import dash

import dash_html_components as html

from dash.dependencies import Input, Output, State

from openomics import MultiOmics

from openomics_web.layouts import app_layout

from openomics_web.layouts.clinical_view import ClinicalDataColumnSelect, ClinicalDataTable

from openomics_web.layouts.datatable_view import ExpressionDataTable, DataTableColumnSelect, split_filter_part

from openomics_web.server import server

from openomics_web.utils.io import get_table_columns, get_expression_data, get_clinical_data

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

# running directly with Python

app = dash.Dash(__name__,

                server=server,

                external_stylesheets=external_stylesheets)

app.layout = app_layout.app_main()

user_multiomics = MultiOmics(cohort_name="TEST", )

@app.callback([

    Output('data-table-column-select', 'children'),

    Output('upload-data-table', 'children')

], [

    Input('upload-data-table', 'contents'),

    Input('upload-data-table', 'filename')

], [

    State('data-table-type', 'value'),

])

def update_datatable_metadata(

    list_of_contents,

    list_of_names,

    data_type,

):

    """

    Args:

        list_of_contents:

        list_of_names:

        data_type:

    """

    if list_of_contents is None:

        return None, ['Drag and Drop or ', html.A('Select Files')]

    try:

        columns = get_table_columns(list_of_contents, list_of_names)

    except Exception as e:

        print(e)

        return None, 'There was an error processing this file.'

    return DataTableColumnSelect(columns), "Uploaded {}".format(list_of_names)

@app.callback(Output('output-data-upload', 'children'),

              [Input('upload-data-table-submit', 'n_clicks')], [

                  State('data-table-cohort', 'value'),

                  State('data-table-type', 'value'),

                  State('upload-data-table', 'contents'),

                  State('upload-data-table', 'filename'),

                  State('data-table-genes-col-name', 'value'),

                  State('data-table-columns-select', 'value'),

                  State('data-table-transpose', 'value')

])

def import_datatable_upload(n_clicks, cohort_name, data_type, list_of_contents,

                            list_of_names, genes_col_name, columns_select,

                            transposed):

    """

    Args:

        n_clicks:

        cohort_name:

        data_type:

        list_of_contents:

        list_of_names:

        genes_col_name:

        columns_select:

        transposed:

    """

    if list_of_contents is None:

        return []

    try:

        omics_data = get_expression_data(list_of_contents, list_of_names,

                                         data_type, cohort_name,

                                         genes_col_name, columns_select,

                                         transposed)

        user_multiomics.add_omic(omics_data)

    except Exception as e:

        print(e)

        return html.Div(['There was an error processing this file.'])

    return ExpressionDataTable(omics_data.expressions.head(20))

@app.callback(Output('expression-datatable', "data"), [

    Input('expression-datatable', "page_current"),

    Input('expression-datatable', "page_size"),

    Input('expression-datatable', "sort_by"),

    Input('expression-datatable', "filter_query")

])

def update_table(page_current, page_size, sort_by, filter):

    """

    Args:

        page_current:

        page_size:

        sort_by:

        filter:

    """

    filtering_expressions = filter.split(' && ')

    print(user_multiomics.get_omics_list())

    dff = user_multiomics[user_multiomics.get_omics_list()[0]]

    for filter_part in filtering_expressions:

        col_name, operator, filter_value = split_filter_part(filter_part)

        if operator in ('eq', 'ne', 'lt', 'le', 'gt', 'ge'):

            # these operators match pandas series operator method names

            dff = dff.loc[getattr(dff[col_name], operator)(filter_value)]

        elif operator == 'contains':

            dff = dff.loc[dff[col_name].str.contains(filter_value)]

        elif operator == 'datestartswith':

            # this is a simplification of the front-end filtering logic,

            # only works with complete fields in standard format

            dff = dff.loc[dff[col_name].str.startswith(filter_value)]

    if sort_by:

        dff = dff.sort_values(

            [col['column_id'] for col in sort_by],

            ascending=[col['direction'] == 'asc' for col in sort_by],

            inplace=False)

    return dff.iloc[page_current * page_size:(page_current + 1) *

                    page_size].to_dict('records')

@app.callback(

    [

        Output('clinical-column-select', 'children'),

        Output('upload-clinical', 'children')

    ],

    [

        Input('upload-clinical', 'contents'),

        Input('upload-clinical', 'filename')

    ],

)

def update_clinical_upload_metadata(

    file_content,

    file_name,

):

    """

    Args:

        file_content:

        file_name:

    """

    if file_content is None:

        return None, ['Drag and Drop or ', html.A('Select Files')]

    try:

        columns = get_table_columns([

            file_content,

        ], [

            file_name,

        ])

    except Exception as e:

        print(e)

        return None, 'There was an error processing this file.'

    return ClinicalDataColumnSelect(columns), "Uploaded {}".format(file_name)

@app.callback(Output('output-clinical-upload', 'children'),

              [Input('clinical-submit-button', 'n_clicks')], [

                  State('clinical-cohort', 'value'),

                  State('clinical-data-type', 'value'),

                  State('upload-clinical', 'contents'),

                  State('upload-clinical', 'filename'),

                  State('clinical-patient-col-name', 'value'),

                  State('clinical-data-columns-select', 'value'),

])

def import_datatable_upload(n_clicks, cohort_name, data_type, list_of_contents,

                            list_of_names, patient_id_col, columns_select):

    """

    Args:

        n_clicks:

        cohort_name:

        data_type:

        list_of_contents:

        list_of_names:

        patient_id_col:

        columns_select:

    """

    if list_of_contents is None:

        return []

    try:

        clinical_data = get_clinical_data(list_of_contents, list_of_names,

                                          data_type, cohort_name,

                                          patient_id_col, columns_select)

        user_multiomics.add_clinical_data(clinical_data)

    except Exception as e:

        print(e)

        return html.Div(['There was an error processing this file.'])

    return ClinicalDataTable(clinical_data.patient.head(20))

if __name__ == '__main__':

    app.run_server(debug=False, port=8050)