File risk_service.py has 497 lines of code (exceeds 250 allowed). Consider refactoring.

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import logging
import os

import numpy as np
import pandas as pd

RiskService has 24 functions (exceeds 20 allowed). Consider refactoring.

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class RiskService(object):
    def __init__(self, user_selections):
        # DB Connection
        self.engine = sqlalchemy.create_engine(os.getenv('POSTGRES_URL'))
        self.metadata = sqlalchemy.MetaData(bind=self.engine)

Function calc_risk has a Cognitive Complexity of 16 (exceeds 5 allowed). Consider refactoring.

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    def calc_risk(self):
        """
        Purpose: Runs analysis on the fly instead of using precalcuted results
        (For when users define current protection level, find annual impact themselves)
        Output:

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Function run_stats has a Cognitive Complexity of 10 (exceeds 5 allowed). Consider refactoring.

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    def run_stats(self, dataframe):
        """
        Purpose: Finds the average, min, and max impact for all impact types
        Input:
            dataframe: Data associated with flood, geography, exposure type for all climate models

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Function find_impact has 6 arguments (exceeds 4 allowed). Consider refactoring.

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    def find_impact(self, impact_cc, impact_soc, impact_sub, impact_cc_soc, impact_urb, model):

Function select_projection_data has 5 arguments (exceeds 4 allowed). Consider refactoring.

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    def select_projection_data(self, dataframe, climate, model, socioecon, year):

Function compute_rp_change has 5 arguments (exceeds 4 allowed). Consider refactoring.

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    def compute_rp_change(self, ref_impact, target_impact, rp, min_rp=2, max_rp=1000):

Function user_selections has a Cognitive Complexity of 6 (exceeds 5 allowed). Consider refactoring.

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    def user_selections(self):
        """
        Purpose: Gather all necessary inputs to run any analysis
        Input:
            flood: Riverine of Coastal (User must select)

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Function bench has a Cognitive Complexity of 6 (exceeds 5 allowed). Consider refactoring.

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    def bench(self):
        defaultfn = "precalc_agg_{0}_{1}_{2}".format(self.flood, self.geogunit_type, self.sub_abb)
        print(defaultfn)

        # cols = ['{0} as {1}'.format(col, col.replace(self.exposure, 'bench').replace('urban_damage_v2', 'bench').replace("_"+ self.scen_abb, '')) for col in sqlalchemy.Table(defaultfn, self.metadata).columns.keys() if ((self.exposure in col) or ('urban_damage_v2' in col)) and (self.scen_abb in col) and ("cc" not in col) and ("soc" not in col) and ("sub" not in col) and ("avg" in col)]

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