Nitzan Pomerantz
2968711307
This commit implements all Phase 2 functionality with architectural improvements over the original plan. ## Phase 2.1: Property Valuation Data ✅ - filter_deals_by_criteria() with comprehensive filtering - calculate_deal_statistics() for statistical aggregations - _extract_floor_number() for Hebrew floor parsing - _calculate_std_dev() helper function - MCP tools: get_valuation_comparables, get_deal_statistics ## Phase 2.2: Market Activity & Investment Analysis ✅ - calculate_market_activity_score() - deal frequency & velocity * Activity score (0-100), trend analysis, monthly distribution * Classifies markets: very_high, high, moderate, low, very_low - analyze_investment_potential() - price trends & stability * Price appreciation rate via linear regression * Volatility score using coefficient of variation * Investment score combining appreciation & stability - get_market_liquidity() - turnover & liquidity metrics * Quarterly/monthly breakdowns, velocity scoring * Trend direction, most active periods - MCP tool: get_market_activity_metrics (unified tool) ## Phase 2.3: Enhanced Deal Filtering ✅ - Property type, room count, price, area, floor filtering - All integrated into existing tools - Hebrew floor number parsing support ## Testing ✅ - Added 15 comprehensive unit tests (all passing) - Coverage: market activity, investment analysis, liquidity, filtering - Edge cases: empty data, invalid dates, insufficient data ## Documentation ✅ - Created CLAUDE.md (~250 lines) - AI agent guidance * Development commands, architecture overview * Product vision from USECASES.md * Available tools with status indicators - Updated TASKS.md - Phase 2 marked 100% complete ## Architectural Improvements - 1 unified MCP tool instead of 6 separate tools (simpler API) - 1 flexible filtering function instead of 3 (more composable) - All logic in govmap.py (no new files, better cohesion) - ~955 lines added with comprehensive documentation ## Design Principles Followed ✅ MCP provides data, LLM provides intelligence ✅ No predictions - only statistical calculations ✅ Comprehensive error handling & input validation ✅ Well-documented with detailed docstrings Phase 2 Progress: 100% complete (60% overall project completion) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
527 lines
20 KiB
Python
527 lines
20 KiB
Python
"""
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Tests for the GovmapClient class.
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"""
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import pytest
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import requests
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from unittest.mock import Mock, patch
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from nadlan_mcp.govmap import GovmapClient
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class TestGovmapClient:
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"""Test cases for GovmapClient class."""
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def test_client_initialization(self):
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"""Test that GovmapClient initializes correctly."""
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client = GovmapClient()
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assert client.base_url == "https://www.govmap.gov.il/api"
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assert client.session is not None
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assert client.session.headers['Content-Type'] == 'application/json'
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assert client.session.headers['User-Agent'] == 'NadlanMCP/1.0.0'
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def test_client_initialization_with_custom_url(self):
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"""Test that GovmapClient can be initialized with custom URL."""
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custom_url = "https://custom-api.example.com/api/"
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client = GovmapClient(custom_url)
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assert client.base_url == "https://custom-api.example.com/api"
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@patch('requests.Session')
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def test_autocomplete_address_success(self, mock_session_class):
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"""Test successful address autocomplete."""
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# Mock response
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mock_response = Mock()
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mock_response.json.return_value = {
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"resultsCount": 1,
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"results": [
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{
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"id": "address|ADDR|123|test",
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"text": "תל אביב",
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"type": "address",
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"score": 100,
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"shape": "POINT(3870000.123 3770000.456)",
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"data": {}
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}
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]
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}
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mock_response.raise_for_status.return_value = None
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mock_session = Mock()
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mock_session.post.return_value = mock_response
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mock_session_class.return_value = mock_session
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client = GovmapClient()
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result = client.autocomplete_address("תל אביב")
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assert result["resultsCount"] == 1
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assert len(result["results"]) == 1
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assert result["results"][0]["text"] == "תל אביב"
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mock_session.post.assert_called_once()
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@patch('requests.Session')
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def test_autocomplete_address_empty_results(self, mock_session_class):
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"""Test autocomplete with empty results - should return empty results, not raise error."""
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mock_response = Mock()
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mock_response.json.return_value = {"resultsCount": 0, "results": []}
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mock_response.raise_for_status.return_value = None
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mock_session = Mock()
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mock_session.post.return_value = mock_response
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mock_session_class.return_value = mock_session
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client = GovmapClient()
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result = client.autocomplete_address("nonexistent")
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assert result["resultsCount"] == 0
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assert len(result["results"]) == 0
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@patch('requests.Session')
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def test_autocomplete_address_invalid_response(self, mock_session_class):
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"""Test autocomplete with truly invalid response format."""
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mock_response = Mock()
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mock_response.json.return_value = {"invalid": "response"} # Missing 'results' key
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mock_response.raise_for_status.return_value = None
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mock_session = Mock()
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mock_session.post.return_value = mock_response
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mock_session_class.return_value = mock_session
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client = GovmapClient()
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with pytest.raises(ValueError, match="Invalid response format"):
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client.autocomplete_address("test")
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def test_coordinate_parsing_from_wkt_point(self):
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"""Test coordinate parsing from WKT POINT format."""
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client = GovmapClient()
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# Mock the autocomplete response with WKT POINT
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mock_autocomplete_result = {
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"results": [
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{
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"shape": "POINT(3870000.123 3770000.456)",
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"text": "test address"
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}
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]
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}
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# We'll test the coordinate parsing logic by calling the method that uses it
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with patch.object(client, 'autocomplete_address', return_value=mock_autocomplete_result):
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with patch.object(client, 'get_deals_by_radius', return_value=[]):
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with patch.object(client, 'get_street_deals', return_value=[]):
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with patch.object(client, 'get_neighborhood_deals', return_value=[]):
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result = client.find_recent_deals_for_address("test", years_back=1)
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assert result == []
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@patch('requests.Session')
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def test_get_deals_by_radius_success(self, mock_session_class):
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"""Test successful deals by radius query."""
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mock_response = Mock()
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mock_response.json.return_value = [
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{
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"dealscount": "2",
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"settlementNameHeb": "תל אביב-יפו",
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"polygon_id": "123-456",
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"objectid": 12345
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}
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]
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mock_response.raise_for_status.return_value = None
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mock_session = Mock()
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mock_session.get.return_value = mock_response
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mock_session_class.return_value = mock_session
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client = GovmapClient()
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result = client.get_deals_by_radius((3870000.123, 3770000.456), radius=50)
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assert len(result) == 1
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assert result[0]["polygon_id"] == "123-456"
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mock_session.get.assert_called_once()
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@patch('requests.Session')
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def test_get_street_deals_success(self, mock_session_class):
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"""Test successful street deals query."""
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mock_response = Mock()
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mock_response.json.return_value = {
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"totalCount": "1",
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"data": [
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{
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"objectid": 123,
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"dealAmount": 1000000,
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"dealDate": "2025-01-01T00:00:00.000Z",
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"assetArea": 100,
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"settlementNameHeb": "תל אביב-יפו",
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"propertyTypeDescription": "דירה"
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}
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]
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}
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mock_response.raise_for_status.return_value = None
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mock_session = Mock()
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mock_session.get.return_value = mock_response
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mock_session_class.return_value = mock_session
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client = GovmapClient()
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result = client.get_street_deals("123-456")
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assert len(result) == 1
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assert result[0]["dealAmount"] == 1000000
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assert result[0]["assetArea"] == 100
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mock_session.get.assert_called_once()
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@patch('requests.Session')
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def test_get_neighborhood_deals_success(self, mock_session_class):
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"""Test successful neighborhood deals query."""
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mock_response = Mock()
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mock_response.json.return_value = {
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"totalCount": "1",
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"data": [
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{
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"objectid": 456,
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"dealAmount": 2000000,
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"dealDate": "2025-01-15T00:00:00.000Z",
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"assetArea": 120,
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"settlementNameHeb": "תל אביב-יפו",
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"propertyTypeDescription": "דירה"
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}
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]
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}
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mock_response.raise_for_status.return_value = None
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mock_session = Mock()
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mock_session.get.return_value = mock_response
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mock_session_class.return_value = mock_session
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client = GovmapClient()
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result = client.get_neighborhood_deals("123-456")
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assert len(result) == 1
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assert result[0]["dealAmount"] == 2000000
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assert result[0]["assetArea"] == 120
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mock_session.get.assert_called_once()
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@patch('nadlan_mcp.main.GovmapClient.get_neighborhood_deals')
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@patch('nadlan_mcp.main.GovmapClient.get_street_deals')
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@patch('nadlan_mcp.main.GovmapClient.get_deals_by_radius')
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@patch('nadlan_mcp.main.GovmapClient.autocomplete_address')
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def test_find_recent_deals_for_address_integration(self, mock_autocomplete, mock_radius, mock_street, mock_neighborhood):
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"""Test the main integration function."""
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# Mock autocomplete response
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mock_autocomplete.return_value = {
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"results": [
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{
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"shape": "POINT(3870000.123 3770000.456)",
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"text": "test address"
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}
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]
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}
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# Mock radius response
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mock_radius.return_value = [
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{"polygon_id": "123-456", "objectid": 1}
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]
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# Mock street deals response
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mock_street.return_value = [
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{
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"dealId": "deal1",
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"dealAmount": 1000000,
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"dealDate": "2025-01-01T00:00:00.000Z",
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"address": "Test Street 1"
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}
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]
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# Mock neighborhood deals response
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mock_neighborhood.return_value = [
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{
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"dealId": "deal2",
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"dealAmount": 2000000,
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"dealDate": "2025-01-15T00:00:00.000Z",
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"address": "Test Street 2"
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}
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]
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client = GovmapClient()
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result = client.find_recent_deals_for_address("test address", years_back=1)
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assert len(result) == 2
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assert result[0]["dealDate"] == "2025-01-15T00:00:00.000Z" # Should be sorted by date
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assert result[1]["dealDate"] == "2025-01-01T00:00:00.000Z"
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@patch('requests.Session')
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def test_http_error_handling(self, mock_session_class):
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"""Test that HTTP errors are properly handled."""
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mock_response = Mock()
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mock_response.raise_for_status.side_effect = Exception("HTTP Error")
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mock_session = Mock()
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mock_session.post.return_value = mock_response
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mock_session_class.return_value = mock_session
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client = GovmapClient()
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with pytest.raises(Exception, match="HTTP Error"):
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client.autocomplete_address("test")
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def test_invalid_coordinate_format(self):
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"""Test handling of invalid coordinate formats."""
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client = GovmapClient()
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# Mock autocomplete response with invalid shape
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mock_autocomplete_result = {
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"results": [
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{
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"shape": "INVALID_FORMAT",
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"text": "test address"
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}
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]
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}
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with patch.object(client, 'autocomplete_address', return_value=mock_autocomplete_result):
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with pytest.raises(ValueError, match="Invalid coordinate format"):
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client.find_recent_deals_for_address("test", years_back=1)
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class TestMarketAnalysisFunctions:
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"""Test cases for market analysis functions."""
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def test_calculate_market_activity_score_success(self):
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"""Test successful market activity score calculation."""
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client = GovmapClient()
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# Sample deals with dates
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deals = [
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{"dealDate": "2023-01-15", "dealAmount": 1000000},
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{"dealDate": "2023-01-20", "dealAmount": 1100000},
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{"dealDate": "2023-02-10", "dealAmount": 1200000},
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{"dealDate": "2023-03-05", "dealAmount": 1150000},
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{"dealDate": "2023-04-12", "dealAmount": 1250000},
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]
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result = client.calculate_market_activity_score(deals)
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assert "total_deals" in result
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assert "deals_per_month" in result
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assert "activity_score" in result
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assert "activity_level" in result
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assert "trend" in result
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assert "monthly_distribution" in result
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assert result["total_deals"] == 5
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assert result["deals_per_month"] > 0
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assert 0 <= result["activity_score"] <= 100
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def test_calculate_market_activity_score_empty_deals(self):
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"""Test market activity score with empty deals list."""
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client = GovmapClient()
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with pytest.raises(ValueError, match="Cannot calculate market activity from empty deals list"):
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client.calculate_market_activity_score([])
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def test_calculate_market_activity_score_invalid_dates(self):
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"""Test market activity score with invalid dates."""
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client = GovmapClient()
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# Deals with invalid dates
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deals = [
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{"dealDate": "", "dealAmount": 1000000},
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{"dealAmount": 1100000}, # Missing dealDate
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]
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with pytest.raises(ValueError, match="No valid deal dates found"):
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client.calculate_market_activity_score(deals)
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def test_calculate_market_activity_score_high_activity(self):
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"""Test market activity score with high activity."""
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client = GovmapClient()
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# Generate many deals in short period (high activity)
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deals = [
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{"dealDate": f"2023-01-{i:02d}", "dealAmount": 1000000 + i * 10000}
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for i in range(1, 31) # 30 deals in one month
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]
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result = client.calculate_market_activity_score(deals)
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assert result["activity_level"] == "very_high"
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assert result["activity_score"] >= 90
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def test_analyze_investment_potential_success(self):
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"""Test successful investment potential analysis."""
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client = GovmapClient()
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# Sample deals with price appreciation
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deals = [
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{"dealDate": "2022-01-15", "dealAmount": 1000000, "assetArea": 80, "price_per_sqm": 12500},
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{"dealDate": "2022-06-10", "dealAmount": 1050000, "assetArea": 80, "price_per_sqm": 13125},
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{"dealDate": "2023-01-05", "dealAmount": 1100000, "assetArea": 80, "price_per_sqm": 13750},
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{"dealDate": "2023-06-12", "dealAmount": 1150000, "assetArea": 80, "price_per_sqm": 14375},
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]
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result = client.analyze_investment_potential(deals)
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assert "price_appreciation_rate" in result
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assert "price_volatility" in result
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assert "market_stability" in result
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assert "price_trend" in result
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assert "avg_price_per_sqm" in result
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assert "investment_score" in result
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assert "data_quality" in result
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assert 0 <= result["investment_score"] <= 100
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assert result["price_trend"] in ["increasing", "stable", "decreasing"]
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def test_analyze_investment_potential_empty_deals(self):
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"""Test investment potential with empty deals list."""
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client = GovmapClient()
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with pytest.raises(ValueError, match="Cannot analyze investment potential from empty deals list"):
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client.analyze_investment_potential([])
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def test_analyze_investment_potential_insufficient_data(self):
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"""Test investment potential with insufficient data."""
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client = GovmapClient()
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# Only 2 deals (need at least 3)
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deals = [
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{"dealDate": "2023-01-15", "dealAmount": 1000000, "assetArea": 80, "price_per_sqm": 12500},
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{"dealDate": "2023-06-10", "dealAmount": 1050000, "assetArea": 80, "price_per_sqm": 13125},
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]
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with pytest.raises(ValueError, match="Insufficient data for investment analysis"):
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client.analyze_investment_potential(deals)
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def test_analyze_investment_potential_stable_market(self):
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"""Test investment potential with stable market (low volatility)."""
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client = GovmapClient()
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# Deals with consistent prices (very stable)
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deals = [
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{"dealDate": f"2023-{i:02d}-15", "dealAmount": 1000000 + i * 1000, "assetArea": 80, "price_per_sqm": 12500 + i * 12.5}
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for i in range(1, 13) # 12 months, slight increase
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]
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result = client.analyze_investment_potential(deals)
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assert result["market_stability"] in ["very_stable", "stable"]
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assert result["price_volatility"] < 50
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def test_get_market_liquidity_success(self):
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"""Test successful market liquidity calculation."""
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client = GovmapClient()
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# Sample deals across multiple quarters
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deals = [
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{"dealDate": "2023-01-15", "dealAmount": 1000000},
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{"dealDate": "2023-02-20", "dealAmount": 1100000},
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{"dealDate": "2023-05-10", "dealAmount": 1200000},
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{"dealDate": "2023-06-05", "dealAmount": 1150000},
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{"dealDate": "2023-09-12", "dealAmount": 1250000},
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{"dealDate": "2023-10-18", "dealAmount": 1300000},
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]
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result = client.get_market_liquidity(deals)
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assert "total_deals" in result
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assert "deals_per_month" in result
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assert "deals_per_quarter" in result
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assert "quarterly_breakdown" in result
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assert "monthly_breakdown" in result
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assert "velocity_score" in result
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assert "liquidity_rating" in result
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assert "trend_direction" in result
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assert "most_active_period" in result
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assert result["total_deals"] == 6
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assert 0 <= result["velocity_score"] <= 100
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def test_get_market_liquidity_empty_deals(self):
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"""Test market liquidity with empty deals list."""
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client = GovmapClient()
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with pytest.raises(ValueError, match="Cannot calculate market liquidity from empty deals list"):
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client.get_market_liquidity([])
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def test_get_market_liquidity_quarterly_breakdown(self):
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"""Test market liquidity quarterly breakdown."""
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client = GovmapClient()
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# Deals spread across specific quarters
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deals = [
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{"dealDate": "2023-01-15"}, # Q1
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{"dealDate": "2023-02-20"}, # Q1
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{"dealDate": "2023-05-10"}, # Q2
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{"dealDate": "2023-08-05"}, # Q3
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{"dealDate": "2023-11-12"}, # Q4
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]
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result = client.get_market_liquidity(deals)
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assert "2023-Q1" in result["quarterly_breakdown"]
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assert "2023-Q2" in result["quarterly_breakdown"]
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assert "2023-Q3" in result["quarterly_breakdown"]
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assert "2023-Q4" in result["quarterly_breakdown"]
|
|
assert result["quarterly_breakdown"]["2023-Q1"] == 2
|
|
|
|
def test_filter_deals_by_criteria_property_type(self):
|
|
"""Test filtering deals by property type."""
|
|
client = GovmapClient()
|
|
|
|
deals = [
|
|
{"assetTypeHeb": "דירה", "roomsNum": 3, "dealAmount": 1000000},
|
|
{"assetTypeHeb": "בית", "roomsNum": 5, "dealAmount": 2000000},
|
|
{"assetTypeHeb": "דירה", "roomsNum": 4, "dealAmount": 1500000},
|
|
]
|
|
|
|
filtered = client.filter_deals_by_criteria(deals, property_type="דירה")
|
|
|
|
assert len(filtered) == 2
|
|
assert all(d["assetTypeHeb"] == "דירה" for d in filtered)
|
|
|
|
def test_filter_deals_by_criteria_rooms(self):
|
|
"""Test filtering deals by room count."""
|
|
client = GovmapClient()
|
|
|
|
deals = [
|
|
{"assetRoomNum": 2, "dealAmount": 800000},
|
|
{"assetRoomNum": 3, "dealAmount": 1000000},
|
|
{"assetRoomNum": 4, "dealAmount": 1500000},
|
|
{"assetRoomNum": 5, "dealAmount": 2000000},
|
|
]
|
|
|
|
filtered = client.filter_deals_by_criteria(deals, min_rooms=3, max_rooms=4)
|
|
|
|
assert len(filtered) == 2
|
|
assert all(3 <= d["assetRoomNum"] <= 4 for d in filtered)
|
|
|
|
def test_filter_deals_by_criteria_price_range(self):
|
|
"""Test filtering deals by price range."""
|
|
client = GovmapClient()
|
|
|
|
deals = [
|
|
{"dealAmount": 800000},
|
|
{"dealAmount": 1000000},
|
|
{"dealAmount": 1500000},
|
|
{"dealAmount": 2000000},
|
|
]
|
|
|
|
filtered = client.filter_deals_by_criteria(deals, min_price=900000, max_price=1600000)
|
|
|
|
assert len(filtered) == 2
|
|
assert all(900000 <= d["dealAmount"] <= 1600000 for d in filtered)
|
|
|
|
def test_calculate_deal_statistics_success(self):
|
|
"""Test successful deal statistics calculation."""
|
|
client = GovmapClient()
|
|
|
|
deals = [
|
|
{"dealAmount": 1000000, "assetArea": 80, "price_per_sqm": 12500, "assetRoomNum": 3},
|
|
{"dealAmount": 1200000, "assetArea": 90, "price_per_sqm": 13333, "assetRoomNum": 4},
|
|
{"dealAmount": 900000, "assetArea": 70, "price_per_sqm": 12857, "assetRoomNum": 3},
|
|
]
|
|
|
|
stats = client.calculate_deal_statistics(deals)
|
|
|
|
assert "count" in stats
|
|
assert "price_stats" in stats
|
|
assert "area_stats" in stats
|
|
assert "price_per_sqm_stats" in stats
|
|
assert stats["count"] == 3
|
|
assert stats["price_stats"]["mean"] > 0
|
|
assert stats["area_stats"]["mean"] > 0 |