update: new short subjects; merged general report

.gitattributes

@@ -57,3 +57,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+weather/merged_general_report/merged_general_weather_report.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,5 +1,9 @@
 # WIATS: Weather-centric Intervention-Aware Time Series Multimodal Dataset
 
+## Data source:
+
+- [NYC_Traffic_Speed](https://data.cityofnewyork.us/Transportation/DOT-Traffic-Speeds-NBE/i4gi-tjb9/about_data)
+
 ## Dataset Structure
 
 The dataset is organized into the following structure:
@@ -46,6 +50,10 @@ The dataset is organized into the following structure:
 |   |   |   |-- version_2
 |   |   |   |-- ...
 
+|   |   |-- merged_general_report # merged general report for multiple needed locations (optional)
+|   |   |   |-- xxx.json
+|   |   |   |-- ...
+
 |   |-- scripts # Scripts for data processing, model training, and evaluation
 |   |-- id_info.json # Metadata for whole dataset without preprocessing
 |   |-- static_info.json # Static information for this dataset, including the dataset information, channel information, downtime reasons. 

expand_id_info_short_new.json

@@ -0,0 +1,52 @@
+{
+    "159": {
+        "borough": "Bronx",
+        "link": "BRP N WATSON AVENUE - FORDHAM ROAD",
+        "len": 322911
+    },
+    "351": {
+        "borough": "Staten Island",
+        "link": "MLK S - SIE W WALKER STREET - RICHMOND AVENUE",
+        "len": 322890
+    },
+    "150": {
+        "borough": "Manhattan",
+        "link": "BQE N Atlantic Ave - MAN Bridge Manhattan Side",
+        "len": 317848
+    },
+    "364": {
+        "borough": "Manhattan",
+        "link": "QMT E Manhattan Side - Toll Plaza",
+        "len": 321989
+    },
+    "195": {
+        "borough": "Bronx",
+        "link": "CBE W U/LEV AMSTERDAM AVE - I95 S EXP LNS",
+        "len": 321922
+    },
+    "199": {
+        "borough": "Queens",
+        "link": "CIP N Hempstead Tpk - LIE",
+        "len": 322896
+    },
+    "406": {
+        "borough": "Queens",
+        "link": "TNB S Toll Plaza - Queens Anchorage",
+        "len": 321972
+    },
+    "402": {
+        "borough": "Queens",
+        "link": "TNB N Queens Anchorage - Toll Plaza",
+        "len": 321977
+    },
+    "164": {
+        "borough": "Queens",
+        "link": "BWB N Queens Anchorage - Toll Plaza",
+        "len": 322011
+    },
+    "141": {
+        "borough": "Queens",
+        "link": "BE S TBB EXIT RAMP - QUEENS ANCHORAGE",
+        "len": 322000
+    }
+}

scripts/impute_expand_abandoned.py

@@ -0,0 +1,207 @@
+import pandas as pd
+import numpy as np
+from matplotlib import pyplot as plt
+import json
+import os
+from tqdm import tqdm
+from concurrent.futures import ThreadPoolExecutor
+
+# disable the warning from pandas
+pd.options.mode.chained_assignment = None
+
+id_info = json.load(open('./expand_id_info_short_new.json'))
+id_list = list(id_info.keys())
+
+print('Reading data...')
+dfs = {}
+
+def read_parquet(id):
+    return id, pd.read_parquet(f'./scripts/slim_data/id_{id}.parquet')
+
+with ThreadPoolExecutor(max_workers=100) as executor:
+    futures = [executor.submit(read_parquet, id) for id in id_list]
+    for future in tqdm(futures):
+        id, df = future.result()
+        dfs[id] = df
+
+sensor_downtimes = {}
+
+for id in tqdm(id_list):
+
+    tqdm.write(f"Preparing ID: {id}")
+
+    df = dfs[str(id)].copy()
+
+    df['SPEED'] = pd.to_numeric(df['SPEED'], errors='coerce')
+    df['TRAVEL_TIME'] = pd.to_numeric(df['TRAVEL_TIME'], errors='coerce')
+    df['DATA_AS_OF'] = pd.to_datetime(df['DATA_AS_OF'])
+
+    # round the DATA_AS_OF to the nearest 5 minutes
+    df['DATA_AS_OF'] = df['DATA_AS_OF'].dt.round('5min') 
+    tqdm.write(f"De-duplicating ID: {id}")
+    #find duplicate timestamps
+    missing_gap0 = df['DATA_AS_OF'].diff().dt.total_seconds() == 0
+    # missing_gap5 = df['DATA_AS_OF'].diff().dt.total_seconds() > (60*5)
+    missing_gap0 = missing_gap0[missing_gap0].index
+
+    to_remove=[]
+    for ind in missing_gap0:
+        # if the DATA_AS_OF of ind is 10min smaller than ind+1 then add 5 min to ind
+        if ind+1 == len(df):
+            to_remove.append(ind)
+        elif df['DATA_AS_OF'].iloc[ind] + pd.Timedelta('10min') == df['DATA_AS_OF'].iloc[ind+1]:
+            # print(f'adjust {ind}')
+            df['DATA_AS_OF'].iloc[ind] = df['DATA_AS_OF'].iloc[ind] + pd.Timedelta('5min')
+        else:
+            # remove the row of ind
+            to_remove.append(ind)
+    df = df.drop(to_remove)
+    df = df.reset_index(drop=True)
+    # check missing_gap0 again
+    missing_gap0 = df['DATA_AS_OF'].diff().dt.total_seconds() == 0
+    missing_gap0 = missing_gap0[missing_gap0].index
+    assert len(missing_gap0) == 0, 'There are still duplicate timestamps'
+
+    tqdm.write(f"Small gaps ID: {id}")
+    # get the missing gaps of 5~2h
+    threshold_time = 120 # in min
+
+    missing_gap15 = df['DATA_AS_OF'].diff().dt.total_seconds() <= (60*threshold_time) # make it 2h
+    missing_gap5 = df['DATA_AS_OF'].diff().dt.total_seconds() > (60*5)
+    missing_gap = missing_gap15 & missing_gap5
+
+    missing_gap = missing_gap[missing_gap].index
+
+    # convert the str values in df['SPEED'] and df['TRAVEL_TIME'] to float
+    # convert the timestamp to datetime
+
+    def linear_impute(start_idx, end_idx):
+        start_time = df['DATA_AS_OF'][start_idx]
+        end_time = df['DATA_AS_OF'][end_idx]
+        start_speed = df['SPEED'][start_idx]
+        end_speed = df['SPEED'][end_idx]
+        start_travel_time = df['TRAVEL_TIME'][start_idx]
+        end_travel_time = df['TRAVEL_TIME'][end_idx]
+        gap = end_time - start_time
+        gap = gap.total_seconds()
+        new_rows = []
+        for j in range(1, int(gap // 300)):
+            new_rows.append({
+                'DATA_AS_OF': start_time + pd.Timedelta(f'{j*5}min'),
+                'SPEED': start_speed + (end_speed - start_speed) * j / (gap // 300),
+                'TRAVEL_TIME': start_travel_time + (end_travel_time - start_travel_time) * j / (gap // 300)
+            })
+        return new_rows
+
+    with ThreadPoolExecutor(max_workers=100) as executor:
+        futures = [executor.submit(linear_impute, i - 1, i) for i in missing_gap]
+        results = [future.result() for future in tqdm(futures)]
+
+    # Flatten the list of lists
+    new_rows = [item for sublist in results for item in sublist]
+
+    # Create a DataFrame from the new rows and concatenate it with the original DataFrame
+    new_df = pd.DataFrame(new_rows)
+    df = pd.concat([df, new_df], ignore_index=True)
+
+    # sort by the DATA_AS_OF
+    df = df.sort_values('DATA_AS_OF')
+    df = df.reset_index(drop=True)
+
+    # check again
+    missing_gap15 = df['DATA_AS_OF'].diff().dt.total_seconds() <= (60*threshold_time)
+    missing_gap5 = df['DATA_AS_OF'].diff().dt.total_seconds() > (60*5)
+    missing_gap = missing_gap15 & missing_gap5
+
+    missing_gap = missing_gap[missing_gap].index
+    assert len(missing_gap) == 0, 'There are still missing gaps'
+
+    tqdm.write(f"Large gaps ID: {id}")
+    missing_gaplarge = df['DATA_AS_OF'].diff().dt.total_seconds() > (60*threshold_time) # make it 2h #30 min is ok
+
+    missing_gaplarge = missing_gaplarge[missing_gaplarge].index
+
+    def zero_impute(start_idx, end_idx):
+        start_time = df['DATA_AS_OF'][start_idx]
+        end_time = df['DATA_AS_OF'][end_idx]
+        gap = end_time - start_time
+        gap = gap.total_seconds()
+        new_rows = []
+        for j in range(1, int(gap // 300)):
+            new_rows.append({
+                'DATA_AS_OF': start_time + pd.Timedelta(f'{j*5}min'),
+                'SPEED': 0,
+                'TRAVEL_TIME': 0
+            })
+        return new_rows
+
+    with ThreadPoolExecutor(max_workers=100) as executor:
+        futures = [executor.submit(zero_impute, i - 1, i) for i in missing_gaplarge]
+        results = [future.result() for future in tqdm(futures)]
+
+    # Flatten the list of lists
+    new_rows = [item for sublist in results for item in sublist]
+
+    # Create a DataFrame from the new rows and concatenate it with the original DataFrame
+    new_df = pd.DataFrame(new_rows)
+    df = pd.concat([df, new_df], ignore_index=True)
+
+    # sort by the DATA_AS_OF
+    df = df.sort_values('DATA_AS_OF')
+    df = df.reset_index(drop=True)
+
+    # check again
+    missing_anygap = df['DATA_AS_OF'].diff().dt.total_seconds() > (60*5)
+    missing_anygap = missing_anygap[missing_anygap].index
+    assert len(missing_anygap) == 0, 'There are still missing gaps'
+
+    tqdm.write(f"Sensor downtime ID: {id}")
+    # get sensor downtime
+    # get all the SPEED=0
+    zero_speed = df['SPEED']==0
+    speed_goes_down = df['SPEED'].diff() < 0
+    speed_goes_up = df['SPEED'].diff(-1) < 0
+
+    speed_goto_zero = zero_speed & speed_goes_down
+    speed_goto_zero = speed_goto_zero[speed_goto_zero].index
+
+    speed_gofrom_zero = zero_speed & speed_goes_up
+    speed_gofrom_zero = speed_gofrom_zero[speed_gofrom_zero].index
+
+    threshold_step = threshold_time//5
+    sensor_downtime = {}
+    i=0
+    for start, end in zip(speed_goto_zero, speed_gofrom_zero):
+        if end - start > threshold_step:
+            sensor_downtime[i] = {'time':(df['DATA_AS_OF'][start], df['DATA_AS_OF'][end]), 'index':(start, end)} 
+            i+=1
+
+
+    # if the downtime is between 0:00 to 6:00 then remove the downtime from dictionary
+    def check_22_6(time):
+        if time.hour >= 0 and time.hour < 6:
+            return True
+        elif time.hour >=22:
+            return True
+        else:
+            return False
+    for key in list(sensor_downtime.keys()):
+        if check_22_6(sensor_downtime[key]['time'][0]) and check_22_6(sensor_downtime[key]['time'][1]):
+            del sensor_downtime[key]
+
+    # convert the 'time' segment to string
+    for key in sensor_downtime.keys():
+        sensor_downtime[key]['time'] = (str(sensor_downtime[key]['time'][0]), str(sensor_downtime[key]['time'][1]))
+
+    sensor_downtime = dict(enumerate(sensor_downtime.values()))
+
+    df.to_parquet(f'./time_series_short_new/id_{id}.parquet')
+    sensor_downtimes[id] = sensor_downtime
+    id_info[str(id)]['sensor_downtime'] = sensor_downtime
+    id_info[str(id)]['len'] = len(df)
+
+json.dump(id_info, open('./time_series_short_new/id_info_zero_shot_imputed.json', 'w'), indent=4)
+
+json.dump(sensor_downtimes, open('./time_series_short_new/sensor_downtimes.json', 'w'), indent=4)
+print('Done!')
+

scripts/merge_general_report.py

@@ -0,0 +1,58 @@
+import json
+import os
+import glob
+
+# Configuration for paths and filenames
+base_directory = 'weather'
+output_filename = 'merged_general_weather_report.json'
+
+# Define a search pattern to find the relevant JSON files
+file_pattern = os.path.join(base_directory, '*', 'weather_report', 'fast_general_weather_forecast_*.json')
+all_files = glob.glob(file_pattern)
+
+if not all_files:
+    print(f"No files found for pattern '{file_pattern}'. Please check the path and script location.")
+else:
+    print(f"Found {len(all_files)} files to merge. Starting process...")
+
+    # The master dictionary will hold the final nested structure.
+    # It is structured as: { 'timestamp1': {'regionA': {...}, 'regionB': {...}}, 'timestamp2': ... }
+    master_data = {}
+
+    for file_path in all_files:
+        try:
+            # Extract the region name from the file path.
+            # e.g., 'Bayreuth-TenneT' is the 3rd last part of the path.
+            path_parts = os.path.normpath(file_path).split(os.sep)
+            region = path_parts[-3] 
+
+            # The data in each file is a dictionary like {'20160101': {...}, '20160102': {...}}
+            with open(file_path, 'r', encoding='utf-8') as f:
+                region_data = json.load(f)
+
+            # Iterate through all (timestamp, report_details) pairs in the current file
+            for timestamp, report_details in region_data.items():
+                timestamp_entry = master_data.setdefault(timestamp, {})
+                # Under the entry for this timestamp, add the report details keyed by the region name.
+                timestamp_entry[region] = report_details
+            
+            print(f"  - Processed and integrated: {region}")
+
+        except Exception as e:
+            print(f"An error occurred while processing file {file_path}: {e}")
+
+    # Write the consolidated data to a new JSON file
+    if master_data:
+        print("\nWriting all data to the final file...")
+        
+        # Sort the master dictionary by timestamp (key) for better readability
+        sorted_master_data = dict(sorted(master_data.items()))
+        
+        with open(output_filename, 'w', encoding='utf-8') as f:
+            json.dump(sorted_master_data, f, indent=4, ensure_ascii=False)
+        
+        print(f"Merge complete!")
+        print(f"All data has been aggregated by timestamp into: {output_filename}")
+        print(f"The final file contains {len(master_data)} unique timestamps.")
+    else:
+        print("No files were processed successfully. The output file was not generated.")

static_info_embeddings.pkl

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static_info_embeddings_short_new.pkl

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static_info_short_new.json

@@ -0,0 +1 @@
+{"general_info": "This dataset contains Average Speed of a Vehicle Traveled Between End Points data in km/h collected from various locations in New York City by sensors. The sampling rate is every 5 minutes. When no car is detected in the period, the speed is set to 0.", "downtime_prompt": "The sensor is down for unknown reasons, readings set to 0. ", "channel_info": {"159": "Sensor 159 is located at Bronx, with segment of BRP N WATSON AVENUE - FORDHAM ROAD.", "351": "Sensor 351 is located at Staten Island, with segment of MLK S - SIE W WALKER STREET - RICHMOND AVENUE.", "150": "Sensor 150 is located at Manhattan, with segment of BQE N Atlantic Ave - MAN Bridge Manhattan Side.", "364": "Sensor 364 is located at Manhattan, with segment of QMT E Manhattan Side - Toll Plaza.", "195": "Sensor 195 is located at Bronx, with segment of CBE W U/LEV AMSTERDAM AVE - I95 S EXP LNS.", "199": "Sensor 199 is located at Queens, with segment of CIP N Hempstead Tpk - LIE.", "406": "Sensor 406 is located at Queens, with segment of TNB S Toll Plaza - Queens Anchorage.", "402": "Sensor 402 is located at Queens, with segment of TNB N Queens Anchorage - Toll Plaza.", "164": "Sensor 164 is located at Queens, with segment of BWB N Queens Anchorage - Toll Plaza.", "141": "Sensor 141 is located at Queens, with segment of BE S TBB EXIT RAMP - QUEENS ANCHORAGE."}}

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