def store_magellan_metadata(self, magellan_directory):
print('Collecting Metadata: ' + magellan_directory)
magellan = MagellanDataset(magellan_directory)
self.all_data = magellan.as_array(stitched=True)
# Get size of dataset
size_array = self.all_data.shape
num_slices = size_array[2]
total_height = size_array[3]
total_width = size_array[4]
voxel_size_z_um = magellan.summary_metadata['z-step_um']
pixel_size_xy_um = magellan.summary_metadata['PixelSize_um']
num_positions = magellan.get_num_xy_positions()
num_frames = magellan.get_num_frames()
print('Time List')
time_list = []
for t in range(num_frames):
metadata_dictionary = magellan.read_metadata(t_index=t)
print(metadata_dictionary)
try:
time_list.append(metadata_dictionary['TimeReceivedByCore'])
except Exception:
time_list.append(metadata_dictionary['Time'])
# Version of Magellan dependent
print('Channels')
try:
num_channels = len(magellan.summary_metadata['ChNames'])
channel_names = magellan.summary_metadata['ChNames']
except Exception:
num_channels = len(magellan.get_channel_names())
channel_names = magellan.get_channel_names()
image_height = magellan.summary_metadata['Height']
image_width = magellan.summary_metadata['Width']
print('Data type')
if magellan.summary_metadata['PixelType'] == 'GRAY16':
data_type = np.uint16
else:
data_type = np.uint8
# Add it all to the local dictionary
local_dictionary = {
'directory': magellan_directory,
'slices': num_slices,
'width': total_width,
'height': total_height,
'zSize': voxel_size_z_um,
'xySize': pixel_size_xy_um,
'frames': num_frames,
'time_list': time_list,
'positions': num_positions,
'channels': num_channels,
'channel_names': channel_names,
'single_image_height': image_height,
'single_image_width': image_width,
'data_type': data_type
}
# append to global dictionary
self.magellan_dataset_dictionary[magellan_directory] = local_dictionary
def get_working_directory(self):
# Get the string version
magellan_directory_str = str(
QFileDialog.getExistingDirectory(self, "Select Directory"))
# Convert to path version for OS compatibility
magellan_directory_path = pathlib.Path(magellan_directory_str)
try:
magellan = MagellanDataset(magellan_directory_path)
except Exception:
print('Not a Magellan Dataset')
self.show_not_magellan_dialog()
return
print('PATH: ' + str(magellan_directory_path))
self._ui.magellan_dataset_listWidget.addItem(magellan_directory_str)
self.store_magellan_metadata(magellan_directory_str)
def run(self):
"""
Run the thread to create and package files based on the inout from the GUI, which is packaged in the
output dictionary.
"""
for files in range(len(self.output_list)):
#print('In threading')
self.file = create_h5.create_h5(
self.output_dictionary[self.output_list[files]])
self.name_signal.emit(
self.output_dictionary[self.output_list[files]]['file_name'])
#print('After signal')
# Re-open Magellan directory to have the data on hand
magellan_directory = self.output_dictionary[
self.output_list[files]]['magellan_directory']
magellan = MagellanDataset(magellan_directory)
all_data = magellan.as_array(stitched=True)
# Gather pertinent information for packing the file
time_start = self.output_dictionary[
self.output_list[files]]['time_start']
time_end = self.output_dictionary[
self.output_list[files]]['time_end']
channel_list = self.output_dictionary[
self.output_list[files]]['file_channel_list']
x_min = self.output_dictionary[self.output_list[files]]['x_min']
x_max = self.output_dictionary[self.output_list[files]]['x_max']
y_min = self.output_dictionary[self.output_list[files]]['y_min']
y_max = self.output_dictionary[self.output_list[files]]['y_max']
z_min = self.output_dictionary[self.output_list[files]]['z_min']
z_max = self.output_dictionary[self.output_list[files]]['z_max']
total_width = x_max - x_min + 1
total_height = y_max - y_min + 1
num_slices = z_max - z_min + 1
#print(num_slices)
num_channels = len(channel_list)
#print(num_channels)
num_time = (time_end - time_start + 1)
#print(num_time)
data_type = self.output_dictionary[
self.output_list[files]]['data_type']
count = 0
# Status bar information passed to other function via emit
self.progress_bar_signal.emit(count, num_slices, num_channels,
num_time)
#print('to time')
# Time and channel information packaged in .ims file
for t in range(time_start, (time_end + 1)):
t = t - 1
time_name_string = '/TimePoint ' + str(t)
for c in channel_list:
channel_name_string = '/Channel ' + str(c)
channel_group_data = self.file.create_group(
"DataSet/ResolutionLevel 0" + time_name_string +
channel_name_string)
create_h5.write_attribute(channel_group_data,
'HistogramMax', '65535.000')
create_h5.write_attribute(channel_group_data,
'HistogramMin', '0.000')
create_h5.write_attribute(channel_group_data,
'ImageBlockSizeX', '256')
create_h5.write_attribute(channel_group_data,
'ImageBlockSizeY', '256')
create_h5.write_attribute(channel_group_data,
'ImageBlockSizeZ', '256')
create_h5.write_attribute(channel_group_data, 'ImageSizeX',
str(total_width))
create_h5.write_attribute(channel_group_data, 'ImageSizeY',
str(total_height))
create_h5.write_attribute(channel_group_data, 'ImageSizeZ',
str(num_slices))
#print('to data temp')
data_temp = self.file.create_dataset(
"DataSet/ResolutionLevel 0" + time_name_string +
channel_name_string + "/Data",
(1, total_height, total_width),
chunks=(8, 256, 256),
maxshape=(num_slices, total_height, total_width),
compression="gzip",
compression_opts=2,
dtype=data_type)
#print('to write direct')
data_temp.write_direct(np.array(all_data[t, c, z_min - 1]))
count = count + 1
#print('to count')
# Status bar information passed to other function via emit
self.progress_bar_signal.emit(count, num_slices,
num_channels, num_time)
# Package z data into .ims file
for z in range(z_min, z_max):
count = count + 1
#print(count)
data_temp.resize(data_temp.shape[0] + 1, axis=0)
data_temp[z, :, :] = np.array(all_data[t, c, z])
print('T:' + str(t) + ', C:' + str(c) + ', Z:' +
str(z))
time.sleep(0.05)
# Status bar information passed to other function via emit
self.progress_bar_signal.emit(count, num_slices,
num_channels, num_time)
# Close the files upon completion of packing data
self.file.close()
# Return signal to GUI to announce all files are have completed
self.completed_signal.emit('All Files Complete')
return
import numpy as np
from pygellan.magellan_data import MagellanDataset
import napari
#This path is to the top level of the magellan dataset (i.e. the one that contains the Full resolution folder)
data_path = '/path/to/data'
#open the dataset
magellan = MagellanDataset(data_path)
#read tiles or tiles + metadata by channel, slice, time, and position indices
#img is a numpy array and md is a python dictionary
img, img_metadata = magellan.read_image(channel_index=0,
z_index=30,
pos_index=20,
read_metadata=True)
#Alternatively, all data can be opened at once in a single dask array. Using dask arrays enables all_data to be
#held in a single memory-mapped array (i.e. the data are not loaded in RAM until they are used, enabing a convenient
#way to work with data larger than the computer's memory. Dask arrays also enable visulization in Napari (see below),
#and allow for code to be prototyped on a small computers and scaled up to clusters without having to rewrite code.
#More information can be found at https://dask.org/
all_data = magellan.as_array(
stitched=True
) #returns an array with 5 dimensions corresponding to time-channel-z-y-x
# all_data = magellan.as_array(stitched=False) #this version has a leading axis for position
#dask array can be used just like numpy array
#take max intenisty z projection of z stack at time point 0 in channel 0
max_intensity = np.max(all_data[0, 0], axis=0)
import numpy as np
import h5py
from write_functions import write_attribute
from pygellan.magellan_data import MagellanDataset
"""
data_path = 'E:\\...\\201978_AgedOvary_Nobox_3umSteps_2'
save_path = 'E:\\...\\MagellanTest'
save_name = 'E:\\...\\201978_AgedOvary_Nobox_3umSteps_2\\test.ims'
"""
data_path = 'E:\\...\\Cortex_3umStep_1'
save_path = 'E:\\...\\SPIM'
save_name = 'E:\\...\\Cortex_3umStep_1_test.ims'
magellan = MagellanDataset(data_path)
all_data = magellan.as_array(stitched=True)
size_array = all_data.shape
print('All Data')
print(size_array)
num_slices = size_array[2]
print(num_slices)
total_height = size_array[3]
print(total_height)
total_width = size_array[4]
print(total_width)
# Get pertinent information
voxel_size_z_um = magellan.summary_metadata['z-step_um']
print(voxel_size_z_um)
pixel_size_xy_um = magellan.summary_metadata['PixelSize_um']
print(pixel_size_xy_um)
import numpy as np
from pygellan.magellan_data import MagellanDataset
import matplotlib.pyplot as plt
#data_path = 'C:\\...\\test_1'
#data_path = 'J:\\...\\Cortex_10um_2timepoints_1'
data_path = 'E:\\...\\PyMARIS_test_z10_c4_t3_1'
magellan = MagellanDataset(data_path)
#channel_list = magellan.get_channel_names()
num_frames = magellan.get_num_frames()
time_list = []
for t in range(num_frames):
metadata_dictionary = magellan.read_metadata(t_index=t)
time_list.append(metadata_dictionary['TimeReceivedByCore'])
print(time_list)
#'TimeReceivedByCore': '2019-10-15 13:48:14.036429'
#'TimeReceivedByCore': '2019-10-15 13:50:55.130478'
#rint(channel_list)
#img, img_metadata = magellan.read_image(channel_index=0, z_index=0, pos_index=0, read_metadata=True)
#print(img_metadata)
"""
all_data = magellan.as_array(stitched=True)
print(all_data)
sub_data = np.array(all_data[0, 2, 5])
import numpy as np
import json
from pygellan.magellan_data import MagellanDataset
import tifffile as tf
import matplotlib.pyplot as plt
#data_path = 'C:\\...\\test_1'
data_path = 'J:\\...\\Cortex_10um_2timepoints_1'
save_path = 'J:\\...\\Cortex_10um_2timepoints_1'
save_name = 'J:\\...\\Cortex_10um_2timepoints_1\\test.tif'
magellan = MagellanDataset(data_path)
all_data = magellan.as_array(stitched=True)
z_steps = magellan.summary_metadata['z-step_um']
print("Z Steps")
print(z_steps)
img, img_metadata = magellan.read_image(channel_index=0,
z_index=0,
pos_index=0,
read_metadata=True)
xres = float(img_metadata['PixelSizeUm'])
print(img_metadata['PixelSizeUm'])
print(img_metadata)
# Find xy pixel data type
#data_type = xy_data.dtype
#print('Data Type: ' + str(data_type))