diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..8235df3 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,5 @@ +*.shp filter=lfs diff=lfs merge=lfs -text +*.dbf filter=lfs diff=lfs merge=lfs -text +*.shx filter=lfs diff=lfs merge=lfs -text +*.cpg filter=lfs diff=lfs merge=lfs -text +*.prj filter=lfs diff=lfs merge=lfs -text diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml new file mode 100644 index 0000000..afb2ff7 --- /dev/null +++ b/.github/workflows/codeql.yml @@ -0,0 +1,94 @@ +# For most projects, this workflow file will not need changing; you simply need +# to commit it to your repository. +# +# You may wish to alter this file to override the set of languages analyzed, +# or to provide custom queries or build logic. +# +# ******** NOTE ******** +# We have attempted to detect the languages in your repository. Please check +# the `language` matrix defined below to confirm you have the correct set of +# supported CodeQL languages. +# +name: "CodeQL Advanced" + +on: + push: + branches: [ "main", "development" ] + pull_request: + branches: [ "main", "development" ] + # schedule: + # - cron: '44 14 * * 1' + workflow_dispatch: + + +jobs: + analyze: + name: Analyze (${{ matrix.language }}) + # Runner size impacts CodeQL analysis time. To learn more, please see: + # - https://gh.io/recommended-hardware-resources-for-running-codeql + # - https://gh.io/supported-runners-and-hardware-resources + # - https://gh.io/using-larger-runners (GitHub.com only) + # Consider using larger runners or machines with greater resources for possible analysis time improvements. + runs-on: ${{ (matrix.language == 'swift' && 'macos-latest') || 'ubuntu-latest' }} + permissions: + # required for all workflows + security-events: write + + # required to fetch internal or private CodeQL packs + packages: read + + # only required for workflows in private repositories + actions: read + contents: read + + strategy: + fail-fast: false + matrix: + include: + - language: python + build-mode: none + # CodeQL supports the following values keywords for 'language': 'c-cpp', 'csharp', 'go', 'java-kotlin', 'javascript-typescript', 'python', 'ruby', 'swift' + # Use `c-cpp` to analyze code written in C, C++ or both + # Use 'java-kotlin' to analyze code written in Java, Kotlin or both + # Use 'javascript-typescript' to analyze code written in JavaScript, TypeScript or both + # To learn more about changing the languages that are analyzed or customizing the build mode for your analysis, + # see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/customizing-your-advanced-setup-for-code-scanning. + # If you are analyzing a compiled language, you can modify the 'build-mode' for that language to customize how + # your codebase is analyzed, see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/codeql-code-scanning-for-compiled-languages + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + # Initializes the CodeQL tools for scanning. + - name: Initialize CodeQL + uses: github/codeql-action/init@v3 + with: + languages: ${{ matrix.language }} + build-mode: ${{ matrix.build-mode }} + # If you wish to specify custom queries, you can do so here or in a config file. + # By default, queries listed here will override any specified in a config file. + # Prefix the list here with "+" to use these queries and those in the config file. + + # For more details on CodeQL's query packs, refer to: https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs + # queries: security-extended,security-and-quality + + # If the analyze step fails for one of the languages you are analyzing with + # "We were unable to automatically build your code", modify the matrix above + # to set the build mode to "manual" for that language. Then modify this step + # to build your code. + # ℹī¸ Command-line programs to run using the OS shell. + # 📚 See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun + - if: matrix.build-mode == 'manual' + shell: bash + run: | + echo 'If you are using a "manual" build mode for one or more of the' \ + 'languages you are analyzing, replace this with the commands to build' \ + 'your code, for example:' + echo ' make bootstrap' + echo ' make release' + exit 1 + + - name: Perform CodeQL Analysis + uses: github/codeql-action/analyze@v3 + with: + category: "/language:${{matrix.language}}" diff --git a/.github/workflows/prevent_merge_when_branch_behind.yml b/.github/workflows/prevent_merge_when_branch_behind.yml new file mode 100644 index 0000000..f041018 --- /dev/null +++ b/.github/workflows/prevent_merge_when_branch_behind.yml @@ -0,0 +1,43 @@ +name: Require Branch to Be Up-to-Date + +# Trigger this workflow on pull request events targeting a specific branch. +on: + pull_request: + branches: + - main + - development + + workflow_dispatch: # enables manual triggering + +jobs: + check-branch: + runs-on: ubuntu-latest + steps: + - name: Determine Comparison Branch + id: comparison + run: | + if [[ "${{ github.event.pull_request.base.ref }}" == "main" && "${{ github.event.pull_request.head.ref }}" == "development" ]]; then + echo "branch=main" >> $GITHUB_ENV + else + echo "branch=development" >> $GITHUB_ENV + fi + + - name: Checkout pull request branch + uses: actions/checkout@v3 + with: + ref: ${{ github.event.pull_request.head.ref }} + + - name: Fetch comparison branch + run: | + git fetch --unshallow + git fetch origin ${{ env.branch }} + + - name: Compare branch with ${{ env.branch }} + run: | + if git merge-base --is-ancestor origin/${{ env.branch }} HEAD; then + echo "::notice ::Branch is up-to-date with ${{ env.branch }}." + else + echo "::error ::Merge Blocked: Your branch is behind the latest commits on ${{ env.branch }}. Please update your branch before attempting to merge." + + exit 1 + fi \ No newline at end of file diff --git a/.github/workflows/publish_package.yml b/.github/workflows/publish_package.yml new file mode 100644 index 0000000..5968e97 --- /dev/null +++ b/.github/workflows/publish_package.yml @@ -0,0 +1,38 @@ +name: Publish Package + +on: + release: + types: + - published + workflow_dispatch: # enables manual triggering + +jobs: + publish: + runs-on: ubuntu-latest + + steps: + - name: Checkout repository + uses: actions/checkout@v3 + + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: "3.11" + + - name: Install Poetry + run: | + curl -sSL https://install.python-poetry.org | python3 - + + - name: Install Dependencies + run: | + python -m pip install --upgrade pip + python -m pip install toml requests packaging + + - name: Validate Version + run: | + latest_version=$(curl -s https://pypi.org/pypi/views-pipeline-core/json | jq -r .info.version) + new_version=$(python -c "import toml; print(toml.load('pyproject.toml')['tool']['poetry']['version'])") + python -c "from packaging.version import parse; assert parse('$new_version') > parse('$latest_version'), 'Version must be higher than $latest_version'" + + - name: Publish to PyPI + run: poetry publish --build --username __token__ --password ${{ secrets.PYPI_TOKEN }} diff --git a/.github/workflows/run_pytest.yml b/.github/workflows/run_pytest.yml new file mode 100644 index 0000000..6f6cea6 --- /dev/null +++ b/.github/workflows/run_pytest.yml @@ -0,0 +1,38 @@ +name: Run Pytest + +on: + push: + branches: + - main + - development + pull_request: + branches: + - main + - development + workflow_dispatch: + +jobs: + test: + runs-on: ubuntu-latest + + steps: + - name: Checkout repository + uses: actions/checkout@v3 + + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: "3.11" + + - name: Install Poetry + run: | + curl -sSL https://install.python-poetry.org | python3 - + + - name: Install dependencies + run: | + poetry install + + - name: Run tests + run: | + set -e + poetry run pytest tests/ diff --git a/poetry.lock b/poetry.lock deleted file mode 100644 index ee486c7..0000000 --- a/poetry.lock +++ /dev/null @@ -1,4923 +0,0 @@ -# This file is automatically @generated by Poetry 2.1.4 and should not be changed by hand. - 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a/views_postprocessing/shapefiles/GAUL_2024_L2/GAUL_2024_L2.sbx b/views_postprocessing/shapefiles/GAUL_2024_L2/GAUL_2024_L2.sbx new file mode 100644 index 0000000..a7c6eca Binary files /dev/null and b/views_postprocessing/shapefiles/GAUL_2024_L2/GAUL_2024_L2.sbx differ diff --git a/views_postprocessing/shapefiles/GAUL_2024_L2/GAUL_2024_L2.shp b/views_postprocessing/shapefiles/GAUL_2024_L2/GAUL_2024_L2.shp new file mode 100644 index 0000000..f50aaac --- /dev/null +++ b/views_postprocessing/shapefiles/GAUL_2024_L2/GAUL_2024_L2.shp @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c150365c48c26648f2b07c7322db589594c465aefcf1d44e843e46d46fd44872 +size 774793140 diff --git a/views_postprocessing/shapefiles/GAUL_2024_L2/GAUL_2024_L2.shp.xml b/views_postprocessing/shapefiles/GAUL_2024_L2/GAUL_2024_L2.shp.xml new file mode 100644 index 0000000..89916ff --- /dev/null +++ b/views_postprocessing/shapefiles/GAUL_2024_L2/GAUL_2024_L2.shp.xml @@ -0,0 +1,2 @@ + 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--git a/views_postprocessing/shapefiles/priogrid_cellshp/priogrid_cell.shx b/views_postprocessing/shapefiles/priogrid_cellshp/priogrid_cell.shx new file mode 100644 index 0000000..642c7cc --- /dev/null +++ b/views_postprocessing/shapefiles/priogrid_cellshp/priogrid_cell.shx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b7e37604cc28f71e20369d4e27b411289575afc44012a1417fd4025cb56659b3 +size 2073700 diff --git a/views_postprocessing/unfao/__init__.py b/views_postprocessing/unfao/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/views_postprocessing/unfao/managers/README.md b/views_postprocessing/unfao/managers/README.md new file mode 100644 index 0000000..2e126d2 --- /dev/null +++ b/views_postprocessing/unfao/managers/README.md @@ -0,0 +1,387 @@ +# UNFAOPostProcessorManager + +A postprocessor manager for transforming ViEWS pipeline predictions into UN FAO-compatible formats with geographic metadata enrichment. + +## Overview + +The `UNFAOPostProcessorManager` is a specialized postprocessor that prepares VIEWS conflict prediction data for delivery to the United Nations Food and Agriculture Organization (UN FAO). It handles: + +- Reading historical observation data from Viewser +- Downloading forecast predictions from the Appwrite datastore +- Enriching data with geographic metadata (coordinates, country codes, admin boundaries) +- Validating output schema compliance +- Uploading processed data to the UN FAO Appwrite storage bucket + +## Architecture + +``` +┌─────────────────────────────────────────────────────────────────┐ +│ UNFAOPostProcessorManager │ +│ (extends PostprocessorManager + ForecastingModelManager) │ +├─────────────────────────────────────────────────────────────────┤ +│ │ +│ ┌──────────────────┐ ┌──────────────────┐ │ +│ │ Historical Data │ │ Forecast Data │ │ +│ │ (Viewser) │ │ (Appwrite) │ │ +│ └────────â”Ŧ─────────┘ └────────â”Ŧ─────────┘ │ +│ │ │ │ +│ └───────────â”Ŧ───────────┘ │ +│ â–ŧ │ +│ ┌───────────────────────┐ │ +│ │ PriogridCountryMapper │ │ +│ │ (Geographic Metadata) │ │ +│ └───────────â”Ŧ───────────┘ │ +│ â–ŧ │ +│ ┌───────────────────────┐ │ +│ │ Validation Layer │ │ +│ └───────────â”Ŧ───────────┘ │ +│ â–ŧ │ +│ ┌───────────────────────┐ │ +│ │ UN FAO Appwrite │ │ +│ │ (Output Storage) │ │ +│ └───────────────────────┘ │ +└─────────────────────────────────────────────────────────────────┘ +``` + +## Data Flow + +1. **Read Phase** (`_read`) + - Fetches historical data from ViewsER via `ViewsDataLoader` + - Downloads latest forecast predictions from Appwrite production forecasts bucket + +2. **Transform Phase** (`_transform`) + - Enriches both historical and forecast dataframes with geographic metadata + - Uses `PriogridCountryMapper` to map PRIO-GRID cells to administrative boundaries + +3. **Validate Phase** (`_validate`) + - Ensures all required metadata columns are present + - Checks for schema compliance before output + +4. **Save Phase** (`_save`) + - Saves processed data to local parquet files + - Uploads to UN FAO-specific Appwrite bucket with metadata + +## Installation + +Part of `views-postprocessing` package: + +```bash +pip install views-postprocessing +``` + +## Configuration + +### Required Environment Variables + +The manager requires several environment variables for Appwrite connectivity. These should be set in the `.env` file found at the root of views-models: + +```bash +# Appwrite Connection +APPWRITE_ENDPOINT=https://cloud.appwrite.io/v1 +APPWRITE_DATASTORE_PROJECT_ID=your_project_id +APPWRITE_DATASTORE_API_KEY=your_api_key + +# Production Forecasts Bucket (Input) +APPWRITE_PROD_FORECASTS_BUCKET_ID=production_forecasts +APPWRITE_PROD_FORECASTS_BUCKET_NAME=Production Forecasts +APPWRITE_PROD_FORECASTS_COLLECTION_ID=forecasts_metadata +APPWRITE_PROD_FORECASTS_COLLECTION_NAME=Forecasts Metadata + +# UN FAO Bucket (Output) +APPWRITE_UNFAO_BUCKET_ID=unfao_data +APPWRITE_UNFAO_BUCKET_NAME=UN FAO Data +APPWRITE_UNFAO_COLLECTION_ID=unfao_metadata +APPWRITE_UNFAO_COLLECTION_NAME=UN FAO Metadata + +# Metadata Database +APPWRITE_METADATA_DATABASE_ID=file_metadata +APPWRITE_METADATA_DATABASE_NAME=File Metadata +``` + +## Usage + +### Basic Usage + +```python +from views_pipeline_core.managers.postprocessor import PostprocessorPathManager +from views_postprocessing.unfao.managers import UNFAOPostProcessorManager + +# Initialize with path manager +path_manager = PostprocessorPathManager("un_fao") +manager = UNFAOPostProcessorManager( + model_path=path_manager, + wandb_notifications=True, + use_prediction_store=False +) + +# Execute the full pipeline +manager.execute() +``` + +### CLI Execution + +From the postprocessor directory: + +```bash +python main.py +``` + +### Step-by-Step Execution + +```python +# Initialize +manager = UNFAOPostProcessorManager(path_manager) + +# Read data +manager._read() # Loads historical + forecast data + +# Transform (add geographic metadata) +manager._transform() + +# Validate schema +manager._validate() + +# Save and upload +manager._save() +``` + +## Class Reference + +### UNFAOPostProcessorManager + +```python +class UNFAOPostProcessorManager(PostprocessorManager, ForecastingModelManager): + def __init__( + self, + model_path: PostprocessorPathManager, + wandb_notifications: bool = True, + use_prediction_store: bool = False, + ) -> None +``` + +#### Parameters + +| Parameter | Type | Default | Description | +|-----------|------|---------|-------------| +| `model_path` | PostprocessorPathManager | Required | Path manager for the postprocessor | +| `wandb_notifications` | bool | True | Enable Weights & Biases logging | +| `use_prediction_store` | bool | False | Whether to use prediction store (legacy) | + +#### Attributes + +| Attribute | Type | Description | +|-----------|------|-------------| +| `_historical_dataframe` | pd.DataFrame | Historical observation data from ViewsER | +| `_forecast_dataframe` | pd.DataFrame | Forecast predictions from ensemble | +| `_historical_dataset` | PGMDataset | Wrapped historical data with utilities | +| `_forecast_dataset` | PGMDataset | Wrapped forecast data with utilities | +| `_mapper` | PriogridCountryMapper | Geographic mapping utility | +| `ensemble_path_manager` | EnsemblePathManager | Path manager for source ensemble | + +## Methods + +### _read_historical_data() + +Fetches historical observation data from ViewsER. + +```python +manager._read_historical_data() +# Populates: _historical_dataframe, _historical_dataset +``` + +**Process:** +1. Uses `ViewsDataLoader` to fetch data for "forecasting" partition +2. Reads saved dataframe from `data_raw` directory +3. Creates `PGMDataset` wrapper with configured targets + +### _read_forecast_data() + +Downloads latest forecast predictions from Appwrite. + +```python +manager._read_forecast_data() +# Populates: _forecast_dataframe, _forecast_dataset +``` + +**Process:** +1. Reads ensemble name from config +2. Initializes `EnsemblePathManager` for the source ensemble +3. Configures Appwrite connection from ensemble's `.env` +4. Downloads latest forecast file with `category="forecast"` filter +5. Creates `PGMDataset` wrapper + +**Raises:** +- `ValueError`: If ensemble name not configured +- `Exception`: If download fails + +### _append_metadata(dataset) + +Enriches a dataset with geographic metadata. + +```python +enriched_df = manager._append_metadata(dataset) +``` + +**Parameters:** +- `dataset`: PGMDataset to enrich + +**Returns:** DataFrame with added metadata columns + +**Added Columns:** +| Column | Description | +|--------|-------------| +| `pg_xcoord` | PRIO-GRID cell X coordinate (longitude) | +| `pg_ycoord` | PRIO-GRID cell Y coordinate (latitude) | +| `country_iso_a3` | ISO 3166-1 alpha-3 country code | +| `admin1_gaul1_code` | GAUL level 1 admin code | +| `admin1_gaul1_name` | GAUL level 1 admin name | +| `admin1_gaul0_code` | Parent country GAUL code | +| `admin1_gaul0_name` | Parent country name | +| `admin2_gaul2_code` | GAUL level 2 admin code | +| `admin2_gaul2_name` | GAUL level 2 admin name | + +### _transform() + +Applies geographic metadata to both dataframes. + +```python +manager._transform() +# Updates: _historical_dataframe, _forecast_dataframe +``` + +### _validate() + +Validates that required metadata columns are present. + +```python +manager._validate() +``` + +**Required Columns:** +- `pg_xcoord`, `pg_ycoord` +- `country_iso_a3` +- `admin1_gaul1_code`, `admin1_gaul1_name` +- `admin1_gaul0_code`, `admin1_gaul0_name` +- `admin2_gaul2_code`, `admin2_gaul2_name` + +**Raises:** +- `ValueError`: If any required column is missing + +### _save() + +Saves and uploads processed data to UN FAO Appwrite bucket. + +```python +manager._save() +``` + +**Output Files:** +- `historical_dataset_{timestamp}.parquet` - Historical data with metadata +- `forecast_dataset_{timestamp}.parquet` - Forecast data with metadata + +**Upload Metadata:** +| Field | Historical | Forecast | +|-------|-----------|----------| +| `name` | Postprocessor name | Ensemble name | +| `loa` | "pgm" | "pgm" | +| `type` | "model" | "model" | +| `category` | "historical" | "forecast" | +| `targets` | From config | Prediction columns | + +## Output Schema Example + +The processed dataframes have the following structure: + +``` +Index: (month_id, priogrid_gid) + +Columns: +├── Target Variables +│ ├── ged_sb_dep (or configured targets) +│ ├── ged_ns_dep +│ └── ged_os_dep +│ +├── Predictions (forecast only) +│ ├── pred_ln_sb_best +│ ├── pred_ln_ns_best +│ ├── pred_ln_os_best +│ ├── pred_ln_sb_prob +│ ├── pred_ln_ns_prob +│ └── pred_ln_os_prob +│ +└── Geographic Metadata + ├── pg_xcoord + ├── pg_ycoord + ├── country_iso_a3 + ├── admin1_gaul1_code + ├── admin1_gaul1_name + ├── admin1_gaul0_code + ├── admin1_gaul0_name + ├── admin2_gaul2_code + └── admin2_gaul2_name +``` + +## Error Handling + +### Common Errors + +**Appwrite Connection Failed:** +``` +Error while trying to download the latest forecast data... +``` +*Solution:* Verify environment variables are set correctly in the ensemble's `.env` file + +**Missing Metadata Columns:** +``` +ValueError: Historical dataframe is missing required metadata column: country_iso_a3 +``` +*Solution:* Ensure the `PriogridCountryMapper` has access to required shapefiles + +## Dependencies + +- `views-pipeline-core`: Core pipeline infrastructure +- `views-postprocessing`: Postprocessor base classes and mapping utilities +- `pandas`: DataFrame operations +- `polars`: Alternative DataFrame operations +- `python-dotenv`: Environment variable management + +## Example Workflow + +```python +import logging +from views_pipeline_core.managers.postprocessor import PostprocessorPathManager +from views_postprocessing.unfao.managers import UNFAOPostProcessorManager + +# Setup logging +logging.basicConfig(level=logging.INFO) + +# Initialize +path_manager = PostprocessorPathManager("un_fao") +manager = UNFAOPostProcessorManager(path_manager) + +# Full execution +try: + # 1. Load data + manager._read() + print(f"Historical shape: {manager._historical_dataframe.shape}") + print(f"Forecast shape: {manager._forecast_dataframe.shape}") + + # 2. Add geographic metadata + manager._transform() + print(f"Added columns: {manager._historical_dataframe.columns.tolist()}") + + # 3. Validate output + manager._validate() + print("Validation passed!") + + # 4. Save and upload + manager._save() + print("Data uploaded to UN FAO bucket") + +except Exception as e: + print(f"Pipeline failed: {e}") +``` + +## See Also + +- [PriogridCountryMapper](../mapping/README.md) - Geographic mapping documentation \ No newline at end of file diff --git a/views_postprocessing/unfao/managers/__init__.py b/views_postprocessing/unfao/managers/__init__.py new file mode 100644 index 0000000..225b9d3 --- /dev/null +++ b/views_postprocessing/unfao/managers/__init__.py @@ -0,0 +1 @@ +from .unfao import UNFAOPostProcessorManager \ No newline at end of file diff --git a/views_postprocessing/unfao/managers/unfao.py b/views_postprocessing/unfao/managers/unfao.py new file mode 100644 index 0000000..c32d18c --- /dev/null +++ b/views_postprocessing/unfao/managers/unfao.py @@ -0,0 +1,276 @@ +from views_pipeline_core.managers.postprocessor.postprocessor import ( + PostprocessorManager, + PostprocessorPathManager, +) +from views_pipeline_core.files.utils import read_dataframe +from views_pipeline_core.configs.pipeline import PipelineConfig +import logging +from views_pipeline_core.data.handlers import PGMDataset + +from views_pipeline_core.modules.appwrite.file import AppwriteConfig +from views_pipeline_core.modules.datastore import DatastoreModule +from views_pipeline_core.managers.model import ForecastingModelManager + +from views_pipeline_core.managers.ensemble import EnsembleManager, EnsemblePathManager +import polars as pl +import pandas as pd +import io +from argparse import Namespace +from datetime import datetime +import os +from dotenv import load_dotenv +from views_postprocessing.unfao.mapping.mapping import get_default_mapper +from pathlib import Path + +logger = logging.getLogger(__name__) + + +class UNFAOPostProcessorManager(PostprocessorManager, ForecastingModelManager): + def __init__( + self, + model_path: PostprocessorPathManager, + wandb_notifications: bool = True, + use_prediction_store: bool = False, + ) -> None: + super().__init__(model_path, wandb_notifications, use_prediction_store) + + # Add your custom initialization below + logger.info(f"Initializing {self.__class__.__name__}") + self._historical_dataframe = None + self._forecast_dataframe = None + + self._historical_dataset = None + self._forecast_dataset = None + self._mapper = get_default_mapper() + self.ensemble_path_manager = None + + def _read_historical_data(self): + # Historical Data + path_raw = self._model_path.data_raw # Path to raw data + path_artifacts = self._model_path.artifacts # Path to save model artifacts + run_type = "forecasting" # e.g., "calibration", "validation", "forecasting" + + self._data_loader.get_data( + use_saved=False, + validate=False, + self_test=False, + partition=run_type + ) + current_month = datetime.now().strftime("%Y-%m") + artifact_name = f"{run_type}_viewser_df_{current_month}" + self._historical_dataframe = read_dataframe( + path_raw / f"{run_type}_viewser_df{PipelineConfig.dataframe_format}" + ) # Dataframe obtained from viewser + partitioner_dict = ( + self._data_loader.partition_dict + ) # Partition dict from ViewsDataLoader + self._historical_dataset = PGMDataset( + source=self._historical_dataframe, targets=self.configs.get("targets") + ) + + def _read_forecast_data(self): + # Forecast Data + ensemble_name = self.configs.get("ensemble", None) + if not ensemble_name: + raise ValueError("Ensemble name must be provided in configs with the `ensemble` key for forecasting. Cannot proceed.") + + self.ensemble_path_manager = EnsemblePathManager(ensemble_name_or_path=ensemble_name, validate=False) + # ensemble_configs = EnsembleManager( + # ensemble_path=self.ensemble_path_manager, + # ).configs + + # loa = ensemble_configs.get("level", None) + loa = "pgm" + if not loa: + raise ValueError("level must be defined in the ensemble configurations (e.g, pgm, cm). Cannot proceed.") + + # Force it to the correct .env just to be safe + load_dotenv(dotenv_path=str(self.ensemble_path_manager.dotenv)) + + # appwrite_config = AppwriteConfig( + # path_manager=self.ensemble_path_manager, + # endpoint=os.getenv("APPWRITE_ENDPOINT"), + # project_id=os.getenv("APPWRITE_DATASTORE_PROJECT_ID"), + # credentials=os.getenv("APPWRITE_DATASTORE_API_KEY"), + # auth_method="api_key", + # cache_ttl_hours=24, + # bucket_id=os.getenv("APPWRITE_UNFAO_BUCKET_ID"), + # bucket_name=os.getenv("APPWRITE_UNFAO_BUCKET_NAME"), + # collection_name=os.getenv("APPWRITE_UNFAO_COLLECTION_NAME"), + # collection_id=os.getenv("APPWRITE_UNFAO_COLLECTION_ID"), + # database_id=os.getenv("APPWRITE_DATABASE_ID"), + # database_name=os.getenv("APPWRITE_DATABASE_NAME"), + # ) + # appwrite_config = AppwriteConfig( + # path_manager=self._model_path, + # endpoint=os.getenv("APPWRITE_ENDPOINT"), + # project_id=os.getenv("APPWRITE_DATASTORE_PROJECT_ID"), + # credentials=os.getenv("APPWRITE_DATASTORE_API_KEY"), + # auth_method="api_key", + # cache_ttl_hours=24, + # bucket_id=os.getenv("APPWRITE_UNFAO_FORECASTS_BUCKET_ID"), + # bucket_name=os.getenv("APPWRITE_UNFAO_FORECASTS_BUCKET_NAME"), + # collection_id=os.getenv("APPWRITE_UNFAO_COLLECTION_ID"), + # collection_name=os.getenv("APPWRITE_UNFAO_COLLECTION_NAME"), + # database_id=os.getenv("APPWRITE_METADATA_DATABASE_ID"), + # database_name=os.getenv("APPWRITE_METADATA_DATABASE_NAME"), + # ) + + appwrite_config = AppwriteConfig( + path_manager=self.ensemble_path_manager, + endpoint=os.getenv("APPWRITE_ENDPOINT"), + project_id=os.getenv("APPWRITE_DATASTORE_PROJECT_ID"), + credentials=os.getenv("APPWRITE_DATASTORE_API_KEY"), + auth_method="api_key", + cache_ttl_hours=24, + bucket_id=os.getenv("APPWRITE_PROD_FORECASTS_BUCKET_ID"), + bucket_name=os.getenv("APPWRITE_PROD_FORECASTS_BUCKET_NAME"), + collection_id=os.getenv("APPWRITE_PROD_FORECASTS_COLLECTION_ID"), + collection_name=os.getenv("APPWRITE_PROD_FORECASTS_COLLECTION_NAME"), + database_id=os.getenv("APPWRITE_METADATA_DATABASE_ID"), + database_name=os.getenv("APPWRITE_METADATA_DATABASE_NAME"), + ) + + try: + prediction_store_manager = DatastoreModule(appwrite_file_manager_config=appwrite_config) + self._forecast_dataframe = pd.read_parquet(io.BytesIO(prediction_store_manager.download_latest_file(filters={"category": "forecast"}).to_dict().get("data", {}).get("file_bytes", None))) + + self._forecast_dataset = PGMDataset(self._forecast_dataframe) + except Exception as e: + logger.error( + f"Encountered an error while trying to download the latest forecast data for level {loa} from Datastore: {e}", + exc_info=True, + ) + raise + + def _read(self) -> any: + self._read_historical_data() + self._read_forecast_data() + + def _append_metadata(self, dataset: PGMDataset) -> pd.DataFrame: + filter_cols = [ + dataset._time_id, + dataset._entity_id, + "pg_xcoord", + "pg_ycoord", + "country_iso_a3", + "admin1_gaul1_code", + "admin1_gaul1_name", + "admin1_gaul0_code", + "admin1_gaul0_name", + "admin2_gaul2_code", + "admin2_gaul2_name", + ] + raw_result = self._mapper.enrich_dataframe_with_pg_info( + dataset.dataframe.reset_index(), + pg_id_col=dataset._entity_id, + time_id_col=dataset._time_id, + only_metadata=True, + batch_size=1000 + ) + + raw_result = raw_result[filter_cols].set_index([dataset._time_id, dataset._entity_id]) + return dataset.dataframe.join(raw_result) + + + # def _append_lat_lon(self): + # self._historical_dataframe = self._historical_dataframe.join(self._historical_dataset.get_lat_lon()) + # self._forecast_dataframe = self._forecast_dataframe.join(self._forecast_dataset.get_lat_lon()) + + # def _append_isoa3(self): + # self._historical_dataframe = self._historical_dataframe.join(self._historical_dataset.get_isoab()) + # self._forecast_dataframe = self._forecast_dataframe.join(self._forecast_dataset.get_isoab()) + + # def _append_name(self): + # self._historical_dataframe = self._historical_dataframe.join(self._historical_dataset.get_name()) + # self._forecast_dataframe = self._forecast_dataframe.join(self._forecast_dataset.get_name()) + + def _transform( + self, + ) -> list: + # self._append_m49() + # self._append_lat_lon() + # self._append_isoa3() + # self._append_name() + self._historical_dataframe = self._append_metadata(self._historical_dataset) + self._forecast_dataframe = self._append_metadata(self._forecast_dataset) + + def _validate(self) -> pd.DataFrame: + _necessary_metadata_cols = ["pg_xcoord", + "pg_ycoord", + "country_iso_a3", + "admin1_gaul1_code", + "admin1_gaul1_name", + "admin1_gaul0_code", + "admin1_gaul0_name", + "admin2_gaul2_code", + "admin2_gaul2_name"] + + for col in _necessary_metadata_cols: + if col in self._historical_dataframe.columns: + # if self._historical_dataframe[col].isnull().any(): + # raise ValueError(f"Historical dataframe is missing values in required metadata column: {col}. Found {self._historical_dataframe[col].isnull().sum()} null values.") + continue + else: + raise ValueError(f"Historical dataframe is missing required metadata column: {col}. Found columns: {self._historical_dataframe.columns.tolist()}") + logger.info("Historical dataframe metadata validation passed.") + + for col in _necessary_metadata_cols: + if col in self._forecast_dataframe.columns: + # if self._forecast_dataframe[col].isnull().any(): + # raise ValueError(f"Forecast dataframe is missing values in required metadata column: {col}. Found {self._forecast_dataframe[col].isnull().sum()} null values.") + continue + else: + raise ValueError(f"Forecast dataframe is missing required metadata column: {col}. Found columns: {self._forecast_dataframe.columns.tolist()}") + logger.info("Forecast dataframe metadata validation passed.") + + def _save(self) -> list: + if self._historical_dataset is None or self._forecast_dataset is None: + raise ValueError("Datasets could not be initialized properly.") + + # self._historical_dataset.dataframe.to_parquet( + # self._model_path.data_generated / "historical_dataset.parquet" + # ) + # self._forecast_dataset.dataframe.to_parquet( + # self._model_path.data_generated / "forecast_dataset.parquet" + # ) + + unfao_appwrite_config = AppwriteConfig( + path_manager=self._model_path, + endpoint=os.getenv("APPWRITE_ENDPOINT"), + project_id=os.getenv("APPWRITE_DATASTORE_PROJECT_ID"), + credentials=os.getenv("APPWRITE_DATASTORE_API_KEY"), + auth_method="api_key", + cache_ttl_hours=24, + bucket_id=os.getenv("APPWRITE_UNFAO_BUCKET_ID"), + bucket_name=os.getenv("APPWRITE_UNFAO_BUCKET_NAME"), + collection_id=os.getenv("APPWRITE_UNFAO_COLLECTION_ID"), + collection_name=os.getenv("APPWRITE_UNFAO_COLLECTION_NAME"), + database_id=os.getenv("APPWRITE_METADATA_DATABASE_ID"), + database_name=os.getenv("APPWRITE_METADATA_DATABASE_NAME"), + ) + dsm = DatastoreModule(appwrite_file_manager_config=unfao_appwrite_config) + + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + historical_file_path = self._model_path.data_generated / f"historical_dataset_{timestamp}.parquet" + forecast_file_path = self._model_path.data_generated / f"forecast_dataset_{timestamp}.parquet" + + self._historical_dataframe.to_parquet( + historical_file_path + ) + dsm.upload_data(file=historical_file_path, + filename=Path(historical_file_path).name, + name=self._model_path.model_name, + loa="pgm", + type="model", targets=self.configs.get("targets", []), + description="This is a test DataFrame.", category="historical") + + self._forecast_dataframe.to_parquet( + forecast_file_path + ) + dsm.upload_data(file=forecast_file_path, + filename=Path(forecast_file_path).name, + name=self.ensemble_path_manager.model_name, + loa="pgm", + type="model", targets=["pred_ln_sb_best", "pred_ln_ns_best", "pred_ln_os_best", "pred_ln_sb_prob", "pred_ln_ns_prob", "pred_ln_os_prob"], + description="This is a test DataFrame.", category="forecast") \ No newline at end of file diff --git a/views_postprocessing/unfao/mapping/README.md b/views_postprocessing/unfao/mapping/README.md new file mode 100644 index 0000000..a754de8 --- /dev/null +++ b/views_postprocessing/unfao/mapping/README.md @@ -0,0 +1,579 @@ +# PRIO-GRID Spatial Mapping Module + +## Overview + +The `mapping.py` module provides a comprehensive spatial mapping solution for the VIEWS platform. It enables bidirectional mapping between **PRIO-GRID cells** (a standardized global grid system with ~50×50 km cells) and administrative boundaries at multiple levels: + +- **Country level** (using Natural Earth data) +- **Admin Level 1** (GAUL 2024 - provinces/states) +- **Admin Level 2** (GAUL 2024 - districts/counties) + +This module is critical for the UN FAO postprocessing pipeline, where conflict predictions made at the PRIO-GRID level need to be aggregated and reported at various administrative boundary levels. + +--- + +## Table of Contents + +1. [Key Concepts](#key-concepts) +2. [Data Sources](#data-sources) +3. [Classes](#classes) +4. [Mapping Decision Logic](#mapping-decision-logic) +5. [Caching Architecture](#caching-architecture) +6. [Key Methods](#key-methods) +7. [Usage Examples](#usage-examples) +8. [Performance Considerations](#performance-considerations) +9. [Configuration](#configuration) + +--- + +## Key Concepts + +### PRIO-GRID +The **Peace Research Institute Oslo GRID** (PRIO-GRID) is a standardized global grid system that divides the world into cells of approximately 0.5° × 0.5° (roughly 50×50 km at the equator). Each cell is identified by a unique **GID** (Grid ID). This grid provides a consistent spatial unit for conflict research and prediction. + +### Administrative Boundaries +- **Country (GAUL Level 0)**: National boundaries using ISO A3 codes (e.g., 'USA', 'TZA', 'FRA') +- **Admin Level 1 (GAUL Level 1)**: First-level subdivisions (states, provinces, regions) +- **Admin Level 2 (GAUL Level 2)**: Second-level subdivisions (counties, districts) + +### The Mapping Challenge +PRIO-GRID cells do not align perfectly with administrative boundaries. A single grid cell may: +- Fall entirely within one country/admin region +- Span multiple countries (e.g., border regions) +- Partially overlap water bodies +- Have its centroid in a different region than its majority area + +This module implements sophisticated decision rules to handle these edge cases consistently. + +--- + +## Data Sources + +The module relies on the following shapefiles (paths configured relative to the module location): + +| Data Source | Path | Description | +|------------|------|-------------| +| Natural Earth | `shapefiles/ne_110m_admin_0_countries/` | Country boundaries (110m resolution) | +| PRIO-GRID | `shapefiles/priogrid_cellshp/` | Global grid cell definitions | +| GAUL Admin 1 | `shapefiles/GAUL_2024_L1/` | First-level administrative boundaries | +| GAUL Admin 2 | `shapefiles/GAUL_2024_L2/` | Second-level administrative boundaries | + +All shapefiles use **EPSG:4326** (WGS84) coordinate reference system. + +--- + +## Classes + +### `DistanceCache` + +A simple LRU (Least Recently Used) cache for distance calculations. + +```python +class DistanceCache: + def __init__(self, maxsize=1000) + def get(self, key) -> Optional[float] + def set(self, key, value) -> None +``` + +**Purpose**: Caches haversine distance calculations to avoid redundant computations when calculating distances between grid cells and capital cities. + +--- + +### `PriogridCountryMapper` + +The main class that handles all spatial mapping operations. + +#### Initialization + +```python +mapper = PriogridCountryMapper( + use_disk_cache=True, # Enable persistent disk caching + cache_dir=None, # Custom cache directory (default: ~/.priogrid_mapper_cache) + cache_ttl=None # Cache time-to-live in seconds (None = no expiration) +) +``` + +#### Attributes + +| Attribute | Type | Description | +|-----------|------|-------------| +| `countries_gdf` | GeoDataFrame | Natural Earth country boundaries | +| `priogrid_gdf` | GeoDataFrame | PRIO-GRID cell geometries with centroids | +| `admin1_gdf` | GeoDataFrame | GAUL Level 1 boundaries | +| `admin2_gdf` | GeoDataFrame | GAUL Level 2 boundaries | +| `priogrid_sindex` | SpatialIndex | R-tree index for fast PRIO-GRID queries | +| `admin1_sindex` | SpatialIndex | R-tree index for fast Admin1 queries | +| `admin2_sindex` | SpatialIndex | R-tree index for fast Admin2 queries | + +--- + +## Mapping Decision Logic + +### Country Assignment for a PRIO-GRID Cell + +The `find_country_for_gid()` method uses a simple, deterministic **largest overlap** approach: + +``` +┌─────────────────────────────────────────────────────────────┐ +│ INPUT: PRIO-GRID GID │ +└─────────────────────────────────────────────────────────────┘ + │ + â–ŧ +┌─────────────────────────────────────────────────────────────┐ +│ Step 1: Find all countries that intersect the grid cell │ +│ using spatial index for performance │ +└─────────────────────────────────────────────────────────────┘ + │ + â–ŧ +┌─────────────────────────────────────────────────────────────┐ +│ Step 2: Calculate overlap ratio for each country │ +│ overlap_ratio = intersection_area / cell_area │ +└─────────────────────────────────────────────────────────────┘ + │ + â–ŧ +┌─────────────────────────────────────────────────────────────┐ +│ Step 3: Sort by overlap ratio (descending) │ +└─────────────────────────────────────────────────────────────┘ + │ + â–ŧ +┌─────────────────────────────────────────────────────────────┐ +│ Step 4: Assign to country with LARGEST OVERLAP │ +│ Method tag: "largest overlap" │ +└─────────────────────────────────────────────────────────────┘ +``` + +#### Why Largest Overlap? + +The decision to use **only the largest overlap rule** (rather than a multi-rule hierarchy) provides several benefits: + +1. **Simplicity** - One consistent rule is easier to understand, explain, and maintain +2. **Determinism** - The same input always produces the same output, regardless of centroid position +3. **Intuitive for aggregation** - When aggregating predictions, assigning to the region with the most area coverage makes sense (more of the cell's "land" belongs there) +4. **Handles all edge cases** - Works correctly for: + - Cells entirely within one country (100% overlap) + - Border cells spanning multiple countries + - Coastal cells with partial water coverage (assigns to the country with most land area) + +### Admin Level Assignment + +The `find_admin1_for_gid()` and `find_admin2_for_gid()` methods use the same largest overlap approach: + +1. **First determine the country** - Uses `find_country_for_gid()` to ensure admin regions are filtered to the correct country +2. **Calculate overlaps** - For all admin regions in that country that intersect the grid cell +3. **Assign to largest overlap** - The admin region with the highest overlap ratio wins + +This approach ensures that: +- Admin regions are always consistent with the assigned country +- The assignment method is consistent across all administrative levels +- Results are deterministic and reproducible + +--- + +## Caching Architecture + +The module implements a sophisticated multi-layer caching system to optimize performance: + +### Cache Types + +``` +┌─────────────────────────────────────────────────────────────┐ +│ CACHING ARCHITECTURE │ +└─────────────────────────────────────────────────────────────┘ + +┌─────────────────────┐ ┌─────────────────────┐ +│ DISK CACHE │ │ MEMORY CACHE │ +│ (Persistent) │ │ (Session-based) │ +├─────────────────────┤ ├─────────────────────┤ +│ â€ĸ Survives restarts │ │ â€ĸ Faster access │ +│ â€ĸ Shared across │ │ â€ĸ Limited size │ +│ processes │ │ â€ĸ LRU or TTL-based │ +│ â€ĸ Uses joblib │ │ â€ĸ Uses cachetools │ +│ Memory │ │ │ +└─────────────────────┘ └─────────────────────┘ + +Default location: ~/.priogrid_mapper_cache/ +├── country/ # GID → Country mappings +├── admin1/ # GID → Admin1 mappings +├── admin2/ # GID → Admin2 mappings +└── gid/ # Point → GID mappings +``` + +### Cache Configuration + +| Cache Type | Max Size | Purpose | +|------------|----------|---------| +| Country Cache | 100,000 | GID → Country mappings | +| GID Cache | 100,000 | Point → GID lookups | +| GIDs for Country | 10,000,000 | Country → [GIDs] reverse mapping | +| Admin1 Cache | 100,000 | GID → Admin1 mappings | +| Admin2 Cache | 100,000 | GID → Admin2 mappings | + +### Cache Methods + +```python +# Clear all or specific caches +mapper.clear_cache(cache_type="all") # "all", "country", "admin1", "admin2", "gid" + +# Get cache statistics +stats = mapper.get_cache_stats() + +# Pre-warm cache for frequently accessed data +mapper.warm_cache(gid_list=[123, 456, 789], iso_a3_list=['TZA', 'KEN']) +``` + +--- + +## Key Methods + +### Core Mapping Methods + +#### `find_country_for_gid(gid: int) -> dict` + +Find country information for a PRIO-GRID cell. + +**Returns:** +```python +{ + "gid": 123456, + "iso_a3": "TZA", + "country_name": "Tanzania", + "overlap_ratio": 0.85, + "method": "largest overlap", + # ... additional Natural Earth fields +} +``` + +#### `find_admin1_for_gid(gid: int) -> dict` + +Find Admin Level 1 region for a PRIO-GRID cell. + +**Returns:** +```python +{ + "gid": 123456, + "gaul1_code": 45678, + "gaul1_name": "Dar es Salaam", + "iso3_code": "TZA", + "method": "largest overlap", + "gaul0_code": 123, + "gaul0_name": "Tanzania", + # ... additional GAUL fields +} +``` + +#### `find_admin2_for_gid(gid: int) -> dict` + +Find Admin Level 2 region for a PRIO-GRID cell. + +**Returns:** +```python +{ + "gid": 123456, + "gaul2_code": 78901, + "gaul2_name": "Kinondoni", + "iso3_code": "TZA", + "method": "largest overlap", + "gaul1_code": 45678, + "gaul1_name": "Dar es Salaam", + # ... additional GAUL fields +} +``` + +#### `find_gids_for_country(iso_a3: str) -> List[int]` + +Find all PRIO-GRID cells belonging to a country (reverse mapping). + +```python +gids = mapper.find_gids_for_country("TZA") +# Returns: [123456, 123457, 123458, ...] +``` + +### Batch Processing Methods + +#### `batch_country_mapping(gid_list: List[int]) -> pd.DataFrame` + +Process multiple GIDs with cache optimization. + +```python +df = mapper.batch_country_mapping([123, 456, 789, ...]) +``` + +#### `batch_country_mapping_parallel(gid_list, batch_size=1000) -> pd.DataFrame` + +Process large datasets using multiprocessing. + +```python +df = mapper.batch_country_mapping_parallel(gid_list, batch_size=1000) +``` + +#### `batch_admin_mapping(gid_list, admin_level="admin1") -> pd.DataFrame` + +Batch mapping for admin regions. + +```python +df = mapper.batch_admin_mapping(gid_list, admin_level="admin2") +``` + +### DataFrame Enrichment Methods + +#### `enrich_dataframe_with_pg_info(df, ...)` + +Enrich a DataFrame containing PRIO-GRID IDs with geographic metadata. + +```python +enriched_df = mapper.enrich_dataframe_with_pg_info( + df, + pg_id_col="priogrid_id", + time_id_col="month_id", + include_country=True, + include_admin1=True, + include_admin2=True, + include_pg_info=True, + batch_size=1000, + use_multiprocessing=True, + show_progress=True +) +``` + +**Result columns added:** +- `country_iso_a3`, `country_name`, `country_overlap_ratio`, `country_method` +- `admin1_gaul1_code`, `admin1_gaul1_name`, `admin1_method` +- `admin2_gaul2_code`, `admin2_gaul2_name`, `admin2_method` +- `pg_xcoord`, `pg_ycoord`, `pg_geometry`, etc. + +### Visualization Methods + +#### `visualize_grid_and_country(gid: int)` + +Visualize a PRIO-GRID cell and its assigned country. + +```python +mapper.visualize_grid_and_country(123456) +``` + +#### `visualize_grid_and_admin(gid, admin_level="admin1", show_all_admins=False)` + +Visualize a PRIO-GRID cell with admin boundaries. + +```python +mapper.visualize_grid_and_admin(123456, admin_level="admin2", show_all_admins=True) +``` + +### Utility Methods + +#### `find_gid_for_point(point_geometry) -> int` + +Find which PRIO-GRID cell contains a point. + +```python +from shapely.geometry import Point +gid = mapper.find_gid_for_point(Point(36.8, -1.3)) # Nairobi +``` + +#### `find_country_by_iso_a3(iso_a3: str) -> dict` + +Get detailed country information by ISO code. + +```python +info = mapper.find_country_by_iso_a3("TZA") +``` + +#### `search_countries_by_name(name_pattern, exact_match=False) -> List[dict]` + +Search for countries by name pattern. + +```python +results = mapper.search_countries_by_name("tanzania") +results = mapper.search_countries_by_name("united", exact_match=False) +``` + +--- + +## Usage Examples + +### Basic Usage + +```python +from views_postprocessing.unfao.mapping.mapping import PriogridCountryMapper + +# Initialize mapper (loads shapefiles, builds spatial indices) +mapper = PriogridCountryMapper(use_disk_cache=True) + +# Map a single PRIO-GRID cell to country +result = mapper.find_country_for_gid(148345) +print(f"GID 148345 belongs to {result['country_name']} ({result['iso_a3']})") +print(f"Assignment method: {result['method']}") + +# Get all admin levels at once +all_info = mapper.find_all_admin_for_gid(148345) +``` + +### Batch Processing for Large Datasets + +```python +import pandas as pd + +# Your prediction data with PRIO-GRID IDs +predictions_df = pd.DataFrame({ + 'priogrid_id': [148345, 148346, 148347, ...], + 'month_id': [553, 553, 553, ...], + 'predicted_fatalities': [0.5, 1.2, 0.3, ...] +}) + +# Enrich with geographic metadata +enriched_df = mapper.enrich_dataframe_with_pg_info( + predictions_df, + pg_id_col='priogrid_id', + include_country=True, + include_admin1=True, + include_admin2=True, + use_multiprocessing=True, + show_progress=True +) + +# Now you can aggregate by country, admin1, or admin2 +country_totals = enriched_df.groupby('country_iso_a3')['predicted_fatalities'].sum() +``` + +### Using the Default Global Mapper + +```python +from views_postprocessing.unfao.mapping.mapping import get_default_mapper + +# A global mapper is automatically initialized when the module is imported +mapper = get_default_mapper() + +# Use it directly +result = mapper.find_country_for_gid(148345) +``` + +### Cache Management + +```python +# Check cache statistics +stats = mapper.get_cache_stats() +print(f"Cache type: {stats['cache_type']}") +print(f"Country cache size: {stats['country_cache_size']}") + +# Clear cache before fresh analysis +mapper.clear_cache(cache_type="all") + +# Pre-warm cache for a specific region +african_countries = ['TZA', 'KEN', 'UGA', 'RWA', 'BDI'] +mapper.warm_cache(iso_a3_list=african_countries) +``` + +--- + +## Performance Considerations + +### Spatial Indexing + +The module uses **R-tree spatial indices** for all geographic datasets: +- Reduces point-in-polygon queries from O(n) to O(log n) +- Critical for batch processing thousands of grid cells + +### Recommended Batch Sizes + +| Operation | Recommended Batch Size | Notes | +|-----------|----------------------|-------| +| Country mapping | 1,000 | Good balance of memory and parallelization | +| Admin mapping | 500-1,000 | Admin2 is more complex | +| DataFrame enrichment | 1,000 | Depends on available memory | + +### Memory Management + +- Enable disk caching (`use_disk_cache=True`) for large datasets +- Use `only_metadata=True` in `enrich_dataframe_with_pg_info()` to minimize memory +- Clear caches periodically for long-running processes + +### Multiprocessing vs Threading + +The module uses **ThreadPoolExecutor** instead of multiprocessing to avoid: +- Pickling issues with GeoDataFrames +- Memory duplication across processes +- Complex IPC overhead + +For CPU-bound tasks, consider running multiple single-threaded instances. + +--- + +## Configuration + +### Global Constants + +```python +# Cache directory (default: ~/.priogrid_mapper_cache) +CACHE_DIR = Path.home() / ".priogrid_mapper_cache" + +# Cache sizes +COUNTRY_CACHE_MAXSIZE = 100_000 +GID_CACHE_MAXSIZE = 100_000 +GIDS_FOR_COUNTRY_CACHE_MAXSIZE = 10_000_000 +ADMIN1_CACHE_MAXSIZE = 100_000 +ADMIN2_CACHE_MAXSIZE = 100_000 +``` + +### Shapefile Paths + +Paths are configured relative to the module location: + +```python +NATURAL_EARTH_COUNTRY_PATH = Path(__file__).parent.parent.parent / "shapefiles" / "ne_110m_admin_0_countries" / "ne_110m_admin_0_countries.shp" +PRIOGRID_SHAPEFILE_PATH = Path(__file__).parent.parent.parent / "shapefiles" / "priogrid_cellshp" / "priogrid_cell.shp" +ADM_1_SHAPEFILE_PATH = Path(__file__).parent.parent.parent / "shapefiles" / "GAUL_2024_L1" / "GAUL_2024_L1.shp" +ADM_2_SHAPEFILE_PATH = Path(__file__).parent.parent.parent / "shapefiles" / "GAUL_2024_L2" / "GAUL_2024_L2.shp" +``` + +--- + +## Helper Functions + +### `cached_haversine(lat1, lon1, lat2, lon2) -> float` + +Calculate great-circle distance between two points with caching. + +```python +distance_km = cached_haversine( + lat1=-6.8, lon1=39.3, # Dar es Salaam + lat2=-1.3, lon2=36.8 # Nairobi +) +``` + +### `set_default_mapper() -> PriogridCountryMapper` + +Initialize the global default mapper. + +### `get_default_mapper() -> PriogridCountryMapper` + +Retrieve the global default mapper instance. + +--- + +## Error Handling + +The module handles various edge cases: + +| Scenario | Behavior | +|----------|----------| +| Invalid GID | Returns `None` | +| GID in water (no country) | Returns `None` or uses water handling rule | +| Invalid ISO A3 code | Returns `None` with warning | +| Missing shapefile | Raises `FileNotFoundError` during initialization | +| Invalid geometry | Logged as warning, skipped in calculations | +| Cache miss | Computes and caches result | + +--- + +## Dependencies + +``` +pandas +geopandas +shapely +numpy +joblib +cachetools +matplotlib (optional, for visualization) +tqdm (optional, for progress bars) +``` diff --git a/views_postprocessing/unfao/mapping/__init__.py b/views_postprocessing/unfao/mapping/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/views_postprocessing/unfao/mapping/mapping.py b/views_postprocessing/unfao/mapping/mapping.py new file mode 100644 index 0000000..c201929 --- /dev/null +++ b/views_postprocessing/unfao/mapping/mapping.py @@ -0,0 +1,2771 @@ +import pandas as pd +import geopandas as gpd +from datetime import datetime +from shapely.geometry import shape +from shapely.geometry import Point as ShapelyPoint +from shapely import wkt +import warnings +from abc import ABC, abstractmethod +from typing import Optional, Union, List, Dict, Any + +from functools import lru_cache, partial +from collections import OrderedDict +import math +import numpy as np +import logging +from pathlib import Path +import os + +from multiprocessing import Pool, Manager, cpu_count +from joblib import Memory +from cachetools import LRUCache, TTLCache + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +warnings.filterwarnings('ignore') + +NATURAL_EARTH_COUNTRY_PATH = Path(__file__).parent.parent.parent / "shapefiles" / "ne_110m_admin_0_countries" / "ne_110m_admin_0_countries.shp" + +PRIOGRID_SHAPEFILE_PATH = Path(__file__).parent.parent.parent / "shapefiles" / "priogrid_cellshp" / "priogrid_cell.shp" +ADM_1_SHAPEFILE_PATH = Path(__file__).parent.parent.parent / "shapefiles" / "GAUL_2024_L1" / "GAUL_2024_L1.shp" +ADM_2_SHAPEFILE_PATH = Path(__file__).parent.parent.parent / "shapefiles" / "GAUL_2024_L2" / "GAUL_2024_L2.shp" + +# Configure disk cache +CACHE_DIR = Path.home() / ".priogrid_mapper_cache" +os.makedirs(CACHE_DIR, exist_ok=True) + +# Cache sizes +COUNTRY_CACHE_MAXSIZE = 100000 +GID_CACHE_MAXSIZE = 100000 +GIDS_FOR_COUNTRY_CACHE_MAXSIZE = 10000000 +ADMIN1_CACHE_MAXSIZE = 100000 +ADMIN2_CACHE_MAXSIZE = 100000 + +# Global disk caches (shared across instances) +_disk_country_cache = Memory(location=CACHE_DIR / "country", verbose=0) +_disk_admin1_cache = Memory(location=CACHE_DIR / "admin1", verbose=0) +_disk_admin2_cache = Memory(location=CACHE_DIR / "admin2", verbose=0) +_disk_gid_cache = Memory(location=CACHE_DIR / "gid", verbose=0) + + +class DistanceCache: + """Simple LRU cache for distance calculations.""" + + def __init__(self, maxsize=1000): + self.cache = OrderedDict() + self.maxsize = maxsize + + def get(self, key): + if key in self.cache: + self.cache.move_to_end(key) + return self.cache[key] + return None + + def set(self, key, value): + self.cache[key] = value + self.cache.move_to_end(key) + if len(self.cache) > self.maxsize: + self.cache.popitem(last=False) + + +# Global distance cache +_distance_cache = DistanceCache() + + +def cached_haversine(lat1, lon1, lat2, lon2): + """Calculate haversine distance with caching.""" + # Validate coordinates + if ( + not (-90 <= lat1 <= 90) + or not (-180 <= lon1 <= 180) + or not (-90 <= lat2 <= 90) + or not (-180 <= lon2 <= 180) + ): + raise ValueError( + "Invalid coordinates: latitudes must be between -90 and 90, longitudes between -180 and 180" + ) + + # Create a cache key based on the coordinates (rounded to 6 decimal places) + cache_key = (round(lat1, 6), round(lon1, 6), round(lat2, 6), round(lon2, 6)) + + # Check cache first + cached_result = _distance_cache.get(cache_key) + if cached_result is not None: + return cached_result + + # Not in cache, perform the calculation + lat1, lon1, lat2, lon2 = map(math.radians, [lat1, lon1, lat2, lon2]) + dlon = lon2 - lon1 + dlat = lat2 - lat1 + a = ( + math.sin(dlat / 2) ** 2 + + math.cos(lat1) * math.cos(lat2) * math.sin(dlon / 2) ** 2 + ) + c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a)) + result = 6371.0 * c # Earth radius in kilometers + + # Update cache + _distance_cache.set(cache_key, result) + return result + + +class PriogridCountryMapper: + """ + A comprehensive class for mapping between PRIO-GRID cells and countries using Natural Earth shapefiles. + Now with persistent disk caching for improved performance across sessions. + + Attributes: + countries_gdf (GeoDataFrame): Processed Natural Earth country data + priogrid_gdf (GeoDataFrame): Processed PRIO-GRID data with cell geometries and centroids + priogrid_sindex (SpatialIndex): Spatial index for faster PRIO-GRID queries + """ + + def __init__(self, use_disk_cache=True, cache_dir=None, cache_ttl=None): + """ + Initialize the PriogridCountryMapper with optional disk caching. + + Parameters: + use_disk_cache (bool): Whether to use persistent disk caching + cache_dir (str or Path): Custom cache directory path + cache_ttl (int): Time-to-live for cache entries in seconds (None for no expiration) + """ + # Initialize attributes used in __del__ immediately to prevent AttributeError + # if an exception occurs during initialization (e.g., missing shapefiles). + self._process_pool = None + self._max_workers = cpu_count() + + # Load Natural Earth country data + # NOTE: This is the line that was crashing in your traceback (line 134) + country_path = str(NATURAL_EARTH_COUNTRY_PATH) + self.countries_gdf = self._load_and_preprocess_naturalearth(country_path) + + # Load PRIO-GRID data + self.priogrid_gdf = self._load_priogrid(str(PRIOGRID_SHAPEFILE_PATH)) + + self.priogrid_sindex = ( + self.priogrid_gdf.sindex if hasattr(self.priogrid_gdf, "sindex") else None + ) + + # Configure caching + self.use_disk_cache = use_disk_cache + self.cache_ttl = cache_ttl + + if use_disk_cache: + # Set up custom cache directory if provided + if cache_dir: + self.cache_dir = Path(cache_dir) + os.makedirs(self.cache_dir, exist_ok=True) + else: + self.cache_dir = CACHE_DIR + + # Initialize disk caches + self._disk_country_cache = Memory(location=self.cache_dir / "country", verbose=0) + self._disk_admin1_cache = Memory(location=self.cache_dir / "admin1", verbose=0) + self._disk_admin2_cache = Memory(location=self.cache_dir / "admin2", verbose=0) + self._disk_gid_cache = Memory(location=self.cache_dir / "gid", verbose=0) + + logger.info(f"Using disk cache at: {self.cache_dir}") + else: + # Initialize in-memory caches as instance variables + if cache_ttl: + self._country_cache = TTLCache(maxsize=COUNTRY_CACHE_MAXSIZE, ttl=cache_ttl) + self._gid_cache = TTLCache(maxsize=GID_CACHE_MAXSIZE, ttl=cache_ttl) + self._gids_for_country_cache = TTLCache(maxsize=GIDS_FOR_COUNTRY_CACHE_MAXSIZE, ttl=cache_ttl) + self._admin1_cache = TTLCache(maxsize=ADMIN1_CACHE_MAXSIZE, ttl=cache_ttl) + self._admin2_cache = TTLCache(maxsize=ADMIN2_CACHE_MAXSIZE, ttl=cache_ttl) + else: + self._country_cache = LRUCache(maxsize=COUNTRY_CACHE_MAXSIZE) + self._gid_cache = LRUCache(maxsize=GID_CACHE_MAXSIZE) + self._gids_for_country_cache = LRUCache(maxsize=GIDS_FOR_COUNTRY_CACHE_MAXSIZE) + self._admin1_cache = LRUCache(maxsize=ADMIN1_CACHE_MAXSIZE) + self._admin2_cache = LRUCache(maxsize=ADMIN2_CACHE_MAXSIZE) + + logger.info("Using in-memory caching") + + # Load admin1 and admin2 data if paths are provided + self.admin1_path = str(ADM_1_SHAPEFILE_PATH) + self.admin2_path = str(ADM_2_SHAPEFILE_PATH) + + self.admin1_gdf = None + self.admin2_gdf = None + + if self.admin1_path: + self.admin1_gdf = self._load_admin_data(self.admin1_path, "admin1") + self.admin1_sindex = ( + self.admin1_gdf.sindex if hasattr(self.admin1_gdf, "sindex") else None + ) + else: + self.admin1_sindex = None + + if self.admin2_path: + self.admin2_gdf = self._load_admin_data(self.admin2_path, "admin2") + self.admin2_sindex = ( + self.admin2_gdf.sindex if hasattr(self.admin2_gdf, "sindex") else None + ) + else: + self.admin2_sindex = None + + def _get_gid_cache_key(self, gid): + """Generate a consistent cache key for GID-based lookups""" + return f"gid_{gid}" + + def _get_point_cache_key(self, point_geometry): + """Generate a consistent cache key for point-based lookups""" + return f"point_{round(point_geometry.x, 6)}_{round(point_geometry.y, 6)}" + + def _get_country_cache_key(self, iso_a3): + """Generate a consistent cache key for country-based lookups""" + return f"country_{iso_a3.upper() if iso_a3 else iso_a3}" + + def _get_admin_cache_key(self, gid, admin_level): + """Generate a consistent cache key for admin-based lookups""" + return f"{admin_level}_{gid}" + + def clear_cache(self, cache_type="all"): + """ + Clear cached data. + + Parameters: + cache_type (str): Type of cache to clear ("all", "country", "admin1", "admin2", "gid") + """ + if self.use_disk_cache: + if cache_type == "all" or cache_type == "country": + self._disk_country_cache.clear() + logger.info("Cleared country cache") + + if cache_type == "all" or cache_type == "admin1": + self._disk_admin1_cache.clear() + logger.info("Cleared admin1 cache") + + if cache_type == "all" or cache_type == "admin2": + self._disk_admin2_cache.clear() + logger.info("Cleared admin2 cache") + + if cache_type == "all" or cache_type == "gid": + self._disk_gid_cache.clear() + logger.info("Cleared GID cache") + else: + if cache_type == "all" or cache_type == "country": + self._country_cache.clear() + logger.info("Cleared country cache") + + if cache_type == "all" or cache_type == "admin1": + self._admin1_cache.clear() + logger.info("Cleared admin1 cache") + + if cache_type == "all" or cache_type == "admin2": + self._admin2_cache.clear() + logger.info("Cleared admin2 cache") + + if cache_type == "all" or cache_type == "gid": + self._gid_cache.clear() + logger.info("Cleared GID cache") + + def get_cache_stats(self): + """ + Get statistics about cache usage. + + Returns: + dict: Cache statistics + """ + if self.use_disk_cache: + stats = { + "cache_type": "disk", + "cache_dir": str(self.cache_dir), + "country_cache_size": len(self._disk_country_cache), + "admin1_cache_size": len(self._disk_admin1_cache), + "admin2_cache_size": len(self._disk_admin2_cache), + "gid_cache_size": len(self._disk_gid_cache), + } + + # Calculate total cache size on disk + total_size = 0 + for cache_dir in [self.cache_dir / d for d in ["country", "admin1", "admin2", "gid"]]: + if cache_dir.exists(): + for file in cache_dir.glob("*"): + if file.is_file(): + total_size += file.stat().st_size + + stats["total_disk_size_mb"] = round(total_size / (1024 * 1024), 2) + else: + stats = { + "cache_type": "memory", + "country_cache_size": len(self._country_cache), + "country_cache_maxsize": self._country_cache.maxsize, + "gid_cache_size": len(self._gid_cache), + "gid_cache_maxsize": self._gid_cache.maxsize, + "admin1_cache_size": len(self._admin1_cache), + "admin1_cache_maxsize": self._admin1_cache.maxsize, + "admin2_cache_size": len(self._admin2_cache), + "admin2_cache_maxsize": self._admin2_cache.maxsize, + } + + if hasattr(self._country_cache, 'currsize'): + stats["country_cache_hit_rate"] = f"{self._country_cache.hits}/{self._country_cache.hits + self._country_cache.misses}" + stats["gid_cache_hit_rate"] = f"{self._gid_cache.hits}/{self._gid_cache.hits + self._gid_cache.misses}" + + return stats + + def warm_cache(self, gid_list=None, iso_a3_list=None): + """ + Warm up the cache with frequently accessed data. + + Parameters: + gid_list (list): List of GIDs to pre-cache + iso_a3_list (list): List of ISO A3 codes to pre-cache + """ + if gid_list: + logger.info(f"Warming cache with {len(gid_list)} GIDs") + for gid in gid_list: + self.find_country_for_gid(gid) + self.find_admin1_for_gid(gid) + self.find_admin2_for_gid(gid) + + if iso_a3_list: + logger.info(f"Warming cache with {len(iso_a3_list)} countries") + for iso_a3 in iso_a3_list: + self.find_country_by_iso_a3(iso_a3) + self.find_gids_for_country(iso_a3) + + logger.info("Cache warming complete") + + def __del__(self): + if self._process_pool: + self._process_pool.close() + self._process_pool.join() + + def _init_process_pool(self): + """Initialize the process pool if not already done""" + if self._process_pool is None: + self._process_pool = Pool(processes=self._max_workers) + + def batch_country_mapping_parallel(self, gid_list, batch_size=1000): + """ + Find countries for multiple PRIO-GRID cells using multiprocessing. + + Parameters: + gid_list (list): List of PRIO-GRID cell IDs + batch_size (int): Number of GIDs to process in each batch + + Returns: + DataFrame: Results of the batch mapping + """ + self._init_process_pool() + + # Split into batches + batches = [gid_list[i:i + batch_size] for i in range(0, len(gid_list), batch_size)] + + # Create partial function for mapping + map_func = partial(self._process_gid_batch) + + # Process batches in parallel + results = [] + for batch_result in self._process_pool.imap_unordered(map_func, batches): + results.extend(batch_result) + + return pd.DataFrame(results) + + def _process_gid_batch(self, gid_batch): + """Process a batch of GIDs in a single process""" + batch_results = [] + for gid in gid_batch: + result = self.find_country_for_gid(gid) + batch_results.append(result) + return batch_results + + def _load_and_preprocess_naturalearth(self, country_path): + """ + Load and preprocess the Natural Earth country dataset from the provided path. + + Parameters: + country_path (str): Path to the Natural Earth country dataset file + + Returns: + GeoDataFrame: Processed Natural Earth data with proper geometry + + Raises: + FileNotFoundError: If the file cannot be found + ValueError: If the file format is unsupported or data cannot be parsed + """ + # Load Natural Earth data + if country_path.endswith(".shp"): + countries_gdf = gpd.read_file(country_path) + else: + # Assume it's a CSV with WKT geometry + countries_df = pd.read_csv(country_path) + + # Parse the WKT geometry, removing the SRID prefix if present + def parse_wkt(wkt_str): + if wkt_str.startswith("SRID=4326;"): + wkt_str = wkt_str.replace("SRID=4326;", "") + return wkt.loads(wkt_str) + + countries_df["geometry"] = countries_df["geometry"].apply(parse_wkt) + countries_gdf = gpd.GeoDataFrame(countries_df, geometry="geometry") + + # Set CRS if not already set + if countries_gdf.crs is None: + countries_gdf.set_crs(epsg=4326, inplace=True) + + # Validate data + self._validate_naturalearth_data(countries_gdf) + + return countries_gdf + + def _validate_naturalearth_data(self, countries_gdf): + """Validate Natural Earth data for consistency and correctness.""" + # Check for required columns + required_columns = ["ISO_A3", "NAME_EN", "geometry"] + missing_columns = [ + col for col in required_columns if col not in countries_gdf.columns + ] + if missing_columns: + raise ValueError( + f"Missing required columns in Natural Earth data: {missing_columns}" + ) + + # Check geometry validity + invalid_geometries = countries_gdf[~countries_gdf["geometry"].is_valid] + if not invalid_geometries.empty: + logger.warning( + f"Found {len(invalid_geometries)} invalid geometries in Natural Earth data" + ) + + def _load_priogrid(self, priogrid_path): + """ + Load and preprocess the PRIO-GRID data from the provided path. + + Parameters: + priogrid_path (str): Path to the PRIO-GRID dataset file + + Returns: + GeoDataFrame: Processed PRIO-GRID data with geometry and centroid fields + + Raises: + FileNotFoundError: If the file cannot be found + ValueError: If the file format is unsupported or data cannot be parsed + """ + # Load PRIO-GRID data + priogrid_gdf = gpd.read_file(priogrid_path) + + # Ensure CRS is set + if priogrid_gdf.crs is None: + priogrid_gdf.set_crs(epsg=4326, inplace=True) + + # Calculate centroid of each grid cell for point-in-polygon testing + priogrid_gdf["centroid"] = priogrid_gdf["geometry"].centroid + + # Validate data + self._validate_priogrid_data(priogrid_gdf) + + return priogrid_gdf + + def _validate_priogrid_data(self, priogrid_gdf): + """Validate PRIO-GRID data for consistency and correctness.""" + # Check for required columns + required_columns = ["gid", "geometry", "centroid"] + missing_columns = [ + col for col in required_columns if col not in priogrid_gdf.columns + ] + if missing_columns: + raise ValueError( + f"Missing required columns in PRIO-GRID data: {missing_columns}" + ) + + # Check geometry validity + invalid_geometries = priogrid_gdf[~priogrid_gdf["geometry"].is_valid] + if not invalid_geometries.empty: + logger.warning( + f"Found {len(invalid_geometries)} invalid geometries in PRIO-GRID data" + ) + + def find_gid_for_point(self, point_geometry): + """ + Find the PRIO-GRID cell ID that contains a point using spatial index for faster query. + + Parameters: + point_geometry (shapely.geometry.Point): Point geometry to locate + + Returns: + int or None: PRIO-GRID cell ID containing the point, or None if not found + """ + if self.use_disk_cache: + # Use disk cache + @self._disk_gid_cache.cache() + def _find_gid_for_point_impl(point_geometry): + # Validate input geometry + if not point_geometry.is_valid: + raise ValueError("Invalid point geometry") + + # Not in cache, perform the expensive operation + if self.priogrid_sindex: + possible_matches_index = list( + self.priogrid_sindex.intersection(point_geometry.bounds) + ) + possible_matches = self.priogrid_gdf.iloc[possible_matches_index] + precise_matches = possible_matches[ + possible_matches.intersects(point_geometry) + ] + + if len(precise_matches) > 0: + result = precise_matches.iloc[0]["gid"] + else: + result = None + else: + # Fallback to iterative search + result = None + for _, grid_cell in self.priogrid_gdf.iterrows(): + if grid_cell["geometry"].contains(point_geometry): + result = grid_cell["gid"] + break + + return result + + return _find_gid_for_point_impl(point_geometry) + else: + # Use in-memory cache + # Validate input geometry + if not point_geometry.is_valid: + raise ValueError("Invalid point geometry") + + # Create a consistent cache key + cache_key = self._get_point_cache_key(point_geometry) + + # Check cache first + if cache_key in self._gid_cache: + return self._gid_cache[cache_key] + + # Not in cache, perform the expensive operation + if self.priogrid_sindex: + possible_matches_index = list( + self.priogrid_sindex.intersection(point_geometry.bounds) + ) + possible_matches = self.priogrid_gdf.iloc[possible_matches_index] + precise_matches = possible_matches[ + possible_matches.intersects(point_geometry) + ] + + if len(precise_matches) > 0: + result = precise_matches.iloc[0]["gid"] + else: + result = None + else: + # Fallback to iterative search + result = None + for _, grid_cell in self.priogrid_gdf.iterrows(): + if grid_cell["geometry"].contains(point_geometry): + result = grid_cell["gid"] + break + + # Update cache + self._gid_cache[cache_key] = result + return result + + def find_country_for_gid(self, gid): + """ + Find the country information for a PRIO-GRID cell using majority area-based method. + Now with persistent disk caching. + """ + if self.use_disk_cache: + # Use disk cache + @self._disk_country_cache.cache() + def _find_country_for_gid_impl(gid): + # Get the PRIO-GRID cell + grid_cell = self.priogrid_gdf[self.priogrid_gdf["gid"] == gid] + if len(grid_cell) == 0: + return None + + grid_geometry = grid_cell["geometry"].iloc[0] + grid_centroid = grid_cell["centroid"].iloc[0] + + # Use spatial index to find potentially intersecting countries + if hasattr(self.countries_gdf, 'sindex'): + possible_countries_idx = list(self.countries_gdf.sindex.intersection(grid_geometry.bounds)) + candidate_countries = self.countries_gdf.iloc[possible_countries_idx] + candidate_countries = candidate_countries[candidate_countries.intersects(grid_geometry)] + else: + candidate_countries = self.countries_gdf[self.countries_gdf.intersects(grid_geometry)] + + # Calculate area overlaps + overlaps = [] + for _, country in candidate_countries.iterrows(): + try: + intersection = country["geometry"].intersection(grid_geometry) + overlap_area = intersection.area + total_area = grid_geometry.area + overlap_ratio = overlap_area / total_area + + overlaps.append({ + "country_data": country, + "overlap_area": overlap_area, + "overlap_ratio": overlap_ratio, + }) + except Exception as e: + logger.debug(f"Overlap calculation error for GID {gid}: {e}") + continue + + # Sort by overlap ratio (descending) + overlaps.sort(key=lambda x: x["overlap_ratio"], reverse=True) + + # Assignment rule: always use largest overlap + result = None + if overlaps: + country = overlaps[0]["country_data"] + method_used = "largest overlap" + + # Prepare result + if overlaps: + result = { + "gid": int(gid), + "iso_a3": country["ISO_A3"], + "country_name": country["NAME_EN"], + "overlap_ratio": float(overlaps[0]["overlap_ratio"]) if overlaps else 0.0, + "method": method_used, + } + + # Add all available country data + for col in country.index: + if col not in ["geometry", "ISO_A3", "NAME_EN"]: + result[col] = country[col] + + return result + + return _find_country_for_gid_impl(gid) + else: + # Use in-memory cache + # Generate consistent cache key + cache_key = self._get_gid_cache_key(gid) + + # Check cache first + if cache_key in self._country_cache: + cached_value = self._country_cache[cache_key] + if cached_value is None: + return None + result = cached_value.copy() + return result + + # Get the PRIO-GRID cell + grid_cell = self.priogrid_gdf[self.priogrid_gdf["gid"] == gid] + if len(grid_cell) == 0: + self._country_cache[cache_key] = None + return None + + grid_geometry = grid_cell["geometry"].iloc[0] + grid_centroid = grid_cell["centroid"].iloc[0] + + # Use spatial index to find potentially intersecting countries + if hasattr(self.countries_gdf, 'sindex'): + possible_countries_idx = list(self.countries_gdf.sindex.intersection(grid_geometry.bounds)) + candidate_countries = self.countries_gdf.iloc[possible_countries_idx] + candidate_countries = candidate_countries[candidate_countries.intersects(grid_geometry)] + else: + candidate_countries = self.countries_gdf[self.countries_gdf.intersects(grid_geometry)] + + # Calculate area overlaps + overlaps = [] + for _, country in candidate_countries.iterrows(): + try: + intersection = country["geometry"].intersection(grid_geometry) + overlap_area = intersection.area + total_area = grid_geometry.area + overlap_ratio = overlap_area / total_area + + overlaps.append({ + "country_data": country, + "overlap_area": overlap_area, + "overlap_ratio": overlap_ratio, + }) + except Exception as e: + logger.debug(f"Overlap calculation error for GID {gid}: {e}") + continue + + # Sort by overlap ratio (descending) + overlaps.sort(key=lambda x: x["overlap_ratio"], reverse=True) + + # Assignment rule: always use largest overlap + result = None + if overlaps: + country = overlaps[0]["country_data"] + method_used = "largest overlap" + + # Prepare result + if overlaps: + result = { + "gid": int(gid), + "iso_a3": country["ISO_A3"], + "country_name": country["NAME_EN"], + "overlap_ratio": float(overlaps[0]["overlap_ratio"]) if overlaps else 0.0, + "method": method_used, + } + + # Add all available country data + for col in country.index: + if col not in ["geometry", "ISO_A3", "NAME_EN"]: + result[col] = country[col] + + # Update cache + self._country_cache[cache_key] = result + return result + + def batch_country_mapping(self, gid_list): + """ + Optimized batch mapping using vectorized operations where possible. + """ + results = [] + cache_hits = 0 + + for gid in gid_list: + # Check cache first + cache_key = self._get_gid_cache_key(gid) + if cache_key in self._country_cache: + cached_value = self._country_cache[cache_key] + if cached_value is not None: + results.append(cached_value.copy()) + else: + results.append(None) + cache_hits += 1 + continue + + # Not in cache, process this GID + result = self.find_country_for_gid(gid) + results.append(result) + + if cache_hits > 0: + logger.info(f"Cache hits: {cache_hits}/{len(gid_list)} ({cache_hits/len(gid_list)*100:.1f}%)") + + return pd.DataFrame([r for r in results if r is not None]) + + def find_gids_for_country(self, iso_a3): + """ + Find all PRIO-GRID cells that belong to a specific country. + Optimized using spatial indexing and vectorized operations. + """ + if self.use_disk_cache: + # Use disk cache + @self._disk_country_cache.cache() + def _find_gids_for_country_impl(iso_a3): + # Filter Natural Earth data for the specific country + country = self.countries_gdf[self.countries_gdf["ISO_A3"] == iso_a3] + + if len(country) == 0: + return [] + else: + # Get the country geometry + country_geometry = country.iloc[0]["geometry"] + + # OPTIMIZATION: Use spatial index to quickly find intersecting grid cells + if self.priogrid_sindex: + possible_matches_index = list(self.priogrid_sindex.intersection(country_geometry.bounds)) + candidate_grid_cells = self.priogrid_gdf.iloc[possible_matches_index] + intersecting_cells = candidate_grid_cells[candidate_grid_cells.intersects(country_geometry)] + else: + intersecting_cells = self.priogrid_gdf[self.priogrid_gdf.intersects(country_geometry)] + + if len(intersecting_cells) == 0: + result = [] + else: + # OPTIMIZATION: Process with early termination + matching_gids = self._find_dominant_country_gids( + intersecting_cells, country_geometry, iso_a3 + ) + result = matching_gids + + return result + + return _find_gids_for_country_impl(iso_a3) + else: + # Use in-memory cache + # Create consistent cache key + cache_key = self._get_country_cache_key(iso_a3) + + # Check cache first + if cache_key in self._gids_for_country_cache: + result = self._gids_for_country_cache[cache_key] + return result + + # Not in cache, perform optimized operation + # Filter Natural Earth data for the specific country + country = self.countries_gdf[self.countries_gdf["ISO_A3"] == iso_a3] + + if len(country) == 0: + result = [] + else: + # Get the country geometry + country_geometry = country.iloc[0]["geometry"] + + # OPTIMIZATION: Use spatial index to quickly find intersecting grid cells + if self.priogrid_sindex: + possible_matches_index = list(self.priogrid_sindex.intersection(country_geometry.bounds)) + candidate_grid_cells = self.priogrid_gdf.iloc[possible_matches_index] + intersecting_cells = candidate_grid_cells[candidate_grid_cells.intersects(country_geometry)] + else: + intersecting_cells = self.priogrid_gdf[self.priogrid_gdf.intersects(country_geometry)] + + if len(intersecting_cells) == 0: + result = [] + else: + # OPTIMIZATION: Process with early termination + matching_gids = self._find_dominant_country_gids( + intersecting_cells, country_geometry, iso_a3 + ) + result = matching_gids + + # Update cache + self._gids_for_country_cache[cache_key] = result + return result + + def _find_dominant_country_gids(self, intersecting_cells, country_geometry, iso_a3): + """Find grid cells where target country has dominant area using optimized approach.""" + matching_gids = [] + + for _, grid_cell in intersecting_cells.iterrows(): + grid_geometry = grid_cell["geometry"] + grid_gid = grid_cell["gid"] + + # OPTIMIZATION 1: Early check for complete containment + if country_geometry.contains(grid_geometry): + matching_gids.append(int(grid_gid)) + continue + + # Calculate overlap with the target country + try: + intersection = country_geometry.intersection(grid_geometry) + overlap_ratio = intersection.area / grid_geometry.area + + # If overlap is significant, include it + if overlap_ratio > 0.5: + matching_gids.append(int(grid_gid)) + except Exception as e: + # Handle potential geometry errors gracefully + logger.debug(f"Geometry error for GID {grid_gid}: {e}") + continue + + return matching_gids + + def get_all_iso_a3_codes(self): + """ + Get a list of all unique ISO A3 codes in the Natural Earth dataset. + + Returns: + list: List of unique ISO A3 codes + """ + return self.countries_gdf["ISO_A3"].unique().tolist() + + def visualize_grid_and_country(self, gid): + """ + Visualize a PRIO-GRID cell and the country it belongs to. + + Parameters: + gid (int): PRIO-GRID cell ID + + Raises: + ImportError: If matplotlib is not available + ValueError: If the PRIO-GRID cell ID is not found + """ + try: + import matplotlib.pyplot as plt + except ImportError: + logger.error("Matplotlib is required for visualization") + return + + # Get the grid cell + grid_cell = self.priogrid_gdf[self.priogrid_gdf["gid"] == gid] + + if len(grid_cell) == 0: + raise ValueError(f"PRIO-GRID cell with ID {gid} not found") + + # Find which country contains the grid cell based on majority area + country_info = self.find_country_for_gid(gid) + containing_country = None + + if country_info: + iso_a3 = country_info["iso_a3"] + containing_country = self.countries_gdf[self.countries_gdf["ISO_A3"] == iso_a3].iloc[0] + + # Create plot + fig, ax = plt.subplots(1, 1, figsize=(10, 8)) + + # Plot all countries + self.countries_gdf.plot(ax=ax, color="lightgray", edgecolor="white", alpha=0.5) + + # Highlight the containing country + if containing_country is not None: + gpd.GeoDataFrame([containing_country], geometry="geometry").plot( + ax=ax, color="lightblue", edgecolor="blue", alpha=0.7 + ) + + # Plot the grid cell + grid_cell.plot(ax=ax, color="red", alpha=0.7) + + # Add centroid marker + centroid = grid_cell["centroid"].iloc[0] + ax.scatter( + centroid.x, + centroid.y, + color="yellow", + s=100, + marker="*", + edgecolor="black", + label="Grid Centroid", + ) + + # Set title and legend + country_name = ( + containing_country["NAME_EN"] + if containing_country is not None + else "No Country" + ) + method = country_info.get("method", "unknown") if country_info else "unknown" + overlap_ratio = country_info.get("overlap_ratio", 0) if country_info else 0 + + ax.set_title( + f'PRIO-GRID Cell {gid} in {country_name}\n' + f'Method: {method} | Overlap Ratio: {overlap_ratio:.3f}' + ) + ax.legend() + + plt.tight_layout() + plt.show() + + def calculate_capital_distance(self, grid_lat, grid_lon, capital_coords): + """ + Calculate distance from grid cell to capital. + + Parameters: + grid_lat: Latitude of grid centroid + grid_lon: Longitude of grid centroid + capital_coords: Tuple of (capital_lon, capital_lat) or None + + Returns: + float or None: Distance in kilometers or None if calculation fails + """ + if capital_coords is None or None in capital_coords: + return None + + try: + capital_lon, capital_lat = capital_coords + return cached_haversine(grid_lat, grid_lon, capital_lat, capital_lon) + except Exception as e: + logger.error(f"Error calculating capital distance: {e}") + return None + + def get_gid_from_point(self, point: ShapelyPoint) -> Optional[int]: + """ + Find the PRIO-GRID cell ID that contains a point. + + Parameters: + point (ShapelyPoint): Point geometry to locate + + Returns: + int or None: PRIO-GRID cell ID containing the point, or None if not found + """ + for _, grid_cell in self.priogrid_gdf.iterrows(): + if grid_cell["geometry"].contains(point): + return grid_cell["gid"] + return None + + def get_point_from_gid(self, gid: int) -> Optional["Point"]: + """ + Get the centroid point of a PRIO-GRID cell. + + Parameters: + gid (int): PRIO-GRID cell ID + + Returns: + Point or None: Point object representing the centroid, or None if not found + """ + # from ..space import Point + grid_cell = self.priogrid_gdf[self.priogrid_gdf["gid"] == gid] + if len(grid_cell) == 0: + return None + centroid = grid_cell["centroid"].iloc[0] + return Point(lat=centroid.y, lon=centroid.x) + + def _load_admin_data(self, admin_path, admin_level): + """ + Load and preprocess admin1 or admin2 data from the provided path. + + Parameters: + admin_path (str): Path to the admin dataset file + admin_level (str): Either "admin1" or "admin2" + + Returns: + GeoDataFrame: Processed admin data with proper geometry + """ + # Load admin data + if admin_path.endswith(".shp"): + admin_gdf = gpd.read_file(admin_path) + else: + # Assume it's a CSV with WKT geometry + admin_df = pd.read_csv(admin_path) + + # Parse the WKT geometry, removing the SRID prefix if present + def parse_wkt(wkt_str): + if wkt_str.startswith("SRID=4326;"): + wkt_str = wkt_str.replace("SRID=4326;", "") + return wkt.loads(wkt_str) + + admin_df["geometry"] = admin_df["geometry"].apply(parse_wkt) + admin_gdf = gpd.GeoDataFrame(admin_df, geometry="geometry") + + # Set CRS if not already set + if admin_gdf.crs is None: + admin_gdf.set_crs(epsg=4326, inplace=True) + + # Validate data + self._validate_admin_data(admin_gdf, admin_level) + + return admin_gdf + + def _validate_admin_data(self, admin_gdf, admin_level): + """Validate admin data for consistency and correctness.""" + # Check for required columns based on admin level + if admin_level == "admin1": + required_columns = ["gaul1_code", "gaul1_name", "iso3_code", "geometry"] + else: # admin2 + required_columns = ["gaul2_code", "gaul2_name", "iso3_code", "geometry"] + + missing_columns = [ + col for col in required_columns if col not in admin_gdf.columns + ] + if missing_columns: + raise ValueError( + f"Missing required columns in {admin_level} data: {missing_columns}" + ) + + # Check geometry validity + invalid_geometries = admin_gdf[~admin_gdf["geometry"].is_valid] + if not invalid_geometries.empty: + logger.warning( + f"Found {len(invalid_geometries)} invalid geometries in {admin_level} data" + ) + + def find_admin1_for_gid(self, gid): + """ + Find the admin1 information for a PRIO-GRID cell. + First determines the country, then finds the admin1 region within that country. + Now with persistent disk caching. + """ + if self.admin1_gdf is None: + logger.warning("Admin1 data not loaded. Cannot find admin1 for GID.") + return None + + if self.use_disk_cache: + # Use disk cache + @self._disk_admin1_cache.cache() + def _find_admin1_for_gid_impl(gid): + # First find the country for this GID + country_info = self.find_country_for_gid(gid) + if not country_info: + return None + + iso_a3 = country_info["iso_a3"] + + # Get the PRIO-GRID cell + grid_cell = self.priogrid_gdf[self.priogrid_gdf["gid"] == gid] + if len(grid_cell) == 0: + return None + + grid_geometry = grid_cell["geometry"].iloc[0] + + # Filter admin1 regions to only those in the same country + country_admin1 = self.admin1_gdf[self.admin1_gdf["iso3_code"] == iso_a3] + + if len(country_admin1) == 0: + return None + + # Use spatial index to find potentially intersecting admin1 regions + if self.admin1_sindex: + possible_admin1_idx = list(self.admin1_sindex.intersection(grid_geometry.bounds)) + candidate_admin1 = self.admin1_gdf.iloc[possible_admin1_idx] + candidate_admin1 = candidate_admin1[candidate_admin1.intersects(grid_geometry)] + # Filter to only those in the same country + candidate_admin1 = candidate_admin1[candidate_admin1["iso3_code"] == iso_a3] + else: + candidate_admin1 = country_admin1[country_admin1.intersects(grid_geometry)] + + if len(candidate_admin1) == 0: + return None + + # Calculate overlaps and use largest overlap + overlaps = [] + for _, admin1 in candidate_admin1.iterrows(): + try: + intersection = admin1["geometry"].intersection(grid_geometry) + overlap_area = intersection.area + total_area = grid_geometry.area + overlap_ratio = overlap_area / total_area + + overlaps.append({ + "admin1_data": admin1, + "overlap_area": overlap_area, + "overlap_ratio": overlap_ratio, + }) + except Exception as e: + logger.debug(f"Overlap calculation error for GID {gid}: {e}") + continue + + # Sort by overlap ratio (descending) + overlaps.sort(key=lambda x: x["overlap_ratio"], reverse=True) + + if not overlaps: + return None + + admin1 = overlaps[0]["admin1_data"] + method_used = "largest overlap" + + # Prepare result + result = { + "gid": int(gid), + "gaul1_code": int(admin1["gaul1_code"]) if admin1["gaul1_code"] is not None else None, + "gaul1_name": admin1["gaul1_name"], + "iso3_code": admin1["iso3_code"], + "method": method_used, + } + + # Add additional fields if available + if "gaul0_code" in admin1: + result["gaul0_code"] = int(admin1["gaul0_code"]) if admin1["gaul0_code"] is not None else None + if "gaul0_name" in admin1: + result["gaul0_name"] = admin1["gaul0_name"] + if "continent" in admin1: + result["continent"] = admin1["continent"] + if "disp_en" in admin1: + result["disp_en"] = admin1["disp_en"] + + return result + + return _find_admin1_for_gid_impl(gid) + else: + # Use in-memory cache + # Generate consistent cache key + cache_key = self._get_admin_cache_key(gid, "admin1") + + # Check cache first + if cache_key in self._admin1_cache: + cached_value = self._admin1_cache[cache_key] + if cached_value is not None: + result = cached_value.copy() + return result + else: + return None + + # First find the country for this GID + country_info = self.find_country_for_gid(gid) + if not country_info: + self._admin1_cache[cache_key] = None + return None + + iso_a3 = country_info["iso_a3"] + + # Get the PRIO-GRID cell + grid_cell = self.priogrid_gdf[self.priogrid_gdf["gid"] == gid] + if len(grid_cell) == 0: + self._admin1_cache[cache_key] = None + return None + + grid_geometry = grid_cell["geometry"].iloc[0] + + # Filter admin1 regions to only those in the same country + country_admin1 = self.admin1_gdf[self.admin1_gdf["iso3_code"] == iso_a3] + + if len(country_admin1) == 0: + self._admin1_cache[cache_key] = None + return None + + # Use spatial index to find potentially intersecting admin1 regions + if self.admin1_sindex: + possible_admin1_idx = list(self.admin1_sindex.intersection(grid_geometry.bounds)) + candidate_admin1 = self.admin1_gdf.iloc[possible_admin1_idx] + candidate_admin1 = candidate_admin1[candidate_admin1.intersects(grid_geometry)] + # Filter to only those in the same country + candidate_admin1 = candidate_admin1[candidate_admin1["iso3_code"] == iso_a3] + else: + candidate_admin1 = country_admin1[country_admin1.intersects(grid_geometry)] + + if len(candidate_admin1) == 0: + self._admin1_cache[cache_key] = None + return None + + # Calculate overlaps and use largest overlap + overlaps = [] + for _, admin1 in candidate_admin1.iterrows(): + try: + intersection = admin1["geometry"].intersection(grid_geometry) + overlap_area = intersection.area + total_area = grid_geometry.area + overlap_ratio = overlap_area / total_area + + overlaps.append({ + "admin1_data": admin1, + "overlap_area": overlap_area, + "overlap_ratio": overlap_ratio, + }) + except Exception as e: + logger.debug(f"Overlap calculation error for GID {gid}: {e}") + continue + + # Sort by overlap ratio (descending) + overlaps.sort(key=lambda x: x["overlap_ratio"], reverse=True) + + if not overlaps: + self._admin1_cache[cache_key] = None + return None + + admin1 = overlaps[0]["admin1_data"] + method_used = "largest overlap" + + # Prepare result + result = { + "gid": int(gid), + "gaul1_code": int(admin1["gaul1_code"]) if admin1["gaul1_code"] is not None else None, + "gaul1_name": admin1["gaul1_name"], + "iso3_code": admin1["iso3_code"], + "method": method_used, + } + + # Add additional fields if available + if "gaul0_code" in admin1: + result["gaul0_code"] = int(admin1["gaul0_code"]) if admin1["gaul0_code"] is not None else None + if "gaul0_name" in admin1: + result["gaul0_name"] = admin1["gaul0_name"] + if "continent" in admin1: + result["continent"] = admin1["continent"] + if "disp_en" in admin1: + result["disp_en"] = admin1["disp_en"] + + # Update cache + self._admin1_cache[cache_key] = result + return result + + def find_admin2_for_gid(self, gid): + """ + Find the admin2 information for a PRIO-GRID cell. + First determines the country, then finds the admin2 region within that country. + Now with persistent disk caching. + """ + if self.admin2_gdf is None: + logger.warning("Admin2 data not loaded. Cannot find admin2 for GID.") + return None + + if self.use_disk_cache: + # Use disk cache + @self._disk_admin2_cache.cache() + def _find_admin2_for_gid_impl(gid): + # First find the country for this GID + country_info = self.find_country_for_gid(gid) + if not country_info: + return None + + iso_a3 = country_info["iso_a3"] + + # Get the PRIO-GRID cell + grid_cell = self.priogrid_gdf[self.priogrid_gdf["gid"] == gid] + if len(grid_cell) == 0: + return None + + grid_geometry = grid_cell["geometry"].iloc[0] + + # Filter admin2 regions to only those in the same country + country_admin2 = self.admin2_gdf[self.admin2_gdf["iso3_code"] == iso_a3] + + if len(country_admin2) == 0: + return None + + # Use spatial index to find potentially intersecting admin2 regions + if self.admin2_sindex: + possible_admin2_idx = list(self.admin2_sindex.intersection(grid_geometry.bounds)) + candidate_admin2 = self.admin2_gdf.iloc[possible_admin2_idx] + candidate_admin2 = candidate_admin2[candidate_admin2.intersects(grid_geometry)] + # Filter to only those in the same country + candidate_admin2 = candidate_admin2[candidate_admin2["iso3_code"] == iso_a3] + else: + candidate_admin2 = country_admin2[country_admin2.intersects(grid_geometry)] + + if len(candidate_admin2) == 0: + return None + + # Calculate overlaps and use largest overlap + overlaps = [] + for _, admin2 in candidate_admin2.iterrows(): + try: + intersection = admin2["geometry"].intersection(grid_geometry) + overlap_area = intersection.area + total_area = grid_geometry.area + overlap_ratio = overlap_area / total_area + + overlaps.append({ + "admin2_data": admin2, + "overlap_area": overlap_area, + "overlap_ratio": overlap_ratio, + }) + except Exception as e: + logger.debug(f"Overlap calculation error for GID {gid}: {e}") + continue + + # Sort by overlap ratio (descending) + overlaps.sort(key=lambda x: x["overlap_ratio"], reverse=True) + + if not overlaps: + return None + + admin2 = overlaps[0]["admin2_data"] + method_used = "largest overlap" + + # Prepare result + result = { + "gid": int(gid), + "gaul2_code": int(admin2["gaul2_code"]) if admin2["gaul2_code"] is not None else None, + "gaul2_name": admin2["gaul2_name"], + "iso3_code": admin2["iso3_code"], + "method": method_used, + } + + # Add additional fields if available + if "gaul0_code" in admin2: + result["gaul0_code"] = int(admin2["gaul0_code"]) if admin2["gaul0_code"] is not None else None + if "gaul0_name" in admin2: + result["gaul0_name"] = admin2["gaul0_name"] + if "gaul1_code" in admin2: + result["gaul1_code"] = int(admin2["gaul1_code"]) if admin2["gaul1_code"] is not None else None + if "gaul1_name" in admin2: + result["gaul1_name"] = admin2["gaul1_name"] + if "continent" in admin2: + result["continent"] = admin2["continent"] + if "disp_en" in admin2: + result["disp_en"] = admin2["disp_en"] + + return result + + return _find_admin2_for_gid_impl(gid) + else: + # Use in-memory cache + # Generate consistent cache key + cache_key = self._get_admin_cache_key(gid, "admin2") + + # Check cache first + if cache_key in self._admin2_cache: + cached_value = self._admin2_cache[cache_key] + if cached_value is not None: + result = cached_value.copy() + return result + else: + return None + + # First find the country for this GID + country_info = self.find_country_for_gid(gid) + if not country_info: + self._admin2_cache[cache_key] = None + return None + + iso_a3 = country_info["iso_a3"] + + # Get the PRIO-GRID cell + grid_cell = self.priogrid_gdf[self.priogrid_gdf["gid"] == gid] + if len(grid_cell) == 0: + self._admin2_cache[cache_key] = None + return None + + grid_geometry = grid_cell["geometry"].iloc[0] + + # Filter admin2 regions to only those in the same country + country_admin2 = self.admin2_gdf[self.admin2_gdf["iso3_code"] == iso_a3] + + if len(country_admin2) == 0: + self._admin2_cache[cache_key] = None + return None + + # Use spatial index to find potentially intersecting admin2 regions + if self.admin2_sindex: + possible_admin2_idx = list(self.admin2_sindex.intersection(grid_geometry.bounds)) + candidate_admin2 = self.admin2_gdf.iloc[possible_admin2_idx] + candidate_admin2 = candidate_admin2[candidate_admin2.intersects(grid_geometry)] + # Filter to only those in the same country + candidate_admin2 = candidate_admin2[candidate_admin2["iso3_code"] == iso_a3] + else: + candidate_admin2 = country_admin2[country_admin2.intersects(grid_geometry)] + + if len(candidate_admin2) == 0: + self._admin2_cache[cache_key] = None + return None + + # Calculate overlaps and use largest overlap + overlaps = [] + for _, admin2 in candidate_admin2.iterrows(): + try: + intersection = admin2["geometry"].intersection(grid_geometry) + overlap_area = intersection.area + total_area = grid_geometry.area + overlap_ratio = overlap_area / total_area + + overlaps.append({ + "admin2_data": admin2, + "overlap_area": overlap_area, + "overlap_ratio": overlap_ratio, + }) + except Exception as e: + logger.debug(f"Overlap calculation error for GID {gid}: {e}") + continue + + # Sort by overlap ratio (descending) + overlaps.sort(key=lambda x: x["overlap_ratio"], reverse=True) + + if not overlaps: + self._admin2_cache[cache_key] = None + return None + + admin2 = overlaps[0]["admin2_data"] + method_used = "largest overlap" + + # Prepare result + result = { + "gid": int(gid), + "gaul2_code": int(admin2["gaul2_code"]) if admin2["gaul2_code"] is not None else None, + "gaul2_name": admin2["gaul2_name"], + "iso3_code": admin2["iso3_code"], + "method": method_used, + } + + # Add additional fields if available + if "gaul0_code" in admin2: + result["gaul0_code"] = int(admin2["gaul0_code"]) if admin2["gaul0_code"] is not None else None + if "gaul0_name" in admin2: + result["gaul0_name"] = admin2["gaul0_name"] + if "gaul1_code" in admin2: + result["gaul1_code"] = int(admin2["gaul1_code"]) if admin2["gaul1_code"] is not None else None + if "gaul1_name" in admin2: + result["gaul1_name"] = admin2["gaul1_name"] + if "continent" in admin2: + result["continent"] = admin2["continent"] + if "disp_en" in admin2: + result["disp_en"] = admin2["disp_en"] + + # Update cache + self._admin2_cache[cache_key] = result + return result + + def find_all_admin_for_gid(self, gid): + """ + Find country, admin1, and admin2 information for a PRIO-GRID cell. + + Parameters: + gid (int): PRIO-GRID cell ID + + Returns: + dict: Combined information for country, admin1, and admin2 + """ + result = {"gid": int(gid)} + + # Find country information + country_info = self.find_country_for_gid(gid) + if country_info: + result["country"] = country_info + + # Find admin1 information + admin1_info = self.find_admin1_for_gid(gid) + if admin1_info: + result["admin1"] = admin1_info + + # Find admin2 information + admin2_info = self.find_admin2_for_gid(gid) + if admin2_info: + result["admin2"] = admin2_info + + return result + + def batch_admin_mapping(self, gid_list, admin_level="admin1"): + """ + Batch mapping for admin1 or admin2 using vectorized operations. + + Parameters: + gid_list (list): List of PRIO-GRID cell IDs + admin_level (str): Either "admin1" or "admin2" + + Returns: + DataFrame: Results of the batch mapping + """ + if admin_level not in ["admin1", "admin2"]: + raise ValueError("admin_level must be either 'admin1' or 'admin2'") + + if admin_level == "admin1" and self.admin1_gdf is None: + raise ValueError("Admin1 data not loaded") + if admin_level == "admin2" and self.admin2_gdf is None: + raise ValueError("Admin2 data not loaded") + + # Select the appropriate find function + if admin_level == "admin1": + find_func = self.find_admin1_for_gid + cache = self._admin1_cache + else: # admin2 + find_func = self.find_admin2_for_gid + cache = self._admin2_cache + + results = [] + cache_hits = 0 + + for gid in gid_list: + # Check cache first + cache_key = self._get_admin_cache_key(gid, admin_level) + if cache_key in cache: + cached_value = cache[cache_key] + if cached_value is not None: + results.append(cached_value.copy()) + else: + results.append(None) + cache_hits += 1 + continue + + # Not in cache, process this GID + result = find_func(gid) + results.append(result) + + if cache_hits > 0: + logger.info(f"Cache hits: {cache_hits}/{len(gid_list)} ({cache_hits/len(gid_list)*100:.1f}%)") + + return pd.DataFrame([r for r in results if r is not None]) + + def batch_admin_mapping_parallel(self, gid_list, admin_level="admin1", batch_size=1000): + """ + Batch mapping for admin1 or admin2 using multiprocessing. + + Parameters: + gid_list (list): List of PRIO-GRID cell IDs + admin_level (str): Either "admin1" or "admin2" + batch_size (int): Number of GIDs to process in each batch + + Returns: + DataFrame: Results of the batch mapping + """ + if admin_level not in ["admin1", "admin2"]: + raise ValueError("admin_level must be either 'admin1' or 'admin2'") + + if admin_level == "admin1" and self.admin1_gdf is None: + raise ValueError("Admin1 data not loaded") + if admin_level == "admin2" and self.admin2_gdf is None: + raise ValueError("Admin2 data not loaded") + + self._init_process_pool() + + # Split into batches + batches = [gid_list[i:i + batch_size] for i in range(0, len(gid_list), batch_size)] + + # Select the appropriate find function + if admin_level == "admin1": + find_func = self.find_admin1_for_gid + else: # admin2 + find_func = self.find_admin2_for_gid + + # Create partial function for mapping + map_func = partial(self._process_gid_batch_admin, find_func=find_func) + + # Process batches in parallel + results = [] + for batch_result in self._process_pool.imap_unordered(map_func, batches): + results.extend(batch_result) + + return pd.DataFrame(results) + + def _process_gid_batch_admin(self, gid_batch, find_func): + """Process a batch of GIDs in a single process for admin mapping""" + batch_results = [] + for gid in gid_batch: + result = find_func(gid) + batch_results.append(result) + return batch_results + + def find_all_admin_for_point(self, point_geometry): + """ + Find country, admin1, and admin2 information for a point. + + Parameters: + point_geometry (shapely.geometry.Point): Point geometry to locate + + Returns: + dict: Combined information for country, admin1, and admin2 + """ + # Find the GID for the point + gid = self.find_gid_for_point(point_geometry) + if gid is None: + return None + + # Use the existing method to find all admin information + return self.find_all_admin_for_gid(gid) + + def extend_find_country_for_gid(self, gid): + """ + Extended version of find_country_for_gid that also includes admin1 and admin2 information. + + Parameters: + gid (int): PRIO-GRID cell ID + + Returns: + dict: Combined information for country, admin1, and admin2 + """ + # Get country information using the existing method + result = self.find_country_for_gid(gid) + + if result is None: + return None + + # Add admin1 information + admin1_info = self.find_admin1_for_gid(gid) + if admin1_info: + result["admin1"] = admin1_info + + # Add admin2 information + admin2_info = self.find_admin2_for_gid(gid) + if admin2_info: + result["admin2"] = admin2_info + + return result + + def visualize_grid_and_admin(self, gid, admin_level="admin1", show_all_admins=False): + """ + Visualize a PRIO-GRID cell and its corresponding admin1 or admin2 boundary. + + Parameters: + gid (int): PRIO-GRID cell ID + admin_level (str): Either "admin1" or "admin2" + show_all_admins (bool): If True, shows all admin regions in the area, + otherwise only shows the one containing the grid cell + + Raises: + ImportError: If matplotlib is not available + ValueError: If the PRIO-GRID cell ID is not found or admin data is not loaded + """ + try: + import matplotlib.pyplot as plt + import matplotlib.patches as mpatches + except ImportError: + logger.error("Matplotlib is required for visualization") + return + + if admin_level not in ["admin1", "admin2"]: + raise ValueError("admin_level must be either 'admin1' or 'admin2'") + + # Check if admin data is loaded + if admin_level == "admin1" and self.admin1_gdf is None: + raise ValueError("Admin1 data not loaded. Cannot visualize admin1.") + if admin_level == "admin2" and self.admin2_gdf is None: + raise ValueError("Admin2 data not loaded. Cannot visualize admin2.") + + # Get the grid cell + grid_cell = self.priogrid_gdf[self.priogrid_gdf["gid"] == gid] + if len(grid_cell) == 0: + raise ValueError(f"PRIO-GRID cell with ID {gid} not found") + + grid_geometry = grid_cell["geometry"].iloc[0] + grid_centroid = grid_cell["centroid"].iloc[0] + + # Select appropriate admin data + if admin_level == "admin1": + admin_gdf = self.admin1_gdf + admin_code_col = "gaul1_code" + admin_name_col = "gaul1_name" + find_func = self.find_admin1_for_gid + else: # admin2 + admin_gdf = self.admin2_gdf + admin_code_col = "gaul2_code" + admin_name_col = "gaul2_name" + find_func = self.find_admin2_for_gid + + # Find the admin region containing the grid cell + admin_info = find_func(gid) + + # Create plot + fig, ax = plt.subplots(1, 1, figsize=(12, 10)) + + if show_all_admins: + # Show all admin regions that intersect with the grid cell's bounds + if admin_level == "admin1" and self.admin1_sindex: + possible_admins_idx = list(self.admin1_sindex.intersection(grid_geometry.bounds)) + nearby_admins = self.admin1_gdf.iloc[possible_admins_idx] + elif admin_level == "admin2" and self.admin2_sindex: + possible_admins_idx = list(self.admin2_sindex.intersection(grid_geometry.bounds)) + nearby_admins = self.admin2_gdf.iloc[possible_admins_idx] + else: + nearby_admins = admin_gdf[admin_gdf.intersects(grid_geometry.buffer(1))] + + # Plot all nearby admin regions + nearby_admins.plot(ax=ax, color="lightgray", edgecolor="white", alpha=0.5, linewidth=0.5) + + # Highlight the containing admin region if found + if admin_info: + containing_admin = admin_gdf[admin_gdf[admin_code_col] == admin_info[admin_code_col]] + if len(containing_admin) > 0: + containing_admin.plot(ax=ax, color="lightblue", edgecolor="blue", alpha=0.7, linewidth=1.5) + else: + # Only show the containing admin region + if admin_info: + containing_admin = admin_gdf[admin_gdf[admin_code_col] == admin_info[admin_code_col]] + if len(containing_admin) > 0: + containing_admin.plot(ax=ax, color="lightblue", edgecolor="blue", alpha=0.7, linewidth=1.5) + + # Plot the grid cell + grid_cell.plot(ax=ax, color="red", alpha=0.8, edgecolor="darkred", linewidth=2) + + # Add centroid marker + ax.scatter( + grid_centroid.x, + grid_centroid.y, + color="yellow", + s=150, + marker="*", + edgecolor="black", + linewidth=1, + label="Grid Centroid", + zorder=5 + ) + + # Set title and legend + if admin_info: + admin_name = admin_info[admin_name_col] + admin_code = admin_info[admin_code_col] + method = admin_info.get("method", "unknown") + + title = f'PRIO-GRID Cell {gid} in {admin_level.upper()} {admin_name} (Code: {admin_code})\n' + title += f'Method: {method}' + else: + title = f'PRIO-GRID Cell {gid} - No {admin_level.upper()} region found' + + ax.set_title(title, fontsize=14, fontweight='bold') + + # Create legend + legend_elements = [ + mpatches.Patch(color='red', alpha=0.8, label='PRIO-GRID Cell'), + mpatches.Patch(color='lightblue', alpha=0.7, label=f'Containing {admin_level.upper()}'), + mpatches.Patch(color='lightgray', alpha=0.5, label='Other Admin Regions' if show_all_admins else None), + ] + + # Filter out None values + legend_elements = [e for e in legend_elements if e.get_label() is not None] + + ax.legend(handles=legend_elements, loc='upper right', bbox_to_anchor=(1, 1)) + + # Add grid coordinates as text + ax.text( + grid_centroid.x, + grid_centroid.y - 0.1, + f'GID: {gid}\n({grid_cell["xcoord"].iloc[0]:.1f}, {grid_cell["ycoord"].iloc[0]:.1f})', + ha='center', + va='top', + fontsize=9, + bbox=dict(boxstyle="round,pad=0.3", facecolor="white", alpha=0.8) + ) + + # Set aspect ratio and remove unnecessary axes + ax.set_aspect('equal') + ax.set_xlabel('Longitude') + ax.set_ylabel('Latitude') + ax.grid(True, alpha=0.3) + + plt.tight_layout() + plt.show() + + def visualize_admin_regions_for_gids(self, gid_list, admin_level="admin1", show_grid_cells=True, + color_by_country=True, figsize=(15, 10)): + """ + Visualize admin1 or admin2 regions for multiple PRIO-GRID cells. + + Parameters: + gid_list (list): List of PRIO-GRID cell IDs + admin_level (str): Either "admin1" or "admin2" + show_grid_cells (bool): Whether to show the PRIO-GRID cells on the map + color_by_country (bool): If True, colors admin regions by country + figsize (tuple): Figure size (width, height) + + Raises: + ImportError: If matplotlib is not available + ValueError: If admin data is not loaded + """ + try: + import matplotlib.pyplot as plt + import matplotlib.patches as mpatches + from matplotlib.colors import LinearSegmentedColormap + import matplotlib.cm as cm + except ImportError: + logger.error("Matplotlib is required for visualization") + return + + if admin_level not in ["admin1", "admin2"]: + raise ValueError("admin_level must be either 'admin1' or 'admin2'") + + # Check if admin data is loaded + if admin_level == "admin1" and self.admin1_gdf is None: + raise ValueError("Admin1 data not loaded. Cannot visualize admin1.") + if admin_level == "admin2" and self.admin2_gdf is None: + raise ValueError("Admin2 data not loaded. Cannot visualize admin2.") + + # Get admin information for all GIDs + if admin_level == "admin1": + admin_results = self.batch_admin_mapping(gid_list, admin_level="admin1") + admin_gdf = self.admin1_gdf + admin_code_col = "gaul1_code" + admin_name_col = "gaul1_name" + else: + admin_results = self.batch_admin_mapping(gid_list, admin_level="admin2") + admin_gdf = self.admin2_gdf + admin_code_col = "gaul2_code" + admin_name_col = "gaul2_name" + + if admin_results.empty: + logger.warning(f"No {admin_level} regions found for the provided GIDs") + return + + # Get unique admin regions + unique_admin_codes = admin_results[admin_code_col].unique() + admin_regions = admin_gdf[admin_gdf[admin_code_col].isin(unique_admin_codes)] + + # Get grid cells + grid_cells = self.priogrid_gdf[self.priogrid_gdf["gid"].isin(gid_list)] + + # Create plot + fig, ax = plt.subplots(1, 1, figsize=figsize) + + # Plot all admin regions in the area + if color_by_country: + # Create a colormap for countries + unique_countries = admin_results["iso3_code"].unique() + country_colors = plt.cm.tab20(np.linspace(0, 1, len(unique_countries))) + country_color_map = {country: color for country, color in zip(unique_countries, country_colors)} + + # Plot with color based on country + for _, admin in admin_regions.iterrows(): + admin_code = admin[admin_code_col] + admin_row = admin_results[admin_results[admin_code_col] == admin_code] + if not admin_row.empty: + country = admin_row["iso3_code"].iloc[0] + color = country_color_map[country] + admin_gdf[admin_gdf[admin_code_col] == admin_code].plot( + ax=ax, color=[color], edgecolor='black', alpha=0.7, linewidth=1 + ) + + # Create legend for countries + legend_elements = [ + mpatches.Patch(color=country_color_map[country], label=country) + for country in unique_countries + ] + else: + # Plot with uniform color + admin_regions.plot(ax=ax, color="lightblue", edgecolor="black", alpha=0.7, linewidth=1) + legend_elements = [ + mpatches.Patch(color='lightblue', alpha=0.7, label=f'{admin_level.upper()} Regions') + ] + + # Plot grid cells if requested + if show_grid_cells: + grid_cells.plot(ax=ax, color="red", alpha=0.8, edgecolor="darkred", linewidth=1.5) + + # Add labels for grid cells + for _, grid_cell in grid_cells.iterrows(): + centroid = grid_cell["centroid"] + ax.text( + centroid.x, + centroid.y, + str(grid_cell["gid"]), + ha='center', + va='center', + fontsize=8, + color='white', + fontweight='bold', + bbox=dict(boxstyle="circle,pad=0.2", facecolor="red", alpha=0.7) + ) + + # Add labels for admin regions + for _, admin in admin_regions.iterrows(): + centroid = admin["geometry"].centroid + admin_name = admin[admin_name_col] + admin_code = admin[admin_code_col] + + # Truncate long names + if len(admin_name) > 20: + admin_name = admin_name[:17] + "..." + + ax.text( + centroid.x, + centroid.y, + f'{admin_name}\n({admin_code})', + ha='center', + va='center', + fontsize=9, + bbox=dict(boxstyle="round,pad=0.3", facecolor="white", alpha=0.8) + ) + + # Set title + ax.set_title( + f'{admin_level.upper()} Regions for {len(gid_list)} PRIO-GRID Cells\n' + f'Found {len(unique_admin_codes)} unique {admin_level.upper()} regions', + fontsize=14, + fontweight='bold' + ) + + # Add grid cells to legend if shown + if show_grid_cells: + legend_elements.insert(0, mpatches.Patch(color='red', alpha=0.8, label='PRIO-GRID Cells')) + + ax.legend(handles=legend_elements, loc='upper right', bbox_to_anchor=(1, 1)) + + # Set aspect ratio and labels + ax.set_aspect('equal') + ax.set_xlabel('Longitude') + ax.set_ylabel('Latitude') + ax.grid(True, alpha=0.3) + + # Add summary statistics as text + stats_text = f"Total GIDs: {len(gid_list)}\n" + stats_text += f"Unique {admin_level.upper()} regions: {len(unique_admin_codes)}\n" + if color_by_country: + stats_text += f"Unique countries: {len(unique_countries)}" + + ax.text( + 0.02, 0.98, + stats_text, + transform=ax.transAxes, + fontsize=10, + verticalalignment='top', + bbox=dict(boxstyle="round,pad=0.5", facecolor="white", alpha=0.8) + ) + + plt.tight_layout() + plt.show() + + def find_country_by_iso_a3(self, iso_a3): + """ + Find country information by ISO A3 code from the Natural Earth dataset. + + Parameters: + iso_a3 (str): ISO A3 code of the country (e.g., 'USA', 'FRA', 'TZA') + + Returns: + dict: Country information with all available fields from Natural Earth, + or None if country is not found + """ + # Validate input + if not isinstance(iso_a3, str) or len(iso_a3) != 3: + logger.warning(f"Invalid ISO A3 code format: {iso_a3}. Expected 3-character string.") + return None + + # Convert to uppercase for consistency + iso_a3 = iso_a3.upper() + + if self.use_disk_cache: + # Use disk cache + @self._disk_country_cache.cache() + def _find_country_by_iso_a3_impl(iso_a3): + # Find the country in the Natural Earth dataset + country_rows = self.countries_gdf[self.countries_gdf["ISO_A3"] == iso_a3] + + if len(country_rows) == 0: + return None + + # Get the first (and should be only) matching country + country = country_rows.iloc[0] + + # Prepare result with all available country data + result = { + "iso_a3": country["ISO_A3"], + "country_name": country["NAME_EN"], + } + + # Add all other available fields from Natural Earth + for col in country.index: + if col not in ["geometry", "ISO_A3", "NAME_EN"]: + # Convert numpy types to native Python types + value = country[col] + if pd.isna(value): + result[col] = None + elif isinstance(value, (np.integer, np.int64, np.int32)): + result[col] = int(value) + elif isinstance(value, (np.floating, np.float64, np.float32)): + result[col] = float(value) + elif isinstance(value, np.bool_): + result[col] = bool(value) + else: + result[col] = str(value) + + # Add geometry as WKT string + if hasattr(country["geometry"], "wkt"): + result["geometry_wkt"] = country["geometry"].wkt + + # Add some commonly useful derived fields + result["data_source"] = "Natural Earth" + result["centroid"] = { + "lon": float(country["geometry"].centroid.x), + "lat": float(country["geometry"].centroid.y) + } + + # Calculate bounding box + bounds = country["geometry"].bounds + result["bounds"] = { + "min_lon": float(bounds[0]), + "min_lat": float(bounds[1]), + "max_lon": float(bounds[2]), + "max_lat": float(bounds[3]) + } + + return result + + return _find_country_by_iso_a3_impl(iso_a3) + else: + # Use in-memory cache + # Generate consistent cache key + cache_key = self._get_country_cache_key(iso_a3) + + # Check cache first + if cache_key in self._country_cache: + cached_value = self._country_cache[cache_key] + if cached_value is None: + return None + result = cached_value.copy() + return result + + # Find the country in the Natural Earth dataset + country_rows = self.countries_gdf[self.countries_gdf["ISO_A3"] == iso_a3] + + if len(country_rows) == 0: + logger.warning(f"Country with ISO A3 code '{iso_a3}' not found in Natural Earth dataset") + # Update cache with None to avoid repeated lookups + self._country_cache[cache_key] = None + return None + + # Get the first (and should be only) matching country + country = country_rows.iloc[0] + + # Prepare result with all available country data + result = { + "iso_a3": country["ISO_A3"], + "country_name": country["NAME_EN"], + } + + # Add all other available fields from Natural Earth + for col in country.index: + if col not in ["geometry", "ISO_A3", "NAME_EN"]: + # Convert numpy types to native Python types + value = country[col] + if pd.isna(value): + result[col] = None + elif isinstance(value, (np.integer, np.int64, np.int32)): + result[col] = int(value) + elif isinstance(value, (np.floating, np.float64, np.float32)): + result[col] = float(value) + elif isinstance(value, np.bool_): + result[col] = bool(value) + else: + result[col] = str(value) + + # Add geometry as WKT string + if hasattr(country["geometry"], "wkt"): + result["geometry_wkt"] = country["geometry"].wkt + + # Add some commonly useful derived fields + result["data_source"] = "Natural Earth" + result["centroid"] = { + "lon": float(country["geometry"].centroid.x), + "lat": float(country["geometry"].centroid.y) + } + + # Calculate bounding box + bounds = country["geometry"].bounds + result["bounds"] = { + "min_lon": float(bounds[0]), + "min_lat": float(bounds[1]), + "max_lon": float(bounds[2]), + "max_lat": float(bounds[3]) + } + + # Update cache + self._country_cache[cache_key] = result + return result + + def find_multiple_countries_by_iso_a3(self, iso_a3_list): + """ + Find information for multiple countries by their ISO A3 codes. + + Parameters: + iso_a3_list (list): List of ISO A3 codes (e.g., ['USA', 'FRA', 'TZA']) + + Returns: + dict: Dictionary with ISO A3 codes as keys and country info as values. + Countries not found will have None as values. + """ + if not isinstance(iso_a3_list, (list, tuple, set)): + raise ValueError("iso_a3_list must be a list, tuple, or set of ISO A3 codes") + + results = {} + cache_hits = 0 + + for iso_a3 in iso_a3_list: + # Check cache first + cache_key = self._get_country_cache_key(iso_a3.upper()) + if cache_key in self._country_cache: + cached_value = self._country_cache[cache_key] + results[iso_a3.upper()] = cached_value.copy() if cached_value is not None else None + cache_hits += 1 + else: + # Not in cache, fetch the country info + country_info = self.find_country_by_iso_a3(iso_a3) + results[iso_a3.upper()] = country_info + + if cache_hits > 0: + logger.info(f"Cache hits: {cache_hits}/{len(iso_a3_list)} ({cache_hits/len(iso_a3_list)*100:.1f}%)") + + return results + + def get_country_summary(self, iso_a3): + """ + Get a summary of country information including related grid cells and admin regions. + + Parameters: + iso_a3 (str): ISO A3 code of the country + + Returns: + dict: Summary including country info, grid cell count, and admin region counts + """ + # Get basic country information + country_info = self.find_country_by_iso_a3(iso_a3) + if not country_info: + return None + + # Get all grid cells for this country + gids = self.find_gids_for_country(iso_a3) + + # Count admin1 regions if available + admin1_count = 0 + if self.admin1_gdf is not None: + admin1_count = len(self.admin1_gdf[self.admin1_gdf["iso3_code"] == iso_a3]) + + # Count admin2 regions if available + admin2_count = 0 + if self.admin2_gdf is not None: + admin2_count = len(self.admin2_gdf[self.admin2_gdf["iso3_code"] == iso_a3]) + + # Create summary + summary = { + "country_info": country_info, + "priogrid": { + "gid_count": len(gids), + "gid_list": gids[:100], # Return first 100 GIDs to avoid huge responses + "has_more": len(gids) > 100 + }, + "admin_regions": { + "admin1_count": admin1_count, + "admin2_count": admin2_count + }, + "data_source": "Natural Earth" + } + + return summary + + def search_countries_by_name(self, name_pattern, exact_match=False, case_sensitive=False): + """ + Search for countries by name pattern. + + Parameters: + name_pattern (str): Name or pattern to search for + exact_match (bool): If True, only exact matches are returned + case_sensitive (bool): If True, search is case sensitive + + Returns: + list: List of matching countries with their information + """ + if not isinstance(name_pattern, str): + raise ValueError("name_pattern must be a string") + + # Prepare the search pattern + if not case_sensitive: + name_pattern = name_pattern.lower() + search_column = self.countries_gdf["NAME_EN"].str.lower() + else: + search_column = self.countries_gdf["NAME_EN"] + + # Find matches + if exact_match: + matching_indices = search_column == name_pattern + else: + matching_indices = search_column.str.contains(name_pattern, na=False) + + matching_countries = self.countries_gdf[matching_indices] + + if len(matching_countries) == 0: + logger.info(f"No countries found matching pattern: {name_pattern}") + return [] + + # Convert matches to list of dictionaries + results = [] + for _, country in matching_countries.iterrows(): + country_info = { + "iso_a3": country["ISO_A3"], + "country_name": country["NAME_EN"], + } + + # Add a few key fields + key_fields = ["CONTINENT", "REGION_UN", "SUBREGION", "POP_EST", "GDP_MD", "INCOME_GRP"] + for field in key_fields: + if field in country: + country_info[field.lower()] = country[field] + + results.append(country_info) + + logger.info(f"Found {len(results)} countries matching pattern: {name_pattern}") + return results + + def enrich_dataframe_with_pg_info(self, df, pg_id_col="priogrid_id", time_id_col="month_id", + include_country=True, include_admin1=True, include_admin2=True, + include_pg_info=True, country_cols=None, admin1_cols=None, admin2_cols=None, pg_cols=None, + batch_size=1000, use_multiprocessing=True, show_progress=True, only_metadata=True): + """ + Enrich a DataFrame with country, admin1, admin2, and PRIO-GRID information based on PRIO-GRID IDs. + + Parameters: + df (pd.DataFrame): Input DataFrame containing PRIO-GRID IDs + pg_id_col (str): Name of the column containing PRIO-GRID IDs + include_country (bool): Whether to include country information + include_admin1 (bool): Whether to include admin1 information + include_admin2 (bool): Whether to include admin2 information + include_pg_info (bool): Whether to include PRIO-GRID information + country_cols (list): Specific country columns to include (None for all) + admin1_cols (list): Specific admin1 columns to include (None for all) + admin2_cols (list): Specific admin2 columns to include (None for all) + pg_cols (list): Specific PRIO-GRID columns to include (None for all) + batch_size (int): Number of PRIO-GRID IDs to process in each batch + use_multiprocessing (bool): Whether to use multiprocessing for faster processing + show_progress (bool): Whether to show a progress bar + + Returns: + pd.DataFrame: Enriched DataFrame with additional columns + + Raises: + ValueError: If pg_id_col is not found in the DataFrame + """ + # Validate input + if pg_id_col not in df.columns: + raise ValueError(f"Column '{pg_id_col}' not found in DataFrame") + + if only_metadata: + df = pd.DataFrame(df[[pg_id_col, time_id_col]]) + # Create a copy to avoid modifying the original DataFrame + result_df = df.copy() + + # Get unique PRIO-GRID IDs to process + unique_pg_ids = df[pg_id_col].unique() + total_ids = len(unique_pg_ids) + total_batches = (total_ids - 1) // batch_size + 1 + + logger.info(f"Starting enrichment of {total_ids} unique PRIO-GRID IDs in {total_batches} batches") + logger.info(f"Parameters: country={include_country}, admin1={include_admin1}, admin2={include_admin2}, pg_info={include_pg_info}") + + # Ensure cache directories exist + if self.use_disk_cache: + for cache_type in ["country", "admin1", "admin2", "gid"]: + cache_path = self.cache_dir / cache_type + if not cache_path.exists(): + os.makedirs(cache_path, exist_ok=True) + logger.info(f"Created cache directory: {cache_path}") + + # Process in batches + all_pg_data = {} + processed_count = 0 + + # Initialize progress bar if requested + pbar = None + if show_progress: + try: + from tqdm import tqdm + pbar = tqdm(total=total_ids, desc="Processing PRIO-GRID IDs", unit="ids") + except ImportError: + logger.warning("tqdm not installed. Progress bar will not be shown. Install with: pip install tqdm") + show_progress = False + + if use_multiprocessing and total_ids > batch_size: + # Use threading instead of multiprocessing to avoid pickling issues + from concurrent.futures import ThreadPoolExecutor, as_completed + + # Split into batches + batches = [unique_pg_ids[i:i+batch_size] for i in range(0, total_ids, batch_size)] + + # Process batches in parallel using threads + with ThreadPoolExecutor(max_workers=self._max_workers) as executor: + # Submit all batches for processing + future_to_batch = { + executor.submit(self._process_pg_batch, batch_ids, + include_country, include_admin1, include_admin2, + include_pg_info, country_cols, admin1_cols, admin2_cols, pg_cols): batch_idx + for batch_idx, batch_ids in enumerate(batches) + } + + # Collect results as they complete + for future in as_completed(future_to_batch): + batch_idx = future_to_batch[future] + try: + batch_result = future.result() + all_pg_data.update(batch_result) + processed_count += len(batches[batch_idx]) + + # Update progress + if pbar: + pbar.update(len(batches[batch_idx])) + + # Log progress every 10% or every 5 batches + if batch_idx % max(5, total_batches // 10) == 0 or batch_idx == total_batches - 1: + progress_pct = (processed_count / total_ids) * 100 + logger.info(f"Progress: {processed_count}/{total_ids} ({progress_pct:.1f}%) - Batch {batch_idx + 1}/{total_batches}") + + except Exception as e: + logger.error(f"Error processing batch {batch_idx}: {str(e)}") + # Continue with other batches even if one fails + continue + else: + # Process sequentially for smaller datasets + for i in range(0, total_ids, batch_size): + batch_ids = unique_pg_ids[i:i+batch_size] + batch_num = i // batch_size + 1 + + logger.info(f"Processing batch {batch_num}/{total_batches} ({len(batch_ids)} IDs)") + + try: + batch_result = self._process_pg_batch(batch_ids, + include_country, include_admin1, include_admin2, + include_pg_info, country_cols, admin1_cols, admin2_cols, pg_cols) + all_pg_data.update(batch_result) + processed_count += len(batch_ids) + + # Update progress + if pbar: + pbar.update(len(batch_ids)) + + # Log progress + progress_pct = (processed_count / total_ids) * 100 + logger.info(f"Completed batch {batch_num}/{total_batches} - {progress_pct:.1f}% complete") + + except Exception as e: + logger.error(f"Error processing batch {batch_num}: {str(e)}") + continue + + # Close progress bar + if pbar: + pbar.close() + + # Convert the collected data to a DataFrame + logger.info("Converting collected data to DataFrame") + pg_info_df = pd.DataFrame.from_dict(all_pg_data, orient="index") + + # Merge with the original DataFrame + if not pg_info_df.empty: + logger.info("Merging enriched data with original DataFrame") + result_df = result_df.merge( + pg_info_df, + left_on=pg_id_col, + right_on="pg_id", + how="left" + ) + + # Drop the redundant pg_id column + if "pg_id" in result_df.columns: + result_df.drop(columns=["pg_id"], inplace=True) + else: + logger.warning("No enrichment data was generated") + + logger.info(f"Enrichment complete. Added {len(result_df.columns) - len(df.columns)} new columns") + return result_df + + def _process_pg_batch(self, pg_ids, include_country, include_admin1, include_admin2, + include_pg_info, country_cols, admin1_cols, admin2_cols, pg_cols): + """Process a batch of PRIO-GRID IDs and return their information""" + batch_data = {} + + for pg_id in pg_ids: + pg_data = {"pg_id": pg_id} + + # Get PRIO-GRID information + if include_pg_info: + grid_cell = self.priogrid_gdf[self.priogrid_gdf["gid"] == pg_id] + if len(grid_cell) > 0: + grid_info = grid_cell.iloc[0].to_dict() + + # Select specific columns if requested + if pg_cols is not None: + for col in pg_cols: + if col in grid_info: + pg_data[f"pg_{col}"] = grid_info[col] + else: + # Include all grid info with pg_ prefix + for key, value in grid_info.items(): + if key != "gid": # Skip gid as it's already the key + pg_data[f"pg_{key}"] = value + + # Get country information + if include_country: + country_info = self.find_country_for_gid(pg_id) + if country_info: + # Select specific columns if requested + if country_cols is not None: + for col in country_cols: + if col in country_info: + pg_data[f"country_{col}"] = country_info[col] + else: + # Include all country info with country_ prefix + for key, value in country_info.items(): + if key != "gid": # Skip gid as it's already the key + pg_data[f"country_{key}"] = value + + # Get admin1 information + if include_admin1: + admin1_info = self.find_admin1_for_gid(pg_id) + if admin1_info: + # Select specific columns if requested + if admin1_cols is not None: + for col in admin1_cols: + if col in admin1_info: + pg_data[f"admin1_{col}"] = admin1_info[col] + else: + # Include all admin1 info with admin1_ prefix + for key, value in admin1_info.items(): + if key != "gid": # Skip gid as it's already the key + pg_data[f"admin1_{key}"] = value + + # Get admin2 information + if include_admin2: + admin2_info = self.find_admin2_for_gid(pg_id) + if admin2_info: + # Select specific columns if requested + if admin2_cols is not None: + for col in admin2_cols: + if col in admin2_info: + pg_data[f"admin2_{col}"] = admin2_info[col] + else: + # Include all admin2 info with admin2_ prefix + for key, value in admin2_info.items(): + if key != "gid": # Skip gid as it's already the key + pg_data[f"admin2_{key}"] = value + + batch_data[pg_id] = pg_data + + return batch_data + + def enrich_dataframe_with_country_info(self, df, iso_a3_col="iso_a3", + include_admin1=True, include_admin2=True, + country_cols=None, admin1_cols=None, admin2_cols=None, + batch_size=1000, use_multiprocessing=True, show_progress=True): + """ + Enrich a DataFrame with country, admin1, and admin2 information based on ISO A3 codes. + + Parameters: + df (pd.DataFrame): Input DataFrame containing ISO A3 codes + iso_a3_col (str): Name of the column containing ISO A3 codes + include_admin1 (bool): Whether to include admin1 information + include_admin2 (bool): Whether to include admin2 information + country_cols (list): Specific country columns to include (None for all) + admin1_cols (list): Specific admin1 columns to include (None for all) + admin2_cols (list): Specific admin2 columns to include (None for all) + batch_size (int): Number of ISO A3 codes to process in each batch + use_multiprocessing (bool): Whether to use multiprocessing for faster processing + show_progress (bool): Whether to show a progress bar + + Returns: + pd.DataFrame: Enriched DataFrame with additional columns + + Raises: + ValueError: If iso_a3_col is not found in the DataFrame + """ + # Validate input + if iso_a3_col not in df.columns: + raise ValueError(f"Column '{iso_a3_col}' not found in DataFrame") + + # Create a copy to avoid modifying the original DataFrame + result_df = df.copy() + + # Get unique ISO A3 codes to process + unique_iso_a3 = df[iso_a3_col].unique() + total_codes = len(unique_iso_a3) + total_batches = (total_codes - 1) // batch_size + 1 + + logger.info(f"Starting enrichment of {total_codes} unique ISO A3 codes in {total_batches} batches") + logger.info(f"Parameters: admin1={include_admin1}, admin2={include_admin2}") + + # Prepare parameters for batch processing + process_params = { + "include_admin1": include_admin1, + "include_admin2": include_admin2, + "country_cols": country_cols, + "admin1_cols": admin1_cols, + "admin2_cols": admin2_cols + } + + # Process in batches + all_country_data = {} + processed_count = 0 + + # Initialize progress bar if requested + pbar = None + if show_progress: + try: + from tqdm import tqdm + pbar = tqdm(total=total_codes, desc="Processing ISO A3 codes", unit="codes") + except ImportError: + logger.warning("tqdm not installed. Progress bar will not be shown. Install with: pip install tqdm") + show_progress = False + + if use_multiprocessing and total_codes > batch_size: + # Use threading instead of multiprocessing to avoid pickling issues + from concurrent.futures import ThreadPoolExecutor, as_completed + + # Split into batches + batches = [unique_iso_a3[i:i+batch_size] for i in range(0, total_codes, batch_size)] + + # Process batches in parallel using threads + with ThreadPoolExecutor(max_workers=self._max_workers) as executor: + # Submit all batches for processing + future_to_batch = { + executor.submit(self._process_country_batch, batch_codes, **process_params): batch_idx + for batch_idx, batch_codes in enumerate(batches) + } + + # Collect results as they complete + for future in as_completed(future_to_batch): + batch_idx = future_to_batch[future] + try: + batch_result = future.result() + all_country_data.update(batch_result) + processed_count += len(batches[batch_idx]) + + # Update progress + if pbar: + pbar.update(len(batches[batch_idx])) + + # Log progress every 10% or every 5 batches + if batch_idx % max(5, total_batches // 10) == 0 or batch_idx == total_batches - 1: + progress_pct = (processed_count / total_codes) * 100 + logger.info(f"Progress: {processed_count}/{total_codes} ({progress_pct:.1f}%) - Batch {batch_idx + 1}/{total_batches}") + + except Exception as e: + logger.error(f"Error processing batch {batch_idx}: {str(e)}") + # Continue with other batches even if one fails + continue + else: + # Process sequentially for smaller datasets + for i in range(0, total_codes, batch_size): + batch_codes = unique_iso_a3[i:i+batch_size] + batch_num = i // batch_size + 1 + + logger.info(f"Processing batch {batch_num}/{total_batches} ({len(batch_codes)} codes)") + + try: + batch_result = self._process_country_batch(batch_codes, **process_params) + all_country_data.update(batch_result) + processed_count += len(batch_codes) + + # Update progress + if pbar: + pbar.update(len(batch_codes)) + + # Log progress + progress_pct = (processed_count / total_codes) * 100 + logger.info(f"Completed batch {batch_num}/{total_batches} - {progress_pct:.1f}% complete") + + except Exception as e: + logger.error(f"Error processing batch {batch_num}: {str(e)}") + continue + + # Close progress bar + if pbar: + pbar.close() + + # Convert the collected data to a DataFrame + logger.info("Converting collected data to DataFrame") + country_info_df = pd.DataFrame.from_dict(all_country_data, orient="index") + + # Merge with the original DataFrame + if not country_info_df.empty: + logger.info("Merging enriched data with original DataFrame") + result_df = result_df.merge( + country_info_df, + left_on=iso_a3_col, + right_on="iso_a3_code", + how="left" + ) + + # Drop the redundant iso_a3_code column + if "iso_a3_code" in result_df.columns: + result_df.drop(columns=["iso_a3_code"], inplace=True) + else: + logger.warning("No enrichment data was generated") + + logger.info(f"Enrichment complete. Added {len(result_df.columns) - len(df.columns)} new columns") + return result_df + + def _process_country_batch(self, iso_a3_codes, include_admin1, include_admin2, + country_cols, admin1_cols, admin2_cols): + """Process a batch of ISO A3 codes and return their information""" + batch_data = {} + + for iso_a3 in iso_a3_codes: + # Skip empty or invalid codes + if pd.isna(iso_a3) or not isinstance(iso_a3, str) or len(iso_a3) != 3: + batch_data[iso_a3] = {"iso_a3_code": iso_a3} + continue + + country_data = {"iso_a3_code": iso_a3} + + # Get country information + country_info = self.find_country_by_iso_a3(iso_a3) + if country_info: + # Select specific columns if requested + if country_cols is not None: + for col in country_cols: + if col in country_info: + country_data[f"country_{col}"] = country_info[col] + else: + # Include all country info with country_ prefix + for key, value in country_info.items(): + if key != "iso_a3": # Skip iso_a3 as it's already the key + country_data[f"country_{key}"] = value + + # Get admin1 information + if include_admin1 and self.admin1_gdf is not None: + admin1_regions = self.admin1_gdf[self.admin1_gdf["iso3_code"] == iso_a3] + + if not admin1_regions.empty: + # Create a list of admin1 regions + admin1_list = [] + for _, admin1 in admin1_regions.iterrows(): + admin1_dict = {} + + # Select specific columns if requested + if admin1_cols is not None: + for col in admin1_cols: + if col in admin1: + admin1_dict[col] = admin1[col] + else: + # Include all admin1 info + for key, value in admin1.items(): + if key != "iso3_code": # Skip iso3_code as it's already known + admin1_dict[key] = value + + admin1_list.append(admin1_dict) + + country_data["admin1_regions"] = admin1_list + country_data["admin1_count"] = len(admin1_list) + + # Get admin2 information + if include_admin2 and self.admin2_gdf is not None: + admin2_regions = self.admin2_gdf[self.admin2_gdf["iso3_code"] == iso_a3] + + if not admin2_regions.empty: + # Create a list of admin2 regions + admin2_list = [] + for _, admin2 in admin2_regions.iterrows(): + admin2_dict = {} + + # Select specific columns if requested + if admin2_cols is not None: + for col in admin2_cols: + if col in admin2: + admin2_dict[col] = admin2[col] + else: + # Include all admin2 info + for key, value in admin2.items(): + if key != "iso3_code": # Skip iso3_code as it's already known + admin2_dict[key] = value + + admin2_list.append(admin2_dict) + + country_data["admin2_regions"] = admin2_list + country_data["admin2_count"] = len(admin2_list) + + batch_data[iso_a3] = country_data + + return batch_data + + def add_country_info_to_dataframe(self, df, iso_a3_col="iso_a3", + name_col="country_name", + additional_cols=None): + """ + Add basic country information to a DataFrame based on ISO A3 codes. + + Parameters: + df (pd.DataFrame): Input DataFrame containing ISO A3 codes + iso_a3_col (str): Name of the column containing ISO A3 codes + name_col (str): Name of the column to create for country names + additional_cols (list): Additional country columns to include + + Returns: + pd.DataFrame: DataFrame with added country information + """ + # Create a copy to avoid modifying the original DataFrame + result_df = df.copy() + + # Get unique ISO A3 codes to process + unique_iso_a3 = df[iso_a3_col].unique() + logger.info(f"Processing {len(unique_iso_a3)} unique ISO A3 codes for country names") + + # Create a mapping of ISO A3 to country name + iso_to_name = {} + iso_to_additional = {col: {} for col in additional_cols or []} + + for iso_a3 in unique_iso_a3: + # Skip empty or invalid codes + if pd.isna(iso_a3) or not isinstance(iso_a3, str) or len(iso_a3) != 3: + iso_to_name[iso_a3] = None + for col in iso_to_additional: + iso_to_additional[col][iso_a3] = None + continue + + country_info = self.find_country_by_iso_a3(iso_a3) + if country_info: + iso_to_name[iso_a3] = country_info.get("country_name") + + # Add additional columns if requested + for col in iso_to_additional: + iso_to_additional[col][iso_a3] = country_info.get(col) + else: + iso_to_name[iso_a3] = None + for col in iso_to_additional: + iso_to_additional[col][iso_a3] = None + + # Add the country names to the DataFrame + result_df[name_col] = result_df[iso_a3_col].map(iso_to_name) + + # Add additional columns if requested + for col in iso_to_additional: + result_df[f"country_{col}"] = result_df[iso_a3_col].map(iso_to_additional[col]) + + logger.info(f"Added country information to DataFrame") + return result_df + + +# Global default mapper +_DEFAULT_MAPPER = None + + +def set_default_mapper(): + """ + Set the default PriogridCountryMapper instance to be used by SpatialEntity classes. + + This function creates a global default mapper that will be automatically used + by Point, Priogrid, and Country classes when no explicit mapper is provided. + + Returns: + PriogridCountryMapper: The created default mapper instance + """ + global _DEFAULT_MAPPER + _DEFAULT_MAPPER = PriogridCountryMapper() + return _DEFAULT_MAPPER + + +def get_default_mapper(): + """ + Get the default PriogridCountryMapper instance. + + Returns: + PriogridCountryMapper: The default mapper instance + + Raises: + ValueError: If no default mapper has been set + """ + if _DEFAULT_MAPPER is None: + raise ValueError("No default mapper set. Call set_default_mapper() first.") + return _DEFAULT_MAPPER + +# Set default mapper +set_default_mapper() \ No newline at end of file