prado_spec.py

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# -*- coding: utf-8 -*-
"""
Created on Fri May 15 13:48:33 2020
 
@author: cd
"""
 
import numpy as np
import pandas as pd
from efo.rule import RuleBase
        
        
class OcwdSeepage(RuleBase):
    def __init__(self, name, time, seepPolys, cumSeepStrat, constants):
        # Call super class constructor
        super().__init__(name, time)
        self.seepPolysseepPolys = seepPolys
        self.cumSeepStratcumSeepStrat = cumSeepStrat
        self.qSeepqSeep = np.zeros(self.T.nSteps)
        self.cumSeepcumSeep = np.zeros(self.T.nSteps)
        self.constantsconstants = constants
        
    def calc_seepage(self, stor):
        idxPoly = min(self.cumSeepStratcumSeepStrat.size-2,
                      np.argwhere(self.cumSeepStratcumSeepStrat <= self.cumSeepcumSeep[max(0,self.T.step-1)])[-1])
        qSeepCur = max(0, self.seepPolysseepPolys[idxPoly,0].item()*stor**3 + 
                    self.seepPolysseepPolys[idxPoly,1].item()*stor**2 + 
                    self.seepPolysseepPolys[idxPoly,2].item()*stor + self.seepPolysseepPolys[idxPoly,3].item())
        self.qSeepqSeep[self.T.step] = qSeepCur
        if self.T.curDT.month == 10 and self.T.curDT.day == 1:
            self.cumSeepcumSeep[self.T.step] = qSeepCur*self.constantsconstants.cfs2af
        else:
            self.cumSeepcumSeep[self.T.step] = self.cumSeepcumSeep[max(0,self.T.step-1)] + qSeepCur*self.constantsconstants.cfs2af
        return qSeepCur
        
        
def calc_vireo_impacts(*, hypso, T, df, scenarios):
    returnDf = pd.DataFrame()
    for j, curScen in enumerate(scenarios):
        storPrdo = df.xs(curScen)['stor_prado']
        curDateTime = df.xs(curScen).index
        years = np.unique(curDateTime.year)
        elevChngMeters = np.empty(years.size)
        idxName = np.full(years.size, scenarios[j])
        for i, curYr in enumerate(years):
            iCurVirioPer = ((curDateTime.year == curYr) & (curDateTime.month == 3) & (curDateTime.day >= 21))\
                | ((curDateTime.year == curYr) & (curDateTime.month == 4))
            rowsCurVirioPer = np.ix_(iCurVirioPer)[0]
            rowMaxStor = rowsCurVirioPer[0] + np.argmax(storPrdo[iCurVirioPer], axis=0)
            storMax = storPrdo[rowMaxStor]
            elevMax = hypso.stor2elev(storMax)
            rowsBeforMaxStor = rowsCurVirioPer[rowsCurVirioPer < rowMaxStor]
            storMin = np.min(storPrdo[rowsBeforMaxStor]) if rowsBeforMaxStor.size>0 else storMax
            elevMin = hypso.stor2elev(storMin)
            elevChngMeters[i] = (elevMax - elevMin)*0.3048
        dfIndices = [idxName, years]
        dfMultiIdx = pd.MultiIndex.from_arrays(dfIndices, names=('name_scenario', 'water_year'))
        curDf = pd.DataFrame({'elev_chng_meters': elevChngMeters},
                        columns = ['elev_chng_meters'], index=dfMultiIdx)
        returnDf = returnDf.append(curDf)
    return returnDf
 
 
def calc_vireo_impacts2(*, hypso, T, df, scenarios):
    returnDf = pd.DataFrame()
    for j, curScen in enumerate(scenarios):
        curDf = df.xs(curScen)
        storPrdo = curDf['stor_prado']
        curDateTime = df.xs(curScen).index
        years = np.unique(curDateTime.year)
        for i, curYr in enumerate(years):
            iCurVireoPer = ((curDateTime.year == curYr) & (curDateTime.month == 3) & (curDateTime.day >= 21))\
                | ((curDateTime.year == curYr) & (curDateTime.month == 4))
            elevCurPer = hypso.stor2elev(storPrdo[iCurVireoPer])
            elevDiff = elevCurPer - elevCurPer[0]
            elevDiffMeters = elevDiff*0.3048
            dfIndices = [np.full(elevDiffMeters.size-1, scenarios[j]), curDf.index[iCurVireoPer][1:]]
            dfMultiIdx = pd.MultiIndex.from_arrays(dfIndices, names=('name_scenario', 'date_time'))
            appDf = pd.DataFrame({'elev_chng_meters': elevDiffMeters[1:]},
                            columns = ['elev_chng_meters'], index=dfMultiIdx)
            returnDf = returnDf.append(appDf)
    return returnDf
 
 
def calc_vol_not_perc(*, T, df, scenarios):
    vDate = T.get_vdate()
    years = np.unique(vDate[:, 0])
    returnDf = pd.DataFrame()
    volNotPerc = np.empty(years.size)
    for j, curScen in enumerate(scenarios):
        idxName = np.full(years.size, scenarios[j])
        curDf = df.xs(curScen)
        for i, curYr in enumerate(years):
            iCurWY = (curDf.index.year == curYr-1) & (curDf.index.month >= 10) | \
                (curDf.index.year == curYr) & (curDf.index.month <= 9)
            qDiff = curDf.loc[iCurWY]['q_sar_ocwd'].to_numpy() - curDf.loc[iCurWY]['div_ocwd'].to_numpy()
            qNotPerc = np.amax(np.vstack((np.zeros(qDiff.size), qDiff)), axis=0)
            volNotPerc[i] = np.sum(qNotPerc*1.983471)
        dfIndices = [idxName, years]
        dfMultiIdx = pd.MultiIndex.from_arrays(dfIndices, names=('name_scenario', 'water_year'))
        curDf = pd.DataFrame({'vol_not_percolated': volNotPerc},
                             columns = ['vol_not_percolated'], index=dfMultiIdx)
        returnDf = returnDf.append(curDf)
    return returnDf