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toolbox.py
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toolbox.py
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import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.graph_objects as go
from tqdm.auto import tqdm
def plot_l96_traj(
x,
model,
linewidth,
):
fig = plt.figure(figsize=(linewidth, linewidth/3))
plt.grid(False)
im = plt.imshow(
x.T,
aspect = 'auto',
origin = 'lower',
interpolation = 'spline36',
cmap = sns.diverging_palette(240, 60, as_cmap=True),
extent = [0, (model.dt/model.lyap_time)*x.shape[0], 0, model.Nx],
vmin = -10,
vmax = 15,
)
plt.colorbar(im)
plt.xlabel('Time (Lyapunov time)')
plt.ylabel('Lorenz 96 variables')
plt.tick_params(direction='out', left=True, bottom=True)
plt.show()
def plot_l96_compare_traj(
x_ref,
x_pred,
model,
linewidth,
):
error = x_pred - x_ref
fig = plt.figure(figsize=(linewidth, linewidth))
ax = plt.subplot(311)
ax.grid(False)
im = plt.imshow(
x_ref.T,
aspect = 'auto',
origin = 'lower',
interpolation = 'spline36',
cmap = sns.diverging_palette(240, 60, as_cmap=True),
extent = [0, (model.dt/model.lyap_time)*x_pred.shape[0], 0, model.Nx],
vmin = -10,
vmax = 15,
)
ax.set_title('true model integration')
plt.colorbar(im)
ax.set_ylabel('Lorenz 96 variables')
ax.tick_params(direction='out', left=True, bottom=True)
ax.set_xticklabels([])
ax = plt.subplot(312)
ax.grid(False)
im = plt.imshow(
x_pred.T,
aspect = 'auto',
origin = 'lower',
interpolation = 'spline36',
cmap = sns.diverging_palette(240, 60, as_cmap=True),
extent = [0, (model.dt/model.lyap_time)*x_pred.shape[0], 0, model.Nx],
vmin = -10,
vmax = 15,
)
ax.set_title('surrogate model integration')
plt.colorbar(im)
ax.set_ylabel('Lorenz 96 variables')
ax.tick_params(direction='out', left=True, bottom=True)
ax.set_xticklabels([])
ax = plt.subplot(313)
ax.grid(False)
im = ax.imshow(
error.T,
aspect = 'auto',
origin = 'lower',
interpolation = 'spline36',
cmap = sns.diverging_palette(240, 10, as_cmap=True),
extent = [0, (model.dt/model.lyap_time)*error.shape[0], 0, model.Nx],
vmin = -15,
vmax = 15,
)
ax.set_title('signed error')
plt.colorbar(im)
ax.set_xlabel('Time (Lyapunov time)')
ax.set_ylabel('Lorenz 96 variables')
ax.tick_params(direction='out', left=True, bottom=True)
plt.show()
def get_plotly_color_palette(alpha=None):
if alpha is None:
return [
'rgb(99, 110, 250)',
'rgb(239, 85, 59)',
'rgb(0, 204, 150)',
'rgb(171, 99, 250)',
'rgb(255, 161, 90)',
'rgb(25, 211, 243)',
'rgb(255, 102, 146)',
'rgb(182, 232, 128)',
'rgb(255, 151, 255)',
'rgb(254, 203, 82)'
]
else:
return [
f'rgba(99, 110, 250, {alpha})',
f'rgba(239, 85, 59, {alpha})',
f'rgba(0, 204, 150, {alpha})',
f'rgba(171, 99, 250, {alpha})',
f'rgba(255, 161, 90, {alpha})',
f'rgba(25, 211, 243, {alpha})',
f'rgba(255, 102, 146, {alpha})',
f'rgba(182, 232, 128, {alpha})',
f'rgba(255, 151, 255, {alpha})',
f'rgba(254, 203, 82, {alpha})'
]
def plot_l96_forecast_skill(
fss,
model,
p1,
p2,
xmax,
linewidth,
):
fig = go.Figure()
palette = get_plotly_color_palette()
spalette = get_plotly_color_palette(alpha=0.2)
for (index, key) in enumerate(fss):
time = (model.dt/model.lyap_time)*np.arange(fss[key].shape[0])
rmse_m = fss[key].mean(axis=1) / model.model_var
rmse_p1 = np.percentile(fss[key], p1, axis=1) / model.model_var
rmse_p2 = np.percentile(fss[key], p2, axis=1) / model.model_var
fig.add_scatter(
x=time,
y=rmse_m,
name=key,
customdata=np.arange(len(time)),
hovertemplate='index = %{customdata}, value = %{y:.3f}',
line_color=palette[index]
)
fig.add_scatter(
x=np.concatenate([time, time[::-1]]),
y=np.concatenate([rmse_p1, rmse_p2[::-1]]),
fill='toself',
name=key+' (CI)',
hoverinfo='skip',
fillcolor=spalette[index],
line_width=0,
mode='lines'
)
fig.update_xaxes(title_text='Time (Lyapunov time)')
fig.update_yaxes(title_text='normalised RMSE')
fig.update_layout(
title='Forecast skill',
xaxis_range=[0, xmax],
yaxis_range=[0, 2],
width=linewidth,
height=0.7*linewidth,
hovermode='x unified',
)
fig.add_hline(
y=np.sqrt(2),
line_width=1,
line_dash='dash',
line_color='black',
label_text=r'$\sqrt{2}$',
label_textposition='start',
)
fig.show()
def plot_learning_curve(
loss,
val_loss,
title,
linewidth,
):
fig = go.Figure()
palette = get_plotly_color_palette()
fig.add_scatter(
x=np.arange(len(loss)),
y=loss,
name='training loss',
customdata=np.arange(len(loss)),
hovertemplate='epoch = %{customdata}, value = %{y:.3f}',
line_color=palette[0]
)
fig.add_scatter(
x=np.arange(len(val_loss)),
y=val_loss,
name='validation loss',
customdata=np.arange(len(val_loss)),
hovertemplate='epoch = %{customdata}, value = %{y:.3f}',
line_color=palette[1]
)
fig.update_xaxes(title_text='Number of epochs')
fig.update_yaxes(title_text='MSE', type='log')
fig.update_layout(title=title, width=linewidth, height=0.7*linewidth, hovermode='x unified')
fig.show()
class TQDMCallback(tf.keras.callbacks.Callback):
def __init__(self, desc, loss=None, val_loss=None):
super().__init__()
self.desc = desc
self.metrics = {'loss':loss, 'val_loss':val_loss}
def on_train_begin(self, logs=None):
self.epoch_bar = tqdm(total=self.params['epochs'], desc=self.desc)
def on_train_end(self, logs=None):
self.epoch_bar.close()
def on_epoch_end(self, epoch, logs=None):
for name in self.metrics:
self.metrics[name] = logs.get(name, self.metrics[name])
self.epoch_bar.set_postfix(mse=self.metrics['loss'], val_mse=self.metrics['val_loss'], refresh=False)
self.epoch_bar.update()