From 0157f0afb2b6174031ba17761f9c5a735b7b1b6c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lo=C3=AFc=20Le=20Guyader?= <loic.le.guyader@xfel.eu>
Date: Thu, 16 May 2024 13:03:51 +0200
Subject: [PATCH] Improved formatting
---
src/toolbox_scs/routines/boz.py | 46 ++++++++++++++++++++-------------
1 file changed, 28 insertions(+), 18 deletions(-)
diff --git a/src/toolbox_scs/routines/boz.py b/src/toolbox_scs/routines/boz.py
index 613b2dc..348d993 100644
--- a/src/toolbox_scs/routines/boz.py
+++ b/src/toolbox_scs/routines/boz.py
@@ -1,7 +1,7 @@
"""
Beam splitting Off-axis Zone plate analysis routines.
-Copyright (2021) SCS Team.
+Copyright (2021, 2022, 2023, 2024) SCS Team.
"""
import time
@@ -126,12 +126,14 @@ class parameters():
self.arr = None
self.tid = None
- def dask_load_persistently(self, dark_data_size_Gb=None, data_size_Gb=None):
+ def dask_load_persistently(self, dark_data_size_Gb=None,
+ data_size_Gb=None):
"""Load dask data array in memory.
Inputs
------
- dark_data_size_Gb: float, optional size of dark to load in memory, in Gb
+ dark_data_size_Gb: float, optional size of dark to load in memory,
+ in Gb
data_size_Gb: float, optional size of data to load in memory, in Gb
"""
self.arr_dark, self.tid_dark = load_dssc_module(self.proposal,
@@ -361,7 +363,9 @@ class parameters():
f += f'rois: {self.rois}\n'
f += f'flat-field type: {self.ff_type}\n'
- f += f'flat-field p: {self.flat_field} prod:{self.flat_field_prod_th} ratio:{self.flat_field_ratio_th}\n'
+ f += f'flat-field p: {self.flat_field} '
+ f += f'prod:{self.flat_field_prod_th} '
+ f += f'ratio:{self.flat_field_ratio_th}\n'
f += f'plane guess fit: {self.plane_guess_fit}\n'
f += f'use hexagons: {self.use_hex}\n'
f += f'enforce mirror symmetry: {self.force_mirror}\n'
@@ -683,7 +687,8 @@ def find_rois(data_mean, threshold, extended=False):
# along X
lowX = int(np.argmax(pX > threshold) - 1) # 1st occurrence returned
- highX = int(pX.shape[0] - np.argmax(pX[::-1] > threshold)) # last occ. returned
+ highX = int(pX.shape[0] -
+ np.argmax(pX[::-1] > threshold)) # last occ. returned
midX = int(0.5*(lowX+highX))
@@ -695,7 +700,8 @@ def find_rois(data_mean, threshold, extended=False):
# along Y
lowY = int(np.argmax(pY > threshold) - 1) # 1st occurrence returned
- highY = int(pY.shape[0] - np.argmax(pY[::-1] > threshold)) # last occ. returned
+ highY = int(pY.shape[0]
+ - np.argmax(pY[::-1] > threshold)) # last occ. returned
# define rois
rois = {}
@@ -972,7 +978,8 @@ def compute_polyline_flat_field_correction(rois, params, plot=False):
return flat_field
-def inspect_flat_field_domain(avg, rois, prod_th, ratio_th, vmin=None, vmax=None):
+def inspect_flat_field_domain(avg, rois, prod_th, ratio_th, vmin=None,
+ vmax=None):
"""Extract beams roi from average image and compute the ratio.
Inputs
@@ -1029,7 +1036,8 @@ def inspect_flat_field_domain(avg, rois, prod_th, ratio_th, vmin=None, vmax=None
if ratio_vmin is None:
ratio_vmin = np.percentile(v, 5)
ratio_vmax = np.percentile(v, 99.8)
- im3 = axs[2, k].imshow(v, vmin=ratio_vmin, vmax=ratio_vmax, cmap='RdBu_r')
+ im3 = axs[2, k].imshow(v, vmin=ratio_vmin, vmax=ratio_vmax,
+ cmap='RdBu_r')
axs[2,k].contour(v, ratio_th, cmap=cm.get_cmap(cm.cool, 2))
cbar = fig.colorbar(im, ax=axs[0, :], orientation="horizontal")
@@ -1545,7 +1553,7 @@ def nl_crit(p, domain, alpha, arr_dark, arr, tid, rois, mask, flat_field,
def nl_crit_sk(p, domain, alpha, arr_dark, arr, tid, rois, mask, flat_field,
sat_level=511, use_gpu=False):
- """Criteria for the non linear correction, combining 'n' and 'p' as reference
+ """Non linear correction criteria, combining 'n' and 'p' as reference.
Inputs
------
@@ -1619,8 +1627,9 @@ def nl_fit(params, domain, ff=None, crit=None):
if crit is None:
crit = nl_crit
- fixed_p = (domain, params.nl_alpha, params.arr_dark, params.arr, params.tid,
- fitrois, params.get_mask(), ff, params.sat_level, params._using_gpu)
+ fixed_p = (domain, params.nl_alpha, params.arr_dark, params.arr,
+ params.tid, fitrois, params.get_mask(), ff, params.sat_level,
+ params._using_gpu)
def fit_callback(x):
if not hasattr(fit_callback, "counter"):
@@ -1679,7 +1688,7 @@ def inspect_nl_fit(res_fit):
def snr(sig, ref, methods=None, verbose=False):
- """ Compute mean, std and SNR from transmitted signal sig and I0 signal ref.
+ """ Compute mean, std and SNR from transmitted and I0 signals.
Inputs
------
@@ -1844,7 +1853,7 @@ def inspect_correction(params, gain=None):
[photon_scale, np.linspace(0.95, 1.05, 150)*m],
cmap='Blues',
norm=LogNorm(vmin=0.2, vmax=200),
- # alpha=0.5 # make the plot looks ugly with lots of white lines
+ # alpha=0.5 # make the plot looks ugly with lots of white lines
)
h, xedges, yedges, img2 = axs[l, k].hist2d(
g*scale*sat_d[r].values.flatten(),
@@ -1852,7 +1861,7 @@ def inspect_correction(params, gain=None):
[photon_scale, np.linspace(0.95, 1.05, 150)*m],
cmap='Reds',
norm=LogNorm(vmin=0.2, vmax=200),
- # alpha=0.5 # make the plot looks ugly with lots of white lines
+ # alpha=0.5 # make the plot looks ugly with lots of white lines
)
v = snr_v['direct']['mu']/snr_v['direct']['s']
@@ -1862,8 +1871,8 @@ def inspect_correction(params, gain=None):
axs[l, k].text(0.4, 0.05, r'SNR$_\mathrm{w}$: ' + f'{v:.0f}',
transform = axs[l, k].transAxes)
- # axs[l, k].plot(3*nbins, 1+np.sqrt(2/(1e6*nbins)), c='C1', ls='--')
- # axs[l, k].plot(3*nbins, 1-np.sqrt(2/(1e6*nbins)), c='C1', ls='--')
+ #axs[l, k].plot(3*nbins, 1+np.sqrt(2/(1e6*nbins)), c='C1', ls='--')
+ #axs[l, k].plot(3*nbins, 1-np.sqrt(2/(1e6*nbins)), c='C1', ls='--')
axs[l, k].set_ylim([0.95*m, 1.05*m])
@@ -2313,8 +2322,9 @@ def inspect_saturation(data, gain, Nbins=200):
# compute density normalization on all data
norm = w*(np.sum(h[k+'_nosat']) + np.sum(h[k+'_sat']))
- ax.fill_between(bins_c, h[k+'_sat']/norm + h[k+'_nosat']/norm, h[k+'_nosat']/norm,
- facecolor=f"C{kk}", edgecolor='none', alpha=0.2)
+ ax.fill_between(bins_c, h[k+'_sat']/norm + h[k+'_nosat']/norm,
+ h[k+'_nosat']/norm, facecolor=f"C{kk}",
+ edgecolor='none', alpha=0.2)
ax.plot(bins_c, h[k+'_nosat']/norm, label=k,
c=f'C{kk}', alpha=0.4)
--
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