:param p: Parameters (amplitude, x,y center position, width, offset)
:param xi: x positions
:param yi: y positions
:return: The Gaussian 2D peak.
"""
arg = -(np.square(xi - p[1]) + np.square(yi - p[2])) / (2 * p[3] * p[3])
y = p[0] * np.exp(arg) + p[4]
return y
if __name__ == '__main__':
if not gf.cuda_available():
raise RuntimeError(gf.get_last_error())
# number of fits and fit points
number_fits = 10000
size_x = 12
number_points = size_x * size_x
number_parameters = 5
# set input arguments
# true parameters
true_parameters = np.array((10, 5.5, 5.5, 3, 10), dtype=np.float32)
# initialize random number generator
np.random.seed(0)
y = np.zeros((n_fits, x.shape[0], x.shape[1]), dtype=np.float32)
# loop over each fit
for i in range(n_fits):
pi = p[i, :]
arg = -(np.square(xi - pi[1]) + np.square(yi - pi[2])) / (2 * pi[3] *
pi[3])
y[i, :, :] = pi[0] * np.exp(arg) + pi[4]
return y
if __name__ == '__main__':
# cuda available checks
print('CUDA available: {}'.format(gf.cuda_available()))
if not gf.cuda_available():
raise RuntimeError(gf.get_last_error())
print(
'CUDA versions runtime: {}, driver: {}'.format(*gf.get_cuda_version()))
# number of fit points
size_x = 5
number_points = size_x * size_x
# set input arguments
# true parameters
mean_true_parameters = np.array((100, 2, 2, 1, 10), dtype=np.float32)
# average noise level
import numba
import numpy as np
import warnings
warnings.filterwarnings("ignore")
import picasso.io as io
import picasso.localize as localize
import picasso.gausslq as gausslq
import picasso.postprocess as postprocess
import picasso_addon
### Test if GPU fitting can be used
try:
from pygpufit import gpufit as gf
gpufit_available = True
gpufit_available = gf.cuda_available()
except ImportError:
gpufit_available = False
#%%
@numba.jit(nopython=True, nogil=True, cache=False)
def gradient_at(frame, y, x, i):
gy = frame[y + 1, x] - frame[y - 1, x]
gx = frame[y, x + 1] - frame[y, x - 1]
return gy, gx
#%%
@numba.jit(nopython=True, nogil=True, cache=False)
def boxvals_in_frame(frame, y, x, box):