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artiq/examples/master/repository/remote_exec_processing.py

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import numpy as np
from numba import jit
from scipy.optimize import least_squares
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import logging
logger = logging.getLogger(__name__)
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@jit(nopython=True)
def compute_gaussian(r, img_w, img_h,
gaussian_w, gaussian_h,
gaussian_cx, gaussian_cy):
for y in range(img_h):
for x in range(img_w):
ds = ((gaussian_cx-x)/gaussian_w)**2
ds += ((gaussian_cy-y)/gaussian_h)**2
r[x, y] = np.exp(-ds/2)
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def fit(data, get_dataset):
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img_w, img_h = data.shape
def err(parameters):
r = np.empty((img_w, img_h))
compute_gaussian(r, img_w, img_h, *parameters)
r -= data
return r.ravel()
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guess = [
get_dataset("rexec_demo.gaussian_w", 12),
get_dataset("rexec_demo.gaussian_h", 15),
get_dataset("rexec_demo.gaussian_cx", img_w/2),
get_dataset("rexec_demo.gaussian_cy", img_h/2)
]
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res = least_squares(err, guess)
return res.x
def get_and_fit():
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if "dataset_db" in globals():
logger.info("using dataset DB for Gaussian fit guess")
def get_dataset(name, default):
try:
return dataset_db.get(name)
except KeyError:
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return default
else:
logger.info("using defaults for Gaussian fit guess")
def get_dataset(name, default):
return default
get_dataset = lambda name, default: default
return fit(controller_driver.get_picture(), get_dataset)