root().add_child(macro)
'''
Save completed map viz to an appropriate folder
'''
map_GDP.save(
os.path.join(script_dir_path, '..', 'webapp', 'templates',
'GDP_viz.html'))
print('Successfully created the GDP viz!')