Beispiel #1
0
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)
Beispiel #4
0
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)