Removes all tight_layout calls

doc/changelog.rst

@@ -9,6 +9,7 @@ unreleased
 
 - **Improvements**
 
+    - remove calls to matplotlib tight_layout :mr:`206`
 
 - **New Features**
 

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
 ++++++++++++++++++++++++++++++++++++++++
 

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
 

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'):

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

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'],

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()