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_lat_lon_coordinates_in_range_of_given_point.py b/contrib/experimental/great_expectations_experimental/expectations/expect_column_values_to_be_lat_lon_coordinates_in_range_of_given_point.py
new file mode 100644
--- /dev/null
+++ b/contrib/experimental/great_expectations_experimental/expectations/expect_column_values_to_be_lat_lon_coordinates_in_range_of_given_point.py
@@ -0,0 +1,445 @@
+"""
+This is a template for creating custom ColumnMapExpectations.
+For detailed instructions on how to use it, please see:
+    https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations
+"""
+
+import json
+import math
+from math import cos, pi, sqrt
+from typing import Optional
+
+import numpy as np
+
+from great_expectations.execution_engine import (
+    PandasExecutionEngine,
+    SparkDFExecutionEngine,
+)
+from great_expectations.expectations.expectation import (
+    ColumnMapExpectation,
+    ExpectationConfiguration,
+    InvalidExpectationConfigurationError,
+)
+from great_expectations.expectations.metrics import (
+    ColumnMapMetricProvider,
+    MetricDomainTypes,
+    MetricPartialFunctionTypes,
+    column_condition_partial,
+)
+from great_expectations.expectations.metrics.import_manager import F, sparktypes
+
+
+# This class defines a Metric to support your Expectation.
+# For most ColumnMapExpectations, the main business logic for calculation will live in this class.
+class ColumnValuesAreLatLonCoordinatesInRange(ColumnMapMetricProvider):
+
+    # This is the id string that will be used to reference your metric.
+    condition_metric_name = "column_values.coordinates.in_range"
+    condition_value_keys = ("center_point", "range", "unit", "projection")
+
+    # This method implements the core logic for the PandasExecutionEngine
+    @column_condition_partial(engine=PandasExecutionEngine)
+    def _pandas(cls, column, **kwargs):
+        center_point = kwargs.get("center_point")
+        unit = kwargs.get("unit")
+        range = kwargs.get("range")
+        projection = kwargs.get("projection")
+
+        if projection == "fcc":
+            if unit == "kilometers":
+                distances = column.apply(lambda x, y=center_point: fcc_projection(x, y))
+            elif unit == "miles":
+                distances = column.apply(
+                    lambda x, y=center_point: fcc_projection(x, y) * 1.609344
+                )
+                range = range * 1.609344
+
+        elif projection == "pythagorean":
+            if unit == "kilometers":
+                distances = column.apply(
+                    lambda x, y=center_point: pythagorean_projection(x, y)
+                )
+            elif unit == "miles":
+                distances = column.apply(
+                    lambda x, y=center_point: pythagorean_projection(x, y) * 1.609344
+                )
+                range = range * 1.609344
+
+        return distances.le(range)
+
+    # This method defines the business logic for evaluating your metric when using a SparkDFExecutionEngine
+    @column_condition_partial(engine=SparkDFExecutionEngine)
+    def _spark(cls, column, **kwargs):
+        center_point = kwargs.get("center_point")
+        unit = kwargs.get("unit")
+        range = kwargs.get("range")
+        projection = kwargs.get("projection")
+
+        if projection == "fcc":
+            if unit == "kilometers":
+                distances = F.udf(
+                    lambda x, y=center_point: fcc_projection(x, y),
+                    sparktypes.FloatType(),
+                )
+            elif unit == "miles":
+                distances = F.udf(
+                    lambda x, y=center_point: fcc_projection(x, y) * 1.609344,
+                    sparktypes.FloatType(),
+                )
+                range = range * 1.609344
+
+            return F.when(distances(column) < range, F.lit(True)).otherwise(
+                F.lit(False)
+            )
+
+        elif projection == "pythagorean":
+            if unit == "kilometers":
+                distances = F.udf(
+                    lambda x, y=center_point: pythagorean_projection(x, y),
+                    sparktypes.FloatType(),
+                )
+            elif unit == "miles":
+                distances = F.udf(
+                    lambda x, y=center_point: pythagorean_projection(x, y) * 1.609344,
+                    sparktypes.FloatType(),
+                )
+                range = range * 1.609344
+
+            return F.when(distances(column) < range, F.lit(True)).otherwise(
+                F.lit(False)
+            )
+
+
+def fcc_projection(loc1, loc2):
+    """
+    Application of the Pythagorean theorem to calculate the distance in kilometers between two lat/lon points on an ellipsoidal earth
+    projected to a plane, as prescribed by the FCC for distances not exceeding 475km/295mi.
+
+    See https://www.govinfo.gov/content/pkg/CFR-2016-title47-vol4/pdf/CFR-2016-title47-vol4-sec73-208.pdf
+
+    :param loc1: A tuple of lat/lon
+    :param loc2: A tuple of lat/lon
+    :return distance: Distance between points in km
+    """
+
+    lat1, lat2 = float(loc1[0]), float(loc2[0])
+    lon1, lon2 = float(loc1[1]), float(loc2[1])
+
+    mean_lat = (lat1 + lat2) / 2
+    delta_lat = lat2 - lat1
+    delta_lon = lon2 - lon1
+
+    k1 = 111.13209 - (0.56605 * cos(2 * mean_lat)) + (0.0012 * cos(4 * mean_lat))
+    k2 = (
+        (111.41513 * cos(mean_lat))
+        - (0.09455 * cos(3 * mean_lat))
+        + (0.00012 * cos(5 * mean_lat))
+    )
+
+    distance = sqrt((k1 * delta_lat) ** 2 + (k2 * delta_lon) ** 2)
+
+    return distance
+
+
+def pythagorean_projection(loc1, loc2):
+    """
+    Application of the pythagorean theorem to calculate the distance in kilometers between two lat/lon points on
+    a spherical earth projected to a plane.
+
+    :param loc1: A tuple of lat/lon
+    :param loc2: A tuple of lat/lon
+    :return distance: Distance between points in km
+    """
+
+    lat1, lat2 = float(loc1[0]), float(loc2[0])
+    lon1, lon2 = float(loc1[1]), float(loc2[1])
+
+    mean_lat = (lat1 + lat2) / 2
+    delta_lat = (lat2 - lat1) * (pi / 180)  # converting from degree-decimal to radians
+    delta_lon = (lon2 - lon1) * (pi / 180)  # converting from degree-decimal to radians
+
+    radius = 6371.009
+
+    distance = radius * sqrt((delta_lat**2) + (cos(mean_lat) * delta_lon) ** 2)
+
+    return distance
+
+
+# This class defines the Expectation itself
+class ExpectColumnValuesToBeLatLonCoordinatesInRangeOfGivenPoint(ColumnMapExpectation):
+    """Expect values in a column to be tuples of degree-decimal (latitude, longitude) within a specified range of a given degree-decimal (latitude, longitude) point.
+
+    expect_column_values_to_be_lat_lon_coordinates_in_range_of_given_point is a :func:`column_map_expectation \
+    <great_expectations.dataset.dataset.MetaDataset.column_map_expectation>`.
+
+    Args:
+        column (str): \
+            The column name.
+
+
+    Keyword Args:
+        center_point (tuple(float, float) or list(float, float)): \
+            The point from which to measure to the column points.
+            Must be a tuple or list of exactly two (2) floats.
+
+        unit (str or None): \
+            The unit of distance with which to measure.
+            Must be one of: [miles, kilometers]
+            Default: kilometers
+
+        range (int or float): \
+            The range in [miles, kilometers] from your specified center_point to measure.
+
+        projection (str or None): \
+            The method by which to calculate distance between points.
+            Must be one of: [fcc, pythagorean]
+            Default: pythagorean
+
+    Other Parameters:
+        result_format (str or None): \
+            Which output mode to use: `BOOLEAN_ONLY`, `BASIC`, `COMPLETE`, or `SUMMARY`.
+            For more detail, see :ref:`result_format <result_format>`.
+        include_config (boolean): \
+            If True, then include the expectation config as part of the result object. \
+            For more detail, see :ref:`include_config`.
+        catch_exceptions (boolean or None): \
+            If True, then catch exceptions and include them as part of the result object. \
+            For more detail, see :ref:`catch_exceptions`.
+        meta (dict or None): \
+            A JSON-serializable dictionary (nesting allowed) that will be included in the output without
+            modification. For more detail, see :ref:`meta`.
+
+    Returns:
+        An ExpectationSuiteValidationResult
+
+        Exact fields vary depending on the values passed to :ref:`result_format <result_format>` and
+        :ref:`include_config`, :ref:`catch_exceptions`, and :ref:`meta`.
+
+    Projections:
+        fcc: \
+            Calculates the distance in kilometers between two lat/lon points on an ellipsoidal earth
+            projected to a plane. Prescribed by the FCC for distances up to and not exceeding 475km/295mi.
+
+        pythagorean: \
+            Calculates the distance in kilometers between two lat/lon points on
+            a spherical earth projected to a plane. Very fast but error increases rapidly as distances increase.
+    """
+
+    def validate_configuration(cls, configuration: Optional[ExpectationConfiguration]):
+        """
+        Validates that a configuration has been set, and sets a configuration if it has yet to be set. Ensures that
+        necessary configuration arguments have been provided for the validation of the expectation.
+        Args:
+            configuration (OPTIONAL[ExpectationConfiguration]): \
+                An optional Expectation Configuration entry that will be used to configure the expectation
+        Returns:
+            True if the configuration has been validated successfully. Otherwise, raises an exception
+        """
+
+        # Setting up a configuration
+        super().validate_configuration(configuration)
+        if configuration is None:
+            configuration = cls.configuration
+
+        center_point = configuration.kwargs["center_point"]
+        range = configuration.kwargs["range"]
+        unit = configuration.kwargs["unit"]
+        projection = configuration.kwargs["projection"]
+
+        try:
+            assert (
+                center_point is not None and range is not None
+            ), "center_point and range must be specified"
+            assert (
+                isinstance(center_point, tuple) or isinstance(center_point, list)
+            ) and all(
+                isinstance(n, float) for n in center_point
+            ), "center_point must be a tuple or list of lat/lon floats"
+            assert (center_point[0] >= -90 and center_point[0] <= 90) and (
+                center_point[1] >= -180 and center_point[1] <= 180
+            ), "center_point must be a valid lat/lon pair"
+            assert isinstance(range, (float, int)), "range must be a numeric value"
+            assert isinstance(unit, str) and unit in [
+                "miles",
+                "kilometers",
+            ], "unit must be a string specifying miles or kilometers"
+            assert isinstance(projection, str) and projection in [
+                "fcc",
+                "pythagorean",
+            ], "projection must be a string specifying fcc or pythagorean"
+        except AssertionError as e:
+            raise InvalidExpectationConfigurationError(str(e))
+
+        return True
+
+    # These examples will be shown in the public gallery.
+    # They will also be executed as unit tests for your Expectation.
+    examples = [
+        {
+            "data": {
+                "lat_lon": [
+                    (62.75955799999999, -164.483752),
+                    (62.7673475, -164.4996625),
+                    (62.7698675, -164.5034575),
+                    (62.76901333333333, -164.50339),
+                    (62.76906333333334, -164.50353333333337),
+                ],
+                "mixed_types": [
+                    (62.75955799999999, -164.483752),
+                    ("62.7673475", "-164.4996625"),
+                    True,
+                    5,
+                    "null",
+                ],
+            },
+            "tests": [
+                {
+                    "title": "basic_positive_test",
+                    "exact_match_out": False,
+                    "include_in_gallery": True,
+                    "in": {
+                        "column": "lat_lon",
+                        "center_point": (62.7596, -164.484),
+                        "range": 8,
+                        "unit": "miles",
+                        "projection": "fcc",
+                        "mostly": 1,
+                    },
+                    "out": {"success": True},
+                },
+                {
+                    "title": "basic_negative_test",
+                    "exact_match_out": False,
+                    "include_in_gallery": True,
+                    "in": {
+                        "column": "lat_lon",
+                        "range": 8,
+                        "center_point": (61.7668, -162.578),
+                        "unit": "kilometers",
+                        "projection": "pythagorean",
+                        "mostly": 0.2,
+                    },
+                    "out": {"success": False},
+                },
+                {
+                    "title": "bad_type_error_test",
+                    "exact_match_out": False,
+                    "include_in_gallery": True,
+                    "in": {
+                        "column": "mixed_types",
+                        "range": 8,
+                        "center_point": (62.7596, -164.484),
+                        "unit": "kilometers",
+                        "projection": None,
+                        "mostly": 1,
+                        "catch_exceptions": True,
+                    },
+                    "out": {},
+                    "error": {"traceback_substring": "bad operand type for unary ~:"},
+                },
+                {
+                    "title": "bad_range_parameter_input",
+                    "exact_match_out": False,
+                    "include_in_gallery": True,
+                    "in": {
+                        "column": "lat_lon",
+                        "range": "8",
+                        "center_point": (62.7596, -164.484),
+                        "unit": "kilometers",
+                        "projection": "fcc",
+                        "catch_exceptions": True,
+                    },
+                    "out": {},
+                    "error": {"traceback_substring": "range must be a numeric value"},
+                },
+            ],
+            "only_for": ["pandas"],
+        },
+        {
+            "data": {
+                "lat_lon": [
+                    (62.75955799999999, -164.483752),
+                    (62.7673475, -164.4996625),
+                    (62.7698675, -164.5034575),
+                    (62.76901333333333, -164.50339),
+                    (62.76906333333334, -164.50353333333337),
+                ],
+            },
+            "tests": [
+                {
+                    "title": "basic_positive_test",
+                    "exact_match_out": False,
+                    "include_in_gallery": True,
+                    "in": {
+                        "column": "lat_lon",
+                        "center_point": (62.7596, -164.484),
+                        "range": 8,
+                        "unit": "miles",
+                        "projection": "fcc",
+                        "mostly": 1,
+                    },
+                    "out": {"success": True},
+                },
+                {
+                    "title": "basic_negative_test",
+                    "exact_match_out": False,
+                    "include_in_gallery": True,
+                    "in": {
+                        "column": "lat_lon",
+                        "range": 8,
+                        "center_point": (61.7668, -162.578),
+                        "unit": "kilometers",
+                        "projection": "pythagorean",
+                        "mostly": 0.2,
+                    },
+                    "out": {"success": False},
+                },
+                {
+                    "title": "bad_range_parameter_input",
+                    "exact_match_out": False,
+                    "include_in_gallery": True,
+                    "in": {
+                        "column": "lat_lon",
+                        "range": "8",
+                        "center_point": (62.7596, -164.484),
+                        "unit": "kilometers",
+                        "projection": "fcc",
+                        "catch_exceptions": True,
+                    },
+                    "out": {},
+                    "error": {"traceback_substring": "range must be a numeric value"},
+                },
+            ],
+            "only_for": ["spark"],
+        },
+    ]
+
+    # 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.coordinates.in_range"
+
+    # This is a list of parameter names that can affect whether the Expectation evaluates to True or False
+    success_keys = ("mostly", "center_point", "projection", "range", "unit")
+
+    # This dictionary contains default values for any parameters that should have default values
+    default_kwarg_values = {
+        "projection": "pythagorean",
+        "unit": "kilometers",
+        "mostly": 1,
+    }
+
+    # This object contains metadata for display in the public Gallery
+    library_metadata = {
+        "tags": [
+            "geospatial",
+            "hackathon-2022",
+            "distance projection",
+        ],  # Tags for this Expectation in the Gallery
+        "contributors": [  # Github handles for all contributors to this Expectation.
+            "@austiezr",  # Don't forget to add your github handle here!
+        ],
+    }
+
+
+if __name__ == "__main__":
+    ExpectColumnValuesToBeLatLonCoordinatesInRangeOfGivenPoint().print_diagnostic_checklist()


Test patch: