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