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Rename data into counts for hdf5 zebra data

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This commit is contained in:
usov_i
2022-01-28 10:08:27 +01:00
parent 4ba08366df
commit bbe7b7d305
3 changed files with 19 additions and 19 deletions
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8
pyzebra/app/panel_hdf_param_study.py
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@ -260,10 +260,10 @@ def create():
def update_overview_plot(): def update_overview_plot():
scan = _get_selected_scan() scan = _get_selected_scan()
h5_data = scan["data"] counts = scan["counts"]
n_im, n_y, n_x = h5_data.shape n_im, n_y, n_x = counts.shape
overview_x = np.mean(h5_data, axis=1) overview_x = np.mean(counts, axis=1)
overview_y = np.mean(h5_data, axis=2) overview_y = np.mean(counts, axis=2)
# normalize for simpler colormapping # normalize for simpler colormapping
overview_max_val = max(np.max(overview_x), np.max(overview_y)) overview_max_val = max(np.max(overview_x), np.max(overview_y))

18
pyzebra/app/panel_hdf_viewer.py
View File

@ -115,7 +115,7 @@ def create():
print("Could not read data from the file.") print("Could not read data from the file.")
return return
last_im_index = scan["data"].shape[0] - 1 last_im_index = scan["counts"].shape[0] - 1
index_spinner.value = 0 index_spinner.value = 0
index_spinner.high = last_im_index index_spinner.high = last_im_index
@ -157,7 +157,7 @@ def create():
if index is None: if index is None:
index = index_spinner.value index = index_spinner.value
current_image = scan["data"][index] current_image = scan["counts"][index]
proj_v_line_source.data.update( proj_v_line_source.data.update(
x=np.arange(0, IMAGE_W) + 0.5, y=np.mean(current_image, axis=0) x=np.arange(0, IMAGE_W) + 0.5, y=np.mean(current_image, axis=0)
) )
@ -223,10 +223,10 @@ def create():
) )
def update_overview_plot(): def update_overview_plot():
h5_data = scan["data"] counts = scan["counts"]
n_im, n_y, n_x = h5_data.shape n_im, n_y, n_x = counts.shape
overview_x = np.mean(h5_data, axis=1) overview_x = np.mean(counts, axis=1)
overview_y = np.mean(h5_data, axis=2) overview_y = np.mean(counts, axis=2)
# normalize for simpler colormapping # normalize for simpler colormapping
overview_max_val = max(np.max(overview_x), np.max(overview_y)) overview_max_val = max(np.max(overview_x), np.max(overview_y))
@ -438,13 +438,13 @@ def create():
def box_edit_callback(_attr, _old, new): def box_edit_callback(_attr, _old, new):
if new["x"]: if new["x"]:
h5_data = scan["data"] counts = scan["counts"]
x_val = np.arange(h5_data.shape[0]) x_val = np.arange(counts.shape[0])
left = int(np.floor(new["x"][0])) left = int(np.floor(new["x"][0]))
right = int(np.ceil(new["x"][0] + new["width"][0])) right = int(np.ceil(new["x"][0] + new["width"][0]))
bottom = int(np.floor(new["y"][0])) bottom = int(np.floor(new["y"][0]))
top = int(np.ceil(new["y"][0] + new["height"][0])) top = int(np.ceil(new["y"][0] + new["height"][0]))
y_val = np.sum(h5_data[:, bottom:top, left:right], axis=(1, 2)) y_val = np.sum(counts[:, bottom:top, left:right], axis=(1, 2))
else: else:
x_val = [] x_val = []
y_val = [] y_val = []

12
pyzebra/h5.py
View File

@ -68,13 +68,13 @@ def read_detector_data(filepath, cami_meta=None):
ndarray: A 3D array of data, omega, gamma, nu. ndarray: A 3D array of data, omega, gamma, nu.
""" """
with h5py.File(filepath, "r") as h5f: with h5py.File(filepath, "r") as h5f:
data = h5f["/entry1/area_detector2/data"][:] counts = h5f["/entry1/area_detector2/data"][:]
# reshape data to a correct shape (2006 issue) # reshape images (counts) to a correct shape (2006 issue)
n, cols, rows = data.shape n, cols, rows = counts.shape
data = data.reshape(n, rows, cols) counts = counts.reshape(n, rows, cols)
scan = {"data": data} scan = {"counts": counts}
scan["original_filename"] = filepath scan["original_filename"] = filepath
if "/entry1/zebra_mode" in h5f: if "/entry1/zebra_mode" in h5f:
@ -145,7 +145,7 @@ def read_detector_data(filepath, cami_meta=None):
def fit_event(scan, fr_from, fr_to, y_from, y_to, x_from, x_to): def fit_event(scan, fr_from, fr_to, y_from, y_to, x_from, x_to):
data_roi = scan["data"][fr_from:fr_to, y_from:y_to, x_from:x_to] data_roi = scan["counts"][fr_from:fr_to, y_from:y_to, x_from:x_to]
model = GaussianModel() model = GaussianModel()
fr = np.arange(fr_from, fr_to) fr = np.arange(fr_from, fr_to)