Working imports for statistics and short terms.
This commit is contained in:
@@ -205552,6 +205552,4 @@
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205936,,,,,,,23034526,586731372,156,1694343685.248867,1694340000,
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206058,,,"0.184692485805","0.109","0.547",,,,154,1694350873.0925798,1694347200,
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206059,,,,,,,29418950,672995157,155,1694350873.0926156,1694347200,
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206060,,,,,,,27980500,591677346,156,1694350873.0926518,1694347200,
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ALTER TABLE ONLY "public"."statistics" ADD CONSTRAINT "statistics_metadata_id_fkey" FOREIGN KEY (metadata_id) REFERENCES statistics_meta(id) ON DELETE CASCADE NOT DEFERRABLE;
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206060,,,,,,,27980500,591677346,156,1694350873.0926518,1694347200
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11260,2023-09-30 03:55:00
|
||||
11261,2023-09-30 04:00:00
|
||||
11262,2023-09-30 04:05:00
|
||||
11263,2023-09-30 04:10:00
|
||||
11264,2023-09-30 04:15:00
|
||||
11265,2023-09-30 04:20:00
|
||||
11266,2023-09-30 04:25:00
|
||||
11267,2023-09-30 04:30:00
|
||||
11268,2023-09-30 04:35:00
|
||||
11269,2023-09-30 04:40:00
|
||||
11270,2023-09-30 04:45:00
|
||||
11271,2023-09-30 04:50:00
|
||||
11272,2023-09-30 04:55:00
|
||||
11273,2023-09-30 05:00:00
|
||||
11274,2023-09-30 05:05:00
|
||||
11275,2023-09-30 05:10:00
|
||||
11276,2023-09-30 05:15:00
|
||||
11277,2023-09-30 05:20:00
|
||||
11278,2023-09-30 05:25:00
|
||||
11279,2023-09-30 05:30:00
|
||||
11280,2023-09-30 05:35:00
|
||||
11281,2023-09-30 05:40:00
|
||||
11282,2023-09-30 05:45:00
|
||||
11283,2023-09-30 05:50:00
|
||||
11284,2023-09-30 05:55:00
|
||||
11285,2023-09-30 06:00:00
|
||||
11286,2023-09-30 06:05:00
|
||||
11287,2023-09-30 06:10:00
|
||||
11288,2023-09-30 06:15:00
|
||||
11289,2023-09-30 06:20:00
|
||||
11290,2023-09-30 06:25:00
|
||||
11291,2023-09-30 06:30:00
|
||||
11292,2023-09-30 06:35:00
|
||||
11293,2023-09-30 06:40:00
|
||||
11294,2023-09-30 06:45:00
|
||||
11295,2023-09-30 06:50:00
|
||||
11296,2023-09-30 06:55:00
|
||||
11297,2023-09-30 07:00:00
|
||||
11298,2023-09-30 07:05:00
|
||||
11299,2023-09-30 07:10:00
|
||||
11300,2023-09-30 07:15:00
|
||||
11301,2023-09-30 07:20:00
|
||||
11302,2023-09-30 07:25:00
|
||||
11303,2023-09-30 07:30:00
|
||||
11304,2023-09-30 07:35:00
|
||||
11305,2023-09-30 07:40:00
|
||||
11306,2023-09-30 07:45:00
|
||||
11307,2023-09-30 07:50:00
|
||||
11308,2023-09-30 07:55:00
|
||||
11309,2023-09-30 08:00:00
|
||||
11310,2023-09-30 08:05:00
|
||||
11311,2023-09-30 08:10:00
|
||||
11312,2023-09-30 08:15:00
|
||||
11313,2023-09-30 08:20:00
|
||||
11314,2023-09-30 08:25:00
|
||||
11315,2023-09-30 08:30:00
|
||||
11316,2023-09-30 08:35:00
|
||||
11317,2023-09-30 08:40:00
|
||||
11318,2023-09-30 08:45:00
|
||||
11319,2023-09-30 08:50:00
|
||||
11320,2023-09-30 08:55:00
|
||||
11321,2023-09-30 09:00:00
|
||||
11322,2023-09-30 09:05:00
|
||||
11323,2023-09-30 09:10:00
|
||||
11324,2023-09-30 09:15:00
|
||||
11325,2023-09-30 09:20:00
|
||||
11326,2023-09-30 09:25:00
|
||||
11327,2023-09-30 09:30:00
|
||||
11328,2023-09-30 09:35:00
|
||||
11329,2023-09-30 09:40:00
|
||||
11330,2023-09-30 09:45:00
|
||||
11331,2023-09-30 09:50:00
|
||||
11332,2023-09-30 09:55:00
|
||||
11333,2023-09-30 10:00:00
|
||||
11334,2023-09-30 10:05:00
|
||||
11335,2023-09-30 10:10:00
|
||||
11336,2023-09-30 10:15:00
|
||||
11337,2023-09-30 10:20:00
|
||||
11338,2023-09-30 10:25:00
|
||||
11339,2023-09-30 10:30:00
|
||||
11340,2023-09-30 10:35:00
|
||||
11341,2023-09-30 10:40:00
|
||||
11342,2023-09-30 10:45:00
|
||||
11343,2023-09-30 10:50:00
|
||||
11344,2023-09-30 10:55:00
|
||||
11345,2023-09-30 11:00:00
|
||||
11346,2023-09-30 11:05:00
|
||||
11347,2023-09-30 11:10:00
|
||||
11348,2023-09-30 11:15:00
|
||||
11349,2023-09-30 11:20:00
|
||||
11350,2023-09-30 11:25:00
|
||||
11351,2023-09-30 11:30:00
|
||||
11352,2023-09-30 11:35:00
|
||||
11353,2023-09-30 11:40:00
|
||||
11354,2023-09-30 11:45:00
|
||||
11355,2023-09-30 11:50:00
|
||||
11356,2023-09-30 11:55:00
|
||||
11357,2023-09-30 12:00:00
|
||||
11358,2023-09-30 12:05:00
|
||||
11359,2023-09-30 12:10:00
|
||||
11360,2023-09-30 12:15:00
|
||||
11361,2023-09-30 12:20:00
|
||||
11362,2023-09-30 12:25:00
|
||||
11363,2023-09-30 12:30:00
|
||||
11364,2023-09-30 12:35:00
|
||||
11365,2023-09-30 12:40:00
|
||||
11366,2023-09-30 12:45:00
|
||||
11367,2023-09-30 12:50:00
|
||||
11368,2023-09-30 12:55:00
|
||||
11369,2023-09-30 13:00:00
|
||||
11370,2023-09-30 13:05:00
|
||||
11371,2023-09-30 13:10:00
|
||||
11372,2023-09-30 13:15:00
|
||||
11373,2023-09-30 13:20:00
|
||||
11374,2023-09-30 13:25:00
|
||||
11375,2023-09-30 13:30:00
|
||||
11376,2023-09-30 13:35:00
|
||||
11377,2023-09-30 13:40:00
|
||||
11378,2023-09-30 13:45:00
|
||||
11379,2023-09-30 13:50:00
|
||||
11380,2023-09-30 13:55:00
|
||||
11381,2023-09-30 14:00:00
|
||||
11382,2023-09-30 14:05:00
|
||||
11383,2023-09-30 14:10:00
|
||||
11384,2023-09-30 14:15:00
|
||||
11385,2023-09-30 14:20:00
|
||||
11386,2023-09-30 14:25:00
|
||||
11387,2023-09-30 14:30:00
|
||||
11388,2023-09-30 14:35:00
|
||||
11389,2023-09-30 14:40:00
|
||||
11390,2023-09-30 14:45:00
|
||||
11391,2023-09-30 14:50:00
|
||||
11392,2023-09-30 14:55:00
|
||||
11393,2023-09-30 15:00:00
|
||||
11394,2023-09-30 15:05:00
|
||||
11395,2023-09-30 15:10:00
|
||||
11396,2023-09-30 15:15:00
|
||||
11397,2023-09-30 15:20:00
|
||||
11398,2023-09-30 15:25:00
|
||||
11399,2023-09-30 15:30:00
|
||||
11400,2023-09-30 15:35:00
|
||||
11401,2023-09-30 15:40:00
|
||||
11402,2023-09-30 15:45:00
|
||||
11403,2023-09-30 15:50:00
|
||||
11404,2023-09-30 15:55:00
|
||||
11405,2023-09-30 16:00:00
|
||||
11406,2023-09-30 16:05:00
|
||||
11407,2023-09-30 16:10:00
|
||||
11408,2023-09-30 16:15:00
|
||||
11409,2023-09-30 16:20:00
|
||||
11410,2023-09-30 16:25:00
|
||||
11411,2023-09-30 16:30:00
|
||||
11412,2023-09-30 16:35:00
|
||||
11413,2023-09-30 16:40:00
|
||||
11414,2023-09-30 16:45:00
|
||||
11415,2023-09-30 16:50:00
|
||||
11416,2023-09-30 16:55:00
|
||||
11417,2023-09-30 17:00:00
|
||||
11418,2023-09-30 17:05:00
|
||||
11419,2023-09-30 17:10:00
|
||||
11420,2023-09-30 17:15:00
|
||||
11421,2023-09-30 17:20:00
|
||||
11422,2023-09-30 17:25:00
|
||||
11423,2023-09-30 17:30:00
|
||||
11424,2023-09-30 17:35:00
|
||||
11425,2023-09-30 17:40:00
|
||||
11426,2023-09-30 17:45:00
|
||||
11427,2023-09-30 17:50:00
|
||||
11428,2023-09-30 17:55:00
|
||||
11429,2023-09-30 18:00:00
|
||||
11430,2023-09-30 18:05:00
|
||||
11431,2023-09-30 18:10:00
|
||||
11432,2023-09-30 18:15:00
|
||||
11433,2023-09-30 18:20:00
|
||||
11434,2023-09-30 18:25:00
|
||||
11435,2023-09-30 18:30:00
|
||||
11436,2023-09-30 18:35:00
|
||||
11437,2023-09-30 18:40:00
|
||||
11438,2023-09-30 18:45:00
|
||||
11439,2023-09-30 18:50:00
|
||||
11440,2023-09-30 18:55:00
|
||||
11441,2023-09-30 19:00:00
|
||||
11442,2023-09-30 19:05:00
|
||||
11443,2023-09-30 19:10:00
|
||||
11444,2023-09-30 19:15:00
|
||||
11445,2023-09-30 19:20:00
|
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11446,2023-09-30 19:25:00
|
||||
11447,2023-09-30 19:30:00
|
||||
11448,2023-09-30 19:35:00
|
||||
11449,2023-09-30 19:40:00
|
||||
11450,2023-09-30 19:45:00
|
||||
11451,2023-09-30 19:50:00
|
||||
11452,2023-09-30 19:55:00
|
||||
11453,2023-09-30 20:00:00
|
||||
11454,2023-09-30 20:05:00
|
||||
11455,2023-09-30 20:10:00
|
||||
11456,2023-09-30 20:15:00
|
||||
11457,2023-09-30 20:20:00
|
||||
11458,2023-09-30 20:25:00
|
||||
11459,2023-09-30 20:30:00
|
||||
11460,2023-09-30 20:35:00
|
||||
11461,2023-09-30 20:40:00
|
||||
11462,2023-09-30 20:45:00
|
||||
11463,2023-09-30 20:50:00
|
||||
11464,2023-09-30 20:55:00
|
||||
11465,2023-09-30 21:00:00
|
||||
11466,2023-09-30 21:05:00
|
||||
11467,2023-09-30 21:10:00
|
||||
11468,2023-09-30 21:15:00
|
||||
11469,2023-09-30 21:20:00
|
||||
11470,2023-09-30 21:25:00
|
||||
11471,2023-09-30 21:30:00
|
||||
11472,2023-09-30 21:35:00
|
||||
11473,2023-09-30 21:40:00
|
||||
11474,2023-09-30 21:45:00
|
||||
11475,2023-09-30 21:50:00
|
||||
11476,2023-09-30 21:55:00
|
||||
11477,2023-09-30 22:00:00
|
||||
11478,2023-09-30 22:05:00
|
||||
11479,2023-09-30 22:10:00
|
||||
11480,2023-09-30 22:15:00
|
||||
11481,2023-09-30 22:20:00
|
||||
11482,2023-09-30 22:25:00
|
||||
11483,2023-09-30 22:30:00
|
||||
11484,2023-09-30 22:35:00
|
||||
11485,2023-09-30 22:40:00
|
||||
11486,2023-09-30 22:45:00
|
||||
11487,2023-09-30 22:50:00
|
||||
11488,2023-09-30 22:55:00
|
||||
11489,2023-09-30 23:00:00
|
||||
11490,2023-09-30 23:05:00
|
||||
11491,2023-09-30 23:10:00
|
||||
11492,2023-09-30 23:15:00
|
||||
11493,2023-09-30 23:20:00
|
||||
11494,2023-09-30 23:25:00
|
||||
11495,2023-09-30 23:30:00
|
||||
11496,2023-09-30 23:35:00
|
||||
11497,2023-09-30 23:40:00
|
||||
11498,2023-09-30 23:45:00
|
||||
11499,2023-09-30 23:50:00
|
||||
11500,2023-09-30 23:55:00
|
||||
11501,2023-10-01 00:00:00
|
||||
11502,2023-10-01 00:05:00
|
||||
11503,2023-10-01 00:10:00
|
||||
11504,2023-10-01 00:15:00
|
||||
11505,2023-10-01 00:20:00
|
||||
11506,2023-10-01 00:25:00
|
||||
11507,2023-10-01 00:30:00
|
||||
11508,2023-10-01 00:35:00
|
||||
|
File diff suppressed because it is too large
Load Diff
100
make_import.py
100
make_import.py
@@ -1,18 +1,33 @@
|
||||
import csv
|
||||
import pandas as pd
|
||||
|
||||
############
|
||||
# read files
|
||||
############
|
||||
|
||||
# file locations
|
||||
statistics_meta_archive_file = "input/raw/postgres/statistics_meta.csv"
|
||||
statistics_meta_export_file = "input/raw/sqlite/statistics_meta-export.csv"
|
||||
statistics_archive_file = "input/raw/postgres/statistics.csv"
|
||||
statistics_export_file = "input/raw/sqlite/statistics-export.csv"
|
||||
statistics_import_file = "output/statistics-import.csv"
|
||||
statistics_short_term_archive_file = "input/raw/postgres/statistics_short_term.csv"
|
||||
statistics_short_term_export_file = "input/raw/sqlite/statistics_short_term-export.csv"
|
||||
statistics_short_term_import_file = "output/statistics_short_term-import.csv"
|
||||
|
||||
# read in current export, and the archive
|
||||
meta_df = pd.read_csv(statistics_meta_export_file)
|
||||
meta_archive_df = pd.read_csv(statistics_meta_archive_file)
|
||||
statistics_df = pd.read_csv(statistics_export_file, index_col='id')
|
||||
statistics_archive_df = pd.read_csv(statistics_archive_file, index_col='id')
|
||||
statistics_short_term_df = pd.read_csv(statistics_short_term_export_file, index_col='id')
|
||||
statistics_short_term_archive_df = pd.read_csv(statistics_short_term_archive_file, index_col='id')
|
||||
|
||||
|
||||
#################
|
||||
# statistics_meta
|
||||
#################
|
||||
|
||||
statistics_meta_archive_file = "statistics_meta.csv"
|
||||
statistics_meta_export_file = "statistics_meta-export.csv"
|
||||
|
||||
# read in current export, and the archive
|
||||
meta_df = pd.read_csv(statistics_meta_export_file)
|
||||
meta_archive_df = pd.read_csv(statistics_meta_archive_file)
|
||||
|
||||
# find the id's and the unique statistics from each
|
||||
meta_df = meta_df[['id','statistic_id']]
|
||||
meta_archive_df = meta_archive_df[['id','statistic_id']]
|
||||
@@ -25,66 +40,73 @@ meta_lookup.set_index('id_x').to_csv("meta_merge.csv")
|
||||
meta_lookup = meta_lookup[['id_y','id_x']]
|
||||
meta_lookup = meta_lookup.T.to_dict('records')[0]
|
||||
|
||||
|
||||
############
|
||||
# statistics
|
||||
############
|
||||
|
||||
statistics_archive_file = "statistics.csv"
|
||||
statistics_export_file = "statistics-export.csv"
|
||||
statistics_import_file = "statistics-import.csv"
|
||||
|
||||
statistics_df = pd.read_csv(statistics_export_file, index_col='id')
|
||||
statistics_archive_df = pd.read_csv(statistics_archive_file, index_col='id')
|
||||
|
||||
# make unique indexes
|
||||
statistics_max_id = statistics_df.last_valid_index()
|
||||
statistics_df.reset_index(inplace=True)
|
||||
statistics_df['id'] += statistics_max_id
|
||||
statistics_df.set_index('id',drop=True,inplace=True)
|
||||
statistics_archive_df.reset_index(inplace=True)
|
||||
statistics_archive_df['id'] += statistics_max_id
|
||||
statistics_archive_df.set_index('id',drop=True,inplace=True)
|
||||
|
||||
# find any duplicates where tuple (start_ts,metadata_id)
|
||||
# exist in export and archive, drop the archive
|
||||
# read in current export, and the archive
|
||||
print(statistics_df.info())
|
||||
print(statistics_archive_df.info())
|
||||
statistics_df['unique_tuple'] = statistics_df.apply(lambda row: (row['start_ts'],row['metadata_id']), axis=1)
|
||||
statistics_archive_df['unique_tuple'] = statistics_archive_df.apply(lambda row: (row['start_ts'],row['metadata_id']), axis=1)
|
||||
statistics_df_copy = statistics_df.copy()
|
||||
statistics_df_copy = statistics_df_copy[['start_ts','metadata_id','unique_tuple']]
|
||||
statistics_archive_df = statistics_archive_df[['start_ts','metadata_id','unique_tuple']]
|
||||
unique_lookup = statistics_df_copy.merge(statistics_archive_df, on=['unique_tuple'], how='left', indicator=True)
|
||||
statistics_archive_df_copy = statistics_archive_df.copy()
|
||||
statistics_archive_df_copy = statistics_archive_df_copy[['start_ts','metadata_id','unique_tuple']]
|
||||
statistics_df = statistics_df[['start_ts','metadata_id','unique_tuple']]
|
||||
unique_lookup = statistics_archive_df_copy.merge(statistics_df, on=['unique_tuple'], how='left', indicator=True)
|
||||
unique_lookup = unique_lookup[unique_lookup['_merge']=="both"]
|
||||
unique_lookup.to_csv("unique_merge.csv")
|
||||
unique_tuples = unique_lookup['unique_tuple']
|
||||
statistics_df = statistics_df[~statistics_df['unique_tuple'].isin(unique_tuples)]
|
||||
statistics_df.drop(columns='unique_tuple',inplace=True)
|
||||
print(statistics_df.info())
|
||||
|
||||
statistics_archive_df = statistics_archive_df[~statistics_archive_df['unique_tuple'].isin(unique_tuples)]
|
||||
statistics_archive_df.drop(columns='unique_tuple',inplace=True)
|
||||
print(statistics_archive_df.info())
|
||||
|
||||
# drop any statistics not in the existing systems metadata
|
||||
statistics_df = statistics_df[statistics_df['metadata_id'].isin(meta_lookup.keys())]
|
||||
statistics_archive_df = statistics_archive_df[statistics_archive_df['metadata_id'].isin(meta_lookup.keys())]
|
||||
|
||||
# correct the meta column
|
||||
statistics_df.replace({'metadata_id': meta_lookup}, inplace=True)
|
||||
statistics_archive_df.replace({'metadata_id': meta_lookup}, inplace=True)
|
||||
|
||||
#######################
|
||||
# statistics_short_term
|
||||
#######################
|
||||
|
||||
statistics_short_term_archive_file = "statistics_short_term.csv"
|
||||
statistics_short_term_export_file = "statistics_short_term-export.csv"
|
||||
statistics_short_term_import_file = "statistics_short_term-import.csv"
|
||||
# make unique indexes
|
||||
statistics_short_term_max_id = statistics_short_term_df.last_valid_index()
|
||||
statistics_short_term_archive_df.reset_index(inplace=True)
|
||||
statistics_short_term_archive_df['id'] += statistics_short_term_max_id
|
||||
statistics_short_term_archive_df.set_index('id',drop=True,inplace=True)
|
||||
|
||||
statistics_short_term_df = pd.read_csv(statistics_short_term_export_file, index_col='id')
|
||||
|
||||
# OBEY UNIQUE HERE TOO!!!!!
|
||||
# find any duplicates where tuple (start_ts,metadata_id)
|
||||
# exist in export and archive, drop the archive
|
||||
# read in current export, and the archive
|
||||
print(statistics_short_term_archive_df.info())
|
||||
statistics_short_term_df['unique_tuple'] = statistics_short_term_df.apply(lambda row: (row['start_ts'],row['metadata_id']), axis=1)
|
||||
statistics_short_term_archive_df['unique_tuple'] = statistics_short_term_archive_df.apply(lambda row: (row['start_ts'],row['metadata_id']), axis=1)
|
||||
statistics_short_term_archive_df_copy = statistics_short_term_archive_df.copy()
|
||||
statistics_short_term_archive_df_copy = statistics_short_term_archive_df_copy[['start_ts','metadata_id','unique_tuple']]
|
||||
statistics_short_term_df = statistics_short_term_df[['start_ts','metadata_id','unique_tuple']]
|
||||
unique_lookup = statistics_short_term_archive_df_copy.merge(statistics_short_term_df, on=['unique_tuple'], how='left', indicator=True)
|
||||
#unique_lookup.to_csv(statistics_short_term_import_file)
|
||||
unique_lookup = unique_lookup[unique_lookup['_merge']=="both"]
|
||||
#unique_lookup.to_csv("unique_merge.csv")
|
||||
unique_tuples = unique_lookup['unique_tuple']
|
||||
statistics_short_term_archive_df = statistics_short_term_archive_df[~statistics_short_term_archive_df['unique_tuple'].isin(unique_tuples)]
|
||||
statistics_short_term_archive_df.drop(columns='unique_tuple',inplace=True)
|
||||
print(statistics_short_term_archive_df.info())
|
||||
|
||||
|
||||
# drop any statistics not in the existing systems metadata
|
||||
statistics_short_term_df = statistics_short_term_df[statistics_short_term_df['metadata_id'].isin(meta_lookup.keys())]
|
||||
statistics_short_term_archive_df = statistics_short_term_archive_df[statistics_short_term_archive_df['metadata_id'].isin(meta_lookup.keys())]
|
||||
|
||||
# correct the meta column
|
||||
statistics_short_term_df.replace({'metadata_id': meta_lookup}, inplace=True)
|
||||
statistics_short_term_archive_df.replace({'metadata_id': meta_lookup}, inplace=True)
|
||||
|
||||
|
||||
|
||||
@@ -92,7 +114,7 @@ statistics_short_term_df.replace({'metadata_id': meta_lookup}, inplace=True)
|
||||
# write files for importing
|
||||
###########################
|
||||
|
||||
statistics_df.to_csv(statistics_import_file)
|
||||
statistics_short_term_df.to_csv(statistics_short_term_import_file)
|
||||
statistics_archive_df.to_csv(statistics_import_file)
|
||||
statistics_short_term_archive_df.to_csv(statistics_short_term_import_file)
|
||||
|
||||
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
86261
output/statistics_short_term-import.csv
Normal file
86261
output/statistics_short_term-import.csv
Normal file
File diff suppressed because it is too large
Load Diff
30237
statistics-import.csv
30237
statistics-import.csv
File diff suppressed because it is too large
Load Diff
204
unique_merge.csv
204
unique_merge.csv
@@ -1,103 +1,103 @@
|
||||
,start_ts_x,metadata_id_x,unique_tuple,start_ts_y,metadata_id_y,_merge
|
||||
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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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||||
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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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||||
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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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||||
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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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||||
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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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|
||||
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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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|
||||
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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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|
||||
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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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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
2058,1677114000,2,"(1677114000.0, 2.0)",1677114000.0,2.0,both
|
||||
2060,1677117600,2,"(1677117600.0, 2.0)",1677117600.0,2.0,both
|
||||
2062,1677121200,2,"(1677121200.0, 2.0)",1677121200.0,2.0,both
|
||||
2064,1677124800,2,"(1677124800.0, 2.0)",1677124800.0,2.0,both
|
||||
2066,1677128400,2,"(1677128400.0, 2.0)",1677128400.0,2.0,both
|
||||
2138,1677139200,2,"(1677139200.0, 2.0)",1677139200.0,2.0,both
|
||||
2140,1677142800,2,"(1677142800.0, 2.0)",1677142800.0,2.0,both
|
||||
2142,1677146400,2,"(1677146400.0, 2.0)",1677146400.0,2.0,both
|
||||
2144,1677150000,2,"(1677150000.0, 2.0)",1677150000.0,2.0,both
|
||||
2146,1677153600,2,"(1677153600.0, 2.0)",1677153600.0,2.0,both
|
||||
2148,1677157200,2,"(1677157200.0, 2.0)",1677157200.0,2.0,both
|
||||
2150,1677160800,2,"(1677160800.0, 2.0)",1677160800.0,2.0,both
|
||||
2152,1677164400,2,"(1677164400.0, 2.0)",1677164400.0,2.0,both
|
||||
2154,1677168000,2,"(1677168000.0, 2.0)",1677168000.0,2.0,both
|
||||
2156,1677171600,2,"(1677171600.0, 2.0)",1677171600.0,2.0,both
|
||||
5999,1676916000,2,"(1676916000.0, 2.0)",1676916000.0,2.0,both
|
||||
6001,1676919600,2,"(1676919600.0, 2.0)",1676919600.0,2.0,both
|
||||
6003,1676923200,2,"(1676923200.0, 2.0)",1676923200.0,2.0,both
|
||||
6005,1676926800,2,"(1676926800.0, 2.0)",1676926800.0,2.0,both
|
||||
6007,1676930400,2,"(1676930400.0, 2.0)",1676930400.0,2.0,both
|
||||
6009,1676934000,2,"(1676934000.0, 2.0)",1676934000.0,2.0,both
|
||||
6011,1676937600,2,"(1676937600.0, 2.0)",1676937600.0,2.0,both
|
||||
6013,1676941200,2,"(1676941200.0, 2.0)",1676941200.0,2.0,both
|
||||
6015,1676944800,2,"(1676944800.0, 2.0)",1676944800.0,2.0,both
|
||||
6017,1676948400,2,"(1676948400.0, 2.0)",1676948400.0,2.0,both
|
||||
6019,1676952000,2,"(1676952000.0, 2.0)",1676952000.0,2.0,both
|
||||
6021,1676955600,2,"(1676955600.0, 2.0)",1676955600.0,2.0,both
|
||||
6023,1676959200,2,"(1676959200.0, 2.0)",1676959200.0,2.0,both
|
||||
6025,1676962800,2,"(1676962800.0, 2.0)",1676962800.0,2.0,both
|
||||
6027,1676966400,2,"(1676966400.0, 2.0)",1676966400.0,2.0,both
|
||||
6029,1676970000,2,"(1676970000.0, 2.0)",1676970000.0,2.0,both
|
||||
6031,1676973600,2,"(1676973600.0, 2.0)",1676973600.0,2.0,both
|
||||
6033,1676977200,2,"(1676977200.0, 2.0)",1676977200.0,2.0,both
|
||||
6044,1676980800,2,"(1676980800.0, 2.0)",1676980800.0,2.0,both
|
||||
6046,1676984400,2,"(1676984400.0, 2.0)",1676984400.0,2.0,both
|
||||
6048,1676988000,2,"(1676988000.0, 2.0)",1676988000.0,2.0,both
|
||||
6050,1676991600,2,"(1676991600.0, 2.0)",1676991600.0,2.0,both
|
||||
6052,1676995200,2,"(1676995200.0, 2.0)",1676995200.0,2.0,both
|
||||
6054,1676998800,2,"(1676998800.0, 2.0)",1676998800.0,2.0,both
|
||||
6056,1677002400,2,"(1677002400.0, 2.0)",1677002400.0,2.0,both
|
||||
6058,1677006000,2,"(1677006000.0, 2.0)",1677006000.0,2.0,both
|
||||
6060,1677009600,2,"(1677009600.0, 2.0)",1677009600.0,2.0,both
|
||||
6062,1677013200,2,"(1677013200.0, 2.0)",1677013200.0,2.0,both
|
||||
6064,1677016800,2,"(1677016800.0, 2.0)",1677016800.0,2.0,both
|
||||
6066,1677132000,2,"(1677132000.0, 2.0)",1677132000.0,2.0,both
|
||||
6068,1677135600,2,"(1677135600.0, 2.0)",1677135600.0,2.0,both
|
||||
6071,1677175200,2,"(1677175200.0, 2.0)",1677175200.0,2.0,both
|
||||
6073,1677178800,2,"(1677178800.0, 2.0)",1677178800.0,2.0,both
|
||||
6075,1677182400,2,"(1677182400.0, 2.0)",1677182400.0,2.0,both
|
||||
6077,1677186000,2,"(1677186000.0, 2.0)",1677186000.0,2.0,both
|
||||
6079,1677189600,2,"(1677189600.0, 2.0)",1677189600.0,2.0,both
|
||||
6081,1677193200,2,"(1677193200.0, 2.0)",1677193200.0,2.0,both
|
||||
6083,1677196800,2,"(1677196800.0, 2.0)",1677196800.0,2.0,both
|
||||
|
||||
|
Reference in New Issue
Block a user