def visualize_stacked_features(sample, image_dir, verbose=True):
if not os.path.exists(image_dir):
os.makedirs(image_dir)
feature = sample["feature"]
for i, f in enumerate(feature):
name = f"{i}.png"
fig = vis_hm.protein_distogram_heatmap(f)
if verbose:
print(f"Saving image: {image_dir}/{name}")
fig.write_image(f"{image_dir}/{name}")
def togle_het_atoms(n_clicks, slider_val):
triger_id = dash.callback_context.triggered[0]['prop_id'].split('.')[0]
if triger_id == "toggle_het_atoms":
app_state["show_het_atoms"] = not app_state["show_het_atoms"]
df = pdbStructure.get_pandas_structure(
test_pdb_file, het_atom=app_state["show_het_atoms"])
heatmap_fig = protein_distogram_heatmap(pdbStructure.get_pairwise_euclidean_atom(df),
clip_min=slider_val[0], clip_max=slider_val[1])
print(f"Clicked toggle: {app_state}")
print(f"Data shape: {df.shape}")
print()
return heatmap_fig
def visualize_batch(batch, image_dir, verbose=True):
batch_size = len(batch["pdb_path"])
for i in range(batch_size):
for k in batch:
if k != "pdb_path":
name = f"{k}.png"
fig = vis_hm.protein_distogram_heatmap(batch[k][i])
dirname = f"{image_dir}/batch_{i}"
if not os.path.exists(dirname):
os.makedirs(dirname)
if verbose:
print(f"Saving image: {dirname}/{name}")
fig.write_image(f"{dirname}/{name}")
def visualize_features(sample, image_dir, verbose=True):
if not os.path.exists(image_dir):
os.makedirs(image_dir)
print(f"Sample name: {sample['pdb_path']}")
for k in sample:
if k != "pdb_path":
name = f"{k}.png"
fig = vis_hm.protein_distogram_heatmap(sample[k])
if verbose:
print(f"Saving image: {image_dir}/{name}")
fig.write_image(f"{image_dir}/{name}")
return True
f"{config.folder_structure_cfg.log_path}/{experiment_root}/nn_vis/0/{sample}/")
return layers
# image_directory = '/home/erikj/projects/insidrug/py_proj/erikj/loggs/2020_10_30_10_36_18_fullrun/images/'
# list_of_images = [os.path.basename(x)
# for x in glob.glob(f'{image_directory}*.png')]
static_image_route = '/static/'
app_state = {"show_het_atoms": False}
pdbStructure = PandasMolStructure()
df = pdbStructure.get_pandas_structure(
test_pdb_file, het_atom=app_state["show_het_atoms"])
heatmap_fig = protein_distogram_heatmap(
pdbStructure.get_pairwise_euclidean_atom(df))
app.layout = html.Div(children=[
dcc.Tabs([
dcc.Tab(label='Experiment results', children=[
dcc.Interval(
id='interval-component',
interval=1*1000, # in milliseconds
n_intervals=0
),
html.H3("Filter weight distributions"),
html.P("Select experiment"),
dcc.Dropdown(id="experiment-dropdown",
options=[{"label": f"{x[1]}", "value": f"{x[1]}"}
for x in list(map(lambda x: os.path.split(x), logs))]
