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| # -*- coding: utf-8 -*- | ||
| """ | ||
| Created on Thu Apr 27 13:49:36 2023 | ||
| @author: zacha | ||
| """ | ||
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| import os | ||
| import pandas as pd | ||
| import statsmodels.api as sm | ||
| import matplotlib.pyplot as plt | ||
| from sklearn.linear_model import LinearRegression | ||
| import matplotlib.colors as mcolors | ||
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| ## import Northern Water Res Rights #### | ||
| Northern_Res_Rights = pd.read_csv('NorthernWaterResRights.csv', header=None) | ||
| Northern_Res_Rights.columns =['WDID', 'Name', 'Adjudication Date', 'Absolute', 'Conditional', 'APEX Absolute', 'APEX Conditional'] | ||
| Northern_Res_Rights['Adjudication Date'] = pd.to_datetime(Northern_Res_Rights['Adjudication Date']) | ||
| Northern_Res_Rights['Sum'] = (Northern_Res_Rights['Absolute'] + Northern_Res_Rights['Conditional'] + Northern_Res_Rights['APEX Absolute'] + Northern_Res_Rights['APEX Conditional']) | ||
| Northern_Res_Rights.index = Northern_Res_Rights['Adjudication Date'] | ||
| Northern_Res_Rights['Year'] = Northern_Res_Rights.index.year | ||
| Northern_Res_Rights_Yearly = (Northern_Res_Rights.groupby(['Year'])["Sum"].sum()).astype(int) | ||
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| Northern_Total_Rights = Northern_Res_Rights_Yearly | ||
| Northern_Total_Rights = Northern_Total_Rights.groupby(['Year']).sum() | ||
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| Northern_Cum_Sum = Northern_Total_Rights.cumsum() | ||
| Northern_Cum_Sum_extended = Northern_Cum_Sum.reindex(range(1920,2013)) | ||
| Northern_Cum_Sum_extended = Northern_Cum_Sum_extended.ffill() | ||
| Northern_Cum_Sum_extended = Northern_Cum_Sum_extended[Northern_Cum_Sum_extended.index >= 1950] | ||
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| #os.chdir('C:/Users/zacha/Documents/UNC/SP2016_StateMod/historicalbaseline/xddparquet') | ||
| #os.chdir('C:/Users/zacha/Documents/UNC/SP2016_StateMod/SP_update_test/xddparquet_03_29') | ||
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| os.chdir('C:/Users/zacha/Documents/UNC/SP2016_StateMod/historicalbaseline/xddparquet') | ||
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| ThorntonIndoor = pd.read_parquet('02_Thorn_I.parquet', engine='pyarrow') | ||
| ThorntonOutdoor = pd.read_parquet('02_Thorn_O.parquet', engine='pyarrow') | ||
| WestminsterIndoor = pd.read_parquet('02_Westy_I.parquet', engine='pyarrow') | ||
| WestminsterOutdoor = pd.read_parquet('02_Westy_O.parquet', engine='pyarrow') | ||
| LaffayetteIndoor = pd.read_parquet('06LAFFYT_I.parquet', engine='pyarrow') | ||
| LaffayetteOutdoor = pd.read_parquet('06LAFFYT_O.parquet', engine='pyarrow') | ||
| LouisvilleIndoor = pd.read_parquet('06LOUIS_I.parquet', engine='pyarrow') | ||
| LouisvilleOutdoor = pd.read_parquet('06LOUIS_O.parquet', engine='pyarrow') | ||
| BoulderOutdoor = pd.read_parquet('06BOULDER_O.parquet', engine='pyarrow') | ||
| BoulderIndoor = pd.read_parquet('06BOULDER_I.parquet', engine='pyarrow') | ||
| LongmontIndoor = pd.read_parquet('05LONG_IN.parquet', engine='pyarrow') | ||
| LongmontOutdoor = pd.read_parquet('05LONG_OUT.parquet', engine='pyarrow') | ||
| LovelandIndoor = pd.read_parquet('04_Lovelnd_I.parquet', engine='pyarrow') | ||
| LovelandOutdoor = pd.read_parquet('04_Lovelnd_O.parquet', engine='pyarrow') | ||
| ArvadaIndoor = pd.read_parquet('07_Arvada_I.parquet', engine='pyarrow') | ||
| ArvadaOutdoor = pd.read_parquet('07_Arvada_O.parquet', engine='pyarrow') | ||
| ConMutIndoor = pd.read_parquet('07_ConMut_I.parquet', engine='pyarrow') | ||
| ConMutOutdoor = pd.read_parquet('07_ConMut_O.parquet', engine='pyarrow') | ||
| GoldenIndoor = pd.read_parquet('07_Golden_I.parquet', engine='pyarrow') | ||
| GoldenOutdoor = pd.read_parquet('07_Golden_O.parquet', engine='pyarrow') | ||
| DenverIndoor = pd.read_parquet('08_Denver_I.parquet', engine='pyarrow') | ||
| DenverOutdoor = pd.read_parquet('08_Denver_O.parquet', engine='pyarrow') | ||
| EnglewoodIndoor = pd.read_parquet('08_Englwd_I.parquet', engine='pyarrow') | ||
| EnglewoodOutdoor = pd.read_parquet('08_Englwd_O.parquet', engine='pyarrow') | ||
| AuroraIndoor = pd.read_parquet('08_Aurora_I.parquet', engine='pyarrow') | ||
| AuroraOutdoor = pd.read_parquet('08_Aurora_O.parquet', engine='pyarrow') | ||
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| EstesParkIndoor = pd.read_parquet('0400518_I.parquet', engine='pyarrow') | ||
| EstesParkOutdoor = pd.read_parquet('0400518_O.parquet', engine='pyarrow') | ||
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| # convert municipal demands to pandas series | ||
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| Thornton_Indoor_demand = ThorntonIndoor['demand'] | ||
| Thornton_Outdoor_demand = ThorntonOutdoor['demand'] | ||
| Westminster_Indoor_demand = WestminsterIndoor['demand'] | ||
| Westminster_Outdoor_demand = WestminsterOutdoor['demand'] | ||
| Laffayette_Indoor_demand = LaffayetteIndoor['demand'] | ||
| Laffayette_Outdoor_demand = LaffayetteOutdoor['demand'] | ||
| Louisville_Indoor_demand = LouisvilleIndoor['demand'] | ||
| Louisville_Outdoor_demand = LouisvilleOutdoor['demand'] | ||
| Boulder_Outdoor_demand = BoulderIndoor['demand'] | ||
| Boulder_Indoor_demand = BoulderOutdoor['demand'] | ||
| Longmont_Indoor_demand = LongmontIndoor['demand'] | ||
| Longmont_Outdoor_demand = LongmontOutdoor['demand'] | ||
| Loveland_Indoor_demand = LovelandIndoor['demand'] | ||
| Loveland_Outdoor_demand = LovelandOutdoor['demand'] | ||
| Arvada_Indoor_demand = ArvadaIndoor['demand'] | ||
| Arvada_Outdoor_demand = ArvadaOutdoor['demand'] | ||
| ConMut_Indoor_demand = ConMutIndoor['demand'] | ||
| ConMut_Outdoor_demand = ConMutOutdoor['demand'] | ||
| Golden_Indoor_demand = GoldenIndoor['demand'] | ||
| Golden_Outdoor_demand = GoldenOutdoor['demand'] | ||
| Denver_Indoor_demand = DenverIndoor['demand'] | ||
| Denver_Outdoor_demand = DenverOutdoor['demand'] | ||
| Englewood_Indoor_demand = EnglewoodIndoor['demand'] | ||
| Englewood_Outdoor_demand = EnglewoodOutdoor['demand'] | ||
| Aurora_Indoor_demand = AuroraIndoor['demand'] | ||
| Aurora_Outdoor_demand = AuroraOutdoor['demand'] | ||
| EstesPark_Indoor_demand = EstesParkIndoor['demand'] | ||
| EstesPark_Outdoor_demand = EstesParkOutdoor['demand'] | ||
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| Thornton_Total_demand = Thornton_Indoor_demand + Thornton_Outdoor_demand | ||
| Westminster_Total_demand = Westminster_Indoor_demand + Westminster_Outdoor_demand | ||
| Laffayette_Total_demand = Laffayette_Indoor_demand + Laffayette_Outdoor_demand | ||
| Louisville_Total_demand = Louisville_Indoor_demand + Louisville_Outdoor_demand | ||
| Boulder_Total_demand = Boulder_Indoor_demand + Boulder_Outdoor_demand | ||
| Longmont_Total_demand = Longmont_Indoor_demand + Longmont_Outdoor_demand | ||
| Loveland_Total_demand = Loveland_Indoor_demand + Loveland_Outdoor_demand | ||
| Arvada_Total_demand = Arvada_Indoor_demand + Arvada_Outdoor_demand | ||
| ConMut_Total_demand = ConMut_Indoor_demand + ConMut_Outdoor_demand | ||
| Golden_Total_demand = Golden_Indoor_demand + Golden_Outdoor_demand | ||
| Denver_Total_demand = Denver_Indoor_demand + Denver_Outdoor_demand | ||
| Englewood_Total_demand = Englewood_Indoor_demand + Englewood_Outdoor_demand | ||
| Aurora_Total_demand = Aurora_Indoor_demand + Aurora_Outdoor_demand | ||
| EstesPark_Total_demand = EstesPark_Indoor_demand + EstesPark_Outdoor_demand | ||
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| Thornton_Total_demand_Sum = pd.DataFrame().assign(Year=ThorntonIndoor['year'],demand=Thornton_Total_demand) | ||
| Thornton_Total_demand_Sum = Thornton_Total_demand_Sum.groupby('Year').sum() | ||
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| Westminster_Total_demand_Sum = pd.DataFrame().assign(Year=WestminsterIndoor['year'],demand=Westminster_Total_demand) | ||
| Westminster_Total_demand_Sum = Westminster_Total_demand_Sum.groupby('Year').sum() | ||
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| Laffayette_Total_demand_Sum = pd.DataFrame().assign(Year=LaffayetteIndoor['year'],demand=Laffayette_Total_demand) | ||
| Laffayette_Total_demand_Sum = Laffayette_Total_demand_Sum.groupby('Year').sum() | ||
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| Louisville_Total_demand_Sum = pd.DataFrame().assign(Year=LouisvilleIndoor['year'],demand=Louisville_Total_demand) | ||
| Louisville_Total_demand_Sum = Louisville_Total_demand_Sum.groupby('Year').sum() | ||
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| Boulder_Total_demand_Sum = pd.DataFrame().assign(Year=BoulderIndoor['year'],demand=Boulder_Total_demand) | ||
| Boulder_Total_demand_Sum = Boulder_Total_demand_Sum.groupby('Year').sum() | ||
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| Boulder_Total_demand_Sum_H = pd.DataFrame().assign(Year=BoulderIndoor['year'],demand=Boulder_Total_demand) | ||
| Boulder_Total_demand_Sum_H = Boulder_Total_demand_Sum_H.groupby('Year').sum() | ||
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| Longmont_Total_demand_Sum = pd.DataFrame().assign(Year=LongmontIndoor['year'],demand=Longmont_Total_demand) | ||
| Longmont_Total_demand_Sum = Longmont_Total_demand_Sum.groupby('Year').sum() | ||
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| Loveland_Total_demand_Sum = pd.DataFrame().assign(Year=LovelandIndoor['year'],demand=Loveland_Total_demand) | ||
| Loveland_Total_demand_Sum = Loveland_Total_demand_Sum.groupby('Year').sum() | ||
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| Arvada_Total_demand_Sum = pd.DataFrame().assign(Year=ArvadaIndoor['year'],demand=Arvada_Total_demand) | ||
| Arvada_Total_demand_Sum = Arvada_Total_demand_Sum.groupby('Year').sum() | ||
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| ConMut_Total_demand_Sum = pd.DataFrame().assign(Year=ConMutIndoor['year'],demand=ConMut_Total_demand) | ||
| ConMut_Total_demand_Sum = ConMut_Total_demand_Sum.groupby('Year').sum() | ||
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| Golden_Total_demand_Sum = pd.DataFrame().assign(Year=GoldenIndoor['year'],demand=Golden_Total_demand) | ||
| Golden_Total_demand_Sum = Golden_Total_demand_Sum.groupby('Year').sum() | ||
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| Denver_Total_demand_Sum = pd.DataFrame().assign(Year=DenverIndoor['year'],demand=Denver_Total_demand) | ||
| Denver_Total_demand_Sum = Denver_Total_demand_Sum.groupby('Year').sum() | ||
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| Englewood_Total_demand_Sum = pd.DataFrame().assign(Year=EnglewoodIndoor['year'],demand=Englewood_Total_demand) | ||
| Englewood_Total_demand_Sum = Englewood_Total_demand_Sum.groupby('Year').sum() | ||
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| Aurora_Total_demand_Sum = pd.DataFrame().assign(Year=AuroraIndoor['year'],demand=Aurora_Total_demand) | ||
| Aurora_Total_demand_Sum = Aurora_Total_demand_Sum.groupby('Year').sum() | ||
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| EstesPark_Total_demand_Sum = pd.DataFrame().assign(Year=EstesParkIndoor['year'],demand=EstesPark_Total_demand) | ||
| EstesPark_Total_demand_Sum = EstesPark_Total_demand_Sum.groupby('Year').sum() | ||
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| ## Sum all Northern Water municipalities shortages' ### | ||
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| Northern_Water_Muni_demands = (Loveland_Total_demand_Sum + Longmont_Total_demand_Sum + Louisville_Total_demand_Sum + Laffayette_Total_demand_Sum + Boulder_Total_demand_Sum) | ||
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| ## FIGURE 1 ######################### | ||
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| af_to_m3 = 1233.4818375475 | ||
| af_to_km3 = 810713.18210885 | ||
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| color1 = '#277DA1' | ||
| color2 = '#90BE6D' | ||
| ax1 = plt.subplot() | ||
| plt.style.use('default') | ||
| ax1.plot(Northern_Water_Muni_demands*af_to_m3/1000000, label = 'Municipal Demands', linewidth = 1.5, color = color1) | ||
| ax1.plot(Northern_Cum_Sum_extended*af_to_m3/1000000, label = 'Water Rights Holdings', linewidth = 1.5, color = color2) | ||
| ax1.set_yticklabels(ax1.get_yticklabels()) | ||
| ax1.set_xticklabels(ax1.get_xticklabels(), rotation=0) | ||
| ax1.text(-.1, .5, 'Water Quantity (MCM)', ha='center', va='center', transform=ax1.transAxes, rotation='vertical', color = 'black', fontsize = 10) | ||
| ax1.text(.5, -.1, 'Year', ha='center', va='center', transform=ax1.transAxes, color = 'black', fontsize = 10) | ||
| ax1.legend() | ||
| plt.savefig('figure_1_hi_res.png', format = 'png', dpi=600) | ||
| #legend_prop = {'weight': 'bold'} | ||
| #ax1.legend(prop = legend_prop , framealpha = 0.5) | ||
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