import os
import climate_plots as cp
from Figures import ReportFigures
import pandas as pd
figures = ReportFigures()
figures.set_style(style='timeseries', width='single')
inputfolder = 'D:/ATLData/Fox-Wolf/data'
outpdf = 'D:/ATLData/Fox-Wolf/Fox-Wolf_growing_season.pdf'
csvs = [os.path.join(inputfolder, c) for c in os.listdir(inputfolder) if 'growing_season' in c]
dfs = {}
for c in csvs:
df = pd.read_csv(c, index_col=0, parse_dates=True)
scenario = os.path.split(c)[-1].split('_')[0]
dfs[scenario] = df
props = {'sresa1b': {'color': 'Tomato', 'zorder': -2, 'alpha': 0.5},
'sresa2': {'color': 'SteelBlue', 'zorder': -3, 'alpha': 0.5},
'sresb1': {'color': 'Yellow', 'zorder': -1, 'alpha': 0.5},
}
fig, ax = cp.timeseries(dfs, ylabel='Growing season length (days)', props=props, title='',
plotstyle=figures.plotstyle) # plotstyle dict as argument to override some Seaborn settings
fig.savefig(outpdf, dpi=300)
import os
import numpy as np
import pandas as pd
from collections import defaultdict
import datetime as dt
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import PRMSio
import textwrap
import sys
sys.path.append('../Postprocessing')
import climate_plots as cp
try:
from Figures import ReportFigures
figures = ReportFigures()
figures.set_style()
except:
'Figures module not found; needed for USGS report formatting'
# inputs
datadir = 'D:/ATLData/BlackEarth/input' # contains existing PRMS .data files
# growing season parameters
uniform = False # T/F; T: one growing season for entire domain (incomplete option), F: growing season by hru
nhru = 880 # only needed if growing_output = True
frost_temp = 28.0
growing_output = False # if True, generate .day files, otherwise just plots (much faster)
real_data_periods = ['1961-2000', '2046-2065',
'2081-2100'] # for labeling non-synthetic data on plots
import plot_and_table_functions as ptf
from matplotlib.backends.backend_pdf import PdfPages
from Figures import ReportFigures
from scipy.interpolate import InterpolatedUnivariateSpline
import glob
# 31.9, 34.3 = min, max precipitation values for central sands for 1981-2010
# mean min temp (C) = 0.8 to 2 (33.4 to 35.6 F)
# mean max temp (C) = 12.07 to 13.68 (53.7 to 56.6 F)
if len(sys.argv) >= 1:
output_path = sys.argv[1]
else:
output_path = '.'
rf = ReportFigures()
rf.set_style()
# functions to convert between feet and meters
def meters_to_feet(x):
return (x * 3.28084)
def feet_to_meters(x):
return (x * 0.3048)
# functions to convert between cubic meter per day and cubic feet per second
def cu_meters_day_to_cfs(x):
return (x * 0.000408734569)
import os
import climate_plots as cp
from Figures import ReportFigures
import pandas as pd
figures = ReportFigures()
figures.set_style(style='timeseries', width='single')
inputfolder = 'D:/ATLData/Fox-Wolf/data'
outpdf = 'D:/ATLData/Fox-Wolf/Fox-Wolf_growing_season.pdf'
csvs = [
os.path.join(inputfolder, c) for c in os.listdir(inputfolder)
if 'growing_season' in c
]
dfs = {}
for c in csvs:
df = pd.read_csv(c, index_col=0, parse_dates=True)
scenario = os.path.split(c)[-1].split('_')[0]
dfs[scenario] = df
props = {
'sresa1b': {
'color': 'Tomato',
'zorder': -2,
'alpha': 0.5
},
'sresa2': {
'color': 'SteelBlue',
'zorder': -3,
import os
import numpy as np
import pandas as pd
from collections import defaultdict
import datetime as dt
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import PRMSio
import textwrap
import sys
sys.path.append('../Postprocessing')
import climate_plots as cp
try:
from Figures import ReportFigures
figures = ReportFigures()
figures.set_style()
except:
'Figures module not found; needed for USGS report formatting'
# inputs
datadir = 'D:/ATLData/BlackEarth/input' # contains existing PRMS .data files
# growing season parameters
uniform = False # T/F; T: one growing season for entire domain (incomplete option), F: growing season by hru
nhru = 880 # only needed if growing_output = True
frost_temp = 28.0
growing_output = False # if True, generate .day files, otherwise just plots (much faster)
real_data_periods = ['1961-2000', '2046-2065', '2081-2100'] # for labeling non-synthetic data on plots
__author__ = 'aleaf'
import os
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import ImageGrid
import matplotlib.patheffects as PathEffects
from matplotlib.colors import LinearSegmentedColormap
import textwrap
import climate_stats as cs
import GSFLOW_utils as GSFu
from Figures import ReportFigures
rf = ReportFigures()
rf.set_style()
'''
#--modify the base rcParams for a few items
newparams = {'font.family': 'Univers 57 Condensed Light',
'legend.fontsize': 8,
'axes.labelsize': 9,
'xtick.labelsize': 8,
'ytick.labelsize': 8,
'pdf.fonttype': 42,
'pdf.compression': 0,
'axes.formatter.limits': [-7, 9]}
# Update the global rcParams dictionary with the new parameter choices
plt.rcParams.update(newparams)
import sys, os
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import pandas as pd
import datetime
from Figures import ReportFigures
from matplotlib import cm
from datetime import date
from datetime import timedelta
from dateutil.parser import parse
import matplotlib.dates as mdates
from dateutil.relativedelta import relativedelta
# Use USGS report styles
rf = ReportFigures()
rf.set_style()
sys.path.append('..')
python_exe = sys.executable
#open irrigation segment file again to read lines
fname1 = open(r"..\input\prms\jh_coef_month_high.in", "r")
fname2 = open(r"..\input\prms\jh_coef_month_low.in", "r")
Kc_high, Kc_low = [], []
# Read Kc values
lines1 = fname1.readlines()
lines2 = fname2.readlines()
# set high Kc values
import sys, os
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import pandas as pd
import datetime
from Figures import ReportFigures
from matplotlib import cm
from datetime import date
from datetime import timedelta
from dateutil.parser import parse
import matplotlib.dates as mdates
from dateutil.relativedelta import relativedelta
# Load USGS report styles
rf = ReportFigures()
rf.set_style()
sys.path.append('..')
python_exe = sys.executable
# calculate number of lines in file can be any gage file
num_lines = 0
with open(r"..\output_GSFLOW_only\modflow\sagehensfr18_HighTrig.out",
'r') as f:
for line in f:
num_lines += 1
# set dates for daily values; this date is simulation starte date
dates = []
for i in range(num_lines - 1):
dates.append(datetime.date(1990, 10, 1) + datetime.timedelta(days=i))
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import pandas as pd
import datetime
from Figures import ReportFigures
from matplotlib import cm
from datetime import date
from datetime import timedelta
from dateutil.parser import parse
import matplotlib.dates as mdates
from dateutil.relativedelta import relativedelta
#%matplotlib inline
# Use USGS report styles
rf = ReportFigures()
rf.set_style()
sys.path.append('..')
python_exe = sys.executable
# calculate number of lines in file can be any gage file
num_lines = 0
with open(r".\output\Agwater1SW_high.ts9", 'r') as f:
for line in f:
num_lines += 1
# set dates for daily values; this date is simulation starte date
dates = []
for i in range(num_lines - 1):
dates.append(datetime.date(2014, 10, 1) + datetime.timedelta(days=i))