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climplotlib.py
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climplotlib.py
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# Library of plotting functions for climate analysis
import sys
from numpy.lib.histograms import _get_bin_edges, histogramdd
from pandas.core.groupby.generic import NamedAgg
sys.path.append('/mnt/e/')
import matplotlib.pyplot as plt
import icetools.climate as clm
import icetools.plotting.plotutils as plu
import pandas as pd
from scipy import optimize
import xarray as xr
import numpy as np
def monthCycler(months, start):
cycled_months = [(i-(start-1) if i>=start else (i+(12-start+1))) for i in months]
return cycled_months
def subplotGridLabels(fig, title, xlabel, ylabel):
fig.add_subplot(111, frameon=False)
plt.grid(None)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.suptitle(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
def getColor(name):
colors = {'ecco': '#0073FF', #blue
'icesdk': '#73FF00', #green
'hadley': '#FF8C00', #orange
'noaa': '#8C00FF', #purple
'default': 'royalblue'}
color = colors[name]
return color
def getMarker(name):
markers = {'ecco': 'o',
'icesdk': '^',
'hadley': 's',
'noaa': 'v',
'default': '.'}
def piecewise_linear(x, y0, k1, k2):
'''Piecewise linear function that breaks at x0=2003'''
x0 = 2003.
return np.piecewise(x, [x < x0], [lambda x: k1*x + y0 - k1*x0, lambda x: k2*x + y0 - k2*x0])
def regional_correction(coordinate, wood_data, ecco_data):
'''Regional correction to get ECCO to match Wood et al (2021) data'''
# -- Get data for curve fit
years = np.array([1995., 2003., 2013.])
wood_total_mean = wood_data.loc['1992-2017'].mean()
wood = np.array([
wood_data.loc['1992-1997'].mean() - wood_total_mean,
wood_data.loc['1998-2007'].mean() - wood_total_mean,
wood_data.loc['2008-2017'].mean() - wood_total_mean])
mine_total_mean = ecco_data.sel(time=slice('1992-01-01', '2017-12-31')).mean().values
mine = np.array([
float(ecco_data.sel(time=slice('1992-01-01', '1997-12-31')).mean().values - mine_total_mean),
float(ecco_data.sel(time=slice('1998-01-01', '2007-12-31')).mean().values - mine_total_mean),
float(ecco_data.sel(time=slice('2008-01-01', '2017-12-31')).mean().values - mine_total_mean)])
diff = mine - wood
# -- fit curve to data (difference between my data and wood's)
p, e = optimize.curve_fit(piecewise_linear, years, diff)
return p
def subSurfaceTemperatureAnomaly(ax, depth, idx, coordinate, ecco=None, ecco_dtype='ecco5', icesdk=None, wood_data=None, dec_mean=False, spgrid=False, style='pub-jog'):
if ecco is not None:
# get ECCO data at point for bottom 60% of water column
ecco_deepwater = clm.selectPointData(data=ecco, dtype='ecco5', coordinate=coordinate)
ecco_depth = ecco_deepwater.sel(
DEPTH_T=slice(
ecco_deepwater.temperature.dropna(dim='DEPTH_T').DEPTH_T.max().values*0.4,ecco_deepwater.temperature.dropna(dim='DEPTH_T').DEPTH_T.max().values)
).temperature.mean(dim='DEPTH_T')
# apply regional correction (piecewise linear fit)
p = regional_correction(coordinate, wood_data, ecco_depth)
# use regional correction to calculate corrected ECCO temperature anomaly
years = pd.to_datetime(ecco_depth.time).year.astype('float')
correction = piecewise_linear(years, *p)
ecco_depth_bias_corrected = ecco_depth - correction
ecco_mean = ecco_depth_bias_corrected.sel(
time=slice('1992-01-01', '2017-12-31')).mean().values
ecco_anomaly = ecco_depth_bias_corrected - ecco_mean
# determine mean and anomaly for entire time series
# ecco_mean = ecco_depth_bias_corrected.values.mean()
# ecco_anomaly = ecco_depth_bias_corrected.values - ecco_mean
time = ecco_depth_bias_corrected.time.values
eagc, = ax.plot(time, ecco_anomaly, marker=getMarker('ecco'), color=getColor('ecco'), alpha=0.7, label='ECCO annual mean (corrected)')
eagu, = ax.plot(time, ecco_depth - ecco_depth.mean(), marker=getMarker('ecco'), color='gray', linestyle='--', alpha=0.7, label='ECCO annual mean (uncorrected)')
ax.annotate(text='ECCO mean={:.2f} $^oC$'.format(ecco_mean), xy=(0.05, 0.9), xycoords='axes fraction')
# plu.designProperties(ax, [eag], style)
if dec_mean is True:
df = pd.DataFrame(data={'anomaly': ecco_anomaly}, index=time)
df['decade'] = df.index.year - (df.index.year % 10)
ecco_df_decade = df.groupby('decade').mean()
decade_starts = pd.to_datetime(ecco_df_decade.index.values, format='%Y')
decade_ends = pd.to_datetime(ecco_df_decade.index.values + 10, format='%Y')
edg = ax.hlines(ecco_df_decade.anomaly.values, xmin=decade_starts, xmax=decade_ends, color=getColor('ecco'), linewidth=5, alpha=0.5, zorder=2, label='ECCO decadal mean')
else:
eagc, = ax.plot(0,0, visible=False)
eagu, = ax.plot(0,0, visible=False)
edg, = ax.plot(0,0, visible=False)
ecco_df_decade = []
if icesdk is not None:
# icesdk_depth_point = clm.selectPointData(icesdk, dtype='icesdk', depth=depth, depth_tolerance=25, coordinate=coordinate, coordinate_tolerance=0.51)
icesdk_point = clm.selectPointData(icesdk, dtype='icesdk', coordinate=coordinate, coordinate_tolerance=0.51)
# icesdk_depth_point = icesdk_point.where(icesdk_point.depth > depth).dropna()
icesdk_depth_point = icesdk_point.where(
icesdk_point.depth > icesdk_point.depth.values.max()*0.4).dropna()
icesdk_mean = clm.icesAnnualMean(icesdk_depth_point)
icesdk_anomaly = icesdk_mean.avg.values - icesdk_mean.avg.mean()
time = pd.to_datetime(icesdk_mean.index.values, format='%Y')
iag, = ax.plot(time, icesdk_anomaly, marker=getMarker('icesdk'), color=getColor('icesdk'), alpha=0.7, label='ICES annual mean')
ax.annotate(text='ICES mean={:.2f} $^oC$'.format(icesdk_mean.avg.mean()), xy=(0.05, 0.8), xycoords='axes fraction')
if dec_mean is True:
ices_decadal_mean = clm.icesDecadalMean(icesdk_depth_point)
ices_decadal_anomaly = ices_decadal_mean.avg.values - ices_decadal_mean.avg.mean()
decade_starts = pd.to_datetime(ices_decadal_mean.index, format='%Y')
decade_ends = pd.to_datetime(ices_decadal_mean.index+10, format='%Y')
ices_df_decade = pd.DataFrame(data={'decade': decade_starts, 'anomaly': ices_decadal_anomaly})
idg = ax.hlines(ices_decadal_anomaly, xmin=decade_starts, xmax=decade_ends, color=getColor('icesdk'), linewidth=5, alpha=0.5, zorder=2, label='ICES decadal mean')
else:
iag, = ax.plot(0,0, visible=False)
idg, = ax.plot(0,0, visible=False)
ices_df_decade = []
if spgrid is True:
ax.set_title('{} N, {} W'.format(coordinate[0], -coordinate[1]))
# ax.legend()
elif spgrid is False:
ax.legend()
ax.set_xlabel('Time')
ax.set_ylabel('Temperature anomaly ($^oC$)')
ax.set_title('Ocean Temperature Anomaly at {}m ({} N, {} W)'.format(depth, coordinate[0], -coordinate[1]))
return eagc, eagu, edg, iag, idg, ecco_df_decade, ices_df_decade
def seaSurfaceTemperatureAnomaly(ax, coordinate, ecco=None, ecco_dtype='ecco5', hadley=None, dec_mean=False, spgrid=False, style='pub-jog'):
if ecco is not None:
ecco_sfc_point = clm.selectPointData(ecco, dtype=ecco_dtype, depth=0, coordinate=coordinate)
ecco_mean = ecco_sfc_point.temperature.mean().values
ecco_anomaly = ecco_sfc_point.temperature.values - ecco_mean
time = ecco.temperature.time.values
eag, = ax.plot(time, ecco_anomaly, marker=getMarker('ecco'), color=getColor('ecco'), alpha=0.7, label='ECCO annual mean')
ax.annotate(text='ECCO mean={:.2f} $^oC$'.format(ecco_mean), xy=(0.05, 0.9), xycoords='axes fraction')
# plu.designProperties(ax, [eag], style)
if dec_mean is True:
df = pd.DataFrame(data={'anomaly': ecco_anomaly}, index=time)
df['decade'] = df.index.year - (df.index.year % 10)
ecco_df_decade = df.groupby('decade').mean()
decade_starts = pd.to_datetime(ecco_df_decade.index.values, format='%Y')
decade_ends = pd.to_datetime(ecco_df_decade.index.values + 10, format='%Y')
edg = ax.hlines(ecco_df_decade.anomaly.values, xmin=decade_starts, xmax=decade_ends, color=getColor('ecco'), linewidth=5, alpha=0.5, zorder=2, label='ECCO decadal mean')
# plu.designProperties(ax, [edg], style)
else:
eag, = ax.plot(0,0, visible=False)
edg, = ax.plot(0,0, visible=False)
ecco_df_decade = []
if hadley is not None:
hadley_sfc_point = clm.selectPointData(hadley, dtype='hadley', coordinate=coordinate)
hadley_mean = hadley_sfc_point.SST.mean().values
hadley_anomaly = hadley_sfc_point.SST.values - hadley_mean
time = hadley.SST.time.values
hag, = ax.plot(time, hadley_anomaly, marker=getMarker('hadley'), color=getColor('hadley'), alpha=0.7, label='Hadley-OI annual mean')
ax.annotate(text='Hadley-OI mean={:.2f} $^oC$'.format(hadley_mean), xy=(0.05, 0.8), xycoords='axes fraction')
# plu.designProperties(ax, [hag], style)
if dec_mean is True:
df = pd.DataFrame(data={'anomaly': hadley_anomaly}, index=time)
df['decade'] = df.index.year - (df.index.year % 10)
hadley_df_decade = df.groupby('decade').mean()
decade_starts = pd.to_datetime(hadley_df_decade.index.values, format='%Y')
decade_ends = pd.to_datetime(hadley_df_decade.index.values + 10, format='%Y')
hdg = ax.hlines(hadley_df_decade.anomaly.values, xmin=decade_starts, xmax=decade_ends, color=getColor('hadley'), linewidth=5, alpha=0.5, zorder=2, label='Hadley-OI decadal mean')
# plu.designProperties(ax, [hdg], style)
else:
hag, = ax.plot(0,0, visible=False)
hdg, = ax.plot(0,0, visible=False)
hadley_df_decade = []
if spgrid is True:
ax.set_title('{} N, {} W'.format(coordinate[0], -coordinate[1]))
elif spgrid is False:
ax.legend()
ax.set_xlabel('Time')
ax.set_ylabel('Temperature anomaly ($^oC$)')
ax.set_title('Sea Surface Temperature Anomaly ({} N, {} W)'.format(coordinate[0], -coordinate[1]))
# plu.designProperties(ax, [], style)
return eag, edg, hag, hdg, ecco_df_decade, hadley_df_decade
def seaIceConcentration(ax, coordinate, season_start, ecco=None, hadley=None, noaa=None, noaa_var=None, dec_mean=False, spgrid=False, style='pub-jog'):
if ecco is not None:
ecco_coord = clm.dataAtCoord(ecco, coordinate, dtype='ecco4')
ecco_season = clm.seasonMean(ecco_coord, 'SIarea', season_start)
ecco_sic = 100 * ecco_season.values
time = ecco_season.time.values
eg, = ax.plot(time, ecco_sic, '.-', color=getColor('ecco'), alpha=0.7, label='ECCO seasonal mean')
# plu.designProperties(ax, [graph], style)
else: eg, = ax.plot(0,0, visible=False)
if hadley is not None:
hadley_coord = clm.dataAtCoord(hadley, coordinate, dtype='hadley')
hadley_season = clm.seasonMean(hadley_coord, 'SEAICE', season_start)
hadley_sic = hadley_season.values
time = hadley_season.time.values
hag, = ax.plot(time, hadley_sic, marker=getMarker('hadley'), color=getColor('hadley'), alpha=0.7, label='Hadley-OI seasonal mean')
# plu.designProperties(ax, [graph], style)
if dec_mean is True:
df = pd.DataFrame(data={'sic': hadley_sic}, index=time)
df['decade'] = df.index.year - (df.index.year % 10)
df_decade = df.groupby('decade').mean()
decade_starts = pd.to_datetime(df_decade.index.values, format='%Y')
decade_ends = pd.to_datetime(df_decade.index.values + 10, format='%Y')
# decadal_hadley_sic = df.resample('10AS', loffset='-2A').mean()
# decade_starts = pd.to_datetime(decadal_hadley_sic.index.year, format='%Y')
# decade_ends = pd.to_datetime(decadal_hadley_sic.index.year+10, format='%Y')
hdg = ax.hlines(df_decade.sic.values, xmin=decade_starts, xmax=decade_ends, color=getColor('hadley'), linewidth=5, alpha=0.5, zorder=2, label='Hadley-OI decadal seasonal mean')
# plu.designProperties(ax, [hdg], style)
else:
hag, = ax.plot(0,0, visible=False)
hdg, = ax.plot(0,0, visible=False)
if noaa is not None and noaa_var is not None:
noaa_coord = clm.dataAtCoord(noaa, coordinate, dtype='noaa')
noaa_season = clm.seasonMean(noaa_coord, noaa_var, season_start)
noaa_sic = 100 * noaa_season.values
time = noaa_season.time.values
nag, = ax.plot(time, noaa_sic, marker=getMarker('noaa'), color=getColor('noaa'), alpha=0.7, label='NOAA seasonal mean')
# plu.designProperties(ax, [graph], style)
if dec_mean is True:
df = pd.DataFrame(data={'sic': noaa_sic}, index=time)
df['decade'] = df.index.year - (df.index.year % 10)
df_decade = df.groupby('decade').mean()
decade_starts = pd.to_datetime(df_decade.index.values, format='%Y')
decade_ends = pd.to_datetime(df_decade.index.values + 10, format='%Y')
# decadal_noaa_sic = df.resample('10AS', loffset='+1A').mean()
# decade_starts = pd.to_datetime(decadal_noaa_sic.index.year, format='%Y')
# decade_ends = pd.to_datetime(decadal_noaa_sic.index.year+10, format='%Y')
ndg = ax.hlines(df_decade.sic.values, xmin=decade_starts, xmax=decade_ends, color=getColor('noaa'), linewidth=5, alpha=0.5, zorder=2, label='NOAA decadal seasonal mean')
# plu.designProperties(ax, [ndg], style)
else:
nag, = ax.plot(0,0, visible=False)
ndg, = ax.plot(0,0, visible=False)
if spgrid is True:
ax.set_title('{} N, {} W'.format(coordinate[0], -coordinate[1]))
elif spgrid is False:
ax.legend()
ax.set_xlabel('Time')
ax.set_ylabel('% concentration')
ax.set_title('Sea Ice Concentration ({} N, {} W)'.format(coordinate[0], -coordinate[1]))
# plu.designProperties(ax, [eg, hag, hdg, nag, ndg], style)
return eg, hag, hdg, nag, ndg
def seaIceSeason(ax, coordinate, sic_minimum, ecco=None, hadley=None, noaa=None, noaa_var=None, spgrid=False, style='pub-jog'):
if ecco is not None:
try:
ecco_ice_season_length = clm.seaIceSeasonLength(ecco, 'SIarea', dtype='ecco4', minimum=sic_minimum, coordinate=coordinate)
ecco_ice_onset = clm.seaIceSeasonOnset(ecco, 'SIarea', dtype='ecco5', minimum=sic_minimum, coordinate=coordinate)
months = [d.month for d in pd.to_datetime(ecco_ice_onset.values)][:-1]
months = [m+12 if m<9 else m for m in months]
lengths = ecco_ice_season_length.values[:-1]
years = pd.to_datetime(ecco_ice_season_length.time.values).year.values[:-1]
graph = ax.vlines(years-0.25, ymin=months, ymax=months+lengths-1, color=getColor('ecco'), alpha=0.8, label='ECCO')
# plu.designProperties(ax, [graph], style)
except: None
if hadley is not None:
try:
hadley_ice_season_length = clm.seaIceSeasonLength(hadley, 'SEAICE', dtype='hadley', minimum=sic_minimum, coordinate=coordinate)
hadley_ice_onset = clm.seaIceSeasonOnset(hadley, 'SEAICE', dtype='hadley', minimum=sic_minimum, coordinate=coordinate)
months = [d.month for d in pd.to_datetime(hadley_ice_onset.values)][:-1]
months = [m+12 if m<9 else m for m in months]
lengths = hadley_ice_season_length.values[:-1]
years = pd.to_datetime(hadley_ice_season_length.time.values).year.values[:-1]
graph = ax.vlines(years, ymin=months, ymax=months+lengths-1, color=getColor('hadley'), alpha=0.8, label='Hadley-OI')
# plu.designProperties(ax, [graph], style)
except: None
if noaa is not None and noaa_var is not None:
try:
noaa_ice_season_length = clm.seaIceSeasonLength(noaa, noaa_var, dtype='noaa', minimum=sic_minimum, coordinate=coordinate)
noaa_ice_onset = clm.seaIceSeasonOnset(noaa, noaa_var, dtype='noaa', minimum=sic_minimum, coordinate=coordinate)
months = [d.month for d in pd.to_datetime(noaa_ice_onset.values)][:-1]
months = [m+12 if m<9 else m for m in months]
lengths = noaa_ice_season_length.values[:-1]
years = pd.to_datetime(noaa_ice_season_length.time.values).year.values[:-1]
graph = ax.vlines(years+0.25, ymin=months, ymax=months+lengths-1, color=getColor('noaa'), alpha=0.8, label='NOAA')
# plu.designProperties(ax, [graph], style)
except: None
ax.set_yticks([9,11,13,15,17,19,21])
ax.set_yticklabels(['Sep', 'Nov', 'Jan', 'Mar', 'May', 'Jul', 'Sep'])
if spgrid is True:
ax.set_title('{} N, {} W'.format(coordinate[0], -coordinate[1]))
elif spgrid is False:
ax.legend()
ax.set_xlabel('Years')
ax.set_ylabel('Months')
ax.set_title('Sea Ice Season Onset and Length ({} N, {} W)'.format(coordinate[0], -coordinate[1]))
# plu.designProperties(ax, [graph], style)
def seaIceSeasonLength(ax, coordinate, sic_minimum, ecco=None, hadley=None, noaa=None, noaa_var=None, dec_mean=False, spgrid=False, style='pub-jog'):
if ecco is not None:
ecco_ice_season_length = clm.seaIceSeasonLength(ecco, 'SIarea', dtype='ecco4', minimum=sic_minimum, coordinate=coordinate)
ecco_time = ecco_ice_season_length.time.values[:-1]
ecco_vals = ecco_ice_season_length.values[:-1]
eg, = ax.plot(ecco_time, ecco_vals, '.-', color=getColor('ecco'), alpha=0.7, label='ECCO')
# plu.designProperties(ax, [graph], style)
# ax.bar(ecco_time-width, ecco_vals, width, color=getColor('ecco'), label='ECCO')
ecco_df = pd.DataFrame(data=ecco_vals, index=ecco_time)
else:
ecco_df = None
eg, = ax.plot(0,0, visible=False)
if hadley is not None:
hadley_ice_season_length = clm.seaIceSeasonLength(hadley, 'SEAICE', dtype='hadley', minimum=sic_minimum, coordinate=coordinate)
time = hadley_ice_season_length.time.values[:-1]
lengths = hadley_ice_season_length.values[:-1]
hag, = ax.plot(time, lengths, marker=getMarker('hadley'), color=getColor('hadley'), alpha=0.7, label='Hadley-OI annual mean')
# plu.designProperties(ax, [graph], style)
# ax.bar(hadley_time, hadley_vals, width, color=getColor('hadley'), label='Hadley-OI')
if dec_mean is True:
df = pd.DataFrame(data={'lengths': lengths}, index=time)
df['decade'] = df.index.year - (df.index.year % 10)
df_decade = df.groupby('decade').mean()
decade_starts = pd.to_datetime(df_decade.index.values, format='%Y')
decade_ends = pd.to_datetime(df_decade.index.values + 10, format='%Y')
# decadal_hadley_length = hadley_df.resample('10AS', loffset='-2A').mean()
# decade_starts = pd.to_datetime(decadal_hadley_length.index.year, format='%Y')
# decade_ends = pd.to_datetime(decadal_hadley_length.index.year+10, format='%Y')
hdg = ax.hlines(df_decade.lengths.values, xmin=decade_starts, xmax=decade_ends, color=getColor('hadley'), linewidth=5, alpha=0.5, zorder=2, label='Hadley-OI decadal mean')
# plu.designProperties(ax, [hdg], style)
else:
hag, = ax.plot(0,0, visible=False)
hdg, = ax.plot(0,0, visible=False)
if noaa is not None and noaa_var is not None:
noaa_ice_season_length = clm.seaIceSeasonLength(noaa, noaa_var, dtype='noaa', minimum=sic_minimum, coordinate=coordinate)
time = noaa_ice_season_length.time.values[:-1]
lengths = noaa_ice_season_length.values[:-1]
nag, = ax.plot(time, lengths, marker=getMarker('noaa'), color=getColor('noaa'), alpha=0.7, label='NOAA annual mean')
# plu.designProperties(ax, [graph], style)
# ax.bar(noaa_time+width, noaa_vals, width, color=getColor('noaa'), label='NOAA')
if dec_mean is True:
df = pd.DataFrame(data={'lengths': lengths}, index=time)
df['decade'] = df.index.year - (df.index.year % 10)
df_decade = df.groupby('decade').mean()
decade_starts = pd.to_datetime(df_decade.index.values, format='%Y')
decade_ends = pd.to_datetime(df_decade.index.values + 10, format='%Y')
# decadal_noaa_length = noaa_df.resample('10AS', loffset='+1A').mean()
# decade_starts = pd.to_datetime(decadal_noaa_length.index.year, format='%Y')
# decade_ends = pd.to_datetime(decadal_noaa_length.index.year+10, format='%Y')
ndg = ax.hlines(df_decade.lengths.values, xmin=decade_starts, xmax=decade_ends, color=getColor('noaa'), linewidth=5, alpha=0.5, zorder=2, label='NOAA decadal mean')
# plu.designProperties(ax, [ndg], style)
else:
nag, = ax.plot(0,0, visible=False)
ndg, = ax.plot(0,0, visible=False)
if spgrid is True:
ax.set_title('{} N, {} W'.format(coordinate[0], -coordinate[1]))
elif spgrid is False:
ax.legend()
ax.set_xlabel('Time')
ax.set_ylabel('Months')
ax.set_title('Duration of sea ice season ({} N, {} W)'.format(coordinate[0], -coordinate[1]))
# plu.designProperties(ax, [eg, hag, nag], style)
return eg, hag, hdg, nag, ndg
def seaIceSeasonOnset(ax, coordinate, sic_minimum, ecco=None, hadley=None, noaa=None, noaa_var=None, spgrid=False, style='pub-jog'):
shift = 0.15
if ecco is not None:
ecco_ice_onset = clm.seaIceSeasonOnset(ecco, 'SIarea', dtype='ecco4', minimum=sic_minimum, coordinate=coordinate)
ecco_years = pd.to_datetime(ecco_ice_onset.values).year
ecco_months = pd.to_datetime(ecco_ice_onset.values).month
ecco_years = pd.to_datetime(
[ecco_years[yi]-1 if ecco_months[yi]<9 else
ecco_years[yi] for yi in range(len(ecco_years))],
format='%Y')
ecco_months = [m+12-shift if m<9 else m-shift for m in ecco_months]
eg = ax.scatter(ecco_years, ecco_months, marker='o', color=getColor('ecco'), alpha=0.7, edgecolor=None, label='ECCO')
# plu.designProperties(ax, [graph], style)
else: eg, = ax.plot(0,0, visible=False)
if hadley is not None:
hadley_ice_onset = clm.seaIceSeasonOnset(hadley, 'SEAICE', dtype='hadley', minimum=sic_minimum, coordinate=coordinate)
hadley_years = pd.to_datetime(hadley_ice_onset.values).year
hadley_months = pd.to_datetime(hadley_ice_onset.values).month
hadley_years = pd.to_datetime(
[hadley_years[yi]-1 if hadley_months[yi]<9 else
hadley_years[yi] for yi in range(len(hadley_years))],
format='%Y')
hadley_months = [m+12 if m<9 else m for m in hadley_months]
hg = ax.scatter(hadley_years, hadley_months, marker='o', color=getColor('hadley'), alpha=0.7, edgecolor=None, label='Hadley-OI')
# plu.designProperties(ax, [graph], style)
else: hg, = ax.plot(0,0, visible=False)
if noaa is not None and noaa_var is not None:
noaa_ice_onset = clm.seaIceSeasonOnset(noaa, noaa_var, dtype='noaa', minimum=sic_minimum, coordinate=coordinate)
noaa_years = pd.to_datetime(noaa_ice_onset.values).year
noaa_months = pd.to_datetime(noaa_ice_onset.values).month
noaa_years = pd.to_datetime(
[noaa_years[yi]-1 if noaa_months[yi]<9 else
noaa_years[yi] for yi in range(len(noaa_years))],
format='%Y')
noaa_months = [m+12+shift if m<9 else m+shift for m in noaa_months]
ng = ax.scatter(noaa_years, noaa_months, marker='o', color=getColor('noaa'), alpha=0.7, edgecolor=None, label='NOAA')
# plu.designProperties(ax, [graph], style)
else: ng, = ax.plot(0,0, visible=False)
ax.set_yticks([9,11,13,15,17,19,21])
ax.set_ylim(bottom=8, top=21)
ax.set_yticklabels(['Sep', 'Nov', 'Jan', 'Mar', 'May', 'Jul', 'Sep'])
if spgrid is True:
ax.set_title('{} N, {} W'.format(coordinate[0], -coordinate[1]))
elif spgrid is False:
ax.legend()
ax.set_xlabel('Year')
ax.set_ylabel('Month')
ax.set_title('Onset of sea ice formation ({} N, {} W)'.format(coordinate[0], -coordinate[1]))
# plu.designProperties(ax, [eg, hg, ng], style)
return eg, hg, ng
def marAverage(ax, data, dvar, spgrid=False, style='pub-jog'):
dvars = {'SMB': 'Surface Mass Balance',
'RU': 'Runoff',
'SF': 'Snowfall',
'RF': 'Rainfall',
'ME': 'Meltwater Production'}
time = data[dvar].TIME.values
vals = clm.mmweday2myr(data[dvar].values)
graph, = ax.plot(time, vals, color=getColor('default'))
if spgrid is True:
ax.set_title('{}'.format('[region, TBD]'))
elif spgrid is False:
ax.set_xlabel('Year')
ax.set_ylabel('{} (m/yr)'.format(dvars[dvar]))
ax.set_title('{} at {}'.format(dvars[dvar], '[region, TBD]'))
# plu.designProperties(ax, [graph], style)
def marAnomaly(ax, data, dvar, spgrid=False, style='pub-jog'):
dvars = {'SMB': 'Surface Mass Balance',
'RU': 'Runoff',
'SF': 'Snowfall',
'RF': 'Rainfall',
'ME': 'Meltwater Production'}
time = data[dvar].TIME.values
anomaly = data[dvar].values - data[dvar].mean().values
anomaly = clm.mmweday2myr(anomaly)
graph, = ax.plot(time, anomaly, color=getColor('default'))
if spgrid is True:
ax.set_title('{}'.format('[region, TBD]'))
elif spgrid is False:
ax.set_xlabel('Year')
ax.set_ylabel('{} anomaly (m/yr)'.format(dvars[dvar]))
ax.set_title('{} Anomaly at {}'.format(dvars[dvar], '[region, TBD]'))
# plu.designProperties(ax, [graph], style)
def marAnomalyBulk(ax, data, coords, dvar, individual=False, ann_mean=False, error=False, dec_mean=False):
dvars = {'SMB': 'Surface mass balance',
'RU': 'Runoff',
'SF': 'Snowfall',
'RF': 'Rainfall',
'ME': 'Meltwater production'}
# bulk_anomaly = pd.DataFrame(
# columns=pd.to_datetime(data.TIME.values),
# index=range(len(coords)))
bulk_anomaly = clm.bulkAnomalyMAR(data, coords, dvar)
time = bulk_anomaly.keys()
# plot annual mean variable for each coordinate (nearest on grid)
if individual is True:
for c in range(len(coords)):
anomaly = bulk_anomaly.loc[c].values
time = bulk_anomaly.loc[c].index
ax.plot(time, anomaly, color='silver', alpha=0.3, zorder=1, label='individual glaciers')
# plot overall annual mean(+std) and decadal means
if ann_mean is True:
bulk_mean = clm.bulkMeanMAR(data, coords, dvar)
ax.plot(time, bulk_anomaly.mean(), '.-', color=getColor('default'), zorder=3, label='annual anomaly')
bulk_anomaly.mean().mean()
ax.annotate(text='mean={:.2f} m/yr'.format(bulk_mean), xy=(0.05, 0.9), xycoords='axes fraction')
if error is True:
ax.fill_between(time, bulk_anomaly.mean()-bulk_anomaly.std(), bulk_anomaly.mean()+bulk_anomaly.std(), color=getColor('default'), edgecolor=None, alpha=0.2, label='annual std')
if dec_mean is True:
decadal_mean_anomaly = clm.decadalAnomalyMAR(data, coords, dvar)
decade_starts = pd.to_datetime(decadal_mean_anomaly.index.year[:-1], format='%Y')
decade_ends = pd.to_datetime(decadal_mean_anomaly.index.year[:-1]+10, format='%Y')
ax.hlines(decadal_mean_anomaly[:-1], xmin=decade_starts, xmax=decade_ends, color=getColor('default'), linewidth=5, alpha=0.6, zorder=4, label='decadal mean anomaly')
ax.set_xlabel('Year')
ax.set_ylabel('Anomaly (m a$^{-1}$)')
ax.set_title('{}'.format(dvars[dvar]))
def marMeltDaysAnomaly(ax, data, coords, individual=False, ann_mean=False, error=False, dec_mean=False):
bulk_melt = pd.DataFrame(
columns=pd.to_datetime(data.resample(TIME='AS').count().TIME.values),
index=range(len(coords)))
bulk_melt_anomaly = pd.DataFrame(
columns=pd.to_datetime(data.resample(TIME='AS').count().TIME.values),
index=range(len(coords)))
for c in range(len(coords)):
coord = coords[c]
# -- get annual number of melt days
daily_melt_coord = clm.dataAtCoord(data, coord, dtype='mar').ME
annual_melt_days = daily_melt_coord.where(daily_melt_coord != 0).resample(TIME='AS').count(dim='TIME')
bulk_melt.loc[c] = annual_melt_days.values.flatten()
# -- calculate melt days anomaly
anomaly = bulk_melt.loc[c] - bulk_melt.loc[c].mean()
bulk_melt_anomaly.loc[c] = anomaly
time = bulk_melt_anomaly.loc[c].index
if individual is True:
for c in range(len(coords)):
annual_melt_anomaly = bulk_melt_anomaly.loc[c].values
ig = ax.plot(time, annual_melt_anomaly, color='silver', alpha=0.3, zorder=1, label='individual glaciers')
else: ig, = ax.plot(0,0, visible=False)
if ann_mean is True:
bulk_mean = bulk_melt.mean(axis=1).mean()
am, = ax.plot(time, bulk_melt_anomaly.mean(), '.-', color=getColor('default'), zorder=3, label='annual anomaly')
ax.annotate(text='mean={:.0f} days'.format(bulk_mean), xy=(0.05, 0.9), xycoords='axes fraction')
else: am, = ax.plot(0, 0, visible=False)
if error is True:
er = ax.fill_between(time, bulk_melt_anomaly.mean()-bulk_melt_anomaly.std(), bulk_melt_anomaly.mean()+bulk_melt_anomaly.std(), color=getColor('default'), edgecolor=None, alpha=0.2, label='annual std')
else: er, = ax.plot(0,0, visible=False)
if dec_mean is True:
decadal_mean_melt = bulk_melt_anomaly.mean()[1:].resample('10AS').mean()
decade_starts = pd.to_datetime(decadal_mean_melt.index.year[:-1], format='%Y')
decade_ends = pd.to_datetime(decadal_mean_melt.index.year[:-1]+10, format='%Y')
dm = ax.hlines(decadal_mean_melt[:-1], xmin=decade_starts, xmax=decade_ends, color=getColor('default'), linewidth=5, alpha=0.6, zorder=2, label='decadal mean anomaly')
else: dm, = ax.plot(0,0, visible=False)
ax.set_xlabel('Year')
ax.set_ylabel('Anomaly (days)')
ax.set_title('Melt days')
return ig, er, am, dm, decadal_mean_melt
def marRunoffDaysAnomaly(ax, data, coords, individual=False, ann_mean=False, error=False, dec_mean=False):
bulk_runoff = pd.DataFrame(
columns=pd.to_datetime(data.resample(TIME='AS').count().TIME.values),
index=range(len(coords)))
bulk_runoff_anomaly = pd.DataFrame(
columns=pd.to_datetime(data.resample(TIME='AS').count().TIME.values),
index=range(len(coords)))
for c in range(len(coords)):
coord = coords[c]
# -- get annual number of runoff days
daily_runoff_coord = clm.dataAtCoord(data, coord, dtype='mar').RU
annual_runoff_days = daily_runoff_coord.where(daily_runoff_coord != 0).resample(TIME='AS').count(dim='TIME')
bulk_runoff.loc[c] = annual_runoff_days.values.flatten()
# -- calculate runoff days anomaly
anomaly = bulk_runoff.loc[c] - bulk_runoff.loc[c].mean()
bulk_runoff_anomaly.loc[c] = anomaly
time = bulk_runoff_anomaly.loc[c].index
# daily_runoff_mask = daily_runoff_coord.where(daily_runoff_coord==0, other=1)
# annual_runoff_days = daily_runoff_mask.resample(TIME='AS').sum()
# total_mean = annual_runoff_days.mean().values
# annual_runoff_anomaly = annual_runoff_days.values - total_mean
# time = annual_runoff_days.TIME.values
if individual is True:
for c in range(len(coords)):
annual_runoff_anomaly = bulk_runoff_anomaly.loc[c].values
ig, = ax.plot(time, annual_runoff_anomaly, color='silver', alpha=0.3, zorder=1, label='individual glaciers')
else: ig, = ax.plot(0,0, visible=False)
# bulk_runoff.loc[c] = annual_runoff_anomaly.flatten()
if ann_mean is True:
bulk_mean = bulk_runoff.mean(axis=1).mean()
am, = ax.plot(time, bulk_runoff_anomaly.mean(), '.-', color=getColor('default'), zorder=3, label='annual anomaly')
ax.annotate(text='mean={:.0f} days'.format(bulk_mean), xy=(0.05, 0.9), xycoords='axes fraction')
else: am, = ax.plot(0, 0, visible=False)
if error is True:
er = ax.fill_between(time, bulk_runoff_anomaly.mean()-bulk_runoff_anomaly.std(), bulk_runoff_anomaly.mean()+bulk_runoff_anomaly.std(), color=getColor('default'), edgecolor=None, alpha=0.2, label='annual std')
else: er, = ax.plot(0,0, visible=False)
if dec_mean is True:
decadal_mean_runoff = bulk_runoff_anomaly.mean()[1:].resample('10AS').mean()
decade_starts = pd.to_datetime(decadal_mean_runoff.index.year[:-1], format='%Y')
decade_ends = pd.to_datetime(decadal_mean_runoff.index.year[:-1]+10, format='%Y')
dm = ax.hlines(decadal_mean_runoff[:-1], xmin=decade_starts, xmax=decade_ends, color=getColor('default'), linewidth=5, alpha=0.6, zorder=2, label='decadal mean anomaly')
else: dm, = ax.plot(0,0, visible=False)
ax.set_xlabel('Year')
ax.set_ylabel('Anomaly (days)')
ax.set_title('Runoff days')
return ig, er, am, dm, decadal_mean_runoff