def make_cubehelix(*args, **kwargs):
"""make_cubehelix(start=0.5, rotation=-1.5, gamma=1.0,
start_hue=None, end_hue=None,
sat=None, min_sat=1.2, max_sat=1.2,
min_light=0., max_light=1.,
n=256., reverse=False, name='custom_cubehelix')
"""
from palettable.cubehelix import Cubehelix
cmap = Cubehelix.make(*args, **kwargs).mpl_colormap
register = kwargs.setdefault("register", False)
if register:
plt.register_cmap(cmap=cmap)
plt.rc('image', cmap=cmap.name)
return cmap
def make_cubehelix(*args, **kwargs):
"""make_cubehelix(start=0.5, rotation=-1.5, gamma=1.0,
start_hue=None, end_hue=None,
sat=None, min_sat=1.2, max_sat=1.2,
min_light=0., max_light=1.,
n=256., reverse=False, name='custom_cubehelix')
"""
from palettable.cubehelix import Cubehelix
cmap = Cubehelix.make(*args, **kwargs).mpl_colormap
register = kwargs.setdefault("register", False)
if register:
plt.register_cmap(cmap=cmap)
plt.rc('image', cmap=cmap.name)
return cmap
'GOOGLE_ROOT'] # this is system-dependent and could be removed if necessary
# define the font family
mpl.rc('font', family='Arial')
# define the configurations
num_configs = int(
sys.argv[1]) # input from the "run_kepert_analysis.sh" script
configs = [sys.argv[i] for i in range(2, 2 + num_configs)]
# define a list of colors
custom_colormap = Cubehelix.make(start_hue=240,
end_hue=-300,
min_sat=1,
max_sat=2.5,
gamma=1,
min_light=0.3,
max_light=0.8,
n=num_configs,
reverse=False,
name='custom_colormap')
colors = custom_colormap.hex_colors
# define the variables to be plotted
vars_to_plot_all_configs = []
vars_to_plot_short_names_all_configs = []
vars_to_plot_long_names_all_configs = []
vars_to_plot_units_all_configs = []
vars_to_plot_ticks_all_configs = []
plot_NOAA = True # plot reference values from NOAA dropsondes?
plot_betacast = False # plot betacast reference value?
def plot_scatterplot(candidate,
ref,
candidatename,
refname,
hexbin_kwargs=None,
xmin=0.,
xmax=0.7,
ymin=0.,
ymax=0.7,
figsize=4,
ax=None,
f=None,
weights=None,
add_criterion_text=False,
criterion_text_threshold=None,
filename=None,
logmessage=True):
""" Create hexbin between candidate and reference """
try:
from palettable.cubehelix import Cubehelix
palette = Cubehelix.make(start=0.3, rotation=-0.5, n=16, reverse=True)
import palettable
cmap = palette.get_mpl_colormap()
#cmap = palettable.matplotlib.Inferno_20.get_mpl_colormap()
cmap = plt.cm.jet
cmap = plt.cm.cubehelix_r
except ImportError:
cmap = 'cubehelix_r'
if hexbin_kwargs is None:
hexbin_kwargs = {}
if ax is None:
f, ax = plt.subplots(1, 1, figsize=(figsize, figsize))
myax = ax
outvar = f
else:
outvar = ax
myax = ax
x = ref.values.flatten()
y = candidate.values.flatten()
if not weights is None:
broadcasted_weights = xr.where(ref, weights, weights)
#addweights = {'C':np.ones(len(x)),'reduce_C_function':np.sum}
addweights = {
'C': broadcasted_weights.values.flatten(),
'reduce_C_function': np.sum
}
else:
addweights = {}
if not 'gridsize' in hexbin_kwargs: hexbin_kwargs['gridsize'] = 200
if not 'bins' in hexbin_kwargs: hexbin_kwargs['bins'] = 'log'
if not 'cmap' in hexbin_kwargs: hexbin_kwargs['cmap'] = cmap
im = myax.hexbin(x, y, **addweights, **hexbin_kwargs)
myax.set_xlim(xmin, xmax)
myax.set_ylim(xmin, ymax)
myax.add_line(mlines.Line2D([0., 1.], [0., 1.], color='k'))
myax.set_ylabel(candidatename)
myax.set_xlabel(refname)
f.subplots_adjust(right=0.8)
cbar_ax = f.add_axes([0.85, 0.15, 0.05, 0.7])
f.colorbar(im, cax=cbar_ax)
if add_criterion_text:
c = my.stats.compute_crit(candidate,
ref,
candidatename,
refname,
multiplied=1.,
weights=weights)
text = f'{c.candidatename} vs {c.refname} :\nBias={c.bias} RMSE={c.rmsd}\n' # & {c.r2} & {c.equation[0]} * x + {c.equation[1]}'
text = f'{c.candidatename} vs {c.refname} :\nBias={c.bias} RMSE={c.rmsd}\n' # & {c.r2} & {c.equation[0]} * x + {c.equation[1]}'
if criterion_text_threshold:
def t(x):
if x is None: return None
return x.where(ref < criterion_text_threshold)
c1 = my.stats.compute_crit(t(candidate),
t(ref),
candidatename,
refname,
multiplied=1.,
weights=t(weights))
text += f'Bias={c1.bias} RMSE={c1.rmsd} (<{criterion_text_threshold})\n' # & {c.r2} & {c.equation[0]} * x + {c.equation[1]}'
def t(x):
if x is None: return None
return x.where(ref >= criterion_text_threshold)
c1 = my.stats.compute_crit(t(candidate),
t(ref),
candidatename,
refname,
multiplied=1.,
weights=t(weights))
text += f'Bias={c1.bias} RMSE={c1.rmsd} (>{criterion_text_threshold})\n' # & {c.r2} & {c.equation[0]} * x + {c.equation[1]}'
try:
ax.text(0.02, 0.75, text, transform=ax.transAxes)
except:
f.text(0.02, 0.75, text, transform=ax.transFigure
) # may work, or may not, if f is figure or ax, maybe
if filename:
my.io.ensure_dir(filename)
f.savefig(filename)
if logmessage:
logging.info(f'Saved plot in {filename}')
return outvar
def cluster(X, y, dim, name):
print('clustering')
svd = TruncatedSVD(n_components=50, n_iter=3, random_state=42)
X1 = svd.fit_transform(X)
print('finished svd')
if dim == 2:
fig, axes = plt.subplots(3, 5)
for i, perp in enumerate([10, 30, 50]):
for j, met in enumerate(
['euclidean', 'cityblock', 'hamming', 'jaccard', 'cosine']):
X_embedded = TSNE(n_components=2, perplexity=perp,
metric=met).fit_transform(X1)
print('finished tsne')
palette = Cubehelix.make(start_hue=-300.,
end_hue=240.,
min_sat=1.,
max_sat=2.5,
min_light=0.3,
max_light=0.8,
gamma=.9,
n=10).mpl_colors
colors = [
palette[np.nonzero(y[i])[0][0]] for i in range(y.shape[0])
]
#ax.scatter(X1[:,0], X1[:,1], c=colors, alpha=0.5)
axes[i, j].scatter(X_embedded[:, 0],
X_embedded[:, 1],
c=colors,
alpha=0.5)
axes[i, j].set_title(met)
axes[i, j].grid(True)
fig.tight_layout()
plt.show()
plt.savefig(name + '.png')
if dim == 3:
X_embedded = TSNE(n_components=3, perplexity=50).fit_transform(X1)
print('finished tsne')
palette = Cubehelix.make(start_hue=-300.,
end_hue=240.,
min_sat=1.,
max_sat=2.5,
min_light=0.3,
max_light=0.8,
gamma=.9,
n=10).mpl_colors
colors = [palette[np.nonzero(y[i])[0][0]] for i in range(y.shape[0])]
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
#ax.scatter(X1[:,0], X1[:,1], c=colors, alpha=0.5)
ax.scatter(X_embedded[:, 0],
X_embedded[:, 1],
X_embedded[:, 2],
c=colors,
alpha=0.5)
ax.set_title('tsne clustering')
ax.grid(True)
fig.tight_layout()
plt.show()
plt.savefig('tsne_cluster2.png')
pickle.dump(
fig, open('FigureObject.fig.pickle', 'wb')
) # This is for Python 3 - py2 may need `file` instead of `open`
import numpy as np import matplotlib import matplotlib.pyplot as plt import matplotlib.ticker as mticker import cartopy as cart from scipy import signal from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter #from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER from scipy.stats.stats import pearsonr, linregress from sklearn import linear_model from sklearn.preprocessing import StandardScaler from ocean_atmosphere.misc_fns import an_ave, spatial_ave, calc_AMO, running_mean, calc_NA_globeanom, calc_NA_globeanom3D, detrend_separate, detrend_common from thermolib.thermo import w_sat, r_star from palettable.cubehelix import Cubehelix cx4 = Cubehelix.make(reverse=True, start=0., rotation=0.5) #fin = '/Users/cpatrizio/data/ECMWF/' fin = '/Users/cpatrizio/data/MERRA2/' fout = '/Volumes/GoogleDrive/My Drive/PhD/figures/AMO/' #fsst = cdms2.open(fin + 'era_interim_moda_SST_1979to2010.nc') #fTHF = cdms2.open(fin + 'era_interim_moda_THF_1979to2010.nc') fsst = cdms2.open(fin + 'MERRA2_SST_ocnthf_monthly1980to2017.nc') fcf3D = cdms2.open(fin + 'MERRA2_cldfrac3D_monthly1980to2017.nc') fv3D = cdms2.open(fin + 'MERRA2_v3D_monthly1980to2017.nc') fu3D = cdms2.open(fin + 'MERRA2_u3D_monthly1980to2017.nc') fomega = cdms2.open(fin + 'MERRA2_omega_monthly1980to2017.nc') #fqv3D = cdms2.open(fin + 'MERRA2_qv3D_monthly1980to2017.nc') ft3D = cdms2.open(fin + 'MERRA2_t3D_monthly1980to2017.nc')