[HACKATHON] ColumnValuesLinestringKMDistanceBetween

contrib/experimental/great_expectations_experimental/expectations/expect_column_values_to_be_line_km_between.py

@@ -0,0 +1,140 @@
+import json
+from typing import Optional
+
+import geopandas
+from shapely.geometry import LineString
+
+from great_expectations.core.expectation_configuration import ExpectationConfiguration
+from great_expectations.exceptions import InvalidExpectationConfigurationError
+from great_expectations.execution_engine import PandasExecutionEngine
+
+from great_expectations.expectations.expectation import ColumnMapExpectation
+from great_expectations.expectations.metrics import (
+    ColumnMapMetricProvider,
+    column_condition_partial,
+)
+
+
+# This class defines a Metric to support your Expectation
+# For most Expectations, the main business logic for calculation will live here.
+# To learn about the relationship between Metrics and Expectations, please visit {some doc}.
+class ColumnValuesLinestringKMDistanceBetween(ColumnMapMetricProvider):
+
+    # This is the id string that will be used to reference your metric.
+    # Please see {some doc} for information on how to choose an id string for your Metric.
+    condition_metric_name = "column_values.linestring_distance"
+    condition_value_keys = (
+        "min_distance",
+        "max_distance",
+    )
+
+    # This method defines the business logic for evaluating your metric when using a PandasExecutionEngine
+
+    @column_condition_partial(engine=PandasExecutionEngine)
+    def _pandas(cls, column, min_distance, max_distance, **kwargs):
+        column = geopandas.GeoSeries(column)
+        # Set crs to meters
+        column = column.to_crs({"proj": "cea"})
+        #access the length of the column
+        col_len = column.length/1000
+        return (col_len >= min_distance) & (col_len <= max_distance)
+
+
+# This method defines the business logic for evaluating your metric when using a SqlAlchemyExecutionEngine
+#     @column_condition_partial(engine=SqlAlchemyExecutionEngine)
+#     def _sqlalchemy(cls, column, _dialect, **kwargs):
+#         return column.in_([3])
+
+# This method defines the business logic for evaluating your metric when using a SparkDFExecutionEngine
+#     @column_condition_partial(engine=SparkDFExecutionEngine)
+#     def _spark(cls, column, **kwargs):
+#         return column.isin([3])
+
+
+# This class defines the Expectation itself
+# The main business logic for calculation lives here.
+class ExpectColumnValuesToBeLinestringKMDistanceBetween(ColumnMapExpectation):
+    """This expectation will compute the distance of Linestring
+    in kilometers and check if it's between two values."""
+
+
+    # These examples will be shown in the public gallery, and also executed as unit tests for your Expectation
+    examples = [
+        {
+            "data": {
+                "linestring_less_than_1000_km": [
+                    "LINESTRING (0 0, 111319.490793 110568.8124, 0 110568.8124)",
+                    "LINESTRING (0 0, 111319.490793 110568.8124, 111319.490793 0, 0 110568.8124)",
+                    "LINESTRING (0 0, 222638.981587 221104.845779, 222638.981587 0)"
+                ],
+                "linestring_between_1000_and_2000_km": [
+                    "LINESTRING (222638.981587 552188.640112, 111319.490793 772147.013102, 1113194.907933 1209055.279421)",
+                    "LINESTRING (111319.490793 881798.964757, 1001875.417139 221104.845779, 111319.490793 0, 556597.453966 1317466.085138)",
+                    "LINESTRING (556597.453966 552188.640112, 1224514.398726 1209055.279421, 779236.435553 881798.964757)"
+                ],
+            },
+            "tests": [
+                {
+                    "title": "basic_positive_test",
+                    "exact_match_out": False,
+                    "include_in_gallery": True,
+                    "in": {
+                        "column": "linestring_less_than_1000_km",
+                        "min_distance": 0,
+                        "max_distance": 1000,
+                    },
+                    "out": {
+                        "success": True,
+                    },
+                },
+                {
+                    "title": "basic_negative_test",
+                    "exact_match_out": False,
+                    "include_in_gallery": True,
+                    "in": {
+                        "column": "linestring_between_1000_and_2000_km",
+                        "min_distance": 1000,
+                        "max_distance": 2000,
+                    },
+                    "out": {
+                        "success": False,
+                    },
+                },
+            ],
+        }
+    ]
+
+    # This dictionary contains metadata for display in the public gallery
+    library_metadata = {
+        "maturity": "experimental",  # "experimental", "beta", or "production"
+        "tags": [
+            "geospatial",
+            "hackathon-22",
+        ],  # Tags for this Expectation in the gallery
+        "contributors": [  # Github handles for all contributors to this Expectation.
+            "@luismdiaz01","@derekma73"
+        ],
+    }
+
+    # This is the id string of the Metric used by this Expectation.
+    # For most Expectations, it will be the same as the `condition_metric_name` defined in your Metric class above.
+    map_metric = "column_values.linestring_distance"
+
+    # This is a list of parameter names that can affect whether the Expectation evaluates to True or False
+    # Please see {some doc} for more information about domain and success keys, and other arguments to Expectations
+    success_keys = (
+        "mostly",
+        "min_distance",
+        "max_distance",
+    )
+
+    # This dictionary contains default values for any parameters that should have default values
+    default_kwarg_values = {
+        "min_distance": 0,
+        "max_distance": 0,
+        "mostly": 1.0,
+    }
+
+
+if __name__ == "__main__":
+    ExpectColumnValuesToBeLinestringKMDistanceBetween().print_diagnostic_checklist()