Lebovits/issu1015 cleanup new pipeline

data/src/Pipfile

@@ -13,29 +13,29 @@ matplotlib = "*"
 rasterio = "*"
 scikit-learn = "*"
 mapclassify = "*"
-black = "*"
 fiona = "*"
 esridump = "*"
 sqlalchemy = "*"
 psycopg2-binary = "*"
-geoalchemy2 = "*"
 mapbox = "*"
 google-cloud-storage = "*"
 pydantic = "==2.8.2"
-data-diff = {extras = ["postgresql"], version = "*"}
 future = "*"
 slack-sdk = "*"
-pytest = "*"
 networkx = "*"
 libpysal = "*"
 jenkspy = "*"
 pyarrow = "*"
 tqdm = "*"
+geoalchemy2 ="*"
+
+[dev-packages]
+black = "*"
+pytest = "*"
 vulture = "*"
 pylint = "*"
 radon = "*"
-
-[dev-packages]
+ruff = "*"
 
 [requires]
 python_version = "3.11"

data/src/main.py

@@ -1,155 +1,107 @@
 import sys
-
-from new_etl.data_utils.access_process import access_process
-from new_etl.data_utils.contig_neighbors import contig_neighbors
-from new_etl.data_utils.dev_probability import dev_probability
-from new_etl.data_utils.negligent_devs import negligent_devs
-from new_etl.data_utils.opa_properties import opa_properties
-from new_etl.data_utils.priority_level import priority_level
-from new_etl.data_utils.vacant_properties import vacant_properties
-from new_etl.data_utils.pwd_parcels import pwd_parcels
-from new_etl.data_utils.city_owned_properties import city_owned_properties
-from new_etl.data_utils.phs_properties import phs_properties
-from new_etl.data_utils.li_violations import li_violations
-from new_etl.data_utils.li_complaints import li_complaints
-from new_etl.data_utils.rco_geoms import rco_geoms
-from new_etl.data_utils.council_dists import council_dists
-from new_etl.data_utils.tree_canopy import tree_canopy
-from new_etl.data_utils.nbhoods import nbhoods
-from new_etl.data_utils.gun_crimes import gun_crimes
-from new_etl.data_utils.drug_crimes import drug_crimes
-from new_etl.data_utils.delinquencies import delinquencies
-from new_etl.data_utils.unsafe_buildings import unsafe_buildings
-from new_etl.data_utils.imm_dang_buildings import imm_dang_buildings
-from new_etl.data_utils.tactical_urbanism import tactical_urbanism
-from new_etl.data_utils.conservatorship import conservatorship
-from new_etl.data_utils.owner_type import owner_type
-from new_etl.data_utils.community_gardens import community_gardens
-from new_etl.data_utils.park_priority import park_priority
-from new_etl.data_utils.ppr_properties import ppr_properties
-
 import pandas as pd
+import traceback
 
+from config.psql import conn
+from config.config import tiles_file_id_prefix
+
+from new_etl.classes.slack_reporters import send_dataframe_profile_to_slack, send_pg_stats_to_slack, send_error_to_slack
+from new_etl.classes.data_diff import DiffReport
+from new_etl.data_utils import *
+from new_etl.database import to_postgis_with_schema
 
 # Ensure the directory containing awkde is in the Python path
 awkde_path = "/usr/src/app"
 if awkde_path not in sys.path:
     sys.path.append(awkde_path)
 
-services = [
-    # vacant designation
-    vacant_properties,  # needs to run early so that other utils can make use of the `vacant` designation
-    # geometries/areas
-    pwd_parcels,
-    council_dists,
-    nbhoods,
-    rco_geoms,
-    # ownership
-    city_owned_properties,
-    phs_properties,
-    community_gardens,
-    ppr_properties,
-    owner_type,
-    # quality of life
-    li_violations,
-    li_complaints,
-    tree_canopy,
-    gun_crimes,
-    drug_crimes,
-    delinquencies,
-    unsafe_buildings,
-    imm_dang_buildings,
-    # development
-    contig_neighbors,
-    dev_probability,
-    negligent_devs,
-    # access/interventions
-    tactical_urbanism,
-    conservatorship,
-    park_priority,
-]
-
-dataset = opa_properties()
-
-print("Initial Dataset:")
-print("Shape:", dataset.gdf.shape)
-print("Head:\n", dataset.gdf.head())
-print("NA Counts:\n", dataset.gdf.isna().sum())
-
-for service in services:
-    dataset = service(dataset)
-    print(f"After {service.__name__}:")
-    print("Dataset type:", type(dataset.gdf).__name__)
-    print("Shape:", dataset.gdf.shape)
-    print("Head:\n", dataset.gdf.head())
-    print("NA Counts:\n", dataset.gdf.isna().sum())
-
-before_drop = dataset.gdf.shape[0]
-dataset.gdf = dataset.gdf.drop_duplicates(subset="opa_id")
-after_drop = dataset.gdf.shape[0]
-print(
-    f"Duplicate dataset rows dropped after initial services: {before_drop - after_drop}"
-)
-
-# Add Priority Level
-dataset = priority_level(dataset)
-
-# Print the distribution of "priority_level"
-distribution = dataset.gdf["priority_level"].value_counts()
-print("Distribution of priority level:")
-print(distribution)
-
-# Add Access Process
-dataset = access_process(dataset)
-
-# Print the distribution of "access_process"
-distribution = dataset.gdf["access_process"].value_counts()
-print("Distribution of access process:")
-print(distribution)
-
-before_drop = dataset.gdf.shape[0]
-dataset.gdf = dataset.gdf.drop_duplicates(subset="opa_id")
-after_drop = dataset.gdf.shape[0]
-print(f"Duplicate final dataset rows droppeds: {before_drop - after_drop}")
-
-# Convert problematic columns to numeric
-numeric_columns = [
-    "market_value",
-    "sale_price",
-    "total_assessment",
-    "total_due",
-    "num_years_owed",
-    "permit_count",
-]
-for col in numeric_columns:
-    dataset.gdf[col] = pd.to_numeric(dataset.gdf[col], errors="coerce")
-
-dataset.gdf["most_recent_year_owed"] = dataset.gdf["most_recent_year_owed"].astype(str)
-
-print("Column data types before exporting to Parquet:")
-print(dataset.gdf.dtypes)
-
-# Quick dataset profiling
-print("\nQuick dataset profile:")
-
-# 1) Number of NA values per column
-print("\nNumber of NA values per column:")
-print(dataset.gdf.isna().sum())
-
-# 2) Mean, median, and std of numeric columns
-print("\nMean, Median, and Standard Deviation of numeric columns:")
-numeric_columns = dataset.gdf.select_dtypes(include=["float", "int"]).columns
-
-for column in numeric_columns:
-    mean = dataset.gdf[column].mean()
-    median = dataset.gdf[column].median()
-    std = dataset.gdf[column].std()
-    print(f"{column}:\n  Mean: {mean:.2f}\n  Median: {median:.2f}\n  Std: {std:.2f}")
-
-# 3) Number of unique values in string columns
-print("\nNumber of unique values in string columns:")
-string_columns = dataset.gdf.select_dtypes(include=["object", "string"]).columns
-unique_values = dataset.gdf[string_columns].nunique()
-print(unique_values)
-
-dataset.gdf.to_parquet("tmp/test_output.parquet")
+
+try:
+
+    print("Starting ETL process.")
+
+    services = [
+        vacant_properties,  # Run early for other utils to use the `vacant` designation
+        pwd_parcels,
+        council_dists,
+        nbhoods,
+        rco_geoms,
+        city_owned_properties,
+        phs_properties,
+        community_gardens,
+        ppr_properties,
+        owner_type,
+        li_violations,
+        li_complaints,
+        tree_canopy,
+        gun_crimes,
+        drug_crimes,
+        delinquencies,
+        unsafe_buildings,
+        imm_dang_buildings,
+        contig_neighbors,
+        dev_probability,
+        negligent_devs,
+        tactical_urbanism,
+        conservatorship,
+        park_priority,
+    ]
+
+    print("Loading OPA properties dataset.")
+    dataset = opa_properties()
+
+    for service in services:
+        print(f"Running service: {service.__name__}")
+        dataset = service(dataset)
+
+    print("Applying final dataset transformations.")
+    dataset = priority_level(dataset)
+    dataset = access_process(dataset)
+
+    # Drop duplicates
+    before_drop = dataset.gdf.shape[0]
+    dataset.gdf = dataset.gdf.drop_duplicates(subset="opa_id")
+    print(f"Duplicate rows dropped: {before_drop - dataset.gdf.shape[0]}")
+
+    # Convert columns to numeric where necessary
+    numeric_columns = [
+        "market_value",
+        "sale_price",
+        "total_assessment",
+        "total_due",
+        "num_years_owed",
+        "permit_count",
+    ]
+    dataset.gdf[numeric_columns] = dataset.gdf[numeric_columns].apply(pd.to_numeric, errors="coerce")
+    dataset.gdf["most_recent_year_owed"] = dataset.gdf["most_recent_year_owed"].astype(str)
+
+    # Dataset profiling
+    send_dataframe_profile_to_slack(dataset.gdf, "all_properties_end")
+
+    # Save dataset to PostgreSQL
+    to_postgis_with_schema(dataset.gdf, "all_properties_end", conn)
+
+    # Generate and send diff report
+    diff_report = DiffReport()
+    diff_report.run()
+
+    send_pg_stats_to_slack(conn)  # Send PostgreSQL stats to Slack
+
+    # Save local Parquet file
+    parquet_path = "tmp/test_output.parquet"
+    dataset.gdf.to_parquet(parquet_path)
+    print(f"Dataset saved to Parquet: {parquet_path}")
+
+    # Publish only vacant properties
+    dataset.gdf = dataset.gdf[dataset.gdf["vacant"]]
+    dataset.build_and_publish(tiles_file_id_prefix)
+
+    # Finalize
+    conn.commit()
+    conn.close()
+    print("ETL process completed successfully.")
+
+except Exception as e:
+    error_message = f"Error in backend job: {str(e)}\n\n{traceback.format_exc()}"
+    send_error_to_slack(error_message)
+    raise  # Optionally re-raise the exception