def plot_time_series(data_in, param_in, region):
'''
plot_time_series ...
'''
# Read time and param vars
time = data_in.variables['time'][:] # read the time
param = data_in.variables[param_in][:]
# Scale var
[scal_req, scale_factor, add_offset] = findScaleOffset(data_in, param_in)
param_scaled = (scale_factor*param)+add_offset
# create time vector
time_uni = data_in.variables['time'].units # get the time units
time_cal = data_in.variables['time'].calendar # read calendar
cdftime = utime(time_uni, calendar=time_cal)
date = [cdftime.num2date(t) for t in time]
# ############# A plot of Maximum precipitation ##############
plt.figure(region+' '+param_in, figsize=(15, 6))
plt.plot(date, param_scaled[:, 0, 0], label=model)
# window = 10 # date [x:-y], where x+y = window - 1
# param_scaled_smoothed = moving_average(arr=param_scaled[:, 0, 0], win=window)
# plt.plot(date[5:-4], param_scaled_smoothed, label=model+'_smoothed') ##
plt.ylabel("%s Anomaly (%s)" % (data_in.variables[param_in].long_name,
data_in.variables[param_in].units))
plt.ticklabel_format(useOffset=False, axis='y')
plt.xlabel("Time")
plt.title('Annual '+data_in.variables[param_in].long_name+' Anomaly ' + region + ' region', fontweight='bold')
def plot_time_series(data_in, param_in, region):
'''
plot_time_series of RCP4.5 scenario ...
'''
# Read time and param vars
time = data_in.variables['time'][:]
param = data_in.variables[param_in][:]
# Scale var
[scal_req, scale_factor, add_offset] = findScaleOffset(data_in, param_in)
param_scaled = (scale_factor*param)+add_offset
# create time vector
time_uni = data_in.variables['time'].units
time_cal = data_in.variables['time'].calendar
cdftime = utime(time_uni, calendar=time_cal)
date = [cdftime.num2date(t) for t in time]
# ############# A plot of field mean ##############
plt.figure(region+' '+param_in, figsize=(15, 6))
plt.plot(date, param_scaled[:, 0, 0], label=model) # PLOT ORIGINAL DATA
# ############ PLOT SMOOTHED ###############
# window = 8 # date [x:-y], where x+y = window - 1
# param_scaled_smoothed = moving_average(arr=param_scaled[:, 0, 0], win=window)
# plt.plot(date[4:-3], param_scaled_smoothed, label=model) # Plot data smoothed
plt.ylabel("%s (%s)" % (data_in.variables[param_in].long_name,
data_in.variables[param_in].units))
plt.ticklabel_format(useOffset=False, axis='y')
plt.xlabel("Time")
plt.title('RCP 8.5 - Annual Average '+data_in.variables[param_in].long_name
+ ' in the '+region+' region', fontweight='bold')
def plot_time_series(data_in, param_in, title_in):
'''
plot_time_series ...
'''
# Read time and param vars
time = data_in.variables['time'][:]
param = data_in.variables[param_in][:]
# Scale var
[scal_req, scale_factor, add_offset] = findScaleOffset(data_in, param_in)
param_scaled = (scale_factor*param)+add_offset
# create time vector
time_uni = data_in.variables['time'].units
time_cal = data_in.variables['time'].calendar
cdftime = utime(time_uni, calendar=time_cal)
date = [cdftime.num2date(t) for t in time]
plt.figure()
plt.plot(date, param_scaled[:, 0, 0], c='r')
plt.ylabel("%s (%s)" % (data_in.variables[param_in].long_name,
data_in.variables[param_in].units))
plt.ticklabel_format(useOffset=False, axis='y')
plt.xlabel("Time")
plt.title('Maximum '+data_in.variables[param_in].long_name + ' in the '+region+' region')
plt.grid()
# verify file is written correctly
# type: <class 'netCDF4._netCDF4.Dataset'>
# nc_fh = Dataset(nc_spat_avg, 'r') #filehandler
ncdump(data_spat_avg, True)
lats = data_spat_avg.variables['lat'][:] # extract/copy the data
lons = data_spat_avg.variables['lon'][:]
time = data_spat_avg.variables['time'][:]
t2m = data_spat_avg.variables[
't2m'][:] # shape is time, lat, lon as shown above
# in this dataset the times is as hours since 1900-01-01
time_hours = [dt.date(1900, 1, 1) + dt.timedelta(hours=t) for t in time]
[scal_req, scale_factor, add_offset] = findScaleOffset(data_spat_avg, 't2m')
if scal_req:
t2m_scaled = (scale_factor * t2m) + add_offset
# print(type(t2m_scaled[:,0,0]))
# A plot of t2m_scaled
fig = plt.figure()
plt.plot(time_hours, t2m_scaled[:, 0, 0], c='r')
plt.ylabel("%s (%s)" % (data_spat_avg.variables['t2m'].long_name,
data_spat_avg.variables['t2m'].units))
plt.ticklabel_format(useOffset=False, axis='y')
plt.xlabel("Time")
plt.title("%s from\n%s scaled" %
(data_spat_avg.variables['t2m'].long_name, box))
test_file = '../nc_files/cmip5_converted_days/HadGEM2-AO_days/pr/box/pr_day_HadGEM2-AO_historical_r1i1p1_18600101-20051230_Andes_box.nc'
# Create file handlers for field mean and year mean
data_in = Dataset(test_file, mode='r')
# Read time and param vars
time = data_in.variables['time'][:]
# param = data_in.variables['pr'][-365:-1, 0, 0]
param = data_in.variables['pr'][:, 0, 0]
# print(max(param))
# ####################################################################
# Scale var
[scal_req, scale_factor, add_offset] = findScaleOffset(data_in, param)
param_scaled = (scale_factor*param)+add_offset
# create time vector
time_uni = data_in.variables['time'].units
time_cal = data_in.variables['time'].calendar
cdftime = utime(time_uni, calendar=time_cal)
date = [cdftime.num2date(t) for t in time]
# ############# A plot of field mean ##############
plt.figure()
plt.plot(date, param_scaled[:, 0, 0], c='r')
plt.ylabel("%s (%s)" % (data_in.variables[param].long_name,
data_in.variables[param].units))
def plot_time_series(file_path_in_array,
png_name_in=None,
param_in=None,
region=None,
h_line=None):
'''
plot_time_series ...
'''
import pathlib
from useful_functions import findScaleOffset
from netcdftime import utime
import matplotlib.pyplot as plt
import matplotlib.dates as date_plt
from useful_functions import moving_average
from netCDF4 import Dataset # http://code.google.com/p/netcdf4-python/
import datetime as dt # Python standard library datetime module
from bisect import bisect_left
import logging
logger = logging.getLogger('root')
FORMAT = "[%(filename)s:%(lineno)s - %(funcName)20s() ] %(message)s"
logging.basicConfig(format=FORMAT)
logger.setLevel(logging.debug())
plot_each = True
# Read time and param vars
if param_in is None:
return
date_fill = None
rcp45_p25_fill = None
rcp45_p75_fill = None
rcp85_p25_fill = None
rcp85_p75_fill = None
histo_date_fill = None
histo_rcp45_p25_fill = None
histo_rcp45_p75_fill = None
histo_rcp85_p25_fill = None
histo_rcp85_p75_fill = None
for file_path_in in file_path_in_array:
plt.figure(region + ' ' + param_in, figsize=(15, 6))
logger.debug("plot_time_series " + file_path_in)
data_in = Dataset(file_path_in, mode='r')
time = data_in.variables['time'][:]
param = data_in.variables[param_in][:]
# Scale var
[scal_req, scale_factor,
add_offset] = findScaleOffset(data_in, param_in)
param_scaled = (scale_factor * param) + add_offset
# create time vector
time_uni = data_in.variables['time'].units
time_cal = data_in.variables['time'].calendar
cdftime = utime(time_uni, calendar=time_cal)
date = [cdftime.num2date(t) for t in time]
# ############# A plot of Maximum precipitation ##############
# plt.plot(date, param_scaled[:, 0, 0], label=model)
days_2006 = 57160.5 # 2006 value in time:units = "days since 1850-1-1 00:00:00" ; time:calendar = "standard" ;'
index_2006 = bisect_left(time, days_2006)
half_window = 60 # 60-5
window = half_window * 2
date_start = half_window
date_end = half_window - 1 # date [x:-y], where x+y = window - 1
param_scaled_smoothed = moving_average(arr=param_scaled[:, 0, 0],
win=window)
if "25" in file_path_in and "rcp45" in file_path_in:
rcp45_p25_fill = param_scaled_smoothed[index_2006 - 1 - date_end -
1:]
histo_rcp45_p25_fill = param_scaled_smoothed[:index_2006 -
date_start]
# plt.plot(date[index_2006-1:-date_end], param_scaled_smoothed[index_2006-1-date_end-1:], 'g--', label=pathlib.Path(file_path_in).stem.split("45")[0])#.split("_histo")[0])
elif "75" in file_path_in and "rcp45" in file_path_in:
rcp45_p75_fill = param_scaled_smoothed[index_2006 - 1 - date_end -
1:]
date_fill = date[index_2006 - 1:-date_end]
histo_rcp45_p75_fill = param_scaled_smoothed[:index_2006 -
date_start]
histo_date_fill = date[date_start:index_2006]
# plt.plot(date[index_2006-1:-date_end], param_scaled_smoothed[index_2006-1-date_end-1:], 'g--', label=pathlib.Path(file_path_in).stem.split("45")[0])#.split("_histo")[0])
elif "rcp45" in file_path_in:
plt.plot(
date[date_start:index_2006],
param_scaled_smoothed[:index_2006 - date_start], 'k'
) # label=pathlib.Path(file_path_in).stem.split("45")[0])#.split("_histo")[0])
plt.plot(date[index_2006 - 1:-date_end],
param_scaled_smoothed[index_2006 - 1 - date_end - 1:],
'g',
label=pathlib.Path(file_path_in).stem.split("45")
[0]) #.split("_histo")[0])
if "25" in file_path_in and "rcp85" in file_path_in:
rcp85_p25_fill = param_scaled_smoothed[index_2006 - 1 - date_end -
1:]
histo_rcp85_p25_fill = param_scaled_smoothed[:index_2006 -
date_start]
# plt.plot(date[index_2006-1:-date_end], param_scaled_smoothed[index_2006-1-date_end-1:], 'r--', label=pathlib.Path(file_path_in).stem.split("45")[0])#.split("_histo")[0])
elif "75" in file_path_in and "rcp85" in file_path_in:
rcp85_p75_fill = param_scaled_smoothed[index_2006 - 1 - date_end -
1:]
date_fill = date[index_2006 - 1:-date_end]
histo_rcp85_p75_fill = param_scaled_smoothed[:index_2006 -
date_start]
histo_date_fill = date[date_start:index_2006]
# plt.plot(date[index_2006-1:-date_end], param_scaled_smoothed[index_2006-1-date_end-1:], 'r--', label=pathlib.Path(file_path_in).stem.split("45")[0])#.split("_histo")[0])
elif "rcp85" in file_path_in:
plt.plot(date[index_2006 - 1:-date_end],
param_scaled_smoothed[index_2006 - 1 - date_end - 1:],
'r',
label=pathlib.Path(file_path_in).stem.split("45")
[0]) #.split("_histo")[0])
plt.plot(
date[date_start:index_2006],
param_scaled_smoothed[:index_2006 - date_start], 'k'
) # label=pathlib.Path(file_path_in).stem.split("45")[0])#.split("_histo")[0])
# plt.ylabel("%s Anomaly (%s)" % (data_in.variables[param_in].long_name,
# data_in.variables[param_in].units))
#plt.ylabel("Exceedance rate (%)") # TX90P
plt.ylabel(
data_in.variables[param_in].units) # R95P, SDII, RX5DAY, SDII
#plt.ylabel("Days") # FD
plt.ticklabel_format(useOffset=False, axis='y')
plt.xlabel("Year")
plt.title('Annual ' + data_in.variables[param_in].long_name +
' Anomaly ' + 'in the ' + region + ' region (smoothed)',
fontweight='bold')
data_in.close()
############## TO PLOT each model ############
# if plot_each:
# plt.legend()#loc=(0, 0), fontsize=7, frameon=True, ncol=11, bbox_to_anchor=(0, -0.5)) # Legend for smoothed
# plt.tight_layout(rect=[0, 0, 1, 1])
#
# # add horizontal line at y=0
# if h_line is not None:
# plt.axhline(y=h_line, color='k')
# # highligth 1961 to 1990 range
# plt.axvspan(dt.datetime(1961, 1, 1), dt.datetime(1990, 12, 30), color='b', alpha=0.1)
#
# plt.grid(b=True, linestyle='--', linewidth=1)
# plt.show()
if rcp45_p25_fill is not None:
plt.fill_between(
date_fill,
rcp45_p25_fill,
rcp45_p75_fill,
facecolor="g", # The fill color
# color='', # The outline color
alpha=0.2) # Transparency of the fill
if rcp85_p25_fill is not None:
plt.fill_between(
date_fill,
rcp85_p25_fill,
rcp85_p75_fill,
facecolor="r", # The fill color
# color='', # The outline color
alpha=0.2) # Transparency of the fill
if histo_rcp45_p25_fill is not None:
plt.fill_between(
histo_date_fill,
histo_rcp45_p25_fill,
histo_rcp45_p75_fill,
facecolor="k", # The fill color
# color='', # The outline color
alpha=0.1)
if histo_rcp85_p25_fill is not None:
plt.fill_between(
histo_date_fill,
histo_rcp85_p25_fill,
histo_rcp85_p75_fill,
facecolor="k", # The fill color
# color='', # The outline color
alpha=0.1) # Transparency of the fill
# plt.legend(loc=(0, 0), fontsize=7, frameon=True, ncol=11, bbox_to_anchor=(0, -0.5)) # Legend for smoothed
plt.tight_layout(rect=[0, 0, 1, 1])
# add horizontal line at y=0
if h_line is not None:
plt.axhline(y=h_line, color='b', alpha=0.5, linestyle='--')
# logger.debug(clean((clean((list(plt.yticks()[0]))))))
# plt.yticks(list(plt.yticks()[0]) + [h_line])
# highligth 1961 to 1990 range
plt.axvspan(dt.datetime(1961, 1, 1),
dt.datetime(1990, 12, 30),
color='b',
alpha=0.1)
plt.grid(b=True, linestyle='--', linewidth=1)
# cdftime = utime(time_uni, calendar=time_cal)
# date = [cdftime.num2date(time[140])]
# dates = [dt.datetime(1861,1,1),
# dt.datetime(1890,1,1),
# dt.datetime(1961,1,1),
# dt.datetime(1990,1,1),
# dt.datetime(2006,1,1),
# dt.datetime(2061,1,1),
# dt.datetime(2090,1,1)]
#
# dates_plot = [date_plt.date2num(date) for date in dates]
# plt.xticks(dates_plot)
# plt.show()
if png_name_in is None:
plt.show()
else:
logger.debug((png_name_in))
plt.savefig(png_name_in, dpi=150)
def plot_time_series(file_path_in_array,
png_name_in=None,
param_in=None,
region=None,
h_line=None,
lat=0,
lon=0):
'''
plot_time_series ...
'''
import pathlib
from useful_functions import findScaleOffset
from netcdftime import utime
import matplotlib.pyplot as plt
from useful_functions import moving_average
from netCDF4 import Dataset # http://code.google.com/p/netcdf4-python/
import datetime as dt # Python standard library datetime module
# Read time and param vars
if param_in is None:
return
date_fill = None
rcp45_p25_fill = None
rcp45_p75_fill = None
rcp85_p25_fill = None
rcp85_p75_fill = None
for file_path_in in file_path_in_array:
plt.figure(region + ' ' + param_in, figsize=(15, 6))
print("plot_time_series " + file_path_in)
data_in = Dataset(file_path_in, mode='r')
time = data_in.variables['time'][:]
param = data_in.variables[param_in][:]
# Scale var
[scal_req, scale_factor,
add_offset] = findScaleOffset(data_in, param_in)
param_scaled = (scale_factor * param) + add_offset
# create time vector
time_uni = data_in.variables['time'].units
time_cal = data_in.variables['time'].calendar
cdftime = utime(time_uni, calendar=time_cal)
date = [cdftime.num2date(t) for t in time]
print("plot_time_series " + file_path_in)
# ############# A plot of Maximum precipitation ##############
plt.plot(date,
param_scaled[:, lat, lon],
alpha=0.5,
label=pathlib.Path(file_path_in).stem.split("45")
[0]) #.split("_histo")[0l)
window = 10 # date [x:-y], where x+y = window - 1
param_scaled_smoothed = moving_average(arr=param_scaled[:, lat, lon],
win=window)
plt.plot(date[5:-4], param_scaled_smoothed, 'k')
#plt.plot(date[5:145], param_scaled_smoothed[:140], 'k') #label=pathlib.Path(file_path_in).stem.split("45")[0])#.split("_histo")[0])
if True: #"25" in file_path_in and "45" in file_path_in:
rcp45_p25_fill = param_scaled_smoothed[139:]
#plt.plot(date[144:-4], param_scaled_smoothed[139:], 'g--', label=pathlib.Path(file_path_in).stem.split("45")[0])#.split("_histo")[0])
elif "75" in file_path_in and "45" in file_path_in:
rcp45_p75_fill = param_scaled_smoothed[139:]
date_fill = date[144:-4]
#plt.plot(date[144:-4], param_scaled_smoothed[139:], 'g--', label=pathlib.Path(file_path_in).stem.split("45")[0])#.split("_histo")[0])
elif "45" in file_path_in:
plt.plot(date[144:-4],
param_scaled_smoothed[139:],
'g',
label=pathlib.Path(file_path_in).stem.split("45")
[0]) #.split("_histo")[0])
if "25" in file_path_in and "85" in file_path_in:
rcp85_p25_fill = param_scaled_smoothed[139:]
#plt.plot(date[144:-4], param_scaled_smoothed[139:], 'r--', label=pathlib.Path(file_path_in).stem.split("45")[0])#.split("_histo")[0])
elif "75" in file_path_in and "85" in file_path_in:
rcp85_p75_fill = param_scaled_smoothed[139:]
date_fill = date[144:-4]
#plt.plot(date[144:-4], param_scaled_smoothed[139:], 'r--', label=pathlib.Path(file_path_in).stem.split("45")[0])#.split("_histo")[0])
elif "85" in file_path_in:
plt.plot(date[144:-4],
param_scaled_smoothed[139:],
'r',
label=pathlib.Path(file_path_in).stem.split("45")
[0]) #.split("_histo")[0])
plt.ylabel("%s Anomaly (%s)" % (data_in.variables[param_in].long_name,
data_in.variables[param_in].units))
plt.ticklabel_format(useOffset=False, axis='y')
plt.xlabel("Time")
plt.title('Annual ' + data_in.variables[param_in].long_name +
' Anomaly ' + 'in the ' + region + ' region (smoothed)',
fontweight='bold')
data_in.close()
plt.legend()
# add horizontal line at y=0
if h_line is not None:
plt.axhline(y=h_line, color='k')
# highligth 1961 to 1990 range
plt.axvspan(dt.datetime(1961, 1, 1),
dt.datetime(1990, 12, 30),
color='b',
alpha=0.1)
plt.grid(b=True, linestyle='--', linewidth=1)
plt.show()
if rcp45_p25_fill is not None:
plt.fill_between(
date_fill,
rcp45_p25_fill,
rcp45_p75_fill,
facecolor="g", # The fill color
#color='', # The outline color
alpha=0.2) # Transparency of the fill
if rcp85_p25_fill is not None:
plt.fill_between(
date_fill,
rcp85_p25_fill,
rcp85_p75_fill,
facecolor="r", # The fill color
#color='', # The outline color
alpha=0.2) # Transparency of the fill
plt.legend(
) #loc=(0, 0), fontsize=7, frameon=True, ncol=11, bbox_to_anchor=(0, -0.5)) # Legend for smoothed
plt.tight_layout(rect=[0, 0, 1, 1])
# add horizontal line at y=0
if h_line is not None:
plt.axhline(y=h_line, color='k')
# highligth 1961 to 1990 range
plt.axvspan(dt.datetime(1961, 1, 1),
dt.datetime(1990, 12, 30),
color='b',
alpha=0.1)
plt.grid(b=True, linestyle='--', linewidth=1)
#plt.show()
if png_name_in is None:
pass #plt.show()
else:
print(png_name_in)
plt.savefig(png_name_in, dpi=150)
