def test_save_tables(self):
df = pd.DataFrame({"a": [1, 2, 3, 4], "b": [5, 6, 7, 8]})
path = os.path.join(IO_UTILS_DATA, "table.xlsx")
io_utils.save_tables(path, [df])
df2 = pd.read_excel(path, engine="openpyxl")
assert np.all(df == df2)
def __save_quant_data__(self, dirpath: str):
"""Save quantitative data and 2D visualizations of femoral cartilage.
Check which quantitative values (T2, T1rho, etc) are defined for femoral cartilage and analyze these
1. Save 2D total, superficial, and deep visualization maps.
2. Save {'medial', 'lateral'}, {'anterior', 'central', 'posterior'}, {'deep', 'superficial'} data to excel
file
Args:
dirpath (str): Directory path to tissue data.
"""
q_names = []
dfs = []
for quant_val in QuantitativeValueType:
if quant_val.name not in self.quant_vals.keys():
continue
q_names.append(quant_val.name)
q_val = self.quant_vals[quant_val.name]
dfs.append(q_val[1])
q_name_dirpath = io_utils.mkdirs(
os.path.join(dirpath, quant_val.name.lower()))
for q_map_data in q_val[0]:
filepath = os.path.join(q_name_dirpath, q_map_data['filename'])
xlabel = 'Slice'
ylabel = 'Angle (binned)'
title = q_map_data['title']
data_map = q_map_data['data']
plt.clf()
upper_bound = BOUNDS[quant_val]
if preferences.visualization_use_vmax:
# Hard bounds - clipping
plt.imshow(data_map,
cmap='jet',
vmin=0.0,
vmax=BOUNDS[quant_val])
else:
# Try to use a soft bounds
if np.sum(data_map <= upper_bound) == 0:
plt.imshow(data_map,
cmap='jet',
vmin=0.0,
vmax=BOUNDS[quant_val])
else:
warnings.warn(
'%s: Pixel value exceeded upper bound (%0.1f). Using normalized scale.'
% (quant_val.name, upper_bound))
plt.imshow(data_map, cmap='jet')
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(title)
clb = plt.colorbar()
clb.ax.set_title('(ms)')
plt.savefig(filepath)
# Save data
raw_data_filepath = os.path.join(
q_name_dirpath, 'raw_data',
q_map_data['raw_data_filename'])
io_utils.save_pik(raw_data_filepath, data_map)
if len(dfs) > 0:
io_utils.save_tables(os.path.join(dirpath, 'data.xlsx'), dfs,
q_names)
def __save_quant_data__(self, dirpath):
"""Save quantitative data and 2D visualizations of patellar cartilage
Check which quantitative values (T2, T1rho, etc) are defined for
patellar cartilage and analyze these:
1. Save 2D total, superficial, and deep visualization maps
2. Save {'medial', 'lateral'}, {'anterior', 'posterior'},
{'superior', 'inferior', 'total'} data to excel file
:param dirpath: base filepath to save data
"""
q_names = []
dfs = []
for quant_val in QuantitativeValueType:
if quant_val.name not in self.quant_vals.keys():
continue
q_names.append(quant_val.name)
q_val = self.quant_vals[quant_val.name]
dfs.append(q_val[1])
q_name_dirpath = io_utils.mkdirs(
os.path.join(dirpath, quant_val.name.lower()))
for q_map_data in q_val[0]:
filepath = os.path.join(q_name_dirpath, q_map_data["filename"])
xlabel = ""
ylabel = ""
title = q_map_data["title"]
data_map = q_map_data["data"]
axs_bounds = self.__get_axis_bounds__(data_map,
leave_buffer=True)
plt.clf()
upper_bound = BOUNDS[quant_val]
if preferences.visualization_use_vmax:
# Hard bounds - clipping
plt.imshow(data_map,
cmap="jet",
vmin=0.0,
vmax=BOUNDS[quant_val])
else:
# Try to use a soft bounds
if np.sum(data_map <= upper_bound) == 0:
plt.imshow(data_map,
cmap="jet",
vmin=0.0,
vmax=BOUNDS[quant_val])
else:
warnings.warn(
"%s: Pixel value exceeded upper bound (%0.1f). Using normalized scale."
% (quant_val.name, upper_bound))
plt.imshow(data_map, cmap="jet")
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(title)
plt.ylim(axs_bounds[0])
plt.gca().invert_yaxis()
plt.xlim(axs_bounds[1])
# plt.axis('tight')
clb = plt.colorbar()
clb.ax.set_ylabel("(ms)")
plt.savefig(filepath)
# Save data
raw_data_filepath = os.path.join(
q_name_dirpath, "raw_data",
q_map_data["raw_data_filename"])
io_utils.save_pik(raw_data_filepath, data_map)
if len(dfs) > 0:
io_utils.save_tables(os.path.join(dirpath, "data.xlsx"), dfs,
q_names)