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surf slopes added + combined plotting functions #35

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Jul 6, 2024
Merged

surf slopes added + combined plotting functions #35

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c568b5c
updated the plotting similarity function
mansakrishna23 Jul 6, 2024
61e4fcd
deleted some tests
mansakrishna23 Jul 6, 2024
c693429
Merge branch 'ISSMteam:main' into main
mansakrishna23 Jul 6, 2024
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217 changes: 48 additions & 169 deletions pinnicle/utils/plotting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -227,139 +227,6 @@ def plot_data(X, Y, im_data, axs=None, vranges={}, **kwargs):

return axs

def plot_similarity(pinn, feature_name, feat_title=None, sim='MAE', cmap='jet', scale=1, cols=[0, 1, 2], cbar_bins=10):
"""
plotting the similarity between reference and predicted
solutions, mae default

Args:
pinn : pinnicle.model
the trained PINN model
feature_name : str
the name of a predicted feature of the PINN.
for the L-2 norm of two or more predictions, write as a list. e.g., ['u', 'v'].
feat_title : str (default=None, will be set to feature_name if None provided)
the name of the predicted feature in the title.
sim : str (default='MAE')
the similarity/comparison type.
options include: 'MAE', 'MSE', 'RMSE', 'SIMPLE'
(can be written as upper case or lower case) e.g., 'MSE' and 'mse' will give the same result.
cmap : str (default='jet', for similarity default='RdBu')
the matplotlib colormap name as a str.
scale : float (default=1)
the scale by which to multiply predictions (e.g., m/s * yts = m/year, then scale = yts)
cols : list (default=[0, 1, 2])
can specify which columns of the figure to extract. 0 = reference, 1 = prediction, 2 = similarity.
cbar_bins : int (default=10)
the number of bins/ticks on the c-axis.

Returns:
fig, axs
plot of the reference, prediction, and similarity
"""
# setting the figure title
if feat_title == None:
if type(feature_name) == list:
raise TypeError('feat_title must be provided as an input string')
else:
feat_title = feature_name

# initialize figure, default all 3 columns
if len(cols) == 1:
# subplots returns a single Axes if only 1 figure, but we need array later
fig, ax_single = plt.subplots(1, len(cols), figsize=(5*len(cols), 4))
axs = [ax_single]
else:
fig, axs = plt.subplots(1, len(cols), figsize=(5*len(cols), 4))

# inputs and outputs of NN
input_names = pinn.nn.parameters.input_variables
output_names = pinn.nn.parameters.output_variables

# inputs
X_ref = pinn.model_data.data['ISSM'].X_dict
xref = X_ref[input_names[0]].flatten()[:,None]
for i in range(1, len(input_names)):
xref = np.hstack((xref, X_ref[input_names[i]].flatten()[:,None]))
meshx = np.squeeze(xref[:, 0])
meshy = np.squeeze(xref[:, 1])

# predictions
pred = pinn.model.predict(xref)

# reference solution
X_sol = pinn.model_data.data['ISSM'].data_dict
sol = X_sol[output_names[0]].flatten()[:,None] # initializing array
for i in range(1, len(output_names)):
sol = np.hstack((sol, X_sol[output_names[i]].flatten()[:,None]))

# grab feature
# initializing reference and prediction
ref_sol = np.zeros_like(np.squeeze(sol[:, 0:1]*scale))
pred_sol = np.zeros_like(np.squeeze(pred[:, 0:1]*scale))

if type(feature_name) == list:
for feat in feature_name:
fid = output_names.index(feat)
ref_sol += (np.squeeze(sol[:, fid:fid+1]*scale))**2
pred_sol += (np.squeeze(pred[:, fid:fid+1]*scale))**2
ref_sol = np.sqrt(ref_sol)
pred_sol = np.sqrt(pred_sol)
else:
fid = output_names.index(feature_name)
ref_sol = np.squeeze(sol[:, fid:fid+1]*scale)
pred_sol = np.squeeze(pred[:, fid:fid+1]*scale)

[cmin, cmax] = [np.min(np.append(ref_sol, pred_sol)), np.max(np.append(ref_sol, pred_sol))]
levels = np.linspace(cmin*0.9, cmax*1.1, 500)
data_list = [ref_sol, pred_sol]
title_list = [ feat_title + r"$_{ref}$", feat_title + r"$_{pred}$"]

# plotting
for c, col in enumerate(cols):
if col == 2:
if sim.upper() == 'MAE':
diff = np.abs(ref_sol-pred_sol)
diff_val = np.round(np.mean(diff), 2)
title = r"|"+feat_title+r"$_{ref} - $"+feat_title+r"$_{pred}$|, MAE="+str(diff_val)
elif sim.upper() == 'MSE':
diff = (ref_sol-pred_sol)**2
diff_val = np.round(np.mean(diff), 2)
title = r"$($"+feat_title+r"$_{ref} - $"+feat_title+r"$_{pred})^2$, MSE="+str(diff_val)
elif sim.upper() == 'RMSE':
diff = (ref_sol-pred_sol)**2
diff_val = np.round(np.sqrt(np.mean(diff)), 2)
diff = np.sqrt(diff)
title = r"$(($"+feat_title+r"$_{ref} - $"+feat_title+r"$_{pred})^2)^{1/2}$, RMSE="+str(diff_val)
elif sim.upper() == 'SIMPLE':
diff = ref_sol-pred_sol
diff_val = np.round(np.mean(diff), 2)
title = feat_title+r"$_{ref} - $"+feat_title+r"$_{pred}$, AVG. DIFF="+str(diff_val)
else:
print('Default similarity MAE implemented.')
diff = np.abs(ref_sol-pred_sol)
diff_val = np.round(np.mean(diff), 2)
title = r"|"+feat_title+r"$_{ref} - $"+feat_title+r"$_{pred}$|, MAE="+str(diff_val)

levels = np.linspace(np.min(diff)*0.9, np.max(diff)*1.1, 500)
data = np.squeeze(diff)
axes = axs[c].tricontourf(meshx, meshy, data, levels=levels, cmap='RdBu', norm=colors.CenteredNorm())
else:
axes = axs[c].tricontourf(meshx, meshy, data_list[col], levels=levels, cmap=cmap)
title = title_list[col]

# common settings
axs[c].set_title(title, fontsize=14)
cb = plt.colorbar(axes, ax=axs[c])
cb.ax.tick_params(labelsize=14)
colorbar_bins = ticker.MaxNLocator(nbins=cbar_bins)
cb.locator = colorbar_bins
cb.update_ticks()
axs[c].axis('off')

# save figure to path as defined
return fig, axs

def plot_residuals(pinn, cmap='RdBu', cbar_bins=10, cbar_limits=[-5e3, 5e3]):
"""plotting the pde residuals
"""
Expand Down Expand Up @@ -416,66 +283,78 @@ def plot_residuals(pinn, cmap='RdBu', cbar_bins=10, cbar_limits=[-5e3, 5e3]):

return fig, axs

def tripcolor_similarity(pinn, feature_name, feat_title=None, sim='MAE', cmap='jet', scale=1, colorbar_bins=10):
"""tripcolor similarity, plot with ISSM triangulation
def plot_similarity(pinn, feature, feat_title=None, mdata='ISSM', figsize=(15, 4), sim='mae', cmap='jet', clim=None, scale=1, colorbar_bins=10, elements=None):
"""plotting function: reference sol, prediction, and difference
"""
if feat_title == None:
if type(feature_name)==list:
raise TypeError('feat_title must be provided as a str type input.')
if feat_title=None:
if type(feature)==list:
raise TypeError('feat_title must be provided as input str')
else:
feat_title = feature_name
feat_title=feature

# initialize figure
fig, axs = plt.subplots(1, 3, figsize=(15, 4))
fig, axs = plt.subplots(1, 3, figsize=figsize)

# neural network features
input_names = pinn.nn.parameters.input_variables
output_names = pinn.nn.parameters.output_variables
# neural network inputs and outputs
ins = pinn.nn.parameters.input_variables
outs = pinn.nn.parameters.output_variables

# inputs
X_ref = pinn.model_data.data['ISSM'].X_dict
xref = X_ref[input_names[0]].flatten()[:,None]
for i in range(1, len(input_names)):
xref = np.hstack((xref, X_ref[input_names[i]].flatten()[:,None]))
X_ref = pinn.model_data.data[mdata].X_dict
xref = X_ref[ins[0]].flatten()[:,None]
for i in range(1, len(ins)):
xref = np.hstack((xref, X_ref[ins[i]].flatten()[:,None]))
meshx = np.squeeze(xref[:, 0])
meshy = np.squeeze(xref[:, 1])

# predictions
pred = pinn.model.predict(xref)

# reference solution
X_sol = pinn.model_data.data['ISSM'].data_dict
sol = X_sol[output_names[0]].flatten()[:,None]
for i in range(1, len(output_names)):
sol = np.hstack((sol, X_sol[output_names[i]].flatten()[:,None]))

# triangulation from ISSM (matlab --> python indexing)
elements = pinn.model_data.data['ISSM'].mesh_dict['elements']-1
triangles = mpl.tri.Triangulation(meshx, meshy, elements)
# reference sol
X_sol = pinn.model_data.data[mdata].data_dict
sol = X_sol[outs[0]].flatten()[:,None]
for i in range(1, len(outs)):
sol = np.hstack((sol, X_sol[outs[i]].flatten()[:,None]))

# elements, if any
if elements!=None:
triangles = elements
if len(triangles)!=len(meshx):
raise ValueError('number of elements must equal number number of vertices (x, y)')
else:
if pinn.model_data.data[mdata].mesh_dict == {}:
triangles = mpl.tri.Triangulation(meshx, meshy)
else:
if pinn.params.param_dict['data'][mdata]['data_path'].endswith('mat'):
elements = pinn.model_data.data[mdata].mesh_dict['elements']-1
else:
elements = pinn.model_data.data[mdata].mesh_dict['elements']
triangles = mpl.tri.Triangulation(meshx, meshy, elements)

# grab feature
# initializing ref and pred
# grabbing the feature
ref_sol = np.zeros_like(np.squeeze(sol[:, 0:1]*scale))
pred_sol = np.zeros_like(np.squeeze(pred[:, 0:1]*scale))
if type(feature_name) == list:
for feat in feature_name:
fid = output_names.index(feat)
if type(feature)==list:
for feat in feature:
fid = outs.index(feat)
ref_sol += (np.squeeze(sol[:, fid:fid+1]*scale))**2
pred_sol += (np.squeeze(pred[:, fid:fid+1]*scale))**2
ref_sol = np.sqrt(ref_sol)
pred_sol = np.sqrt(pred_sol)
else:
fid = output_names.index(feature_name)
fid = outs.index(feature)
ref_sol = np.squeeze(sol[:, fid:fid+1]*scale)
pred_sol = np.squeeze(pred[:, fid:fid+1]*scale)

[cmin, cmax] = [0.9*np.min(np.append(ref_sol, pred_sol)), 1.1*np.max(np.append(ref_sol, pred_sol))]
if clim==None:
[cmin, cmax] = [0.9*np.min(np.append(ref_sol, pred_sol)), 1.1*np.max(np.append(ref_sol, pred_sol))]
else:
[cmin, cmax] = clim
data_list = [ref_sol, pred_sol]
title_list = [ feat_title + r"$_{ref}$", feat_title + r"$_{pred}$"]
title_list = [feat_title + r"$_{ref}$", feat_title + r"$_{pred}$"]

# looping through the columns of the plot
# looping through plot
for c in range(3):
if c == 2:
if c==2:
if sim.upper() == 'MAE':
diff = np.abs(ref_sol-pred_sol)
diff_val = np.round(np.mean(diff), 2)
Expand All @@ -484,7 +363,7 @@ def tripcolor_similarity(pinn, feature_name, feat_title=None, sim='MAE', cmap='j
diff = (ref_sol-pred_sol)**2
diff_val = np.round(np.mean(diff), 2)
title = r"$($"+feat_title+r"$_{ref} - $"+feat_title+r"$_{pred})^2$, MSE="+str(diff_val)
elif sim.upper() == 'RMSE':
elif sim.upper() == 'RSME':
diff = (ref_sol-pred_sol)**2
diff_val = np.round(np.sqrt(np.mean(diff)), 2)
diff = np.sqrt(diff)
Expand All @@ -506,7 +385,7 @@ def tripcolor_similarity(pinn, feature_name, feat_title=None, sim='MAE', cmap='j
axes = axs[c].tripcolor(triangles, data_list[c], cmap=cmap, norm=colors.Normalize(vmin=cmin, vmax=cmax))
title = title_list[c]

# common settings
# common plot settings
axs[c].set_title(title, fontsize=14)
axs[c].axis('off')
cb = plt.colorbar(axes, ax=axs[c])
Expand Down
55 changes: 0 additions & 55 deletions tests/test_pinn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -217,32 +217,6 @@ def test_plot(tmp_path):
assert len(im_data) == 5
assert im_data['u'].shape == (10,10)

def test_similarity(tmp_path):
hp["save_path"] = str(tmp_path)
hp["is_save"] = False
issm["data_size"] = {"u":100, "v":100, "s":100, "H":100, "C":None}
hp["data"] = {"ISSM": issm}
experiment = pinn.PINN(params=hp)
experiment.compile()
# plot_similarity(pinn, feature_name, sim='MAE', cmap='jet', scale=1, cols=[0, 1, 2])
# default
fig, axs = plot_similarity(experiment, feature_name='s')
assert (fig is not None) and (np.size(axs) == 3)
fig, axs = plot_similarity(experiment, feature_name='H', cols=[0])
assert (fig is not None) and (np.size(axs) == 1)
fig, axs = plot_similarity(experiment, feature_name="u", sim="mae", cols=[2])
assert (fig is not None) and (np.size(axs) == 1)
fig, axs = plot_similarity(experiment, feature_name="v", sim="Mse", cols=[2, 1])
assert (fig is not None) and (np.size(axs) == 2)
fig, axs = plot_similarity(experiment, feature_name="C", sim="rmse", cols=[0, 2, 1])
assert (fig is not None) and (np.size(axs) == 3)
fig, axs = plot_similarity(experiment, feature_name="H", sim="SIMPLE")
assert (fig is not None) and (np.size(axs) == 3)
fig, axs = plot_similarity(experiment, feature_name=['u', 'v'], feat_title='vel')
assert (fig is not None) and (np.size(axs) == 3)
with pytest.raises(TypeError):
fig, axs = plot_similarity(experiment, feature_name=['u', 'v'])

def test_residuals(tmp_path):
hp["save_path"] = str(tmp_path)
hp["is_save"] = False
Expand Down Expand Up @@ -273,35 +247,6 @@ def test_residuals(tmp_path):
fig, axs = plot_residuals(experiment)
assert (fig is not None) and (np.size(axs)==3)

def test_trisimilarity(tmp_path):
hp["equations"] = {"SSA":SSA}
hp["save_path"] = str(tmp_path)
hp["is_save"] = False
issm["data_size"] = {"u":100, "v":100, "s":100, "H":100, "C":None}
hp["data"] = {"ISSM": issm}
experiment = pinn.PINN(params=hp)
experiment.compile()
# plot_similarity(pinn, feature_name, sim='MAE', cmap='jet', scale=1, cols=[0, 1, 2])
# default
fig, axs = tripcolor_similarity(experiment, feature_name='s')
assert (fig is not None) and (np.size(axs) == 3)
fig, axs = tripcolor_similarity(experiment, feature_name='s', sim='mae')
assert (fig is not None) and (np.size(axs) == 3)
fig, axs = tripcolor_similarity(experiment, feature_name='s', sim='SIMPLE')
assert (fig is not None) and (np.size(axs) == 3)
fig, axs = tripcolor_similarity(experiment, feature_name='s', cmap='terrain')
assert (fig is not None) and (np.size(axs) == 3)
fig, axs = tripcolor_similarity(experiment, feature_name='s', sim='Rmse')
assert (fig is not None) and (np.size(axs) == 3)
fig, axs = tripcolor_similarity(experiment, feature_name='s', sim='mse')
assert (fig is not None) and (np.size(axs) == 3)
fig, axs = tripcolor_similarity(experiment, feature_name='s', colorbar_bins=5)
assert (fig is not None) and (np.size(axs) == 3)
fig, axs = tripcolor_similarity(experiment, feature_name=['u', 'v'], feat_title='vel', scale=experiment.model_data.yts)
assert (fig is not None) and (np.size(axs) == 3)
with pytest.raises(TypeError):
fig, axs = tripcolor_similarity(experiment, feature_name=['u', 'v'])

def test_triresiduals(tmp_path):
hp["equations"] = {"SSA":SSA}
hp["save_path"] = str(tmp_path)
Expand Down
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