diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..b138a52 --- /dev/null +++ b/.dockerignore @@ -0,0 +1,49 @@ +# Ignore unnecessary files for Docker build +.git +.gitignore +README.rst +docs/ +notebooks/ +tests/ +.pytest_cache/ +__pycache__/ +*.pyc +*.pyo +*.pyd +.Python +*.egg-info/ +.coverage +.tox +venv/ +env/ +ENV/ +.venv/ +.env +.DS_Store +Thumbs.db +*.log +.mypy_cache/ +.idea/ +.vscode/ +*.swp +*.swo +*~ + +# Build artifacts +build/ +dist/ +*.egg-info/ + +# Docker files (don't include docker-compose in container) +docker-compose.yml +Dockerfile +.dockerignore + +# Development and documentation +CHANGELOG.rst +CODE_OF_CONDUCT.md +CONTRIBUTING.rst +LICENSE +Makefile +MANIFEST.in +deploy.sh \ No newline at end of file diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 11e99e9..1aef4cc 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -42,7 +42,7 @@ jobs: run: python3 -m pip install -e ".[dev]" - name: Run tests and collect coverage run: | - pytest --cov snowexsql --cov-report=xml + pytest --cov snowexsql --cov-report=xml -m "not integration" - name: Upload coverage to Codecov uses: codecov/codecov-action@v5 with: diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index b2c9381..8674a08 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -54,4 +54,4 @@ jobs: python3 -m pip install -e ".[dev]" - name: Test with pytest run: | - pytest -s + pytest -s -m "not integration" diff --git a/deployment/README.md b/deployment/README.md new file mode 100644 index 0000000..c219a1b --- /dev/null +++ b/deployment/README.md @@ -0,0 +1,31 @@ +# Deployment + +This directory contains all the infrastructure and deployment configurations for +running snowexsql on AWS Lambda. + +## Structure + +- **`docker/`** - Docker container configuration for Lambda + - `Dockerfile` - Lambda-compatible container definition + - `.dockerignore` - Optimization for container builds + - `requirements-lambda.txt` - Lightweight dependencies + +- **`aws/`** - AWS IAM policies and configurations + - `ecr_policy.json` - ECR repository permissions for Lambda + - `secrets_policy.json` - Secrets Manager access policy + +- **`scripts/`** - Deployment automation scripts + - `deploy.sh` - Main deployment script (container-based) + - `test_lambda.sh` - Automated testing script + +## Quick Start + +1. Run `scripts/deploy.sh` to deploy the Lambda function +2. Test with `scripts/test_lambda.sh` + +## Prerequisites + +- AWS CLI configured +- Docker installed and running +- Existing ECR repository named `snowexsql` +- Lambda function with container image support \ No newline at end of file diff --git a/deployment/aws/ecr_policy.json b/deployment/aws/ecr_policy.json new file mode 100644 index 0000000..27b3a1b --- /dev/null +++ b/deployment/aws/ecr_policy.json @@ -0,0 +1,33 @@ +{ + "Version": "2008-10-17", + "Statement": [ + { + "Sid": "LambdaECRImageRetrievalPolicy", + "Effect": "Allow", + "Principal": { + "Service": "lambda.amazonaws.com" + }, + "Action": [ + "ecr:BatchGetImage", + "ecr:GetDownloadUrlForLayer" + ], + "Condition": { + "StringLike": { + "aws:sourceArn": "arn:aws:lambda:us-west-2:390402539674:function:*" + } + } + }, + { + "Sid": "LambdaECRImageCrossAccount", + "Effect": "Allow", + "Principal": { + "AWS": "arn:aws:iam::390402539674:root" + }, + "Action": [ + "ecr:BatchGetImage", + "ecr:GetDownloadUrlForLayer", + "ecr:GetAuthorizationToken" + ] + } + ] +} \ No newline at end of file diff --git a/deployment/aws/secrets_policy.json b/deployment/aws/secrets_policy.json new file mode 100644 index 0000000..e549b63 --- /dev/null +++ b/deployment/aws/secrets_policy.json @@ -0,0 +1,12 @@ +{ + "Version": "2012-10-17", + "Statement": [ + { + "Effect": "Allow", + "Action": [ + "secretsmanager:GetSecretValue" + ], + "Resource": "arn:aws:secretsmanager:us-west-2:390402539674:secret:rds/snowexsql/credentials-*" + } + ] +} \ No newline at end of file diff --git a/deployment/docker/.dockerignore b/deployment/docker/.dockerignore new file mode 100644 index 0000000..b138a52 --- /dev/null +++ b/deployment/docker/.dockerignore @@ -0,0 +1,49 @@ +# Ignore unnecessary files for Docker build +.git +.gitignore +README.rst +docs/ +notebooks/ +tests/ +.pytest_cache/ +__pycache__/ +*.pyc +*.pyo +*.pyd +.Python +*.egg-info/ +.coverage +.tox +venv/ +env/ +ENV/ +.venv/ +.env +.DS_Store +Thumbs.db +*.log +.mypy_cache/ +.idea/ +.vscode/ +*.swp +*.swo +*~ + +# Build artifacts +build/ +dist/ +*.egg-info/ + +# Docker files (don't include docker-compose in container) +docker-compose.yml +Dockerfile +.dockerignore + +# Development and documentation +CHANGELOG.rst +CODE_OF_CONDUCT.md +CONTRIBUTING.rst +LICENSE +Makefile +MANIFEST.in +deploy.sh \ No newline at end of file diff --git a/deployment/docker/Dockerfile b/deployment/docker/Dockerfile new file mode 100644 index 0000000..c410fda --- /dev/null +++ b/deployment/docker/Dockerfile @@ -0,0 +1,11 @@ +FROM public.ecr.aws/lambda/python:3.12 + +# Copy requirements and install dependencies +COPY deployment/docker/requirements-lambda.txt ${LAMBDA_TASK_ROOT}/ +RUN pip install --no-cache-dir -r requirements-lambda.txt + +# Copy the snowexsql package +COPY snowexsql/ ${LAMBDA_TASK_ROOT}/snowexsql/ + +# Set the CMD to your handler +CMD ["snowexsql.lambda_handler.lambda_handler"] \ No newline at end of file diff --git a/deployment/docker/requirements-lambda.txt b/deployment/docker/requirements-lambda.txt new file mode 100644 index 0000000..3790fdf --- /dev/null +++ b/deployment/docker/requirements-lambda.txt @@ -0,0 +1,8 @@ +# Lambda-optimized requirements without heavy dependencies +utm>=0.5.0,<1.0 +geoalchemy2>=0.6,<1.0 +shapely>=2.0.0,<3.0 +pandas>=1.5.0,<3.0 +psycopg2-binary>=2.9.0,<2.10.0 +SQLAlchemy>=2.0.0 +boto3>=1.26.0 \ No newline at end of file diff --git a/deployment/scripts/deploy.sh b/deployment/scripts/deploy.sh new file mode 100755 index 0000000..db581ba --- /dev/null +++ b/deployment/scripts/deploy.sh @@ -0,0 +1,84 @@ +#!/bin/bash + +# AWS Lambda Deployment Script for SnowEx SQL +# This script builds and deploys the Docker container to AWS Lambda + +set -e + +# Configuration +AWS_ACCOUNT_ID="390402539674" +AWS_REGION="us-west-2" +ECR_REPOSITORY="snowexsql" +LAMBDA_FUNCTION_NAME="lambda-snowex-sql" +IMAGE_TAG="$(git rev-parse --short HEAD)" + +# Colors for output +RED='\033[0;31m' +GREEN='\033[0;32m' +YELLOW='\033[1;33m' +NC='\033[0m' # No Color + +echo -e "${GREEN}Starting SnowEx SQL Lambda deployment...${NC}" + +# Check if AWS CLI is installed and configured +if ! command -v aws &> /dev/null; then + echo -e "${RED}Error: AWS CLI is not installed${NC}" + exit 1 +fi + +# Check if Docker is running +if ! docker info &> /dev/null; then + echo -e "${RED}Error: Docker is not running${NC}" + exit 1 +fi + +# Build the Docker image +echo -e "${YELLOW}Building Docker image...${NC}" +DOCKER_BUILDKIT=0 docker build -f ../docker/Dockerfile -t ${ECR_REPOSITORY}:${IMAGE_TAG} ../.. + +# Get ECR login token +echo -e "${YELLOW}Logging into ECR...${NC}" +aws ecr get-login-password --region ${AWS_REGION} | docker login --username AWS --password-stdin ${AWS_ACCOUNT_ID}.dkr.ecr.${AWS_REGION}.amazonaws.com + +# Tag the image for ECR +ECR_URI="${AWS_ACCOUNT_ID}.dkr.ecr.${AWS_REGION}.amazonaws.com/${ECR_REPOSITORY}:${IMAGE_TAG}" +echo -e "${YELLOW}Tagging image for ECR: ${ECR_URI}${NC}" +docker tag ${ECR_REPOSITORY}:${IMAGE_TAG} ${ECR_URI} + +# Push to ECR +echo -e "${YELLOW}Pushing image to ECR...${NC}" +docker push ${ECR_URI} + +# Update Lambda function +echo -e "${YELLOW}Updating Lambda function...${NC}" +aws lambda update-function-code \ + --region ${AWS_REGION} \ + --function-name ${LAMBDA_FUNCTION_NAME} \ + --image-uri ${ECR_URI} + +# Wait for the update to complete +echo -e "${YELLOW}Waiting for Lambda function update to complete...${NC}" +aws lambda wait function-updated \ + --region ${AWS_REGION} \ + --function-name ${LAMBDA_FUNCTION_NAME} + +echo -e "${GREEN}Deployment completed successfully!${NC}" +echo -e "${GREEN}Lambda function '${LAMBDA_FUNCTION_NAME}' has been updated with the new image.${NC}" + +# Optional: Test the function +read -p "Would you like to test the Lambda function? (y/n): " -n 1 -r +echo +if [[ $REPLY =~ ^[Yy]$ ]]; then + echo -e "${YELLOW}Testing Lambda function...${NC}" + aws lambda invoke \ + --region ${AWS_REGION} \ + --function-name ${LAMBDA_FUNCTION_NAME} \ + --cli-binary-format raw-in-base64-out \ + --payload '{"action":"test_connection"}' \ + response.json + + echo -e "${GREEN}Test response:${NC}" + cat response.json + echo + rm -f response.json +fi \ No newline at end of file diff --git a/deployment/scripts/test_lambda.sh b/deployment/scripts/test_lambda.sh new file mode 100755 index 0000000..4884de3 --- /dev/null +++ b/deployment/scripts/test_lambda.sh @@ -0,0 +1,105 @@ +#!/bin/bash + +# Test script for the deployed Lambda function +# This script tests the basic functionality of the deployed Lambda + +set -e + +AWS_REGION="us-west-2" +LAMBDA_FUNCTION_NAME="lambda-snowex-sql" + +# Colors for output +GREEN='\033[0;32m' +YELLOW='\033[1;33m' +RED='\033[0;31m' +NC='\033[0m' # No Color + +echo -e "${YELLOW}Testing Lambda function: ${LAMBDA_FUNCTION_NAME}${NC}" + +# Test 1: Basic connectivity test +echo -e "${YELLOW}Test 1: Basic database connectivity...${NC}" +aws lambda invoke \ + --region ${AWS_REGION} \ + --function-name ${LAMBDA_FUNCTION_NAME} \ + --cli-binary-format raw-in-base64-out \ + --payload '{"action":"test_connection"}' \ + test_response.json + +if [ $? -eq 0 ]; then + echo -e "${GREEN}✓ Lambda invocation successful${NC}" + echo -e "${YELLOW}Response:${NC}" + if command -v jq >/dev/null 2>&1; then + # Show full response and decoded body + echo -e "${YELLOW}Full invoke response:${NC}" + jq . test_response.json + echo -e "${YELLOW}Decoded body:${NC}" + jq -r '.body' test_response.json | jq . 2>/dev/null || jq -r '.body' test_response.json + else + echo -e "${YELLOW}jq not found; using Python to pretty-print JSON${NC}" + if command -v python3 >/dev/null 2>&1; then + echo -e "${YELLOW}Full invoke response:${NC}" + python3 -m json.tool < test_response.json || cat test_response.json + echo -e "${YELLOW}Decoded body:${NC}" + python3 - "$AWS_REGION" << 'PY' +import json,sys +try: + data=json.load(open('test_response.json','r')) + body=data.get('body') + if isinstance(body,str): + try: + print(json.dumps(json.loads(body), indent=2)) + except Exception: + print(body) + else: + print(json.dumps(body, indent=2)) +except Exception as e: + print(open('test_response.json','r').read()) +PY + else + cat test_response.json + fi + fi +else + echo -e "${RED}✗ Lambda invocation failed${NC}" + exit 1 +fi + +############################################# +# Test 2: Check logs (best-effort) +############################################# +echo -e "${YELLOW}Test 2: Checking recent logs...${NC}" +LOG_GROUP=$(aws logs describe-log-groups \ + --region ${AWS_REGION} \ + --log-group-name-prefix "/aws/lambda/${LAMBDA_FUNCTION_NAME}" \ + --query 'logGroups[0].logGroupName' \ + --output text 2>/dev/null || echo "") + +if [ -z "$LOG_GROUP" ] || [ "$LOG_GROUP" = "None" ]; then + echo -e "${YELLOW}No log group found yet for ${LAMBDA_FUNCTION_NAME}. Skipping log fetch.${NC}" +else + LOG_STREAM=$(aws logs describe-log-streams \ + --region ${AWS_REGION} \ + --log-group-name "$LOG_GROUP" \ + --order-by LastEventTime \ + --descending \ + --max-items 1 \ + --query 'logStreams[0].logStreamName' \ + --output text 2>/dev/null || echo "") + + if [ -z "$LOG_STREAM" ] || [ "$LOG_STREAM" = "None" ]; then + echo -e "${YELLOW}No recent log stream found. It can take a few seconds for logs to appear.${NC}" + else + aws logs get-log-events \ + --region ${AWS_REGION} \ + --log-group-name "$LOG_GROUP" \ + --log-stream-name "$LOG_STREAM" \ + --limit 10 \ + --query 'events[*].message' \ + --output text || true + fi +fi + +# Cleanup +rm -f test_response.json + +echo -e "${GREEN}Testing completed!${NC}" \ No newline at end of file diff --git a/deployment/scripts/update_lambda_config.sh b/deployment/scripts/update_lambda_config.sh new file mode 100755 index 0000000..268ffce --- /dev/null +++ b/deployment/scripts/update_lambda_config.sh @@ -0,0 +1,44 @@ +#!/bin/bash + +# Update Lambda function configuration (timeout, memory, etc.) +# Run this after deploy.sh if you need to adjust Lambda settings + +set -e + +# Configuration +AWS_REGION="us-west-2" +LAMBDA_FUNCTION_NAME="lambda-snowex-sql" +TIMEOUT=90 # seconds (max is 900 for Lambda) +MEMORY=1024 # MB (default is 512, max is 10240) + +# Colors for output +GREEN='\033[0;32m' +YELLOW='\033[1;33m' +NC='\033[0m' # No Color + +echo -e "${GREEN}Updating Lambda configuration...${NC}" +echo -e "${YELLOW}Function: ${LAMBDA_FUNCTION_NAME}${NC}" +echo -e "${YELLOW}Timeout: ${TIMEOUT}s${NC}" +echo -e "${YELLOW}Memory: ${MEMORY}MB${NC}" + +# Update Lambda configuration +aws lambda update-function-configuration \ + --region ${AWS_REGION} \ + --function-name ${LAMBDA_FUNCTION_NAME} \ + --timeout ${TIMEOUT} \ + --memory-size ${MEMORY} + +echo -e "${GREEN}Configuration updated successfully!${NC}" + +# Wait a moment for the update to propagate +sleep 2 + +# Show current configuration +echo -e "${YELLOW}Current configuration:${NC}" +aws lambda get-function-configuration \ + --region ${AWS_REGION} \ + --function-name ${LAMBDA_FUNCTION_NAME} \ + --query '{Timeout:Timeout,Memory:MemorySize,Runtime:Runtime,LastModified:LastModified}' \ + --output table + +echo -e "${GREEN}Done!${NC}" diff --git a/docs/gallery/api_plot_pit_density_example.ipynb b/docs/gallery/api_plot_pit_density_example.ipynb index 86647af..d7d7fc5 100644 --- a/docs/gallery/api_plot_pit_density_example.ipynb +++ b/docs/gallery/api_plot_pit_density_example.ipynb @@ -22,13 +22,71 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "🔍 Testing Lambda connection...\n", + "✅ Connected: True\n", + "📊 Database: PostgreSQL 16.10 on x86_64-conda-linux-gnu, compiled by x86_64-conda-linux-gnu-cc (conda-forge gcc 14.3.0-4) 14.3.0, 64-bit\n" + ] + } + ], "source": [ + "from snowexsql.lambda_client import SnowExLambdaClient\n", + "\n", + "# Initialize client\n", + "client = SnowExLambdaClient()\n", + "\n", + "# Get all measurement classes dynamically\n", + "classes = client.get_measurement_classes()\n", + "PointMeasurements = classes['PointMeasurements']\n", + "LayerMeasurements = classes['LayerMeasurements']\n", + "RasterMeasurements = classes['RasterMeasurements']\n", + "\n", + "\n", + "print(\"🔍 Testing Lambda connection...\")\n", + "connection_test = client.test_connection()\n", + "print(f\"✅ Connected: {connection_test.get('connected', False)}\")\n", + "if connection_test.get('connected'):\n", + " print(f\"📊 Database: {connection_test.get('version', 'Unknown version')}\")\n", + "else:\n", + " print(\"❌ Connection failed\")\n", + "\n", "# imports\n", "from datetime import date\n", "import geopandas as gpd\n", - "import matplotlib.pyplot as plt\n", - "from snowexsql.api import PointMeasurements, LayerMeasurements" + "import matplotlib.pyplot as plt\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Density Cutter',\n", + " 'Manual',\n", + " 'A2 Sensor',\n", + " 'Thermometer',\n", + " 'SnowMicroPen',\n", + " 'SnowMicroPen',\n", + " 'IS3-SP-11-01F',\n", + " 'IRIS',\n", + " 'IS3-SP-15-01US',\n", + " 'Digital Thermometer']" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "LayerMeasurements.all_instruments" ] }, { @@ -40,32 +98,34 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[('Cameron Pass',), ('Sagehen Creek',), ('Fraser Experimental Forest',), ('Mammoth Lakes',), ('Niwot Ridge',), ('Boise River Basin',), ('Little Cottonwood Canyon',), ('East River',), ('American River Basin',), ('Senator Beck',), ('Jemez River',), ('Grand Mesa',)]\n" + "['5S43-M0708', 'EA229', '1S8-M0932', 'CAMLPD_20191218_1128', 'COERGT_20200219_1500', '2S3-M0844', '5N15-M1193', '1S17-M1293', 'COERIB_20200312_0938', 'COGM9C28_20200131', '9N29-M0881', '6N17-M1341', '8N38-M0849', 'COFEB1_20210421_0805', 'CONWOF_20200122_1140', '2S48-M0683', 'COGM8N34_20200130', '3N22-M0766', 'EB237', '2N4-M0610', 'COER12_20200226_1226', 'COGMSO_20200328_0900', 'COER14_20200201_1300', '3S47-M0743', 'EA410', 'IDBRLO_20200212_1519', 'COER14_20200512_1300', 'COFEB2_20210421_0905', '3S33-M1052', 'EA092', '2N4-M0620', 'COGM2C3_20200131', 'MTCASX_20210217_1147', '2S7-M1240', '2S11-M0948', 'COGM5S43_20200129', 'UTLCAC_20200227_1000', '6N17-M1349', 'IDBRBU_20200226_1445', 'COER13_20200226_1115', 'WN485', '1S1-M0817', '6N18-M0710', '2N12-M0903', '2S7-M1251', '5N15-M1191', '2S11-M0956', '5S29-M1093', 'COFEB1_20200304_1530', 'COER13_20200428_1230', 'COGM5S49_20200204', 'COCPJW_20191218_1125', '7C15-M0824', '5S43-M0716', '6N18-M0708', 'CAMLPD_20200212_1029', '5S29-M1092', 'COFEB2_20210428_0926', 'COGM2N13_20200206', 'IDBRLT_20210316_1235', 'NMJRHQ_20200205_1100', 'COGM2C4_20200131', '2N12-M0870', '3S14-M0983', '3S33-M1063', '2S3-M0831', '2N12-M0867', '8C11-M1162', '2N12-M0900', '5N10-M1368', '2S48-M0681', '1S2-M1217', '2S16-M1264', '1S8-M0925', '3N22-M0764', 'COERO2_20200201_0950', '9S51-M0774', 'TLS-FL3A-M1320', '5S21-M1013', 'COFESL04_20210122_0745', '2N4-M0606', '5N10-M0678', 'TLS-FL3A-M1315', 'A790', '9N29-M0876', 'COFEB1_20210428_0846', '5N15-M1207', '2S7-M1243', 'WB472', '5N15-M1188', 'COFEFC01_20210303_0938', '3S47-M0740', '3S33-M1065', 'COGM9N29_20200130', '3S33-M1061', 'COGMTraining_20200127', '2S48-M0695', '5N19-M0735', 'CONWFN_20200311_1100', 'IDBRBT_20210225_1225', '2S11-M0951', '3S14-M0994', '9S51-M0770', '6S34-M1045', '1S8-M0919', 'COSBSA_20210127_1210', '7C15-M0821', 'CASHFO_20200129_1053', 'COFEB1_20210217_1109', '2N12-M0911', '3N22-M0746', 'COERIB_20200129_1145', '3S33-M1074', '8C18-M1100', '8N38-M0847', 'COCPCP_20210406_0855', '8C18-M1108', 'IDBRBO_20200305_1245', 'WA282', 'COFEJ1_20200318_1440', '5S21-M1007', 'COERO6_20200427_1345', '1S2-M1214', '2N12-M0905', 'COCPJW_20200124_1135', '2S11-M0960', 'COGM5N19_20200128', 'COGM2N14_20200211', 'WB498', 'DB254', 'COCPCP_20210423_1048', 'COFESL16_20210224_1329', 'COFEJ1_20191219_1100', 'COFEFC15_20210317_1109', '3N22-M0763', '1S1-M0800', 'D672', 'COERO4_20200513_0845', '3S33-M1071', 'COERAP_20200512_1430', '1S1-M0819', '2S16-M1283', '6S34-M1048', 'COERO2_20200201_1220', '6S34-M1027', '1N3-M1446', 'CONWFF_20200122_1020', 'COFEJ2_20200318_1400', '2S16-M1272', 'COFEJ1_20200205_1044', '2S16-M1262', 'N524', '2S16-M1268', '1N3-M1442', '8N38-M0857', '1S8-M0912', '7C15-M0829', 'EA221', 'COERUP_20200429_1030', 'COGM5N15_20200206', 'COGM8C36_20200205', '5N11-M1404', '8N38-M0855', '5N10-M1373', '2C12-M1479', '6S34-M1032', 'COERGT_20200117_1015', 'COFEJ2_20191029_1254', 'COGM6S34_20200204', 'COERTR_20200202_1034', '5N10-M0674', '2N13-M1186', 'COGMCT_20200131_1016', '8N38-M0860', 'TLS-FL3A-M1312', '9C17-M0787', 'IDBRLT_20210302_1345', 'A759', 'EA219', 'COGM8N9_20200205', 'CAMLCP_20200311_0908', 'WB242', 'COFEB1_20200131_1230', '2N12-M0882', 'EB257', 'COGMST_20200328_0715', '2N13-M1173', '2S11-M0972', 'COFESL01_20210127_1511', '1N6-M0643', 'WN475', 'I775', 'COFEFC02_20210303_1222', 'WA081', 'COFESL09_20210122_1008', '9C16-M1130', '5N10-M1358', '5S21-M1006', 'IDBRBT_20210318_1500', 'COFEFC04_20210303_1529', '9C16-M1142', '1S2-M1220', '3N22-M0753', '5C21-M0862', 'COERIB_20200422_1130', 'COSBSB_20200219_1240', '6N18-M0701', '8N38-M0837', 'COFEB1_20210303_0933', '5N15-M1212', '9N29-M0885', '2S3-M0834', 'CASHFO_20200226_1000', 'COCPMR_20200124_1115', 'COFEB2_20210303_1115', 'COGM2C6_20200131', 'COCPCP_20210202_1311', 'TLS-FL2A-M1415', 'GML-M1022', '6S34-M1049', 'COCPMR_20210202_0937', 'CONWFF_20200212_1110', 'COFEJ2_20200210_1345', 'COERO6_20200201_1500', 'IDBRLO_20200226_0928', 'CASHT4_20200219_1230', 'COGMWO_20200409_1218', '6S34-M1025', 'IDBRBS_20210210_1136', 'COERO6_20200201_1400', 'EB280', '5N19-M0720', 'COGMCT_20200226_0855', '3S14-M0976', 'COGM4N27_20200130', 'COERUP_20200202_1030', '3S33-M1060', '8C18-M1123', 'CONWC1_20200212_0910', '1N3-M1436', '2N4-M0611', '3S14-M0984', 'EB035', '7C15-M0831', '9N29-M0892', '2N13-M1174', 'UTLCAC_20200220_1400', 'COGM6S19_20200129', 'COSBSA_20210317_1241', 'COGM8N52_20200204', '1S8-M0923', '2S16-M1279', 'CONWFF_20200122_1025', 'DN346', 'COFEB2_20200226_1440', '6N17-M1328', '2N4-M0621', 'COFEB2_20210224_1033', '5C21-M0854', 'A523', '5S43-M0720', '5S29-M1081', '6N18-M0715', 'COFEJ1_20200210_1300', '5S43-M0722', 'TLS-FL2A-M1416', '2N4-M0607', 'COGM6C37_20200129', 'A665', 'MTCASX_20210120_1100', '2C12-M1482', 'COFEFC13_20210303_1055', '2S16-M1277', '1S2-M1230', '9S51-M0769', '1S1-M0804', '5N10-M0670', 'IDBRBL_20210202_1530', 'A763', '8N25-M0791', '2S48-M0687', '5N11-M1383', '9N29-M0878', 'COGMCO_20200325_1015', 'COGMST_20200219_1500', 'A548', 'DB093', 'COFEB1_20210203_0936', '2S3-M0829', 'COFEB1_20200212_1030', 'IDBRLO_20210115_1335', '7C15-M0825', '2N4-M0624', 'COGM2N4_20200128', '6S34-M1041', 'COFESL16_20210210_1152', 'COER13_20200512_1130', '8C18-M1124', '2S11-M0950', '2N12-M0873', 'COGM8N37_20200204', 'IDBRLO_20201125_1215', '2N12-M0877', '6N17-M1343', 'DN086', 'COGMCO_20200122_1015', 'COFESL17_20210224_1546', 'COFEB2_20200205_1419', '6N18-M0712', 'COCPCP_20210120_1410', 'CONWFS_20200122_1406', '5S29-M1077', '1N3-M1433', '5N10-M1366', 'COGM9S39_20200201', 'COGM6S26_20200212', '5N11-M1385', '5C21-M0856', '1N6-M0646', '2S48-M0685', '3N22-M0756', '8N38-M0858', 'COGM7S23_20200206', 'COGM1C5_20200212', 'COGM2S11_20200201', '1S1-M0801', '1S8-M0906', 'WB493', 'COFESL09_20210127_1231', 'COERO4_20200201_1115', '5N10-M0665', 'A654', 'N659', '2S16-M1281', 'DB343', 'TLS-FL2A-M1421', 'COFEJ2_20210428_1241', 'COER12_20200226_1241', '6S34-M1042', '9S51-M0777', '2N12-M0909', 'TLS-FL3A-M1325', '2S48-M0689', 'IDBRBU_20200124_1315', '2N13-M1187', 'COGM6N46_20200201', 'WB488', 'N762', 'COERGT_20200311_1025', 'CONWFN_20200513_1024', 'EB252', '8C18-M1126', '5C21-M0858', '2S11-M0963', 'COGM9N39_20200210', '5S21-M1002', '1S8-M0915', '3N22-M0765', 'COGM8C32_20200209', 'EA207', 'IDBRBT_20210217_1222', '5N11-M1388', '6N17-M1326', 'COGM1N7_20200211', 'CB068', 'TLS-FL3A-M1305', '1N6-M0650', 'COERO6_20200427_1430', '8N25-M0776', 'DA087', '5N10-M0677', 'COSBSA_20200219_1446', 'NMJRBA_20200226_1139', 'COGMWO_20200409_0550', 'COGM5N50_20200201', '2N12-M0892', '5N10-M0672', 'COFEJ2_20200304_1320', 'CONWOF_20200212_1310', '9C16-M1132', 'IDBRBL_20210127_1330', 'CASHOP_20200129_1057', '5N19-M0744', '7C15-M0812', '2N4-M0618', 'I679', '6S34-M1035', 'CASHT4_20200311_1045', '2S48-M0688', 'COGM5S42_20200204', '2N12-M0861', '2N13-M1177', '5N11-M1381', '3S14-M0987', '2S16-M1271', '1S1-M0813', 'COFEJ1_20210310_0954', '5S21-M1008', 'COGM1C1_20200131', 'COGMST_20200421_1507', '3N22-M0762', 'COERGT_20200212_1400', 'COFEJ1_20191216_1130', '2S3-M0826', 'UTLCAW_20210120_1330', 'COFEJ1_20200104_1110', 'IDBRBS_20201208_1149', 'UTLCAW_20200220_1350', '2S16-M1275', 'COFESL04_20210217_1030', 'COGM3S47_20200129', 'COGM1S8_20200201', '7C15-M0827', '2N12-M0879', '3S47-M0746', 'COSBSB_20200304_1215', 'IDBRBS_20200130_1200', '9C17-M0796', '1S8-M0907', '2S3-M0839', 'IDBRLT_20200212_1400', 'COFEJ1_20191223_1120', '6N17-M1339', '5S29-M1089', '1S8-M0921', 'COFESL06_20210210_1057', 'COGM1C14_20200131', 'COFEJ1_20210317_1340', '8N25-M0785', 'CAAMCL_20191220_1300', 'IDBRBL_20210114_1300', '6N17-M1336', 'IDBRBL_20210107_1245', '1N6-M0647', 'COGM2C12_20200212', 'CONWFS_20200122_1400', 'COGM8C25_20200130', 'COFEJ2_20200108_1208', '2S11-M0954', '1N6-M0641', '3N22-M0769', 'CONWFF_20200513_0930', 'COGMST_20200421_0726', '2S7-M1244', '1S8-M0914', '1S8-M0913', '5S43-M0705', 'COGM8C35_20200205', 'COGM8N33_20200206', 'IDBRBT_20201216_1415', 'CONWFN_20200122_1231', 'DB327', 'IDBRBO_20200123_1430', 'COCPMR_20200219_1314', 'COGM8N55_20200128', 'IDBRBS_20200213_1130', 'COERIB_20200122_1200', 'COGM5N41_20200130', 'COGM9C23_20200209', 'CONWFS_20200122_1405', '2N12-M0908', '2S48-M0703', 'IDBRBU_20200207_1230', '2S16-M1276', 'COFEJ1_20210115_1120', '8N25-M0772', 'CASHFO_20200311_0823', '5N15-M1203', '5C21-M0853', 'COFEJ1_20210203_1137', '5N15-M1196', '3S47-M0733', 'TLS-FL2A-M1425', 'COGM1N23_20200211', 'COFEJ1_20191029_1210', '2N12-M0875', '2S3-M0827', '5S43-M0721', 'D698', 'WB283', 'COSBSA_20200311_1125', '2S7-M1254', 'COERUP_20200429_1130', '1S8-M0926', '6N17-M1337', '2N51-M1474', 'CONWFS_20200311_0931', '1S17-M1284', '8C18-M1103', '9N29-M0891', 'WB032', '7C15-M0818', 'N546', 'COFEJ1_20191210_1158', 'COGM5C27_20200209', 'IDBRLT_20210216_1420', '5S21-M1018', '5S43-M0704', 'IDBRLT_20200122_1324', '8C18-M1098', 'COGMST_20200503_0653', 'IDBRBS_20191218_1000', 'COGMCO_20200219_0930', 'COFESL03_20210127_1348', 'COGM1C1_20200208', 'COER14_20200512_1230', '2N12-M0874', 'WA489', '9C17-M0785', '1N3-M1443', '1N6-M0626', '2C12-M1486', 'SA326', 'IDBRBS_20200206_1200', 'CASHOP_20200212_0938', 'COERAP_20200201_1524', 'CONWFS_20200513_0945', '2S3-M0823', '8C18-M1113', '5N10-M1364', '6S34-M1040', 'COERUP_20200202_1215', 'IDBRBO_20191218_1424', 'A738', 'COGMST_20200328_1430', 'I549', 'COGM3S14_20200201', 'COERUP_20200512_0930', 'SB377', '3S14-M0995', 'COFEJ1_20200212_1342', '9C16-M1129', 'EN096', '2N12-M0898', 'IDBRLO_20210209_1130', 'WB241', 'COER14_20200226_1007', 'COFEJ2_20200205_1200', '8C18-M1105', '1S1-M0802', 'COFESL10_20210127_1113', '8N25-M0780', 'COGMCT_20200304_1252', 'IDBRMM_20210203_1210', '1S1-M0799', 'CONWOF_20200422_1155', 'IDBRBL_20210122_1200', '3S33-M1066', 'COFESL06_20210217_1155', 'TLS-FL2A-M1408', 'COCPMR_20210527_1145', '1N6-M0631', 'DA085', 'COFEFC13_20210317_0945', '2S11-M0968', '1S17-M1291', '1N3-M1453', '2S48-M0693', 'IDBRBU_20200108_1155', '5S29-M1087', 'COERIB_20191219_1110', '3S47-M0751', '1S17-M1289', 'COGM9N42_20200204', '5S21-M0999', '2S11-M0947', '9C17-M0798', 'COER14_20200226_1040', '2S3-M0836', 'COER14_20200226_1024', '2S7-M1255', '1N6-M0642', 'NMJRHQ_20200129_1026', 'COGM1S1_20200129', '5N10-M0660', '3N22-M0749', '2S16-M1280', '7C15-M0820', 'COGMST_20200422_1618', '8N25-M0790', 'CONWSA_20200212_1045', 'COGM8C22_20200131', 'COGM6N31_20200130', 'COCPMR_20210113_0945', 'DN341', '9C17-M0793', 'CONWFF_20200311_0900', 'UTLCAW_20210224_1215', '1S1-M0812', '6S34-M1026', 'COGM5C20_20200130', '5C21-M0857', 'TLS-FL2A-M1418', '5S29-M1078', 'WB025', 'N501', '8C18-M1107', '2S11-M0952', 'COGM5N10_20200210', '5S29-M1083', '5N11-M1400', 'WA474', 'IDBRBO_20200109_1411', 'IDBRBU_20200220_1100', 'COGMCO_20191219_1220', 'IDBRBL_20210309_1500', 'NMJRHQ_20200226_1157', '1S2-M1216', 'CONWSA_20200205_1145', 'WA082', 'A765', 'COGM2N8_20200208', 'N787', '2N13-M1185', 'TLS-FL2A-M1422', '8C18-M1114', 'CASHT4_20200304_1058', 'COGM2S7_20200208', 'CAAMCL_20200306_1145', '1S8-M0939', 'WA494', '1S2-M1221', 'IDBRBS_20201218_1100', 'IDBRLT_20210121_1124', '6N17-M1327', 'WN103', 'CAMLCP_20200212_1015', 'CONWFN_20200122_1230', 'COGM1S13_20200205', 'COFEJ1_20200311_0915', '6N18-M0707', 'COERGT_20200129_0940', 'COGMCT_20200318_1005', '1N6-M0655', 'CONWFS_20200212_1108', '8N38-M0844', '2S7-M1242', 'UTLCAC_20200124_1222', '5N19-M0730', 'COERO2_20200226_1018', '9C17-M0797', '2S7-M1250', '2N14-M1455', 'COGM2N48_20200201', '2N12-M0906', '5S21-M1014', 'COFEB1_20200226_1350', 'COERGT_20191218_1316', '1N3-M1450', 'WN483', 'COERO6_20200201_1430', 'NMJRHQ_20200304_1105', 'COGMSO_20200421_0553', '1S8-M0930', 'IDBRBS_20200227_1030', '7C15-M0826', '2S3-M0840', '1N3-M1454', 'COFEB1_20200205_1300', '5N11-M1386', '3S47-M0742', '6N18-M0694', '2N13-M1180', 'COGMSO_20200421_1626', '5N15-M1194', 'EN255', '3S47-M0731', '5C21-M0874', 'CONWFF_20200212_1145', '1N6-M0652', '8N38-M0838', 'COFEJ2_20200119_1140', '3N22-M0771', 'COER12_20200512_1045', 'COCPCP_20210309_1230', 'COFEB2_20191219_1510', '2N14-M1464', 'COGMWO_20200316_0841', '2S7-M1246', '6S34-M1024', '8C18-M1099', 'COSBSA_20191218_0915', 'COGM2S35_20200130', 'IDBRBL_20210324_1130', 'N656', 'NMJRBA_20200205_1114', 'COGMSO_20200131_1225', '1N6-M0645', '5N19-M0724', 'COGMSO_20200321_1006', '1S2-M1235', 'COER13_20200512_1115', '6N17-M1342', '9C17-M0808', '9C17-M0807', 'EA039', 'COFEB2_20200131_1304', 'UTLCAC_20210115_1300', '2N12-M0894', 'COGM2C2_20200131', '1S17-M1296', '3S47-M0734', 'COFESL09_20210224_1351', 'UTLCAW_20210310_0930', 'EA225', 'WA079', '5C21-M0863', '6N18-M0698', 'TLS-FL3A-M1319', '3S38-M1095', 'COCPMR_20210224_0940', '9C16-M1152', '7C15-M0833', 'IDBRBS_20200312_1000', 'COER13_20200226_1130', '5N19-M0719', 'I684', '9C16-M1144', '5N10-M1356', '3S47-M0755', 'COCPMR_20210303_0940', 'COERAP_20200201_1610', 'COGMWO_20200331_1530', '8N25-M0797', '9S51-M0767', '8C18-M1125', 'N612', '2N12-M0868', 'IDBRLO_20210121_1000', '6N18-M0711', 'COCPCP_20210127_1400', 'COGMCO_20200212_0852', '1S1-M0808', 'CN063', 'CONWC1_20200226_0820', '1S8-M0916', '2N4-M0612', 'IDBRLT_20200311_1117', 'COGMWO_20200331_0934', '8N25-M0774', '1N23-M1478', '2S11-M0969', 'DA406', 'DB468', '8C18-M1106', 'SB029', 'DN013', '5N10-M1357', '2S3-M0824', '5N19-M0722', 'IDBRMC_20200212_1100', 'CAMLPD_20200129_1030', 'N613', 'COFEB1_20191219_1404', '2S7-M1241', 'IDBRLO_20210216_1230', '9C17-M0790', 'IDBRBT_20201209_1345', 'CAMLCP_20200129_0950', '2S3-M0845', 'GML-M1020', '9C16-M1131', '5N19-M0729', '8N25-M0794', '2S11-M0970', '1S1-M0810', 'TLS-FL2A-M1428', 'COGM8N38_20200130', '2N12-M0876', '6N17-M1338', '1S8-M0920', '9C17-M0786', '1S8-M0936', 'COER14_20200201_1350', 'COFEJ1_20210303_1410', 'TLS-FL3A-M1310', '6S34-M1047', 'I668', 'EN097', 'COFEB1_20210210_0915', '3S14-M0977', '5S43-M0711', 'COGM2S45_20200210', '1N6-M0632', 'A652', '9C17-M0805', '5N19-M0721', '1S8-M0922', '6N18-M0689', 'COERIB_20200219_1130', 'COERUP_20200512_0900', 'COER14_20200428_1015', 'COGMST_20200401_0900', '9C16-M1145', 'N742', '1N6-M0630', 'COCPCP_20210322_1335', '3S33-M1075', 'COCPMR_20210120_0942', '5N19-M0732', 'UTLCAC_20200305_1100', '7C15-M0822', 'MTCAWH_20210224_1115', 'COSBSA_20200122_1235', 'COFEJ2_20210224_1504', '2S16-M1267', 'COGMCO_20200304_0921', '5N10-M0675', '9S51-M0772', '5S29-M1094', 'DA464', 'COCPMR_20210218_1020', 'IDBRLT_20210309_1205', '2S16-M1261', 'COCPMR_20210520_1205', 'MTCASX_20210305_1052', 'EN038', 'NMJRBA_20200220_1234', '2N12-M0864', 'CASHFO_20200212_0919', '2S3-M0841', '8C11-M1157', 'COGM2S20_20200206', '9N29-M0887', 'COGM8S41_20200210', '1N3-M1448', 'CONWFS_20200513_0930', '2C12-M1488', 'COGMSO_20200306_1610', '3S33-M1056', 'WA034', 'COFESL14_20210210_1452', '7C15-M0811', 'COCPJW_20200212_1108', '1N23-M1477', '2N12-M0910', '3S14-M0975', 'COGM3S5_20200129', '6S34-M1029', '9S51-M0766', 'CONWOF_20200513_1115', '2S3-M0835', '5S21-M1015', 'DA470', '8C18-M1111', 'COERIB_20200117_1030', 'COER14_20200428_1045', 'COGMSO_20200406_1600', '5S43-M0717', '9C16-M1147', '8C18-M1110', '6N18-M0702', 'WN104', '5S21-M1000', '6N17-M1345', 'IDBRLT_20210209_1300', '5N10-M0659', 'EB100', 'CAAMCL_20200313_1030', 'DB263', '2S48-M0696', '2N13-M1167', 'UTLCAW_20210322_0950', '5S43-M0710', '1S8-M0931', 'COFESL04_20210224_1020', 'WN239', 'COFEFC05_20210317_1316', 'COFEFC15_20210303_1150', 'COFESL14_20210121_1446', '6N17-M1330', 'WN487', 'UTLCAW_20200131_1200', 'COERGT_20200124_1205', 'COFEJ1_20210428_1208', 'I529', 'CONWC1_20200129_1020', '5N15-M1201', 'COCPMR_20191218_1350', '1N3-M1452', 'IDBRLO_20210204_1035', 'CASHFO_20191220_1231', 'IDBRLO_20200131_0926', 'COERO4_20200513_0900', '2N13-M1181', '2S48-M0686', '5N11-M1395', 'COFEJ1_20210127_1048', 'CASHOP_20200311_0850', 'DA405', 'COGMCO_20200312_0850', 'A766', 'MTCAWX_20210120_1350', '5N10-M0671', '5N19-M0728', 'COERAP_20200201_1550', 'COERO4_20200428_0900', 'UTLCAC_20200213_1345', 'COER13_20200226_1155', 'IDBRBU_20191219_1000', 'COERIB_20200520_0930', '9C17-M0804', '2N14-M1461', 'CONWFN_20200513_0944', 'CONWFN_20200212_1335', '5C21-M0855', '2S16-M1273', 'WA256', '1N6-M0657', 'COGM9S40_20200201', '1S17-M1285', 'COERIB_20200408_1042', 'COGM2S6_20200211', '2N12-M0885', '2S16-M1263', 'CAMLCP_20200122_1000', '5C21-M0872', '3S33-M1068', 'COGM9C17_20200130', '8N38-M0848', '2N4-M0602', 'COER13_20200512_1200', 'COGMSO_20191219_1600', '1N3-M1441', 'UTLCAW_20210115_1510', 'COERTR_20200202_1033', '9C16-M1148', 'CONWOF_20200122_1100', '9S51-M0773', 'WA020', 'COGMST_20200503_1732', 'DB247', 'IDBRLO_20210302_1200', 'COGM5S31_20200130', 'COGMCO_20200318_0825', '1N6-M0629', 'COGMSO_20200328_1630', 'DA458', 'TLS-FL3A-M1323', 'COCPMR_20200226_0949', 'COGM1C7_20200131', '5S21-M1016', 'COERGT_20200205_0945', 'COSBSA_20200212_1236', '9S51-M0758', '5N19-M0731', 'DN091', '2S3-M0822', '9S51-M0775', '1S2-M1233', 'WB490', 'COGMCT_20200325_1230', 'IDBRLT_20210115_1457', 'COFEJ1_20191024_1243', 'CONWC1_20200304_0903', '7C15-M0832', 'IDBRLT_20210223_1245', 'IDBRBS_20210120_1120', '9S51-M0763', 'COERAP_20200427_0845', 'COERIB_20200226_1145', '1S1-M0820', 'COSBSA_20210317_1018', 'IDBRBO_20200219_1222', 'N547', 'COFEJ2_20200311_1030', 'CAMLCP_20200226_0954', '3S47-M0748', '1N6-M0634', 'COGM9N59_20200128', 'N556', '2N14-M1460', 'COER13_20200428_1300', 'COERUP_20200226_1400', 'COFEJ1_20200226_1208', '2S7-M1245', 'CONWFS_20200513_1000', 'COFEFC12_20210317_0845', 'COGM8N51_20200204', '3S14-M0985', 'COGMFL2A_20200211', '2N12-M0865', 'DA411', 'UTLCAC_20210204_1350', '2N4-M0622', '3N22-M0757', 'COFESL01_20210224_1440', '2S11-M0961', '2N12-M0899', 'TLS-FL3A-M1313', 'COERAP_20200427_1015', 'CONWFN_20200311_1130', 'CASHT4_20200129_1415', 'COCPJW_20200226_0947', '5S21-M0997', 'MTCASX_20210224_1231', 'COERO4_20200201_1215', 'COFEB2_20201217_1330', 'N657', '3S14-M0988', 'COGM8C18_20200205', '2S48-M0699', '6N17-M1347', '6S34-M1028', 'N730', '2S48-M0682', '8C11-M1160', '2S48-M0701', 'TLS-FL3A-M1324', 'COGMCO_20191220_1030', '8C18-M1101', '9S51-M0771', 'COGM4C30_20200131', 'CONWOF_20200513_1050', 'COGM8C29_20200205', '5N10-M1360', '2S7-M1249', '5S29-M1091', 'COERO4_20200513_0915', '2S3-M0837', '8N38-M0839', 'COFEB1_20210505_0815', '5N10-M1376', '5N11-M1387', 'COFEJ1_20210407_0754', '1S1-M0811', 'TLS-FL3A-M1317', 'TLS-FL3A-M1307', '9S51-M0764', 'IDBRBL_20210225_1330', 'COGM2C33_20200130', 'COFEB2_20210217_1248', '1N6-M0639', 'IDBRBK_20210120_1310', '2S3-M0833', '6S34-M1034', 'COGM6S53_20200206', 'COGM6N18_20200128', '9S51-M0778', 'COGM7N57_20200128', 'COCPJW_20200219_1004', '9C16-M1140', '5C21-M0867', 'SB027', '3S14-M0993', '2S16-M1278', 'COGM2S36_20200129', '1S1-M0805', '5N11-M1393', '2N4-M0609', 'COFEJ1_20191124_1112', 'A761', 'COGMWO_20200316_1519', '2N12-M0881', 'IDBRBS_20210310_1150', 'COGM8N45_20200201', 'COER12_20200201_1232', 'DB106', 'IDBRLO_20200220_0845', '3S14-M0981', 'COGM8N35_20200210', 'COCPJW_20200311_0900', 'COGM8N54_20200204', '3S47-M0744', 'UTLCAW_20200305_1230', '1S1-M0814', '2S11-M0959', 'IDBRBS_20210128_1200', 'COFESL16_20210128_1215', '6N17-M1333', 'UTLCAW_20200227_1145', '5N15-M1209', 'CAMLCP_20191218_1108', 'NMJRHQ_20191220_1100', '1N3-M1449', '5S43-M0707', '6S34-M1046', 'COER12_20200512_1100', '5S21-M1010', 'COGMWT_20200409_0725', '2N12-M0880', 'COSBSA_20210120_1130', 'CONWC1_20191218_0915', '3S47-M0750', 'COFEJ1_20200119_1040', '5C21-M0873', 'COFEJ2_20191203_1310', '8N25-M0778', '2S48-M0702', 'COGM5N10_20200128', '5S43-M0725', 'COGM2S16_20200208', 'SB011', '3S14-M0974', 'COER12_20200428_1445', 'TLS-FL2A-M1411', '2S16-M1282', '5S29-M1079', '6S34-M1036', '3S47-M0753', 'COGM9N56_20200204', '7C15-M0834', 'COGM7C15_20200130', 'CONWFF_20200311_0920', 'A664', 'COGMCT_20200312_1030', 'CONWFF_20200122_1010', 'COERTR_20200202_1030', '3S33-M1064', '1S1-M0798', 'COFEB1_20200311_1200', '1S8-M0928', '5S21-M1005', 'N788', 'COERAP_20200226_1600', 'DN469', '1N3-M1444', '2C12-M1489', '2S7-M1239', 'WN017', 'COGM5N24_20200212', 'DB107', '9C17-M0784', '3S47-M0732', 'CAAMCL_20200228_1130', 'UTLCAW_20200124_1100', 'IDBRLT_20200304_1111', '5S43-M0728', 'COSBSB_20191218_1415', '5N10-M0676', 'N667', 'COFESL15_20210121_1546', 'COGM9C19_20200130', 'IDBRLO_20210316_1130', '2S11-M0955', 'CN069', '5N11-M1390', 'COERO4_20200428_0830', '2N4-M0615', '2S3-M0838', 'COCPMR_20210210_1015', 'CASHFO_20200219_1000', '3S33-M1073', 'IDBRBO_20200312_1215', '8N25-M0793', '2N4-M0616', 'IDBRLO_20200311_0930', 'COER12_20200428_1400', '3S33-M1072', 'COGM1C8_20200131', 'IDBRBL_20210407_1200', 'COFEB2_20200212_1120', '6N18-M0690', 'COGM3S52_20200204', 'IDBRLO_20210323_1207', '1S8-M0934', '9C17-M0794', 'COGM2C9_20200131', 'COGM9N30_20200206', '3S33-M1058', 'COERTR_20200202_1032', 'COFEB2_20210331_1120', 'COFEJ2_20191024_1322', '5N10-M0673', '1S8-M0940', '6S34-M1033', 'COGMFL1B_20200212', '1N3-M1451', 'COFEJ2_20210303_1601', '2C12-M1481', 'COGM6S22_20200205', 'COGM2S46_20200204', '7C15-M0814', '5S21-M1017', '5S43-M0718', 'CB056', '1S1-M0807', '6N18-M0699', 'COGMWO_20200316_1358', '5S29-M1080', 'COFEB2_20210407_0932', '5N10-M1374', 'COGM1N20_20200205', '2S48-M0690', '1N6-M0653', 'COCPMR_20200212_1348', 'CONWFS_20200212_1040', 'COGMST_20200306_1815', 'TLS-FL2A-M1410', '1S8-M0917', '2C12-M1490', 'IDBRBT_20210122_1400', 'I505', 'UTLCAC_20200312_0930', 'COFEJ2_20200104_1200', '9N29-M0884', '5N19-M0742', 'UTLCAW_20210317_0850', '3S33-M1057', 'COGM8C11_20200205', 'COERUP_20200226_1330', '5N11-M1391', 'SA356', 'COGMSO_20200406_0650', 'COER14_20200512_1330', '1S8-M0911', '5N10-M1354', '1S8-M0924', 'COER12_20200201_1305', '1S1-M0803', 'WA240', '5N15-M1198', '8C18-M1121', '8N25-M0789', '5S21-M1011', 'WN432', 'COGM3N22_20200128', '8C18-M1112', 'COCPCP_20210507_0905', 'GML-M1019', '5N10-M1370', '8N38-M0856', '9N29-M0893', '1N3-M1447', 'COERO2_20200226_1120', '6N18-M0706', 'COERO4_20200428_0915', 'EB234', '7C15-M0815', 'DA375', '2S3-M0830', 'NMJRHQ_20200212_1305', 'IDBRBU_20200304_1455', '9C16-M1128', 'CONWOF_20200122_1115', '1N3-M1435', 'COCPMR_20210322_0930', 'COGMSO_20200122_1455', '3S47-M0738', '3N22-M0758', '5N19-M0725', 'COGMWO_20200409_1615', 'IDBRLO_20200122_1512', 'CONWFN_20200513_1048', 'IDBRBU_20200214_1230', '5N11-M1403', 'UTLCAC_20210303_1220', 'TLS-FL3A-M1304', 'COGM8C26_20200131', '5C21-M0871', 'COFEFC01_20210317_0833', 'COFEJ2_20210421_1149', 'UTLCAW_20210517_1120', 'COGM9N47_20200204', 'COGM4N2_20200128', 'COGMST_20200312_1345', '2N12-M0862', 'COERUP_20200512_0945', 'COGM2N21_20200211', 'COGM3N53_20200128', 'COGM9S51_20200129', '6N17-M1348', 'GML-M1023', '5C21-M0852', '5S21-M1001', '9C16-M1150', 'COCPMR_20210127_1025', 'COERO2_20200427_1215', '7C15-M0819', '1N3-M1440', 'UTLCAC_20210120_1330', '3N22-M0747', 'CONWFS_20200212_1100', '7C15-M0816', 'N611', 'CONWSA_20200304_1006', 'UTLCAC_20210210_1100', 'COGM1S2_20200208', '2S11-M0949', 'COERTR_20200202_1031', 'CONWOF_20200513_1130', 'CAMLPD_20200219_1015', 'COGMST_20200418_1217', '8N25-M0779', 'WN037', '5N11-M1397', '3S33-M1067', '5N19-M0723', 'COGMST_20200321_1155', '5N10-M1362', '5N19-M0734', 'COGMCT_20200122_1225', '5N15-M1200', 'DB337', 'IDBRBS_20210322_1144', '9C17-M0788', 'COGM8S30_20200129', 'COSBSA_20210310_1322', 'COGM1N3_20200211', 'TLS-FL2A-M1424', '5S43-M0724', 'WN276', 'COFEFC10_20210303_1059', 'IDBRBS_20200109_1030', '3S14-M0978', '5C21-M0868', 'COSBSA_20200226_1444', '5N10-M1371', '8C18-M1109', '6N18-M0717', '1S1-M0818', 'COFEJ1_20191230_1115', 'COGMST_20200212_1430', 'COFEJ1_20210210_1318', 'CASHT4_20200212_1050', '2N12-M0863', 'IDBRBT_20210309_1653', 'WA491', 'COER12_20200428_1415', 'A784', 'COGMSO_20210318_1215', 'CASHOP_20200304_0957', 'CAMLCP_20200206_1335', 'IDBRLT_20200131_1115', '2N14-M1465', 'COGM9N43_20200204', 'CONWOF_20200212_1300', '2S7-M1247', '1N6-M0640', 'COFEJ2_20210331_1330', '3S14-M0980', 'IDBRBL_20210302_1226', '1S2-M1226', '5N10-M1361', '5C21-M0859', '8C11-M1159', 'COSBSA_20210115_1115', '2N12-M0871', '5N11-M1389', 'EN220', '9N29-M0890', '6N18-M0696', 'N502', 'COFESL06_20210122_0839', 'IDBRBL_20201216_1430', '2S16-M1274', 'CONWFF_20200422_1310', 'COFEB2_20210210_1055', 'COFESL13_20210127_0916', 'COERAP_20200226_1550', '9N29-M0888', 'COGM2S10_20200205', '3S38-M1096', '9N29-M0875', 'COGM2S9_20200205', 'NMJRBA_20200122_1420', 'COFEB1_20210120_1500', '1S8-M0935', 'COGMTLSFL2A_20200210', '1S8-M0905', 'COFEJ1_20210421_1059', 'COGMSO_20200312_1520', 'COGMST_20200401_1535', '5S29-M1082', '3S33-M1051', 'COCPCP_20210520_0854', '2S16-M1260', 'WA098', 'CAMLCP_20200219_1005', '2N12-M0891', '8C18-M1102', '5N10-M0667', 'COGM6N17_20200210', 'COERO6_20200427_1445', 'COFEJ2_20191230_1227', 'IDBRBT_20210107_1430', '3N22-M0761', '1N6-M0648', 'COGM5N11_20200210', '2N4-M0623', '2S16-M1270', 'NMJRBA_20200304_1116', '5N11-M1396', '2S48-M0692', '2N13-M1178', 'COFEFC17_20210303_1404', 'UTLCAC_20210310_1245', 'COGM2S25_20200129', '3S47-M0754', 'COCPCP_20210218_1418', '5N11-M1399', '3N22-M0767', '5C21-M0869', 'CASHOP_20200226_1016', 'COGM5S29_20200204', '5N10-M1369', 'COGMSO_20200422_1512', 'COFESL07_20210127_1307', 'CONWFS_20200311_1010', '1S8-M0909', '1S1-M0806', '8N25-M0775', 'COER14_20200428_1115', '2S48-M0700', 'A739', '5N15-M1208', '8N25-M0782', 'COFEFC10_20210317_1026', 'COER12_20200512_1030', '1N6-M0654', 'UTLCAC_20200131_1130', '5N10-M0668', 'IDBRBL_20210318_1330', 'COGM2N49_20200210', 'CAAMCL_20200131_1215', 'NMJRHQ_20200220_1156', '8N25-M0796', 'COGM1S17_20200208', '6N18-M0714', 'COERGT_20200226_1020', 'DN407', '9S51-M0776', 'A767', 'COFEJ1_20200221_1105', 'EB231', 'COGMWT_20200305_0935', 'GML-M1021', 'COGM6S15_20200205', 'CONWC1_20200311_0716', '3S33-M1069', '7C15-M0836', 'COCPCP_20210115_1515', 'COGM5S24_20200129', 'COCPCP_20210303_1345', 'COGMST_20200225_1640', 'COGMCT_20200212_1030', 'TLS-FL3A-M1303', 'COFEJ1_20210331_1400', 'IDBRBO_20200213_1230', 'IDBRBS_20210225_1134', '5N15-M1210', '1S8-M0937', '5N19-M0726', '5C21-M0851', 'COGM5S21_20200201', 'COGMCO_20200226_0715', '9N29-M0894', 'COERO4_20200226_1230', 'COFEJ1_20210322_1204', '2S3-M0843', 'COFESL02_20210127_1429', '6N18-M0705', 'UTLCAW_20210127_1320', 'COGM2S3_20200129', 'COFEJ1_20210224_1308', '8N25-M0792', '2S16-M1265', '7C15-M0817', '9N29-M0882', '2N14-M1457', 'IDBRBO_20210318_1202', 'A557', 'COFEB1_20210331_0935', '3S14-M0979', '2S11-M0964', '3N22-M0755', '3N22-M0768', 'WA437', '8N38-M0845', '8N38-M0843', '7C15-M0830', 'CONWFS_20200311_0930', '5S43-M0719', 'CONWOF_20200513_1105', 'COFEJ1_20200304_1220', 'CONWSA_20200226_1016', 'COGMSO_20200212_1252', '9C17-M0806', 'COGMSO_20200225_1500', '5C21-M0870', 'COGM6S44_20200204', 'COGM8S28_20200131', 'IDBRBL_20201201_1149', 'TLS-FL3A-M1301', '3S47-M0745', '5S43-M0723', 'COFEB1_20210407_0835', '1S2-M1225', 'N746', 'COERO6_20200226_1428', '3S33-M1055', '5N19-M0739', 'UTLCAC_20210127_1145', '6N17-M1350', '8N25-M0783', '6N18-M0697', 'COGMSO_20200306_0602', 'EN233', 'UTLCAW_20210219_1205', 'CONWSA_20200129_1030', '2N51-M1473', 'COGM8N58_20200128', 'COERUP_20200202_1120', '2N12-M0869', '2S11-M0962', 'TLS-FL2A-M1431', '5N15-M1197', '8C18-M1116', 'N764', '8N25-M0788', '2S7-M1256', '5C21-M0861', '2N4-M0613', '5S21-M0998', '6S34-M1050', '6N17-M1334', '2N12-M0893', 'COGMCT_20191219_1420', '2N14-M1470', 'COSBSB_20200201_0950', 'COGMSO_20200306_1705', '5N10-M0662', '2C12-M1484', 'COSBSA_20210210_1310', '9S51-M0759', '1N6-M0651', 'CAMLPD_20200124_0945', '8N38-M0854', '5S43-M0706', '9C17-M0800', '6S34-M1039', '5N11-M1392', '2C12-M1480', 'COGM6S32_20200206', 'EB227', 'CONWFS_20200513_0915', '1S17-M1294', '5N19-M0736', '3S47-M0735', 'COCPMR_20210309_0945', '3S14-M0986', '2N12-M0890', '8N38-M0840', '6N17-M1344', '9C16-M1134', '5N11-M1405', 'DN409', 'COERO4_20200226_1330', 'COER13_20200428_1245', 'COGMWT_20200316_1050', '5N15-M1189', 'CONWSA_20191218_1300', '2S48-M0684', '1S8-M0904', 'COGMWT_20200331_1310', '2N12-M0897', '9C17-M0799', 'COGM2S27_20200204', '2N51-M1475', '3S47-M0739', 'TLS-FL2A-M1426', 'COFESL08_20210122_0929', 'CB006', '1S8-M0941', 'CONWOF_20200311_1100', 'COGM9N28_20200208', 'TLS-FL2A-M1419', '6S34-M1043', 'NMJRBA_20191220_1450', 'UTLCAW_20210303_0925', 'DN471', 'COFEJ2_20210322_1326', 'IDBRBO_20200130_1340', '6N18-M0700', 'COGM1N5_20200211', 'EB108', 'IDBRBT_20210302_1500', '5N10-M0664', '6N18-M0695', '6N18-M0704', '6N17-M1335', '3S33-M1059', 'IDBRMC_20200311_1100', 'COFEFC17_20210317_1212', 'CONWOF_20200212_1254', '9C16-M1151', 'WA492', 'NMJRHQ_20200122_1115', '2C12-M1483', 'COGM1S12_20200211', 'COERO2_20200427_1245', '2N13-M1175', '1S8-M0927', '8N25-M0781', '1S2-M1218', '1N6-M0638', 'CAMLCP_20200304_1028', 'COGM9C16_20200205', 'WN486', 'N789', '9C17-M0803', 'CASHOP_20191220_1123', 'COFEB2_20200221_1450', '9C17-M0810', 'UTLCAW_20210507_0910', 'COERO2_20200201_1120', 'COCPMR_20200304_1229', 'COER12_20200226_1242', 'CASHT4_20200226_1201', 'COFESL02_20210224_1308', 'COFEJ2_20191124_1155', 'IDBRLO_20200304_1210', 'IDBRBL_20210211_1015', 'COFEB2_20210322_0957', 'TLS-FL3A-M1309', '5N10-M0669', '1S17-M1287', '2S16-M1269', 'WN101', '5N19-M0745', '8N38-M0851', '2N14-M1469', 'WN105', 'COFEB1_20210322_0827', '2S7-M1257', 'IDBRMC_20210407_1215', '2N4-M0608', 'IDBRBS_20201120_1150', 'UTLCAW_20200312_1248', '5N19-M0737', '5N15-M1206', 'IDBRBU_20200131_1330', '9N29-M0883', '9N29-M0880', 'CAAMCL_20200221_1200', '2N12-M0889', '9S51-M0761', 'COCPJW_20200131_1000', '2N12-M0872', 'CONWOF_20200311_1118', 'WA331', 'WN281', '2N13-M1179', '2N13-M1172', 'COGM3N26_20200208', 'COGMSO_20200219_1340', '2S3-M0832', '8N25-M0786', 'COGM9N44_20200201', 'CONWFN_20200122_1245', '1N6-M0637', 'COERUP_20200429_1115', '2N12-M0904', 'COGMST_20200122_1655', '1S2-M1215', 'IDBRLO_20210126_1205', '2N12-M0888', '6N18-M0713', 'COGM6C24_20200131', 'SB028', '1S2-M1234', '1S1-M0821', 'COGMCO_20200417_1509', '8C18-M1120', '5N10-M1379', '2S3-M0828', 'COGM6C34_20200201', '1S1-M0816', 'DN248', 'CONWFN_20200311_1101', 'COCPMR_20210318_0900', '5N10-M0666', 'IDBRBS_20210204_1030', '1N3-M1437', 'IDBRBU_20200311_1400', '2S48-M0694', 'COERO2_20200427_1145', 'SA328', 'COGMWO_20200305_0809', '6N18-M0692', '2N14-M1456', '5N10-M1377', '2N4-M0619', '2S11-M0958', '7C15-M0828', 'COFEB1_20210310_1349', '1S1-M0815', '2N12-M0901', '5N11-M1382', '9C17-M0783', '1S8-M0908', 'IDBRBS_20200219_1045', 'COFEB1_20210317_0942', 'COGM5N32_20200212', 'IDBRBL_20201116_1100', 'COERIB_20200506_1132', '6S34-M1030', '2N4-M0625', '5N11-M1384', 'COFEJ2_20210210_1443', 'CASHOP_20200219_1021', '1N6-M0633', 'IDBRBS_20200123_1100', '3S38-M1097', '1N3-M1445', '8N38-M0846', '5S21-M0996', '2N14-M1468', 'COERO2_20200226_1000', 'IDBRBU_20200116_1100', 'CAMLPD_20200205_1000', '1N6-M0644', 'NMJRBA_20200129_1119', 'COGM2S37_20200201', 'TLS-FL2A-M1412', '3S5-M0846', '6S34-M1037', 'IDBRBS_20200305_1040', '3N22-M0759', 'COFEJ2_20210127_1139', 'CONWFF_20200311_0945', 'COGM6C10_20200131', 'CONWC1_20200219_0930', '2N12-M0886', '2S3-M0825', 'EB099', '8N25-M0787', '5N19-M0727', 'COGM1N1_20200208', 'SB454', 'TLS-FL3A-M1321', '2S7-M1237', 'COGM2N12_20200131', 'COGMSO_20200419_1600', '3S14-M0992', '3S14-M0982', '1S8-M0903', 'COGM2S48_20200129', '9C17-M0782', '2N12-M0878', '3S14-M0973', '2N13-M1176', '5S43-M0729', 'COGM8N25_20200128', 'COFEB1_20200221_1350', 'COGMCT_20200219_1115', 'COERO4_20200201_1000', 'COCPCP_20210113_1340', '8N38-M0852', '3S33-M1070', '2S11-M0953', '6N18-M0709', '8N25-M0773', '6N18-M0716', '5N11-M1394', '5N10-M1365', 'COFESL17_20210128_0856', '9N29-M0889', '1S8-M0910', '2C12-M1487', '5N19-M0733', '1N3-M1438', 'IDBRBO_20200206_1300', '5C21-M0860', '2N4-M0605', 'CONWOF_20200422_1054', 'A760', '8N38-M0841', 'IDBRLT_20210126_1335', '7C15-M0835', '6N18-M0693', '5N11-M1398', 'COFEJ1_20200131_1030', 'COGMCO_20200131_0830', 'N786', '6N17-M1329', '1S2-M1228', '2S48-M0698', '8N25-M0795', '1N6-M0635', 'UTLCAW_20210204_0945', 'CONWC1_20200122_1145', '2N14-M1458', 'EB036', 'COSBSB_20200226_1250', 'CONWSA_20200219_1032', 'COCPJW_20200304_0927', '9C16-M1127', 'COGMSO_20200304_1550', '5S29-M1088', 'COGM7N40_20200204', 'COSBSA_20210203_1315', 'COCPMR_20200131_1200', 'WN473', '8N25-M0777', 'WB497', '6N17-M1331', 'COFESL11_20210127_1142', 'WA018', '9N29-M0879', '3N22-M0748', '2N12-M0902', '5N10-M1353', '5S21-M1004', 'COFESL04_20210210_0954', 'CAAMCL_20200214_1200', 'UTLCAC_20210223_1230', '5N15-M1190', 'COGM8C31_20200209', '2S16-M1266', 'COFEFC04_20210317_1359', 'COFEJ2_20210120_1051', 'COERIB_20200212_1145', 'COFEB2_20210127_1447', 'MTCAWX_20210224_1328', '8C11-M1161', '5C21-M0866', 'COCPMR_20200311_1200', 'EB230', 'COERUP_20200226_1310', '9C17-M0802', '1S8-M0944', '8N38-M0859', 'COGM1N6_20200128', '1S17-M1290', '5S21-M1003', '1S8-M0929', '2S11-M0967', 'COGM3S38_20200201', 'COGMWT_20200331_1145', '1S2-M1224', 'COCPMR_20210115_0950', '6S34-M1031', 'COGMGML_20200203', 'UTLCAC_20210322_1245', 'CASHFO_20200304_0925', '1N23-M1476', '3N22-M0770', '5N19-M0738', 'COGM3S33_20200204', '2N4-M0617', '2N4-M0614', 'COSBSA_20210321_1231', '5N15-M1192', 'IDBRBS_20210317_0845', '5N10-M1367', 'IDBRMC_20210304_1318', '2S3-M0842', '5N10-M1355', '2S11-M0971', '1S8-M0938', 'COSBSA_20200129_1205', '9C16-M1133', 'MTCAWX_20210217_1430', '8N38-M0853', '5C21-M0865', 'COERIB_20200205_1140', 'COER12_20200201_1154', 'COFESL14_20210224_1436', 'COFESL12_20210127_1024', '1S8-M0933', '5S29-M1085', 'COERO6_20200226_1458', '1S8-M0918', 'IDBRLO_20210309_1015', 'COSBSA_20200304_1410', '5N19-M0718', '5N19-M0743', '5N10-M1372', 'COFESL17_20210210_1254', 'UTLCAW_20200213_1510', 'A500', 'CONWC1_20200205_1020', '5N15-M1195', 'COGM2S4_20200205', 'TLS-FL2A-M1432', 'A522', '9S51-M0765', '5S21-M1012', 'COGM2C13_20200212', '5S21-M1009', 'COERAP_20200427_0930', '3S47-M0747', 'IDBRBS_20210115_1215', '1N6-M0636', 'DN040', 'COCPCP_20210318_1335', 'IDBRLT_20200226_1115', '9C16-M1143', '2N12-M0896', 'N729', 'COGM5C21_20200130', '3N22-M0750', '3N22-M0751', '8N38-M0850', '9C16-M1146', 'TLS-FL3A-M1306', '3S47-M0737', '1N6-M0649', 'COFEJ1_20200124_1200', 'COFEJ2_20191216_1232', '2N13-M1182', '9C16-M1141', '2N4-M0604', '2N12-M0895', '2N4-M0603', '9C17-M0791', '6N17-M1332', 'COFEJ2_20200221_1145', '9S51-M0768', '1S2-M1222', 'NMJRBA_20200212_1337', '3N22-M0760', 'COGM6N36_20200130', 'COGMWT_20200316_1228', '1S17-M1292', 'COSBSA_20210224_1145', '5C21-M0864', 'SA378', '5N15-M1199', '3S47-M0741', 'CN062', '8N38-M0842', 'IDBRBL_20201209_1200', 'COERO4_20200226_1425', 'DN050', '3S33-M1062', 'COGMST_20200131_1423', '2S48-M0680', 'COGMWT_20200409_1440', 'CONWFN_20200513_1000', '9S51-M0762', 'COGM6N16_20200208', 'TLS-FL2A-M1423', 'COFEJ2_20191210_1245', '8C11-M1158', '5S43-M0730', '3S14-M0991', 'COFEFC12_20210303_0955', 'COER14_20200201_1040', 'UTLCAW_20210210_1220', 'CONWFF_20200513_0955', '2S7-M1236', '5N15-M1205', '1S1-M0809', '2N12-M0887', '6N18-M0691', 'COFEB2_20210505_0842', 'COGM7S50_20200206', 'COGM8S18_20200205', '5N15-M1211', '3S47-M0757', 'COFEJ1_20191203_1208', '2S48-M0691', 'CONWFF_20200513_1030', '8C18-M1115', '2N12-M0907', 'CONWOF_20200311_1145', 'IDBRBO_20200227_1240', '3N22-M0752', 'IDBRLT_20200220_1042', 'COERO6_20200226_1459', 'COGMWO_20200408_1551']\n" ] } ], "source": [ "# Find site names we can use\n", - "print(LayerMeasurements().all_site_names)" + "print(LayerMeasurements.all_sites)" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + " \n", + " geo_json_2766324f69fa51f5dcad31ab7ea010b4.addTo(map_8201170c90bb668ea27934b0b2e25768);\n", + " \n", + "</script>\n", + "</html>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen>" ], "text/plain": [ - "" + "" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -240,12 +309,10 @@ "# Get the first 1000 measurements from the Boise River Basin Site\n", "df = LayerMeasurements.from_filter(\n", " type=\"density\",\n", - " site_name=\"Boise River Basin\",\n", + " campaign=\"2021 Timeseries\",\n", " limit=1000\n", ")\n", - "\n", - "# Explore the pits so we can find an interesting site\n", - "df.loc[:, [\"site_id\", \"geom\"]].drop_duplicates().explore()" + "df.explore()" ] }, { @@ -3001,7 +3068,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "snowexsql", "language": "python", "name": "python3" }, @@ -3015,7 +3082,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.12.6" } }, "nbformat": 4, diff --git a/docs/gallery/lambda_example.ipynb b/docs/gallery/lambda_example.ipynb new file mode 100644 index 0000000..1c56bdd --- /dev/null +++ b/docs/gallery/lambda_example.ipynb @@ -0,0 +1,368 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d4d8e20d", + "metadata": {}, + "source": [ + "# Lambda Client Examples using the new database schema\n", + "\n", + "These are some initial examples to illustrate how to work with the new database schema using the AWS Lambda client. We will continue updating the existing gallery to follow these patterns." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aca5a2c4", + "metadata": {}, + "outputs": [], + "source": [ + "from datetime import date\n", + "import geopandas as gpd\n", + "import matplotlib.pyplot as plt\n", + "import contextily as ctx\n", + "import numpy as np\n", + "import pandas as pd\n", + "from shapely.geometry import box\n", + "\n", + "from snowexsql.lambda_client import SnowExLambdaClient" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15159007", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "🔍 Testing Lambda connection...\n", + "✅ Connected: True\n", + "📊 Database: PostgreSQL 16.10 on x86_64-conda-linux-gnu, compiled by x86_64-conda-linux-gnu-cc (conda-forge gcc 14.3.0-4) 14.3.0, 64-bit\n" + ] + } + ], + "source": [ + "# Initialize client\n", + "client = SnowExLambdaClient()\n", + "\n", + "# Get all measurement classes dynamically\n", + "classes = client.get_measurement_classes()\n", + "PointMeasurements = classes['PointMeasurements']\n", + "LayerMeasurements = classes['LayerMeasurements']\n", + "RasterMeasurements = classes['RasterMeasurements']\n", + "\n", + "\n", + "print(\"🔍 Testing Lambda connection...\")\n", + "connection_test = client.test_connection()\n", + "print(f\"✅ Connected: {connection_test.get('connected', False)}\")\n", + "if connection_test.get('connected'):\n", + " print(f\"📊 Database: {connection_test.get('version', 'Unknown version')}\")\n", + "else:\n", + " print(\"❌ Connection failed\")" + ] + }, + { + "cell_type": "markdown", + "id": "638e23a2", + "metadata": {}, + "source": [ + "## Query and access data using spatial bounding box\n", + "\n", + "Many people will want to explore what types of SnowEx data might be available in a region of interest. Here is an example showing how to do this for layer dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "id": "b5b2a3b9", + "metadata": {}, + "outputs": [], + "source": [ + "# Create polygon from bounding box (minx, miny, maxx, maxy)\n", + "bbox_polygon = box(\n", + " minx=-116.14, # min longitude (west)\n", + " miny=43.73, # min latitude (south)\n", + " maxx=-116.04, # max longitude (east)\n", + " maxy=43.8 # max latitude (north)\n", + ")\n", + "\n", + "# Convert to WKT for query\n", + "bbox_wkt = bbox_polygon.wkt\n", + "\n", + "# Create a GeoDataFrame from the bounding box polygon\n", + "bbox_gdf = gpd.GeoDataFrame([1], geometry=[bbox_polygon], crs='EPSG:4326')\n", + "\n", + "# Reproject to Web Mercator for basemap\n", + "bbox_gdf_web = bbox_gdf.to_crs(epsg=3857)\n" + ] + }, + { + "cell_type": "markdown", + "id": "e7c4b7c8", + "metadata": {}, + "source": [ + "We currently don't have a method for showing which data types exist within a user-defined bounding box. So instead we start by showing all possible data types that currently exist in the database." + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "cd2cb76d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available types: ['comments', 'swe', 'snow_temperature', 'reflectance', 'depth', 'grain_size', 'equivalent_diameter', 'liquid_water_content', 'manual_wetness', 'specific_surface_area', 'two_way_travel', 'permittivity', 'grain_type', 'hand_hardness', 'force', 'density', 'sample_signal']\n" + ] + } + ], + "source": [ + "# Get all available types first\n", + "all_types = LayerMeasurements.all_types\n", + "print(f\"Available types: {all_types}\")" + ] + }, + { + "cell_type": "markdown", + "id": "756c0036", + "metadata": {}, + "source": [ + "Now we can query the database by area and type. If no data are returned, try a different area or type." + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "id": "4a53cbe1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Getting layer data within bounding box...\n", + "Retrieved 592 records.\n" + ] + } + ], + "source": [ + "print(\"Getting layer data within bounding box...\")\n", + "df = LayerMeasurements.from_area(\n", + " shp=bbox_polygon,\n", + " start_date=date(2022, 1, 1),\n", + " end_date=date(2022, 12, 31),\n", + " crs=4326,\n", + " type='snow_temperature',\n", + " limit=8000\n", + ")\n", + "\n", + "print(f\"Retrieved {len(df)} records.\")" + ] + }, + { + "cell_type": "markdown", + "id": "835101f2", + "metadata": {}, + "source": [ + "Now plot on a basemap." + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "id": "588cf2f5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import geopandas as gpd\n", + "import matplotlib.pyplot as plt\n", + "import contextily as ctx\n", + "\n", + "# Reproject to Web Mercator for basemap\n", + "df_web = df.to_crs(epsg=3857)\n", + "bbox_web = bbox_gdf.to_crs(epsg=3857)\n", + "\n", + "# Plot\n", + "fig, ax = plt.subplots(figsize=(12, 10))\n", + "\n", + "# Plot the data points\n", + "df_web.plot(ax=ax, color='blue', markersize=20, alpha=0.6, label='Layer Measurements')\n", + "\n", + "# Plot bounding box\n", + "bbox_web.boundary.plot(ax=ax, color='red', linewidth=2, label='Query Area')\n", + "\n", + "# Add basemap\n", + "ctx.add_basemap(ax, source=ctx.providers.OpenStreetMap.Mapnik, alpha=0.5)\n", + "\n", + "ax.set_xlabel('Longitude')\n", + "ax.set_ylabel('Latitude')\n", + "ax.set_title(f'Layer Measurements within Bounding Box (n={len(df)})')\n", + "ax.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c5389717", + "metadata": {}, + "source": [ + "Let's look at what the query returns for columns. Note that the 'value' column is the measured value associated with the variable type you requested in the `from_area` query above. Note that it also returns values as strings, so we'll need to convert to numeric below. " + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "id": "e0dd1fc7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['depth', 'bottom_depth', 'value', 'site_id', 'measurement_type_id',\n", + " 'instrument_id', 'id', 'geom_wkt', 'geom', 'geometry'],\n", + " dtype='object')" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "id": "655eeecd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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aOXKk/vjHPzYuj4uL09q1azV37lxdvHhRffu2vot9+/ZVRERE5+wIAADoFQoKCjR27Fjl5OQoMTHR7DheLzwySlfHxJodw2uFnKmUJA0aHKV+V/HvCPPxGQmgI7xuoHOHwyEfHx8NGDCgzTYWi6XNgpQkFRYWavDgwYqNjdXs2bN17NixTk4LAAAAAACAlph+p1RH1NTU6OGHH9bdd98ti8XSYpuzZ89q9erVWrx4cZt9TZgwQS+88IKGDx+u06dPa82aNUpOTtann36qgQMHtriO0+mU0+lsnGZgTgAAep/q6mpJUn5+vslJvIfL5VJFRYVCQ0PVp8/XfxMtKiqS4XDIWcOYUugenDU1MhwO5eXlyeFwmB3nsrR0rHlaw2djw2clALTFa4pSdXV1mj17tlwul373u9+12MYwDN1666267rrr9Otf/7rN/r75yGBCQoImTpyouLg47dixQ0uWLGlxnfT09GaDowMAgN7FbrdLkubOnWtukB6i+IRdcbZ4s2MAKj5h10cH32t86RKujN1uV0pKitkxAHRzXlGUqqur05133qnjx4/rz3/+c4t3SZ0/f17f//731a9fP+3du1d+fn4d2kZISIgSEhJUWFjYapvly5c3KVgZhqEhQ4Z0aDsAAMC7xcTESJJ27typ+HiKKe3R2p1Sax9fr6joGHPDAf9PVHSMxk1M1iMP/dJrz/G7y51Sc+fObfysBIC2dPuiVENBqrCwUO+++26Lj9YZhqFbbrlFAQEBev311xUYGNjh7TidTuXn5ys1NbXVNgEBAQoICOhw3wAAoOcICgqSJMXHxzOIbzu5XC6VlZUpPDy88ULZarXKYrUq4DLO24CuEBAYKIvVqpEjRyouLs7sOJelpWPNLA2flQDQFtMHOr9w4YJyc3OVm5srSTp+/Lhyc3N18uRJXbx4UTNnztShQ4f04osvqr6+XqWlpSotLVVtba2kr++QmjJliiorK5WRkSHDMBrb1NfXN27npptu0tNPP904vWzZMu3fv1/Hjx/XBx98oJkzZ8owDM2fP9+j+w8AALyLzWZTTk6ObDab2VEAoNvhMxJAR5h+p9ShQ4c0efLkxumGx+Pmz5+vlStX6vXXX5ckjR49usl67777riZNmqScnBx98MEHkqTvfOc7TdocP3688bbRo0eP6syZM43LTp06pTlz5ujMmTMKCwtTUlKS3n//fUVHR3f2LgIAgB4kODiYO6Q6UVlJsdkRvNoA4+t/v9NfFuvchRCT03g3fhc7B5+RADrC9KLUpEmT5Ha7W13e1rL2rN+gYVDSBn/4wx/alQ8AAACdz2KxKCQoUJnbNpsdxauF+tWqNHyIsg5nqKLO3+w4Xi8kKLDVt3wDADqf6UUpAAAA9D5hYWF6bssWGYZhdpQegXecdQ6LxaKwsDCzYwBAr0FRCgAAAKYICwujAAAAQC9m+kDnAAAAAAAA6H0oSgEAAAAAAMDjKEoBAAAAAADA4xhTCgAAAJelvLy8XQOVu1wunT17VufPn1efPvxNFOgqHGvmYZB84PKYXpQ6cOCAnnjiCeXk5KikpER79+5VWlqaJKmurk6PPvqo3nzzTR07dkxWq1U333yzHnvsMQ0ePFiSZLfbFRsb22Lfr7zyimbNmtXispUrV2rVqlVN5g0aNEilpaWdt3NeoL6+XtnZ2SopKVFkZKRSU1Pl6+trdiwAANDNlZeX697Fi1VZXXPpxm6p7mKd/Pr6ST5dnw1XLtSvVtPCS5VVFqGKOn+z46C9ONZMExIUqOe2bKEwBXSQ6UWpyspKjRo1SgsWLNCMGTOaLKuqqtLhw4e1YsUKjRo1Sl999ZUeeOAB3X777Tp06JAkaciQISopKWmy3tatW7V+/XpNnTq1zW2PGDFC+/bta5zubcWYPXv2aOnSpbLb7Y3zYmJitGHDBk2fPt28YOiRqqqqVFBQIJvNpuDgYLPjAACukGEYqqyuUdrC+xUeGXWJ1m7V1tbJ399PXCl7hwHG55ry4f2KmP6IzlmGmx0H7caxZoaykmJlbtsswzAoSvUiVVVV+uSTT5ScnKx+/fqZHcdrmV6Umjp1aqvFI6vVqnfeeafJvE2bNmn8+PE6efKkhg4dKl9fX0VERDRps3fvXt11112X/MXo27dvs3V7iz179mjmzJmaNm2adu3apZEjRyovL0/r1q3TzJkztXv3bgpT6FQFBQUaO3ascnJylJiYaHYcAEAnCY+M0tUxLd+13sAtt2qdtfIP8JcPF8peIeRMpSRp0OAo9buq7Z8vug+ONcBzCgoKdMstt+ijjz7S9ddfb3Ycr+V1Dxo7HA75+PhowIABLS7PyclRbm6uFi1adMm+CgsLNXjwYMXGxmr27Nk6duxYJ6ftnurr67V06VJNmzZNmZmZSkpKUr9+/ZSUlKTMzExNmzZNy5YtU319vdlRAQAAAABAD2X6nVIdUVNTo4cfflh33323LBZLi20yMjIUHx+v5OTkNvuaMGGCXnjhBQ0fPlynT5/WmjVrlJycrE8//VQDBw5scR2n0ymn09k43Z6BPbuj7Oxs2e127dq1q9kAiH369NHy5cuVnJys7OxsTZo0yZyQ6HGqq6slSfn5+SYnAb7mcrlUUVGh0NBQBoMFLkNRUZEMh0POmnaMKQUAPZizpkaGw6G8vDw5HA6z48BDCgoKJH1dp8Dl85qiVF1dnWbPni2Xy6Xf/e53Lbaprq7WSy+9pBUrVlyyv28+MpiQkKCJEycqLi5OO3bs0JIlS1pcJz09vdng6N6oYQyukSNHtri8Yf63x+oCrkTD2GVz5841NwgAoFMVn7ArzhZvdgwAME3xCbs+Ovhe4wu70LvY7XbdcMMNZsfwWl5RlKqrq9Odd96p48eP689//nOrd0nt3r1bVVVVmjdvXoe3ERISooSEBBUWFrbaZvny5U0KVoZhaMiQIR3eltkiIyMlSXl5eUpKSmq2PC8vr0k7oDPExMRIknbu3Kn4eC5eYD7ulAKuTFFRkdY+vl5R0TFmRwEAU0VFx2jcxGQ98tAvvfL6EJfns88+0z333NN4nYPL0+2LUg0FqcLCQr377rutPlonff3o3u23335ZbzxwOp3Kz89Xampqq20CAgIUEBDQ4b67m9TUVMXExGjdunXKzMxscjHmcrmUnp6u2NjYNv8tgI4KCgqSJMXHxzPQOboFl8ulsrIyhYeHU5QCLoPVapXFalVAYKDZUQDAVAGBgbJYrRo5cqTi4uLMjgMPcblckqRAvgeviOln4RcuXFBubq5yc3MlScePH1dubq5OnjypixcvaubMmTp06JBefPFF1dfXq7S0VKWlpaqtrW3SzxdffKEDBw7o3nvvbXE7N910k55++unG6WXLlmn//v06fvy4PvjgA82cOVOGYWj+/Pldtq/dha+vrzZs2KCsrCylpaXp4MGDOn/+vA4ePKi0tDRlZWXpySeflK+vr9lR0YPYbDbl5OTIZrOZHQUAAAAArojNZtPbb7/N9c0VMv1OqUOHDmny5MmN0w2Px82fP18rV67U66+/LkkaPXp0k/XefffdJoNwb9u2TVFRUZoyZUqL2zl69KjOnDnTOH3q1CnNmTNHZ86cUVhYmJKSkvT+++8rOjq6k/ase5s+fbp2796tpUuXNhkUPjY2Vrt379b06dNNTIeeKDg4mDukAKAHKispbkcrt2pr6+Tv7yfxmnqvMMD4+ud6+stinbsQYnIatB/Hmhna9zmIniY4OFjf/e53FRwcbHYUr+bjdrvdZofwVoZhyGq1yuFwtDrOVXdXX1+v7OxslZSUKDIyUqmpqdwhZSIeJwI8h+MNuDLl5eW6d/FiVVa3461DbqnuYp38+vpxnewlQv1qNS28VFllEaqo8zc7DtqLY800IUGBem7LlssaSgbeiXPJtrW3XmL6nVIwl6+vb5M7zgAAANojLCxMz23ZIsMwLtnW5XLp7NmzGjhwICfuXibF7ADoEI4181gsFgpSwGWgKAUAAIDLEhYW1q6LMJfLpf79+/PXZKCLcawB8DZ8UgEAAAAAAMDjKEoBAAAAAADA4yhKAQAAAAAAwOMoSgEAAAAAAMDjTC9KHThwQLfddpsGDx4sHx8fZWZmNlm+cuVK2Ww2hYSE6J/+6Z90880364MPPmhcXlFRoX//93/Xtddeq+DgYA0dOlQ/+9nP5HA42tzuypUr5ePj0+R/ERERXbGLAAAAAACgB/mqqEBFL/xEXxUVmB3Fq5lelKqsrNSoUaP09NNPt7h8+PDhevrpp/X3v/9df/3rXxUTE6MpU6aovLxckvTll1/qyy+/1JNPPqm///3v2r59u/70pz9p0aJFl9z2iBEjVFJS0vi/v//97526bwAAAAAAoOf58shhjav6X3155LDZUbxaX7MDTJ06VVOnTm11+d13391k+je/+Y0yMjL0ySef6KabbtLIkSP1xz/+sXF5XFyc1q5dq7lz5+rixYvq27f1Xezbty93RwEAAAAAgA7Jzc1VQsP/T5lrdhyvZfqdUh1RW1urrVu3ymq1atSoUa22czgcslgsbRakJKmwsFCDBw9WbGysZs+erWPHjnV2ZAAAAAAAALTA9Dul2iMrK0uzZ89WVVWVIiMj9c477+iqq65qse3Zs2e1evVqLV68uM0+J0yYoBdeeEHDhw/X6dOntWbNGiUnJ+vTTz/VwIEDW1zH6XTK6XQ2ThuGcfk7BQAAAAAA0It5xZ1SkydPVm5urt577z19//vf15133qmysrJm7QzD0K233qrrrrtOv/71r9vsc+rUqZoxY4YSEhJ0880364033pAk7dixo9V10tPTZbVaG/83ZMiQK9sxAAAAAACAXsorilIhISH6zne+o6SkJGVkZKhv377KyMho0ub8+fP6/ve/r379+mnv3r3y8/Pr8DYSEhJUWFjYapvly5fL4XA0/q+oqOiy9gcAAAAAAKC384rH977N7XY3e4zulltuUUBAgF5//XUFBgZ2uE+n06n8/Hy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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Depth range: 0.0 - 190.0 cm\n", + "Temperature range: -8.9 - 0.2 °C\n", + "Number of depth bands: 31\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# Convert value to numeric, coercing errors to NaN\n", + "df['value'] = pd.to_numeric(df['value'], errors='coerce')\n", + "\n", + "# Remove any NaN values that resulted from conversion\n", + "df = df.dropna(subset=['value', 'depth'])\n", + "\n", + "# Create depth bands (bins) every 5 cm\n", + "bin_width = 5.0\n", + "min_depth = np.floor(df['depth'].min())\n", + "max_depth = np.ceil(df['depth'].max())\n", + "bins = np.arange(min_depth, max_depth + bin_width, bin_width)\n", + "\n", + "# Assign each measurement to a depth band\n", + "df['depth_band'] = pd.cut(df['depth'], bins=bins, labels=bins[:-1] + bin_width/2, include_lowest=True)\n", + "\n", + "# Get unique depth bands\n", + "depth_bands = sorted(df['depth_band'].dropna().unique())\n", + "\n", + "# Prepare data for box plot - group by depth band\n", + "data_by_band = [df[df['depth_band'] == band]['value'].values for band in depth_bands]\n", + "\n", + "# Create the plot\n", + "fig, ax = plt.subplots(figsize=(12, 8))\n", + "\n", + "# Create box plot\n", + "bp = ax.boxplot(data_by_band, positions=depth_bands, vert=False, \n", + " patch_artist=True, widths=3.0)\n", + "\n", + "# Customize box colors\n", + "for patch in bp['boxes']:\n", + " patch.set_facecolor('lightblue')\n", + " patch.set_alpha(0.7)\n", + "\n", + "# Invert y-axis so depth increases downward\n", + "ax.invert_yaxis()\n", + "\n", + "ax.set_xlabel('Temperature (°C)', fontsize=12)\n", + "ax.set_ylabel('Depth (cm)', fontsize=12)\n", + "ax.set_title(f'Snow Temperature vs Depth (5 cm bands, n={len(df)} measurements)', fontsize=14)\n", + "ax.grid(True, alpha=0.3, axis='x')\n", + "\n", + "# Add legend explaining box plot elements\n", + "from matplotlib.lines import Line2D\n", + "legend_elements = [\n", + " Line2D([0], [0], color='lightblue', marker='s', markersize=10, \n", + " label='Box: 25th-75th percentile', linestyle=''),\n", + " Line2D([0], [0], color='orange', marker='|', markersize=10, \n", + " label='Median', linestyle='', markeredgewidth=2),\n", + " Line2D([0], [0], color='black', marker='o', markersize=6, \n", + " label='Outliers', linestyle='', markerfacecolor='white')\n", + "]\n", + "ax.legend(handles=legend_elements, loc='best', fontsize=10)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Print summary statistics\n", + "print(f\"\\nDepth range: {df['depth'].min():.1f} - {df['depth'].max():.1f} cm\")\n", + "print(f\"Temperature range: {df['value'].min():.1f} - {df['value'].max():.1f} °C\")\n", + "print(f\"Number of depth bands: {len(depth_bands)}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "snowexsql", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pyproject.toml b/pyproject.toml index b197b4a..be65765 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,6 +27,7 @@ dependencies = [ "psycopg2-binary <3.0", "rasterio <2.0", "SQLAlchemy <3.0", + "boto3 <2.0", ] @@ -69,3 +70,10 @@ local_scheme = "no-local-version" [tool.hatch.build.targets.sdist] exclude = ["/tests"] + +[tool.pytest.ini_options] +markers = [ + "integration: marks tests as integration tests that require AWS credentials (deselect with '-m \"not integration\"')", + "handler: marks tests as Lambda handler tests that require local database credentials (deselect with '-m \"not handler\"')", + "lambda: marks tests as Lambda-specific tests", +] \ No newline at end of file diff --git a/snowexsql/api.py b/snowexsql/api.py index 950aff4..945c54e 100644 --- a/snowexsql/api.py +++ b/snowexsql/api.py @@ -1,22 +1,89 @@ +""" +SnowEx API for querying measurement data. + +LAMBDA INTEGRATION CONVENTIONS: +=============================== +If you're adding a new measurement class that should be available +via the Lambda API, follow these naming conventions: + +1. Class name MUST end with 'Measurements' + (e.g., WeatherMeasurements) +2. Class MUST have a 'MODEL' attribute pointing to the SQLAlchemy + model +3. Class MUST inherit from BaseDataset + +Example: + class WeatherMeasurements(BaseDataset): + MODEL = WeatherData # Required for Lambda auto-discovery! + + # Your implementation here... + +The Lambda handler will automatically discover and expose your +class as: + client.weather_measurements.from_filter() + client.weather_measurements.all_instruments + etc. + +See snowexsql.lambda_handler._get_measurement_classes() for +implementation details. +""" import logging import os from contextlib import contextmanager -import geoalchemy2.functions as gfunc -import geopandas as gpd -from geoalchemy2.shape import from_shape -from geoalchemy2.types import Raster -from shapely.geometry import box from sqlalchemy.sql import func -from sqlalchemy import cast, Numeric +from sqlalchemy import cast, Numeric, exists + +# Initialize logger first +LOG = logging.getLogger(__name__) -from snowexsql.conversions import query_to_geopandas, raster_to_rasterio +# Import pandas - always available +import pandas as pd +from sqlalchemy.dialects import postgresql + +def query_to_geopandas(query, engine, **kwargs): + """ + Convert SQLAlchemy query to GeoDataFrame (if geopandas available) + or DataFrame. + + Execution context: + - Local power users: Returns GeoDataFrame with proper geometry objects + - Lambda environment: Returns pandas DataFrame (no geopandas dependency) + - DataFrame is serialized to JSON by lambda_handler + - lambda_client receives JSON and converts to GeoDataFrame client-side + + Args: + query: SQLAlchemy Query object + engine: SQLAlchemy engine + **kwargs: Additional arguments passed to read_postgis or read_sql + + Returns: + geopandas.GeoDataFrame if geopandas available, + otherwise pandas.DataFrame + """ + sql = query.statement.compile(dialect=postgresql.dialect()) + + try: + import geopandas as gpd + return gpd.read_postgis(sql, engine.connect(), **kwargs) + except ImportError: + # Geopandas not available (e.g., Lambda environment) + # Returns pandas DataFrame with geometry as WKB/WKT + # lambda_client will convert to GeoDataFrame client-side + return pd.read_sql(sql, engine, **kwargs) + +def raster_to_rasterio(rasters): + """Raster functionality requires rasterio""" + raise ImportError( + "Raster functionality not available in Lambda environment. " + "Use local API for raster operations." + ) from snowexsql.db import get_db -from snowexsql.tables import Campaign, DOI, ImageData, Instrument, LayerData, \ +from snowexsql.tables import ( + Campaign, DOI, ImageData, Instrument, LayerData, MeasurementType, Observer, PointData, PointObservation, Site - -LOG = logging.getLogger(__name__) -DB_NAME = 'snow:hackweek@db.snowexdata.org/snowex' +) +from snowexsql.db import db_session_with_credentials # TODO: # * Possible enums @@ -26,19 +93,9 @@ class LargeQueryCheckException(RuntimeError): pass - -@contextmanager -def db_session(db_name): - # use default_name - db_name = db_name or DB_NAME - engine, session = get_db(db_name) - yield session, engine - session.close() - - def get_points(): # Lets grab a single row from the points table - with db_session(DB_NAME) as session: + with db_session_with_credentials() as (_engine, session): qry = session.query(PointData).limit(1) # Execute that query! result = qry.all() @@ -46,8 +103,6 @@ def get_points(): class BaseDataset: MODEL = None - # Use this database name - DB_NAME = DB_NAME ALLOWED_QRY_KWARGS = [ "campaign", "date", "instrument", "type", @@ -59,24 +114,18 @@ class BaseDataset: # Default max record count MAX_RECORD_COUNT = 1000 - @staticmethod - def build_box(xmin, ymin, xmax, ymax, crs): - # build a geopandas box - return gpd.GeoDataFrame( - geometry=[box(xmin, ymin, xmax, ymax)] - ).set_crs(crs) - @staticmethod def retrieve_single_value_result(result): """ - When we only request a single thing we still get a list of lists - this function filters it out. This usually looks like a list of tuples. + When we only request a single thing we still get a list of + lists this function filters it out. This usually looks like a + list of tuples. """ final = [] if len(result) != 0: final = [r[0] for r in result] return final - + @classmethod def _check_size(cls, qry, kwargs): # Safeguard against accidental giant requests @@ -146,8 +195,8 @@ def extend_qry(cls, qry, check_size=True, **kwargs): ) elif "_equal" in k: raise ValueError( - "We cannot compare greater_equal or less_equal" - " with a list" + "We cannot compare greater_equal or " + "less_equal with a list" ) qry = qry.filter(filter_col.in_(v)) LOG.debug( @@ -214,10 +263,15 @@ def extend_qry(cls, qry, check_size=True, **kwargs): @classmethod def from_unique_entries(cls, columns_to_search, **kwargs): - """Returns unique values from a column to help with filtering""" - columns = [getattr(cls.MODEL, column) for column in columns_to_search] + """ + Returns unique values from a column to help with filtering + """ + columns = [ + getattr(cls.MODEL, column) + for column in columns_to_search + ] - with db_session(cls.DB_NAME) as (session, engine): + with db_session_with_credentials() as (_engine, session): try: qry = session.query(*columns) # Hardcode the limit to @@ -226,7 +280,9 @@ def from_unique_entries(cls, columns_to_search, **kwargs): except Exception as e: session.close() - LOG.error("Failed query finding options for filtering") + LOG.error( + "Failed query finding options for filtering" + ) raise e if len(columns_to_search) == 1: @@ -240,14 +296,16 @@ def from_filter(cls, **kwargs): Get data for the class by filtering by allowed arguments. The allowed filters are cls.ALLOWED_QRY_KWARGS. """ - with db_session(cls.DB_NAME) as (session, engine): + with db_session_with_credentials() as (engine, session): try: qry = session.query(cls.MODEL) qry = cls.extend_qry(qry, **kwargs) - # For debugging in the test suite and not recommended - # in production - # https://docs.sqlalchemy.org/en/20/faq/sqlexpressions.html#rendering-postcompile-parameters-as-bound-parameters ## noqa + # For debugging in the test suite and not + # recommended in production + # https://docs.sqlalchemy.org/en/20/faq/ + # sqlexpressions.html#rendering-postcompile- + # parameters-as-bound-parameters if 'DEBUG_QUERY' in os.environ: full_sql_query = qry.statement.compile( compile_kwargs={"literal_binds": True} @@ -264,67 +322,217 @@ def from_filter(cls, **kwargs): return df @classmethod - def from_area(cls, shp=None, pt=None, buffer=None, crs=26912, **kwargs): + def from_area( + cls, shp=None, pt=None, buffer=None, crs=26912, **kwargs + ): """ Get data for the class within a specific shapefile or - within a point and a known buffer + within a point and a known buffer. Uses PostGIS SQL directly + for spatial operations, eliminating dependency on geoalchemy2/shapely. + Args: - shp: shapely geometry in which to filter - pt: shapely point that will have a buffer applied in order - to find search area - buffer: in same units as point - crs: integer crs to use + shp: shapely geometry in which to filter, or WKT string + pt: shapely point that will have a buffer applied, or WKT string + buffer: buffer distance in same units as point + (meters if using geography) + crs: integer SRID/EPSG code (default 26912 = UTM Zone 12N) kwargs: for more filtering or limiting (cls.ALLOWED_QRY_KWARGS) - Returns: Geopandas dataframe of results - + + Returns: + pandas DataFrame with results (includes geom column with WKT) """ + from sqlalchemy import text + if shp is None and pt is None: raise ValueError( - "Inputs must be a shape description or a point and buffer" + "Inputs must be a shape description or a point " + "and buffer" ) - if (pt is not None and buffer is None) or \ - (buffer is not None and pt is None): - raise ValueError("pt and buffer must be given together") - with db_session(cls.DB_NAME) as (session, engine): + if ((pt is not None and buffer is None) or + (buffer is not None and pt is None)): + raise ValueError( + "pt and buffer must be given together" + ) + + # Convert shapely objects to WKT if needed + if shp is not None and hasattr(shp, 'wkt'): + shp_wkt = shp.wkt + elif isinstance(shp, str): + shp_wkt = shp + else: + shp_wkt = None + + if pt is not None: + if hasattr(pt, 'wkt'): + pt_wkt = pt.wkt + elif isinstance(pt, str): + pt_wkt = pt + elif isinstance(pt, (tuple, list)) and len(pt) == 2: + # Handle (x, y) tuple format + pt_wkt = f"POINT ({pt[0]} {pt[1]})" + else: + pt_wkt = None + else: + pt_wkt = None + + # Determine table structure + table_name = cls.MODEL.__tablename__ + needs_site_join = (table_name == 'layers') + + with db_session_with_credentials() as (engine, session): try: - if shp is not None: - qry = session.query(cls.MODEL) - # Filter geometry based on Site for LayerData - if cls.MODEL == LayerData: - qry = qry.join(cls.MODEL.site).filter( - func.ST_Within( - Site.geom, from_shape(shp, srid=crs) - ) + # Detect database SRID to avoid transforming indexed column + # Query first non-null geometry to determine database SRID + if needs_site_join: + db_srid_query = text(f""" + SELECT ST_SRID(s.geom) + FROM {table_name} + JOIN sites s ON {table_name}.site_id = s.id + WHERE s.geom IS NOT NULL + LIMIT 1 + """) + else: + db_srid_query = text(f""" + SELECT ST_SRID(geom) + FROM {table_name} + WHERE geom IS NOT NULL + LIMIT 1 + """) + + try: + db_srid_result = session.execute(db_srid_query).first() + if not db_srid_result or db_srid_result[0] is None: + # No data in table yet - use input CRS as default + # This allows empty table queries to work (will return empty) + LOG.warning( + f"No geometries found in {table_name}, " + f"using input CRS {crs} as default" ) + db_srid = crs else: - qry = qry.filter( - func.ST_Within( - cls.MODEL.geom, from_shape(shp, srid=crs) - ) - ) - qry = cls.extend_qry(qry, check_size=True, **kwargs) - df = query_to_geopandas(qry, engine) + db_srid = db_srid_result[0] + LOG.debug(f"Detected database SRID: {db_srid} for table {table_name}") + except Exception as srid_error: + # If SRID detection fails, fall back to input CRS + LOG.warning( + f"SRID detection failed for {table_name}: {srid_error}. " + f"Using input CRS {crs} as default" + ) + db_srid = crs + + # Build PostGIS search geometry + # Transform search geometry to match database SRID for index usage + if pt_wkt: + # Create point in input CRS, buffer it, then transform to DB SRID + # Buffer before transform to ensure correct distance units + search_geom_sql = ( + f"ST_Transform(" + f"ST_Buffer(ST_GeomFromText('{pt_wkt}', {crs}), " + f"{buffer}), {db_srid})" + ) + elif shp_wkt: + # Transform shape from input CRS to database SRID + search_geom_sql = ( + f"ST_Transform(ST_GeomFromText('{shp_wkt}', {crs}), " + f"{db_srid})" + ) else: - qry_pt = from_shape(pt) - qry = session.query( - gfunc.ST_SetSRID( - func.ST_Buffer(qry_pt, buffer), crs - ) + raise ValueError("Unable to parse geometry input") + + # Build WHERE clauses for filters + where_clauses = [] + params = {} + + # Add spatial filter - DB geometry stays in native SRID + # This allows PostGIS to use the spatial index efficiently + if needs_site_join: + where_clauses.append( + f"ST_Intersects(s.geom, ({search_geom_sql}))" ) - - buffered_pt = qry.all()[0][0] - qry = session.query(cls.MODEL) - # Filter geometry based on Site for LayerData - if cls.MODEL == LayerData: - qry = qry.join(cls.MODEL.site).filter( - func.ST_Within(Site.geom, buffered_pt) + else: + where_clauses.append( + f"ST_Intersects({table_name}.geom, ({search_geom_sql}))" + ) + + # Add standard filters + for key, value in kwargs.items(): + if key == 'limit': + continue + elif key == 'type': + where_clauses.append( + f"{table_name}.measurement_type_id IN " + f"(SELECT id FROM measurement_type WHERE " + f"name = :type_name)" ) - else: - qry = qry.filter( - func.ST_Within(cls.MODEL.geom, buffered_pt) + params['type_name'] = value + elif key == 'instrument': + where_clauses.append( + f"{table_name}.instrument_id IN " + f"(SELECT id FROM instrument WHERE " + f"name = :instrument_name)" + ) + params['instrument_name'] = value + elif key == 'campaign': + if needs_site_join: + where_clauses.append( + f"s.campaign_id IN (SELECT id FROM campaign " + f"WHERE name = :campaign_name)" + ) + else: + where_clauses.append( + f"{table_name}.site_id IN (SELECT id FROM " + f"sites WHERE campaign_id IN (SELECT id FROM " + f"campaign WHERE name = :campaign_name))" + ) + params['campaign_name'] = value + elif key == 'date_greater_equal': + where_clauses.append(f"{table_name}.date >= :date_gte") + params['date_gte'] = value + elif key == 'date_less_equal': + where_clauses.append(f"{table_name}.date <= :date_lte") + params['date_lte'] = value + elif key == 'value_greater_equal': + where_clauses.append( + f"{table_name}.value >= :value_gte" + ) + params['value_gte'] = value + elif key == 'value_less_equal': + where_clauses.append( + f"{table_name}.value <= :value_lte" ) - qry = cls.extend_qry(qry, check_size=True, **kwargs) - df = query_to_geopandas(qry, engine) + params['value_lte'] = value + elif key in cls.ALLOWED_QRY_KWARGS: + where_clauses.append(f"{table_name}.{key} = :{key}") + params[key] = value + + where_sql = " AND ".join(where_clauses) + limit = kwargs.get('limit', cls.MAX_RECORD_COUNT) + + # Construct query based on table structure + if needs_site_join: + query = text(f""" + SELECT {table_name}.*, + ST_AsText(s.geom) as geom_wkt, + s.geom as geom + FROM {table_name} + JOIN sites s ON {table_name}.site_id = s.id + WHERE {where_sql} + LIMIT :limit + """) + else: + query = text(f""" + SELECT *, ST_AsText(geom) as geom_wkt + FROM {table_name} + WHERE {where_sql} + LIMIT :limit + """) + params['limit'] = limit + + # Execute and convert to DataFrame + result = session.execute(query, params) + rows = [dict(row._mapping) for row in result] + df = pd.DataFrame(rows) + except Exception as e: session.close() raise e @@ -336,7 +544,7 @@ def all_campaigns(self): """ Return all campaign names """ - with db_session(self.DB_NAME) as (session, engine): + with db_session_with_credentials() as (_engine, session): qry = session.query(Campaign.name).distinct() result = qry.all() return self.retrieve_single_value_result(result) @@ -346,7 +554,7 @@ def all_types(self): """ Return all types of the data """ - with db_session(self.DB_NAME) as (session, engine): + with db_session_with_credentials() as (_engine, session): qry = session.query(MeasurementType.name).distinct() result = qry.all() return self.retrieve_single_value_result(result) @@ -356,7 +564,7 @@ def all_dates(self): """ Return all distinct dates in the data """ - with db_session(self.DB_NAME) as (session, engine): + with db_session_with_credentials() as (_engine, session): qry = session.query(self.MODEL.date).distinct() result = qry.all() return self.retrieve_single_value_result(result) @@ -366,7 +574,7 @@ def all_observers(self): """ Return all distinct observers in the data """ - with db_session(self.DB_NAME) as (session, engine): + with db_session_with_credentials() as (_engine, session): qry = session.query(Observer.name).distinct() result = qry.all() return self.retrieve_single_value_result(result) @@ -376,7 +584,7 @@ def all_dois(self): """ Return all distinct DOIs in the data """ - with db_session(self.DB_NAME) as (session, engine): + with db_session_with_credentials() as (_engine, session): qry = session.query(DOI.doi).distinct() result = qry.all() return self.retrieve_single_value_result(result) @@ -386,7 +594,7 @@ def all_units(self): """ Return all distinct units in the data """ - with db_session(self.DB_NAME) as (session, engine): + with db_session_with_credentials() as (_engine, session): qry = session.query(self.MODEL.units).distinct() result = qry.all() return self.retrieve_single_value_result(result) @@ -396,10 +604,14 @@ def all_instruments(self): """ Return all distinct instruments in the data """ - with db_session(self.DB_NAME) as (session, engine): - qry = session.query(Instrument.name).join( - self.MODEL, Instrument.id == self.MODEL.instrument_id - ).distinct() + with db_session_with_credentials() as (_engine, session): + # Use EXISTS for better performance on large datasets + # (29GB+ tables) + qry = session.query(Instrument.name).filter( + exists().where( + self.MODEL.instrument_id == Instrument.id + ) + ) result = qry.all() return self.retrieve_single_value_result(result) @@ -455,10 +667,12 @@ def all_instruments(self): """ Return all distinct instruments in the data """ - with db_session(self.DB_NAME) as (session, engine): + with db_session_with_credentials() as (_engine, session): result = session.query(Instrument.name).filter( Instrument.id.in_( - session.query(PointObservation.instrument_id).distinct() + session.query( + PointObservation.instrument_id + ).distinct() ) ).all() return self.retrieve_single_value_result(result) @@ -466,8 +680,8 @@ def all_instruments(self): class TooManyRastersException(Exception): """ - Exception to report to users that their query will produce too many - rasters + Exception to report to users that their query will produce too + many rasters """ pass @@ -518,7 +732,7 @@ def all_sites(self): """ Return all specific site names """ - with db_session(self.DB_NAME) as (session, engine): + with db_session_with_credentials() as (_engine, session): result = session.query( Site.name ).distinct().all() @@ -529,7 +743,7 @@ def all_dates(self): """ Return all distinct dates in the data """ - with db_session(self.DB_NAME) as (session, engine): + with db_session_with_credentials() as (_engine, session): result = session.query( Site.date ).distinct().all() @@ -540,7 +754,7 @@ def all_units(self): """ Return all distinct units in the data """ - with db_session(self.DB_NAME) as (session, engine): + with db_session_with_credentials() as (_engine, session): result = session.query( MeasurementType.units ).distinct().all() @@ -548,11 +762,13 @@ def all_units(self): class RasterMeasurements(BaseDataset): MODEL = ImageData - ALLOWED_QRY_KWARGS = BaseDataset.ALLOWED_QRY_KWARGS + ['description'] + ALLOWED_QRY_KWARGS = ( + BaseDataset.ALLOWED_QRY_KWARGS + ['description'] + ) @property def all_descriptions(self): - with db_session(self.DB_NAME) as (session, engine): + with db_session_with_credentials() as (_engine, session): qry = session.query(self.MODEL.description).distinct() result = qry.all() return self.retrieve_single_value_result(result) @@ -560,20 +776,33 @@ def all_descriptions(self): @classmethod def check_for_single_dataset(cls, **kwargs): """ - At the moment there is not a clear path to how to deal with multiple rasters so - check that the user only requested one dataset + At the moment there is not a clear path to how to deal with + multiple rasters so check that the user only requested one + dataset """ - LOG.info("Checking raster query for single raster dataset...") - multi_raster_indicators = ['instrument', 'date', 'observers', 'doi', 'type', 'description'] - with db_session(cls.DB_NAME) as (session, engine): + LOG.info( + "Checking raster query for single raster dataset..." + ) + multi_raster_indicators = [ + 'instrument', 'date', 'observers', 'doi', 'type', + 'description' + ] + with db_session_with_credentials() as (_engine, session): try: - # Form query and check if the query spans multipl rasters + # Form query and check if the query spans + # multiple rasters for column in multi_raster_indicators: - values = cls.from_unique_entries([column], **kwargs) + values = cls.from_unique_entries( + [column], **kwargs + ) if len(values) > 1: options = [f"'{v}'" for v in values] - raise TooManyRastersException(f"More than one `{column}` suggests there are multiple raster datasets. " - f"Try filter {column} to one of the following values {', '.join(options)}.") + raise TooManyRastersException( + f"More than one `{column}` suggests " + f"there are multiple raster datasets. " + f"Try filter {column} to one of the " + f"following values {', '.join(options)}." + ) except Exception as e: session.close() @@ -583,13 +812,13 @@ def check_for_single_dataset(cls, **kwargs): @classmethod def from_filter(cls, **kwargs): """ - Get data for the class by filtering by allowed arguments. The allowed - filters are cls.ALLOWED_QRY_KWARGS. + Get data for the class by filtering by allowed arguments. + The allowed filters are cls.ALLOWED_QRY_KWARGS. """ cls.check_for_single_dataset(**kwargs) - with db_session(cls.DB_NAME) as (session, engine): + with db_session_with_credentials() as (_engine, session): try: # Rebuild the query and form the raster base_query = cls.MODEL.raster @@ -612,15 +841,28 @@ def from_filter(cls, **kwargs): return datasets @classmethod - def from_area(cls, shp=None, pt=None, buffer=None, crs=26912, **kwargs): + def from_area( + cls, shp=None, pt=None, buffer=None, crs=26912, **kwargs + ): if shp is None and pt is None: raise ValueError( - "We need a shape description or a point and buffer") - if (pt is not None and buffer is None) or ( - buffer is not None and pt is None): - raise ValueError("pt and buffer must be given together") + "We need a shape description or a point and buffer" + ) + if ((pt is not None and buffer is None) or + (buffer is not None and pt is None)): + raise ValueError( + "pt and buffer must be given together" + ) + + # Check if geometric operations are available + if not HAS_CORE_GIS: + raise ImportError( + "geoalchemy2 and shapely are required for " + "geometric filtering. Install with: " + "pip install geoalchemy2 shapely" + ) - with db_session(cls.DB_NAME) as (session, engine): + with db_session_with_credentials() as (_engine, session): try: # Get shape ready for cropping with rasters @@ -636,10 +878,18 @@ def from_area(cls, shp=None, pt=None, buffer=None, crs=26912, **kwargs): db_shp = qry.all()[0][0] # Grab the rasters, union and clip them - base_query = func.ST_AsTiff(func.ST_Clip(func.ST_Union(ImageData.raster, type_=Raster), db_shp, True)) + base_query = func.ST_AsTiff( + func.ST_Clip( + func.ST_Union(ImageData.raster, type_=Raster), + db_shp, + True + ) + ) q = session.query(base_query) # Find all the tiles that - q = q.filter(gfunc.ST_Intersects(ImageData.raster, db_shp)) + q = q.filter( + gfunc.ST_Intersects(ImageData.raster, db_shp) + ) limit = kwargs.get("limit") diff --git a/snowexsql/lambda_client.py b/snowexsql/lambda_client.py new file mode 100644 index 0000000..61bfb53 --- /dev/null +++ b/snowexsql/lambda_client.py @@ -0,0 +1,641 @@ +""" +SnowEx Lambda API Client + +Lightweight client for accessing SnowEx database via AWS Lambda +function. Provides serverless access to snow data without requiring +heavy geospatial dependencies. +""" + +import boto3 +import json +import pandas as pd +from typing import Dict, Any +from datetime import datetime, date + + +class SnowExLambdaClient: + """ + Client for accessing SnowEx data via AWS Lambda + + This client provides serverless access to the SnowEx database through + a deployed Lambda function, eliminating the need for direct database + connections or heavy geospatial dependencies. + + The client mirrors the api.py class structure, providing access to: + - PointMeasurements: Point data measurements + - LayerMeasurements: Layer/profile data measurements + - RasterMeasurements: Raster/image data + - System functions: DOI queries, connection testing + + Example: + >>> client = SnowExLambdaClient() + >>> client.test_connection() + {'connected': True, 'version': 'PostgreSQL 17.6...'} + + >>> # Use class-based approach (mirrors api.py) + >>> data = client.layer_measurements.from_filter( + ... instrument='reflectance', limit=10 + ... ) + >>> instruments = client.point_measurements.all_instruments + """ + + def __init__( + self, + region: str = 'us-west-2', + function_name: str = 'lambda-snowex-sql' + ): + """ + Initialize the Lambda client + + Args: + region: AWS region where the Lambda function is deployed + function_name: Name of the deployed Lambda function + """ + self.lambda_client = boto3.client('lambda', region_name=region) + self.function_name = function_name + + # Dynamically create class-based accessors from available + # measurement classes + self._create_measurement_clients() + + def query(self, sql_query: str) -> pd.DataFrame: + """ + Execute a raw SQL query against the database via Lambda. + + Args: + sql_query: Raw SQL query string to execute + + Returns: + pd.DataFrame: Query results as a DataFrame + """ + payload = { + 'action': 'query', + 'sql': sql_query + } + + response = self.lambda_client.invoke( + FunctionName=self.function_name, + InvocationType='RequestResponse', + Payload=json.dumps(payload) + ) + + result = json.loads(response['Payload'].read()) + + if result.get('statusCode') != 200: + raise Exception(f"Lambda query failed: {result.get('body')}") + + body = json.loads(result['body']) + return pd.DataFrame(body.get('data', [])) + + def _create_measurement_clients(self): + """ + Dynamically create measurement client attributes based on + available measurement classes. + + This method discovers measurement classes using the same + convention as the Lambda handler: + - Classes ending in 'Measurements' + - Available in the snowexsql.api module + - Creates snake_case attributes + (e.g., PointMeasurements -> point_measurements) + """ + try: + # Import here to avoid circular imports + from snowexsql import api + + # Get all measurement classes using the same discovery + # logic as lambda_handler + measurement_classes = [ + name for name in dir(api) + if name.endswith('Measurements') and hasattr(api, name) + ] + + # Create client attributes dynamically + for class_name in measurement_classes: + # Convert CamelCase to snake_case for attribute name + attr_name = ''.join([ + '_' + c.lower() if c.isupper() else c + for c in class_name + ]).lstrip('_') + + # Create the client accessor + setattr( + self, + attr_name, + _LambdaDatasetClient(self, class_name) + ) + + except ImportError as e: + # If local discovery fails + raise ImportError( + f"Could not auto-discover measurement classes from " + f"snowexsql.api: {e}. " + "This usually indicates a packaging or import issue. " + "Check that the snowexsql package is properly installed." + ) + + def get_measurement_classes(self): + """ + Get all measurement client objects as a dictionary for easy unpacking. + + This method dynamically discovers all available measurement classes + and returns them with their original CamelCase names, making it easy + to use as drop-in replacements for direct API imports. + + Returns: + Dict mapping class names (str) to client objects + + Example: + >>> from snowexsql.lambda_client import SnowExLambdaClient + >>> client = SnowExLambdaClient() + >>> + >>> # Get all measurement classes + >>> classes = client.get_measurement_classes() + >>> PointMeasurements = classes['PointMeasurements'] + >>> LayerMeasurements = classes['LayerMeasurements'] + >>> + >>> # Use exactly like the direct API + >>> df = PointMeasurements.from_filter(type='depth', limit=10) + >>> df.plot(column='value', cmap='jet') + """ + try: + from snowexsql import api + + # Get all measurement classes + measurement_classes = [ + name for name in dir(api) + if name.endswith('Measurements') and hasattr(api, name) + ] + + # Build dictionary mapping class names to client objects + result = {} + for class_name in measurement_classes: + # Convert CamelCase to snake_case to get the attribute name + attr_name = ''.join([ + '_' + c.lower() if c.isupper() else c + for c in class_name + ]).lstrip('_') + + # Get the client object and map it to the original class name + if hasattr(self, attr_name): + result[class_name] = getattr(self, attr_name) + + return result + + except ImportError as e: + raise ImportError( + f"Could not discover measurement classes: {e}" + ) + + def _serialize_payload(self, obj): + """ + Recursively serialize payload objects to JSON-compatible format. + + Handles datetime objects and Shapely geometry objects by converting + them to JSON-serializable formats. + + Args: + obj: Object to serialize + + Returns: + JSON-serializable version of the object + """ + if isinstance(obj, (datetime, date)): + return obj.isoformat() + elif hasattr(obj, '__geo_interface__'): + # Handle Shapely geometry objects (Point, Polygon, etc.) + return obj.__geo_interface__ + elif isinstance(obj, dict): + return {key: self._serialize_payload(value) for key, value in obj.items()} + elif isinstance(obj, (list, tuple)): + return [self._serialize_payload(item) for item in obj] + else: + return obj + + def _invoke_lambda(self, action: str, **kwargs) -> dict: + """ + Internal method to invoke Lambda function + + Args: + action: The action to perform + (e.g., 'test_connection', 'get_layer_measurements') + **kwargs: Additional parameters to pass to the Lambda + function + + Returns: + Dict containing the Lambda function response + + Raises: + Exception: If Lambda invocation fails or returns an error + """ + payload = {'action': action, **kwargs} + + # Serialize datetime objects and other non-JSON-serializable types + payload = self._serialize_payload(payload) + + try: + response = self.lambda_client.invoke( + FunctionName=self.function_name, + InvocationType='RequestResponse', + Payload=json.dumps(payload) + ) + + result = json.loads(response['Payload'].read().decode('utf-8')) + + # Check if result has the expected structure + if 'body' not in result: + raise Exception(f"Unexpected Lambda response format: {result}") + + body = json.loads(result['body']) + + # Check for errors in the response + if 'error' in body: + raise Exception(f"Lambda returned error: {body['error']}") + + return body + + except json.JSONDecodeError as e: + raise Exception(f"Failed to parse Lambda response: {str(e)}") + except Exception as e: + raise Exception(f"Lambda invocation failed: {str(e)}") + + def test_connection(self) -> Dict[str, Any]: + """ + Test database connection through Lambda + + Returns: + Dict with connection status and database version info + """ + return self._invoke_lambda('test_connection') + +class _LambdaDatasetClient: + """ + Dynamic proxy client that automatically mirrors api.py + BaseDataset classes + + This class uses Python's __getattr__ magic method to dynamically + handle any method or property call, eliminating the need to + manually synchronize with changes in the underlying API classes. + + Supported patterns: + - Properties starting with 'all_': all_instruments, + all_campaigns, etc. + - Known methods: from_filter, from_unique_entries, from_area + - Class-specific properties: all_sites (LayerMeasurements only) + """ + + # Known methods that return DataFrames + _DATAFRAME_METHODS = {'from_filter', 'from_area'} + + # Known methods that take special parameters + _KNOWN_METHODS = { + 'from_filter': ['filters'], + 'from_unique_entries': ['columns', 'filters'], + 'from_area': ['shp', 'pt', 'buffer', 'crs'] + } + + def __init__( + self, + parent_client: SnowExLambdaClient, + class_name: str + ): + self._client = parent_client + self._class_name = class_name + + def __getattr__(self, name: str): + """ + Dynamic attribute access - handles any property or method call + + This magic method is called when an attribute is accessed that + doesn't exist on the object. It routes the call to the + appropriate handler based on naming patterns. + """ + + # Pattern 1: Properties starting with 'all_' + if name.startswith('all_'): + return self._get_property(name) + + # Pattern 2: Known methods from BaseDataset + elif name in self._KNOWN_METHODS: + return self._create_method_proxy(name) + + # Pattern 3: Other potential methods (extensible) + elif name.startswith('get_') or name.startswith('find_'): + return self._create_method_proxy(name) + + # Pattern 4: Handle unknown attributes with helpful error + else: + methods_list = list(self._KNOWN_METHODS.keys()) + raise AttributeError( + f"'{self._class_name}' has no attribute '{name}'. " + f"Available patterns: all_* (properties), " + f"{methods_list} (methods)" + ) + + def _create_method_proxy(self, method_name: str): + """ + Create a proxy function for a method that will invoke Lambda + + Returns a callable that matches the signature of the original + method + """ + def method_proxy(*args, as_geodataframe=True, **kwargs): + # Convert positional args to kwargs based on known method + # signatures + if args and method_name in self._KNOWN_METHODS: + param_names = self._KNOWN_METHODS[method_name] + for i, arg in enumerate(args): + if i < len(param_names): + kwargs[param_names[i]] = arg + + # Shape the payload to match what the Lambda handler + # expects from_filter: expects a single 'filters' dict + if method_name == 'from_filter': + provided_filters = {} + # If user provided an explicit filters dict, start + # with it + if 'filters' in kwargs and isinstance( + kwargs['filters'], dict + ): + provided_filters.update(kwargs['filters']) + kwargs.pop('filters', None) + # Move any remaining kwargs into filters + for k in list(kwargs.keys()): + provided_filters[k] = kwargs.pop(k) + # Now set the shaped kwargs + kwargs = {'filters': provided_filters} + + # from_unique_entries: expects 'columns' list and + # optional 'filters' dict + elif method_name == 'from_unique_entries': + columns = kwargs.get('columns') + if columns is None and 'filters' in kwargs: + # In case user passed columns positionally earlier, + # it's already mapped + pass + # Start filters from explicit dict if present + provided_filters = {} + if 'filters' in kwargs and isinstance( + kwargs['filters'], dict + ): + provided_filters.update(kwargs['filters']) + # Pull out recognized key 'columns' + shaped = {} + if columns is not None: + shaped['columns'] = columns + # Move any unrecognized keys (besides + # 'columns'/'filters') into filters + for k in list(kwargs.keys()): + if k in ('columns', 'filters'): + continue + provided_filters[k] = kwargs[k] + if provided_filters: + shaped['filters'] = provided_filters + kwargs = shaped if shaped else kwargs + + # from_area: Handle server-side spatial filtering using PostGIS + # Lambda uses PostGIS for efficient database-side spatial queries + elif method_name == 'from_area': + return self._handle_from_area_server_side(kwargs, as_geodataframe) + + # Invoke Lambda with the method call + action = f'{self._class_name}.{method_name}' + result = self._client._invoke_lambda(action, **kwargs) + + if 'error' in result: + raise Exception( + f"Method call failed: {result['error']}" + ) + + # Smart return type handling based on method + if method_name in self._DATAFRAME_METHODS: + df = pd.DataFrame(result['data']) + + # Convert to GeoDataFrame if requested and possible + if as_geodataframe and self._can_convert_to_geodataframe(df): + return self._to_geodataframe(df) + + return df + else: + return result['data'] + + # Add helpful docstring to the proxy function + method_proxy.__doc__ = ( + f"Proxy for {self._class_name}.{method_name}() - " + f"calls Lambda backend\n\n" + f"Args:\n" + f" as_geodataframe (bool): If True (default), return GeoDataFrame " + f"when geometry data is available.\n" + f" If False, return regular DataFrame.\n" + f" Requires geopandas to be installed." + ) + method_proxy.__name__ = method_name + + return method_proxy + + def _handle_from_area_server_side(self, kwargs: dict, as_geodataframe: bool): + """ + Handle from_area() with server-side PostGIS spatial filtering + + Lambda uses PostGIS for efficient database-side spatial queries: + 1. Convert geometry to WKT (Well-Known Text) format + 2. Send to Lambda with other filters + 3. Lambda constructs PostGIS spatial query + 4. Database performs spatial filtering efficiently + 5. Return filtered results + + Args: + kwargs: Parameters including pt/shp, buffer, crs, and other filters + as_geodataframe: Whether to return as GeoDataFrame + + Returns: + Filtered GeoDataFrame or DataFrame + """ + try: + from shapely.geometry import Point + except ImportError: + raise ImportError( + "shapely is required for from_area(). " + "Install with: pip install shapely" + ) + + # Extract spatial parameters + pt = kwargs.pop('pt', None) + shp = kwargs.pop('shp', None) + buffer_dist = kwargs.pop('buffer', None) + crs = kwargs.pop('crs', 4326) # Default to WGS84 + + # Validate parameters + if pt is None and shp is None: + raise ValueError("Either 'pt' or 'shp' parameter is required for from_area") + + if pt is not None and buffer_dist is None: + raise ValueError("'buffer' parameter is required when using 'pt'") + + # Convert geometry to WKT for transmission to Lambda + if pt is not None: + # Convert point to WKT + if isinstance(pt, Point): + pt_wkt = pt.wkt + elif isinstance(pt, (tuple, list)) and len(pt) == 2: + pt_wkt = Point(pt[0], pt[1]).wkt + else: + raise ValueError("pt must be a shapely Point or (x, y) tuple") + + kwargs['pt_wkt'] = pt_wkt + kwargs['buffer'] = buffer_dist + else: + # Convert shape to WKT + if hasattr(shp, 'wkt'): + kwargs['shp_wkt'] = shp.wkt + else: + raise ValueError("shp must be a shapely geometry object") + + kwargs['crs'] = crs + + # Remaining kwargs are filters + filters = {} + for k, v in list(kwargs.items()): + if k not in ['pt_wkt', 'shp_wkt', 'buffer', 'crs']: + filters[k] = kwargs.pop(k) + + if filters: + kwargs['filters'] = filters + + # Invoke Lambda with PostGIS spatial query + action = f'{self._class_name}.from_area' + result = self._client._invoke_lambda(action, **kwargs) + + # Convert result to DataFrame + df = pd.DataFrame(result.get('data', [])) + + if df.empty: + return df + + # Convert to GeoDataFrame if requested + if as_geodataframe: + df = self._to_geodataframe(df) + + return df + + def _can_convert_to_geodataframe(self, df: pd.DataFrame) -> bool: + """ + Check if DataFrame can be converted to GeoDataFrame + + Args: + df: DataFrame to check + + Returns: + bool: True if conversion is possible + """ + # Check for PostGIS geometry columns + has_geometry = 'geometry' in df.columns + has_geom = 'geom' in df.columns # PostGIS column name + + return has_geometry or has_geom + + def _to_geodataframe(self, df: pd.DataFrame): + """ + Convert pandas DataFrame to GeoDataFrame + + Handles PostGIS geometry columns returned from Lambda: + - geom column from PostGIS databases (WKB hex, WKT, or GeoJSON dict) + - geometry column already present + + Args: + df: DataFrame to convert + + Returns: + GeoDataFrame if conversion successful, otherwise original DataFrame + """ + try: + import geopandas as gpd + from shapely import wkb, wkt + from shapely.geometry import shape + + # Case 1: DataFrame has 'geom' column (PostGIS standard) + if 'geom' in df.columns: + if df['geom'].dtype == 'object': + # Try to parse as WKB hex string (most common from PostGIS) + try: + df['geometry'] = df['geom'].apply( + lambda x: wkb.loads(x, hex=True) if x else None + ) + return gpd.GeoDataFrame(df, geometry='geometry', crs='EPSG:4326') + except Exception: + # Try as WKT string + try: + df['geometry'] = df['geom'].apply(lambda x: wkt.loads(x) if x else None) + return gpd.GeoDataFrame(df, geometry='geometry', crs='EPSG:4326') + except Exception: + # Try as GeoJSON __geo_interface__ dict + try: + df['geometry'] = df['geom'].apply(lambda x: shape(x) if x else None) + return gpd.GeoDataFrame(df, geometry='geometry', crs='EPSG:4326') + except: + pass # Fall through to return original df + + # Case 2: DataFrame already has geometry column + elif 'geometry' in df.columns: + # Try to parse as WKT if it's a string + if df['geometry'].dtype == 'object': + try: + df['geometry'] = df['geometry'].apply(lambda x: wkt.loads(x) if x else None) + except: + pass # Already valid geometry or will fail below + + return gpd.GeoDataFrame(df, geometry='geometry', crs='EPSG:4326') + + # Case 3: No spatial data available + return df + + except ImportError: + # If geopandas not available, return regular DataFrame + import warnings + warnings.warn( + "geopandas not installed. Returning pandas DataFrame. " + "Install geopandas for spatial plotting: pip install geopandas", + UserWarning + ) + return df + except Exception as e: + # If conversion fails for any other reason + import warnings + warnings.warn( + f"Could not convert to GeoDataFrame: {e}. " + f"Returning pandas DataFrame.", + UserWarning + ) + return df + + def _get_property(self, property_name: str): + """Handle property access via Lambda""" + action = f'{self._class_name}.{property_name}' + result = self._client._invoke_lambda(action) + if 'error' in result: + raise Exception( + f"Property access failed: {result['error']}" + ) + return result['data'] + + def __repr__(self): + """Helpful representation for debugging""" + return f"<{self._class_name}Client via Lambda>" + + +# Convenience function for quick client creation +def create_client( + region: str = 'us-west-2', + function_name: str = 'lambda-snowex-sql' +) -> SnowExLambdaClient: + """ + Create a SnowExLambdaClient instance + + Args: + region: AWS region where the Lambda function is deployed + function_name: Name of the deployed Lambda function + + Returns: + SnowExLambdaClient instance + """ + return SnowExLambdaClient(region=region, function_name=function_name) \ No newline at end of file diff --git a/snowexsql/lambda_handler.py b/snowexsql/lambda_handler.py new file mode 100644 index 0000000..272531c --- /dev/null +++ b/snowexsql/lambda_handler.py @@ -0,0 +1,468 @@ +""" +Lambda-specific helper that exposes a function the container entrypoint +can call. + +This module adapts the existing snowexsql db helpers to accept credentials +provided at runtime via a temporary credentials.json file written from +Secrets Manager. It also exposes the core API functionality from api.py +for serverless usage. + +DEVELOPER NOTE: This module automatically discovers measurement classes +from api.py based on naming conventions. See _get_measurement_classes() +for detailed requirements. +""" +import json +import logging +import os +from pathlib import Path +from typing import Dict, Any +from datetime import datetime, date +import pandas as pd +import numpy as np + +from snowexsql import db as sled_db +from sqlalchemy import text + +LOG = logging.getLogger(__name__) + +LOG.info("Using standard API classes") + +def deserialize_geometry(geom_dict): + """Convert GeoJSON dict back to Shapely geometry""" + try: + from shapely.geometry import shape + return shape(geom_dict) + except ImportError: + raise ImportError( + "shapely is required for geometric operations. " + "Install with: pip install shapely" + ) + +def serialize_for_json(obj): + """Convert pandas DataFrame records to JSON-serializable format""" + if isinstance(obj, list): + return [serialize_for_json(item) for item in obj] + elif isinstance(obj, dict): + return { + key: serialize_for_json(value) + for key, value in obj.items() + } + elif isinstance(obj, (datetime, date)): + return obj.isoformat() + elif isinstance(obj, (pd.Timestamp, pd.NaT.__class__)): + return obj.isoformat() if pd.notna(obj) else None + elif isinstance(obj, (np.integer, np.floating)): + return obj.item() + elif pd.isna(obj): + return None + elif hasattr(obj, '__geo_interface__'): + # Handle shapely geometries + return obj.__geo_interface__ + elif str(type(obj)).endswith("WKBElement'>"): + # Handle geoalchemy2 WKBElement objects + try: + # Convert to WKT (Well-Known Text) format for JSON + return str(obj) + except Exception: + return None + else: + return obj + + +def _test_connection(engine): + """Test database connectivity and return version info.""" + with engine.connect() as conn: + result = conn.execute(text("SELECT version();")) + ver = result.fetchone()[0] + return {'connected': True, 'version': ver} + +def _create_response(action: str, data, **kwargs): + """ + Create a standardized response format for successful operations. + """ + response = { + 'action': action, + 'data': data + } + + # Add count if data is a list + if isinstance(data, list): + response['count'] = len(data) + + # Add any additional metadata + response.update(kwargs) + + return response + +def _create_error_response(action: str, error: Exception): + """Create a standardized error response format.""" + return {'error': f'{action} failed: {str(error)}'} + +def _get_measurement_classes(): + """ + Dynamically discover measurement classes from the api module. + + To make a new measurement class available via Lambda, + it must follow these conventions: + + 1. Class name MUST end with 'Measurements' + (e.g., WeatherMeasurements) + 2. Class MUST have a 'MODEL' attribute pointing to the + SQLAlchemy model + 3. Class MUST inherit from BaseDataset (directly or indirectly) + + Example: + class WeatherMeasurements(BaseDataset): + MODEL = WeatherData # Required! + # ... your implementation ... + + The class will then be automatically available as: + client.weather_measurements.from_filter() + client.weather_measurements.all_instruments + etc. + + Returns: + dict: Mapping of class names to class objects + """ + import snowexsql.api as api_module + + measurement_classes = {} + discovered_classes = [] + + for name in dir(api_module): + if name.endswith('Measurements'): + cls = getattr(api_module, name) + # Verify it's a proper measurement class + if hasattr(cls, 'MODEL') and callable(cls): + measurement_classes[name] = cls + discovered_classes.append(name) + + # Log what was discovered for debugging + LOG.info( + f"Auto-discovered measurement classes: {discovered_classes}" + ) + + if not measurement_classes: + LOG.warning( + "No measurement classes found! Check naming conventions " + "in api.py" + ) + + return measurement_classes + + +def validate_measurement_class(cls, class_name: str) -> bool: + """ + Validate that a class follows the measurement class conventions. + + This is a helper for developers to check if their new classes + will work. You can call this manually in tests or during + development. + """ + issues = [] + + if not class_name.endswith('Measurements'): + issues.append( + f"Class name '{class_name}' should end with 'Measurements'" + ) + + if not hasattr(cls, 'MODEL'): + issues.append( + f"Class '{class_name}' missing required MODEL attribute" + ) + + if not callable(cls): + issues.append( + f"'{class_name}' is not callable (not a class?)" + ) + + # Check if it has expected BaseDataset methods + expected_methods = ['from_filter', 'from_unique_entries'] + for method in expected_methods: + if not hasattr(cls, method): + issues.append( + f"Class '{class_name}' missing expected method " + f"'{method}'" + ) + + if issues: + LOG.warning( + f"Class '{class_name}' validation issues: {issues}" + ) + return False + + LOG.info(f"Class '{class_name}' passes validation ✓") + return True + +def _handle_class_action( + class_name: str, + method_name: str, + event: dict, + tmp_cred_path: str +): + """Handle class-based actions that mirror the api.py structure.""" + try: + # Dynamically discover available measurement classes + allowed_classes = _get_measurement_classes() + + if class_name not in allowed_classes: + available = list(allowed_classes.keys()) + raise ValueError( + f'Unknown class: {class_name}. Available: {available}' + ) + + api_class = allowed_classes[class_name] + + # Handle different method types + if method_name == 'from_filter': + filters = event.get('filters', {}) + records = _get_measurements_by_class(api_class, filters) + action = f'{class_name}.{method_name}' + return _create_response(action, records, filters=filters) + + elif method_name == 'from_area': + # Call api.py from_area method directly (it now uses PostGIS SQL) + pt_wkt = event.get('pt_wkt') + shp_wkt = event.get('shp_wkt') + buffer_dist = event.get('buffer') + crs = event.get('crs', 26912) + filters = event.get('filters', {}) + + # Set credentials for api.py to use + os.environ['SNOWEX_DB_CREDENTIALS_FILE'] = tmp_cred_path + + try: + df = api_class.from_area( + shp=shp_wkt, + pt=pt_wkt, + buffer=buffer_dist, + crs=crs, + **filters + ) + records = df.to_dict('records') + action = f'{api_class.__name__}.from_area' + return _create_response(action, serialize_for_json(records), count=len(records)) + except Exception as e: + raise Exception(f"from_area query failed: {str(e)}") + + elif method_name == 'from_unique_entries': + columns = event.get('columns', []) + filters = event.get('filters', {}) + if not columns: + raise ValueError('columns parameter is required') + result = api_class.from_unique_entries(columns, **filters) + action = f'{class_name}.{method_name}' + return _create_response( + action, + serialize_for_json(result), + columns=columns + ) + + elif method_name.startswith('all_'): + # Handle property-like methods + # (all_instruments, all_campaigns, etc.) + api_instance = api_class() + if hasattr(api_instance, method_name): + result = getattr(api_instance, method_name) + action = f'{class_name}.{method_name}' + return _create_response(action, result) + else: + raise ValueError( + f'Property {method_name} not found on {class_name}' + ) + + else: + raise ValueError(f'Unsupported method: {method_name}') + + except Exception as e: + return _create_error_response( + f'{class_name}.{method_name}', + e + ) + +def _get_measurements_by_class(api_class, filters: dict): + """ + Get measurements by calling api.py methods directly. + Single source of truth for query logic. + """ + df = api_class.from_filter(**filters) + records = df.to_dict('records') if hasattr(df, 'to_dict') else [] + return serialize_for_json(records) + +def _write_temp_credentials(creds: Dict[str, Any], dest: Path): + """ + Write credentials in flat format expected by snowexsql.db.load_credentials. + + AWS Secrets Manager secret should contain: + - username (or user or db_user) + - password + - host (or address) + - dbname (or database or db_name) + """ + cred_entry = { + 'username': (creds.get('username') or + creds.get('user') or + creds.get('db_user')), + 'password': creds.get('password'), + 'address': creds.get('host') or creds.get('address'), + 'db_name': (creds.get('dbname') or + creds.get('database') or + creds.get('db_name')) + } + + # Validate all required fields are present + missing = [k for k, v in cred_entry.items() if not v] + if missing: + LOG.error(f"Missing credential fields: {missing}") + LOG.error(f"Available secret keys: {list(creds.keys())}") + raise ValueError(f"Missing required credential fields: {missing}") + + # Write flat structure (NOT nested with production/tests keys) + dest.parent.mkdir(parents=True, exist_ok=True) + with open(dest, 'w') as fh: + json.dump(cred_entry, fh, indent=2) + + LOG.info(f"Wrote credentials to {dest}") + +def handle_event_with_secret(event: Dict[str, Any], secret_dict: Dict[str, Any]) -> Dict[str, Any]: + """ + Handle an event with credentials from AWS Secrets Manager. + + Args: + event: Lambda event containing action and parameters + secret_dict: Credentials from Secrets Manager + + Returns: + Response dictionary with results or error + """ + tmp_creds = Path('/tmp/credentials.json') + + try: + # Write credentials in flat format expected by snowexsql.db + _write_temp_credentials(secret_dict, tmp_creds) + + # Verify credentials file was written and is readable + if not tmp_creds.exists(): + raise FileNotFoundError(f"Failed to write credentials to {tmp_creds}") + + # Log for debugging (without exposing password) + with open(tmp_creds) as f: + creds_check = json.load(f) + LOG.info(f"Credentials file keys: {list(creds_check.keys())}") + + # Set environment variable so API classes can find credentials + # This is critical because api.py classes call db_session_with_credentials() + # without passing credentials_path parameter + os.environ['SNOWEX_DB_CREDENTIALS'] = str(tmp_creds) + + # Get database connection with explicit credentials path + engine, session = sled_db.get_db(credentials_path=str(tmp_creds)) + + # Test connection + if event.get('action') == 'test_connection': + result = _test_connection(engine) + session.close() + return result + + # Handle class-based actions (e.g., PointMeasurements.from_filter) + action = event.get('action', '') + if '.' in action: + class_name, method_name = action.split('.', 1) + result = _handle_class_action( + class_name, + method_name, + event, + str(tmp_creds) + ) + session.close() + return result + + # Handle raw SQL queries + if action == 'query': + sql = event.get('sql') + if not sql: + raise ValueError('SQL query not provided') + + result = session.execute(text(sql)) + rows = [dict(row._mapping) for row in result] + session.close() + return _create_response('query', serialize_for_json(rows)) + + session.close() + raise ValueError(f'Unknown action: {action}') + + except Exception as e: + LOG.error(f"Error in handle_event_with_secret: {str(e)}", exc_info=True) + if 'session' in locals(): + session.close() + return { + 'error': str(e), + 'action': event.get('action', 'unknown') + } + +def _get_secret( + secret_name: str, + region_name: str = None +) -> Dict[str, Any]: + """ + Retrieve a secret from AWS Secrets Manager and return it as a dict. + + This duplicates the minimal logic previously in the lambda-api + wrapper so the package can be used as the Lambda entrypoint directly. + """ + import boto3 + import json + import base64 + from botocore.exceptions import ClientError + + client_kwargs = {} + if region_name: + client_kwargs['region_name'] = region_name + client = boto3.client('secretsmanager', **client_kwargs) + try: + resp = client.get_secret_value(SecretId=secret_name) + except ClientError: + LOG.exception('Error fetching secret %s', secret_name) + raise + + if 'SecretString' in resp and resp['SecretString']: + try: + return json.loads(resp['SecretString']) + except json.JSONDecodeError: + return {'raw': resp['SecretString']} + else: + decoded = base64.b64decode(resp['SecretBinary']).decode('utf-8') + return json.loads(decoded) + + +def lambda_handler(event: Dict[str, Any], context: Any): + """ + AWS Lambda entrypoint: fetch DB secret and delegate to + handle_event_with_secret. + + This is the function the Lambda runtime will call when we set the + handler to `snowexsql.lambda_handler.lambda_handler` in the + container CMD. + """ + secret_name = os.environ.get('DB_SECRET_NAME') + region = os.environ.get('DB_AWS_REGION') + + if not secret_name: + LOG.error('DB_SECRET_NAME not set in environment') + error_body = json.dumps('DB_SECRET_NAME not set') + return {'statusCode': 500, 'body': error_body} + + try: + secret = _get_secret(secret_name, region) + except Exception as e: + error_body = json.dumps({'error': str(e)}) + return {'statusCode': 500, 'body': error_body} + + try: + result = handle_event_with_secret(event, secret) + return {'statusCode': 200, 'body': json.dumps(result)} + except Exception as e: + LOG.exception('Handler failed') + error_body = json.dumps({'error': str(e)}) + return {'statusCode': 500, 'body': error_body} + \ No newline at end of file diff --git a/tests/api/test_layer_measurements.py b/tests/api/test_layer_measurements.py index 3529910..29a02fb 100644 --- a/tests/api/test_layer_measurements.py +++ b/tests/api/test_layer_measurements.py @@ -165,16 +165,16 @@ def test_from_filter_fails(self, kwargs, expected_error): with pytest.raises(expected_error): self.subject.from_filter(**kwargs) - def test_from_area(self, point_data_x_y, point_data_srid): + def test_from_area(self, layer_data, point_data_x_y, point_data_srid): shp = gpd.points_from_xy( [point_data_x_y.x], [point_data_x_y.y], crs=f"epsg:{point_data_srid}" ).buffer(10)[0] - result = self.subject.from_area(shp=shp) + result = self.subject.from_area(shp=shp, crs=point_data_srid) assert len(result) == 1 - def test_from_area_point(self, point_data_x_y, point_data_srid): + def test_from_area_point(self, layer_data, point_data_x_y, point_data_srid): pts = gpd.points_from_xy( [point_data_x_y.x], [point_data_x_y.y], diff --git a/tests/api/test_point_measurements.py b/tests/api/test_point_measurements.py index 814c4a8..7a15405 100644 --- a/tests/api/test_point_measurements.py +++ b/tests/api/test_point_measurements.py @@ -184,7 +184,7 @@ def test_from_area(self, point_data_x_y, point_data_srid): [point_data_x_y.y], crs=f"epsg:{point_data_srid}" ).buffer(10)[0] - result = self.subject.from_area(shp=shp) + result = self.subject.from_area(shp=shp, crs=point_data_srid) assert len(result) == 1 def test_from_area_point(self, point_data_x_y, point_data_srid): diff --git a/tests/conftest.py b/tests/conftest.py index ae06ce2..6861c01 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -66,10 +66,10 @@ def db_test_session(monkeypatch, sqlalchemy_engine): the API when initiating a session. """ @contextmanager - def db_session(*args, **kwargs): - yield SESSION(), sqlalchemy_engine + def db_session_with_credentials(*args, **kwargs): + yield sqlalchemy_engine, SESSION() - monkeypatch.setattr(snowexsql.api, "db_session", db_session) + monkeypatch.setattr(snowexsql.api, "db_session_with_credentials", db_session_with_credentials) @pytest.fixture(scope='function') diff --git a/tests/deployment/conftest.py b/tests/deployment/conftest.py new file mode 100644 index 0000000..55435c4 --- /dev/null +++ b/tests/deployment/conftest.py @@ -0,0 +1,8 @@ +"""Fixtures for Lambda integration tests.""" +import pytest + +@pytest.fixture(scope="module") +def lambda_client(): + """Fixture to provide a SnowExLambdaClient instance""" + from snowexsql.lambda_client import SnowExLambdaClient + return SnowExLambdaClient() \ No newline at end of file diff --git a/tests/deployment/test_lambda_client.py b/tests/deployment/test_lambda_client.py new file mode 100644 index 0000000..1ddd083 --- /dev/null +++ b/tests/deployment/test_lambda_client.py @@ -0,0 +1,272 @@ +""" +Test the Lambda CLIENT (lambda_client.py) functionality. + +Tests the SnowExLambdaClient class that makes requests to the deployed +Lambda function. This tests: +- Client-side logic and API interface +- Full round-trip: client → Lambda → database → client +- GeoDataFrame conversion on client-side + +These are END-TO-END tests requiring a deployed Lambda function. +Mark with @pytest.mark.integration to run separately. + +REQUIREMENTS: +- Lambda function must be deployed with latest code +- Lambda timeout should be 60+ seconds (default 35s may be too short) +- Lambda must have access to database via secrets manager +- AWS credentials must be configured locally (AWS CLI / boto3) + +To deploy/update Lambda: + cd deployment + ./scripts/deploy.sh +""" + +import pytest +import pandas as pd +from snowexsql.lambda_client import SnowExLambdaClient + +# Check if geopandas is available for testing +try: + import geopandas as gpd + HAS_GEOPANDAS = True +except ImportError: + HAS_GEOPANDAS = False + + +@pytest.fixture(scope="module") +def lambda_client(): + """Fixture to provide a SnowExLambdaClient instance for all tests""" + return SnowExLambdaClient() + + +# ======================================================================== +# CONNECTION TESTS +# ======================================================================== + +@pytest.mark.integration +class TestClientConnection: + """Test client connection and basic functionality""" + + def test_lambda_connection(self, lambda_client): + """Test the deployed Lambda function connection""" + result = lambda_client.test_connection() + assert result.get('connected'), "Lambda connection failed" + assert result.get('version'), "Database version not returned" + assert 'PostgreSQL' in result.get('version', '') + + def test_raw_query(self, lambda_client): + """Test raw SQL query through client""" + result = lambda_client.query("SELECT 1 as test_value;") + assert isinstance(result, pd.DataFrame) + assert len(result) > 0 + + +# ======================================================================== +# MEASUREMENT CLASS INTERFACE TESTS +# ======================================================================== + +@pytest.mark.integration +class TestPointMeasurementsClient: + """Test PointMeasurements through client""" + + def test_point_all_instruments(self, lambda_client): + """Test accessing all_instruments property""" + try: + instruments = lambda_client.point_measurements.all_instruments + assert isinstance(instruments, list) + assert len(instruments) > 0 + except Exception as e: + if 'timed out' in str(e).lower() or 'Sandbox.Timedout' in str(e): + pytest.skip( + "Lambda timeout - ensure Lambda is deployed with latest code " + "and timeout is set appropriately (60+ seconds recommended)" + ) + raise + + def test_point_all_campaigns(self, lambda_client): + """Test accessing all_campaigns property""" + campaigns = lambda_client.point_measurements.all_campaigns + assert isinstance(campaigns, list) + assert len(campaigns) > 0 + + def test_point_from_filter(self, lambda_client): + """Test from_filter method""" + df = lambda_client.point_measurements.from_filter(limit=5) + assert isinstance(df, (pd.DataFrame, gpd.GeoDataFrame)) + assert len(df) <= 5 + + def test_point_from_filter_with_filters(self, lambda_client): + """Test from_filter with multiple filters""" + instruments = lambda_client.point_measurements.all_instruments + if instruments: + df = lambda_client.point_measurements.from_filter( + instrument=instruments[0], + limit=3 + ) + assert isinstance(df, (pd.DataFrame, gpd.GeoDataFrame)) + assert len(df) <= 3 + + +@pytest.mark.integration +class TestLayerMeasurementsClient: + """Test LayerMeasurements through client""" + + def test_layer_all_instruments(self, lambda_client): + """Test accessing all_instruments property""" + try: + instruments = lambda_client.layer_measurements.all_instruments + assert isinstance(instruments, list) + except Exception as e: + if 'timed out' in str(e).lower() or 'Sandbox.Timedout' in str(e): + pytest.skip( + "Lambda timeout - ensure Lambda is deployed with latest code " + "and timeout is set appropriately (60+ seconds recommended)" + ) + raise + + def test_layer_from_filter(self, lambda_client): + """Test from_filter method""" + df = lambda_client.layer_measurements.from_filter(limit=5) + assert isinstance(df, (pd.DataFrame, gpd.GeoDataFrame)) + assert len(df) <= 5 + + def test_layer_from_unique_entries(self, lambda_client): + """Test from_unique_entries method""" + result = lambda_client.layer_measurements.from_unique_entries( + columns=['depth'], + limit=10 + ) + assert isinstance(result, list) + + +# ======================================================================== +# SPATIAL QUERY TESTS +# ======================================================================== + +@pytest.mark.integration +class TestClientSpatialQueries: + """Test spatial queries through client""" + + def test_from_area_with_point_buffer(self, lambda_client): + """Test from_area with point and buffer""" + # Use a point in Grand Mesa area + df = lambda_client.point_measurements.from_area( + pt=(743683, 4321095), + buffer=1000, + crs=26912, + limit=10 + ) + assert isinstance(df, (pd.DataFrame, gpd.GeoDataFrame)) + + def test_from_area_with_geodataframe_conversion(self, lambda_client): + """Test that from_area returns GeoDataFrame when requested""" + df = lambda_client.point_measurements.from_area( + pt=(743683, 4321095), + buffer=500, + crs=26912, + as_geodataframe=True, + limit=5 + ) + + if HAS_GEOPANDAS and len(df) > 0: + # Should be GeoDataFrame with geometry column + assert isinstance(df, gpd.GeoDataFrame) + assert 'geometry' in df.columns or 'geom' in df.columns + + def test_from_area_as_dataframe(self, lambda_client): + """Test that from_area can return plain DataFrame""" + df = lambda_client.point_measurements.from_area( + pt=(743683, 4321095), + buffer=500, + crs=26912, + as_geodataframe=False, + limit=5 + ) + + # Should be plain DataFrame (not GeoDataFrame) + assert isinstance(df, pd.DataFrame) + + +# ======================================================================== +# CLIENT-SIDE CONVERSION TESTS +# ======================================================================== + +@pytest.mark.integration +@pytest.mark.skipif(not HAS_GEOPANDAS, reason="geopandas required") +class TestClientGeoConversion: + """Test client-side GeoDataFrame conversion""" + + def test_geodataframe_conversion(self, lambda_client): + """Test that client converts to GeoDataFrame properly""" + df = lambda_client.point_measurements.from_filter( + limit=3, + as_geodataframe=True + ) + + if len(df) > 0: + assert isinstance(df, gpd.GeoDataFrame) + # Should have geometry column + assert hasattr(df, 'geometry') + + def test_geometry_column_parsing(self, lambda_client): + """Test that geometry is properly parsed from WKT/WKB""" + df = lambda_client.point_measurements.from_filter( + limit=1, + as_geodataframe=True + ) + + if len(df) > 0 and isinstance(df, gpd.GeoDataFrame): + # Geometry should be Shapely objects, not strings + geom = df.iloc[0].geometry + assert hasattr(geom, 'geom_type') + assert geom.geom_type in ['Point', 'Polygon', 'LineString'] + + +# ======================================================================== +# ERROR HANDLING TESTS +# ======================================================================== + +@pytest.mark.integration +class TestClientErrorHandling: + """Test client error handling""" + + def test_invalid_method(self, lambda_client): + """Test client handles invalid method gracefully""" + with pytest.raises(Exception): + lambda_client.point_measurements.nonexistent_method() + + def test_invalid_filter_parameter(self, lambda_client): + """Test client handles invalid filter parameters""" + # This should still work but might return empty results + df = lambda_client.point_measurements.from_filter( + instrument='definitely_not_a_real_instrument_name', + limit=1 + ) + assert isinstance(df, (pd.DataFrame, gpd.GeoDataFrame)) + + +# ======================================================================== +# RESPONSE FORMAT TESTS +# ======================================================================== + +@pytest.mark.integration +class TestClientResponseFormats: + """Test that client properly formats responses""" + + def test_property_returns_list(self, lambda_client): + """Test that property accessors return lists""" + result = lambda_client.point_measurements.all_instruments + assert isinstance(result, list) + + def test_from_filter_returns_dataframe(self, lambda_client): + """Test that from_filter returns DataFrame""" + result = lambda_client.point_measurements.from_filter(limit=1) + assert isinstance(result, (pd.DataFrame, gpd.GeoDataFrame)) + + def test_from_unique_entries_returns_list(self, lambda_client): + """Test that from_unique_entries returns list""" + result = lambda_client.point_measurements.from_unique_entries( + columns=['value'], + limit=5 + ) + assert isinstance(result, list) diff --git a/tests/deployment/test_lambda_handler.py b/tests/deployment/test_lambda_handler.py new file mode 100644 index 0000000..b2bf0cf --- /dev/null +++ b/tests/deployment/test_lambda_handler.py @@ -0,0 +1,527 @@ +""" +Test the Lambda HANDLER (lambda_handler.py) functionality locally. + +Tests the handle_event_with_secret() function and Lambda handler logic +without deploying to AWS. Connects directly to the database using local +credentials. This tests the server-side/handler logic, NOT the client. + +These tests require a credentials.json file in the repository root +directory. Mark with @pytest.mark.handler to run separately from client +tests. +""" +import json +import os +from pathlib import Path +import pytest + +# Set up the environment to simulate Lambda +os.environ['DB_SECRET_NAME'] = 'dummy_secret' +os.environ['DB_AWS_REGION'] = 'us-west-2' + +from snowexsql.lambda_handler import handle_event_with_secret +from snowexsql.tables import PointData, LayerData + + +# ======================================================================== +# FIXTURES +# ======================================================================== + +@pytest.fixture(scope="module") +def local_credentials(): + """ + Load credentials using snowexsql.db functions. + + Uses the same credential loading logic as the main package: + - SNOWEX_DB_CONNECTION environment variable (user:pass@host/db) + - SNOWEX_DB_CREDENTIALS environment variable (path to JSON file) + - credentials.json in current directory + + Set environment: + export SNOWEX_DB_CONNECTION=user:pass@host/dbname + Or: + export SNOWEX_DB_CREDENTIALS=/path/to/credentials.json + """ + from snowexsql.db import load_credentials + + # Check if using connection string format + if os.getenv("SNOWEX_DB_CONNECTION"): + # Parse connection string: user:pass@host/dbname + conn_str = os.getenv("SNOWEX_DB_CONNECTION") + try: + # Split user:pass@host/dbname + auth, location = conn_str.split('@') + username, password = auth.split(':') + host, dbname = location.split('/') + + return { + 'username': username, + 'password': password, + 'host': host, + 'dbname': dbname + } + except ValueError as e: + pytest.skip( + f"Invalid SNOWEX_DB_CONNECTION format: {e}\n" + "Expected format: user:pass@host/dbname" + ) + + # Otherwise use load_credentials for JSON file + try: + creds = load_credentials() + + # Convert to the format expected by the Lambda handler + return { + 'username': creds.get('username') or creds.get('user'), + 'password': creds.get('password'), + 'host': creds.get('address') or creds.get('host'), + 'dbname': (creds.get('db_name') or + creds.get('database') or + creds.get('dbname')) + } + except FileNotFoundError as e: + pytest.skip( + f"Database credentials not found: {str(e)}\n" + "Set SNOWEX_DB_CONNECTION or SNOWEX_DB_CREDENTIALS environment variable" + ) + + +@pytest.fixture +def test_point_data(point_data_factory, db_session): + """Create test point data for spatial queries""" + # Create a point in the Grand Mesa area (UTM Zone 12N) + point_data_factory.create() + return db_session.query(PointData).all() + + +@pytest.fixture +def test_layer_data(layer_data_factory, db_session): + """Create test layer data for spatial queries""" + layer_data_factory.create() + return db_session.query(LayerData).all() + + +# ======================================================================== +# CONNECTION TESTS +# ======================================================================== + +@pytest.mark.handler +def test_handler_connection(local_credentials): + """Test basic database connection through handler""" + event = {'action': 'test_connection'} + + result = handle_event_with_secret(event, local_credentials) + + assert result.get('connected'), "Handler connection failed" + assert result.get('version'), "Database version not returned" + version_str = result.get('version', '') + assert 'PostgreSQL' in version_str, "Expected PostgreSQL version" + + +# ======================================================================== +# LAYER MEASUREMENTS TESTS +# ======================================================================== + +@pytest.mark.handler +class TestLayerMeasurementsHandler: + """Test LayerMeasurements actions through the handler""" + + def test_layer_all_instruments(self, local_credentials): + """Test LayerMeasurements.all_instruments property""" + event = { + 'action': 'LayerMeasurements.all_instruments' + } + + result = handle_event_with_secret(event, local_credentials) + + error_msg = f"Handler returned error: {result.get('error')}" + assert 'error' not in result, error_msg + assert 'data' in result, "Response missing 'data' field" + assert isinstance(result['data'], list), ( + "Expected list of instruments" + ) + + def test_layer_all_campaigns(self, local_credentials): + """Test LayerMeasurements.all_campaigns property""" + event = { + 'action': 'LayerMeasurements.all_campaigns' + } + + result = handle_event_with_secret(event, local_credentials) + + error_msg = f"Handler returned error: {result.get('error')}" + assert 'error' not in result, error_msg + assert 'data' in result, "Response missing 'data' field" + assert isinstance(result['data'], list), ( + "Expected list of campaigns" + ) + + def test_layer_from_filter(self, local_credentials): + """Test LayerMeasurements.from_filter with limit""" + event = { + 'action': 'LayerMeasurements.from_filter', + 'filters': { + 'limit': 5 + } + } + + result = handle_event_with_secret(event, local_credentials) + + error_msg = f"Handler returned error: {result.get('error')}" + assert 'error' not in result, error_msg + assert 'data' in result, "Response missing 'data' field" + assert isinstance(result['data'], list), ( + "Expected list of records" + ) + assert len(result['data']) <= 5, "Limit not respected" + + def test_layer_from_unique_entries_single_column( + self, local_credentials + ): + """Test from_unique_entries with single column""" + event = { + 'action': 'LayerMeasurements.from_unique_entries', + 'columns': ['depth'], + 'filters': {'limit': 10} + } + + result = handle_event_with_secret(event, local_credentials) + + error_msg = f"Handler returned error: {result.get('error')}" + assert 'error' not in result, error_msg + assert 'data' in result, "Response missing 'data' field" + assert isinstance(result['data'], list), ( + "Expected list of unique depths" + ) + + def test_layer_from_unique_entries_multiple_columns( + self, local_credentials + ): + """Test from_unique_entries with multiple columns""" + event = { + 'action': 'LayerMeasurements.from_unique_entries', + 'columns': ['depth', 'value'], + 'filters': {'limit': 5} + } + + result = handle_event_with_secret(event, local_credentials) + + error_msg = f"Handler returned error: {result.get('error')}" + assert 'error' not in result, error_msg + assert 'data' in result, "Response missing 'data' field" + # Multiple columns should return list of tuples/lists + + def test_layer_filter_by_instrument(self, local_credentials): + """Test from_filter with instrument filter""" + # First get available instruments + event1 = { + 'action': 'LayerMeasurements.all_instruments' + } + result1 = handle_event_with_secret(event1, local_credentials) + + if result1.get('data'): + instrument = result1['data'][0] + + # Now filter by that instrument + event2 = { + 'action': 'LayerMeasurements.from_filter', + 'filters': { + 'instrument': instrument, + 'limit': 3 + } + } + result2 = handle_event_with_secret(event2, local_credentials) + + error_msg = f"Filter failed: {result2.get('error')}" + assert 'error' not in result2, error_msg + assert 'data' in result2 + + +# ======================================================================== +# POINT MEASUREMENTS TESTS +# ======================================================================== + +@pytest.mark.handler +class TestPointMeasurementsHandler: + """Test PointMeasurements actions through the handler""" + + def test_point_all_instruments(self, local_credentials): + """Test PointMeasurements.all_instruments property""" + event = { + 'action': 'PointMeasurements.all_instruments' + } + + result = handle_event_with_secret(event, local_credentials) + + error_msg = f"Handler returned error: {result.get('error')}" + assert 'error' not in result, error_msg + assert 'data' in result, "Response missing 'data' field" + assert isinstance(result['data'], list), ( + "Expected list of instruments" + ) + + def test_point_from_filter(self, local_credentials): + """Test PointMeasurements.from_filter""" + event = { + 'action': 'PointMeasurements.from_filter', + 'filters': { + 'limit': 5 + } + } + + result = handle_event_with_secret(event, local_credentials) + + error_msg = f"Handler returned error: {result.get('error')}" + assert 'error' not in result, error_msg + assert 'data' in result, "Response missing 'data' field" + assert isinstance(result['data'], list), ( + "Expected list of records" + ) + + def test_point_from_unique_entries(self, local_credentials): + """Test PointMeasurements.from_unique_entries""" + event = { + 'action': 'PointMeasurements.from_unique_entries', + 'columns': ['value'], # Use 'type' instead of 'instrument' - direct column + 'filters': {'limit': 10} + } + + result = handle_event_with_secret(event, local_credentials) + + error_msg = f"Handler returned error: {result.get('error')}" + assert 'error' not in result, error_msg + assert 'data' in result, "Response missing 'data' field" + + +# ======================================================================== +# QUERY TESTS +# ======================================================================== + +@pytest.mark.handler +class TestRawQueryHandler: + """Test raw SQL query functionality""" + + def test_simple_query(self, local_credentials): + """Test simple SQL query""" + event = { + 'action': 'query', + 'sql': 'SELECT version();' + } + + result = handle_event_with_secret(event, local_credentials) + + error_msg = f"Query failed: {result.get('error')}" + assert 'error' not in result, error_msg + assert 'data' in result, "Response missing 'data' field" + assert len(result['data']) > 0, "Expected query results" + + def test_query_with_limit(self, local_credentials): + """Test query with LIMIT clause""" + event = { + 'action': 'query', + 'sql': 'SELECT * FROM points LIMIT 3;' + } + + result = handle_event_with_secret(event, local_credentials) + + error_msg = f"Query failed: {result.get('error')}" + assert 'error' not in result, error_msg + assert 'data' in result, "Response missing 'data' field" + + +# ======================================================================== +# ERROR HANDLING TESTS +# ======================================================================== + +@pytest.mark.handler +class TestHandlerErrorHandling: + """Test error handling in the handler""" + + def test_invalid_action(self, local_credentials): + """Test handler response to invalid action""" + event = { + 'action': 'invalid_action_name' + } + + result = handle_event_with_secret(event, local_credentials) + + assert 'error' in result, "Expected error for invalid action" + + def test_invalid_class_name(self, local_credentials): + """Test handler response to invalid class name""" + event = { + 'action': 'InvalidClass.from_filter', + 'filters': {} + } + + result = handle_event_with_secret(event, local_credentials) + + assert 'error' in result, "Expected error for invalid class" + + def test_missing_required_parameter(self, local_credentials): + """Test handler response to missing required parameter""" + event = { + 'action': 'LayerMeasurements.from_unique_entries', + # Missing 'columns' parameter + 'filters': {} + } + + result = handle_event_with_secret(event, local_credentials) + + assert 'error' in result, "Expected error for missing parameter" + + def test_invalid_sql_query(self, local_credentials): + """Test handler response to invalid SQL""" + event = { + 'action': 'query', + 'sql': 'SELECT * FROM nonexistent_table_xyz;' + } + + result = handle_event_with_secret(event, local_credentials) + + assert 'error' in result, "Expected error for invalid SQL" + + +# ======================================================================== +# SPATIAL QUERY TESTS (from_area) +# ======================================================================== + +@pytest.mark.handler +@pytest.mark.usefixtures("db_test_session") +@pytest.mark.usefixtures("db_test_connection") +class TestSpatialQueryHandler: + """Test spatial query functionality using PostGIS""" + + def test_point_from_area_with_buffer(self, test_point_data, local_credentials): + """Test from_area with point and buffer""" + event = { + 'action': 'PointMeasurements.from_area', + 'pt_wkt': 'POINT(743683 4321095)', + 'buffer': 1000, # 1km buffer + 'crs': 26912, # UTM Zone 12N + 'filters': {'limit': 10} + } + + result = handle_event_with_secret(event, local_credentials) + + error_msg = f"from_area failed: {result.get('error')}" + assert 'error' not in result, error_msg + assert 'data' in result, "Response missing 'data' field" + assert isinstance(result['data'], list) + + def test_layer_from_area_with_shape(self, test_layer_data, local_credentials): + """Test from_area with polygon shape""" + # Small bounding box + event = { + 'action': 'LayerMeasurements.from_area', + 'shp_wkt': 'POLYGON((743000 4321000, 744000 4321000, 744000 4322000, 743000 4322000, 743000 4321000))', + 'crs': 26912, + 'filters': {'limit': 10} + } + + result = handle_event_with_secret(event, local_credentials) + + error_msg = f"from_area failed: {result.get('error')}" + assert 'error' not in result, error_msg + assert 'data' in result, "Response missing 'data' field" + + def test_from_area_with_filters(self, local_credentials): + """Test from_area with additional filters""" + event = { + 'action': 'PointMeasurements.from_area', + 'pt_wkt': 'POINT(743683 4321095)', + 'buffer': 5000, + 'crs': 26912, + 'filters': { + 'type': 'depth', + 'limit': 5 + } + } + + result = handle_event_with_secret(event, local_credentials) + + error_msg = f"from_area with filters failed: {result.get('error')}" + assert 'error' not in result, error_msg + assert 'data' in result + + +# ======================================================================== +# RESPONSE FORMAT TESTS +# ======================================================================== + +@pytest.mark.handler +class TestHandlerResponseFormat: + """Test that handler responses follow expected format""" + + def test_connection_response_format(self, local_credentials): + """Test connection response has expected fields""" + event = {'action': 'test_connection'} + result = handle_event_with_secret(event, local_credentials) + + assert 'connected' in result + assert 'version' in result + + def test_data_response_format(self, local_credentials): + """Test data query response has expected fields""" + event = { + 'action': 'PointMeasurements.from_filter', + 'filters': {'limit': 1} + } + result = handle_event_with_secret(event, local_credentials) + + assert 'data' in result + assert 'count' in result + assert result['count'] == len(result['data']) + + def test_property_response_format(self, local_credentials): + """Test property response has expected fields""" + event = { + 'action': 'LayerMeasurements.all_instruments' + } + result = handle_event_with_secret(event, local_credentials) + + assert 'data' in result + assert isinstance(result['data'], list) + + +# ======================================================================== +# ARCHITECTURE VERIFICATION +# ======================================================================== + +@pytest.mark.handler +class TestHandlerArchitecture: + """Verify handler uses api.py as single source of truth""" + + def test_handler_calls_api_from_filter(self, local_credentials): + """ + Verify from_filter goes through api.py methods. + This ensures handler isn't duplicating query logic. + """ + event = { + 'action': 'PointMeasurements.from_filter', + 'filters': {'limit': 1} + } + + result = handle_event_with_secret(event, local_credentials) + + # If this works, handler successfully called api.py + assert 'error' not in result + assert 'data' in result + + def test_handler_calls_api_from_area(self, local_credentials): + """ + Verify from_area goes through api.py methods. + This ensures spatial logic is in api.py, not duplicated. + """ + event = { + 'action': 'PointMeasurements.from_area', + 'pt_wkt': 'POINT(743683 4321095)', + 'buffer': 100, + 'crs': 26912, + 'filters': {'limit': 1} + } + + result = handle_event_with_secret(event, local_credentials) + + # If this works, handler successfully called api.py + assert 'error' not in result + assert 'data' in result \ No newline at end of file