resturcured for reproducability
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H.T. Kruitbosch committed
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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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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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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
|
||||
}
|
||||
File diff suppressed because it is too large.
Load diff
@@ -1,83 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Gabmap format\n",
|
||||
"\n",
|
||||
"Exploration of the format of the lines in example Gabmap files Martijn had sent."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"with open('../data/martijn_format/Dutch613-coordinates.txt') as f:\n",
|
||||
" coordinates = list(f)\n",
|
||||
" \n",
|
||||
"with open('../data/martijn_format/Nederlands-ipa.utxt') as f:\n",
|
||||
" table = list(f)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"coordinates[0].split('\\t')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"coordinates[1].split('\\t')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"table[0].split('\\t')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"table[1].split('\\t')"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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
|
||||
}
|
||||
@@ -1,458 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Geographical pronunciation tables, simple example\n",
|
||||
"\n",
|
||||
"Simple example to create gabmap files for two words with few pronunciations an two regions."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import sys\n",
|
||||
"sys.path.append('..')\n",
|
||||
"\n",
|
||||
"import pandas\n",
|
||||
"import MySQLdb\n",
|
||||
"import json\n",
|
||||
"import copy\n",
|
||||
"\n",
|
||||
"db = MySQLdb.connect(user='root', passwd='Nmmxhjgt1@', db='stimmen', charset='utf8')\n",
|
||||
"\n",
|
||||
"from shapely.geometry import shape, Point\n",
|
||||
"\n",
|
||||
"from gabmap import create_gabmap_dataframes\n",
|
||||
"\n",
|
||||
"from stimmen.geojson import merge_features"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"with open('../data/Friesland_wijken.geojson') as f:\n",
|
||||
" regions = json.load(f)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Load and simplify"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Answers to how participants state a word should be pronounced\n",
|
||||
"\n",
|
||||
"answers = pandas.read_sql('''\n",
|
||||
"SELECT prediction_quiz_id, user_lat, user_lng, question_text, answer_text\n",
|
||||
"FROM core_surveyresult as survey\n",
|
||||
"INNER JOIN core_predictionquizresult as result ON survey.id = result.survey_result_id\n",
|
||||
"INNER JOIN core_predictionquizresultquestionanswer as answer\n",
|
||||
" ON result.id = answer.prediction_quiz_id\n",
|
||||
"''', db)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"regions_simple = merge_features(copy.deepcopy(regions),\n",
|
||||
" condition=lambda feature: feature['properties']['GM_NAAM'] == 'Heerenveen',\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"regions_simple = merge_features(\n",
|
||||
" regions_simple,\n",
|
||||
" condition=lambda feature: feature['properties']['GM_NAAM'] == 'Leeuwarden',\n",
|
||||
")\n",
|
||||
"regions_simple['features'] = regions_simple['features'][-2:]\n",
|
||||
"\n",
|
||||
"regions_simple['features'][0]['properties']['name'] = 'Heerenveen'\n",
|
||||
"regions_simple['features'][1]['properties']['name'] = 'Leeuwarden'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"answers_simple = answers[\n",
|
||||
" (answers['question_text'] == '\"blad\" (aan een boom)') |\n",
|
||||
" (answers['question_text'] == '\"vis\"')\n",
|
||||
"].copy()\n",
|
||||
"\n",
|
||||
"answers_simple['question_text'] = answers_simple['question_text'].map(\n",
|
||||
" lambda x: x.replace('\"', '').replace('*', ''))\n",
|
||||
"\n",
|
||||
"answers_simple['answer_text'] = answers_simple['answer_text'].map(\n",
|
||||
" lambda x: x[x.find('('):x.find(')')][1:])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Two words, boom and vis, with each 4 and 2 pronunciations"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>answer_text</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>question_text</th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>blad (aan een boom)</th>\n",
|
||||
" <td>4</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>vis</th>\n",
|
||||
" <td>2</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" answer_text\n",
|
||||
"question_text \n",
|
||||
"blad (aan een boom) 4\n",
|
||||
"vis 2"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"answers_simple.groupby('question_text').agg({'answer_text': lambda x: len(set(x))})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"centroids_example, pronunciations_example, counts_example = create_gabmap_dataframes(\n",
|
||||
" regions_simple, answers_simple,\n",
|
||||
" latitude_column='user_lat', longitude_column='user_lng',\n",
|
||||
" word_column='question_text', pronunciation_column='answer_text',\n",
|
||||
" region_name_property='name'\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Resulting tables\n",
|
||||
"\n",
|
||||
"Stored as tab separated files for gabmap"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>latitude</th>\n",
|
||||
" <th>longitude</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>#name</th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>Heerenveen</th>\n",
|
||||
" <td>52.996076</td>\n",
|
||||
" <td>5.977925</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Leeuwarden</th>\n",
|
||||
" <td>53.169940</td>\n",
|
||||
" <td>5.797613</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" latitude longitude\n",
|
||||
"#name \n",
|
||||
"Heerenveen 52.996076 5.977925\n",
|
||||
"Leeuwarden 53.169940 5.797613"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"centroids_example"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>blad (aan een boom)</th>\n",
|
||||
" <th>vis</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>Heerenveen</th>\n",
|
||||
" <td>blet / blɑt / blɔd / blɛ:t</td>\n",
|
||||
" <td>fisk / fɪs</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Leeuwarden</th>\n",
|
||||
" <td>blet / blɑt / blɔd / blɛ:t</td>\n",
|
||||
" <td>fisk / fɪs</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" blad (aan een boom) vis\n",
|
||||
" \n",
|
||||
"Heerenveen blet / blɑt / blɔd / blɛ:t fisk / fɪs\n",
|
||||
"Leeuwarden blet / blɑt / blɔd / blɛ:t fisk / fɪs"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pronunciations_example"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>blad (aan een boom): blet</th>\n",
|
||||
" <th>blad (aan een boom): blɑt</th>\n",
|
||||
" <th>blad (aan een boom): blɔd</th>\n",
|
||||
" <th>blad (aan een boom): blɛ:t</th>\n",
|
||||
" <th>vis: fisk</th>\n",
|
||||
" <th>vis: fɪs</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>Heerenveen</th>\n",
|
||||
" <td>31.654676</td>\n",
|
||||
" <td>2.158273</td>\n",
|
||||
" <td>2.158273</td>\n",
|
||||
" <td>64.028777</td>\n",
|
||||
" <td>52.517986</td>\n",
|
||||
" <td>47.482014</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Leeuwarden</th>\n",
|
||||
" <td>7.865169</td>\n",
|
||||
" <td>7.022472</td>\n",
|
||||
" <td>8.707865</td>\n",
|
||||
" <td>76.404494</td>\n",
|
||||
" <td>75.000000</td>\n",
|
||||
" <td>25.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" blad (aan een boom): blet blad (aan een boom): blɑt \\\n",
|
||||
" \n",
|
||||
"Heerenveen 31.654676 2.158273 \n",
|
||||
"Leeuwarden 7.865169 7.022472 \n",
|
||||
"\n",
|
||||
" blad (aan een boom): blɔd blad (aan een boom): blɛ:t vis: fisk \\\n",
|
||||
" \n",
|
||||
"Heerenveen 2.158273 64.028777 52.517986 \n",
|
||||
"Leeuwarden 8.707865 76.404494 75.000000 \n",
|
||||
"\n",
|
||||
" vis: fɪs \n",
|
||||
" \n",
|
||||
"Heerenveen 47.482014 \n",
|
||||
"Leeuwarden 25.000000 "
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"counts_example"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pronunciations_example.to_csv('../data/Pronunciations_example.gabmap.tsv', sep='\\t')\n",
|
||||
"counts_example.to_csv('../data/Pronunciation_percentages_example.gabmap.tsv', sep='\\t')\n",
|
||||
"centroids_example.to_csv('../data/Centroids_example.gabmap.tsv', sep='\\t', columns=['longitude', 'latitude'])\n",
|
||||
"with open('../data/Gabmap_example.geojson', 'w') as f:\n",
|
||||
" json.dump(regions_simple, f, indent=1)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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
|
||||
}
|
||||
@@ -1,157 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Geographical pronunciation tables\n",
|
||||
"\n",
|
||||
"Creates gabmap files with region centroids, percentages and pronunciations for wijken in Friesland."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import sys\n",
|
||||
"sys.path.append('..')\n",
|
||||
"\n",
|
||||
"import pandas\n",
|
||||
"import MySQLdb\n",
|
||||
"import json\n",
|
||||
"import copy\n",
|
||||
"\n",
|
||||
"db = MySQLdb.connect(user='root', passwd='Nmmxhjgt1@', db='stimmen', charset='utf8')\n",
|
||||
"\n",
|
||||
"from shapely.geometry import shape, Point\n",
|
||||
"\n",
|
||||
"from gabmap import create_gabmap_dataframes"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"with open('../data/Friesland_wijken.geojson') as f:\n",
|
||||
" regions = json.load(f)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Answers to how participants state a word should be pronounced\n",
|
||||
"\n",
|
||||
"answers = pandas.read_sql('''\n",
|
||||
"SELECT prediction_quiz_id, user_lat, user_lng, question_text, answer_text\n",
|
||||
"FROM core_surveyresult as survey\n",
|
||||
"INNER JOIN core_predictionquizresult as result ON survey.id = result.survey_result_id\n",
|
||||
"INNER JOIN core_predictionquizresultquestionanswer as answer\n",
|
||||
" ON result.id = answer.prediction_quiz_id\n",
|
||||
"''', db)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"zero_latlng_questions = {\n",
|
||||
" q\n",
|
||||
" for q, row in answers.groupby('question_text').agg('std').iterrows()\n",
|
||||
" if row['user_lat'] == 0 and row['user_lng'] == 0\n",
|
||||
"}\n",
|
||||
"answers_filtered = answers[answers['question_text'].map(lambda x: x not in zero_latlng_questions)].copy()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array(['gegaan', 'avond', 'heel', 'dag', 'bij (insect)', 'sprak (toe)',\n",
|
||||
" 'oog', 'armen (lichaamsdeel)', 'kaas', 'deurtje', 'koken',\n",
|
||||
" 'borst (lichaamsdeel)', 'vis', 'zaterdag', 'trein', 'geel', 'tand',\n",
|
||||
" 'gezet', 'blad (aan een boom)'], dtype=object)"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"answers_filtered['question_text'].unique()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"answers_filtered['question_text'] = answers_filtered['question_text'].map(\n",
|
||||
" lambda x: x.replace('\"', '').replace('*', ''))\n",
|
||||
"\n",
|
||||
"answers_filtered['answer_text'] = answers_filtered['answer_text'].map(\n",
|
||||
" lambda x: x[x.find('('):x.find(')')][1:])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"centroids, pronunciations, counts = create_gabmap_dataframes(\n",
|
||||
" regions, answers_filtered,\n",
|
||||
" latitude_column='user_lat', longitude_column='user_lng',\n",
|
||||
" word_column='question_text', pronunciation_column='answer_text',\n",
|
||||
" region_name_property='gemeente_en_wijk_naam'\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pronunciations.to_csv('../data/Friesland_wijken_pronunciations.gabmap.tsv', sep='\\t')\n",
|
||||
"counts.to_csv('../data/Friesland_wijken_pronunciation_percentages.gabmap.tsv', sep='\\t')\n",
|
||||
"centroids.to_csv('../data/Friesland_wijken_centroids.gabmap.tsv', sep='\\t', columns=['longitude', 'latitude'])"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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
|
||||
}
|
||||
@@ -1,265 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Group recordings in 4 Frysian dialect regions\n",
|
||||
"\n",
|
||||
" * Klaaifrysk\n",
|
||||
" * Waldfrysk\n",
|
||||
" * Sudwesthoeksk\n",
|
||||
" * Noardhoeksk\n",
|
||||
" \n",
|
||||
"First run `Dialect Regions from image.ipynb`.\n",
|
||||
"\n",
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from math import floor\n",
|
||||
"import json\n",
|
||||
"import pandas\n",
|
||||
"import MySQLdb\n",
|
||||
"from collections import Counter\n",
|
||||
"\n",
|
||||
"from math import sqrt\n",
|
||||
"import numpy as np\n",
|
||||
"from shapely.geometry import shape, Point\n",
|
||||
"from vincenty import vincenty\n",
|
||||
"\n",
|
||||
"from jupyter_progressbar import ProgressBar\n",
|
||||
"\n",
|
||||
"db = MySQLdb.connect(user='root', passwd='Nmmxhjgt1@', db='stimmen', charset='utf8')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Input\n",
|
||||
"\n",
|
||||
"Load the geojson with the dialect region and create shapely shapes."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"with open('../data/fryslan_dialect_regions.geojson', 'r') as f:\n",
|
||||
" geojson = json.load(f)\n",
|
||||
"\n",
|
||||
"dialect_regions = [region['properties']['dialect'] for region in geojson['features']]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"shapes = {\n",
|
||||
" feature['properties']['dialect']: shape(feature['geometry'])\n",
|
||||
" for feature in geojson['features']\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"def regions_for(coordinate):\n",
|
||||
" regions = {\n",
|
||||
" region_name\n",
|
||||
" for region_name, shape in shapes.items()\n",
|
||||
" if shape.contains(Point(*coordinate))\n",
|
||||
" }\n",
|
||||
" return regions\n",
|
||||
"\n",
|
||||
"def distance_to_shape(shape, longitude, latitude):\n",
|
||||
" ext = shape.exterior\n",
|
||||
" p = ext.interpolate(ext.project(Point(longitude, latitude)))\n",
|
||||
" return vincenty((latitude, longitude), (p.y, p.x))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Query and process\n",
|
||||
"\n",
|
||||
"Query all picture game and free speech recordings and assign the dialect region."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def dialect_regions_and_distance(data):\n",
|
||||
" return[\n",
|
||||
" {\n",
|
||||
" 'dialects': [\n",
|
||||
" {\n",
|
||||
" 'dialect': dialect,\n",
|
||||
" 'boundary_distance': distance_to_shape(shapes[dialect], longitude, latitude),\n",
|
||||
" }\n",
|
||||
" for dialect in regions_for((longitude, latitude))\n",
|
||||
" ],\n",
|
||||
" 'filename': filename,\n",
|
||||
" }\n",
|
||||
" for filename, (latitude, longitude) in ProgressBar(\n",
|
||||
" data[['latitude', 'longitude']].iterrows(),\n",
|
||||
" size=len(data)\n",
|
||||
" )\n",
|
||||
" ]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"picture_games = pandas.read_sql('''\n",
|
||||
"SELECT language.name as language, item.name as picture,\n",
|
||||
" survey.user_lat as latitude, survey.user_lng as longitude,\n",
|
||||
" survey.area_name as area, survey.country_name as country,\n",
|
||||
" result.recording as filename,\n",
|
||||
" result.submitted_at as date\n",
|
||||
"FROM core_surveyresult as survey\n",
|
||||
"INNER JOIN core_picturegameresult as result ON survey.id = result.survey_result_id\n",
|
||||
"INNER JOIN core_language as language ON language.id = result.language_id\n",
|
||||
"INNER JOIN core_picturegameitem as item\n",
|
||||
" ON result.picture_game_item_id = item.id\n",
|
||||
"''', db)\n",
|
||||
"picture_games.set_index('filename', inplace=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "5825449a737b4fcab38a4f4ac2adfd87",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"VBox(children=(HBox(children=(FloatProgress(value=0.0, max=1.0), HTML(value='<b>0</b>s passed', placeholder='0…"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"dialect_region_per_picture_game = dialect_regions_and_distance(picture_games)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = pandas.DataFrame([\n",
|
||||
" [r['filename'], r['dialects'][0]['dialect'], r['dialects'][0]['boundary_distance']]\n",
|
||||
" for r in dialect_region_per_picture_game\n",
|
||||
" if len(r['dialects']) == 1\n",
|
||||
"], columns = ['filename', 'dialect', 'boundary_distance'])\n",
|
||||
"\n",
|
||||
"df.to_excel('../data/picture_game_recordings_by_dialect.xlsx')\n",
|
||||
"df.to_csv('../data/picture_game_recordings_by_dialect.csv')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"free_speech_games = pandas.read_sql('''\n",
|
||||
"SELECT language.name as language,\n",
|
||||
" survey.user_lat as latitude, survey.user_lng as longitude,\n",
|
||||
" survey.area_name as area, survey.country_name as country,\n",
|
||||
" result.recording as filename,\n",
|
||||
" result.submitted_at as date\n",
|
||||
"FROM core_surveyresult as survey\n",
|
||||
"INNER JOIN core_freespeechresult as result ON survey.id = result.survey_result_id\n",
|
||||
"INNER JOIN core_language as language ON language.id = result.language_id\n",
|
||||
"''', db)\n",
|
||||
"free_speech_games.set_index('filename', inplace=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "8afad9f71e544658b554b828932d7769",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"VBox(children=(HBox(children=(FloatProgress(value=0.0, max=1.0), HTML(value='<b>0</b>s passed', placeholder='0…"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"dialect_region_per_free_speech = dialect_regions_and_distance(free_speech_games)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = pandas.DataFrame([\n",
|
||||
" [r['filename'], r['dialects'][0]['dialect'], r['dialects'][0]['boundary_distance']]\n",
|
||||
" for r in dialect_region_per_free_speech\n",
|
||||
" if len(r['dialects']) == 1\n",
|
||||
"], columns = ['filename', 'dialect', 'boundary_distance'])\n",
|
||||
"\n",
|
||||
"df.to_excel('../data/free_speech_recordings_by_dialect.xlsx')\n",
|
||||
"df.to_csv('../data/free_speech_recordings_by_dialect.csv')"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": 1
|
||||
}
|
||||
-1647
File diff suppressed because it is too large.
Load diff
@@ -7,13 +7,11 @@
|
||||
"# Segment provinces\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Create wijk and gemeente level segmentations for all Dutch provinces and save as geojson and Gabmap KML.\n",
|
||||
"Create wijk and gemeente level segmentations for two Dutch provinces, Groningen and Friesland, and save as geojson and Gabmap KML.\n",
|
||||
"\n",
|
||||
"All is based on CBS data.\n",
|
||||
"All is based on [CBS data](https://www.cbs.nl/nl-nl/dossier/nederland-regionaal/geografische%20data/wijk-en-buurtkaart-2017)\n",
|
||||
"\n",
|
||||
"For Friesland, several wijken are merged.\n",
|
||||
"\n",
|
||||
"Note: only applied to Groningen and Friesland, because other provinces give gemetry errors."
|
||||
"For Friesland, several wijken are merged, in particular those of the municipalities Ameland, Harlingen, Schiermonnikoog, Terschelling and Vlieland, and those of Leeuwarden with centroid above 53.167. These neighborhoods are small in area and hence we decided to merge, to avoid a "
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -29,7 +27,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -53,14 +51,37 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Groningen\n",
|
||||
"0\n",
|
||||
"1\n",
|
||||
"2\n",
|
||||
"3\n",
|
||||
"4\n",
|
||||
"5\n",
|
||||
"6\n",
|
||||
"Friesland\n",
|
||||
"0\n",
|
||||
"1\n",
|
||||
"2\n",
|
||||
"3\n",
|
||||
"4\n",
|
||||
"5\n",
|
||||
"6\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for province in ['Groningen', 'Friesland']:\n",
|
||||
" wijken_geojson = gwb_in_province(province, 'wijk', 2018)\n",
|
||||
" gemeente_geojson = gwb_in_province(province, 'gem', 2018)\n",
|
||||
"\n",
|
||||
" wijken_geojson = gwb_in_province(province, 'wijk', 2018, polygon_simplification=None)\n",
|
||||
" gemeente_geojson = gwb_in_province(province, 'gem', 2018, polygon_simplification=None)\n",
|
||||
" \n",
|
||||
" if province == 'Friesland':\n",
|
||||
" for gemeente in {'Ameland', 'Harlingen', 'Schiermonnikoog', 'Terschelling', 'Vlieland'}:\n",
|
||||
" merged_geojson = merge_features(\n",
|
||||
@@ -83,14 +104,14 @@
|
||||
" for gemeente in [feature['properties']['GM_NAAM'] for feature in gemeente_geojson['features']]:\n",
|
||||
" gemeente_geojson = merge_features(\n",
|
||||
" gemeente_geojson, condition=lambda feature: feature['properties']['GM_NAAM'] == gemeente)\n",
|
||||
" \n",
|
||||
" \n",
|
||||
" for feature in wijken_geojson['features']:\n",
|
||||
" feature['properties']['gemeente_en_wijk_naam'] = (\n",
|
||||
" feature['properties']['GM_NAAM'] +\n",
|
||||
" ', ' +\n",
|
||||
" feature['properties'].get('WK_NAAM', '')\n",
|
||||
" ).replace('&', 'en').replace('/', ' ').replace('\"', ' ').replace(\"'\", ' ')\n",
|
||||
" \n",
|
||||
" \n",
|
||||
" for feature in gemeente_geojson['features']:\n",
|
||||
" feature['properties']['gemeente_naam'] = (\n",
|
||||
" feature['properties']['GM_NAAM']\n",
|
||||
@@ -106,6 +127,13 @@
|
||||
" with open('../data/{}_gemeentes.kml'.format(province), 'w') as f:\n",
|
||||
" f.write(as_gabmap_kml(gemeente_geojson, name_property='gemeente_naam'))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
||||
@@ -11,7 +11,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -22,7 +22,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -57,10 +57,10 @@
|
||||
{
|
||||
"data": {
|
||||
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|
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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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"<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"data:text/html;charset=utf-8;base64,PCFET0NUWVBFIGh0bWw+CjxoZWFkPiAgICAKICAgIDxtZXRhIGh0dHAtZXF1aXY9ImNvbnRlbnQtdHlwZSIgY29udGVudD0idGV4dC9odG1sOyBjaGFyc2V0PVVURi04IiAvPgogICAgPHNjcmlwdD5MX1BSRUZFUl9DQU5WQVM9ZmFsc2U7IExfTk9fVE9VQ0g9ZmFsc2U7IExfRElTQUJMRV8zRD1mYWxzZTs8L3NjcmlwdD4KICAgIDxzY3JpcHQgc3JjPSJodHRwczovL2Nkbi5qc2RlbGl2ci5uZXQvbnBtL2xlYWZsZXRAMS4yLjAvZGlzdC9sZWFmbGV0LmpzIj48L3NjcmlwdD4KICAgIDxzY3JpcHQgc3JjPSJodHRwczovL2FqYXguZ29vZ2xlYXBpcy5jb20vYWpheC9saWJzL2pxdWVyeS8xLjExLjEvanF1ZXJ5Lm1pbi5qcyI+PC9zY3JpcHQ+CiAgICA8c2NyaXB0IHNyYz0iaHR0cHM6Ly9tYXhjZG4uYm9vdHN0cmFwY2RuLmNvbS9ib290c3RyYXAvMy4yLjAvanMvYm9vdHN0cmFwLm1pbi5qcyI+PC9zY3JpcHQ+CiAgICA8c2NyaXB0IHNyYz0iaHR0cHM6Ly9jZG5qcy5jbG91ZGZsYXJlLmNvbS9hamF4L2xpYnMvTGVhZmxldC5hd2Vzb21lLW1hcmtlcnMvMi4wLjIvbGVhZmxldC5hd2Vzb21lLW1hcmtlcnMuanMiPjwvc2NyaXB0PgogICAgPGxpbmsgcmVsPSJzdHlsZXNoZWV0IiBocmVmPSJodHRwczovL2Nkbi5qc2RlbGl2ci5uZXQvbnBtL2xlYWZsZXRAMS4yLjAvZGlzdC9sZWFmbGV0LmNzcyIvPgogICAgPGxpbmsgcmVsPSJzdHlsZXNoZWV0IiBocmVmPSJodHRwczovL21heGNkbi5ib290c3RyYXBjZG4uY29tL2Jvb3RzdHJhcC8zLjIuMC9jc3MvYm9vdHN0cmFwLm1pbi5jc3MiLz4KICAgIDxsaW5rIHJlbD0ic3R5bGVzaGVldCIgaHJlZj0iaHR0cHM6Ly9tYXhjZG4uYm9vdHN0cmFwY2RuLmNvbS9ib290c3RyYXAvMy4yLjAvY3NzL2Jvb3RzdHJhcC10aGVtZS5taW4uY3NzIi8+CiAgICA8bGluayByZWw9InN0eWxlc2hlZXQiIGhyZWY9Imh0dHBzOi8vbWF4Y2RuLmJvb3RzdHJhcGNkbi5jb20vZm9udC1hd2Vzb21lLzQuNi4zL2Nzcy9mb250LWF3ZXNvbWUubWluLmNzcyIvPgogICAgPGxpbmsgcmVsPSJzdHlsZXNoZWV0IiBocmVmPSJodHRwczovL2NkbmpzLmNsb3VkZmxhcmUuY29tL2FqYXgvbGlicy9MZWFmbGV0LmF3ZXNvbWUtbWFya2Vycy8yLjAuMi9sZWFmbGV0LmF3ZXNvbWUtbWFya2Vycy5jc3MiLz4KICAgIDxsaW5rIHJlbD0ic3R5bGVzaGVldCIgaHJlZj0iaHR0cHM6Ly9yYXdnaXQuY29tL3B5dGhvbi12aXN1YWxpemF0aW9uL2ZvbGl1bS9tYXN0ZXIvZm9saXVtL3RlbXBsYXRlcy9sZWFmbGV0LmF3ZXNvbWUucm90YXRlLmNzcyIvPgogICAgPHN0eWxlPmh0bWwsIGJvZHkge3dpZHRoOiAxMDAlO2hlaWdodDogMTAwJTttYXJnaW46IDA7cGFkZGluZzogMDt9PC9zdHlsZT4KICAgIDxzdHlsZT4jbWFwIHtwb3NpdGlvbjphYnNvbHV0ZTt0b3A6MDtib3R0b206MDtyaWdodDowO2xlZnQ6MDt9PC9zdHlsZT4KICAgIAogICAgPHN0eWxlPiNtYXBfM2YwZjM2N2NkZjE5NDE4Njg1NzFiZWU2Mzc3ZjQ4N2YgewogICAgICAgIHBvc2l0aW9uOiByZWxhdGl2ZTsKICAgICAgICB3aWR0aDogMTAwLjAlOwogICAgICAgIGhlaWdodDogMTAwLjAlOwogICAgICAgIGxlZnQ6IDAuMCU7CiAgICAgICAgdG9wOiAwLjAlOwogICAgICAgIH0KICAgIDwvc3R5bGU+CjwvaGVhZD4KPGJvZHk+ICAgIAogICAgCiAgICA8ZGl2IGNsYXNzPSJmb2xpdW0tbWFwIiBpZD0ibWFwXzNmMGYzNjdjZGYxOTQxODY4NTcxYmVlNjM3N2Y0ODdmIiA+PC9kaXY+CjwvYm9keT4KPHNjcmlwdD4gICAgCiAgICAKICAgIAogICAgICAgIHZhciBib3VuZHMgPSBudWxsOwogICAgCgogICAgdmFyIG1hcF8zZjBmMzY3Y2RmMTk0MTg2ODU3MWJlZTYzNzdmNDg3ZiA9IEwubWFwKAogICAgICAgICdtYXBfM2YwZjM2N2NkZjE5NDE4Njg1NzFiZWU2Mzc3ZjQ4N2YnLCB7CiAgICAgICAgY2VudGVyOiBbNTEuNDc4MTIwMzk1MDI1NCwgMy44MTUxNjA2MDI2OTM2Mzk1XSwKICAgICAgICB6b29tOiA5LAogICAgICAgIG1heEJvdW5kczogYm91bmRzLAogICAgICAgIGxheWVyczogW10sCiAgICAgICAgd29ybGRDb3B5SnVtcDogZmFsc2UsCiAgICAgICAgY3JzOiBMLkNSUy5FUFNHMzg1NywKICAgICAgICB6b29tQ29udHJvbDogdHJ1ZSwKICAgICAgICB9KTsKCiAgICAKICAgIAogICAgdmFyIHRpbGVfbGF5ZXJfOTUwOWZmZDBhZGZkNDkxYzhkODYyMGZiZmQ2ZDJmYzggPSBMLnRpbGVMYXllcigKICAgICAgICAnaHR0cHM6Ly9zdGFtZW4tdGlsZXMte3N9LmEuc3NsLmZhc3RseS5uZXQvdG9uZXIve3p9L3t4fS97eX0ucG5nJywKICAgICAgICB7CiAgICAgICAgImF0dHJpYnV0aW9uIjogbnVsbCwKICAgICAgICAiZGV0ZWN0UmV0aW5hIjogZmFsc2UsCiAgICAgICAgIm1heE5hdGl2ZVpvb20iOiAxOCwKICAgICAgICAibWF4Wm9vbSI6IDE4LAogICAgICAgICJtaW5ab29tIjogMCwKICAgICAgICAibm9XcmFwIjogZmFsc2UsCiAgICAgICAgInN1YmRvbWFpbnMiOiAiYWJjIgp9KS5hZGRUbyhtYXBfM2YwZjM2N2NkZjE5NDE4Njg1NzFiZWU2Mzc3ZjQ4N2YpOwogICAgCgogICAgICAgICAgICB2YXIgcG9seWdvbl82OWNmMTA4YjFjNmM0ZmY3YTIyNzUwNWI1ODU4ZWExMCA9IEwucG9seWdvbigKICAgICAgICAgICAgICAgIFtbWzUxLjQxNjkwNTY3MzcxOTk4LCAzLjg1NTg4MTgwNzc2NTI1Nl0sIFs1MS40MTk2OTM1MjM4MjkyOTUsIDMuODU2MDgxMTgxNDM3ODQxN10sIFs1MS40MjI4NjU2OTI2MTk2NDUsIDMuODY4NDQxNzYyMjk5NDE0NV0sIFs1MS40MjE5OTc2OTY4NTI0MzYsIDMuODcyNDAyNjQxNDQyNjczXSwgWzUxLjQyNTM1NjEwNjc5Mjc5LCAzLjg4MTA2MzY3MTM3MzIzOV0sIFs1MS40MjY1MTk3NTEzMjMwMSwgMy44ODA4MDI2MDE4MTU4MzQ2XSwgWzUxLjQyNjkwNjQ5NjQzMjM0NSwgMy44ODUyODE3NDc3MzYyODRdLCBbNTEuNDE4NDQ2OTcxNDEyNCwgMy44ODUwMDMyOTI3MDIzODQ4XSwgWzUxLjQxNzE0NjExNzY0MjAxLCAzLjg5MTMxMDQzOTAyNjM0XSwgWzUxLjQxODI4MjQ4Mzc2MjUzLCAzLjg5OTc0MjQ1ODEzOTYwMDNdLCBbNTEuNDE2MDU4MDYwODA3OTMsIDMuOTExODM0OTY5NDQ2OTQzNF0sIFs1MS4zOTU3NDE5Mjk2NTcyMiwgMy45MDA3MTY5MjYyNjk3ODIzXSwgWzUxLjM5MzUwOTY3MTcxMzM0LCAzLjg5Mzk2MzUzMDI4MTI3M10sIFs1MS4zOTc0NDEzNTkwNTMxNywgMy44NzYyMDk1OTgxNDg3MzY2XSwgWzUxLjM5OTg5ODIzODc0NzYxLCAzLjg3NjY4OTgwOTQxMzE4M10sIFs1MS40MDIxODk4OTMwMTE3NCwgMy44NzMyMjczNjY2MzMyOTNdLCBbNTEuNDAzMzUyNjg0NTMzMDEsIDMuODU4MTE0MzY0NTE2MzU1NF0sIFs1MS40MDQzNjY0MTAyMTY3LCAzLjg1OTIzNjA0ODE5ODExNzZdLCBbNTEuNDA0NjY0MDY5MTc1MzEsIDMuODU3NTMzMDE1NzE1MzQ3M10sIFs1MS40MDk0Nzk0Njc1NjQxNzUsIDMuODYwNjQ4ODcyNTUwNjM2XSwgWzUxLjQwOTQ1MzAzNjcxODI3LCAzLjg2MjgxNTk3NTY0NzYzNDZdLCBbNTEuNDExNzY4NDM3MDk4MTcsIDMuODYzODgzMzEyOTIzNjYyXSwgWzUxLjQxMzg5NjI3NTU5NTk0NSwgMy44NjMzODk0MDgyMzY3OTAzXSwgWzUxLjQxMzMzOTkyMTk3ODk0NSwgMy44NTg2NTk5MDU2NjgzNzA0XSwgWzUxLjQxNDcyNDE0ODk4Nzg0NCwgMy44NTk0MTMyNjg5OTA4OThdLCBbNTEuNDE2OLine truncated
|
||||
],
|
||||
"text/plain": [
|
||||
"<folium.folium.Map at 0x7f3200f766d8>"
|
||||
"<folium.folium.Map at 0x7f2f954805f8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -121,12 +121,20 @@
|
||||
" polygon = folium.Polygon(coordinates, fill_color=None, color='blue').add_to(m)\n",
|
||||
" \n",
|
||||
" for feature in gemeentes['features']:\n",
|
||||
"# print(feature['properties']['GM_NAAM'])\n",
|
||||
" coordinates = reverse_latitude_longitude(feature['geometry']['coordinates'])\n",
|
||||
" polygon = folium.Polygon(coordinates, fill_color=None, color='red').add_to(m)\n",
|
||||
" \n",
|
||||
" print(province)\n",
|
||||
" display(m)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
||||
Reference in new issue
Block a user