def plot_bar_plot(data, title, ylabel, filename=None, formatter='{:.1f}'):
""" Helper function used to compute bar plots.
:param y_values:
:param title:
:param xlabel:
:param filename:
:return:
"""
y_vals = data.values
y_pos = np.arange(len(y_vals))
x_ticks_labels = data.index.values
matplotlib = import_matplotlib(
) # this need to be imported before matplotlib plt.
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 1, figsize=(PP_TEXT_WIDTH_IN, PP_TEXT_HEIGHT_IN))
rect = ax.bar(y_pos, y_vals, align='center', alpha=0.5)
ax.set_ylabel(ylabel)
autolabel(rect, ax, False, formatter) # add annotations to plot
plt.xticks(y_pos, x_ticks_labels, rotation=45)
plt.title(title)
plt.subplots_adjust(hspace=0.5, top=0.9, bottom=0.2, left=0.07, right=0.97)
#plt.legend()
if filename:
plt.savefig(os.path.join(base, save_dir, '{}.png'.format(filename)))
return
def plot_2grouped_bar_plot(list_y_values,
labels,
x_ticks_labels,
title,
ylabel,
filename=None):
""" Helper function used to compute bar plots.
TODO add list_labels,
:param list_y_values:
:param title:
:param xlabel:
:param filename:
:return:
"""
y_pos = np.arange(len(list_y_values[0]))
width = 0.35
matplotlib = import_matplotlib(
) # this need to be imported before matplotlib plt.
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 1, figsize=(PP_TEXT_WIDTH_IN, PP_TEXT_HEIGHT_IN))
#for i, y_values in enumerate(list_y_values):
rect = ax.bar(y_pos - width / 2,
list_y_values[0],
width,
align='center',
alpha=0.5,
label='{}'.format(labels[0]))
autolabel(rect, ax) # add annotations to plot
rect2 = ax.bar(y_pos + width / 2,
list_y_values[1],
width,
align='center',
alpha=0.5,
label='{}'.format(labels[1]))
autolabel(rect2, ax) # add annotations to plot
ax.set_ylabel(ylabel)
plt.xticks(y_pos, x_ticks_labels, rotation=45)
plt.title(title)
plt.legend(loc='lower left')
plt.subplots_adjust(hspace=0.5, top=0.9, bottom=0.2, left=0.07, right=0.97)
if filename:
plt.savefig(os.path.join(base, save_dir, '{}.png'.format(filename)))
return
import xarray as xr
import seaborn as sns
import glob
import os
from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,
AutoMinorLocator)
from sclouds.helpers import (path_input, path_stats_results, VARIABLES,
UNITS, LONGNAME, STATISTICS, LONGNAME_STATISTICS)
from sclouds.io.utils import get_xarray_dataset_for_period
from sclouds.plot.helpers import (TEXT_WIDTH_IN, TEXT_HEIGHT_IN,
path_python_figures, import_matplotlib,
cmap_contour_plot, levels_contourplot,
color_maps, add_ticks)
mat = import_matplotlib() #for mye
import matplotlib.pyplot as plt
import matplotlib as mpl
fig, axes = plt.subplots(nrows = 3, ncols = 1, sharex=True, sharey=False)
fig.set_size_inches(w = TEXT_WIDTH_IN, h = TEXT_HEIGHT_IN-2)
################## The AR model
folders = ['AR-B-5']
degree = 'L5'
files = glob.glob('/home/hanna/EX3_Results_AR/AR-B-5/*performance*AR*L5*')
name = '-'.join(files[0].split('_')[-3].split('-5-'))
data = xr.open_mfdataset(files, combine='by_coords')
data['latitude'] = data.latitude.values.astype(float)
import os
import glob
import numpy as np
import xarray as xr
import pandas as pd
from sclouds.plot.helpers import import_matplotlib
mat = import_matplotlib()
import matplotlib.pyplot as plt
def read_global_statistics_to_dataframe():
""" Read computed statistics to dataframe.
"""
read_dir = '/home/hanna/lagrings/ERA5_monthly/'
save_dir = '/home/hanna/lagrings/ERA5_stats/results/'
save_dir = '/home/hanna/lagrings/results/stats/'
STATS = ['mean', 'std', 'min', 'max', 'median', 'mad'] # 'median',
VALID_VARS = ['r', 'q', 't2m', 'sp', 'tcc']
VALID_FILTERS = ['coast', 'sea', 'land', 'artefact', 'all']
results = {}
for f in VALID_FILTERS:
results[f] = {}
for var in VALID_VARS:
files = glob.glob(
os.path.join(save_dir, '*global*{}*{}*.nc'.format(var, f)))
if len(files) != 1:
def save_test_ar(test_fil):
import os
import glob
import numpy as np
import xarray as xr
from sclouds.plot.helpers import TEXT_HEIGHT_IN, TEXT_WIDTH_IN, import_matplotlib
mat = import_matplotlib()
import matplotlib.pyplot as plt
from sclouds.ml.regression.AR_model_loader import AR_model_loader
m1 = AR_model_loader().load_model_to_xarray(test_fil)
save_dir = '/home/hanna/MS-thesis/python_figs/test/{}'.format(
test_fil.split('.nc')[0].split('/')[-1])
if not os.path.isdir(save_dir):
try:
os.makedirs(save_dir, exist_ok=True)
#print("Directory '%s' created successfully" %save_dir)
except OSError as error:
#print("Directory '%s' can not be created")
print(error)
per = ['mse', 'ase', 'r2']
fig, axes = plt.subplots(len(per),
1,
figsize=(TEXT_WIDTH_IN, TEXT_HEIGHT_IN))
for i, p in enumerate(per):
m1[p].plot(ax=axes[i])
fig.suptitle('Sig:{}, Trans:{}, bias:{}, order:{}'.format(
m1['sigmoid'].values, m1['transform'].values, m1['bias'].values,
m1['order'].values))
plt.savefig(os.path.join(save_dir, 'performance.png'))
per = ['num_train_samples', 'num_test_samples']
fig, axes = plt.subplots(len(per),
1,
figsize=(TEXT_WIDTH_IN, TEXT_HEIGHT_IN))
for i, p in enumerate(per):
m1[p].plot(ax=axes[i])
fig.suptitle('Sig:{}, Trans:{}, bias:{}, order:{}'.format(
m1['sigmoid'].values, m1['transform'].values, m1['bias'].values,
m1['order'].values))
plt.savefig(os.path.join(save_dir, 'num_samples.png'))
va = []
for i in range(1, +1):
va.append('W{}'.format(i))
try:
print(m1['Wt2m'])
var = ['Wt2m', 'Wsp', 'Wr', 'Wq']
except KeyError:
#print('failed ...')
var = []
order = m1['order'].values
per = va + var
if m1['bias'].values:
per += ['bias']
fig, axes = plt.subplots(len(per) + order,
1,
figsize=(TEXT_WIDTH_IN, TEXT_HEIGHT_IN))
for i, p in enumerate(per):
m1[p].plot(ax=axes[i])
fig.suptitle('Sig:{}, Trans:{}, bias:{}, order:{}'.format(
m1['sigmoid'].values, m1['transform'].values, m1['bias'].values,
m1['order'].values))
plt.savefig(os.path.join(save_dir, 'weights.png'))
return
