# %%
from ORSModel import Channel, orsObj, Progress, MultiROI, ArrayUnsignedLong, Color
import numpy as np
from matplotlib import pyplot as plt
#%%
multi_roi = orsObj('F3EE6FB4E7BA4A08A733B7963349EA2BCxvLabeledMultiROI')
preview = orsObj('3F9C5390478C4A5B99BA5F47A2DE39EACxvLabeledMultiROI')
#%%
# multi_roi_array = multi_roi.getAsArray(tIndex=1,)
# multi_roi_array = np.zeros((1,644,644))
# multi_roi_array = multi_roi.getNDArray()
# multi_roi_array = ArrayUnsignedLong()
# multi_roi.getAsArray(tIndex=0,pOutputArray=multi_roi_array)
# Convert both the multi ROI and preview ROI to numpy arrays
multi_roi_channel = Channel()
multi_roi_progress = Progress()
multi_roi.getAsChannel(inOutStructuredGrid=multi_roi_channel,
IProgress=multi_roi_progress)
multi_roi_array = multi_roi_channel.getNDArray()
preview_channel = Channel()
preview_progress = Progress()
preview.getAsChannel(inOutStructuredGrid=preview_channel,
IProgress=preview_progress)
preview_array = preview_channel.getNDArray()
#%%
#%%
from ORSModel import Channel,orsObj
import numpy as np
from matplotlib import pyplot as plt
# %%
xrf_channel = orsObj('2DD11443C547404A9C115E7BAFEDB166CxvChannel')
#%%
xrf_array = xrf_channel.getNDArray()
xrf_array = xrf_array.swapaxes(0,2)
xrf_array = xrf_array.squeeze()
# xrf_array = xrf_array.reshape(xrf_array.shape[0]*xrf_array.shape[1])
print(xrf_array.shape)
#%%
hist = np.histogram(xrf_array,bins=5)
print(hist)
#%%
bins = np.linspace(0,255,6)
inds = np.digitize(xrf_array,bins)
# %%
import numpy as np
from ORSModel import Channel, orsObj
#%%
def save_channel_to_numpy(obj_id):
data_channel = obj_id
data_array = data_channel.getNDArray()
print(data_array.shape)
np.save('data', data_array)
#%%
#! USER INPUT
save_channel_to_numpy(orsObj('51C5EF1F95C742E2A12E6A42AF671C68CxvChannel'))
# %%
from ORSModel import Channel, orsObj
import numpy as np
from matplotlib import pyplot as plt
# %%
xrf_channel = orsObj('51C5EF1F95C742E2A12E6A42AF671C68CxvChannel')
#%% crop
#%%
xrf_stack = xrf_channel.getNDArray()
xrf_stack = xrf_stack.swapaxes(0, 2)
print(xrf_stack.shape)
#%%
# fig = plt.figure()
# plt.imshow(xrf_stack[:,:,0])
# plt.show()
#%%
#! USER INPUT
thresh = 255
binary = np.zeros_like(xrf_stack)
print(binary.shape)
#%%
for i in range(xrf_stack.shape[2]):
for j in range(xrf_stack.shape[1]):
for k in range(xrf_stack.shape[0]):
if xrf_stack[k, j, i] >= thresh:
binary[k, j, i] = 1
# %%
from ORSModel import Channel, orsObj
import numpy as np
# %%
red_channel = orsObj('9DB0BBDD9C0E4661B71C15ABC91C9050CxvChannel')
green_channel = orsObj('C691EEA1AC0A4470994971A2408A8665CxvChannel')
blue_channel = orsObj('72E031C786C849B9AF953AB9EAFF0376CxvChannel')
bins = np.linspace(0, 255, 4)
clusters = 8
# spacing =
# channel = Channel()
# #set it sizes, since we use the default voxel size, the channel is for now 100 meter cube
# channel.setXYZTSize(100,100,100,1)
# channel.setXSpacing(0.01)
# channel.setYSpacing(0.01)
# channel.setZSpacing(0.01)
# #now the channel is 1 meter cube
# #initilize it for float32 data
# channel.initializeDataForFLOAT()
# #publish it so that it is visible in the Object properties list
# channel.publish()
# %%
# array = channel.getNDArray()
# #modify it data