25 changed files with 78972 additions and 8 deletions
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<html><head></head><body> <a href="armen (lichaamsdeel).html">armen (lichaamsdeel)<a><br/> |
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<a href="avond.html">avond<a><br/> |
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<a href="bij (insect).html">bij (insect)<a><br/> |
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<a href="blad (aan een boom).html">blad (aan een boom)<a><br/> |
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<a href="borst (lichaamsdeel).html">borst (lichaamsdeel)<a><br/> |
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<a href="dag.html">dag<a><br/> |
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<a href="deurtje.html">deurtje<a><br/> |
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<a href="geel.html">geel<a><br/> |
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<a href="gegaan.html">gegaan<a><br/> |
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<a href="gezet.html">gezet<a><br/> |
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<a href="heel.html">heel<a><br/> |
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<a href="index.html">index<a><br/> |
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<a href="kaas.html">kaas<a><br/> |
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<a href="koken.html">koken<a><br/> |
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<a href="oog.html">oog<a><br/> |
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<a href="sprak (toe).html">sprak (toe)<a><br/> |
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<a href="tand.html">tand<a><br/> |
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<a href="trein.html">trein<a><br/> |
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<a href="vis.html">vis<a><br/> |
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<a href="zaterdag.html">zaterdag<a></body></html> |
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import folium |
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from jupyter_progressbar import ProgressBar |
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from pygeoif.geometry import mapping |
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from shapely.geometry.geo import shape, box |
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from stimmen.cbs import data_file |
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from html import escape |
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import numpy as np |
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from stimmen.latitude_longitude import reverse_latitude_longitude |
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def get_palette(n, no_black=True, no_white=True): |
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with open(data_file('data', 'glasbey', '{}_colors.txt'.format(n + no_black + no_white))) as f: |
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return [ |
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'#%02x%02x%02x' % tuple(int(c) for c in line.replace('\n', '').split(',')) |
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for line in f |
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if not no_black or line != '0,0,0\n' |
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if not no_white or line != '255,255,255\n' |
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] |
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def colored_name(name, color): |
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return '<span style=\\"color:{}; \\">{}</span>'.format(color, name) |
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def region_area_cdf(region_shape, resolution=10000): |
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xmin, ymin, xmax, ymax = region_shape.bounds |
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shape_area = region_shape.area |
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spaces = np.linspace(xmin, xmax, resolution + 1) |
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return np.array([ |
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box(xmin, ymin, xmax_, ymax).intersection(region_shape).area / shape_area |
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for xmax_ in spaces |
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]) |
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# Only slightly faster than region_area_cdf. |
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# def fast_sliced_shape_areas(region_shape, recursions=13): |
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# results = np.zeros(2 ** recursions) |
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# xmin, ymin, xmax, ymax = region_shape.bounds |
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# total = 0 |
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# |
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# def f(shape_, xmin, ymin, xmax, ymax, recursions, results_): |
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# nonlocal total |
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# shape_ = box(xmin, ymin, xmax, ymax).intersection(shape_) |
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# if recursions == 0: |
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# assert results_.shape == (1,) |
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# results_[0] = shape_.area |
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# total += shape_.area |
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# else: |
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# xmiddle = xmin + (xmax - xmin) / 2 |
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# middle_index = len(results_) // 2 |
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# f(shape_, xmin, ymin, xmiddle, ymax, recursions - 1, results_[:middle_index]) |
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# f(shape_, xmiddle, ymin, xmax, ymax, recursions - 1, results_[middle_index:]) |
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# |
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# f(region_shape, xmin, ymin, xmax, ymax, recursions, results) |
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# return results / results.sum() * region_shape.area |
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def area_adjust_boundaries(region_shape, boundaries, region_cdf_cache=None, resolution=10000): |
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"""Adjust the boundaries from percentage of the width of a shape, to percentage of the area of a shape""" |
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if region_cdf_cache is None: |
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region_cdf_cache = region_area_cdf(region_shape, resolution) |
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elif not isinstance(region_cdf_cache, np.ndarray): |
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region_cdf_cache = np.array(region_cdf_cache) |
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return width_adjust_boundaries( |
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region_shape, |
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np.abs(region_cdf_cache[None, :] - boundaries[:, None]).argmin(axis=1) / resolution |
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) |
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def width_adjust_boundaries(region_shape, boundaries): |
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xmin, _, xmax, _ = region_shape.bounds |
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return boundaries * (xmax - xmin) + xmin |
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def pronunciation_bars( |
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regions, dataframe, |
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region_name_property, region_name_column, |
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group_column='answer_text', |
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cutoff_percentage=0.05, |
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normalize_area=True, |
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progress_bar=False, |
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): |
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# all values of group_column that appear at least cutoff_percentage in one of the regions |
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relevant_groups = { |
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group |
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for region_name, region_rows in dataframe.groupby(region_name_column) |
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for group, aggregation in region_rows.groupby( |
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group_column).agg({group_column: len}).iterrows() |
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if aggregation[group_column] >= cutoff_percentage * len(region_rows) |
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} |
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group_to_color = dict(zip(relevant_groups, get_palette(len(relevant_groups)))) |
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group_to_color['other'] = '#ccc' |
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n_other = len(dataframe) - sum( |
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sum(dataframe[group_column] == group_value) |
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for group_value in relevant_groups |
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) |
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# Each FeatureGroup represents all polygons (one for each region) of the relevant_groups |
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feature_groups = { |
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group_value: folium.FeatureGroup( |
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name=colored_name( |
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'{value} ({amount})'.format(value=escape(group_value), amount=amount), |
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color |
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), |
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overlay=True |
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) |
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for group_value, color in group_to_color.items() |
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for amount in [ |
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sum(dataframe[group_column] == group_value) |
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if group_value != 'other' else |
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n_other |
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] # alias |
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} |
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progress_bar = ProgressBar if progress_bar else lambda x: x |
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# for each region, create the bar-polygons. |
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for feature in progress_bar(regions['features']): |
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region_name = feature['properties'][region_name_property] |
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region_rows = dataframe[dataframe[region_name_column] == region_name] |
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region_shape = shape(feature['geometry']) |
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_, ymin, _, ymax = region_shape.bounds |
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group_values_occurrence = { |
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group_value: aggregation[group_column] |
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for group_value, aggregation in region_rows.groupby(group_column).agg({group_column: len}).iterrows() |
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if group_value in relevant_groups |
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} |
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group_values_occurrence['other'] = len(region_rows) - sum(group_values_occurrence.values()) |
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group_values, group_occurrences = zip(*sorted( |
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group_values_occurrence.items(), |
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key=lambda x: (x[0] == 'other', -x[1]) |
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)) |
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group_percentages = np.array(group_occurrences) / len(region_rows) |
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group_boundaries = np.cumsum((0,) + group_occurrences) / len(region_rows) |
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if normalize_area: |
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if '__region_shape_cdf_cache' not in feature['properties']: |
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feature['properties']['__region_shape_cdf_cache'] = region_area_cdf(region_shape).tolist() |
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group_boundaries = area_adjust_boundaries( |
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region_shape, group_boundaries, |
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region_cdf_cache=feature['properties']['__region_shape_cdf_cache'] |
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) |
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else: |
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group_boundaries = width_adjust_boundaries(region_shape, group_boundaries) |
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for group_value, percentage, count, left_boundary, right_boundary in zip( |
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group_values, |
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group_percentages, |
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group_occurrences, |
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group_boundaries[:-1], group_boundaries[1:] |
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): |
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if count == 0 or left_boundary == right_boundary: |
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continue |
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bar_shape = region_shape.intersection(box(left_boundary, ymin, right_boundary, ymax)) |
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if bar_shape.area == 0: |
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continue |
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polygon = folium.Polygon( |
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reverse_latitude_longitude(mapping(bar_shape)['coordinates']), |
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fill_color=group_to_color[group_value], |
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fill_opacity=0.8, |
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color=None, |
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popup='{} ({}, {: 3d}%)'.format(group_value, count, int(round(100 * percentage))) |
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) |
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polygon.add_to(feature_groups[group_value]) |
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return feature_groups |
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