Repository: great-expectations/great_expectations

Issue:
Use cleaner solution for non-truncating division in python 2
Prefer `from __future__ import division` to `1.*x/y`


Discussion:
Done inline during improve_partitions push.

Resolving patch:
diff --git a/contrib/experimental/great_expectations_experimental/expectations/expect_column_values_to_be_line_km_distance_between.py b/contrib/experimental/great_expectations_experimental/expectations/expect_column_values_to_be_line_km_distance_between.py
new file mode 100644
--- /dev/null
+++ b/contrib/experimental/great_expectations_experimental/expectations/expect_column_values_to_be_line_km_distance_between.py
@@ -0,0 +1,182 @@
+import json
+from typing import Optional
+
+import geopandas
+from shapely.geometry import LineString, mapping, shape
+
+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_km"
+    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 = column.apply(shape)
+        # Set crs to meters
+        geo_ser = geopandas.GeoSeries(column, crs={"proj": "cea"})
+        # access the length of the column
+        col_len = geo_ser.length / 1000
+        in_between = (col_len >= min_distance) & (col_len <= max_distance)
+        return in_between
+
+
+# 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 ExpectColumnValuesToBeLineKMDistanceBetween(ColumnMapExpectation):
+    """This expectation will see if a linestring is between the selected distances in kilometers"""
+
+    # 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": [
+                    mapping(
+                        LineString(
+                            [(0, 0), (111319.490793, 110568.8124), (0, 110568.8124)]
+                        )
+                    ),
+                    mapping(
+                        LineString(
+                            [
+                                (0, 0),
+                                (111319.490793, 110568.8124),
+                                (111319.490793, 0),
+                                (0, 110568.8124),
+                            ]
+                        )
+                    ),
+                    mapping(
+                        LineString(
+                            [(0, 0), (222638.981587, 221104.845779), (222638.981587, 0)]
+                        )
+                    ),
+                ],
+                "linestring_between_1000_and_2000_km": [
+                    mapping(
+                        LineString(
+                            [
+                                (222638.981587, 552188.640112),
+                                (111319.490793, 772147.013102),
+                                (1113194.907933, 1209055.279421),
+                            ]
+                        )
+                    ),
+                    mapping(
+                        LineString(
+                            [
+                                (111319.490793, 881798.964757),
+                                (1001875.417139, 221104.845779),
+                                (111319.490793, 0),
+                                (556597.453966, 1317466.085138),
+                            ]
+                        )
+                    ),
+                    mapping(
+                        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",
+        ],
+        "requirements": ["geopandas", "shapely"],
+    }
+
+    # 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_km"
+
+    # 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__":
+    ExpectColumnValuesToBeLineKMDistanceBetween().print_diagnostic_checklist()


Test patch: