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How to use all_first method in Selene
Best Python code snippet using selene_python
waze-real-time.py
Source:waze-real-time.py 
1# -*- coding: utf-8 -*-2"""3Created on Tue Jun 1 16:03:44 20214@author: mayur5"""6import requests7import json8import pandas as pd9import geopandas as gpd10from shapely.geometry import Point, LineString11from datetime import timedelta12import time13import warnings14from catboost import CatBoostClassifier15def waze_download(polygon_path):16 17 with open(polygon_path, 'r') as f:18 poly_data = json.load(f)19 20 responses = []21 for d in poly_data['polygons'][:-1]:22 if d['city_name'].startswith('Sao Paulo'):# or d['city_name'] in ['Xalapa','Quito','Lima','Montivideo']:23 24 try:25 response = requests.get(d['url_base'] + d['polygon'])26 responses.append(response.json())27 except Exception as e:28 # responses[poly_id] = None29 # capture_exception(e)30 print(e)31 32 alerts = []33 jams = []34 for data in responses:35 time_pub = data['endTime'][:-4]36 if 'alerts' in data.keys():37 alerts = alerts + data['alerts']38 if 'jams' in data.keys():39 jams = jams + data['jams']40 41 return alerts, jams,time_pub42 43def process_alerts(alerts_list,pub):44 45 dfa = pd.DataFrame(alerts_list)46 dfa = dfa[dfa.city.isin(['São Paulo'])]#,'Xalapa','Quito','Montevideo','Miraflores'])]47 dfa['pub_utc_date'] = pub48 dfa = dfa[dfa.type=='ACCIDENT'] 49 dfa['geometry'] = gpd.GeoSeries(dfa['location'].apply(50 lambda coord: Point((coord['x'],coord['y']))), crs='EPSG:4326').to_crs('EPSG:22523')51 dfa['longitude'] = dfa['location'].apply(lambda coord: coord['x'])52 dfa['latitude'] = dfa['location'].apply(lambda coord: coord['y'])53 54 dfa = dfa.drop(['location'],axis=1)55 dfa = dfa.drop_duplicates()56 dfa = dfa.drop_duplicates(subset=['pub_utc_date','uuid'],keep='first')57 58 dfa = dfa.reset_index().set_index(['uuid'])59 dfa = dfa.sort_index()60 alerts_uuids = dfa.index.unique()61 62 return dfa, alerts_uuids63 64 65def process_jams(jams_list,dfjtotal,pub):66 dfj = pd.DataFrame(jams_list)67 dfj = dfj[dfj.city.isin(['São Paulo'])]#,'Xalapa','Quito','Montevideo','Miraflores'])]68 dfj['pub_utc_date'] = pub69 70 dfj['geometry'] = gpd.GeoSeries(dfj.line.apply(71 lambda x: LineString([(coord['x'],coord['y']) for coord in x])), crs='EPSG:4326').to_crs('EPSG:22523')72 73 dfj = dfj.drop(['line','segments'],axis=1)74 dfj = dfj.drop_duplicates()75 dfj = dfj.drop_duplicates(subset=['pub_utc_date','uuid'],keep='first')76 dfj['street'] = dfj.street.apply(str)77 78 dfjtotal = pd.concat([dfjtotal,dfj])79 80 #keeps only last 60 minutes of jam data in memory81 if len(dfjtotal.pub_utc_date.unique())>60:82 older_date = sorted(dfjtotal.pub_utc_date.unique().tolist())[0]83 dfjtotal = dfjtotal[dfjtotal.pub_utc_date != older_date] 84 85 return dfjtotal86def search_acc_jams(alerts_uuids,dfa,jams_ix):87 88 jam_acc = []89 # t=len(alerts_uuids)90 # i=091 for au in alerts_uuids: 92 93 acid = dfa.loc[[au]]94 acid = acid.reset_index().set_index(['pub_utc_date', 'street'])95 # print(i,'/',t,au,len(acid),time.ctime()) 96 # i+=197 try:98 jrows = jams_ix.loc[jams_ix.index.intersection(acid.index)]99 jrows['d'] = jrows.geometry.apply(lambda x: acid.iloc[0].geometry.distance(x))100 jrows = jrows[jrows.d<=200]101 except:102 jrows = []103 if len(jrows)>0:104 jrows = jrows[jrows.d==jrows.d.min()]105 106 jrows = jrows.reset_index().drop_duplicates(subset=['pub_utc_date']).set_index(['pub_utc_date','street'])107 108 acid.loc[jrows.index,'juuid'] = jrows.uuid.apply(int)109 jam_acc.extend(acid.reset_index().loc[:,['pub_utc_date','uuid','juuid']].values.tolist())110 return jam_acc111def merge_acc_jams(dfa,jams_ix,jam_acc):112 # print(' preprocessing merge',time.ctime())113 accids2= dfa.reset_index().set_index(['pub_utc_date', 'uuid'])114 accids2= accids2.sort_index()115 jams_ix2 = jams_ix.reset_index().set_index(['pub_utc_date', 'uuid'])116 jams_ix2= jams_ix2.sort_index()117 jadf = pd.DataFrame(jam_acc)118 jadfnn = jadf.dropna()119 120 # print(' merging data',time.ctime())121 joined =[]122 if len(jadfnn)>0:123 for i,j in enumerate(jadfnn[1].unique()):124 # if i%100==0:125 # print(i,time.ctime())126 if len(jadf)>0:127 keys = jadf[jadf[1]==j]128 aa = accids2.loc[list(keys[[0,1]].itertuples(index=False,name=None))]129 130 keys2=jams_ix2.index.intersection(list(keys[[0,2]].itertuples(index=False,name=None)))131 ja = jams_ix2.loc[keys2]132 ja = ja.reset_index().drop_duplicates(subset=['pub_utc_date']).set_index(['pub_utc_date','uuid'])133 134 result = pd.merge(aa.reset_index(), ja.reset_index(), on=["pub_utc_date", "street"]).set_index('index')135 joined.append(result)136 137 if len(joined)>0:138 dfjoined =pd.concat(joined) 139 dfjoined = dfjoined[~dfjoined.index.duplicated(keep='first')]140 notfoundf = accids2[~accids2['index'].isin(dfjoined.index)]141 notfoundf = notfoundf.reset_index().set_index('index')142 notfoundf.rename(columns={'uuid':'uuid_x','type':'type_x','country':'country_x','city':'city_x','roadType':'roadType_x','pubMillis':'pubMillis_x','geometry':'geometry_x'}, inplace=True)143 all_df = pd.concat([dfjoined,notfoundf])144 all_df = all_df.sort_values('pub_utc_date')145 else:146 notfoundf = accids2 147 notfoundf = notfoundf.reset_index().set_index('index')148 notfoundf.rename(columns={'uuid':'uuid_x','type':'type_x','country':'country_x','city':'city_x','roadType':'roadType_x','pubMillis':'pubMillis_x','geometry':'geometry_x'}, inplace=True)149 all_df = notfoundf150 all_df = all_df.sort_values('pub_utc_date')151 all_df['length'] = 0152 all_df['level'] = 0153 all_df['delay'] = 0154 all_df['type_y'] = 'NONE'155 156 return joined, all_df157 158def process_extra_features(all_df,jams_ix):159 160 all_first = all_df.loc[all_df.uuid_x.drop_duplicates(keep='first').index]161 all_first.length.fillna(0,inplace=True)162 all_first.level.fillna(0,inplace=True)163 all_first.delay.fillna(0,inplace=True)164 all_first['pub_utc_date0'] = pd.to_datetime(all_first['pub_utc_date'])165 all_first['pub_utc_date1'] =all_first.pub_utc_date0 - timedelta(minutes=1)166 all_first['pub_utc_date1'] = all_first['pub_utc_date1'].apply(lambda x: str(x))#+'.000')167 168 all_first['pub_utc_date5'] =all_first.pub_utc_date0 - timedelta(minutes=5)169 all_first['pub_utc_date5'] = all_first['pub_utc_date5'].apply(lambda x: str(x))#+'.000')170 171 all_first['pub_utc_date10'] =all_first.pub_utc_date0 - timedelta(minutes=10)172 all_first['pub_utc_date10'] = all_first['pub_utc_date10'].apply(lambda x: str(x))#+'.000')173 174 all_first['pub_utc_date20'] =all_first.pub_utc_date0 - timedelta(minutes=20)175 all_first['pub_utc_date20'] = all_first['pub_utc_date20'].apply(lambda x: str(x))#+'.000')176 177 all_first['pub_utc_date30'] =all_first.pub_utc_date0 - timedelta(minutes=30)178 all_first['pub_utc_date30'] = all_first['pub_utc_date30'].apply(lambda x: str(x))#+'.000')179 180 for dt in ['1','5','10','20','30']:181 # ifo=0182 # inofo=0183 for row in all_first.iterrows():184 # break185 try:186 zzz = jams_ix.loc[(row[1]['pub_utc_date'+dt],row[1].street)]187 zzz['d'] = zzz.geometry.apply(lambda x: x.distance(row[1].geometry_x))188 zzz = zzz[zzz.d<=200]189 zzz = zzz[zzz.d==zzz.d.min()]190 191 all_first.loc[row[0],'len'+dt] = row[1].length - zzz.length[0]192 all_first.loc[row[0],'lev'+dt] = row[1].level - zzz.level[0]193 all_first.loc[row[0],'del'+dt] = row[1].delay - zzz.delay[0]194 # ifo+=1195 except:196 all_first.loc[row[0],'len'+dt] = row[1].length - 0197 all_first.loc[row[0],'lev'+dt] = row[1].level - 0198 all_first.loc[row[0],'del'+dt] = row[1].delay - 0199 return all_first200def merge_extra_features(alerts_uuids,all_first,all_df,dfatotal):201 for au in alerts_uuids:202 203 first = all_first[all_first.uuid_x==au]204 205 cols = ['len1', 'lev1', 'del1', 'len5', 'lev5', 'del5', 'len10', 'lev10',206 'del10', 'len20','lev20', 'del20', 'len30', 'lev30', 'del30']207 zzz=all_df[all_df.uuid_x==first.uuid_x.iloc[0]]208 209 210 firstrep = first.loc[first.index.repeat(len(zzz)),cols]211 firstrep.index = zzz.index 212 all_df.loc[firstrep.index,cols] = firstrep.loc[:,cols]213 214 for k in all_df.uuid_x.unique():215 # break216 temp = all_df[all_df.uuid_x==k]217 218 if temp.length.isnull().all():219 temp['length'] = 0220 temp['level'] = 0221 temp['delay'] = 0 222 temp['type_y'] = 'NONE'223 elif temp.length.isnull().any():224 # if i>5:225 # break226 # i+=1227 temp['c'] = temp.length.mask(temp.length.ffill().notnull(),temp.length.interpolate(limit_area='inside'))228 temp['level'] = temp.level.mask(temp.level.ffill().notnull(),temp.level.interpolate(limit_area='inside'))229 temp['delay'] = temp.delay.mask(temp.delay.ffill().notnull(),temp.delay.interpolate(limit_area='inside'))230 temp['type_y'] = temp.type_y.mask(temp.type_y.ffill().notnull(),temp.type_y.bfill())231 232 temp['length'].fillna(0,inplace=True)233 temp['level'].fillna(0,inplace=True)234 temp['delay'].fillna(0,inplace=True) 235 temp['type_y'].fillna('NONE',inplace=True) 236 237 all_df.loc[temp.index,'length'] = temp['length'] 238 all_df.loc[temp.index,'level'] = temp['level'] 239 all_df.loc[temp.index,'delay'] = temp['delay'] 240 all_df.loc[temp.index,'type_y'] = temp['type_y']241 #shows alerts from last 10 minutes242 dfatotal = pd.concat([dfatotal,all_df])243 if len(dfatotal.pub_utc_date.unique())>10:244 older_date = sorted(dfatotal.pub_utc_date.unique().tolist())[0]245 dfatotal = dfatotal[dfatotal.pub_utc_date != older_date] 246 247 all_df = dfatotal.drop_duplicates('uuid_x',keep='last').reset_index(drop=True)248 249 return all_df,dfatotal250def clean_and_predict_proba(all_df,model):251 #remove unwanted columns252 253 254 if len(joined)>0:255 x_predict = all_df.drop(['pub_utc_date','uuid_x','country_x','city_x','type_x',256 'magvar','street','pubMillis_x','reportDescription',257 'geometry_x','longitude','latitude','uuid_y','country_y',258 'city_y','speedKMH','turnType','endNode','speed',259 'roadType_y','pubMillis_y','blockingAlertUuid','startNode',260 'geometry_y'],axis=1)261 else:262 x_predict = all_df.drop(['pub_utc_date','uuid_x','country_x','city_x','type_x',263 'magvar','street','pubMillis_x','reportDescription',264 'geometry_x','longitude','latitude'],axis=1)265 266 x_predict.rename(columns={'nThumbsUp':'nthumbsup','reportRating':'reportrating','roadType_x':'roadtype'}, inplace=True)267 x_predict['subtype'] = x_predict['subtype'].fillna('not reported')268 x_predict['roadtype'] = x_predict.roadtype.fillna(0)269 x_predict['roadtype'] = x_predict.roadtype.apply(str)270 x_predict = x_predict[['subtype', 'roadtype', 'nthumbsup', 'reliability', 'reportrating',271 'confidence', 'length', 'level', 'delay', 'type_y', 'len1', 'lev1',272 'del1', 'len5', 'lev5', 'del5', 'len10', 'lev10', 'del10', 'len20',273 'lev20', 'del20', 'len30', 'lev30', 'del30']]274 275 276 277 y_prob = model.predict_proba(x_predict) 278 probdf = pd.DataFrame(y_prob)279 return probdf280 281if __name__ == '__main__':282 pd.options.mode.chained_assignment = None283 warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning)284 285 286 #Cities timezones287 # xalapa = 'America/Mexico_City'288 # quito = 'America/Guayaquil'289 # montevideo = 'America/Montevideo'290 # miraflores = 'America/Lima'291 saopaulo = 'America/Sao_Paulo'292 293 polygon_path = 'data/polygons.json' #polygon and url file path294 path = 'data/real_time/' #final csv save location295 model_path = "data/waze_prob_model.cbm" #trained classification model path296 297 model = CatBoostClassifier()298 model.load_model(model_path)299 300 dfatotal = pd.DataFrame()301 302 try:303 dfjtotal = pd.read_pickle('data/dfjtotal')304 except:305 dfjtotal = pd.DataFrame()306 307 while True:308 net=False 309 t0 = time.time()310 try:311 312 alerts,jams,pub = waze_download(polygon_path)313 314 315 dfa, alerts_uuids = process_alerts(alerts,pub)316 dfjtotal = process_jams(jams,dfjtotal,pub)317 318 jams_ix = dfjtotal.set_index(['pub_utc_date', 'street'])319 jams_ix = jams_ix.sort_index()320 321 322 jam_acc = search_acc_jams(alerts_uuids,dfa,jams_ix)323 joined, all_df = merge_acc_jams(dfa,jams_ix,jam_acc)324 325 all_first = process_extra_features(all_df,jams_ix)326 all_df,dfatotal = merge_extra_features(alerts_uuids, all_first, all_df,dfatotal)327 328 probdf = clean_and_predict_proba(dfatotal,model)329 330 331 # datas = pd.to_datetime(all_df['pub_utc_date'])332 #fix cities timezone333 datasp = pd.to_datetime(all_df[all_df.city_x=='São Paulo']['pub_utc_date'],utc=True).dt.tz_convert(saopaulo)334 # dataxa = pd.to_datetime(all_df[all_df.city_x=='Xalapa']['pub_utc_date'],utc=True).dt.tz_convert(xalapa)335 # dataqt = pd.to_datetime(all_df[all_df.city_x=='Quito']['pub_utc_date'],utc=True).dt.tz_convert(quito)336 # datamv = pd.to_datetime(all_df[all_df.city_x=='Montevideo']['pub_utc_date'],utc=True).dt.tz_convert(montevideo)337 # datamf = pd.to_datetime(all_df[all_df.city_x=='Miraflores']['pub_utc_date'],utc=True).dt.tz_convert(miraflores)338 339 # datas = pd.concat([datasp,dataxa,dataqt,datamv,datamf]).sort_index()340 datas = datasp.sort_index()341 342 343 csvtodisk = all_df[['longitude','latitude','street','city_x']]344 csvtodisk['prob'] = probdf[1].round(2)345 csvtodisk['hour'] = datas.apply(lambda x: x.hour)346 csvtodisk['minute'] = datas.apply(lambda x: x.minute)347 csvtodisk['day'] = datas.apply(lambda x: x.day)348 csvtodisk['month'] = datas.apply(lambda x: x.month)349 csvtodisk['year'] = datas.apply(lambda x: x.year)350 csvtodisk['weekday'] = datas.apply(lambda x: x.weekday())351 csvtodisk.rename(columns={'city_x':'city'}, inplace=True)352 353 csvtodisk.to_csv(path+'acc_realtime.csv',index=False)354 dfjtotal.to_pickle('data/dfjtotal')355 except:356 net=True357 pass358 359 t1 = time.time()360 if net:361 print('download error',pub,' call',(t1-t0),'secs. next try in',60-(t1-t0),'secs')362 else:363 print('completed',pub,' call',(t1-t0),'secs. sleeping for',60-(t1-t0),'secs')364 time.sleep(60-(t1-t0))...
neighbor_sep_scatter.py
Source:neighbor_sep_scatter.py
1from astropy.table import Table2import matplotlib.pyplot as plt3import numpy as np4import matplotlib.cm as cm5import pandas as pd6import matplotlib.lines as mlines7import os.path8import scipy.stats9loc = '/Users/josh/projects/intro/sources/'10res = 15011sources = pd.read_csv('sources.csv')12sources = sources['circinus'].values.tolist()13all_first, all_second, all_third = [], [], []14for i in range(len(sources)):15 fp = loc+str(sources[i])+'/'+str(sources[i])+'_'+str(res)+'pc_cloud_stats.csv'16 if os.path.isfile(fp):17 cat = pd.read_csv(fp)18 #ALL GALAXIES19 all_first = all_first + cat['min_dist'].tolist()20 all_second = all_second + cat['min_dist2nd'].tolist()21 all_third = all_third + cat['min_dist3rd'].tolist()22num_bins = 1523bins = scipy.stats.binned_statistic(all_first, all_second, statistic='median', bins=num_bins, range=(0,1000))24medians = bins[0]25xvals = (bins[1][1:] + bins[1][:-1]) / 226bins2 = scipy.stats.binned_statistic(all_first, all_second, statistic='median', bins=num_bins, range=(0,1000))27medians2 = bins2[0]28xvals2 = (bins2[1][1:] + bins2[1][:-1]) / 229#Get percentiles30err1 = np.zeros((2,num_bins))31err2 = np.zeros((2,num_bins))32for i in range(num_bins):33 err1[:,i] = [np.percentile(np.take(all_second, np.where(bins[2]==(i+1))),16),np.percentile(np.take(all_second, np.where(bins[2]==(i+1))),84)]34 err2[:,i] = [np.percentile(np.take(all_third, np.where(bins[2]==(i+1))),16),np.percentile(np.take(all_third, np.where(bins[2]==(i+1))),84)]35err1 = np.abs(err1 - medians)36err2 = np.abs(err2 - medians2)37fig, axes = plt.subplots(1, 2, figsize=(10,5))38axes[0].scatter(all_first, all_second, c='gray', s=3, alpha=0.2, label=r'$\rho$ = '+ str(np.around(scipy.stats.pearsonr(all_first, all_second)[0], 2)))39axes[0].set_xlim((0,1000))40axes[0].set_ylim((0,2000))41axes[0].scatter(xvals, medians, c='b')42axes[0].errorbar(xvals, medians, c='b', capsize=4, ls='none', xerr=None, yerr=err1)43axes[0].set_title(str(res)+'pc Resolution')44axes[0].set_xlabel('Distance to 1st Nearest Neighbor (pc)')45axes[0].set_ylabel('Distance to 2nd Nearest Neighbor (pc)')46axes[1].scatter(all_first, all_third, c='gray', s=3, alpha=0.2, label=r'$\rho$ = '+ str(np.around(scipy.stats.pearsonr(all_first, all_third)[0], 2)))47axes[1].set_xlim((0,1000))48axes[1].set_ylim((0,2000))49axes[1].scatter(xvals2, medians2, c='b')50axes[1].errorbar(xvals2, medians2, c='b', capsize=4, ls='none', xerr=None, yerr=err2)51axes[1].set_title(str(res)+'pc Resolution')52axes[1].set_xlabel('Distance to 1st Nearest Neighbor (pc)')53axes[1].set_ylabel('Distance to 3rd Nearest Neighbor (pc)')54#for i in range(num_bins):55 #axes[0].scatter(xvals[i], medians[i], c='b')56 #axes[0].errorbar(xvals[i], medians[i], c='b', capsize=4, ls='none', xerr=None, yerr=err1)57 #axes[1].scatter(xvals2[i], medians2[i], c='b')58 #axes[1].errorbar(xvals2[i], medians2[i], c='b', capsize=4, ls='none', xerr=None, yerr=[[np.percentile(np.take(all_third, np.where(bins2[2]==(i+1))),16)],[np.percentile(np.take(all_third, np.where(bins2[2]==(i+1))),84)]])59x1 = np.linspace(0,2500, 2501)60axes[0].plot(x1, x1*(np.median(all_second)/np.median(all_first)), c='r', linestyle='--', linewidth=3, label='Slope: Median 2nd / Median 1st')61axes[1].plot(x1, x1*(np.median(all_third)/np.median(all_first)), c='r', linestyle='--', linewidth=3, label='Slope: Median 3rd / Median 1st')62axes[0].legend()63axes[1].legend()64plt.legend()65plt.show()...
stats.py
Source:stats.py
1# %%2token = "df8f82c719ece3df8ff58dbc6cdebc21"3# %%4import httpx5query = {6 "module": "API",7 "token_auth": token,8 "method": "Live.getLastVisitsDetails",9 "idSite": "3",10 "period": "month",11 "date": "today",12 "format": "JSON",13 "filter_limit": "-1",14}15response = httpx.get("https://matomo.schuetze.link/index.php", params=query)16response.raise_for_status()17data = response.json()18# %%19from collections import Counter20import idna21import re22all_domains = list()23all_first = list()24for i in data:25 urls = [action["url"] for action in i["actionDetails"]]26 if any(27 not re.match("https://[a-z-]+\.meine-stadt-transparent.de", url) for url in urls28 ):29 continue30 domains = [idna.decode(url.split("/")[2]).split(".")[0] for url in urls]31 all_domains.extend(domains)32 all_first.append(domains[0])33all_domains = Counter(all_domains)34all_first = Counter(all_first)35print(all_domains)36print(all_first)37#%%38import numpy39import matplotlib.pyplot as plt40labels = list(set(all_domains.keys()) | set(all_first.keys()))41labels.sort(key=lambda x: all_first.get(x, 0))42visitor_values = [all_domains.get(label, 0) for label in labels]43actions_values = [all_first.get(label, 0) for label in labels]44x = numpy.arange(len(labels)) # the label locations45width = 0.35 # the width of the bars46fig, ax = plt.subplots()47rects1 = ax.barh(x - width / 2, visitor_values, width, label="By action")48rects2 = ax.barh(x + width / 2, actions_values, width, label="By visitor")49# Add some text for labels, title and custom x-axis tick labels, etc.50ax.set_yticks(x)51ax.set_yticklabels(labels)52ax.legend()53fig.tight_layout()54plt.title("Visitors and actions per city in the last month")55plt.show()56#%%57import pandas58existing = pandas.read_csv("existing.csv").fillna("")59print(sorted(list(existing["name"])))60print(sorted(all_domains.keys()))...
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