def reset_dn(self, dn, size, simul_z=None, simul_xy=None):
if simul_z is None:
simul_z = 1
if simul_xy is None:
simul_xy = dn.shape[1:][::-1]
# simul_z = 2
# simul_xy = (1024,)*2
# simul_z = 2
# simul_xy = (512,)*2
self.bpm = Bpm3d_img(size=size,
dn=dn,
lam=.5,
simul_z=simul_z,
simul_xy=simul_xy)
if not dn is None:
self.dn_max = np.amax(dn)
z = np.zeros_like(dn)
z[0, 0, 0] = 1.
units = [s / (n - 1.) for s, n in zip(size, dn.shape[::-1])]
self.canvas.setModel(DataModel(NumpyData(z, stackUnits=units)))
def data_model(data):
app = QtWidgets.QApplication(sys.argv)
t = time.time()
app.d = DataModel(NumpyData(data))
print("time to datamodel: ", time.time() - t)
QtCore.QTimer.singleShot(100, app.quit)
app.exec_()
def test_tiffdata():
d = TiffData(rel_path("../data/flybrain.tif"))
m = DataModel(d)
print(m)
for pos in range(m.sizeT()):
print(pos)
print((np.mean(m[pos])))
def test_numpydata():
d = NumpyData(np.ones((10, 100, 100, 100)))
m = DataModel(d)
print(m)
for pos in range(m.sizeT()):
print(pos)
print(np.mean(m[pos]))
def test_spimdata():
d = SpimData(rel_path("../data/spimdata"))
m = DataModel(d)
print(m)
for pos in range(m.sizeT()):
print(pos)
print(np.mean(m[pos]))
return m
def test_tiffdata():
d = TiffData(rel_path("../data/flybrain.tif"))
m = DataModel(d)
print("thread is running: ", m.dataLoadThread.isRunning())
print(m)
for pos in range(m.sizeT()):
print(pos)
print((np.mean(m[pos])))
time.sleep(.1)
def test_rawdata():
d = RawData(rel_path("../data/raw_64_65_66.raw"),
shape=(1, 66, 65, 64),
dtype=np.uint16)
print(d.size())
m = DataModel(d)
print(m)
for pos in range(m.sizeT()):
print(pos)
print((np.mean(m[pos])))
def _with_glwidget(data):
app = QtWidgets.QApplication(sys.argv)
win = GLWidget()
d = DataModel(NumpyData(data))
t = time.time()
win.setModel(d)
print("time to set model in glwidget: ", time.time() - t)
win.show()
win.raise_()
app.win = win
QtCore.QTimer.singleShot(200, app.quit)
app.exec_()
import numpy as np
from PyQt5 import QtCore
import logging
from spimagine import volshow, volfig, logger, qt_exec, NumpyData, DataModel
def single_data(data):
w = volshow(data, raise_window=False)
QtCore.QTimer.singleShot(1000, w.closeMe)
qt_exec()
def test_volumes():
d = np.random.uniform(0, 100, (100, ) * 3)
for dtype in (np.float32, np.int8, np.uint16, np.int32):
print("testing: %s" % dtype)
single_data(d.astype(dtype))
if __name__ == '__main__':
data = np.linspace(0, 255, 100**3).reshape((100, ) * 3).transpose(
(1, 2, 0))
m = DataModel(NumpyData(data.astype(np.uint8)))
w = volshow(m)
QtCore.QTimer.singleShot(1000, w.closeMe)
qt_exec()