cleaned up bar maps
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H.T. Kruitbosch committed
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@@ -74,3 +74,10 @@ This is a simple example for the created gabmap files.
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* [geojson](data/Gabmap_example.geojson)
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* [percentages](data/Pronunciation_percentages_example.gabmap.tsv)
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* [pronunciation](data/Pronunciations_example.gabmap.tsv)
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### Bar Maps per word for Pronounciation Occurence in Frysian Municipalities
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For each word, a map illustrates the pronunciation occurrence as measured by the prediction quiz, per Frysian
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municipality.
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[notebook](notebooks/Bar%20Maps%20per%20word%20for%20Pronounciation%20Occurence%20in%20Frysian%20Municipalities.ipynb)
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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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+409
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Bar Maps per word for Pronounciation Occurence in Frysian Municipalities\n",
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"\n",
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"Each map displays the pronounciation occurence in Frysian municipalities for one word. Each municiplaity region is filled with one bar per pronunciation. The area of a bar is proportional to the represented pronunciation."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"sys.path.append('../')\n",
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"\n",
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"import pandas\n",
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"import MySQLdb\n",
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"import numpy\n",
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"import json\n",
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"\n",
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"db = MySQLdb.connect(user='root', passwd='Nmmxhjgt1@', db='stimmen', charset='utf8')\n",
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"\n",
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"%matplotlib notebook\n",
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"import folium\n",
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"from stimmen.geojson import inject_geojson_regions_into_dataframe\n",
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"from stimmen.folium import pronunciation_bars\n",
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"from stimmen.latitude_longitude import reverse_latitude_longitude\n",
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"from IPython.display import display"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('../data/Friesland_gemeentes.geojson') as f:\n",
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" regions = json.load(f)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Answers to how participants state a word should be pronounces.\n",
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"\n",
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"answers = pandas.read_sql('''\n",
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"SELECT prediction_quiz_id, user_lat, user_lng, question_text, answer_text\n",
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"FROM core_surveyresult as survey\n",
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"INNER JOIN core_predictionquizresult as result ON survey.id = result.survey_result_id\n",
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"INNER JOIN core_predictionquizresultquestionanswer as answer\n",
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" ON result.id = answer.prediction_quiz_id\n",
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"''', db)\n",
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"\n",
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"answers = inject_geojson_regions_into_dataframe(\n",
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" regions, answers,\n",
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" latitude_column='user_lat', longitude_column='user_lng',\n",
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" region_name_property='gemeente_naam',\n",
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" region_name_column='region'\n",
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")\n",
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"\n",
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"zero_latlng_questions = {\n",
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" q\n",
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" for q, row in answers.groupby('question_text').agg('std').iterrows()\n",
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" if row['user_lat'] == 0 and row['user_lng'] == 0\n",
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"}\n",
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"answers = answers[answers['question_text'].map(lambda x: x not in zero_latlng_questions)]\n",
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"\n",
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"answers['question_text'] = answers['question_text'].map(lambda x: x.replace('\"', '').replace('*', ''))\n",
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"\n",
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"answers['answer_text'] = answers['answer_text'].map(lambda x: x[x.find('('):x.find(')')][1:])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"scrolled": false
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "7722c5b30a4d4ee38bb4fb3077715b6f",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"VBox(children=(HBox(children=(FloatProgress(value=0.0, max=1.0), HTML(value='<b>0</b>s passed', placeholder='0…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"../stimmen/folium.py:139: RuntimeWarning: invalid value encountered in true_divide\n",
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" group_percentages = np.array(group_occurrences) / len(region_rows)\n",
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"../stimmen/folium.py:140: RuntimeWarning: invalid value encountered in true_divide\n",
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" group_boundaries = np.cumsum((0,) + group_occurrences) / len(region_rows)\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe 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truncated
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],
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"text/plain": [
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"<folium.folium.Map at 0x7f2ccd1d7b38>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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||||
"text/html": [
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truncated
|
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|
||||
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||||
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|
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|
||||
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|
||||
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||||
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|
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||||
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src=\"data:text/html;charset=utf-8;base64,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 truncated
|
||||
],
|
||||
"text/plain": [
|
||||
"<folium.folium.Map at 0x7f2ccd82d828>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"data:text/html;charset=utf-8;base64,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 truncated
|
||||
],
|
||||
"text/plain": [
|
||||
"<folium.folium.Map at 0x7f2ccd0f04a8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for word_index, (word, word_rows) in enumerate(answers.groupby('question_text')):\n",
|
||||
" m = folium.Map(\n",
|
||||
" (word_rows['user_lat'].median(), word_rows['user_lng'].median()),\n",
|
||||
" tiles='stamentoner', zoom_start=10)\n",
|
||||
"\n",
|
||||
" for pronunciation, feature_group in pronunciation_bars(\n",
|
||||
" regions, word_rows,\n",
|
||||
" region_name_property='gemeente_naam',\n",
|
||||
" region_name_column='region',\n",
|
||||
" group_column='answer_text',\n",
|
||||
" cutoff_percentage=0.1,\n",
|
||||
" normalize_area=True,\n",
|
||||
" progress_bar=word_index == 0\n",
|
||||
" ).items():\n",
|
||||
" feature_group.add_to(m)\n",
|
||||
" \n",
|
||||
" for feature in regions['features']:\n",
|
||||
" folium.Polygon(\n",
|
||||
" reverse_latitude_longitude(feature['geometry']['coordinates']),\n",
|
||||
" fill_color=None, color='#000000'\n",
|
||||
" ).add_to(m)\n",
|
||||
"\n",
|
||||
" folium.map.LayerControl('topright', collapsed=False).add_to(m)\n",
|
||||
" display(m)\n",
|
||||
" m.save('../maps/bar-maps/{}.html'.format(word))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import glob\n",
|
||||
"with open('../maps/bar-maps/index.html', 'w') as f:\n",
|
||||
" f.write('<html><head></head><body>' + \n",
|
||||
" '<br/>\\n'.join(\n",
|
||||
" '\\t<a href=\"{}\">{}<a>'.format(fn, fn[:-5].replace('_', ' '))\n",
|
||||
" for fn in sorted(\n",
|
||||
" glob.glob('../maps/bar-maps/*.html')\n",
|
||||
" )\n",
|
||||
" for fn in [fn[len('../maps/bar-maps/'):]]\n",
|
||||
" ) + \"</body></html>\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -35,15 +35,15 @@
|
||||
"comboBox_Label": null,
|
||||
"comboBox_ObjectType": 1,
|
||||
"heightWidget": {
|
||||
"comboData": 1,
|
||||
"comboText": "Absolute value",
|
||||
"comboData": 101,
|
||||
"comboText": " \"distance 1\"",
|
||||
"editText": "0",
|
||||
"type": 4
|
||||
},
|
||||
"labelHeightWidget": {
|
||||
"comboData": 2,
|
||||
"comboText": "Height from point",
|
||||
"editText": "7309.11282486",
|
||||
"editText": "7500",
|
||||
"type": 6
|
||||
},
|
||||
"radioButton_IntersectingFeatures": true,
|
||||
@@ -62,12 +62,12 @@
|
||||
"styleWidget2": {
|
||||
"comboData": 1,
|
||||
"comboText": "Fixed value",
|
||||
"editText": "1000",
|
||||
"editText": "300",
|
||||
"type": 1
|
||||
},
|
||||
"styleWidget3": {
|
||||
"comboData": 103,
|
||||
"comboText": "\"distance1\"",
|
||||
"comboData": 101,
|
||||
"comboText": "\"distance 1\"",
|
||||
"editText": "300",
|
||||
"type": 1
|
||||
},
|
||||
@@ -77,5 +77,11 @@
|
||||
}
|
||||
},
|
||||
"PluginVersion": "1.4.2",
|
||||
"Template": "3DViewer.html"
|
||||
"Template": "3DViewer.html",
|
||||
"WORLD": {
|
||||
"lineEdit_BaseSize": "100",
|
||||
"lineEdit_Color": "",
|
||||
"lineEdit_zFactor": "1.5",
|
||||
"lineEdit_zShift": "0"
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,172 @@
|
||||
import folium
|
||||
from jupyter_progressbar import ProgressBar
|
||||
from pygeoif.geometry import mapping
|
||||
from shapely.geometry.geo import shape, box
|
||||
|
||||
from stimmen.cbs import data_file
|
||||
from html import escape
|
||||
import numpy as np
|
||||
|
||||
from stimmen.latitude_longitude import reverse_latitude_longitude
|
||||
|
||||
|
||||
def get_palette(n, no_black=True, no_white=True):
|
||||
with open(data_file('data', 'glasbey', '{}_colors.txt'.format(n + no_black + no_white))) as f:
|
||||
return [
|
||||
'#%02x%02x%02x' % tuple(int(c) for c in line.replace('\n', '').split(','))
|
||||
for line in f
|
||||
if not no_black or line != '0,0,0\n'
|
||||
if not no_white or line != '255,255,255\n'
|
||||
]
|
||||
|
||||
|
||||
def colored_name(name, color):
|
||||
return '<span style=\\"color:{}; \\">{}</span>'.format(color, name)
|
||||
|
||||
|
||||
def region_area_cdf(region_shape, resolution=10000):
|
||||
xmin, ymin, xmax, ymax = region_shape.bounds
|
||||
shape_area = region_shape.area
|
||||
spaces = np.linspace(xmin, xmax, resolution + 1)
|
||||
return np.array([
|
||||
box(xmin, ymin, xmax_, ymax).intersection(region_shape).area / shape_area
|
||||
for xmax_ in spaces
|
||||
])
|
||||
|
||||
|
||||
# Only slightly faster than region_area_cdf.
|
||||
# def fast_sliced_shape_areas(region_shape, recursions=13):
|
||||
# results = np.zeros(2 ** recursions)
|
||||
# xmin, ymin, xmax, ymax = region_shape.bounds
|
||||
# total = 0
|
||||
#
|
||||
# def f(shape_, xmin, ymin, xmax, ymax, recursions, results_):
|
||||
# nonlocal total
|
||||
# shape_ = box(xmin, ymin, xmax, ymax).intersection(shape_)
|
||||
# if recursions == 0:
|
||||
# assert results_.shape == (1,)
|
||||
# results_[0] = shape_.area
|
||||
# total += shape_.area
|
||||
# else:
|
||||
# xmiddle = xmin + (xmax - xmin) / 2
|
||||
# middle_index = len(results_) // 2
|
||||
# f(shape_, xmin, ymin, xmiddle, ymax, recursions - 1, results_[:middle_index])
|
||||
# f(shape_, xmiddle, ymin, xmax, ymax, recursions - 1, results_[middle_index:])
|
||||
#
|
||||
# f(region_shape, xmin, ymin, xmax, ymax, recursions, results)
|
||||
# return results / results.sum() * region_shape.area
|
||||
|
||||
|
||||
def area_adjust_boundaries(region_shape, boundaries, region_cdf_cache=None, resolution=10000):
|
||||
"""Adjust the boundaries from percentage of the width of a shape, to percentage of the area of a shape"""
|
||||
if region_cdf_cache is None:
|
||||
region_cdf_cache = region_area_cdf(region_shape, resolution)
|
||||
elif not isinstance(region_cdf_cache, np.ndarray):
|
||||
region_cdf_cache = np.array(region_cdf_cache)
|
||||
return width_adjust_boundaries(
|
||||
region_shape,
|
||||
np.abs(region_cdf_cache[None, :] - boundaries[:, None]).argmin(axis=1) / resolution
|
||||
)
|
||||
|
||||
|
||||
def width_adjust_boundaries(region_shape, boundaries):
|
||||
xmin, _, xmax, _ = region_shape.bounds
|
||||
return boundaries * (xmax - xmin) + xmin
|
||||
|
||||
|
||||
def pronunciation_bars(
|
||||
regions, dataframe,
|
||||
region_name_property, region_name_column,
|
||||
group_column='answer_text',
|
||||
cutoff_percentage=0.05,
|
||||
normalize_area=True,
|
||||
progress_bar=False,
|
||||
):
|
||||
# all values of group_column that appear at least cutoff_percentage in one of the regions
|
||||
relevant_groups = {
|
||||
group
|
||||
for region_name, region_rows in dataframe.groupby(region_name_column)
|
||||
for group, aggregation in region_rows.groupby(
|
||||
group_column).agg({group_column: len}).iterrows()
|
||||
if aggregation[group_column] >= cutoff_percentage * len(region_rows)
|
||||
}
|
||||
|
||||
group_to_color = dict(zip(relevant_groups, get_palette(len(relevant_groups))))
|
||||
group_to_color['other'] = '#ccc'
|
||||
|
||||
n_other = len(dataframe) - sum(
|
||||
sum(dataframe[group_column] == group_value)
|
||||
for group_value in relevant_groups
|
||||
)
|
||||
|
||||
# Each FeatureGroup represents all polygons (one for each region) of the relevant_groups
|
||||
feature_groups = {
|
||||
group_value: folium.FeatureGroup(
|
||||
name=colored_name(
|
||||
'{value} ({amount})'.format(value=escape(group_value), amount=amount),
|
||||
color
|
||||
),
|
||||
overlay=True
|
||||
)
|
||||
for group_value, color in group_to_color.items()
|
||||
for amount in [
|
||||
sum(dataframe[group_column] == group_value)
|
||||
if group_value != 'other' else
|
||||
n_other
|
||||
] # alias
|
||||
}
|
||||
|
||||
progress_bar = ProgressBar if progress_bar else lambda x: x
|
||||
|
||||
# for each region, create the bar-polygons.
|
||||
for feature in progress_bar(regions['features']):
|
||||
region_name = feature['properties'][region_name_property]
|
||||
region_rows = dataframe[dataframe[region_name_column] == region_name]
|
||||
region_shape = shape(feature['geometry'])
|
||||
_, ymin, _, ymax = region_shape.bounds
|
||||
|
||||
group_values_occurrence = {
|
||||
group_value: aggregation[group_column]
|
||||
for group_value, aggregation in region_rows.groupby(group_column).agg({group_column: len}).iterrows()
|
||||
if group_value in relevant_groups
|
||||
}
|
||||
group_values_occurrence['other'] = len(region_rows) - sum(group_values_occurrence.values())
|
||||
group_values, group_occurrences = zip(*sorted(
|
||||
group_values_occurrence.items(),
|
||||
key=lambda x: (x[0] == 'other', -x[1])
|
||||
))
|
||||
|
||||
group_percentages = np.array(group_occurrences) / len(region_rows)
|
||||
group_boundaries = np.cumsum((0,) + group_occurrences) / len(region_rows)
|
||||
if normalize_area:
|
||||
if '__region_shape_cdf_cache' not in feature['properties']:
|
||||
feature['properties']['__region_shape_cdf_cache'] = region_area_cdf(region_shape).tolist()
|
||||
group_boundaries = area_adjust_boundaries(
|
||||
region_shape, group_boundaries,
|
||||
region_cdf_cache=feature['properties']['__region_shape_cdf_cache']
|
||||
)
|
||||
else:
|
||||
group_boundaries = width_adjust_boundaries(region_shape, group_boundaries)
|
||||
|
||||
for group_value, percentage, count, left_boundary, right_boundary in zip(
|
||||
group_values,
|
||||
group_percentages,
|
||||
group_occurrences,
|
||||
group_boundaries[:-1], group_boundaries[1:]
|
||||
):
|
||||
if count == 0 or left_boundary == right_boundary:
|
||||
continue
|
||||
|
||||
bar_shape = region_shape.intersection(box(left_boundary, ymin, right_boundary, ymax))
|
||||
if bar_shape.area == 0:
|
||||
continue
|
||||
polygon = folium.Polygon(
|
||||
reverse_latitude_longitude(mapping(bar_shape)['coordinates']),
|
||||
fill_color=group_to_color[group_value],
|
||||
fill_opacity=0.8,
|
||||
color=None,
|
||||
popup='{} ({}, {: 3d}%)'.format(group_value, count, int(round(100 * percentage)))
|
||||
)
|
||||
polygon.add_to(feature_groups[group_value])
|
||||
|
||||
return feature_groups
|
||||
+38
-1
@@ -1,5 +1,6 @@
|
||||
from pygeoif.geometry import mapping
|
||||
from shapely.geometry import shape
|
||||
from shapely.geometry.point import Point
|
||||
|
||||
|
||||
def merge_features(geojson, condition, aggregate={}):
|
||||
@@ -40,4 +41,40 @@ def merge_features(geojson, condition, aggregate={}):
|
||||
'geometry': mapping(union),
|
||||
'properties': properties
|
||||
})
|
||||
return geojson
|
||||
return geojson
|
||||
|
||||
|
||||
def inject_geojson_regions_into_dataframe(
|
||||
geojson, dataframe,
|
||||
latitude_column='latitude', longitude_column='longitude',
|
||||
region_name_property='name',
|
||||
region_name_column='region'
|
||||
):
|
||||
"""adds a region_name_column column to the dataframe with the region name as specified
|
||||
in the region_name_property of the geojson, by checking which geojson feature geometrically
|
||||
contains the longitude and latitude of the dataframe's row. This allows for faster cross
|
||||
reference between the geojson and the dataframe compared to always checking shape-point
|
||||
containment when cross referencing. Operates in place."""
|
||||
shapes = {
|
||||
feature['properties'][region_name_property]: shape(feature['geometry'])
|
||||
for feature in geojson['features']
|
||||
}
|
||||
|
||||
def get_region_name(point):
|
||||
nonlocal shapes
|
||||
for region_name, region_shape in shapes.items():
|
||||
if region_shape.contains(point):
|
||||
return region_name
|
||||
|
||||
point_to_region_name = {
|
||||
(latitude, longitude): get_region_name(point)
|
||||
for latitude, longitude in set(zip(dataframe[latitude_column], dataframe[longitude_column]))
|
||||
for point in [Point(longitude, latitude)] # alias
|
||||
}
|
||||
|
||||
dataframe[region_name_column] = [
|
||||
point_to_region_name[(latitude, longitude)]
|
||||
for latitude, longitude in zip(dataframe[latitude_column], dataframe[longitude_column])
|
||||
]
|
||||
return dataframe
|
||||
|
||||
Reference in new issue
Block a user