def display_mu_density(
grid, mu_density, grid_resolution=None, cmap=None, vmin=None, vmax=None
):
"""Prints a colour plot of the MU Density.
Examples
--------
See `pymedphys.mudensity.calculate`_.
"""
if grid_resolution is None:
grid_resolution = grid["mlc"][1] - grid["mlc"][0]
x, y = pcolormesh_grid(grid["mlc"], grid["jaw"], grid_resolution)
plt.pcolormesh(x, y, mu_density, cmap=cmap, vmin=vmin, vmax=vmax)
plt.colorbar()
plt.title("MU density")
plt.xlabel("MLC direction (mm)")
plt.ylabel("Jaw direction (mm)")
plt.axis("equal")
plt.gca().invert_yaxis()
def plot_model(width_data, length_data, factor_data):
i, j, k = create_transformed_mesh(width_data, length_data, factor_data)
model_width, model_length, model_factor = i, j, k
# model_width_mesh, model_length_mesh = np.meshgrid(
# model_width, model_length)
vmin = np.nanmin(
np.concatenate([model_factor.ravel(),
factor_data.ravel()]))
vmax = np.nanmax(
np.concatenate([model_factor.ravel(),
factor_data.ravel()]))
# vrange = vmax - vmin
plt.scatter(
width_data,
length_data,
s=100,
c=factor_data,
cmap="viridis",
vmin=vmin,
vmax=vmax,
zorder=2,
)
plt.colorbar()
cs = plt.contour(model_width,
model_length,
model_factor,
20,
vmin=vmin,
vmax=vmax)
plt.clabel(cs, cs.levels[::2], inline=True)
plt.title("Insert model")
plt.xlabel("width (cm)")
plt.ylabel("length (cm)")
def plot_results(
grid_xx, grid_yy, logfile_mu_density, mosaiq_mu_density, diff_colour_scale=0.1
):
min_val = np.min([logfile_mu_density, mosaiq_mu_density])
max_val = np.max([logfile_mu_density, mosaiq_mu_density])
plt.figure()
plt.pcolormesh(grid_xx, grid_yy, logfile_mu_density, vmin=min_val, vmax=max_val)
plt.colorbar()
plt.title("Logfile MU density")
plt.xlabel("MLC direction (mm)")
plt.ylabel("Jaw direction (mm)")
plt.gca().invert_yaxis()
plt.figure()
plt.pcolormesh(grid_xx, grid_yy, mosaiq_mu_density, vmin=min_val, vmax=max_val)
plt.colorbar()
plt.title("Mosaiq MU density")
plt.xlabel("MLC direction (mm)")
plt.ylabel("Jaw direction (mm)")
plt.gca().invert_yaxis()
scaled_diff = (logfile_mu_density - mosaiq_mu_density) / max_val
plt.figure()
plt.pcolormesh(
grid_xx,
grid_yy,
scaled_diff,
vmin=-diff_colour_scale / 2,
vmax=diff_colour_scale / 2,
)
plt.colorbar(label="Limited colour range = {}".format(diff_colour_scale / 2))
plt.title("(Logfile - Mosaiq MU density) / Maximum MU Density")
plt.xlabel("MLC direction (mm)")
plt.ylabel("Jaw direction (mm)")
plt.gca().invert_yaxis()
plt.show()
plt.figure()
plt.pcolormesh(
grid_xx, grid_yy, scaled_diff, vmin=-diff_colour_scale, vmax=diff_colour_scale
)
plt.colorbar(label="Limited colour range = {}".format(diff_colour_scale))
plt.title("(Logfile - Mosaiq MU density) / Maximum MU Density")
plt.xlabel("MLC direction (mm)")
plt.ylabel("Jaw direction (mm)")
plt.gca().invert_yaxis()
plt.show()
absolute_range = np.max([-np.min(scaled_diff), np.max(scaled_diff)])
plt.figure()
plt.pcolormesh(
grid_xx, grid_yy, scaled_diff, vmin=-absolute_range, vmax=absolute_range
)
plt.colorbar(label="No limited colour range")
plt.title("(Logfile - Mosaiq MU density) / Maximum MU Density")
plt.xlabel("MLC direction (mm)")
plt.ylabel("Jaw direction (mm)")
plt.gca().invert_yaxis()
plt.show()
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))
