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How to use record_time method in Molotov

Best Python code snippet using molotov_python

usa_senators_reps_twitter_audits.py

Source:usa_senators_reps_twitter_audits.py

1"""2d:3cd D:\2020\coding\twitter_bot_tweepy4sqlite3 usa_fed_politician_tweets_2.db5dir *.db678#use this to run9d:10cd D:\2020\coding\twitter_bot_tweepy11env_may2022\Scripts\activate.bat12python131415pip install xlrd16https://xlrd.readthedocs.io/en/latest/171819https://ucsd.libguides.com/congress_twitter/home20congress_twitter_040722.xls2122#sql audit queries23select count(*) from user_completed;24select count(*) from err_userid;25select count(*) from users_liking_dict;26select count(*) from err_info_in_tweets;27select count(*) from tweet_dict;28select count(*) from user_completed;29select count(*) from tweet_likes_completed;3031select userID, count(*) as count from completed group by userID;32select userID, count(*) as count from err_info_in_tweets group by userID;33select userID, count(*) as count from err_userid group by userID;34select userID, count(*) as count from completed group by userID;35select userID, tweet_id, count(*) as count from tweet_likes_completed group by userID, tweet_id;3637"""38import urllib.request39import json40import xlrd41import os42import datetime43import time44import tweepy as tw45import pandas as pd4647import sqlite348from sqlite3 import Error49from datetime import datetime5051import requests52#nb: should have these keys loading from environment variables configured outside source code in repo.53#omitting the keys is a temporary fix. 54bearer_token=""55consumer_key=""56consumer_secret=""57access_token=""58access_token_secret=""59#nb: this uses an older method. lazy reuse.60auth = tw.OAuthHandler(consumer_key, consumer_secret)61auth.set_access_token(access_token, access_token_secret)62api = tw.API(auth, wait_on_rate_limit=True)63#64client = tw.Client( bearer_token=bearer_token,65                        consumer_key=consumer_key,66                        consumer_secret=consumer_secret,67                        access_token=access_token,68                        access_token_secret=access_token_secret,69                        return_type = requests.Response,70                        wait_on_rate_limit=True)7172db_filename="usa_fed_politician_tweets_2.db"73conn = sqlite3.connect(db_filename)74757677twitter_names = []7879wb = xlrd.open_workbook('congress_twitter_040722.xls')80sheet_names = wb.sheet_names()81for sheet_name in sheet_names:82    sheet = wb.sheet_by_name(sheet_name)83    print("\n")84    print("Number of Rows: ", sheet.nrows)85    print("Number of Columns: ",sheet.ncols)86    for j in range(2,sheet.nrows):87        twitter_name = sheet.cell(j,4).value88        twitter_name89        if len(twitter_name)>0:90            twitter_names.append(twitter_name)9192num_accounts = len(twitter_names)93print("Number of twitter accounts :", num_accounts)949596def connect(host='http://google.com'):97    try:98        urllib.request.urlopen(host) #Python 3.x99        return True100    except:101        return False102103# test104print( "connected" if connect() else "no internet!" )105106107for i in range(13, num_accounts):108    try:109        userID = twitter_names[i]110        print("userID:", userID)111        tweets = api.user_timeline(screen_name=userID,112                                   # 200 is the maximum allowed count113                                   count=200,114                                   include_rts = False,115                                   # Necessary to keep full_text116                                   # otherwise only the first 140 words are extracted117                                   tweet_mode = 'extended'118                                   )119        num_tweets = len(tweets)120        print("# of tweets:", num_tweets)121        #for info in tweets[:1]:122        #for info in tweets:123        for j in range(num_tweets):124            info = tweets[j]125            try:126                #dir(info)127                print("userID:", userID)128                print("ID: {}".format(info.id))129                print("created_at:", info.created_at)130                print("full_text:", info.full_text)131                print("retweet_count:", info.retweet_count)132                print("favorite_count:", info.favorite_count)133                print("\n")134                liking_users = client.get_liking_users(str(info.id))135                record_time = datetime.now()136                users_liking = json.loads(liking_users.content.decode("utf-8") )['data']137                num_liking_tweet = len(users_liking)138                print("num_liking:", num_liking_tweet)139                tweet_dict = {140                    "userID":userID,141                    'tweet_id':str(info.id),142                    "created_at":info.created_at.strftime("%m/%d/%Y, %H:%M:%S"),143                    "record_time":record_time,144                    "full_text":info.full_text,145                    "retweet_count":info.retweet_count,146                    "favorite_count":info.favorite_count,147                    "num_liking_tweet":num_liking_tweet,148                }149                print("tweet_dict:", tweet_dict)150                df_temp = pd.DataFrame(tweet_dict, index=[0])151                df_temp.to_sql("tweet_dict", conn, schema=None, index=False, if_exists='append')152                num_users_linking = len(users_liking)153                #for user_liking in users_liking:154                for k in range(num_users_linking):155                    user_liking = users_liking[k]156                    try:157                        record_time = datetime.now()158                        users_liking_dict = {159                            "tweet_id":user_liking['id'],160                            "name":user_liking['name'],161                            "username":user_liking['username'],162                            "record_time":record_time,163                        }164                        print("users_liking_dict:", users_liking_dict)165                        df_temp = pd.DataFrame(users_liking_dict, index=[0])166                        df_temp.to_sql("users_liking_dict", conn, schema=None, index=False, if_exists='append')167                    except Exception as e:168                        record_time = datetime.now()169                        err_dict = {170                            "userID":userID,171                            "user_liking":user_liking,172                            "error":str(e),173                            "record_time":record_time,174                        }175                        print("err_user_liking:", err_dict)176                        df_err = pd.DataFrame(err_dict, index=[0])177                        df_err.to_sql("err_user_liking", conn, schema=None, index=False, if_exists='append')178                        if not connect():179                            print("internet not connected.")180                            df_err = pd.DataFrame(err_dict, index=[0])181                            df_err.to_sql("no_internet", conn, schema=None, index=False, if_exists='append')182                            sleep(60)183                #184                record_time = datetime.now()185                completed_dict = {186                    "userID":userID,187                    "tweet_id":str(info.id),188                    "num_likes":num_users_linking,189                    "record_time":record_time,190                }191                print("completed_dict:", completed_dict)192                df_completed = pd.DataFrame(completed_dict, index=[0])193                df_completed.to_sql("tweet_likes_completed", conn, schema=None, index=False, if_exists='append')194            except Exception as e:195                record_time = datetime.now()196                err_dict = {197                    "userID":userID,198                    "info_id":str(info.id),199                    "error":str(e),200                    "record_time":record_time,201                }202                print("err_info_in_tweets:", err_dict)203                df_err = pd.DataFrame(err_dict, index=[0])204                df_err.to_sql("err_info_in_tweets", conn, schema=None, index=False, if_exists='append')205                if not connect():206                    print("internet not connected.")207                    record_time = datetime.now()208                    df_err = pd.DataFrame(err_dict, index=[0])209                    df_err.to_sql("no_internet", conn, schema=None, index=False, if_exists='append')210                    ##move counter back one unit211                    sleep(60)212        record_time = datetime.now()213        completed_dict = {214            "userID":userID,215            "num_tweets":len(tweets),216            "record_time":record_time,217        }218        print("completed_dict:", completed_dict)219        df_completed = pd.DataFrame(completed_dict, index=[0])220        df_completed.to_sql("user_completed", conn, schema=None, index=False, if_exists='append')221    except Exception as e:222        record_time = datetime.now()223        err_dict = {224            "userID":userID,225            "error":str(e),226            "record_time":record_time,227        }228        print("err_userid:", err_dict)229        df_err = pd.DataFrame(err_dict, index=[0])230        df_err = pd.DataFrame(err_dict, index=[0])231        df_err.to_sql("err_userid", conn, schema=None, index=False, if_exists='append')232    #233234completed_dict = {235    "record_time" = datetime.now(),236}237print("completed_dict:", completed_dict)238df_completed = pd.DataFrame(completed_dict, index=[0])
...

tasks.py

Source:tasks.py

1from E_Energy.celeryapp import app2from .models import CachingData, Data, AdapterParameters, Records, CachingRecord3from datetime import datetime4from django.forms.models import model_to_dict5from django.core.exceptions import ObjectDoesNotExist6def preparation_dict(instance):7    dict = model_to_dict(instance)8    dict['record_id'] = dict.pop('id_record')9    dict['adapter_id'] = dict.pop('id_adapter_id')10    return dict11def preparation_record_dict(instance):12    dict = model_to_dict(instance)13    dict['record_id'] = dict.pop('id_record')14    dict['adapter_id'] = dict.pop('id_adapter')15    return dict16@app.task(time_limit=115)17def data_caching():18    #try:19    last_record_id = CachingData.objects.latest('record_time').record_id20    print(last_record_id)21    print(CachingData.objects.latest('record_time').record_time)22    last_caching_record = Records.objects.get(id_record = last_record_id )23    print(last_caching_record.record_time)24    #except AttributeError:25    #    last_caching_record = Records(record_time=datetime(2020, 1,26,0,1))26    new_record_instances = Records.objects.filter(27        record_time__gt=last_caching_record.record_time28    ).order_by('record_time')29    for record_instance in new_record_instances.iterator(chunk_size=50):30        new_data_instances = list(31            Data.objects.filter(id_record=record_instance.id_record)32        )33        print(record_instance.id_record)34        print(record_instance.record_time)35        for data_instance in new_data_instances:36            Data.objects.create(37                record_time=record_instance.record_time,38                adapter_id=record_instance.id_adapter.id_adapter,39                **preparation_dict(data_instance)40            )41@app.task(time_limit=115)42def records_caching():43    try:44        last_record_id = CachingRecord.objects.latest('record_time').record_id45        print(last_record_id)46        print(CachingRecord.objects.latest('record_time').record_time)47        last_caching_record = Records.objects.get(id_record = last_record_id )48        print(last_caching_record.record_time)49    except (AttributeError, ObjectDoesNotExist):50       last_caching_record = Records(record_time=datetime(2020, 1,26,0,1))51    new_input_records = Records.objects.filter(52        record_time__gt=last_caching_record.record_time,53        id_adapter__adapter_name__icontains='Ð²ÑÐ¾Ð´'54    ).order_by('record_time')55    for input_record in new_input_records.iterator(chunk_size=200):56        input_datas = list(57            Data.objects.filter(id_record=input_record.id_record)58        )59        input_params = AdapterParameters.objects.filter(60            id_adapter = input_record.id_adapter_id)61        try:62            output_record = Records.objects.get(63                id_adapter__adapter_name__icontains='Ð²ÑÑÐ¾Ð´',64                id_adapter__device__in=input_record.id_adapter.device_set.all(),65                record_time=input_record.record_time66            )67            output_datas = list(68                Data.objects.filter(id_record=output_record.id_record)69            )70            output_params = AdapterParameters.objects.filter(71                id_adapter = output_record.id_adapter_id)72        except ObjectDoesNotExist:73            output_record = Records()74            output_datas = []75            output_params = []76        p_AU1 = next(77            (data.measure_value for data in input_datas \78                if data.id_parameter==next(79                    (p.id_parameter for p in input_params \80                        if 'ÐÐ°Ð¿ÑÑÐ¶ÐµÐ½Ð¸Ðµ ÑÐ°Ð·Ñ 1' in p.parameter_name),81                    0)),82            0)83        p_BU1 = next(84            (data.measure_value for data in input_datas \85                if data.id_parameter==next(86                    (p.id_parameter for p in input_params \87                        if 'ÐÐ°Ð¿ÑÑÐ¶ÐµÐ½Ð¸Ðµ ÑÐ°Ð·Ñ 2' in p.parameter_name),88                    0)),89            0)90        p_CU1 = next(91            (data.measure_value for data in input_datas \92                if data.id_parameter==next(93                    (p.id_parameter for p in input_params \94                        if 'ÐÐ°Ð¿ÑÑÐ¶ÐµÐ½Ð¸Ðµ ÑÐ°Ð·Ñ 3' in p.parameter_name),95                    0)),96            0)97        p_AI1 = next(98            (data.measure_value for data in input_datas \99                if data.id_parameter==next(100                    (p.id_parameter for p in input_params \101                        if 'Ð¢Ð¾Ðº ÑÐ°Ð·Ñ 1' in p.parameter_name),102                    0)),103            0)104        p_BI1 = next(105            (data.measure_value for data in input_datas \106                if data.id_parameter==next(107                    (p.id_parameter for p in input_params \108                        if 'Ð¢Ð¾Ðº ÑÐ°Ð·Ñ 2' in p.parameter_name),109                    0)),110            0)111        p_CI1 = next(112            (data.measure_value for data in input_datas \113                if data.id_parameter==next(114                    (p.id_parameter for p in input_params \115                        if 'Ð¢Ð¾Ðº ÑÐ°Ð·Ñ 3' in p.parameter_name),116                    0)),117            0)118        p_AU2 = next(119            (data.measure_value for data in output_datas \120                if data.id_parameter==next(121                    (p.id_parameter for p in output_params \122                        if 'ÐÐ°Ð¿ÑÑÐ¶ÐµÐ½Ð¸Ðµ ÑÐ°Ð·Ñ 1' in p.parameter_name),123                    0)),124            0)125        p_BU2 = next(126            (data.measure_value for data in output_datas \127                if data.id_parameter==next(128                    (p.id_parameter for p in output_params \129                        if 'ÐÐ°Ð¿ÑÑÐ¶ÐµÐ½Ð¸Ðµ ÑÐ°Ð·Ñ 2' in p.parameter_name),130                    0)),131            0)132        p_CU2 = next(133            (data.measure_value for data in output_datas \134                if data.id_parameter==next(135                    (p.id_parameter for p in output_params \136                        if 'ÐÐ°Ð¿ÑÑÐ¶ÐµÐ½Ð¸Ðµ ÑÐ°Ð·Ñ 3' in p.parameter_name),137                    0)),138            0)139        p_AI2 = next(140            (data.measure_value for data in output_datas \141                if data.id_parameter==next(142                    (p.id_parameter for p in output_params \143                        if 'Ð¢Ð¾Ðº ÑÐ°Ð·Ñ 1' in p.parameter_name),144                    0)),145            0)146        p_BI2 = next(147            (data.measure_value for data in output_datas \148                if data.id_parameter==next(149                    (p.id_parameter for p in output_params \150                        if 'Ð¢Ð¾Ðº ÑÐ°Ð·Ñ 2' in p.parameter_name),151                    0)),152            0)153        p_CI2 = next(154            (data.measure_value for data in output_datas \155                if data.id_parameter==next(156                    (p.id_parameter for p in output_params \157                        if 'Ð¢Ð¾Ðº ÑÐ°Ð·Ñ 3' in p.parameter_name),158                    0)),159            0)160        total_power = (p_AU1*p_AI1 + p_BU1*p_BI1 + p_CU1*p_CI1)/60161        x1 = p_AI1*p_AU2/60162        x2 = p_AI2*p_AU1/60163        x3 = p_BI1*p_BU2/60164        x4 = p_BI2*p_BU1/60165        x5 = p_CI1*p_CU2/60166        x6 = p_CI2*p_CU1/60167        x0 = x1+x3+x5168        x8 = x2+x4+x6169        xh = x0/x8*100 if x8 !=0 else 0170        xp = 100-xh171        CachingRecord.objects.create(172            p_AU1=p_AU1,173            p_BU1=p_BU1,174            p_CU1=p_CU1,175            p_AI1=p_AI1,176            p_BI1=p_BI1,177            p_CI1=p_CI1,178            p_AU2=p_AU2,179            p_BU2=p_BU2,180            p_CU2=p_CU2,181            p_AI2=p_AI2,182            p_BI2=p_BI2,183            p_CI2=p_CI2,184            total_power=total_power,185            x1=x1,186            x2=x2,187            x3=x3,188            x4=x4,189            x5=x5,190            x6=x6,191            x0=x0,192            x8=x8,193            xh=xh,194            xp=xp,195            **preparation_record_dict(input_record)196        )...

weight_record_features.py

Source:weight_record_features.py

1'''2user_id,weight,record_on,date31,56.000,2013-02-28,2013-02-2841,84.000,2013-03-17,2013-03-1751,63.900,2013-03-18,2013-03-1861,70.200,2013-10-01,2013-10-0171,66.000,2013-10-31,2013-10-3181,62.000,2014-01-11,2014-01-1192,61.500,2013-06-16,2013-06-16102,62.000,2013-06-17,2013-06-17112,62.000,2013-07-07,2013-07-07122,62.000,2013-08-04,2013-08-04132,61.600,2013-08-26,2013-08-3114'''15from datetime import datetime16import numpy as np17import pickle18import pandas as pd19import os20def weight_change_record_frequency_analysis(record_file, output_dir):21    record_frequency = dict()22    with open(record_file,'r') as recf:23        for line in recf:24            line = line.rstrip("\n").split(",")25            if line[0] != "user_id":26                usr = line[0]27                record_time_ = line[2].split("-")28                try:29                    record_time = datetime(int(record_time_[0]), int(record_time_[1]), int(record_time_[2]))30                except:31                    continue32                if usr not in record_frequency: #initialize user data33                    record_frequency[usr] = [1,[record_time,record_time]] #number of record, time intervals (earliest, latest)34                else: #update user data35                    if record_time < datetime(2008, 1, 1): #BOOHEE was found in 2008, any records before this date should be dropped36                        continue37                    if record_time > datetime(2015,12,31):38                        continue39                    if (record_time-record_frequency[usr][-1][0]).days < -30:40                        continue41                    record_frequency[usr][0] += 142                    if (record_time-record_frequency[usr][-1][0]).days <= 0: #update earliest43                        record_frequency[usr][-1][0] = record_time44                    if record_time >= record_frequency[usr][-1][1]: #update latest45                        if (record_time-record_frequency[usr][-1][1]).days < 30:46                            record_frequency[usr][-1][1] = record_time47                        else:48                            record_frequency[usr].append([record_time,record_time])49    for key, value in record_frequency.items():50        interval_num = len(value)-151        time_span = 052        for intv in value[1:]:53            time_span += ((intv[1]-intv[0]).days+1)54        record_frequency[key] = [value[0],time_span,interval_num]55        #if value[0]/time_span > 200:56        #    print(key)57    with open(output_dir+"/weight_change_record_frequency.pkl",'wb') as outf:58        pickle.dump(record_frequency,outf)59def plot_frequency_distribution(record_pkl_file):60    import matplotlib.pyplot as plt61    with open(record_pkl_file,'rb') as infile:62        frequency_record = pickle.load(infile)63    record_number = list()64    record_time_span = list()65    record_interval = list()66    for value in frequency_record.values():67        record_number.append(value[0])68        record_time_span.append(value[1])69        record_interval.append(value[2])70    record_number = np.array(record_number)71    record_interval = np.array(record_interval)72    record_time_span = np.array(record_time_span).astype(float)73    record_density = record_number/record_time_span74    fig,axes = plt.subplots(2,2,figsize=(12,12))75    fig.suptitle("User weight record distribution analysis")76    bins1 = np.array([0,]+list(np.logspace(0,np.log10(2000))))77    axes[0][0].hist(record_number, histtype="stepfilled",alpha=0.6,log=True,bins=bins1)78    axes[0][0].set_title("record number distribution (number of records)")79    axes[0][0].set_xscale('log', basex=10)80    bins2 = np.array([0,]+list(np.logspace(0,np.log10(3000))))81    axes[0][1].hist(record_time_span, histtype="stepfilled",alpha=0.6,log=True,bins=bins2)82    axes[0][1].set_title("record time span (only count those with in intervals) distribution (days)")83    axes[0][1].set_xscale('log', basex=10)84    axes[1][0].hist(record_density, histtype="stepfilled",alpha=0.6,log=True,bins=50)85    axes[1][0].set_title("record density distribution (with in intervals) (record/day)")86    axes[1][1].hist(record_interval, histtype="stepfilled",alpha=0.6,log=True,bins=np.arange(0,35,1))87    axes[1][1].set_title("record interval distribution (number of intervals)")88    plt.savefig('record_frequency_distribution',bbox_inches='tight')89    plt.close()90def get_weight_record_features(record_file, output_dir, user_lists='all'):91    """92    :return: N(nodes) by 4 matrix of weight record features of selected users93    """94    record_frequency = dict()95    out_dict = dict()  # userID: [number of records, span, density, number of intervals]96    with open(record_file, 'r') as recf:97        for line in recf:98            # print(line)99            # exit(-1)100            line = line.rstrip("\n").split(",")101            if line[0] != "user_id":102                usr = line[0]103                record_time_ = line[2].split("-")104                try:105                    record_time = datetime(int(record_time_[0]), int(record_time_[1]), int(record_time_[2]))106                except:107                    continue108                if user_lists != 'all' and usr not in user_lists:109                    continue110                else:111                    if usr not in record_frequency:  # initialize user data112                        record_frequency[usr] = [1, [record_time,113                                                     record_time]]  # number of record, time intervals (earliest, latest)114                    else:  # update user data115                        if record_time < datetime(2008, 1,116                                                  1):  # BOOHEE was found in 2008, any records before this date should be dropped117                            continue118                        if record_time > datetime(2015, 12, 31):119                            continue120                        if (record_time - record_frequency[usr][-1][0]).days < -30:121                            continue122                        record_frequency[usr][0] += 1123                        if (record_time - record_frequency[usr][-1][0]).days <= 0:  # update earliest124                            record_frequency[usr][-1][0] = record_time125                        if record_time >= record_frequency[usr][-1][1]:  # update latest126                            if (record_time - record_frequency[usr][-1][1]).days < 30:127                                record_frequency[usr][-1][1] = record_time128                            else:129                                record_frequency[usr].append([record_time, record_time])130    for key, value in record_frequency.items():131        interval_num = len(value) - 1132        time_span = 0133        for intv in value[1:]:134            time_span += ((intv[1] - intv[0]).days + 1)135        record_frequency[key] = [value[0], time_span, interval_num, float(value[0])/time_span]136    # print(record_frequency)137    df = pd.DataFrame.from_dict(record_frequency)138    df.to_csv(os.path.join(output_dir, 'weight_record_features.csv'), index=False)139if __name__ == '__main__':140    # weight_change_record_frequency_analysis('../data/WeightLoss/weight_record_consolidated.csv','../data/WeightLoss/clean/')141    # plot_frequency_distribution('../data/WeightLoss/clean/weight_change_record_frequency.pkl')...

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