<jupyter_start><jupyter_text>Weather Station Input These scripts built input files for "Write Inpute Tables" --> "Weather Stations"  Actual Stations  Precip<jupyter_code>#raw_data_folder = "E:/Google Drive/WORK/Round_Valley/Data/Climate/"
raw_data_folder = "F:/GIS/Ogden_Valley/SWAT_2017_09_11/climate/climate2/"
processed_data = raw_data_folder + "processed/"

idnum, stats, lat, long, elev = [],[],[],[],[]

i = 0
for file in glob.glob(raw_data_folder+'*.csv'):
    i += 1
    print(file)
    df = pd.read_csv(file, skiprows=16, index_col=0, 
                     usecols=[0,1], parse_dates=True, names = ['datetime','Precipitation'])
    df['Precipitation'] = pd.to_numeric(df['Precipitation'], errors='coerce') 
    df = df.resample('1D').max().interpolate(how='time')
    df.fillna(0.0,inplace=True)
    df['Precipitation'] = df['Precipitation'].apply(lambda x: round(x,2),1)
    df = df[(df.index >= pd.datetime(2000,1,1))&(df.index < pd.datetime(2017,1,1))]
    print(df['Precipitation'].max())
    print(df['Precipitation'].min())
    df.columns = [pd.datetime.strftime(df.index[0],'%Y%m%d')]

    stat = 'P' + str(i).zfill(3)
    df.to_csv(processed_data + stat + '.txt',index=False)
    statinfo = pd.read_csv(file, nrows=6, skiprows=1, index_col=0, names=['name','values'])
    stats.append(stat)
    lat.append(statinfo.loc['Latitude','values'])
    long.append(statinfo.loc['Longitude','values'])
    elev.append(statinfo.loc['Elevation','values'][:-2])
    idnum.append(i)

stations = pd.DataFrame({'ID':idnum,'NAME':stats,'LAT':lat,'LONG':long,'ELEVATION':elev})
stations = stations[['ID','NAME','LAT','LONG','ELEVATION']]
stations.to_csv(processed_data+'pcp.txt',index=False)<jupyter_output>F:/GIS/Ogden_Valley/SWAT_2017_09_11/climate/climate2\UCC_ghcn_USC00422389_2017_09_12_1505215136.csv
50.8
0.0
F:/GIS/Ogden_Valley/SWAT_2017_09_11/climate/climate2\UCC_ghcn_USC00425826_2017_09_12_1505215157.csv
61.0
0.0
F:/GIS/Ogden_Valley/SWAT_2017_09_11/climate/climate2\UCC_ghcn_USC00426405_2017_09_12_1505215146.csv
56.9
0.0
F:/GIS/Ogden_Valley/SWAT_2017_09_11/climate/climate2\UCC_ghcn_USC00426869_2017_09_12_1505215141.csv
80.5
0.0
F:/GIS/Ogden_Valley/SWAT_2017_09_11/climate/climate2\UCC_ghcn_USR0000BUES_2017_09_12_1505215152.csv
0.0
0.0
F:/GIS/Ogden_Valley/SWAT_2017_09_11/climate/climate2\UCC_ghcn_USS0011H08S_2017_09_12_1505215234.csv
152.4
0.0
F:/GIS/Ogden_Valley/SWAT_2017_09_11/climate/climate2\UCC_ghcn_USS0011H21S_2017_09_12_1505215237.csv
58.4
0.0
F:/GIS/Ogden_Valley/SWAT_2017_09_11/climate/climate2\UCC_ghcn_USS0011H25S_2017_09_12_1505215131.csv
96.5
0.0
F:/GIS/Ogden_Valley/SWAT_2017_09_11/climate/climate2\UCC_ghcn_USS0011H30S_2017_09_12_1505215127.csv
121.9
0.0
F:/GIS/Ogden_Valle[...]<jupyter_text>Temp<jupyter_code>#raw_data_folder = "E:/Google Drive/WORK/Round_Valley/Data/Climate/"
#processed_data = raw_data_folder + "processed/"

idnum, stats, lat, long, elev = [],[],[],[],[]

i = 0
for file in glob.glob(raw_data_folder+'*.csv'):
    i += 1
    print(file)
    df = pd.read_csv(file, skiprows=15, index_col=0, 
                     usecols=['Day','Min Temperature','Max Temperature'], parse_dates=True)
    df['Min Temperature'] = pd.to_numeric(df['Min Temperature'], errors='coerce').resample('1D').mean().interpolate(how='time') 
    df['Max Temperature'] = pd.to_numeric(df['Max Temperature'], errors='coerce').resample('1D').mean().interpolate(how='time') 
    #df.fillna(0.0,inplace=True)
    df['Min Temperature'] = df['Min Temperature'].apply(lambda x: round(x,2),1)
    df['Max Temperature'] = df['Max Temperature'].apply(lambda x: round(x,2),1)

    df = df[(df.index >= pd.datetime(2000,1,1))&(df.index < pd.datetime(2017,1,1))]
    #print(df['Precipitation'].max())
    #print(df['Precipitation'].min())


    stat = 'OT' + str(i).zfill(3)
    df = df[['Min Temperature','Max Temperature']]
    df.columns = [pd.datetime.strftime(df.index[0],'%Y%m%d'),'to_drop']
        
    df.to_csv(processed_data + stat + '.txt',index=False)
    statinfo = pd.read_csv(file, nrows=6, skiprows=1, index_col=0, names=['name','values'])
    stats.append(stat[1:])
    lat.append(statinfo.loc['Latitude','values'])
    long.append(statinfo.loc['Longitude','values'])
    elev.append(statinfo.loc['Elevation','values'][:-2])
    idnum.append(i)

stations = pd.DataFrame({'ID':idnum,'NAME':stats,'LAT':lat,'LONG':long,'ELEVATION':elev})
stations = stations[['ID','NAME','LAT','LONG','ELEVATION']]
stations.to_csv(processed_data+'temp.txt',index=False)
outfolder = processed_data+'cleanedtemp/'

word = ',to_drop'

for infile in glob.glob(processed_data+"O*.txt"):
    fin = open(infile)
    outfile = processed_data+os.path.basename(infile)[1:]
    fout = open(outfile, "w+")
    for line in fin:
        line = line.replace(word, "")
        fout.write(line)
    fin.close()
    fout.close()
    print(outfile)<jupyter_output>H:/GIS/Ogden_Valley/SWAT_2017_09_11/climate/climate2/processed/T001.txt
H:/GIS/Ogden_Valley/SWAT_2017_09_11/climate/climate2/processed/T002.txt
H:/GIS/Ogden_Valley/SWAT_2017_09_11/climate/climate2/processed/T003.txt
H:/GIS/Ogden_Valley/SWAT_2017_09_11/climate/climate2/processed/T004.txt
H:/GIS/Ogden_Valley/SWAT_2017_09_11/climate/climate2/processed/T005.txt
H:/GIS/Ogden_Valley/SWAT_2017_09_11/climate/climate2/processed/T006.txt
H:/GIS/Ogden_Valley/SWAT_2017_09_11/climate/climate2/processed/T007.txt
H:/GIS/Ogden_Valley/SWAT_2017_09_11/climate/climate2/processed/T008.txt
H:/GIS/Ogden_Valley/SWAT_2017_09_11/climate/climate2/processed/T009.txt
H:/GIS/Ogden_Valley/SWAT_2017_09_11/climate/climate2/processed/T010.txt
H:/GIS/Ogden_Valley/SWAT_2017_09_11/climate/climate2/processed/T011.txt
H:/GIS/Ogden_Valley/SWAT_2017_09_11/climate/climate2/processed/T012.txt
H:/GIS/Ogden_Valley/SWAT_2017_09_11/climate/climate2/processed/T013.txt
H:/GIS/Ogden_Valley/SWAT_2017_09_11/climate/climate2/processed/T[...]<jupyter_text>PRISM Data Processes files downloaded from http://www.prism.oregonstate.edu/explorer/  Precip<jupyter_code>raw_data_folder = "E:/PROJECTS/Round_Valley/SWAT/climate/"
processed_data = raw_data_folder + "processed/"
stats, lat, long,elev,idnum = [],[],[],[],[]
i = 0

inProj = pyproj.Proj(init='epsg:4326')
outProj = pyproj.Proj('+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=37.5 +lon_0=-96 +x_0=0 +y_0=0 +datum=NAD83 +units=m +no_defs')

for file in glob.glob(raw_data_folder+'*.csv'):
    i += 1
    #print(file)
    df = pd.read_csv(file, skiprows=11, index_col=0, 
                     usecols=[0,1], parse_dates=True, names = ['datetime','Precipitation'])
    df['Precipitation'] = pd.to_numeric(df['Precipitation'], errors='coerce') 
    df = df.resample('1D').max().interpolate(how='time')
    df.fillna(0.0,inplace=True)
    df['Precipitation'] = df['Precipitation'].apply(lambda x: round(x,2),1)
    df = df[(df.index >= pd.datetime(2000,1,1))&(df.index <= pd.datetime(2018,1,1))]
    #print(df['Precipitation'].max(),df['Precipitation'].min())
    df.columns = [pd.datetime.strftime(df.index[0],'%Y%m%d')]

    stat = 'P' + str(i).zfill(3)
    parsename = str(file).split('_')
    
    df.to_csv(processed_data + stat + '.txt',index=False)

    stats.append(stat)
    latitude = parsename[-2]
    longitude = parsename[-1][:-4]
    y2 = parsename[-2]
    x2 = parsename[-1][:-4]
    
    #x2,y2 = pyproj.transform(inProj,outProj,longitude,latitude)
    
    lat.append(y2)
    long.append(x2)
    elevation = wa.get_elev([longitude,latitude])
    elev.append(elevation)
    #print(longitude,latitude,elevation)
    idnum.append(i)

stations = pd.DataFrame({'ID':idnum,'NAME':stats,'LAT':lat,'LONG':long,'ELEVATION':elev})
stations = stations[['ID','NAME','LAT','LONG','ELEVATION']]
stations.to_csv(processed_data + 'pcp.txt',index=False)<jupyter_output><empty_output><jupyter_text>Temp<jupyter_code>#raw_data_folder = "E:/Google Drive/WORK/Round_Valley/Data/Climate/"
#processed_data = raw_data_folder + "processed/"

idnum, stats, lat, long, elev = [],[],[],[],[]

i = 0
for file in glob.glob(raw_data_folder+'*.csv'):
    i += 1
    print(file)
    df = pd.read_csv(file, skiprows=10, index_col=0, 
                     usecols=['Date','tmin (degrees C)','tmax (degrees C)'], parse_dates=True)
    df['Min Temperature'] = pd.to_numeric(df['tmin (degrees C)'], errors='coerce').resample('1D').mean().interpolate(how='time') 
    df['Max Temperature'] = pd.to_numeric(df['tmax (degrees C)'], errors='coerce').resample('1D').mean().interpolate(how='time') 
    #df.fillna(0.0,inplace=True)
    df['Min Temperature'] = df['Min Temperature'].apply(lambda x: round(x,2),1)
    df['Max Temperature'] = df['Max Temperature'].apply(lambda x: round(x,2),1)

    df = df[(df.index >= pd.datetime(2000,1,1))&(df.index <= pd.datetime(2018,1,1))]
    #print(df['Precipitation'].max())
    #print(df['Precipitation'].min())


    stat = 'OT' + str(i).zfill(3)
    df = df[['Min Temperature','Max Temperature']]
    df.columns = [pd.datetime.strftime(df.index[0],'%Y%m%d'),'to_drop']
        
    df.to_csv(processed_data + stat + '.txt',index=False)
    
    parsename = str(file).split('_')
    
    stats.append(stat)
    latitude = parsename[-2]
    longitude = parsename[-1][:-4]
    
    y2 = parsename[-2]
    x2 = parsename[-1][:-4]
    
    #x2,y2 = pyproj.transform(inProj,outProj,longitude,latitude)
    
    lat.append(y2)
    long.append(x2)
    elevation = wa.get_elev([longitude,latitude])
    elev.append(elevation)
    print(longitude,latitude,elevation)
    idnum.append(i)
    


stations = pd.DataFrame({'ID':idnum,'NAME':stats,'LAT':lat,'LONG':long,'ELEVATION':elev})
stations = stations[['ID','NAME','LAT','LONG','ELEVATION']]
stations.to_csv(processed_data+'temp.txt',index=False)<jupyter_output>E:/PROJECTS/Round_Valley/SWAT/climate\PRISM_ppt_tmin_tmax_provisional_4km_20000101_20171231_40.3054_-111.3042.csv
-111.3042 40.3054 2834.65
E:/PROJECTS/Round_Valley/SWAT/climate\PRISM_ppt_tmin_tmax_provisional_4km_20000101_20171231_40.3300_-111.3393.csv
-111.3393 40.3300 2541.11
E:/PROJECTS/Round_Valley/SWAT/climate\PRISM_ppt_tmin_tmax_provisional_4km_20000101_20171231_40.3342_-111.4162.csv
-111.4162 40.3342 2056.37
E:/PROJECTS/Round_Valley/SWAT/climate\PRISM_ppt_tmin_tmax_provisional_4km_20000101_20171231_40.3718_-111.4114.csv
-111.4114 40.3718 1760.83
E:/PROJECTS/Round_Valley/SWAT/climate\PRISM_ppt_tmin_tmax_provisional_4km_20000101_20171231_40.3760_-111.4896.csv
-111.4896 40.3760 2535.04
E:/PROJECTS/Round_Valley/SWAT/climate\PRISM_ppt_tmin_tmax_provisional_4km_20000101_20171231_40.3792_-111.3351.csv
-111.3351 40.3792 2638.79
E:/PROJECTS/Round_Valley/SWAT/climate\PRISM_ppt_tmin_tmax_provisional_4km_20000101_20171231_40.3860_-111.4519.csv
-111.4519 40.3860 1760.66
E:/PROJECTS/Round_Va[...]<jupyter_text>Dams Created Reservoir input data for ArcSWAT. Used in "Edit SWAT Input" --> "Reservoirs" --> "Reservoir Management""RESMONO" and "RESDAYO"<jupyter_code>damdata = pd.read_csv('F:/GIS/Ogden_Valley/SWAT_2017_11_21/Dams/USBR_dam_Data.csv',skiprows=5)
damdata['Site ID'].unique()
def rels_conv(df):
    df['RESOUTFLOW'] = df['Value'].apply(lambda x: round(x*0.0283168,3),1) 

    df['DATE'] = pd.to_datetime(df['Date'])
    df.set_index('DATE',inplace=True)
    df['RESOUTFLOW'] = df['RESOUTFLOW'].interpolate(method='time')
    df.reset_index(inplace=True)
    df = df[['DATE','RESOUTFLOW']]

    df['DATE'] = df['DATE'].apply(lambda x: '{:}/{:}/{:}'.format(x.month,x.day,x.year),1)
    return df
def stor_conv(damdata, dam='causey'):
    df = damdata[(damdata['Site ID']==dam)&(damdata['Parameter']=='Reservoir Storage')]
    
    df['RESTOR'] = df['Value'].apply(lambda x: round(x*0.12*10000,0),1) 

    df['DATE'] = pd.to_datetime(df['Date'])
    df.set_index('DATE',inplace=True)
    df['RESTOR'] = df['RESTOR'].interpolate(method='time')
    df['RESTOR'] = df['RESTOR'].apply(lambda x: int(x),1)
    df = df[(df.index >= pd.datetime(2010,1,1))& (df.index < pd.datetime(2017,1,1))]
    df['sequence'] = range(1,len(df)+1)
    df['rowlab'] = df.index.map('RES_VOL_{0:%Y}_{0:%j}'.format)
    df.reset_index(inplace=True)
    df = df[['sequence','rowlab','RESTOR']]
    df.to_clipboard(index=False)
    return df<jupyter_output><empty_output><jupyter_text>Causey  Daily Input<jupyter_code>stor_conv(damdata)
#caus_stor = damdata[(damdata['Site ID']=='causey')&(damdata['Parameter']=='Reservoir Storage')]
caus_rels = damdata[(damdata['Site ID']=='causey')&(damdata['Parameter']=='Reservoir Release')]
caus_rels = rels_conv(caus_rels)

#caus_rels.to_csv('H:/GIS/Ogden_Valley/SWAT_2017_11_21/Dams/ResdayoCaus.txt',index=False,
#                 header=['"DATE"','"RESOUTFLOW"'],quotechar="\'")
caus_stor
causy = caus_rels.set_index('DATE')
causy.plot()
caus_rels = damdata[(damdata['Site ID']=='causey')&(damdata['Parameter']=='Reservoir Release')]
caus_rels = rels_conv(caus_rels)
caus_rels['DATE'] = caus_rels['DATE'].apply(lambda x: pd.to_datetime(x),1)
caus_rels.set_index('DATE',inplace=True)
caus_mon = caus_rels.groupby(pd.TimeGrouper('MS')).mean()

caus_mon['Year'] = caus_mon.index.year
caus_mon['Month'] = caus_mon.index.month
caus_mon.set_index(['Year','Month'],inplace=True)
caus_mon = caus_mon.unstack()
caus_mon.columns = caus_mon.columns.droplevel(0)
caus_mon.columns = ["Resout1","Resout2","Resout3","Resout4","Resout5","Resout6",
                    "Resout7","Resout8","Resout9","Resout10","Resout11","Resout12"]
caus_mon

caus_mon = caus_mon.unstack()
caus_mon = ["Resout1","Resout2","Resout3","Resout4","Resout5",
                    "Resout6","Resout7","Resout8","Resout9","Resout10","Resout11","Resout12"]
caus_stor = damdata[(damdata['Site ID']=='causey')&(damdata['Parameter']=='Reservoir Storage')]<jupyter_output><empty_output><jupyter_text>Pineview<jupyter_code>pine_stor = damdata[(damdata['Site ID']=='pineview')&(damdata['Parameter']=='Reservoir Storage')]
pine_rels = damdata[(damdata['Site ID']=='pineview')&(damdata['Parameter']=='Reservoir Release')]
pine_rels = rels_conv(pine_rels)
pine_rels.to_csv('F:/GIS/Ogden_Valley/SWAT_2017_11_21/Dams/ResdayoPine.txt',index=False,
                 header=['"DATE"','"RESOUTFLOW"'],quotechar="\'")
pine_stor.plot()
pine_rels = damdata[(damdata['Site ID']=='pineview')&(damdata['Parameter']=='Reservoir Release')]
pine_rels = damdata[(damdata['Site ID']=='pineview')&(damdata['Parameter']=='Reservoir Release')]
pine_rels['RESOUTFLOW'] = pine_rels['Value'].apply(lambda x: round(x*0.0283168,2),1) 
pine_rels['DATE'] = pd.to_datetime(pine_rels['Date'])

pine_rels.set_index('DATE',inplace=True)
pine_rels = pine_rels[(pine_rels.index >= pd.datetime(2010,1,1))& (pine_rels.index < pd.datetime(2017,1,1))]
pine_rels['sequence'] = range(1,len(pine_rels)+1)
pine_rels['rowlab'] = pine_rels.index.map('FLOW_OUT_{0:%Y}_{0:%j}'.format)

pine_rels = pine_rels[['sequence','rowlab','RESOUTFLOW']]

pine_rels.to_clipboard(sep='\t',index=False)
pn_df = pine_rels['RESOUTFLOW'].apply(lambda x: "    {:}".format(x),1)
pn_df.to_clipboard(sep='\t',index=False)
pine_rels['RESOUTFLOW'].plot()
res_df['sequence'] = range(1,len(res_df)+1)
res_df['rowlab'] = res_df.index.map('RES_VOL_{0:%Y}_{0:%j}'.format)

pine_rels
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    13.31
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    9.01
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    7.75
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    5.28
    5.33
    5.81
    5.91
    5.6
    4.9
    4.24
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    4.55
    3.17
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    0.34<jupyter_output><empty_output><jupyter_text>FLow OUT RCH<jupyter_code>dailydata_file = "C:/Users/PAULINKENBRANDT/Downloads/Mon_Oct_30_13_26_49_2017__6735 (1).csv"

dailydata = pd.read_csv(dailydata_file, error_bad_lines=False)

dailydata.dropna(inplace=True)
dailydata.columns = [x.strip() for x in dailydata.columns]
dailydata['Date'] = pd.to_datetime(dailydata['Date'])
dailydata.set_index('Date',inplace=True)
dailydata = dailydata.resample('1D').mean()
dailydata = dailydata.interpolate(method='time')
dailydata = dailydata[dailydata.index < pd.datetime(2017,1,1)]
dailydata['Flow_cms'] = dailydata['Flow'].apply(lambda x: round(x*0.0283168,2),1)
dailydata['Base Flow_cms'] = dailydata['Base Flow'].apply(lambda x: round(x*0.0283168,2),1)
dailydata['sequence'] = range(1,len(dailydata)+1)
dailydata['rowlab'] = dailydata.index.map('FLOW_OUT_{0:%Y}_{0:%j}'.format)


swat_obs_dir = "H:/GIS/Ogden_Valley/SWAT_2017_09_11/swatcup/Daily20171030.Sufi2.SwatCup/SUFI2.IN/"
obs_rch_file = swat_obs_dir + 'observed_rch.txt'
obs_rch_sum_file = swat_obs_dir + 'var_file_rch.txt'
rch_sub_nos = [61]

header = """{:}     : number of observed variables\n""".format(len(sub_nos))

dataset_text = """\nFLOW_OUT_{:}   : this is the name of the variable and the subbasin number to be included in the objective function
{:}   : number of data points for this variable as it follows below. First column is a sequential number from beginning
      : of the simulation, second column is variable name and date (format arbitrary), third column is variable value.\n
"""

with open(obs_rch_file, 'w') as obs_rch:
    obs_rch.write(header)    
    for sub_no in sub_nos:
        dailysub = dailydata[['sequence','rowlab','Flow_cms']]        
        obs_rch.write(dataset_text.format(sub_no,len(dailysub)))
        obs_rch.write(dailysub.to_csv(path_or_buf=None, header=None, index=None, sep='\t'))
obs_rch.close()

with open(obs_rch_sum_file, 'w') as obs_rch_sum:
    rch_file_strs = "FLOW_OUT_{:}.txt\n"*len(sub_nos)
    obs_rch_sum.write(rch_file_strs.format(*sub_nos))
obs_rch_sum.close()<jupyter_output><empty_output>
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{'chain_length': '8', 'dir': 'jupyter-structured-clean-dedup', 'id': '311722', 'license': 'MIT', 'n_tokens_mistral': 23345, 'n_tokens_neox': 19769, 'n_words': 3323, 'path': 'SWAT Input Processor.ipynb', 'repo_name': 'inkenbrandt/Round-Valley', 'size': '40244'}