def set_dynamic_step(min_shade, max_shade):
steps = [1, 2, 5, 10]
shade_range = h.roundup_nearest_ten(max_shade) - h.rounddown_nearest_ten(min_shade)
# default to step that is a whole number in multiple of 1,2 5 or 10, max of 10 shades?
for step in steps:
if shade_range / step <= 10:
return step
def set_dynamic_step(min_shade, max_shade):
steps = [1, 2, 5, 10]
shade_range = h.roundup_nearest_ten(max_shade) - h.rounddown_nearest_ten(
min_shade)
# default to step that is a whole number in multiple of 1,2 5 or 10, max of 10 shades?
for step in steps:
if shade_range / step <= 10:
return step
def create_choropleth(output_path,
json_file,
shade_data_file,
palette_colour,
output_type,
step,
min_range,
max_range,
reverse,
dynamic=True):
reset_output()
if isinstance(shade_data_file, str):
results_data = pd.read_csv(shade_data_file)
else:
results_data = shade_data_file
# calculate the maximum number of shades to show in final output if not user specified
if dynamic:
min_range = h.rounddown_nearest_ten(
np.nanmin(list(results_data.result * 100)))
max_range = h.roundup_nearest_ten(
np.nanmax(list(results_data.result * 100)))
step = set_dynamic_step(min_range, max_range)
# check for a whole number in user defined values - return an error if not
shade_no = int(((max_range + step) - min_range) / step)
plot_dict = {
} # dict used to store each plots data - one for each shade to display.
lower_limit = 0
for upper_limit in range(min_range, max_range + step, step):
temp_df = results_data[(results_data['result'] > lower_limit / 100)
& (results_data['result'] <= upper_limit / 100)]
if len(temp_df.index) > 0:
plot_dict[str(upper_limit)] = dict(
zip(temp_df.district, temp_df.result))
lower_limit = upper_limit
# separate geojson file to match the plots above
geojson_dict = {} # dict used to store each plots geo data
delete_list = [] # districts to delete once all with a colour are assigned
with open(json_file) as base_map:
map_data = json.load(base_map)
id_key = 'LAD11CD' # 'LSOA11CD', 'LAD11CD'
for key, value in plot_dict.items():
geojson_list = []
for feature in map_data['features']:
if str(feature['properties'][id_key]) in value:
geojson_list.append(feature)
# but also remove the feature from the map_data[features] file
delete_list.append(str(feature['properties'][id_key]))
geojson_dict[key] = geojson_list
# if any features have no defined output add them but assign them a zero value.
map_data['features'] = [
feature for feature in map_data['features']
if feature['properties'][id_key] not in delete_list
]
# add a corresponding plot for the shade for those 0 values
if bool(map_data['features']):
plot_dict['0'] = dict((feature['properties'][id_key], 0)
for feature in map_data['features'])
geojson_dict['0'] = [feature for feature in map_data['features']]
# create the colour palette to use
colours = select_palette(shade_no, palette_colour, reverse)
source_dict = {} # a dict that will store all the columndatasources
for key, value in geojson_dict.items():
define_features(value, plot_dict[key], key, source_dict, min_range,
max_range, step, colours, dynamic)
tools = "pan,wheel_zoom,box_zoom,reset,hover,save"
title = output_type + " by LA"
p = figure(width=900, height=900, title=title, tools=tools)
for key in sorted(source_dict.keys(), key=int, reverse=True):
p.patches('x',
'y',
source=source_dict[key],
fill_color='color',
fill_alpha=0.7,
line_color="white",
line_width=0.15,
legend=str(key))
hover = p.select_one(HoverTool)
hover.point_policy = "follow_mouse"
hover.tooltips = [
("Name", "@name"),
(output_type, "@rate%"),
("Code", "@code"),
]
output_dir = os.path.join(output_path, "charts")
if os.path.isdir(output_dir) is False:
os.mkdir(output_dir)
suffix = '.html'
output_filename = os.path.join(output_type + suffix)
output_file_path = os.path.join(output_dir, output_filename)
output_file(output_file_path, title=title, mode='inline')
save(p)
def create_choropleth(output_path, json_file, shade_data_file, palette_colour, output_type, step, min_range, max_range,
reverse, dynamic=True):
reset_output()
if isinstance(shade_data_file, str):
results_data = pd.read_csv(shade_data_file)
else:
results_data = shade_data_file
# calculate the maximum number of shades to show in final output if not user specified
if dynamic:
min_range = h.rounddown_nearest_ten(np.nanmin(list(results_data.result*100)))
max_range = h.roundup_nearest_ten(np.nanmax(list(results_data.result*100)))
step = set_dynamic_step(min_range, max_range)
# check for a whole number in user defined values - return an error if not
shade_no = int(((max_range+step)-min_range)/step)
plot_dict = {} # dict used to store each plots data - one for each shade to display.
lower_limit = 0
for upper_limit in range(min_range, max_range+step, step):
temp_df = results_data[(results_data['result'] > lower_limit/100) & (results_data['result'] <= upper_limit/100)]
if len(temp_df.index) > 0:
plot_dict[str(upper_limit)] = dict(zip(temp_df.district, temp_df.result))
lower_limit = upper_limit
# separate geojson file to match the plots above
geojson_dict = {} # dict used to store each plots geo data
delete_list = [] # districts to delete once all with a colour are assigned
with open(json_file) as base_map:
map_data = json.load(base_map)
id_key = 'LAD11CD' # 'LSOA11CD', 'LAD11CD'
for key, value in plot_dict.items():
geojson_list = []
for feature in map_data['features']:
if str(feature['properties'][id_key]) in value:
geojson_list.append(feature)
# but also remove the feature from the map_data[features] file
delete_list.append(str(feature['properties'][id_key]))
geojson_dict[key] = geojson_list
# if any features have no defined output add them but assign them a zero value.
map_data['features'] = [feature for feature in map_data['features']
if feature['properties'][id_key] not in delete_list]
# add a corresponding plot for the shade for those 0 values
if bool(map_data['features']):
plot_dict['0'] = dict((feature['properties'][id_key], 0) for feature in map_data['features'])
geojson_dict['0'] = [feature for feature in map_data['features']]
# create the colour palette to use
colours = select_palette(shade_no, palette_colour, reverse)
source_dict = {} # a dict that will store all the columndatasources
for key, value in geojson_dict.items():
define_features(value, plot_dict[key], key, source_dict, min_range, max_range, step, colours, dynamic)
tools = "pan,wheel_zoom,box_zoom,reset,hover,save"
title = output_type + " by LA"
p = figure(width=900, height=900, title=title, tools=tools)
for key in sorted(source_dict.keys(), key=int, reverse=True):
p.patches('x', 'y', source=source_dict[key],
fill_color='color', fill_alpha=0.7,
line_color="white", line_width=0.15, legend=str(key))
hover = p.select_one(HoverTool)
hover.point_policy = "follow_mouse"
hover.tooltips = [
("Name", "@name"),
(output_type, "@rate%"),
("Code", "@code"),
]
output_dir = os.path.join(output_path, "charts")
if os.path.isdir(output_dir) is False:
os.mkdir(output_dir)
suffix = '.html'
output_filename = os.path.join(output_type + suffix)
output_file_path = os.path.join(output_dir, output_filename)
output_file(output_file_path, title=title, mode='inline')
save(p)
