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easier to read/distinguish names

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augustin_s
2024-08-02 16:50:04 +02:00
parent 098146c97b
commit 26e2a3f619
1 changed files with 24 additions and 24 deletions
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48
dap/algos/radprof.py
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@ -1,18 +1,18 @@
import numpy as np import numpy as np
def calc_radial_integration(results, data, pixel_mask_pf, center_radial_integration, r_radial_integration): def calc_radial_integration(results, data, pixel_mask_pf, center, rad):
if center_radial_integration is None: if center is None:
center_radial_integration = [ center = [
results["beam_center_x"], results["beam_center_x"],
results["beam_center_y"] results["beam_center_y"]
] ]
r_radial_integration = None rad = None
if r_radial_integration is None: if rad is None:
r_radial_integration, nr_radial_integration = prepare_radial_profile(data, center_radial_integration, keep_pixels=pixel_mask_pf) rad, norm = prepare_radial_profile(data, center, keep_pixels=pixel_mask_pf)
r_min = int(np.min(r_radial_integration)) r_min = int(np.min(rad))
r_max = int(np.max(r_radial_integration)) + 1 r_max = int(np.max(rad)) + 1
apply_threshold = results.get("apply_threshold", False) apply_threshold = results.get("apply_threshold", False)
@ -26,37 +26,37 @@ def calc_radial_integration(results, data, pixel_mask_pf, center_radial_integrat
if threshold_max > threshold_min: if threshold_max > threshold_min:
data[data > threshold_max] = np.nan data[data > threshold_max] = np.nan
rp = radial_profile(data, r_radial_integration, nr_radial_integration, keep_pixels=pixel_mask_pf) rp = radial_profile(data, rad, norm, keep_pixels=pixel_mask_pf)
silent_region_min = results.get("radial_integration_silent_min", None) silent_min = results.get("radial_integration_silent_min", None)
silent_region_max = results.get("radial_integration_silent_max", None) silent_max = results.get("radial_integration_silent_max", None)
if ( if (
silent_region_min is not None and silent_min is not None and
silent_region_max is not None and silent_max is not None and
#TODO: skipping entirely is a guess, but not obvious -- better to ensure the order min < max by switching them if needed #TODO: skipping entirely is a guess, but not obvious -- better to ensure the order min < max by switching them if needed
silent_region_max > silent_region_min and silent_max > silent_min and
silent_region_min > r_min and silent_min > r_min and
silent_region_max < r_max silent_max < r_max
): ):
silent_region = rp[silent_region_min:silent_region_max] silent_region = rp[silent_min:silent_max]
integral_silent_region = np.sum(silent_region) integral_silent_region = np.sum(silent_region)
rp = rp / integral_silent_region rp = rp / integral_silent_region
results["radint_normalised"] = [silent_region_min, silent_region_max] results["radint_normalised"] = [silent_min, silent_max]
results["radint_I"] = rp[r_min:].tolist() results["radint_I"] = rp[r_min:].tolist()
results["radint_q"] = [r_min, r_max] results["radint_q"] = [r_min, r_max]
return center_radial_integration, r_radial_integration return center, rad
def radial_profile(data, r, nr, keep_pixels=None): def radial_profile(data, rad, norm, keep_pixels=None):
if keep_pixels is not None: if keep_pixels is not None:
data = data[keep_pixels] data = data[keep_pixels]
data = data.ravel() data = data.ravel()
tbin = np.bincount(r, data) tbin = np.bincount(rad, data)
rp = tbin / nr rp = tbin / norm
return rp return rp
def prepare_radial_profile(data, center, keep_pixels=None): def prepare_radial_profile(data, center, keep_pixels=None):
@ -66,8 +66,8 @@ def prepare_radial_profile(data, center, keep_pixels=None):
if keep_pixels is not None: if keep_pixels is not None:
rad = rad[keep_pixels] rad = rad[keep_pixels]
rad = rad.astype(int).ravel() rad = rad.astype(int).ravel()
nr = np.bincount(rad) norm = np.bincount(rad)
return rad, nr return rad, norm