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896453f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 | #!/usr/bin/env python3
"""
Extract contacts from dev_mode states (WA, MA, AL, GA, WI)
This script:
1. Loads meetings from the 5 dev states
2. Extracts contacts using the ContactsGoldTableCreator
3. Splits contacts back into state directories
Usage:
python scripts/extract_contacts_dev_mode.py
"""
import sys
from pathlib import Path
import pandas as pd
from loguru import logger
# Add project root to path
sys.path.insert(0, str(Path(__file__).parent.parent))
from pipeline.create_contacts_gold_tables import ContactsGoldTableCreator
# Dev mode states
DEV_STATES = ['WA', 'MA', 'AL', 'GA', 'WI']
def consolidate_dev_meetings():
"""Consolidate meetings from dev states into a single file."""
logger.info("=" * 70)
logger.info("CONSOLIDATING DEV MODE MEETINGS")
logger.info("=" * 70)
states_dir = Path("data/gold/states")
dfs = []
for state in DEV_STATES:
meeting_file = states_dir / state / "meetings.parquet"
if not meeting_file.exists():
logger.warning(f"⚠️ No meetings file for {state}")
continue
df = pd.read_parquet(meeting_file)
logger.info(f" {state}: {len(df):,} meetings")
dfs.append(df)
if not dfs:
logger.error("No meeting data found!")
return None
combined_df = pd.concat(dfs, ignore_index=True)
logger.success(f"✅ Consolidated {len(combined_df):,} total meetings")
# Save temporary consolidated file
output_dir = Path("data/gold")
output_dir.mkdir(parents=True, exist_ok=True)
output_path = output_dir / "meetings_transcripts.parquet"
# Need to ensure we have the required columns
# ContactsGoldTableCreator expects: meeting_id, jurisdiction, transcript_text
# Map columns to expected names
column_mapping = {
'caption_text': 'transcript_text',
'place_name': 'jurisdiction',
'state': 'state' # Keep state
}
# Create meeting_id if it doesn't exist
if 'meeting_id' not in combined_df.columns:
if 'vid_id' in combined_df.columns:
combined_df['meeting_id'] = combined_df['vid_id'].astype(str)
else:
# Fallback: create sequential IDs
combined_df['meeting_id'] = [f"meeting_{i}" for i in range(len(combined_df))]
# Rename columns
for old_col, new_col in column_mapping.items():
if old_col in combined_df.columns and new_col not in combined_df.columns:
combined_df[new_col] = combined_df[old_col]
# Select only needed columns
required_cols = ['meeting_id', 'jurisdiction', 'transcript_text', 'state']
available_cols = [col for col in required_cols if col in combined_df.columns]
output_df = combined_df[available_cols].copy()
output_df.to_parquet(output_path, index=False)
logger.success(f"✅ Saved to {output_path}")
logger.info(f" Columns: {list(output_df.columns)}")
return output_path
def extract_contacts():
"""Extract contacts using ContactsGoldTableCreator."""
logger.info("")
logger.info("=" * 70)
logger.info("EXTRACTING CONTACTS FROM MEETINGS")
logger.info("=" * 70)
creator = ContactsGoldTableCreator(
meetings_gold_dir="data/gold",
output_dir="data/gold"
)
# This creates:
# - data/gold/contacts_local_officials.parquet
# - data/gold/contacts_meeting_attendance.parquet
creator.create_contacts_local_officials()
logger.success("✅ Contacts extraction complete")
def split_contacts_by_state():
"""Split contacts back into state directories."""
logger.info("")
logger.info("=" * 70)
logger.info("SPLITTING CONTACTS BY STATE")
logger.info("=" * 70)
gold_dir = Path("data/gold")
states_dir = gold_dir / "states"
# Load contacts data
officials_file = gold_dir / "contacts_local_officials.parquet"
attendance_file = gold_dir / "contacts_meeting_attendance.parquet"
if not officials_file.exists():
logger.error(f"Officials file not found: {officials_file}")
return
officials_df = pd.read_parquet(officials_file)
logger.info(f" Loaded {len(officials_df):,} unique officials")
if attendance_file.exists():
attendance_df = pd.read_parquet(attendance_file)
logger.info(f" Loaded {len(attendance_df):,} attendance records")
else:
attendance_df = None
# Need to join with meetings to get state
meetings_file = gold_dir / "national" / "meetings_transcripts.parquet"
if meetings_file.exists():
meetings_df = pd.read_parquet(meetings_file)
# Create state mapping from jurisdiction + state
state_map = meetings_df[['jurisdiction', 'state']].drop_duplicates()
# Add state to officials
officials_df = officials_df.merge(
state_map,
on='jurisdiction',
how='left'
)
# Add state to attendance
if attendance_df is not None:
attendance_df = attendance_df.merge(
state_map,
on='jurisdiction',
how='left'
)
# Split by state
for state in DEV_STATES:
state_dir = states_dir / state
state_dir.mkdir(parents=True, exist_ok=True)
# Filter officials for this state
state_officials = officials_df[officials_df['state'] == state].copy()
if len(state_officials) > 0:
# Drop state column before saving
state_officials = state_officials.drop(columns=['state'])
output_file = state_dir / "contacts_local_officials.parquet"
state_officials.to_parquet(output_file, index=False)
logger.success(f" {state}: {len(state_officials):,} officials → {output_file.name}")
else:
logger.warning(f" {state}: No officials found")
# Filter attendance for this state
if attendance_df is not None:
state_attendance = attendance_df[attendance_df['state'] == state].copy()
if len(state_attendance) > 0:
# Drop state column before saving
state_attendance = state_attendance.drop(columns=['state'])
output_file = state_dir / "contacts_meeting_attendance.parquet"
state_attendance.to_parquet(output_file, index=False)
logger.success(f" {state}: {len(state_attendance):,} attendance records → {output_file.name}")
def cleanup_temp_files():
"""Remove temporary consolidated files."""
logger.info("")
logger.info("=" * 70)
logger.info("CLEANUP")
logger.info("=" * 70)
gold_dir = Path("data/gold")
national_dir = gold_dir / "national"
temp_files = [
national_dir / "meetings_transcripts.parquet",
gold_dir / "contacts_local_officials.parquet",
gold_dir / "contacts_meeting_attendance.parquet"
]
for file in temp_files:
if file.exists():
file.unlink()
logger.info(f" Removed {file}")
logger.success("✅ Cleanup complete")
def main():
"""Main execution."""
logger.info("🚀 Extract Contacts - Dev Mode (5 States)")
logger.info(f" States: {', '.join(DEV_STATES)}")
logger.info("")
# Step 1: Consolidate meetings
meetings_path = consolidate_dev_meetings()
if not meetings_path:
logger.error("Failed to consolidate meetings")
return
# Step 2: Extract contacts
extract_contacts()
# Step 3: Split by state
split_contacts_by_state()
# Step 4: Cleanup
cleanup_temp_files()
logger.info("")
logger.info("=" * 70)
logger.success("🎉 CONTACTS EXTRACTION COMPLETE!")
logger.info("=" * 70)
logger.info("")
logger.info("Contacts files created in:")
for state in DEV_STATES:
state_dir = Path(f"data/gold/states/{state}")
officials_file = state_dir / "contacts_local_officials.parquet"
attendance_file = state_dir / "contacts_meeting_attendance.parquet"
if officials_file.exists():
df = pd.read_parquet(officials_file)
logger.info(f" {state}/contacts_local_officials.parquet: {len(df):,} officials")
if attendance_file.exists():
df = pd.read_parquet(attendance_file)
logger.info(f" {state}/contacts_meeting_attendance.parquet: {len(df):,} records")
if __name__ == "__main__":
main()
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