import json

import pandas as pd

#!!! This giant block of imports should be something simpler, such as:
# from great_exepectations.helpers.expectation_creation import *
from great_expectations.execution_engine import (
    PandasExecutionEngine,
    SparkDFExecutionEngine,
    SqlAlchemyExecutionEngine,
)
from great_expectations.expectations.expectation import (
    ColumnMapExpectation,
    Expectation,
    ExpectationConfiguration,
)
from great_expectations.expectations.metrics import (
    ColumnMapMetricProvider,
    column_condition_partial,
)
from great_expectations.expectations.registry import (
    _registered_expectations,
    _registered_metrics,
    _registered_renderers,
)


# 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
# https://docs.greatexpectations.io/en/latest/reference/core_concepts.html#expectations-and-metrics.
class ColumnValuesToChangeBetween(ColumnMapMetricProvider):

    # This is the id string that will be used to reference your metric.
    # Please see https://docs.greatexpectations.io/en/latest/reference/core_concepts/metrics.html#metrics
    # for information on how to choose an id string for your Metric.
    condition_metric_name = "column_values.change_between"
    condition_value_keys = (
        "from_value",
        "to_value",
    )

    # This method defines the business logic for evaluating your metric when using a PandasExecutionEngine

    @column_condition_partial(engine=PandasExecutionEngine)
    def _pandas(cls, column, from_value, to_value, **kwargs):

        # throw an error if one of the values is not numeric
        if not pd.to_numeric(column, errors="coerce").notnull().all():
            raise TypeError("Column values must be numeric !")

        # calculate the difference of the current row with the previous.
        # If previous is NaN fills with the initial value "from_value" to consider it true
        difference = (column - column.shift()).fillna(from_value)

        def is_change_rate_compliant(value: int):
            return True if from_value <= abs(value) <= to_value else False

        return difference.map(lambda x: is_change_rate_compliant(x))


# 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 ExpectColumnValuesToChangeBetween(ColumnMapExpectation):
    """
    Given a list of numeric values,
    check if the difference between the current and the previous row
    is within the expected difference range.

    E.g:
    input = [1,2,5]
    expected difference range = between 1 and 2
    Result: `false` because the difference between 2 and 5 is not between 1 and 2

    parameters:
        from_value: low range value
        to_value: high range value
    """

    # These examples will be shown in the public gallery, and also executed as unit tests for your Expectation
    examples = [
        {
            "data": {
                "numbers_difference_max_3": [1, 3, 5, 7.5, 10, 12, 15],
                "numbers_difference_max_5": [-3, -1, 2, 5, 13, 15, 21],
            },
            "tests": [
                {
                    "title": "positive_test_with_difference_between_1_and_3",
                    "exact_match_out": False,
                    "include_in_gallery": True,
                    "in": {
                        "column": "numbers_difference_max_3",
                        "from_value": 1,
                        "to_value": 3,
                    },
                    "out": {
                        "success": True,
                        "unexpected_index_list": [],
                        "unexpected_list": [],
                    },
                },
                {
                    "title": "positive_test_with_difference_between_1_and_8",
                    "exact_match_out": False,
                    "include_in_gallery": True,
                    "in": {
                        "column": "numbers_difference_max_5",
                        "from_value": 1,
                        "to_value": 8,
                    },
                    "out": {
                        "success": True,
                        "unexpected_index_list": [],
                        "unexpected_list": [],
                    },
                },
                {
                    "title": "negative_test_with_difference_between_1_and_5",
                    "exact_match_out": False,
                    "include_in_gallery": True,
                    "in": {
                        "column": "numbers_difference_max_5",
                        "from_value": 1,
                        "to_value": 5,
                    },
                    "out": {
                        "success": False,
                        "unexpected_index_list": [4, 6],
                        "unexpected_list": [13, 21],
                    },
                },
            ],
        }
    ]

    # This dictionary contains metadata for display in the public gallery
    library_metadata = {
        "maturity": "experimental",  # "experimental", "beta", or "production"
        "tags": ["experimental"],  # Tags for this Expectation in the gallery
        "contributors": ["@maikelpenz"],  # Don't forget to add your github handle here!
        "package": "experimental_expectations",
    }

    # 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.change_between"

    # This is a list of parameter names that can affect whether the Expectation evaluates to True or False
    # Please see https://docs.greatexpectations.io/en/latest/reference/core_concepts/expectations/expectations.html#expectation-concepts-domain-and-success-keys
    # for more information about domain and success keys, and other arguments to Expectations
    success_keys = ("from_value", "to_value")

    # This dictionary contains default values for any parameters that should have default values
    default_kwarg_values = {}

    # This method defines a question Renderer
    # For more info on Renderers, see
    # https://docs.greatexpectations.io/en/latest/guides/how_to_guides/configuring_data_docs/how_to_create_renderers_for_custom_expectations.html
    #!!! This example renderer should render RenderedStringTemplateContent, not just a string


#     @classmethod
#     @renderer(renderer_type="renderer.question")
#     def _question_renderer(
#         cls, configuration, result=None, language=None, runtime_configuration=None
#     ):
#         column = configuration.kwargs.get("column")
#         mostly = configuration.kwargs.get("mostly")

#         return f'Do at least {mostly * 100}% of values in column "{column}" equal 3?'

# This method defines an answer Renderer
#!!! This example renderer should render RenderedStringTemplateContent, not just a string
#     @classmethod
#     @renderer(renderer_type="renderer.answer")
#     def _answer_renderer(
#         cls, configuration=None, result=None, language=None, runtime_configuration=None
#     ):
#         column = result.expectation_config.kwargs.get("column")
#         mostly = result.expectation_config.kwargs.get("mostly")
#         regex = result.expectation_config.kwargs.get("regex")
#         if result.success:
#             return f'At least {mostly * 100}% of values in column "{column}" equal 3.'
#         else:
#             return f'Less than {mostly * 100}% of values in column "{column}" equal 3.'

# This method defines a prescriptive Renderer
#     @classmethod
#     @renderer(renderer_type="renderer.prescriptive")
#     @render_evaluation_parameter_string
#     def _prescriptive_renderer(
#         cls,
#         configuration=None,
#         result=None,
#         language=None,
#         runtime_configuration=None,
#         **kwargs,
#     ):
#!!! This example renderer should be shorter
#         runtime_configuration = runtime_configuration or {}
#         include_column_name = runtime_configuration.get("include_column_name", True)
#         include_column_name = (
#             include_column_name if include_column_name is not None else True
#         )
#         styling = runtime_configuration.get("styling")
#         params = substitute_none_for_missing(
#             configuration.kwargs,
#             ["column", "regex", "mostly", "row_condition", "condition_parser"],
#         )

#         template_str = "values must be equal to 3"
#         if params["mostly"] is not None:
#             params["mostly_pct"] = num_to_str(
#                 params["mostly"] * 100, precision=15, no_scientific=True
#             )
#             # params["mostly_pct"] = "{:.14f}".format(params["mostly"]*100).rstrip("0").rstrip(".")
#             template_str += ", at least $mostly_pct % of the time."
#         else:
#             template_str += "."

#         if include_column_name:
#             template_str = "$column " + template_str

#         if params["row_condition"] is not None:
#             (
#                 conditional_template_str,
#                 conditional_params,
#             ) = parse_row_condition_string_pandas_engine(params["row_condition"])
#             template_str = conditional_template_str + ", then " + template_str
#             params.update(conditional_params)

#         return [
#             RenderedStringTemplateContent(
#                 **{
#                     "content_block_type": "string_template",
#                     "string_template": {
#                         "template": template_str,
#                         "params": params,
#                         "styling": styling,
#                     },
#                 }
#             )
#         ]

if __name__ == "__main__":
    diagnostics_report = ExpectColumnValuesToChangeBetween().run_diagnostics()
    print(json.dumps(diagnostics_report, indent=2))
