def main():
st.write("## Parameters")
number_of_images = st.number_input("Number of Images", 10)
width = st.number_input("Width (mm)", 20)
length = st.number_input("Length (mm)", 24)
edge_lengths = [width, length]
# initial_rotation = 0
bb_diameter = st.number_input("BB Diameter (mm)", 8)
penumbra = st.number_input("Penumbra (mm)", 2)
# files = sorted(IMAGES_DIR.glob("*.jpg"), key=lambda t: -os.stat(t).st_mtime)
# most_recent = files[0:5]
# most_recent
files = get_file_list()
most_recent = sorted(
files, key=lambda t: -os.stat(t).st_mtime)[0:number_of_images]
image_path = st.radio("Image to select", options=most_recent)
plt.imshow(read_image(image_path))
st.pyplot()
if st.button("Calculate"):
img = read_image(image_path)
x, y, img = iview.iview_image_transform(img)
field = imginterp.create_interpolated_field(x, y, img)
initial_centre = findfield.get_centre_of_mass(x, y, img)
(field_centre,
field_rotation) = findfield.field_centre_and_rotation_refining(
field, edge_lengths, penumbra, initial_centre, fixed_rotation=0)
bb_centre = findbb.optimise_bb_centre(field, bb_diameter, edge_lengths,
penumbra, field_centre,
field_rotation)
fig = reporting.image_analysis_figure(
x,
y,
img,
bb_centre,
field_centre,
field_rotation,
bb_diameter,
edge_lengths,
penumbra,
)
st.write(fig)
st.pyplot()
def run_calculation(
reference_results,
evaluation_results,
gamma_options,
escan_directory,
png_output_directory,
):
st.write("Calculating Reference MU Density...")
reference_mudensity = calculate_batch_mudensity(
reference_results["deliveries"])
st.write("Calculating Evaluation MU Density...")
evaluation_mudensity = calculate_batch_mudensity(
evaluation_results["deliveries"])
st.write("Calculating Gamma...")
gamma = calculate_gamma(reference_mudensity, evaluation_mudensity,
gamma_options)
patient_id = reference_results["patient_id"]
st.write("Creating figure...")
output_base_filename = (
f"{patient_id} {reference_results['identifier']} vs "
f"{evaluation_results['identifier']}")
pdf_filepath = str(
escan_directory.joinpath(f"{output_base_filename}.pdf").resolve())
png_record_directory = png_output_directory.joinpath(output_base_filename)
png_record_directory.mkdir(exist_ok=True)
png_filepath = str(png_record_directory.joinpath("report.png").resolve())
try:
patient_name_text = f"Patient Name: {reference_results['patient_name']}\n"
except KeyError:
patient_name_text = ""
header_text = (f"Patient ID: {patient_id}\n"
f"{patient_name_text}"
f"Reference: {reference_results['identifier']}\n"
f"Evaluation: {evaluation_results['identifier']}\n")
reference_path_strings = "\n ".join(
[str(path.resolve()) for path in reference_results["data_paths"]])
evaluation_path_strings = "\n ".join(
[str(path.resolve()) for path in evaluation_results["data_paths"]])
footer_text = (f"reference path(s): {reference_path_strings}\n"
f"evaluation path(s): {evaluation_path_strings}\n"
f"png record: {png_filepath}")
fig = plot_and_save_results(
reference_mudensity,
evaluation_mudensity,
gamma,
gamma_options,
png_record_directory,
header_text=header_text,
footer_text=footer_text,
)
fig.tight_layout()
st.write("Saving figure...")
plt.savefig(png_filepath, dpi=100)
try:
subprocess.check_call(
f'magick convert "{png_filepath}" "{pdf_filepath}"', shell=True)
except subprocess.CalledProcessError:
st.write(
UnableToCreatePDF(
"Please install Image Magick to create PDF reports "
"<https://imagemagick.org/script/download.php#windows>."))
st.write("## Results")
st.pyplot()
def main():
st.write("""
# Electron Insert Factors
""")
patient_id = st.text_input("Patient ID")
if patient_id == "":
st.stop()
rccc_string_search_pattern = r"\\monacoda\FocalData\RCCC\1~Clinical\*~{}\plan\*\*tel.1".format(
patient_id)
rccc_filepath_list = glob(rccc_string_search_pattern)
nbccc_string_search_pattern = r"\\tunnel-nbcc-monaco\FOCALDATA\NBCCC\1~Clinical\*~{}\plan\*\*tel.1".format(
patient_id)
nbccc_filepath_list = glob(nbccc_string_search_pattern)
sash_string_search_pattern = r"\\tunnel-sash-monaco\Users\Public\Documents\CMS\FocalData\SASH\1~Clinical\*~{}\plan\*\*tel.1".format(
patient_id)
sash_filepath_list = glob(sash_string_search_pattern)
filepath_list = np.concatenate(
[rccc_filepath_list, nbccc_filepath_list, sash_filepath_list])
electronmodel_regex = r"RiverinaAgility - (\d+)MeV"
applicator_regex = r"(\d+)X\d+"
insert_data = dict() # type: ignore
for telfilepath in filepath_list:
insert_data[telfilepath] = dict()
with open(telfilepath, "r") as file:
telfilecontents = np.array(file.read().splitlines())
insert_data[telfilepath]["reference_index"] = []
for i, item in enumerate(telfilecontents):
if re.search(electronmodel_regex, item):
insert_data[telfilepath]["reference_index"] += [i]
insert_data[telfilepath]["applicators"] = [
re.search(applicator_regex,
telfilecontents[i + 12]).group(1) # type: ignore
for i in insert_data[telfilepath]["reference_index"]
]
insert_data[telfilepath]["energies"] = [
re.search(electronmodel_regex,
telfilecontents[i]).group(1) # type: ignore
for i in insert_data[telfilepath]["reference_index"]
]
for telfilepath in filepath_list:
with open(telfilepath, "r") as file:
telfilecontents = np.array(file.read().splitlines())
insert_data[telfilepath]["x"] = []
insert_data[telfilepath]["y"] = []
for i, index in enumerate(insert_data[telfilepath]["reference_index"]):
insert_initial_range = telfilecontents[
index +
51::] # coords start 51 lines after electron model name
insert_stop = np.where(insert_initial_range == "0")[0][
0] # coords stop right before a line containing 0
insert_coords_string = insert_initial_range[:insert_stop]
insert_coords = np.fromstring(",".join(insert_coords_string),
sep=",")
insert_data[telfilepath]["x"].append(insert_coords[0::2] / 10)
insert_data[telfilepath]["y"].append(insert_coords[1::2] / 10)
for telfilepath in filepath_list:
insert_data[telfilepath]["width"] = []
insert_data[telfilepath]["length"] = []
insert_data[telfilepath]["circle_centre"] = []
insert_data[telfilepath]["P/A"] = []
for i in range(len(insert_data[telfilepath]["reference_index"])):
width, length, circle_centre = electronfactors.parameterise_insert(
insert_data[telfilepath]["x"][i],
insert_data[telfilepath]["y"][i])
insert_data[telfilepath]["width"].append(width)
insert_data[telfilepath]["length"].append(length)
insert_data[telfilepath]["circle_centre"].append(circle_centre)
insert_data[telfilepath]["P/A"].append(
electronfactors.convert2_ratio_perim_area(width, length))
data_filename = r"S:\Physics\RCCC Specific Files\Dosimetry\Elekta_EFacs\electron_factor_measured_data.csv"
data = pd.read_csv(data_filename)
width_data = data["Width (cm @ 100SSD)"]
length_data = data["Length (cm @ 100SSD)"]
factor_data = data["RCCC Inverse factor (dose open / dose cutout)"]
p_on_a_data = electronfactors.convert2_ratio_perim_area(
width_data, length_data)
for telfilepath in filepath_list:
insert_data[telfilepath]["model_factor"] = []
for i in range(len(insert_data[telfilepath]["reference_index"])):
applicator = float(insert_data[telfilepath]["applicators"][i])
energy = float(insert_data[telfilepath]["energies"][i])
ssd = 100
reference = ((data["Energy (MeV)"] == energy)
& (data["Applicator (cm)"] == applicator)
& (data["SSD (cm)"] == ssd))
number_of_measurements = np.sum(reference)
if number_of_measurements < 8:
insert_data[telfilepath]["model_factor"].append(np.nan)
else:
insert_data[telfilepath]["model_factor"].append(
electronfactors.spline_model_with_deformability(
insert_data[telfilepath]["width"],
insert_data[telfilepath]["P/A"],
width_data[reference],
p_on_a_data[reference],
factor_data[reference],
)[0])
for telfilepath in filepath_list:
st.write("---")
st.write("Filepath: `{}`".format(telfilepath))
for i in range(len(insert_data[telfilepath]["reference_index"])):
applicator = float(insert_data[telfilepath]["applicators"][i])
energy = float(insert_data[telfilepath]["energies"][i])
ssd = 100
st.write("Applicator: `{} cm` | Energy: `{} MeV`".format(
applicator, energy))
width = insert_data[telfilepath]["width"][i]
length = insert_data[telfilepath]["length"][i]
plt.figure()
plot_insert(
insert_data[telfilepath]["x"][i],
insert_data[telfilepath]["y"][i],
insert_data[telfilepath]["width"][i],
insert_data[telfilepath]["length"][i],
insert_data[telfilepath]["circle_centre"][i],
)
reference = ((data["Energy (MeV)"] == energy)
& (data["Applicator (cm)"] == applicator)
& (data["SSD (cm)"] == ssd))
number_of_measurements = np.sum(reference)
plt.figure()
if number_of_measurements < 8:
plt.scatter(
width_data[reference],
length_data[reference],
s=100,
c=factor_data[reference],
cmap="viridis",
zorder=2,
)
plt.colorbar()
else:
plot_model(
width_data[reference],
length_data[reference],
factor_data[reference],
)
reference_data_table = pd.concat(
[
width_data[reference], length_data[reference],
factor_data[reference]
],
axis=1,
)
reference_data_table.sort_values(
["RCCC Inverse factor (dose open / dose cutout)"],
ascending=False,
inplace=True,
)
st.write(reference_data_table)
st.pyplot()
factor = insert_data[telfilepath]["model_factor"][i]
st.write(
"Width: `{0:0.2f} cm` | Length: `{1:0.2f} cm` | Factor: `{2:0.3f}`"
.format(width, length, factor))
def run_calculation(
reference_results,
evaluation_results,
gamma_options,
escan_directory,
png_output_directory,
):
st.write("Calculating Reference MU Density...")
reference_mudensity = calculate_batch_mudensity(
reference_results["deliveries"])
st.write("Calculating Evaluation MU Density...")
evaluation_mudensity = calculate_batch_mudensity(
evaluation_results["deliveries"])
st.write("Calculating Gamma...")
gamma = calculate_gamma(reference_mudensity, evaluation_mudensity,
gamma_options)
patient_id = reference_results["patient_id"]
st.write("Creating figure...")
output_base_filename = (
f"{patient_id} {reference_results['identifier']} vs "
f"{evaluation_results['identifier']}")
pdf_filepath = str(
escan_directory.joinpath(f"{output_base_filename}.pdf").resolve())
png_record_directory = png_output_directory.joinpath(output_base_filename)
png_record_directory.mkdir(exist_ok=True)
png_filepath = str(png_record_directory.joinpath("report.png").resolve())
try:
patient_name_text = f"Patient Name: {reference_results['patient_name']}\n"
except KeyError:
patient_name_text = ""
header_text = (f"Patient ID: {patient_id}\n"
f"{patient_name_text}"
f"Reference: {reference_results['identifier']}\n"
f"Evaluation: {evaluation_results['identifier']}\n")
reference_path_strings = "\n ".join(
[str(path.resolve()) for path in reference_results["data_paths"]])
evaluation_path_strings = "\n ".join(
[str(path.resolve()) for path in evaluation_results["data_paths"]])
footer_text = (f"reference path(s): {reference_path_strings}\n"
f"evaluation path(s): {evaluation_path_strings}\n"
f"png record: {png_filepath}")
fig = plot_and_save_results(
reference_mudensity,
evaluation_mudensity,
gamma,
gamma_options,
png_record_directory,
header_text=header_text,
footer_text=footer_text,
)
fig.tight_layout()
st.write("## Results")
st.pyplot(fig)
st.write("## Saving reports")
st.write("### PNG")
st.write("Saving figure as PNG...")
plt.savefig(png_filepath, dpi=100)
st.write(f"Saved:\n\n`{png_filepath}`")
convert_png_to_pdf(png_filepath, pdf_filepath)
