Working imports for statistics and short terms.
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@@ -205552,6 +205552,4 @@
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205936,,,,,,,23034526,586731372,156,1694343685.248867,1694340000,
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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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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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206059,,,,,,,29418950,672995157,155,1694350873.0926156,1694347200,
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206060,,,,,,,27980500,591677346,156,1694350873.0926518,1694347200,
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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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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 csv
|
||||||
import pandas as pd
|
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
|
||||||
#################
|
#################
|
||||||
|
|
||||||
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
|
# find the id's and the unique statistics from each
|
||||||
meta_df = meta_df[['id','statistic_id']]
|
meta_df = meta_df[['id','statistic_id']]
|
||||||
meta_archive_df = meta_archive_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[['id_y','id_x']]
|
||||||
meta_lookup = meta_lookup.T.to_dict('records')[0]
|
meta_lookup = meta_lookup.T.to_dict('records')[0]
|
||||||
|
|
||||||
|
|
||||||
############
|
############
|
||||||
# statistics
|
# 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
|
# make unique indexes
|
||||||
statistics_max_id = statistics_df.last_valid_index()
|
statistics_max_id = statistics_df.last_valid_index()
|
||||||
statistics_df.reset_index(inplace=True)
|
statistics_archive_df.reset_index(inplace=True)
|
||||||
statistics_df['id'] += statistics_max_id
|
statistics_archive_df['id'] += statistics_max_id
|
||||||
statistics_df.set_index('id',drop=True,inplace=True)
|
statistics_archive_df.set_index('id',drop=True,inplace=True)
|
||||||
|
|
||||||
# find any duplicates where tuple (start_ts,metadata_id)
|
# find any duplicates where tuple (start_ts,metadata_id)
|
||||||
# exist in export and archive, drop the archive
|
# exist in export and archive, drop the archive
|
||||||
# read in current export, and 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_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_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_archive_df_copy = statistics_archive_df.copy()
|
||||||
statistics_df_copy = statistics_df_copy[['start_ts','metadata_id','unique_tuple']]
|
statistics_archive_df_copy = statistics_archive_df_copy[['start_ts','metadata_id','unique_tuple']]
|
||||||
statistics_archive_df = statistics_archive_df[['start_ts','metadata_id','unique_tuple']]
|
statistics_df = statistics_df[['start_ts','metadata_id','unique_tuple']]
|
||||||
unique_lookup = statistics_df_copy.merge(statistics_archive_df, on=['unique_tuple'], how='left', indicator=True)
|
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 = unique_lookup[unique_lookup['_merge']=="both"]
|
||||||
unique_lookup.to_csv("unique_merge.csv")
|
unique_lookup.to_csv("unique_merge.csv")
|
||||||
unique_tuples = unique_lookup['unique_tuple']
|
unique_tuples = unique_lookup['unique_tuple']
|
||||||
statistics_df = statistics_df[~statistics_df['unique_tuple'].isin(unique_tuples)]
|
statistics_archive_df = statistics_archive_df[~statistics_archive_df['unique_tuple'].isin(unique_tuples)]
|
||||||
statistics_df.drop(columns='unique_tuple',inplace=True)
|
statistics_archive_df.drop(columns='unique_tuple',inplace=True)
|
||||||
print(statistics_df.info())
|
print(statistics_archive_df.info())
|
||||||
|
|
||||||
|
|
||||||
# drop any statistics not in the existing systems metadata
|
# 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
|
# 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
|
||||||
#######################
|
#######################
|
||||||
|
|
||||||
statistics_short_term_archive_file = "statistics_short_term.csv"
|
# make unique indexes
|
||||||
statistics_short_term_export_file = "statistics_short_term-export.csv"
|
statistics_short_term_max_id = statistics_short_term_df.last_valid_index()
|
||||||
statistics_short_term_import_file = "statistics_short_term-import.csv"
|
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')
|
# find any duplicates where tuple (start_ts,metadata_id)
|
||||||
|
# exist in export and archive, drop the archive
|
||||||
# OBEY UNIQUE HERE TOO!!!!!
|
# 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
|
# 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
|
# 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
|
# write files for importing
|
||||||
###########################
|
###########################
|
||||||
|
|
||||||
statistics_df.to_csv(statistics_import_file)
|
statistics_archive_df.to_csv(statistics_import_file)
|
||||||
statistics_short_term_df.to_csv(statistics_short_term_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
|
,start_ts_x,metadata_id_x,unique_tuple,start_ts_y,metadata_id_y,_merge
|
||||||
47,1676833200.0,2,"(1676833200.0, 2.0)",1676833200.0,2.0,both
|
1491,1676833200,2,"(1676833200.0, 2.0)",1676833200.0,2.0,both
|
||||||
49,1676836800.0,2,"(1676836800.0, 2.0)",1676836800.0,2.0,both
|
1494,1676836800,2,"(1676836800.0, 2.0)",1676836800.0,2.0,both
|
||||||
51,1676840400.0,2,"(1676840400.0, 2.0)",1676840400.0,2.0,both
|
1496,1676840400,2,"(1676840400.0, 2.0)",1676840400.0,2.0,both
|
||||||
53,1676844000.0,2,"(1676844000.0, 2.0)",1676844000.0,2.0,both
|
1498,1676844000,2,"(1676844000.0, 2.0)",1676844000.0,2.0,both
|
||||||
55,1676847600.0,2,"(1676847600.0, 2.0)",1676847600.0,2.0,both
|
1500,1676847600,2,"(1676847600.0, 2.0)",1676847600.0,2.0,both
|
||||||
57,1676851200.0,2,"(1676851200.0, 2.0)",1676851200.0,2.0,both
|
1502,1676851200,2,"(1676851200.0, 2.0)",1676851200.0,2.0,both
|
||||||
59,1676854800.0,2,"(1676854800.0, 2.0)",1676854800.0,2.0,both
|
1504,1676854800,2,"(1676854800.0, 2.0)",1676854800.0,2.0,both
|
||||||
61,1676858400.0,2,"(1676858400.0, 2.0)",1676858400.0,2.0,both
|
1506,1676858400,2,"(1676858400.0, 2.0)",1676858400.0,2.0,both
|
||||||
63,1676862000.0,2,"(1676862000.0, 2.0)",1676862000.0,2.0,both
|
1508,1676862000,2,"(1676862000.0, 2.0)",1676862000.0,2.0,both
|
||||||
65,1676865600.0,2,"(1676865600.0, 2.0)",1676865600.0,2.0,both
|
1510,1676865600,2,"(1676865600.0, 2.0)",1676865600.0,2.0,both
|
||||||
67,1676869200.0,2,"(1676869200.0, 2.0)",1676869200.0,2.0,both
|
1512,1676869200,2,"(1676869200.0, 2.0)",1676869200.0,2.0,both
|
||||||
69,1676872800.0,2,"(1676872800.0, 2.0)",1676872800.0,2.0,both
|
1514,1676872800,2,"(1676872800.0, 2.0)",1676872800.0,2.0,both
|
||||||
71,1676876400.0,2,"(1676876400.0, 2.0)",1676876400.0,2.0,both
|
1516,1676876400,2,"(1676876400.0, 2.0)",1676876400.0,2.0,both
|
||||||
73,1676880000.0,2,"(1676880000.0, 2.0)",1676880000.0,2.0,both
|
1518,1676880000,2,"(1676880000.0, 2.0)",1676880000.0,2.0,both
|
||||||
75,1676883600.0,2,"(1676883600.0, 2.0)",1676883600.0,2.0,both
|
1520,1676883600,2,"(1676883600.0, 2.0)",1676883600.0,2.0,both
|
||||||
77,1676887200.0,2,"(1676887200.0, 2.0)",1676887200.0,2.0,both
|
1522,1676887200,2,"(1676887200.0, 2.0)",1676887200.0,2.0,both
|
||||||
79,1676890800.0,2,"(1676890800.0, 2.0)",1676890800.0,2.0,both
|
1524,1676890800,2,"(1676890800.0, 2.0)",1676890800.0,2.0,both
|
||||||
81,1676894400.0,2,"(1676894400.0, 2.0)",1676894400.0,2.0,both
|
1526,1676894400,2,"(1676894400.0, 2.0)",1676894400.0,2.0,both
|
||||||
83,1676898000.0,2,"(1676898000.0, 2.0)",1676898000.0,2.0,both
|
1695,1676898000,2,"(1676898000.0, 2.0)",1676898000.0,2.0,both
|
||||||
85,1676901600.0,2,"(1676901600.0, 2.0)",1676901600.0,2.0,both
|
1697,1676901600,2,"(1676901600.0, 2.0)",1676901600.0,2.0,both
|
||||||
87,1676905200.0,2,"(1676905200.0, 2.0)",1676905200.0,2.0,both
|
1699,1676905200,2,"(1676905200.0, 2.0)",1676905200.0,2.0,both
|
||||||
89,1676908800.0,2,"(1676908800.0, 2.0)",1676908800.0,2.0,both
|
1701,1676908800,2,"(1676908800.0, 2.0)",1676908800.0,2.0,both
|
||||||
91,1676912400.0,2,"(1676912400.0, 2.0)",1676912400.0,2.0,both
|
1703,1676912400,2,"(1676912400.0, 2.0)",1676912400.0,2.0,both
|
||||||
93,1676916000.0,2,"(1676916000.0, 2.0)",1676916000.0,2.0,both
|
1773,1677020400,2,"(1677020400.0, 2.0)",1677020400.0,2.0,both
|
||||||
95,1676919600.0,2,"(1676919600.0, 2.0)",1676919600.0,2.0,both
|
1775,1677024000,2,"(1677024000.0, 2.0)",1677024000.0,2.0,both
|
||||||
97,1676923200.0,2,"(1676923200.0, 2.0)",1676923200.0,2.0,both
|
1777,1677027600,2,"(1677027600.0, 2.0)",1677027600.0,2.0,both
|
||||||
99,1676926800.0,2,"(1676926800.0, 2.0)",1676926800.0,2.0,both
|
1779,1677031200,2,"(1677031200.0, 2.0)",1677031200.0,2.0,both
|
||||||
101,1676930400.0,2,"(1676930400.0, 2.0)",1676930400.0,2.0,both
|
1781,1677034800,2,"(1677034800.0, 2.0)",1677034800.0,2.0,both
|
||||||
103,1676934000.0,2,"(1676934000.0, 2.0)",1676934000.0,2.0,both
|
1783,1677038400,2,"(1677038400.0, 2.0)",1677038400.0,2.0,both
|
||||||
105,1676937600.0,2,"(1676937600.0, 2.0)",1676937600.0,2.0,both
|
1785,1677042000,2,"(1677042000.0, 2.0)",1677042000.0,2.0,both
|
||||||
107,1676941200.0,2,"(1676941200.0, 2.0)",1676941200.0,2.0,both
|
1787,1677045600,2,"(1677045600.0, 2.0)",1677045600.0,2.0,both
|
||||||
109,1676944800.0,2,"(1676944800.0, 2.0)",1676944800.0,2.0,both
|
1789,1677049200,2,"(1677049200.0, 2.0)",1677049200.0,2.0,both
|
||||||
111,1676948400.0,2,"(1676948400.0, 2.0)",1676948400.0,2.0,both
|
1791,1677052800,2,"(1677052800.0, 2.0)",1677052800.0,2.0,both
|
||||||
113,1676952000.0,2,"(1676952000.0, 2.0)",1676952000.0,2.0,both
|
1793,1677056400,2,"(1677056400.0, 2.0)",1677056400.0,2.0,both
|
||||||
115,1676955600.0,2,"(1676955600.0, 2.0)",1676955600.0,2.0,both
|
1795,1677060000,2,"(1677060000.0, 2.0)",1677060000.0,2.0,both
|
||||||
117,1676959200.0,2,"(1676959200.0, 2.0)",1676959200.0,2.0,both
|
2030,1677063600,2,"(1677063600.0, 2.0)",1677063600.0,2.0,both
|
||||||
119,1676962800.0,2,"(1676962800.0, 2.0)",1676962800.0,2.0,both
|
2032,1677067200,2,"(1677067200.0, 2.0)",1677067200.0,2.0,both
|
||||||
121,1676966400.0,2,"(1676966400.0, 2.0)",1676966400.0,2.0,both
|
2034,1677070800,2,"(1677070800.0, 2.0)",1677070800.0,2.0,both
|
||||||
123,1676970000.0,2,"(1676970000.0, 2.0)",1676970000.0,2.0,both
|
2036,1677074400,2,"(1677074400.0, 2.0)",1677074400.0,2.0,both
|
||||||
125,1676973600.0,2,"(1676973600.0, 2.0)",1676973600.0,2.0,both
|
2038,1677078000,2,"(1677078000.0, 2.0)",1677078000.0,2.0,both
|
||||||
127,1676977200.0,2,"(1676977200.0, 2.0)",1676977200.0,2.0,both
|
2040,1677081600,2,"(1677081600.0, 2.0)",1677081600.0,2.0,both
|
||||||
129,1676980800.0,2,"(1676980800.0, 2.0)",1676980800.0,2.0,both
|
2042,1677085200,2,"(1677085200.0, 2.0)",1677085200.0,2.0,both
|
||||||
131,1676984400.0,2,"(1676984400.0, 2.0)",1676984400.0,2.0,both
|
2044,1677088800,2,"(1677088800.0, 2.0)",1677088800.0,2.0,both
|
||||||
133,1676988000.0,2,"(1676988000.0, 2.0)",1676988000.0,2.0,both
|
2046,1677092400,2,"(1677092400.0, 2.0)",1677092400.0,2.0,both
|
||||||
135,1676991600.0,2,"(1676991600.0, 2.0)",1676991600.0,2.0,both
|
2048,1677096000,2,"(1677096000.0, 2.0)",1677096000.0,2.0,both
|
||||||
137,1676995200.0,2,"(1676995200.0, 2.0)",1676995200.0,2.0,both
|
2050,1677099600,2,"(1677099600.0, 2.0)",1677099600.0,2.0,both
|
||||||
139,1676998800.0,2,"(1676998800.0, 2.0)",1676998800.0,2.0,both
|
2052,1677103200,2,"(1677103200.0, 2.0)",1677103200.0,2.0,both
|
||||||
141,1677002400.0,2,"(1677002400.0, 2.0)",1677002400.0,2.0,both
|
2054,1677106800,2,"(1677106800.0, 2.0)",1677106800.0,2.0,both
|
||||||
143,1677006000.0,2,"(1677006000.0, 2.0)",1677006000.0,2.0,both
|
2056,1677110400,2,"(1677110400.0, 2.0)",1677110400.0,2.0,both
|
||||||
145,1677009600.0,2,"(1677009600.0, 2.0)",1677009600.0,2.0,both
|
2058,1677114000,2,"(1677114000.0, 2.0)",1677114000.0,2.0,both
|
||||||
147,1677013200.0,2,"(1677013200.0, 2.0)",1677013200.0,2.0,both
|
2060,1677117600,2,"(1677117600.0, 2.0)",1677117600.0,2.0,both
|
||||||
149,1677016800.0,2,"(1677016800.0, 2.0)",1677016800.0,2.0,both
|
2062,1677121200,2,"(1677121200.0, 2.0)",1677121200.0,2.0,both
|
||||||
151,1677020400.0,2,"(1677020400.0, 2.0)",1677020400.0,2.0,both
|
2064,1677124800,2,"(1677124800.0, 2.0)",1677124800.0,2.0,both
|
||||||
153,1677024000.0,2,"(1677024000.0, 2.0)",1677024000.0,2.0,both
|
2066,1677128400,2,"(1677128400.0, 2.0)",1677128400.0,2.0,both
|
||||||
155,1677027600.0,2,"(1677027600.0, 2.0)",1677027600.0,2.0,both
|
2138,1677139200,2,"(1677139200.0, 2.0)",1677139200.0,2.0,both
|
||||||
157,1677031200.0,2,"(1677031200.0, 2.0)",1677031200.0,2.0,both
|
2140,1677142800,2,"(1677142800.0, 2.0)",1677142800.0,2.0,both
|
||||||
159,1677034800.0,2,"(1677034800.0, 2.0)",1677034800.0,2.0,both
|
2142,1677146400,2,"(1677146400.0, 2.0)",1677146400.0,2.0,both
|
||||||
161,1677038400.0,2,"(1677038400.0, 2.0)",1677038400.0,2.0,both
|
2144,1677150000,2,"(1677150000.0, 2.0)",1677150000.0,2.0,both
|
||||||
163,1677042000.0,2,"(1677042000.0, 2.0)",1677042000.0,2.0,both
|
2146,1677153600,2,"(1677153600.0, 2.0)",1677153600.0,2.0,both
|
||||||
165,1677045600.0,2,"(1677045600.0, 2.0)",1677045600.0,2.0,both
|
2148,1677157200,2,"(1677157200.0, 2.0)",1677157200.0,2.0,both
|
||||||
167,1677049200.0,2,"(1677049200.0, 2.0)",1677049200.0,2.0,both
|
2150,1677160800,2,"(1677160800.0, 2.0)",1677160800.0,2.0,both
|
||||||
169,1677052800.0,2,"(1677052800.0, 2.0)",1677052800.0,2.0,both
|
2152,1677164400,2,"(1677164400.0, 2.0)",1677164400.0,2.0,both
|
||||||
171,1677056400.0,2,"(1677056400.0, 2.0)",1677056400.0,2.0,both
|
2154,1677168000,2,"(1677168000.0, 2.0)",1677168000.0,2.0,both
|
||||||
173,1677060000.0,2,"(1677060000.0, 2.0)",1677060000.0,2.0,both
|
2156,1677171600,2,"(1677171600.0, 2.0)",1677171600.0,2.0,both
|
||||||
175,1677063600.0,2,"(1677063600.0, 2.0)",1677063600.0,2.0,both
|
5999,1676916000,2,"(1676916000.0, 2.0)",1676916000.0,2.0,both
|
||||||
177,1677067200.0,2,"(1677067200.0, 2.0)",1677067200.0,2.0,both
|
6001,1676919600,2,"(1676919600.0, 2.0)",1676919600.0,2.0,both
|
||||||
179,1677070800.0,2,"(1677070800.0, 2.0)",1677070800.0,2.0,both
|
6003,1676923200,2,"(1676923200.0, 2.0)",1676923200.0,2.0,both
|
||||||
181,1677074400.0,2,"(1677074400.0, 2.0)",1677074400.0,2.0,both
|
6005,1676926800,2,"(1676926800.0, 2.0)",1676926800.0,2.0,both
|
||||||
183,1677078000.0,2,"(1677078000.0, 2.0)",1677078000.0,2.0,both
|
6007,1676930400,2,"(1676930400.0, 2.0)",1676930400.0,2.0,both
|
||||||
185,1677081600.0,2,"(1677081600.0, 2.0)",1677081600.0,2.0,both
|
6009,1676934000,2,"(1676934000.0, 2.0)",1676934000.0,2.0,both
|
||||||
187,1677085200.0,2,"(1677085200.0, 2.0)",1677085200.0,2.0,both
|
6011,1676937600,2,"(1676937600.0, 2.0)",1676937600.0,2.0,both
|
||||||
189,1677088800.0,2,"(1677088800.0, 2.0)",1677088800.0,2.0,both
|
6013,1676941200,2,"(1676941200.0, 2.0)",1676941200.0,2.0,both
|
||||||
191,1677092400.0,2,"(1677092400.0, 2.0)",1677092400.0,2.0,both
|
6015,1676944800,2,"(1676944800.0, 2.0)",1676944800.0,2.0,both
|
||||||
193,1677096000.0,2,"(1677096000.0, 2.0)",1677096000.0,2.0,both
|
6017,1676948400,2,"(1676948400.0, 2.0)",1676948400.0,2.0,both
|
||||||
195,1677099600.0,2,"(1677099600.0, 2.0)",1677099600.0,2.0,both
|
6019,1676952000,2,"(1676952000.0, 2.0)",1676952000.0,2.0,both
|
||||||
197,1677103200.0,2,"(1677103200.0, 2.0)",1677103200.0,2.0,both
|
6021,1676955600,2,"(1676955600.0, 2.0)",1676955600.0,2.0,both
|
||||||
199,1677106800.0,2,"(1677106800.0, 2.0)",1677106800.0,2.0,both
|
6023,1676959200,2,"(1676959200.0, 2.0)",1676959200.0,2.0,both
|
||||||
201,1677110400.0,2,"(1677110400.0, 2.0)",1677110400.0,2.0,both
|
6025,1676962800,2,"(1676962800.0, 2.0)",1676962800.0,2.0,both
|
||||||
203,1677114000.0,2,"(1677114000.0, 2.0)",1677114000.0,2.0,both
|
6027,1676966400,2,"(1676966400.0, 2.0)",1676966400.0,2.0,both
|
||||||
205,1677117600.0,2,"(1677117600.0, 2.0)",1677117600.0,2.0,both
|
6029,1676970000,2,"(1676970000.0, 2.0)",1676970000.0,2.0,both
|
||||||
207,1677121200.0,2,"(1677121200.0, 2.0)",1677121200.0,2.0,both
|
6031,1676973600,2,"(1676973600.0, 2.0)",1676973600.0,2.0,both
|
||||||
209,1677124800.0,2,"(1677124800.0, 2.0)",1677124800.0,2.0,both
|
6033,1676977200,2,"(1676977200.0, 2.0)",1676977200.0,2.0,both
|
||||||
211,1677128400.0,2,"(1677128400.0, 2.0)",1677128400.0,2.0,both
|
6044,1676980800,2,"(1676980800.0, 2.0)",1676980800.0,2.0,both
|
||||||
213,1677132000.0,2,"(1677132000.0, 2.0)",1677132000.0,2.0,both
|
6046,1676984400,2,"(1676984400.0, 2.0)",1676984400.0,2.0,both
|
||||||
215,1677135600.0,2,"(1677135600.0, 2.0)",1677135600.0,2.0,both
|
6048,1676988000,2,"(1676988000.0, 2.0)",1676988000.0,2.0,both
|
||||||
217,1677139200.0,2,"(1677139200.0, 2.0)",1677139200.0,2.0,both
|
6050,1676991600,2,"(1676991600.0, 2.0)",1676991600.0,2.0,both
|
||||||
219,1677142800.0,2,"(1677142800.0, 2.0)",1677142800.0,2.0,both
|
6052,1676995200,2,"(1676995200.0, 2.0)",1676995200.0,2.0,both
|
||||||
221,1677146400.0,2,"(1677146400.0, 2.0)",1677146400.0,2.0,both
|
6054,1676998800,2,"(1676998800.0, 2.0)",1676998800.0,2.0,both
|
||||||
223,1677150000.0,2,"(1677150000.0, 2.0)",1677150000.0,2.0,both
|
6056,1677002400,2,"(1677002400.0, 2.0)",1677002400.0,2.0,both
|
||||||
225,1677153600.0,2,"(1677153600.0, 2.0)",1677153600.0,2.0,both
|
6058,1677006000,2,"(1677006000.0, 2.0)",1677006000.0,2.0,both
|
||||||
227,1677157200.0,2,"(1677157200.0, 2.0)",1677157200.0,2.0,both
|
6060,1677009600,2,"(1677009600.0, 2.0)",1677009600.0,2.0,both
|
||||||
229,1677160800.0,2,"(1677160800.0, 2.0)",1677160800.0,2.0,both
|
6062,1677013200,2,"(1677013200.0, 2.0)",1677013200.0,2.0,both
|
||||||
231,1677164400.0,2,"(1677164400.0, 2.0)",1677164400.0,2.0,both
|
6064,1677016800,2,"(1677016800.0, 2.0)",1677016800.0,2.0,both
|
||||||
233,1677168000.0,2,"(1677168000.0, 2.0)",1677168000.0,2.0,both
|
6066,1677132000,2,"(1677132000.0, 2.0)",1677132000.0,2.0,both
|
||||||
235,1677171600.0,2,"(1677171600.0, 2.0)",1677171600.0,2.0,both
|
6068,1677135600,2,"(1677135600.0, 2.0)",1677135600.0,2.0,both
|
||||||
237,1677175200.0,2,"(1677175200.0, 2.0)",1677175200.0,2.0,both
|
6071,1677175200,2,"(1677175200.0, 2.0)",1677175200.0,2.0,both
|
||||||
239,1677178800.0,2,"(1677178800.0, 2.0)",1677178800.0,2.0,both
|
6073,1677178800,2,"(1677178800.0, 2.0)",1677178800.0,2.0,both
|
||||||
241,1677182400.0,2,"(1677182400.0, 2.0)",1677182400.0,2.0,both
|
6075,1677182400,2,"(1677182400.0, 2.0)",1677182400.0,2.0,both
|
||||||
243,1677186000.0,2,"(1677186000.0, 2.0)",1677186000.0,2.0,both
|
6077,1677186000,2,"(1677186000.0, 2.0)",1677186000.0,2.0,both
|
||||||
245,1677189600.0,2,"(1677189600.0, 2.0)",1677189600.0,2.0,both
|
6079,1677189600,2,"(1677189600.0, 2.0)",1677189600.0,2.0,both
|
||||||
247,1677193200.0,2,"(1677193200.0, 2.0)",1677193200.0,2.0,both
|
6081,1677193200,2,"(1677193200.0, 2.0)",1677193200.0,2.0,both
|
||||||
249,1677196800.0,2,"(1677196800.0, 2.0)",1677196800.0,2.0,both
|
6083,1677196800,2,"(1677196800.0, 2.0)",1677196800.0,2.0,both
|
||||||
|
|||||||
|
Reference in New Issue
Block a user