616 lines
4.6 MiB
Plaintext
616 lines
4.6 MiB
Plaintext
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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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"# Geographical pronunciation statistics"
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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": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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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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"from matplotlib import pyplot\n",
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"import folium\n",
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"from IPython.display import display\n",
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"from shapely.geometry import Polygon, MultiPolygon, shape, Point, box, mapping\n",
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"from jupyter_progressbar import ProgressBar\n",
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"from collections import defaultdict\n",
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"from ipy_table import make_table\n",
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"from html import escape\n",
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"\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"from matplotlib.colors import LogNorm\n",
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"from sklearn import mixture\n",
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"from skimage.measure import find_contours\n",
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"from collections import Counter\n",
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"from random import shuffle"
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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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"# Borders of Frysian municipalities\n",
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"\n",
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"with open('Friesland_AL8.GeoJson') as f:\n",
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" gemeentes = 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": 34,
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"metadata": {},
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"outputs": [],
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"source": [
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"coords = [feature['geometry'] for feature in gemeentes['features']]\n",
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"coords_folium = [[[[c__[::-1] for c__ in c_] for c_ in c] for c in coords_['coordinates']] for coords_ in coords]\n",
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"shapes = [shape(coords_).simplify(tolerance=0.001) for coords_ in coords]\n",
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"gemeente_names = [feature['properties']['name'] for feature in gemeentes['features']]\n",
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"\n",
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"def get_gemeente(point):\n",
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" for i, shape in enumerate(shapes):\n",
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" if shape.contains(point):\n",
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" return i\n",
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" return -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": 35,
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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)"
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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": 36,
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"metadata": {},
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"outputs": [],
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"source": [
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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_filtered = answers[answers['question_text'].map(lambda x: x not in zero_latlng_questions)]"
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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": 37,
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"metadata": {},
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"outputs": [
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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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"/home/herbert/.virtualenvs/stimmenfryslan/lib/python3.6/site-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
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"A value is trying to be set on a copy of a slice from a DataFrame.\n",
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"Try using .loc[row_indexer,col_indexer] = value instead\n",
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"\n",
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"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
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" # Remove the CWD from sys.path while we load stuff.\n"
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]
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}
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],
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"source": [
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"# Takes approximately 2 minutes\n",
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"\n",
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"gemeente_map = {\n",
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" (lng, lat): get_gemeente(Point(lng, lat))\n",
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" for lng, lat in set(zip(answers_filtered['user_lng'], answers_filtered['user_lat']))\n",
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"}\n",
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"\n",
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"answers_filtered['gemeente'] = [\n",
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" gemeente_map[(lng, lat)]\n",
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" for lat, lng in zip(answers_filtered['user_lat'], answers_filtered['user_lng'])\n",
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"]"
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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": 38,
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"metadata": {},
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"outputs": [
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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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"/home/herbert/.virtualenvs/stimmenfryslan/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
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"A value is trying to be set on a copy of a slice from a DataFrame.\n",
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"Try using .loc[row_indexer,col_indexer] = value instead\n",
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"\n",
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"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
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" \n"
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]
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}
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],
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"source": [
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"answers_filtered['question_text_url'] = answers_filtered['question_text'].map(\n",
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" lambda x: x.replace('\"', '').replace('*', ''))"
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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": 44,
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"metadata": {},
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"outputs": [],
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"source": [
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"def reverse(rd_multipolygon):\n",
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" if len(rd_multipolygon) == 2 and tuple(map(type, rd_multipolygon)) == (float, float):\n",
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" return rd_multipolygon[::-1]\n",
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" return [\n",
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" reverse(element)\n",
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" for element in rd_multipolygon\n",
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" ]"
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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": 46,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_palette(n, no_black=True, no_white=True):\n",
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" with open('glasbey/{}_colors.txt'.format(n + no_black + no_white)) as f:\n",
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" return [\n",
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" '#%02x%02x%02x' % tuple(int(c) for c in line.replace('\\n', '').split(','))\n",
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" for line in f\n",
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" if not no_black or line != '0,0,0\\n'\n",
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" if not no_white or line != '255,255,255\\n'\n",
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" ]"
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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": 89,
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"metadata": {},
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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": "56dab238a8864488a66a23b26dfa4ce3",
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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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"source": [
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"cdfs = [\n",
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" np.array([\n",
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" box(xmin, ymin, xmax_, ymax).intersection(shape).area / shape.area\n",
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" for xmax_ in numpy.linspace(xmin, xmax, 10001)\n",
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" ][1:])\n",
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" for shape in ProgressBar(shapes)\n",
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" for xmin, ymin, xmax, ymax in [shape.bounds]\n",
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"]"
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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": 106,
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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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"model_id": "6a7c2ca1bb1042ff8cfd4b6efbb14c2a",
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"version_major": 2,
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"version_minor": 0
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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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"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 src=\"data:text/html;charset=utf-8;base64,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
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
"<folium.folium.Map at 0x7fba3c826c50>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"cutoff_percentage = 0.05\n",
|
||
|
|
"\n",
|
||
|
|
"for question, rows in ProgressBar(\n",
|
||
|
|
" answers_filtered.groupby('question_text_url'),\n",
|
||
|
|
" size=len(answers_filtered['question_text_url'].unique())\n",
|
||
|
|
"):\n",
|
||
|
|
" m = folium.Map((rows['user_lat'].median(), rows['user_lng'].median()), tiles='stamentoner', zoom_start=10)\n",
|
||
|
|
" answer_texts = {\n",
|
||
|
|
" answer_text\n",
|
||
|
|
" for gemeente, _ in enumerate(gemeente_names)\n",
|
||
|
|
" for rows_ in [rows[rows['gemeente'] == gemeente]]\n",
|
||
|
|
" for answer_text, rows__ in rows_.groupby('answer_text')\n",
|
||
|
|
" if len(rows__) / len(rows_) >= cutoff_percentage\n",
|
||
|
|
" }\n",
|
||
|
|
" palette = dict(zip(answer_texts, get_palette(len(answer_texts))))\n",
|
||
|
|
" n_other = len(rows) - sum(sum(rows['answer_text'] == answer_text) for answer_text in answer_texts)\n",
|
||
|
|
" \n",
|
||
|
|
" groups = {\n",
|
||
|
|
" answer_text: folium.FeatureGroup(name=name, overlay=True)\n",
|
||
|
|
" for answer_text, color in palette.items()\n",
|
||
|
|
" for name in [\n",
|
||
|
|
" '<span style=\\\\\"color:{}; \\\\\">{} ({})'.format(\n",
|
||
|
|
" color,\n",
|
||
|
|
" escape(answer_text),\n",
|
||
|
|
" sum(rows['answer_text'] == answer_text)\n",
|
||
|
|
" )\n",
|
||
|
|
" ]\n",
|
||
|
|
" }\n",
|
||
|
|
" groups['other'] = folium.FeatureGroup(\n",
|
||
|
|
" name='<span style=\\\\\"color: black; \\\\\">{} ({})'.format(\n",
|
||
|
|
" escape('other'),n_other),\n",
|
||
|
|
" overlay=True\n",
|
||
|
|
" )\n",
|
||
|
|
" \n",
|
||
|
|
" for gemeente, gemeente_name in enumerate(gemeente_names):\n",
|
||
|
|
" rows_ = rows[rows['gemeente'] == gemeente]\n",
|
||
|
|
" order = [a for _, a in sorted((\n",
|
||
|
|
" (r['user_lat'], answer)\n",
|
||
|
|
" for answer, r in rows_.groupby('answer_text').count().iterrows()\n",
|
||
|
|
" ), reverse=True)]\n",
|
||
|
|
" gemeente_shape = shapes[gemeente]\n",
|
||
|
|
" xmin, ymin, xmax, ymax = gemeente_shape.bounds\n",
|
||
|
|
" xmin_cum = xmin\n",
|
||
|
|
"# print(sum(sum(rows_['answer_text'] == answer_text) for answer_text in order))\n",
|
||
|
|
"# print(len(rows_))\n",
|
||
|
|
" cum_percentage = 0\n",
|
||
|
|
" for i, answer_text in enumerate(order):\n",
|
||
|
|
" total = sum(rows_['answer_text'] == answer_text)\n",
|
||
|
|
" percentage = total / len(rows_)\n",
|
||
|
|
" cum_percentage += percentage\n",
|
||
|
|
" if i == 0:\n",
|
||
|
|
" max_percentage = percentage\n",
|
||
|
|
" \n",
|
||
|
|
" name = '{} ({}, {}%)'.format(answer_text, total, int(round(100*percentage)))\n",
|
||
|
|
" if percentage < cutoff_percentage:\n",
|
||
|
|
" xmax_ = xmax\n",
|
||
|
|
" color = '#ffffff'\n",
|
||
|
|
" answer_text = 'other'\n",
|
||
|
|
" total = sum(sum(rows_['answer_text'] == answer_text) for answer_text in order[i:])\n",
|
||
|
|
" percentage = total / len(rows_)\n",
|
||
|
|
" done = True\n",
|
||
|
|
" else:\n",
|
||
|
|
" percentage_corrected = numpy.abs(cdfs[gemeente] - cum_percentage).argmin() / 10000\n",
|
||
|
|
" xmax_ = xmin + percentage_corrected * (xmax - xmin)\n",
|
||
|
|
" color = palette[answer_text]\n",
|
||
|
|
" done = False\n",
|
||
|
|
" \n",
|
||
|
|
" answer_shape = gemeente_shape.intersection(box(xmin_cum, ymin, xmax_, ymax))\n",
|
||
|
|
" xmin_cum = xmax_\n",
|
||
|
|
"# color = '#%02x%02x%02x' % tuple(int(255 * c) for c in cmap(percentage / max_percentage)[:3])\n",
|
||
|
|
" \n",
|
||
|
|
" polygon = folium.Polygon(\n",
|
||
|
|
" reverse(mapping(answer_shape)['coordinates']),\n",
|
||
|
|
" fill_color=color,\n",
|
||
|
|
" fill_opacity=0.8,\n",
|
||
|
|
" color=None,\n",
|
||
|
|
" popup='{} ({}, {: 3d}%)'.format(answer_text, total, round(100*percentage))\n",
|
||
|
|
" )\n",
|
||
|
|
" polygon.add_to(groups[answer_text])\n",
|
||
|
|
" if done:\n",
|
||
|
|
" break\n",
|
||
|
|
" \n",
|
||
|
|
" polygon = folium.Polygon(\n",
|
||
|
|
" reverse(mapping(gemeente_shape)['coordinates']),\n",
|
||
|
|
" fill_color=None, color='#000000'\n",
|
||
|
|
" )\n",
|
||
|
|
" polygon.add_to(m)\n",
|
||
|
|
" \n",
|
||
|
|
" for _, group in sorted(groups.items(), key=lambda x: sum(rows['answer_text'] == x[0]), reverse=True):\n",
|
||
|
|
" group.add_to(m)\n",
|
||
|
|
" \n",
|
||
|
|
" folium.map.LayerControl('topright', collapsed=False).add_to(m)\n",
|
||
|
|
" display(m)\n",
|
||
|
|
" m.save('maps/heatmaps-combined/{}.html'.format(question))\n",
|
||
|
|
"# break"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 109,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"import glob\n",
|
||
|
|
"with open('maps/heatmaps-combined/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/heatmaps-combined/*.html')\n",
|
||
|
|
" )\n",
|
||
|
|
" for fn in [fn[len('maps/heatmaps-combined/'):]]\n",
|
||
|
|
" ) + \"</body></html>\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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
|
||
|
|
}
|