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# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES.
# SPDX-FileCopyrightText: All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import math
import numpy as np
import torch
import torch.nn.functional as F
from tqdm import tqdm
import traceback
import matplotlib.pyplot as plt
import matplotlib
from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable
import imageio
import plotly
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.graph_objects as go
from IPython.display import HTML, display
def plot_spectra_mhd(
k,
pred_spectra_kin,
true_spectra_kin,
pred_spectra_mag,
true_spectra_mag,
index_t=-1,
name="Re100",
save_path=None,
save_suffix=None,
font_size=None,
sci_limits=None,
style_kin_pred="b-",
style_kin_true="k-",
style_mag_pred="b--",
style_mag_true="k--",
xmin=0,
xmax=200,
ymin=1e-10,
ymax=None,
):
"Plots spectra of predicted and true outputs"
if font_size is not None:
plt.rcParams.update({"font.size": font_size})
if sci_limits is not None:
plt.rcParams.update({"axes.formatter.limits": sci_limits})
E_kin_pred = pred_spectra_kin[index_t]
E_mag_pred = pred_spectra_mag[index_t]
E_kin_true = true_spectra_kin[index_t]
E_mag_true = true_spectra_mag[index_t]
fig = plt.figure(figsize=(6, 5))
plt.loglog(k, E_kin_pred, style_kin_pred, label="$E_{kin}$ Pred")
plt.loglog(k, E_kin_true, style_kin_true, label="$E_{kin}$ True")
plt.loglog(k, E_mag_pred, style_mag_pred, label="$E_{mag}$ Pred")
plt.loglog(k, E_mag_true, style_mag_true, label="$E_{mag}$ True")
plt.xlabel("k")
plt.ylabel("E(k)")
plt.axis([xmin, xmax, ymin, ymax])
plt.title(f"Spectra ${name}$")
plt.legend(loc="upper right")
if save_path is not None:
if save_suffix is not None:
figure_path = f"{save_path}_spectra_{save_suffix}.png"
else:
figure_path = f"{save_path}_spectra.png"
plt.savefig(figure_path, bbox_inches="tight")
return fig
def plot_predictions_mhd(
pred,
true,
inputs,
index_t=-1,
names=[],
save_path=None,
save_suffix=None,
font_size=None,
sci_limits=None,
shading="auto",
cmap="jet",
):
"Plots images of predictions and absolute error"
if font_size is not None:
plt.rcParams.update({"font.size": font_size})
if sci_limits is not None:
plt.rcParams.update({"axes.formatter.limits": sci_limits})
# Plot
fig = plt.figure(figsize=(24, 5 * len(names)))
# Make plots for each field
for index, name in enumerate(names):
Nt, Nx, Ny, Nfields = pred.shape
u_pred = pred[index_t, ..., index]
u_true = true[index_t, ..., index]
u_err = u_pred - u_true
initial_data = inputs[0, ..., 3:]
u0 = initial_data[..., index]
x = inputs[0, :, 0, 1]
y = inputs[0, 0, :, 2]
X, Y = torch.meshgrid(x, y, indexing="ij")
t = inputs[index_t, 0, 0, 0]
plt.subplot(len(names), 4, index * 4 + 1)
plt.pcolormesh(X, Y, u0, cmap=cmap, shading=shading)
plt.colorbar()
plt.title(f"Intial Condition ${name}_0(x,y)$")
plt.tight_layout()
plt.axis("square")
plt.axis("off")
plt.subplot(len(names), 4, index * 4 + 2)
plt.pcolormesh(X, Y, u_true, cmap=cmap, shading=shading)
plt.colorbar()
plt.title(f"Exact ${name}(x,y,t={t:.2f})$")
plt.tight_layout()
plt.axis("square")
plt.axis("off")
plt.subplot(len(names), 4, index * 4 + 3)
plt.pcolormesh(X, Y, u_pred, cmap=cmap, shading=shading)
plt.colorbar()
plt.title(f"Predict ${name}(x,y,t={t:.2f})$")
plt.axis("square")
plt.tight_layout()
plt.axis("off")
plt.subplot(len(names), 4, index * 4 + 4)
plt.pcolormesh(X, Y, u_pred - u_true, cmap=cmap, shading=shading)
plt.colorbar()
plt.title(f"Absolute Error ${name}(x,y,t={t:.2f})$")
plt.tight_layout()
plt.axis("square")
plt.axis("off")
if save_path is not None:
if save_suffix is not None:
figure_path = f"{save_path}_{save_suffix}.png"
else:
figure_path = f"{save_path}.png"
plt.savefig(figure_path, bbox_inches="tight")
# plt.show()
# return fig
plt.close()
def generate_movie_2D(
preds_y,
test_y,
test_x,
key=0,
plot_title="",
field=0,
val_cbar_index=-1,
err_cbar_index=-1,
val_clim=None,
err_clim=None,
font_size=None,
movie_dir="",
movie_name="movie.gif",
frame_basename="movie",
frame_ext="jpg",
cmap="jet",
shading="gouraud",
remove_frames=True,
):
"Generates a movie of the exact, predicted, and absolute error fields"
frame_files = []
if movie_dir:
os.makedirs(movie_dir, exist_ok=True)
if font_size is not None:
plt.rcParams.update({"font.size": font_size})
pred = preds_y[key][..., field]
true = test_y[key][..., field]
inputs = test_x[key]
error = pred - true
Nt, Nx, Ny = pred.shape
t = inputs[:, 0, 0, 0]
x = inputs[0, :, 0, 1]
y = inputs[0, 0, :, 2]
X, Y = torch.meshgrid(x, y, indexing="ij")
fig, axs = plt.subplots(1, 3, figsize=(18, 5))
ax1 = axs[0]
ax2 = axs[1]
ax3 = axs[2]
pcm1 = ax1.pcolormesh(
X, Y, true[val_cbar_index], cmap=cmap, label="true", shading=shading
)
pcm2 = ax2.pcolormesh(
X, Y, pred[val_cbar_index], cmap=cmap, label="pred", shading=shading
)
pcm3 = ax3.pcolormesh(
X, Y, error[err_cbar_index], cmap=cmap, label="error", shading=shading
)
if val_clim is None:
val_clim = pcm1.get_clim()
if err_clim is None:
err_clim = pcm3.get_clim()
pcm1.set_clim(val_clim)
plt.colorbar(pcm1, ax=ax1)
ax1.axis("square")
ax1.set_axis_off()
pcm2.set_clim(val_clim)
plt.colorbar(pcm2, ax=ax2)
ax2.axis("square")
ax2.set_axis_off()
pcm3.set_clim(err_clim)
plt.colorbar(pcm3, ax=ax3)
ax3.axis("square")
ax3.set_axis_off()
plt.tight_layout()
for i in range(Nt):
# Exact
ax1.clear()
pcm1 = ax1.pcolormesh(X, Y, true[i], cmap=cmap, label="true", shading=shading)
pcm1.set_clim(val_clim)
ax1.set_title(f"Exact {plot_title}: $t={t[i]:.2f}$")
ax1.axis("square")
ax1.set_axis_off()
# Predictions
ax2.clear()
pcm2 = ax2.pcolormesh(X, Y, pred[i], cmap=cmap, label="pred", shading=shading)
pcm2.set_clim(val_clim)
ax2.set_title(f"Predict {plot_title}: $t={t[i]:.2f}$")
ax2.axis("square")
ax2.set_axis_off()
# Error
ax3.clear()
pcm3 = ax3.pcolormesh(X, Y, error[i], cmap=cmap, label="error", shading=shading)
pcm3.set_clim(err_clim)
ax3.set_title(f"Error {plot_title}: $t={t[i]:.2f}$")
ax3.axis("square")
ax3.set_axis_off()
# plt.tight_layout()
fig.canvas.draw()
if movie_dir:
frame_path = os.path.join(movie_dir, f"{frame_basename}-{i:03}.{frame_ext}")
frame_files.append(frame_path)
plt.savefig(frame_path, bbox_inches="tight")
if movie_dir:
movie_path = os.path.join(movie_dir, movie_name)
with imageio.get_writer(movie_path, mode="I") as writer:
for frame in frame_files:
image = imageio.imread(frame)
writer.append_data(image)
if movie_dir and remove_frames:
for frame in frame_files:
try:
os.remove(frame)
except:
pass
def plot_predictions_mhd_plotly(
pred,
true,
inputs,
index=0,
index_t=-1,
name="u",
save_path=None,
font_size=None,
shading="auto",
cmap="jet",
):
"Plots images of predictions and absolute error to be saved to wandb"
Nt, Nx, Ny, Nfields = pred.shape
u_pred = pred[index_t, ..., index]
u_true = true[index_t, ..., index]
ic = inputs[0, ..., 3:]
u_ic = ic[..., index]
u_err = u_pred - u_true
x = inputs[0, :, 0, 1]
y = inputs[0, 0, :, 2]
X, Y = torch.meshgrid(x, y, indexing="ij")
t = inputs[index_t, 0, 0, 0]
zmin = u_true.min().item()
zmax = u_true.max().item()
labels = {"color": name}
# Initial Conditions
title_ic = f"{name}0"
fig_ic = px.imshow(
u_ic,
binary_string=False,
color_continuous_scale=cmap,
labels=labels,
title=title_ic,
)
fig_ic.update_xaxes(showticklabels=False)
fig_ic.update_yaxes(showticklabels=False)
# Predictions
title_pred = f"Predict {name}: t={t:.2f}"
fig_pred = px.imshow(
u_pred,
binary_string=False,
color_continuous_scale=cmap,
labels=labels,
title=title_pred,
)
fig_pred.update_xaxes(showticklabels=False)
fig_pred.update_yaxes(showticklabels=False)
# Ground Truth
title_true = f"Exact {name}: t={t:.2f}"
fig_true = px.imshow(
u_true,
binary_string=False,
color_continuous_scale=cmap,
labels=labels,
title=title_true,
)
fig_true.update_xaxes(showticklabels=False)
fig_true.update_yaxes(showticklabels=False)
# Ground Truth
title_err = f"Error {name}: t={t:.2f}"
fig_err = px.imshow(
u_err,
binary_string=False,
color_continuous_scale=cmap,
labels=labels,
title=title_err,
)
fig_err.update_xaxes(showticklabels=False)
fig_err.update_yaxes(showticklabels=False)
return fig_ic, fig_pred, fig_true, fig_err
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