From 256d6ff1ebfa9bb51e3f62b8c33090443873e4cc Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lo=C3=AFc=20Le=20Guyader?= <loic.le.guyader@xfel.eu>
Date: Mon, 5 Sep 2022 10:22:29 +0200
Subject: [PATCH] Removes all tight_layout calls
---
doc/changelog.rst | 1 +
doc/index.rst | 15 +++++++++++++++
src/toolbox_scs/detectors/digitizers.py | 1 -
src/toolbox_scs/misc/bunch_pattern.py | 1 -
src/toolbox_scs/misc/laser_utils.py | 2 +-
src/toolbox_scs/routines/XAS.py | 1 -
src/toolbox_scs/routines/knife_edge.py | 1 -
7 files changed, 17 insertions(+), 5 deletions(-)
diff --git a/doc/changelog.rst b/doc/changelog.rst
index c7d7dc9..5439780 100644
--- a/doc/changelog.rst
+++ b/doc/changelog.rst
@@ -9,6 +9,7 @@ unreleased
- **Improvements**
+ - remove calls to matplotlib tight_layout :mr:`206`
- **New Features**
diff --git a/doc/index.rst b/doc/index.rst
index a260921..27730a8 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -19,6 +19,21 @@ where in this example 2780 is the proposal number. After this and refreshing the
browser, a new kernel named ``SCS Toolbox (p002780)`` is available and should
be used to run jupyter notebooks on the Maxwell Jupyter hub.
+Figures setup: enabling matplotlib constrained layout
++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+To get the best looking figures generated by the SCS Toolbox, you
+need to enable the experimental constrained_layout_ solver in matplotlib. This
+is done in jupyter notebook with adding at the start the following lines:
+
+.. code:: python
+
+ import matplotlib.pyplot as plt
+ plt.rcParams['figure.constrained_layout.use'] = True
+
+.. _constrained_layout: https://matplotlib.org/stable/tutorials/intermediate/constrainedlayout_guide.html
+
+
Alternative: Manual ToolBox Installation
++++++++++++++++++++++++++++++++++++++++
diff --git a/src/toolbox_scs/detectors/digitizers.py b/src/toolbox_scs/detectors/digitizers.py
index 3dadcb9..297a774 100644
--- a/src/toolbox_scs/detectors/digitizers.py
+++ b/src/toolbox_scs/detectors/digitizers.py
@@ -1102,7 +1102,6 @@ def calibrateTIM(data, rollingWindow=200, mcp=1, plot=False, use_apd=True, intst
ax.set_ylabel(label_trace, size=10)
ax.set_xlabel('sample #', size=10)
ax.legend(fontsize=8)
- plt.tight_layout()
return F
diff --git a/src/toolbox_scs/misc/bunch_pattern.py b/src/toolbox_scs/misc/bunch_pattern.py
index 852f9ed..90deb34 100644
--- a/src/toolbox_scs/misc/bunch_pattern.py
+++ b/src/toolbox_scs/misc/bunch_pattern.py
@@ -171,7 +171,6 @@ def pulsePatternInfo(data, plot=False):
plt.plot(data['npulses_sase1'].trainId, data['npulses_sase1'], '^-',
ms=3, color='C2', label='SASE 1')
plt.legend()
- plt.tight_layout()
def repRate(data=None, runNB=None, proposalNB=None, key='sase3'):
diff --git a/src/toolbox_scs/misc/laser_utils.py b/src/toolbox_scs/misc/laser_utils.py
index 99698e1..0f05296 100644
--- a/src/toolbox_scs/misc/laser_utils.py
+++ b/src/toolbox_scs/misc/laser_utils.py
@@ -110,5 +110,5 @@ def fluenceCalibration(hwp, power_mW, npulses, w0x, w0y=None,
return 1e7*x*np.pi*w0x*w0y/2
ax2 = ax.secondary_yaxis('right', functions=(fTe, eTf))
ax2.set_ylabel(r'Pulse energy [$\mu$J]')
- fig.tight_layout()
+
return F*1e-1, fit_F, E*1e6, fit_E
diff --git a/src/toolbox_scs/routines/XAS.py b/src/toolbox_scs/routines/XAS.py
index 878cc28..0f483c5 100644
--- a/src/toolbox_scs/routines/XAS.py
+++ b/src/toolbox_scs/routines/XAS.py
@@ -235,7 +235,6 @@ def xas(nrun, bins=None, Iokey='SCS_SA3', Itkey='MCP3peaks', nrjkey='nrj',
color='C0', alpha=0.2)
ax2.set_xlabel('Energy (eV)')
ax2.set_ylabel('counts')
- plt.tight_layout()
return {'nrj': bins_c, 'muA': muA, 'sterrA': sterrA,
'sigmaA': nosample['sigmaA'], 'muIo': nosample['muIo'],
diff --git a/src/toolbox_scs/routines/knife_edge.py b/src/toolbox_scs/routines/knife_edge.py
index 78ad7d5..5f1e47e 100644
--- a/src/toolbox_scs/routines/knife_edge.py
+++ b/src/toolbox_scs/routines/knife_edge.py
@@ -93,4 +93,3 @@ def plot_knife_edge(positions, intensities, fit_params, rel_err, title,
plt.ylabel(signalKey)
plt.xlabel(axisKey + ' position [mm]')
plt.title(title)
- plt.tight_layout()
--
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