plot.py 13.82 KiB
from sklearn.metrics import roc_auc_score, roc_curve
import tensorflow as tf
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import cartopy.crs as ccrs
import xarray as xr
import os
import h5py
from util.util import get_grid_values_all, get_cartopy_crs, homedir
import csv
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.markers import MarkerStyle
from util.setup import home_dir
# import mpl_scatter_density
logdir = home_dir+'/tf_logs'
root_imgdir = home_dir
img_name = 'train_curve.png'
run_valid_stepping = 1
try:
matplotlib.use('tkagg')
except Exception as e:
print(e)
cld_phase_clr_list = [[128,128,128], [11,36,250], [45,254,254], [127,15,126], [254,192,203], [0,0,0]]
cld_phase_clr_list_2 = [[128,128,128],[254,192,203],[11,36,250],[127,15,126]]
for i in range(4):
cld_phase_clr_list_2[i][0] /= 255
cld_phase_clr_list_2[i][1] /= 255
cld_phase_clr_list_2[i][2] /= 255
from matplotlib.colors import ListedColormap
cld_phase_cmap = ListedColormap(cld_phase_clr_list_2, 'CldPhase')
cld_mask_list = [[0,220,0], [220, 220, 220]]
for i in range(2):
cld_mask_list[i][0] /= 255
cld_mask_list[i][1] /= 255
cld_mask_list[i][2] /= 255
cld_mask_cmap = ListedColormap(cld_mask_list, 'CldMask')
# # Read colors from CSV file
# colors_file = homedir + 'ICE.ct'
# colors = []
# with open(colors_file, 'r') as csvfile:
# csv_reader = csv.DictReader(csvfile)
# for row in csv_reader:
# colors.append((float(row['R']) / 255, float(row['G']) / 255, float(row['B']) / 255))
# # Define a custom colormap
# cmap_name = 'icing_colormap'
# icing_cmap = LinearSegmentedColormap.from_list(cmap_name, colors, N=256)
def plot_train_curve(rundir=None, title=None):
last_epoch = 0
num_train_steps = 0
step = 0
train_steps = []
train_losses = []
if rundir is not None:
img_name = os.path.split(rundir)[1]+'.png'
rundir = os.path.join(logdir, rundir)
else:
rundir = logdir
events_file = os.listdir(os.path.join(rundir, 'plot_train'))[0]
path_to_events_file = os.path.join(rundir, 'plot_train', events_file)
for e in tf.compat.v1.train.summary_iterator(path_to_events_file):
for v in e.summary.value:
if v.tag == 'loss_trn':
train_losses.append(v.simple_value)
step += run_valid_stepping
if v.tag == 'num_epochs':
last_epoch = v.simple_value
if v.tag == 'num_train_steps':
train_steps.append(v.simple_value)
num_train_steps = v.simple_value
step = 0
lowest_loss = 1000
lowest_loss_step = 0
valid_steps = []
valid_losses = []
valid_acc = []
events_file = os.listdir(os.path.join(rundir, 'plot_valid'))[0]
path_to_events_file = os.path.join(rundir, 'plot_valid', events_file)
for e in tf.compat.v1.train.summary_iterator(path_to_events_file):
for v in e.summary.value:
if v.tag == 'loss_val':
if v.simple_value < lowest_loss:
lowest_loss = v.simple_value
lowest_loss_step = train_steps[step]
valid_steps.append(step)
valid_losses.append(v.simple_value)
step += run_valid_stepping
if v.tag == 'acc_val':
valid_acc.append(v.simple_value)
ratio = last_epoch/num_train_steps
for i in range(len(train_steps)):
train_steps[i] *= ratio
lowest_loss_step *= ratio
plt.figure(1)
fig, ax = plt.subplots(1)
if title is not None:
ax.set_title(title)
ax.set_ylabel('Loss')
ax.set_xlabel('Epoch')
ax2 = ax.twinx()
ax2.set_ylabel('Mean Absolute Error')
plt.grid(True, linestyle=':')
# plt.subplots_adjust(bottom=0.12, top=0.94, right=0.97, left=0.13)
plt.subplots_adjust(bottom=0.12, top=0.94)
plt.ylim([0,1])
train_hdl, = plt.plot(train_steps, train_losses, '-', linewidth=1.5, color='b')
valid_hdl, = plt.plot(train_steps, valid_losses, '-', linewidth=1.5, color=[1.0, 0.5, 0.0])
acc, = plt.plot(train_steps, valid_acc, '-', linewidth=1.5, color=[0.5, 0.0, 0.5])
optim_hdl, = plt.plot(lowest_loss_step, lowest_loss, 'o', color='g')
plt.legend([train_hdl, valid_hdl, acc, optim_hdl], ['Training', 'Validation', 'MAE', 'Best loss'],
loc='center right', fancybox=True, framealpha=0.5)
# plt.xlabel('Epoch')
# plt.ylabel('Loss')
plt.grid(True)
plt.ion()
fig = plt.gcf()
fig.set_size_inches(8,5.3)
plt.show()
plt.savefig(os.path.join(root_imgdir, img_name), dpi=100)
plt.close()
def plot_roc_curve(correct_labels, prob_pos_class):
auc = roc_auc_score(correct_labels, prob_pos_class)
print('AUC: ', auc)
fpr, tpr, _ = roc_curve(correct_labels, prob_pos_class)
plt.figure(figsize=(6,4))
plt.plot(fpr, tpr)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.0])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic')
plt.show()
plt.savefig(homedir+'test_roc', dpi=320)
def plot_image(image, cmap='Greys', vmin=None, vmax=None, fname=None, title=None, do_color_bar=True):
Fig, ax = plt.subplots()
# Plot the image
plt.imshow(image, interpolation="nearest", cmap=cmap, vmin=vmin, vmax=vmax)
if title is not None:
plt.title(os.path.basename(title), fontsize=10, fontweight='bold')
# Add the colorbar
if do_color_bar:
cbar = plt.colorbar()
cbar.ax.set_position([0.90, 0.05, 0.03, 0.90]) # [left, bottom, width, height]
#cbar.ax.set_ylabel(label, rotation=90, fontsize=10)
#cbar.ax.tick_params(labelsize=10)
# *Now* that the colorbar is out, we can set the plot's axis position
ax.set_position([0.05, 0.05, 0.83, 0.90]) # [left, bottom, width, height]
# Finish the figure settings and save to a file
Fig.set_size_inches([10.0, 6.6], forward=True)
Fig.set_dpi(100) # This seems to not get applied. Always 100?
# Save to file
if fname is not None:
ImageDirAndName = os.path.join(homedir, fname)
Fig.savefig(ImageDirAndName)
else:
Fig.show()
# Wrap up
#Fig.clf() # Otherwise fig stays open every time
#plt.close() # Close current figure
# plt.close('all')
def multi_plot(image_s, title_s, cmap, vmin=None, vmax=None, do_color_bar=True):
fig = plt.figure(figsize=(12, 8))
fig.add_subplot(1, 2, 1)
plt.imshow(image_s[0], cmap=cmap, vmin=vmin, vmax=vmax)
plt.title(title_s[0], fontweight='bold')
if do_color_bar:
plt.colorbar()
fig.add_subplot(1, 2, 2)
plt.imshow(image_s[1], cmap=cmap, vmin=vmin, vmax=vmax)
plt.title(title_s[1], fontweight='bold')
if do_color_bar:
plt.colorbar()
def plot_image2(image, cmap='Greys', title='BOEING Icing Reports 2018-2020'):
fig = plt.figure(figsize=(12, 8))
ax = plt.axes(projection=ccrs.PlateCarree())
img_extent = [-180, 180, -90, 90]
ax.imshow(image, origin='lower', extent=img_extent, transform=ccrs.PlateCarree(), cmap=cmap, vmin=0.1, vmax=1)
ax.coastlines(resolution='50m', color='blue', linewidth=0.5)
plt.title(title, loc='left', fontweight='bold', fontsize=16)
# Save to file
ImageDirAndName = os.path.join('/Users/tomrink', 'testimage')
fig.savefig(ImageDirAndName)
# Wrap up
fig.clf() # Otherwise fig stays open every time
plt.close() # Close current figure
def plot_image3(image, bt, cmap='Greys'):
geos, xlen, xmin, xmax, ylen, ymin, ymax = get_cartopy_crs('GOES16', 'CONUS')
fig = plt.figure(figsize=(15, 12))
ax = fig.add_subplot(1, 1, 1, projection=geos)
ax.set_extent([-105, -70, 15, 50], crs=ccrs.PlateCarree())
#ax.imshow(bt, cmap=cmap, origin='upper', extent=(xmin, xmax, ymin, ymax), transform=geos)
#ax.imshow(image, cmap='YlGnBu', origin='upper', extent=(xmin, xmax, ymin, ymax), vmin=0.6, vmax=1.0, transform=geos)
ax.imshow(image, cmap='Purples', origin='upper', extent=(xmin, xmax, ymin, ymax), vmin=0.52, vmax=1.0, transform=geos)
ax.coastlines(resolution='50m', color='blue', linewidth=0.25)
ax.add_feature(ccrs.cartopy.feature.STATES, linewidth=0.25)
plt.title('GOES-16', loc='left', fontweight='bold', fontsize=15)
fig.savefig('/Users/tomrink/image')
def make_icing_image(h5f, probs, ice_lons, ice_lats, clvrx_str_time, satellite, domain, ice_lons_vld=None, ice_lats_vld=None,
ice_intensity=None, imagename='icing', extent=[-110, -60, 10, 55], prob_thresh=0.50):
# keep for reference
# mrkr_styl = MarkerStyle('o', fillstyle=None)
geos, xlen, xmin, xmax, ylen, ymin, ymax = get_cartopy_crs(satellite, domain)
bg_image = None
if h5f is not None:
bg_image = get_grid_values_all(h5f, 'temp_10_4um_nom')
fig = plt.figure(figsize=(48, 30))
ax = fig.add_subplot(1, 1, 1, projection=geos)
if extent is not None:
if satellite == 'H08':
ax.set_extent(extent, crs=geos)
elif satellite == 'H09':
ax.set_extent(extent, crs=geos)
else:
ax.set_extent(extent, crs=ccrs.PlateCarree())
if bg_image is not None:
bg_im = ax.imshow(bg_image, origin='upper', extent=(xmin, xmax, ymin, ymax), cmap='Greys', transform=geos)
if probs is not None:
# p_im = ax.imshow(probs, cmap='Purples', origin='upper', extent=(xmin, xmax, ymin, ymax),
p_im = ax.imshow(probs, cmap='jet', origin='upper', extent=(xmin, xmax, ymin, ymax),
# p_im = ax.imshow(probs, cmap=icing_cmap, origin='upper', extent=(xmin, xmax, ymin, ymax),
vmin=prob_thresh, vmax=1.0, alpha=0.8, transform=geos)
ax.coastlines(resolution='50m', color='green', linewidth=1.50)
ax.add_feature(ccrs.cartopy.feature.STATES, linewidth=1.50, edgecolor='green')
ax.add_feature(ccrs.cartopy.feature.BORDERS, linewidth=1.50, edgecolor='green')
if ice_lons is not None:
ax.scatter(ice_lons, ice_lats, s=40.0, marker='o', color='blue', transform=ccrs.PlateCarree())
if ice_lats_vld is not None:
if ice_intensity is None:
# ax.scatter(ice_lons_vld, ice_lats_vld, s=180.0, marker='o', color='red', transform=ccrs.PlateCarree())
ax.scatter(ice_lons_vld, ice_lats_vld, s=500.0, marker='o', color='white', transform=ccrs.PlateCarree())
ax.scatter(ice_lons_vld, ice_lats_vld, s=200.0, marker='o', color='black', transform=ccrs.PlateCarree())
else:
lons = ice_lons_vld[ice_intensity == 1]
lats = ice_lats_vld[ice_intensity == 1]
ax.scatter(lons, lats, s=500.0, marker='o', color='white', transform=ccrs.PlateCarree())
ax.scatter(lons, lats, s=200.0, marker='o', color='black', transform=ccrs.PlateCarree())
ax.scatter(lons, lats, s=5000.0, marker='o', facecolors='none', edgecolors='white', linewidths=2.0,
transform=ccrs.PlateCarree())
lons = ice_lons_vld[ice_intensity > 1]
lats = ice_lats_vld[ice_intensity > 1]
ax.scatter(lons, lats, s=500.0, marker='^', color='white', transform=ccrs.PlateCarree())
ax.scatter(lons, lats, s=200.0, marker='^', color='black', transform=ccrs.PlateCarree())
ax.scatter(lons, lats, s=5000.0, marker='o', facecolors='none', edgecolors='white', linewidths=2.0,
transform=ccrs.PlateCarree())
lons = ice_lons_vld[ice_intensity == -1]
lats = ice_lats_vld[ice_intensity == -1]
# ax.scatter(lons, lats, s=500.0, marker='o', color='white', transform=ccrs.PlateCarree())
# ax.scatter(lons, lats, s=200.0, marker='o', color='green', transform=ccrs.PlateCarree())
title = satellite+', '+clvrx_str_time+' >50% Probability Icing'
plt.title(title, loc='left', fontweight='bold', fontsize=24)
cbar = plt.colorbar(p_im)
cbar.set_label(label='Icing Probability', fontsize=30)
cbar.ax.tick_params(labelsize=30)
ImageDirAndName = os.path.join(homedir, imagename+'_'+clvrx_str_time)
fig.savefig(ImageDirAndName)
def plot_grid2d(grid_2d, x_map_2d, y_map_2d, proj, vmin=None, vmax=None, cmap='jet'):
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(1, 1, 1, projection=proj)
# Save mappable object returned by pcolormesh
p = ax.pcolormesh(x_map_2d, y_map_2d, grid_2d, vmin=vmin, vmax=vmax, cmap=cmap, shading='auto')
ax.coastlines(resolution='50m', color='green', linewidth=1.50)
# Add gridlines for longitudes and latitudes
gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
linewidth=2, color='gray', alpha=0.5, linestyle='--')
gl.xlabels_top = gl.ylabels_right = False
gl.ylabel_style = {'rotation': 90}
gl.ylabel_padding = 15
# Add color bar
cbar = plt.colorbar(p, orientation="horizontal", pad=0.05)
cbar.set_label('Value', size=15) # Set color bar label
plt.show()
# values, edges in Epoch time
def make_time_domain_hist(values, edges):
# convert the epoch format to matplotlib date format
# mpl_data = mdates.epoch2num(values)
fig, ax = plt.subplots(1, 1)
ax.hist(values, bins=edges, ec='black')
# ax.xaxis.set_major_locator(mdates.MonthLocator())
# ax.xaxis.set_major_formatter(mdates.DateFormatter('%y-%m-%d_%h'))
# fig.autofmt_xdate()
plt.show()
def scatter_density(x, y, color=None, fname=None, fig=None, ax=None, x_rng=None, y_rng=None, dpi=72):
if fig is None:
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection='scatter_density')
if color is not None:
ax.scatter_density(x, y, color=color, dpi=dpi)
else:
ax.scatter_density(x, y, dpi=dpi)
if x_rng is not None and y_rng is not None:
ax.set_xlim(x_rng[0], x_rng[1])
ax.set_ylim(y_rng[0], y_rng[1])
if fname is not None:
fig.savefig(fname)
return fig, ax