diff --git a/.travis.yml b/.travis.yml
index aa2f612e..98fe1e25 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -36,14 +36,12 @@ install:
   - pip install matplotlib
   - python setup.py install
   - pip install wget
-  - pip install pyflakes pycodestyle jupyter
+  - pip install pyflakes jupyter pytest
 
 before_script :
   # static code analysis
   #   pyflakes: mainly warnings, unused code, etc.
   - python -m pyflakes opmd_viewer
-  #   pep8: style only, enforce PEP 8
-  - python -m pycodestyle --ignore=E201,E202,E122,E127,E128,E131,W605 opmd_viewer
 
 script:
   - "python setup.py test"
diff --git a/CHANGELOG.md b/CHANGELOG.md
index b6d5f425..74c26e9a 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,11 @@
 # Change Log / Release Log for openPMD-viewer
 
+## 0.9.0
+
+This release adds two features:
+- Improved calculation of the laser envelope, using the Hilbert transform.
+- Reconstruction of full 3D field from a quasi-3D dataset, when passing `theta=None`.
+
 ## 0.8.2
 
 This is a bug-fix release. It allows the slider to work properly in JupyterLab,
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index c4d9672d..30c155af 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -6,7 +6,7 @@
 
 In order to contribute, please fork the [main repository](https://github.com/openPMD/openPMD-viewer):
 
-- Click 'Fork' on the page of the main repository, in order to create a personal copy of this repository on your Github account. 
+- Click 'Fork' on the page of the main repository, in order to create a personal copy of this repository on your Github account.
 
 - Clone this copy to your local machine:
 ```
@@ -46,13 +46,10 @@ git pull git@github.com:openPMD/openPMD-viewer.git dev
 ```
 
 - Test and check your code:
-  - Use [pyflakes](https://pypi.python.org/pypi/pyflakes) and 
-[pycodestyle](https://pypi.python.org/pypi/pycodestyle) to detect any potential bug, and to 
-ensure that your code complies with some standard style conventions.
+  - Use [pyflakes](https://pypi.python.org/pypi/pyflakes) to detect any potential bug.
   ```
   cd openPMD-viewer/
   pyflakes opmd_viewer
-  pycodestyle --ignore=E201,E202,E122,E127,E128,E131,W605 opmd_viewer
   ```
   - Make sure that the tests pass (please install `wget` and `jupyter` before running the tests: `pip install wget jupyter`)
   ```
@@ -101,7 +98,7 @@ def get_data( dset, i_slice=None, pos_slice=None ) :
 
     i_slice: int, optional
        The index of the slice to be taken
-    
+
     pos_slice: int, optional
        The position at which to slice the array
        When None, no slice is performed
diff --git a/RELEASING.md b/RELEASING.md
index 5194941f..588920d8 100644
--- a/RELEASING.md
+++ b/RELEASING.md
@@ -9,8 +9,7 @@ packages, some of the steps below will be automatized.
 Make sure that your local environment is ready for a full release on
 PyPI and conda. In particular:
 
-- you should install the packages
-[`pypandoc`](https://pypi.python.org/pypi/pypandoc/),
+- you should install the package
 [`twine`](https://pypi.python.org/pypi/twine).
 - you should have a registered account on [PyPI](https://pypi.python.org/pypi) and [test PyPI](https://testpypi.python.org/pypi), and your `$HOME` should contain a file `.pypirc` which contains the following text:
 
diff --git a/conda_recipe/meta.yaml b/conda_recipe/meta.yaml
index 7fc942c8..e34a8cd9 100644
--- a/conda_recipe/meta.yaml
+++ b/conda_recipe/meta.yaml
@@ -1,4 +1,4 @@
-{% set version = "0.8.2" %}
+{% set version = "0.9.0" %}
 
 package:
   name: openpmd_viewer
diff --git a/opmd_viewer/__version__.py b/opmd_viewer/__version__.py
index deded324..3e2f46a3 100644
--- a/opmd_viewer/__version__.py
+++ b/opmd_viewer/__version__.py
@@ -1 +1 @@
-__version__ = "0.8.2"
+__version__ = "0.9.0"
diff --git a/opmd_viewer/addons/pic/lpa_diagnostics.py b/opmd_viewer/addons/pic/lpa_diagnostics.py
index 7cfc5d59..3f3c4718 100644
--- a/opmd_viewer/addons/pic/lpa_diagnostics.py
+++ b/opmd_viewer/addons/pic/lpa_diagnostics.py
@@ -15,6 +15,7 @@
 import scipy.constants as const
 from scipy.optimize import curve_fit
 from opmd_viewer.openpmd_timeseries.plotter import check_matplotlib
+from scipy.signal import hilbert
 try:
     import matplotlib.pyplot as plt
 except ImportError:
@@ -437,8 +438,7 @@ def get_current( self, t=None, iteration=None, species=None, select=None,
         return(current, info)
 
     def get_laser_envelope( self, t=None, iteration=None, pol=None, m='all',
-                            freq_filter=40, index='center', theta=0,
-                            slicing_dir='y', plot=False, **kw ):
+                            index='center', theta=0, slicing_dir='y', plot=False, **kw ):
         """
         Calculate a laser field by filtering out high frequencies. Can either
         return the envelope slice-wise or a full 2D envelope.
@@ -462,11 +462,6 @@ def get_laser_envelope( self, t=None, iteration=None, pol=None, m='all',
            Either 'all' (for the sum of all the modes)
            or an integer (for the selection of a particular mode)
 
-        freq_filter : float, optional
-            Range of frequencies in percent which to filter: Frequencies higher
-            than freq_filter/100 times the dominant frequencies will be
-            filtered out
-
         index : int or str, optional
             Transversal index of the slice from which to calculate the envelope
             Default is 'center', using the center slice.
@@ -504,16 +499,16 @@ def get_laser_envelope( self, t=None, iteration=None, pol=None, m='all',
         info = field[1]
         if index == 'all':
             # Filter the full 2D array
-            envelope = self._fft_filter(field[0], freq_filter)
+            e_complx = hilbert(field[0], axis=1)
         elif index == 'center':
             # Filter the central slice (1D array)
             field_slice = field[0][int( field[0].shape[0] / 2), :]
-            envelope = self._fft_filter(field_slice, freq_filter)
+            e_complx = hilbert(field_slice)
         else:
             # Filter the requested slice (2D array)
             field_slice = field[0][index, :]
-            envelope = self._fft_filter(field_slice, freq_filter)
-
+            e_complx = hilbert(field_slice)
+        envelope = np.abs(e_complx)
         # Restrict the metainformation to 1d if needed
         if index != 'all':
             info.restrict_to_1Daxis( info.axes[1] )
@@ -539,67 +534,6 @@ def get_laser_envelope( self, t=None, iteration=None, pol=None, m='all',
         # Return the result
         return( envelope, info )
 
-    def _fft_filter(self, field, freq_filter):
-        """
-        Filters out high frequencies in input data. Frequencies higher than
-        freq_filter / 100 times the dominant frequency will be filtered.
-
-        Parameters
-        ----------
-        field : 1D array or 2D array
-            Array with input data in time/space domain
-            When a 2D array is provided, filtering is performed along
-            the last dimension.
-
-        freq_filter : float
-            Frequency range in percent around the dominant frequency which will
-            not be filtered out
-
-        Returns
-        -------
-        A 1D array or 2D array with filtered input data in time/space domain
-        """
-        # Number of sample points along the filtered direction
-        N = field.shape[-1]
-        fft_freqs = np.fft.fftfreq(N)
-        # Fourier transform of the field
-        fft_field = np.fft.fft(field, axis=-1)
-        # Find central frequency
-        # (the code below works for both 1D and 2D arrays, and finds
-        # the global maximum across all dimensions in the case of the 2D array)
-        central_freq_i = np.unravel_index( np.argmax( np.abs(fft_field) ),
-                dims=fft_field.shape )[-1]
-        if central_freq_i > int( N / 2 ):
-            # Wrap index around, if it turns out to be in the
-            # negative-frequency part of the fft range
-            central_freq_i = N - central_freq_i
-        central_freq = fft_freqs[central_freq_i]
-        # Filter frequencies higher than central_freq * freq_filter/100
-        filter_bound = central_freq * freq_filter / 100.
-        # Find index from where to filter
-        filter_i = np.argmin(np.abs(filter_bound - fft_freqs))
-        filter_freq_range_i = central_freq_i - filter_i
-        # Write filtered FFT array
-        filtered_fft = np.zeros_like( field, dtype=np.complex )
-        # - Indices in the original fft array
-        i_fft_min = central_freq_i - filter_freq_range_i
-        i_fft_max = central_freq_i + filter_freq_range_i
-        # - Indices in the new filtered array
-        i_filter_min = int(N / 2) - filter_freq_range_i
-        i_filter_max = int(N / 2) + filter_freq_range_i
-        if field.ndim == 2:
-            filtered_fft[ :, i_filter_min:i_filter_max] = \
-                2 * fft_field[ :, i_fft_min:i_fft_max ]
-        elif field.ndim == 1:
-            filtered_fft[ i_filter_min:i_filter_max] = \
-                2 * fft_field[ i_fft_min:i_fft_max ]
-        # Calculate inverse FFT of filtered FFT array (along the last axis)
-        envelope = np.abs( np.fft.ifft(
-            np.fft.fftshift( filtered_fft, axes=-1 ), axis=-1 ) )
-
-        # Return the result
-        return( envelope )
-
     def get_main_frequency( self, t=None, iteration=None, pol=None, m='all',
                             method='max'):
         """
diff --git a/opmd_viewer/openpmd_timeseries/data_reader/field_metainfo.py b/opmd_viewer/openpmd_timeseries/data_reader/field_metainfo.py
index 6015e00e..8d8f3cde 100644
--- a/opmd_viewer/openpmd_timeseries/data_reader/field_metainfo.py
+++ b/opmd_viewer/openpmd_timeseries/data_reader/field_metainfo.py
@@ -124,3 +124,36 @@ def restrict_to_1Daxis(self, axis):
         # Suppress imshow_extent and replace the dictionary
         delattr(self, 'imshow_extent')
         self.axes = {0: axis}
+
+    def _convert_cylindrical_to_3Dcartesian(self):
+        """
+        Convert FieldMetaInformation from cylindrical to 3D Cartesian
+        """
+
+        try:
+            assert self.axes[0] == 'r'
+            assert self.axes[1] == 'z'
+        except (KeyError, AssertionError):
+            raise ValueError('_convert_cylindrical_to_3Dcartesian'
+                ' can only be applied to a timeseries in thetaMode geometry')
+
+        # Create x and y arrays
+        self.x = self.r.copy()
+        self.y = self.r.copy()
+        del self.r
+
+        # Create dx and dy
+        self.dx = self.dr
+        self.dy = self.dr
+        del self.dr
+
+        # Create xmin, xmax, ymin, ymax
+        self.xmin = self.rmin
+        self.ymin = self.rmin
+        del self.rmin
+        self.xmax = self.rmax
+        self.ymax = self.rmax
+        del self.rmax
+
+        # Change axes
+        self.axes = {0:'x', 1:'y', 2:'z'}
diff --git a/opmd_viewer/openpmd_timeseries/data_reader/field_reader.py b/opmd_viewer/openpmd_timeseries/data_reader/field_reader.py
index e42a81e2..465efa63 100644
--- a/opmd_viewer/openpmd_timeseries/data_reader/field_reader.py
+++ b/opmd_viewer/openpmd_timeseries/data_reader/field_reader.py
@@ -12,7 +12,7 @@
 import numpy as np
 from .utilities import get_shape, get_data, get_bpath, join_infile_path
 from .field_metainfo import FieldMetaInformation
-
+from ..utilities import construct_3d_from_circ
 
 def read_field_1d( filename, field_path, axis_labels ):
     """
@@ -103,7 +103,7 @@ def read_field_2d( filename, field_path, axis_labels ):
 def read_field_circ( filename, field_path, m=0, theta=0. ):
     """
     Extract a given field from an HDF5 file in the openPMD format,
-    when the geometry is 2d cartesian.
+    when the geometry is thetaMode
 
     Parameters
     ----------
@@ -117,13 +117,17 @@ def read_field_circ( filename, field_path, m=0, theta=0. ):
     m : int or string, optional
        The azimuthal mode to be extracted
 
-    theta : float, optional
+    theta : float or None
        Angle of the plane of observation with respect to the x axis
+       If `theta` is not None, then this function returns a 2D array
+       corresponding to the plane of observation given by `theta` ;
+       otherwise it returns a full 3D Cartesian array
 
     Returns
     -------
     A tuple with
-       F : a 2darray containing the required field
+       F : a 3darray or 2darray containing the required field,
+           depending on whether `theta` is None or not
        info : a FieldMetaInformation object
        (contains information about the grid; see the corresponding docstring)
     """
@@ -138,40 +142,64 @@ def read_field_circ( filename, field_path, m=0, theta=0. ):
         group.attrs['gridSpacing'], group.attrs['gridGlobalOffset'],
         group.attrs['gridUnitSI'], dset.attrs['position'], thetaMode=True )
 
-    # Extract the modes and recombine them properly
-    F_total = np.zeros( (2 * Nr, Nz ) )
-    if m == 'all':
-        # Sum of all the modes
-        # - Prepare the multiplier arrays
-        mult_above_axis = [1]
-        mult_below_axis = [1]
-        for mode in range(1, int(Nm / 2) + 1):
-            cos = np.cos( mode * theta )
-            sin = np.sin( mode * theta )
-            mult_above_axis += [cos, sin]
-            mult_below_axis += [ (-1) ** mode * cos, (-1) ** mode * sin ]
-        mult_above_axis = np.array( mult_above_axis )
-        mult_below_axis = np.array( mult_below_axis )
-        # - Sum the modes
-        F = get_data( dset )  # (Extracts all modes)
-        F_total[Nr:, :] = np.tensordot( mult_above_axis,
-                                        F, axes=(0, 0) )[:, :]
-        F_total[:Nr, :] = np.tensordot( mult_below_axis,
-                                        F, axes=(0, 0) )[::-1, :]
-    elif m == 0:
-        # Extract mode 0
-        F = get_data( dset, 0, 0 )
-        F_total[Nr:, :] = F[:, :]
-        F_total[:Nr, :] = F[::-1, :]
+    # Convert to a 3D Cartesian array if theta is None
+    if theta is None:
+
+        # Get cylindrical info
+        rmax = info.rmax
+        inv_dr = 1./info.dr
+        Fcirc = get_data( dset )  # (Extracts all modes)
+        nr = Fcirc.shape[1]
+        if m == 'all':
+            modes = [ mode for mode in range(0, int(Nm / 2) + 1) ]
+        else:
+            modes = [ m ]
+        modes = np.array( modes, dtype='int' )
+        nmodes = len(modes)
+
+        # Convert cylindrical data to Cartesian data
+        info._convert_cylindrical_to_3Dcartesian()
+        nx, ny, nz = len(info.x), len(info.y), len(info.z)
+        F_total = np.zeros( (nx, ny, nz) )
+        construct_3d_from_circ( F_total, Fcirc, info.x, info.y, modes,
+            nx, ny, nz, nr, nmodes, inv_dr, rmax )
+
     else:
-        # Extract higher mode
-        cos = np.cos( m * theta )
-        sin = np.sin( m * theta )
-        F_cos = get_data( dset, 2 * m - 1, 0 )
-        F_sin = get_data( dset, 2 * m, 0 )
-        F = cos * F_cos + sin * F_sin
-        F_total[Nr:, :] = F[:, :]
-        F_total[:Nr, :] = (-1) ** m * F[::-1, :]
+
+        # Extract the modes and recombine them properly
+        F_total = np.zeros( (2 * Nr, Nz ) )
+        if m == 'all':
+            # Sum of all the modes
+            # - Prepare the multiplier arrays
+            mult_above_axis = [1]
+            mult_below_axis = [1]
+            for mode in range(1, int(Nm / 2) + 1):
+                cos = np.cos( mode * theta )
+                sin = np.sin( mode * theta )
+                mult_above_axis += [cos, sin]
+                mult_below_axis += [ (-1) ** mode * cos, (-1) ** mode * sin ]
+            mult_above_axis = np.array( mult_above_axis )
+            mult_below_axis = np.array( mult_below_axis )
+            # - Sum the modes
+            F = get_data( dset )  # (Extracts all modes)
+            F_total[Nr:, :] = np.tensordot( mult_above_axis,
+                                            F, axes=(0, 0) )[:, :]
+            F_total[:Nr, :] = np.tensordot( mult_below_axis,
+                                            F, axes=(0, 0) )[::-1, :]
+        elif m == 0:
+            # Extract mode 0
+            F = get_data( dset, 0, 0 )
+            F_total[Nr:, :] = F[:, :]
+            F_total[:Nr, :] = F[::-1, :]
+        else:
+            # Extract higher mode
+            cos = np.cos( m * theta )
+            sin = np.sin( m * theta )
+            F_cos = get_data( dset, 2 * m - 1, 0 )
+            F_sin = get_data( dset, 2 * m, 0 )
+            F = cos * F_cos + sin * F_sin
+            F_total[Nr:, :] = F[:, :]
+            F_total[:Nr, :] = (-1) ** m * F[::-1, :]
 
     # Close the file
     dfile.close()
@@ -213,7 +241,8 @@ def read_field_3d( filename, field_path, axis_labels,
     Returns
     -------
     A tuple with
-       F : a 2darray containing the required field
+       F : a 3darray or 2darray containing the required field,
+           depending on whether `slicing` is None or not
        info : a FieldMetaInformation object
        (contains information about the grid; see the corresponding docstring)
     """
diff --git a/opmd_viewer/openpmd_timeseries/main.py b/opmd_viewer/openpmd_timeseries/main.py
index 695f991a..ac39f5ab 100644
--- a/opmd_viewer/openpmd_timeseries/main.py
+++ b/opmd_viewer/openpmd_timeseries/main.py
@@ -10,7 +10,8 @@
 
 import numpy as np
 import h5py as h5
-from .utilities import list_h5_files, apply_selection, fit_bins_to_grid
+from .utilities import list_h5_files, apply_selection, fit_bins_to_grid, \
+                        combine_cylindrical_components
 from .plotter import Plotter
 from .particle_tracker import ParticleTracker
 from .data_reader.params_reader import read_openPMD_params
@@ -378,9 +379,12 @@ def get_field(self, field=None, coord=None, t=None, iteration=None,
             The iteration at which to obtain the data
             Either `t` or `iteration` should be given by the user.
 
-        theta : float, optional
+        theta : float or None, optional
            Only used for thetaMode geometry
            The angle of the plane of observation, with respect to the x axis
+           If `theta` is not None, then this function returns a 2D array
+           corresponding to the plane of observation given by `theta` ;
+           otherwise it returns a full 3D Cartesian array
 
         slicing : float, optional
            Only used for 3dcartesian geometry
@@ -483,12 +487,7 @@ def get_field(self, field=None, coord=None, t=None, iteration=None,
                 # For Cartesian components, combine r and t components
                 Fr, info = read_field_circ(filename, field + '/r', m, theta)
                 Ft, info = read_field_circ(filename, field + '/t', m, theta)
-                if coord == 'x':
-                    F = np.cos(theta) * Fr - np.sin(theta) * Ft
-                elif coord == 'y':
-                    F = np.sin(theta) * Fr + np.cos(theta) * Ft
-                # Revert the sign below the axis
-                F[: int(F.shape[0] / 2)] *= -1
+                F = combine_cylindrical_components(Fr, Ft, theta, coord, info)
             else:
                 # For cylindrical or scalar components, no special treatment
                 F, info = read_field_circ(filename, field_path, m, theta)
diff --git a/opmd_viewer/openpmd_timeseries/utilities.py b/opmd_viewer/openpmd_timeseries/utilities.py
index 5e6c0173..53395a7a 100644
--- a/opmd_viewer/openpmd_timeseries/utilities.py
+++ b/opmd_viewer/openpmd_timeseries/utilities.py
@@ -166,3 +166,83 @@ def fit_bins_to_grid( hist_size, grid_size, grid_range ):
     hist_range = [ 1.e6 * hist_range[0], 1.e6 * hist_range[1] ]
 
     return( hist_size, hist_range )
+
+
+def combine_cylindrical_components( Fr, Ft, theta, coord, info ):
+    """
+    Calculate the catesian field Fx or Fy,
+    from the cylindrical components Fr and Ft.
+
+    Parameters:
+    -----------
+    Fr, Ft: 3darrays or 2Darrays (depending on whether `theta` is None)
+        Contains the value of the fields
+    theta: float or None
+        Indicates the angle of the plane in which Fr and Ft where taken
+    coord: string
+        Either 'x' or 'y' ; indicates which component to calculate
+    info: FieldMetaInformation object
+        Contains info on the coordinate system
+    """
+    if theta is not None:
+        # Fr and Fr are 2Darrays
+        assert (Fr.ndim == 2) and (Ft.ndim == 2)
+
+        if coord == 'x':
+            F = np.cos(theta) * Fr - np.sin(theta) * Ft
+        elif coord == 'y':
+            F = np.sin(theta) * Fr + np.cos(theta) * Ft
+        # Revert the sign below the axis
+        F[: int(F.shape[0] / 2)] *= -1
+
+    else:
+        # Fr, Ft are 3Darrays, info corresponds to Cartesian data
+        assert (Fr.ndim == 3) and (Ft.ndim == 3)
+
+        # Calculate cos(theta) and sin(theta) in the transverse Cartesian plane
+        # while avoiding divisions by 0
+        r = np.sqrt( info.x[:,np.newaxis]**2 + info.y[np.newaxis,:]**2 )
+        inv_r = 1./np.where( r!=0, r, 1. )
+        # The value `1.`` is a placeholder in the above (to avoid division by 0)
+        # The lines below replace this placeholder value.
+        cos = np.where( r!=0, info.x[:,np.newaxis]*inv_r, 1. )
+        sin = np.where( r!=0, info.y[np.newaxis,:]*inv_r, 0. )
+        if coord == 'x':
+            F = cos[:,:,np.newaxis] * Fr - sin[:,:,np.newaxis] * Ft
+        elif coord == 'y':
+            F = sin[:,:,np.newaxis] * Fr + cos[:,:,np.newaxis] * Ft
+
+    return F
+
+
+def construct_3d_from_circ( F3d, Fcirc, x_array, y_array, modes,
+    nx, ny, nz, nr, nmodes, inv_dr, rmax ):
+    """
+    Reconstruct the field from a quasi-cylindrical simulation (`Fcirc`), as
+    a 3D cartesian array (`F3d`).
+    """
+    for ix in range(nx):
+        x = x_array[ix]
+        for iy in range(ny):
+            y = y_array[iy]
+            r = np.sqrt( x**2 + y**2 )
+            ir = nr - 1 - int( (rmax - r) * inv_dr + 0.5 )
+            # Handle out-of-bounds
+            if ir < 0:
+                ir = 0
+            if ir >= nr:
+                ir = nr-1
+            # Loop over all modes and recontruct data
+            if r == 0:
+                expItheta = 1. + 0.j
+            else:
+                expItheta = (x+1.j*y)/r
+            for im in range(nmodes):
+                mode = modes[im]
+                if mode==0:
+                    F3d[ix, iy, :] += Fcirc[0, ir, :]
+                else:
+                    cos = (expItheta**mode).real
+                    sin = (expItheta**mode).imag
+                    F3d[ix, iy, :] += Fcirc[2*mode-1, ir, :]*cos \
+                                    + Fcirc[2*mode, ir, :]*sin
diff --git a/setup.py b/setup.py
index 14c769f9..89b80401 100644
--- a/setup.py
+++ b/setup.py
@@ -3,13 +3,8 @@
 from setuptools.command.test import test as TestCommand
 
 # Get the long description
-# If possible, use pypandoc to convert the README from Markdown
-# to reStructuredText, as this is the only supported format on PyPI
-try:
-    import pypandoc
-    long_description = pypandoc.convert( './README.md', 'rst')
-except (ImportError, RuntimeError):
-    long_description = open('./README.md').read()
+with open('./README.md') as f:
+    long_description = f.read()
 # Get the package requirements from the requirements.txt file
 with open('./requirements.txt') as f:
     install_requires = [line.strip('\n') for line in f.readlines()]
@@ -44,6 +39,7 @@ def run_tests(self):
       version=__version__,
       description='Visualization tools for openPMD files',
       long_description=long_description,
+      long_description_content_type='text/markdown',
       url='https://github.com/openPMD/openPMD-viewer.git',
       maintainer='Remi Lehe',
       maintainer_email='remi.lehe@lbl.gov',
diff --git a/tutorials/2_Specific-field-geometries.ipynb b/tutorials/2_Specific-field-geometries.ipynb
index 0ae55740..d10bfa5b 100644
--- a/tutorials/2_Specific-field-geometries.ipynb
+++ b/tutorials/2_Specific-field-geometries.ipynb
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,7 +68,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -87,7 +87,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -109,20 +109,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10ba8b240>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Slice across y (i.e. in a plane parallel to x-z)\n",
     "Ez1, info_Ez1 = ts_3d.get_field( field='E', coord='z', iteration=500, \n",
@@ -131,20 +120,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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qvgSl9pf5Yebus0zjlux8ZiZsS8Oa7Dy24dgdkm4+9+7Pb+/mzMxsgvLNZzMze5TBHu7H0LshqwVJT5K0rG55QNI7G9IcKOn+ujQf7lZ+zcwa1Xo+5yzd0Hclhoi4HtgXQNJU4DbgnCZJfxcRL+pk3szMcg25VVLbHAzcGBF/7XZGzMxypUH0HBja5RjgzBbrninpCtKk2O+JiGsaExRT5C0EmLbV1m3LpJlZvUBs9JAY1ZM0AzgCeH+T1ZcDO0fEg5IOB34C7NaYKCIWUUxoMWv+gm4ONGxmk0gEPd3BrXdzNrrDgMsj4s7GFRHxQEQ8WDxeAkyXNK/TGTQza04MZS7d0LclBuCVtKhGkvRY4M6ICEkHkALg3Z3MXFuUOEcq75CWma6bHZ5U5ti5+xzKTVfi4Ln7bEdHr+zvOzNh9v7yC+Q93IqzMkFvlxj6MjAUk16/AHhT3WtvBoiIU4CXAW+RNECaHPuYiHBVkZn1jCpvPkuaC3wd2JMUd14XEf871v31ZWCIiHXANg2vnVL3+GTg5E7ny8wsR6CqJ+r5InBeRLysuP+6+Xh21peBwcysnwWwsaKxkiRtCTwHOA4gIh4GHh7PPnu3ksvMbMISg5kLME/S0rplYcPOngDcBXxT0v9J+rqk2ePJnUsMZmYdFpTq+bw6IvYfYf00YD/gbRFxiaQvAicAHxpr/lxiMDPrghIlhtGsAFZExCXF87NJgWLMHBjMzDosQgzFlKxl9H3FHcCtkp5UvHQw8Kfx5M9VSb2gHX0EKt5ndu/9KdW3V89uAj9YpqNH5rEHMifVGcw/dHba3DzmH5rc2ovc71u5+yuRS2W+8ewzrQf7RaSbz5UOifE24IyiRdJNwD+NZ2cODGZmHVftnM8RsQwY6T5EKQ4MZmYdlm4+92BRpuDAYGbWBR5228zMNmlDz+dKOTCYmXXBkEsMZmZWEwEbhxwYzMyskKqSHBgmp6r7J5Rppp/dXj2zzXjmmRLTSvRjyOzzoMwPSBuzD82UhzL3md3fIf/YU3OHN2vDfAxDud9j5rGzm+KXaLKf3ech98spM+B+B6v9M3s1d4UDg5lZh7m5qpmZNXBVkpmZNejWfM45+jIwSLoZWAMMAgONQ9JKEmlGo8OBdcBxEXF5p/NpZtZMapVU6VhJlerLwFA4KCJWt1h3GLBbsTwd+Grxv5lZ1/V6B7fereQanyOBb0dyMTBX0g7dzpSZWc0Qylq6oV8DQwAXSLqsyTR3APOBW+ueryheG0bSwtp0eYNr17Ypq2Zmw9VaJeUs3dCvVUnPioiVkrYDLpR0XUT8tm59s0/zUa2ZI2IRsAhg1vwFZVo7m5mNi1slVSwiVhb/r5J0DnAAUB8YVgAL6p7vBKzsXA5LasNEPbkd14amZ+5v5lBewhmZ6QBNzUyb+caHNuT/oWkw88bfurxjT92QfWimrc/s2Jc5oU+Z35fBmZmf5cy8/Q3l9jErce7mTv6T23GtF6vyI8RADweG3s1ZC5JmS5pTewwcAlzdkGwx8I9KngHcHxG3dzirZmYtuSqpWtsD56QWqUwDvhcR50l6M0BEnAIsITVVXU5qrjquae7MzKrkns8Vi4ibgH2avH5K3eMA/qWT+TIzK6PqwCBpKrAUuC0iXjSeffVdYDAz63dt6sfwDuBaYMvx7qjv7jGYmU0EVfZjkLQT8PfA16vIm0sMZmYdFgED+RP1zJO0tO75oqKpfb0vAO8F5lSRPwcGM7MuKFGVtLpxPLh6kl4ErIqIyyQdWEXeHBjKKtOXoOL+CdmTolB9/wRtnjcTzazNc2ehgZnT8/aZ+we0fv2M7GMPDMzKSqf78vY3bV1+/8iZD+SlnZI5+U/u5DsAGzfPSzeQe1LmfYyl+lpkf5KqeEKfDqr4HsOzgCMkHU76RraU9N2IeM1Yd+h7DGZmXRChrGX0/cT7I2KniHg8cAxw0XiCArjEYGbWFZ6PwczMNoloTwe3iPg18Ovx7seBwcys48RgfqukjnNgMDPrgpz7B93iwGBm1mEeK8nMzIaLdJ+hVzkwtFPuBUFmVWPuHAsAMT2zf8KsvEH/N98ib8KB+Vvdn5UOYJtZ1c6at3LtVtlpb9mQe+rndR6ZXuKtzLo7r4PC1A153+HQjBLzUGyd+b5z+9ZMyZzfocy89xX/3eR3jOjs3A1ulWRmZpuEbz6bmVkjVyWZmdkwbpVkZmabRPR2YOjdSq4mJC2Q9N+SrpV0jaR3NElzoKT7JS0rlg93I69mZiPxnM/VGQCOj4jLJc0BLpN0YUT8qSHd78Y7tZ2ZWTv5HkNFIuJ24Pbi8RpJ1wLzgcbAYGbWswIx1MOtkno3Z6OQ9Hjg/wGXNFn9TElXSDpX0lNG2MdCSUslLR1cW22bejOzkUTm0g19VWKokbQF8CPgnRHxQMPqy4GdI+LBYuKKnwC7NdtPMT3eIoBZ8xdkfQelqvyyOwnlff1lJuphet4+p83amJVu2zkPZqXbc+7KrHQAu212Z1a66crrhHfVrJ2yj33XmtlZ6YY0MyvdjAfzOqMBzLpzXVY6rcvrVBib5+URIKZkvu/peSfb4PS8k3xK/hxKDOX+3eTvsvf45nO1JE0nBYUzIuLHjesj4oGIeLB4vASYLmleh7NpZjayHi4y9FWJQZKAbwDXRsR/tkjzWODOiAhJB5CC390dzKaZ2ah6ucTQV4GBNLfpa4GrJC0rXjsReBxARJwCvAx4i6QBYD1wTEQv3/83s8kmgKHcOrMu6KvAEBG/Z5Sa+4g4GTi5MzkyMxuDoLMj9pXUd/cYzMwmgoi8ZTQ5HX/L6qsSg5nZhFFdBXdux99sDgxmZh2nym4+t6Pjb3ZgkLQ9cAiwDzAXuA+4ArgwIu4YawaMEhOT5F9iKHOinlmZ/Ri233xNVrq9Zq/ISgfwtFl/zUo3K7Mfw6wpee8F4NLZj8tKdw95k/9MW5eXR4Apdzd2vWku7s9LN2XLOdnHnr759Kx0G7eYlZVuYLO8c3JwsMSPYG5/ndw/hzI/wJ1sp5J/qHmSltY9X1T0wXqUUTr+Zhs1MEh6MvBx4CDgMuBa4A5gDqmF0Bck/Tfw4fEUXczMJo2AyG+VtDoi9h8t0Sgdf0vJKTGcDnwGeHVEPKorpqQZwJGk/gXPHE9mzMwmj+paJY3W8besUQNDRDx9lPUPAz8sFjMzy1FRrVVOx9+y3FzVzKwbqhsSo9bx93l189AcPp6slWqVJOklwAeL7a4DlpFuQC8r7oybmdloKuzgltPxt6yyzVUXAZ8Argb2APYGjgKeAmxRZcbMzCayXh6op2xgWA98OSIGgYtqLxZ1XGZmlquHx0oqe4/hc8A/N77oQerMzMpR5C3dULbEcCFwrqQXAD8n3WO4MiIeqjxnE0Dl/W/KhPHMznDTp+Z1zNpq+vqsdAum549wvuv0vNNvpjbLSnfX4KrsY8+ZkTcJzj2Z383UDfkT9cSavM6Cgw/kNUWfOiX/ynPK+rl56QYyz97Mt638jydfP1+OdnN6tgxlSwznAEtJAeEQ4HvAGknu2GZmlk3pijBn6YKyJYbtgJfXVx0VgzbtXWmuzMwmuglUYvg+8Nz6FyJiTUT8T3VZMjObBIYyly4oGxieAPxQ0rsk7d6ODJmZTXi1fgw9WpVUNjD8kHRf4UjgYklrJP1B0leqz1prkg6VdL2k5ZJOaLJ+pqQfFOsvKUYcNDPrGROpVdJ3irGRAJD0ONIw3B27xyBpKvBl4AXACuBSSYsbRnZ9PXBvROwq6RjgP4BXdCqPZmajmkD3GB6UdKWk70g6HtiNNAz3rtVnraUDgOURcVMRpL5PKsHUOxL4VvH4bOBgd8IzM8szllZJ+xbLPsBbgccBN1Wcr5HMB26te74CaBwBdlOaiBiQdD+wDbC6PpGkhcBCgGlbbd2u/JqZPUq3qolylAoMEXEf8OtiAUDSJyimleuQZlf+jR9xThqKWZAWAcyav6CHvyYzm1CCCTUkRjOfAB51A7iNVgAL6p7vBKxslUbSNGAr4J6O5M7MLEd1w25XrlRgkPRlSW+U9DRJtUlhdwDWVZ+1li4FdpO0SzF73DHA4oY0i4Fji8cvAy7yeE5m1kt6uVVS2RLDbcDzgG8C90i6gWJOBklHSfqbotVQ20TEAOnexvmkG99nRcQ1kj4m6Ygi2TeAbSQtB95NZ0s0Zmaj6+ESQ9l7DJ+sPS7mGN0D2KtY3lj8vy0wq+kOKhIRS4AlDa99uO7xQ8DL25kHM7Nx6eE6jLKtkjaJiI2k2duuqH9dUt7wjWZmk1Q3q4lyjFqVJOntkmaOkmampLfDppZLZmY2kiHlLRlGGw2irJwSw2OB5ZKWAL8BrgfWAHOA3YEDgcOAb483MxNNbmO03CuHKDOgVuYJtXEw75bQ/Rvz5kS4deM2WekAlk/9a1a6WcqbO+HGjTtnH3vNwyNe6zwi87sZnJl/u05z5mSlmzqUd3BtkT+r7tBmeZUEQ9Myz97Mtx1VtH9s1LutPbNUVWLIHA2ilFHPkog4UdJ/AseRhprYC5gL3AtcSarrPzEi8mdoMTOb7KqrSto0GgSApNpoEO0LDAARsRr4bLGYmdl4lLvHME/S0rrni4rOuTU5o0GUMuabz2ZmNg75gWF1ROw/wvqskR7KcGAwM+uCCufBzhkNopSyPZ/b2nnNzMxKyxkNopSybQXOk5TfBMLMzJqrqOdzq9EgxpO1soFhGfA/knasvSDpOZJ+N55MmJlNKpnjJGU3ZY9YEhG7R8QTI+Lfx5u9skNi/KukG0nB4f080nz1M+PNiJnZpNLDPZ/HcvP5D8ADwBmkWdKOiIj1leZqssk9QUqM3x4b8wqDDz00PSvdnevyOmVdNWunrHQADw3lHXu6BvOOvS7/2Petzeuwl2tg8/zbb0PbbJmVTpvldcIb2jyzsx6wccsZWekGZuWda4PT89JFmbuT2T1Dc9OV+AXuZKe5Hg4MZW8+/5g0Sc+PgJcCLySNtmpmZplEapWUs3RD2RLDDcAbIuIeAEk3AYslPT4ivlx57szMJqJ+H0SvXkS8rxYUiudXAX9HGnLbzMxyTZT5GJqJiNskPbuKzJiZTRo9XGKopOdzRKypYj9mZpNFL1cl9c2QGJI+A7wYeBi4EfinZnM/SLqZNCz4IDAwyhgjZmbd0cOBoR2jpLfLhcCeEbE38Gfg/SOkPSgi9nVQMLOeFBOrVVLXRMQFdU8vBl7WjXyUKf5FZlrlztI0mH/w2Ji3z4HMfgx3rckbCeXqKTuOnqhw+0NbZafNsXJt/v7Wr8mblnxW5kf+8Bb511gPbb95VrqpG/LyODQj/9gbtsrrUDCwWd75M5TXLYKhEv0Y2jKpTy9yiaFyrwPObbEugAskXSZp4Ug7kbRQ0lJJSwfXrq08k2ZmrVQ5JEbVeqrEIOmXpKlEG30gIn5apPkAMEDqed3MsyJipaTtgAslXRcRv22WsJjsYhHArPkLejh+m9mE08O/OD0VGCLi+SOtl3Qs8CLg4IjmFTURsbL4f5Wkc0jT3jUNDGZmXdHFPgo5+qYqSdKhwPtIYzOta5FmtqQ5tcfAIcDVnculmdnoRG9XJfVNYABOBuaQqoeWSToFQNKOkpYUabYHfi/pCuCPwC8i4rzuZNfMrLVeDgw9VZU0kojYtcXrK4HDi8c3Aft0Ml9mZmPSw1VJfRMYzMwmFAcGMzPbpMdHV3VgaKfsCXjykmkwfxYRZU7UEw/l7W/dlLzJYG4dmpu3Q2DV9LxOc0OR977Xr8/sbQWwttpTf+Ps/LQPbZN37CkDefsbKvFWNm6e2fGx4g5uZTqt5abNPC2y03VcBwJD7lBCjfrp5rOZ2YTRoSExygwltIkDg5lZF3SiVVJEXBARtbLnxUDW/LeuSjIz67RyHdzmSVpa93xRMWpDWa8DfpCT0IHBzKwb8gPD6pFGiq5oKKFhHBjMzDqs1vO5ClUMJdTIgcHMrAs01P5mSXVDCT231VBCzTgwmJl1WucG0TsZmEkaSgjg4oh482gbOTCUVeLLzG4+nbvPwfxjT9mYl24os2FaZJ4q6wdKTFgzNbMtXmZD9NiQf+ypD2XuM/NLHMjsHwBknxjK/L7L9BEYnJnZPyGv20r2BDxRYqK10yWOAAAMi0lEQVSe7LaSvdxDLEMnst9qKKHRODCYmXVDD8c1BwYzsy7o5QKPA4OZWTc4MJiZ2SZRyXAXbePAYGbWYVX2Y2gHBwYzs27I62vWFX01iJ6kj0q6rZjac5mkw1ukO1TS9ZKWSzqh0/k0MxuNp/as1ucj4rOtVkqaCnwZeAGwArhU0uKI+FOnMlha5pdfalj5zLkbpmQePDIby8dAfi5jSt4+ldmZYMrGMvNVZKbNvHQazGz3nw6eO5FA7v7yD507d0Nuv4Ps/gll5mPo93kWcnSug9uY9FWJIdMBwPKIuCkiHga+DxzZ5TyZmQ3TofkYxqQfA8NbJV0p6TRJWzdZPx+4te75iuK1R5G0UNJSSUsH165tR17NzJpyYChB0i8lXd1kORL4KvBEYF/gduBzzXbR5LWmhbaIWBQR+0fE/lNnl5ib0cxsPIJ08zln6YKeu8cw2hCyNZJOBX7eZNUKYEHd852AlRVkzcysMr3cXLXnSgwjkbRD3dOjgKubJLsU2E3SLpJmAMcAizuRPzOzbJG5dEHPlRhG8WlJ+5I+rpuBNwFI2hH4ekQcHhEDkt4KnA9MBU6LiGu6lWEzs0bu4FahiHhti9dXAofXPV8CLOlUvszMSonoyEQ9Y9VXgcHMbMLo3bjgwNBWFXdcK3MeZTdzy+5NlHncEpMJRWZHr9witzI79ZXZZ/bHU+IvKTubbfjhyJ3Up+qOa6U6o1Vdx9KjHeFclWRmZo8IoIerkvqqVZKZ2YTRwVZJkt4jKSTNy0nvEoOZWRd0qipJ0gLS2HG35G7jEoOZWRdoKLKWCnweeC8lyh8uMZiZdVq5aqJ5kpbWPV8UEYtyNpR0BHBbRFyh3FF9cWAwM+u41MEtOzKsjoj9W+5L+iXw2CarPgCcCBxSNn8ODGZm3VDRyKmtxpeTtBewC1ArLewEXC7pgIi4Y6R9OjD0gjZM1FP1HC+5/RNiqERfgtyEbfh8svt5ZN4hjKklJijKTFp1X4u002rTZR+7xJ3W/H1m77InlSgxjElEXAVst+l40s3A/hGxerRtHRjMzDqtx2dwc2AwM+u4zo+VFBGPz03rwGBm1g1dmoQnhwODmVmnRfem7czhwGBm1g0uMZiZ2TC9GxccGMzMukFDvVuX1DeBQdIPgCcVT+cC90XEvk3S3QysAQaBgZF6DPadElcYVfcRyG5738225WWuwKruIzClzJdT7RwYpfox5Mrtv9HnfQm6Jqisg1s79E1giIhX1B5L+hxw/wjJD8rpxGFm1g0i2t7BbTz6JjDUKPXtPhp4XrfzYmY2Zj0cGPpx2O1nA3dGxA0t1gdwgaTLJC0caUeSFkpaKmnp4Nq1lWfUzKyliLylC3qqxDDSKIER8dPi8SuBM0fYzbMiYqWk7YALJV0XEb9tlrAYunYRwKz5C3o3fJvZxOJ7DPlajRJYI2ka8FLgqSPsY2Xx/ypJ5wAHAE0Dg5lZt/Ryq6R+q0p6PnBdRKxotlLSbElzao9J45Bf3cH8mZllyKxG6lJVUr8FhmNoqEaStKOkJcXT7YHfS7oC+CPwi4g4r8N5NDMbWdDTgaGnqpJGExHHNXltJXB48fgmYJ8OZ8vMrLzerUnqr8BgJVQ8U0/2PCuT9BZ+mY5eyvyQ2tF5rC86pPVDHivgfgxmZjacA4OZmW0SAYO9W5fUbzefzcwmhg7dfJb0NknXS7pG0qdztnGJwcysGzpQlSTpIOBIYO+I2FB0/B2VA4OZWacF0Jk5n98CnBQRGyB1/M3ZyFVJZmYdFxBDecv47A48W9Ilkn4j6Wk5G7nEYGbWaUGZm8/zJC2te76oGOcNGHmMOdJv/NbAM4CnAWdJekLEyPVYDgyTXe+2mOsNuW3qS3yO7ktgQJl7DKtHmnBspDHmJL0F+HERCP4oaQiYB9w10gFdlWRm1g2daZX0E4q5ayTtDswARp3EzCUGM7OO69g4SKcBp0m6GngYOHa0aiRwYDAz67wAOjDsdkQ8DLym7HYODGZm3eAhMczM7BG9PSSGA4OZWacFxPj7KLSNA4OZWTd0pufzmDgwmJl1Qw/fY+i5fgySXl6MAjgkaf+Gde+XtLwYKfCFLbbfpej+fYOkH0ia0Zmc26SmCbZYe0WkVkk5Sxf0XGAArgZeCvy2/kVJe5DmfH4KcCjwFUlTm2z/H8DnI2I34F7g9e3NrpnZGPTwnM89Fxgi4tqIuL7JqiOB70fEhoj4C7AcOKA+gSSRevmdXbz0LeAl7cyvmVl5QQwOZi3d0HOBYQTzgVvrnq8oXqu3DXBfRAyMkMbMrLtqw27nLF3QlZvPI40GGBE/bbVZk9caP7WcNPX5WAgsBJi21datkpmZVc/NVYcbaTTAEawAFtQ93wlY2ZBmNTBX0rSi1NAsTX0+FgGLAGbNX9C7TQTMbEIJIHq4uWo/VSUtBo6RNFPSLsBuwB/rExSDQ/038LLipWOBViUQM7PuiI5N1DMmPRcYJB0laQXwTOAXks4HiIhrgLOAPwHnAf8SEYPFNksk7Vjs4n3AuyUtJ91z+Ean34OZ2Wh6+eazMkZgnRQk3QX8teHleWSMXT6BTfb3D/4M/P6bv/+dI2Lbse5U0nnFvnOsjohDx3qssXBgGIGkpSPNnDTRTfb3D/4M/P4n5/vvuaokMzPrLgcGMzMbxoFhZIu6nYEum+zvH/wZ+P1PQr7HYGZmw7jEYGZmwzgwmJnZMA4MDcY7H8REI+mjkm6TtKxYDu92njpB0qHF97xc0gndzk83SLpZ0lXF97602/lpN0mnSVol6eq61x4j6cJifpcLJU2KQdUcGB5tvPNBTESfj4h9i2VJtzPTbsX3+mXgMGAP4JXF9z8ZHVR875OhLf/ppL/teicAvyrmd/lV8XzCc2BoMJ75IGzCOABYHhE3RcTDwPdJ379NYBHxW+CehpePJM3rApNofhcHhnw580FMVG+VdGVR1J4MRenJ/F3XC+ACSZcVQ9RPRttHxO0Axf/bdTk/HdGVYbe7rY3zQfSlkT4P4KvAx0nv9ePA54DXdS53XTFhv+uSnhURKyVtB1wo6briqtomuEkZGNo4H0Rfyv08JJ0K/LzN2ekFE/a7LiMiVhb/r5J0DqmKbbIFhjsl7RARt0vaAVjV7Qx1gquS8o06H8REVPwx1BxFujk/0V0K7CZpF0kzSI0OFnc5Tx0labakObXHwCFMju++0WLSvC4wieZ3mZQlhpFIOgr4ErAtaT6IZRHxwoi4RlJtPogB6uaDmOA+LWlfUlXKzcCbupud9ouIAUlvBc4HpgKnFfOBTCbbA+dIgvQ78b2IOK+7WWovSWcCBwLzijlhPgKcBJwl6fXALcDLu5fDzvGQGGZmNoyrkszMbBgHBjMzG8aBwczMhnFgMDOzYRwYzMxsGAcGMzMbxoHBzMyGcWCwSU3SpyS9s037/qOkp7Rj32bt5A5uNmlJ2hZYBuwaEevbsP+jgVdExD9UvW+zdnKJwSaz44Al7QgKhcXAQQ3jTZn1PAcGm1AkbSFpsP7HWNKekm6vDQpX5zDgNw3bf0DSV+ueby1po6RZkt4g6XxJX5V0r6Q/S9pD0jsk3SJptaSX1raNiIeAy0gD0Jn1DQcGm1Ai4kHgOmC/updPAj4ZEWsaku8FNM7WtxepeqlmX+D64kd+b2B/4GxgHnAVcG6R7omk+So+2LC/a4F9xvRmzLrEgcEmokspAoOk55Dmbf5ak3RzgWbBojEwXFE83gf4VET8qhhZ90/AVRHxxYjYSBqWunHE4jXFccz6hgODTUSbAgPwaeBDxdzNje4FNlUvFXMvPJFUEqjZh0cCxd4Mn6hojybPr2s4xhzgvpL5N+sqBwabiC4F9pP0D8BmwJkt0l0J7F73fA/gtohYB6A0GcGBwBWSdgZmAH+uS78vw0sXezc8B3gyj5Q4zPqCA4NNRFeQ5rD+HHBCRAy1SLcEeG7d872A7SQ9UdJmpHsGO5MmKNqHVG00BCBpy2LdlXXb70NdEJA0E3gqcGEF78msYxwYbMKJiA2k6qCbI+LcEZJ+Gzi8CAKQAsP5pBvKy4E7gZuADzC8SglS6WB5XeliCrBnQ5ojgF/X5k426xfu4GYTTnGvYDlwdERcPEraTwKrIuILks4Fvh4RP6ooH5cAr4+IyThXsvUxBwabcCT9O/CEiHhlye1WAIdExJ/akzOz/uCqJJswJO0n6X7gOcDbSm67NbAdcEM78mbWT1xiMDOzYVxiMDOzYRwYzMxsGAcGMzMbxoHBzMyGcWAwM7NhHBjMzGwYBwYzMxvm/wOJ2Db0KEUYBgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x114a892b0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Slice across z (i.e. in a plane parallel to x-y)\n",
     "Ez2, info_Ez2 = ts_3d.get_field( field='E', coord='z', iteration=500,\n",
@@ -160,26 +138,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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/aZWg70r6bZzVfhMRL+tm3szMSg2Wt96ZJumKuudzI6LVxOfHAGcPtbO+C/oNhj2rmZmNNmnAteKgvzwiZg+XSNJE4AjgvUOl6/egP9RZ7fmSrgaWAu+OiOsbE0iaA8wBmDRuixHLpJlZvUCs6fwwDIcBV0XE3UMl6tugP8xZ7Spgl4h4UNLhwI+B3RsT5UukuQBTJkzv0TTFZlY1EYxE56xjGaZqB/rwRm6dlme1iHggIh7Mj+cDm0ia1u0Mmpk1JwYLl6K9SZsDLwHOGy5t35b0GeKsJunJwN0REZL2I53c7u1m5iorBnudg+pRP5fdqinobEk/Ih4GtilJ25dBv+6s9pa61/4BICJOA14FvFXSWuAR4JiIcPWNmY0ankSlDc3OajnY1x5/Gfhyt/NlZlYikCdRMTOrigDW9GjsHQd9M7Ouk8fTNzOriqCtHrkd5aBvZtYDLumbmVVEhFzStx7pdLv6wc63jK1qa1uptCQ4UL7TcYX7dNv/EZVu5HZ8GIYiDvpmZl3nOXLNzCoj3ch1nb6ZWWW4R66ZWUW4R66ZWcWUTHo+Ehz0zcy6LALWDDrom5lVQqrecdC3TupR+/sYaKPNeEXb35cq/nSK2/ODKGwbPq7w9+P2/BusVz1y/Y2ZmXVZrclmyVJC0lRJ50q6UdIiSc9vldYlfTOzrut49c4XgQsj4lV5/vDNWyV00Dcz64HS+W+HI2kr4IXACQARsRpY3Sp9XwZ9SbcCq0iDjqyNiNkN60U68x0OPAycEBFXdTufZmbNpNY7HRt756nAPcA3Jc0ErgTeEREPNUvcz3X6B0bErMaAnx0G7J6XOcBXu5ozM7Mh1DpnFdbpT5N0Rd0yp2F3E4B9ga9GxLOAh4CTWx27L0v6BY4Evp0nQ/99vsmxXUTc2euMmZlBW9U7y1sUbmuWAEsi4rL8/FyGCPr9WtIP4GJJVzY56wHsANxe93xJfm0dkubUzp6r49ERyqqZ2bo62XonIu4Cbpf09PzSQcANrdL3a0l//4hYKmkGsEDSjRFxad36Zp/Ues2eI2IuMBdgyoTpbjRuZl3T4dY7/wyclVvu3AK8sVXCvgz6EbE0/79M0vnAfkB90F8C7FT3fEdgafdy2D9ioLATTmGnq7Y6ZxXS+LIbXpq4SflOC/dZrI33HavXlKUbgc+ylDtxjawIsbaDQT8iFgJDVQE9ru++CUmTJW1ZewwcAlzXkGwe8AYlzwNWuj7fzEaTTnbOakc/lvS3Bc7PU8lNAL4XERdK+geAiDgNmE9qrrmY1GSz5aWOmVm3eRKVNkTELcDMJq+fVvc4gH/qZr7MzNrhoG9mVhGeRMXMrGI6NQxDuxz0zcy6LALWehIVM7PqcPWODa+diVEKJz0pbn+/Zm3Z/trIoyZOLEo3bspWZYeeumXxsQcnb1qctsS4hx4rT7tiVVG6wQfK0sVj5cfudA/E4vb8He4W0e9cp29mVjHhoG9mVh2+kWtmVhERrtM3M6sQMeDWO2Zm1eE6fTOzivDYO2ZmVRKpXr8XHPTHqCj8RZWO2R5ry8aA14TyMe3HPWnronSrd51elO7BnScVH/vRrTtbnzrp/vL+CVvcNrko3cRbyxq3D9yzvPjYxf0tSo0r+xzVTh+Tioy979Y7ZmYVEb6Ra2ZWLZ2s3pF0K7AKGADWDjWRuoO+mVkPjEDrnQMjYti6Pgd9M7Mui+hdk82+umMiaSdJv5K0SNL1kt7RJM0BklZKWpiXD/Qir2ZmQ2ljjtxpkq6oW+Y02V0AF0u6ssX6x/VbSX8tcFJEXJUnR79S0oKIuKEh3W8i4mU9yJ+ZWZE26vSXD1VHn+0fEUslzQAWSLoxIi5tlrCvSvoRcWdEXJUfrwIWATv0NldmZu0JxODguKKlaH8RS/P/y4Dzgf1ape2roF9P0q7As4DLmqx+vqSrJV0g6ZlD7GNO7ZJpdTw6Qjk1M1tfFC7DkTQ513wgaTJwCHBdq/T9Vr0DgKQtgB8B74yIBxpWXwXsEhEPSjoc+DGwe7P9RMRcYC7AlAnTe9Q/boQMlnWGKZ8cpezjGTd5s7L9AWt33KYo3b17le1zxZ7lHYA23a5sgpJSK+/cvDjtY1PK3s+0gWlF6SY8+GDxsQdWN/65tEpY1mmv9HfG+L4tX46Mzt7I3RY4XxKkmP69iLiwVeK+C/qSNiEF/LMi4rzG9fUngYiYL+krkqaVNGUyM+uaDhUzI+IWYGZp+r4K+kqnsm8AiyLicy3SPBm4OyJC0n6kKqx7u5hNM7NheZTNMvsDxwHXSlqYXzsF2BkgIk4DXgW8VdJa4BHgmCgdiMbMrAsCGBx00B9WRPwWhh6lKCK+DHy5OzkyM9sAAbikb2ZWHR5a2cysShz0zcyqQqP/Rq6kbUmN/mcCU4EVwNXAgoi4a2SyZ+sYbKNoUNrOup3JLQpoctkEIQAP7VDWXn3l08ve96xZtxQf+xUz/q84bYkfb/us4rRXDzytKN3m95RNCjNlyZbFx2ZlWTv90sl15DYSG65HH92wPSYkPUPSucANpJYzmwB35f+PA66XdK6kPUc0p2ZmY0VADKpo6bSSkv6ZwKeB10XEY40rJU0EjiS1n39+R3NnZjZmjdLqnYh47jDrVwM/zIuZmZXwjVwzswrph6Av6RXA+/N2NwILSTdzF0bEnZ3PnpnZGNRHnbPmAh8lDdu5J7APcBTwTGCLzmbNzGzs6pfOWY8Ap0bEAHBJ7cU8EJqZmZXq0dg77Q5y/VngHxtf9IBmZmbtUZQtndZuSX8BcIGklwA/I9XpXxPhaaf6Vun5uvBiLjbbtPjQj00pK3No20eK0v3t9GuLj/2GrTo7vcKaKD/2tduWzfD5aOFkK1tNmlh8bFRYzhvs8CQq7XQsHF+etG+VTos1Atot6Z8PXEEK9ocA3wNWSWqcmNzMzFpSupFbspTuURov6f8k/WyodO2W9GcAr66vzslzM+7T5n7MzKqt8yX9dwCLgK2GStRuSf8c4EX1L0TEqoj4nzb3Y2ZWbYOFSwFJOwJ/C3x9uLTtBv2nAj+U9C+S9mhzWzMzgyfa6ZdV70yTdEXdMqfJHr8A/BsFp4l2g/4PSfX4RwK/l7RK0u8kfaXN/WwUSYdKuknSYkknN1m/qaTv5/WXSdq1m/kzMxtOG613lkfE7Lpl7jr7kV4GLIuIK0uO226d/nfyWDu1g+1MGmq5a3X6ksYDpwIvAZYAl0uaFxH1N5NPBO6PiN0kHQN8EnhNt/JoZjasztXp7w8cIelwYBKwlaTvRsTrmyVut6T/oKRrJH1H0knA7qQbB7ttVJbbsx+wOCJuySegc0hXHvWOBL6VH58LHOQOZGY2FkXEeyNix4jYFTgGuKRVwIcNa70zKy8zgbcBOwPls1dsvB2A2+ueLwEaRwJ9PE1ErJW0EtgGWKdxdq4bmwMwaZxHkTCz7hmJjlcl2gr6EbEC+HVeAJD0UaCbg601K7E3fnwlach1Y3MBpkyY7l7FZtYdwYgMwxARv6YuPjfTbvVOMx8F1ruZOoKWADvVPd8RWNoqjaQJwBTgvq7kzsysRBQuHdZW0Jd0qqQ3S3qOpNoEntsBD3c+ay1dDuwu6Sl51q5jgHkNaeYBx+fHryLVcbkkb2ajRq/G3mm3pH8H8GLgm8B9km4mj6kv6ShJf5Vb14yYiFhLupdwEekm8g8i4npJH5Z0RE72DWAbSYuBd9HdKxEzs+H1qKTfbp3+x2qPJW1CGlN/77y8Of8/ndRsaMRExHxgfsNrH6h7/Cjw6pHMg5nZRumHG7n1ImINadasq+tflzR1YzNlZjaWjVTVTYlhq3ckvV3SkOPl5h6wb4fHW/iYmdlQBlW2dFhJSf/JwGJJ84H/Bm4CVgFbAnsABwCHAd/ueO5s5JX2WSu8D65HHis+9KYry0aTirvLagt/fs/excfeRGuL03b62IOF72dS4eejR1cPn6gmikfwKks3rvC24Dj3jWw0atvpR8Qpkj4HnEAa3mBvYCpwP3ANqW79lIi4dwTzaWY2tozWoA8QEcuBz+TFzMw2Rg/r9Df4Rq6ZmW0EB30zs+pQ4e2VTmu3R24Vpiw2Mxuz2u2Re6EkD0dpZrax+mHsHdKQC/8jafvaC5JeKOk3nc2WmdkYVjjuzkjc7G13GIZ/lfQnUuB/L0804fx057NmZjaG9dGN3N8BDwBnkWanOiIiHulorqy5djq4jC+8/aLCi70YKEv20ENl+wMm31H2s5ly0+SidAvHP7X42Iu2e3Jx2hKP3bl5cdqpfyz7Hjcv/Hxi1ariY5dS8e/Hna422GgdhqGepPNIA/T/CHgl8FLSqJtmZlZIpNY7JUuntVvSvxl4U0TcByDpFmCepF0j4tSO587MbCzqYH19ntvkUmBTUkw/NyI+2Cp9WyX9iHhPLeDn59cCf0MaVtnMzEp1rvXOY8CLI2Imaf7yQyU9r1Xije6cFRF3SHrBxu7HzKxSOlTSz7MCPpifbpKXlnvvxBy5RETn7ySZmY1hbTTZnCbpirplznr7ksZLWggsAxZExGWtjts3wzBI+jTwcmA18Cfgjc3G7pd0K2no5wFgbUTM7mY+zcyKlJf0lw8XxyJiAJiVJ7E6X9JeEXFds7QdKel3yQJgr4jYB/gj8N4h0h4YEbMc8M1sVIqRab2TC8K/Bg5tlaZvSvoRcXHd098Dr+pVXvpC4eQW2qTsJxCry359gw+Vd9mYsKRsCoZtJpS9l00fKJ+a+dGttyxOW2Lq/eV/nVvcVvYZbXL78qJ0A488Wnzs4n4Zpe30SydRsfV1rvXOdGBNRKyQtBlwMPDJVun79Rv7e+CCFusCuFjSlc3qvupJmlOrJ1sdbfzhmJltpA4Ow7Ad8CtJ1wCXk+r0f9Yq8agq6Uv6BWl6xkbvi4if5DTvA9aSegQ3s39ELJU0A1gg6caIuLRZwoiYC8wFmDJheo/6x5lZJXWu9c41wLNK04+qoB8RBw+1XtLxwMuAg3IzpWb7WJr/XybpfGA/UscFM7PRYYRG0CzRN9U7kg4F3kMa6+fhFmkmS9qy9hg4BGh6B9vMrFdE70bZ7JugD3wZ2JJUZbNQ0mkAkraXND+n2Rb4raSrgT8AP4+IC3uTXTOz1vpiaOVeiojdWry+FDg8P74FmNnNfJmZbZA+GlrZzMw2loO+mVlFjFDVTQkH/TFKpZNblHbCmbBJWboo76Q0eN/9RekmDpRN4LL1veUdrgYnb1qctsS4hx4rTqsVZUNVDT5QOKRV4ecD5Z3xNKEwXenvrLRTWJU46JuZVcdITJBSwkHfzKwHXL1jZlYVPeyc5aBvZtYLDvpmZtVQ65HbCw76ZmY9oMHeRH0HfTOzbnOdvhVpp63zuML2YIXt9AtbYxNttBkvTTu48oGidHqkfAKXcaX9E0q18b4HV68pSlf8WbbxXorb3xdPolL6y7BGrt4xM6sSD61sZlYdnRplU9JOkn4laZGk6yW9Y6j0LumbmfVC50r6a4GTIuKqPJ/IlZIWRMQNzRI76JuZdVt0bhiGiLgTuDM/XiVpEbAD4KBvZjYatNlOf5qkK+qez83ze6+/X2lX0ny5l7XamYO+mVkvNJ/mu5nlETF7uESStgB+BLwzIlo2eeurG7mSPiTpjjxd4kJJh7dId6ikmyQtlnRyt/NpZjacTk6XKGkTUsA/KyLOGyptP5b0Px8Rn2m1UtJ44FTgJcAS4HJJ81rd1Kg6jS887xePm95Gu+3ykk7Z7grbvyftpO2N4rbybXzmHW9/73HyN0wHO2cpTWrwDWBRRHxuuPRj8RvbD1gcEbdExGrgHODIHufJzGwdGixbCuwPHAe8eLhaEOjPkv7bJL0BuILUTKlx+qUdgNvrni8BnttsR5LmAHMAJo3bYgSyambWXAdb7/yW8k7zo6+kL+kXkq5rshwJfBV4GjCL1ETps8120eS1phdSETE3ImZHxOyJmtSx92BmNqQgVW+WLB026kr6EXFwSTpJpwM/a7JqCbBT3fMdgaUdyJqZWcf0auydUVfSH4qk7eqeHgVc1yTZ5cDukp4iaSJwDDCvG/kzMysWhUuHjbqS/jA+JWkW6aO4FXgLgKTtga9HxOERsVbS24CLgPHAGRFxfa8ybGbWyJNTDq7yAAAJxUlEQVSoFIqI41q8vhQ4vO75fGB+t/JlZtaWCE+iYmZWKS7pW0eVdpqJwnZjhZ11RIcnJwFiBFow9AO109GtlDtdjRqu3jEzq4oAXL1jZlYhLumbmVWHq3fMzCrErXfMzKpihDpelXDQNzPrstQ5yyV9M7Pq6NAom+1y0K+6TrfHHtf5X7LKR42tJrep70su6ZuZVYXr9M3MqqR3Y+/4utDMrBc6NImKpDMkLZPUbKj59Tjom5l1W3R0jtwzgUNLD+3qHTOzXujQjdyIuFTSrqXpHfTNzHqhPOZPk3RF3fO5ETF3Qw/roG9m1gMaLG7evDwiZnfquH0T9CV9H3h6fjoVWBERs5qkuxVYBQwAazv5YVkBtxk3G17gzlnDiYjX1B5L+iywcojkB0bE8pHPlZlZ+0T0rHNW3xXLlKYTOho4u9d5MTPbYJ1rsnk28L/A0yUtkXTiUOn7pqRf5wXA3RFxc4v1AVwsKYCvDXXDQ9IcYA7ApHFbdDyjZmYtda71zrHtpB9VQV/SL4AnN1n1voj4SX58LEOX8vePiKWSZgALJN0YEZc2S5hPCHMBpkyYXs2JWM2s+1ynn0TEwUOtlzQBeCXw7CH2sTT/v0zS+cB+QNOgb2bWK2203umofqvTPxi4MSKWNFspabKkLWuPgUOAoq7JZmbdU1ifPwI3e/st6B9DQ9WOpO0lzc9PtwV+K+lq4A/AzyPiwi7n0cxsaEHPgv6oqt4ZTkSc0OS1pcDh+fEtwMwuZ8vMrH2u0zczqw5PomJmViUO+mZmFREBA72p33HQNzPrBZf0zcwqxEHfzKwiAujRHLkO+mZmXRcQrtM3M6uGwDdyzcwqxXX6ZmYV4qBvZlYVIzOuTol+G3DNzKz/BTA4WLYUkHSopJskLZZ08lBpHfTNzHqhc9MljgdOBQ4D9gSOlbRnq/Su3jEz67qODsOwH7A4jzKMpHOAI4EbmiV20Dcz67aAKG+nP03SFXXP5zbM/b0DcHvd8yXAc1vtzEHfzKwXynvkLo+I2UOsV5PXWu7cQd/MrBc613pnCbBT3fMdgaWtEo+6G7mSXi3pekmDkmY3rHtvvjt9k6SXttj+KZIuk3SzpO9LmtidnJuZFYroZOudy4Hdc+ybSJpWdl6rxKMu6JMmMn8lcGn9i/lu9DHAM4FDga/ku9aNPgl8PiJ2B+4HThzZ7JqZbYAOtd6JiLXA24CLgEXADyLi+lbpR131TkQsApDWq6Y6EjgnIh4D/ixpMemu9f/WEiht9GLgtfmlbwEfAr46srk2M2tHEAMDndtbxHxgfkna0VjSb6XZHeodGtJsA6zIZ75WaczMeqs2tHLJ0mE9KelL+gXw5Car3hcRP2m1WZPXGj+Rtu5iS5oDzAGYNG6LVsnMzDqvSkMrR8TBG7BZyR3q5cBUSRNyaX/Iu9i5retcgCkTpvdmIAwzq5wAokeTqPRT9c484BhJm0p6CrA78If6BBERwK+AV+WXjgdaXTmYmfVG5ElUSpYOG3VBX9JRkpYAzwd+LukigHw3+gekrsUXAv8UEQN5m/mSts+7eA/wrnyjdxvgG91+D2Zmw4mBgaKl0xQ9Gt5ztJF0D/CXhpenkaqMqqrq7x/8Gfj9N3//u0TE9A3dqaQL875LLI+IQzf0WOsd20G/NUlXDNP9eUyr+vsHfwZ+/2Pv/Y+66h0zMxs5DvpmZhXioD+0ucMnGdOq/v7Bn4Hf/xjjOn0zswpxSd/MrEIc9M3MKsRBv8HGjuc/1kj6kKQ7JC3My+G9zlM3SDo0f8+LJZ3c6/z0gqRbJV2bv/crht+iv0k6Q9IySdfVvfYkSQvy/BwLJG3dyzx2goP++jZ2PP+x6PMRMSsvRcO39rP8vZ4KHAbsCRybv/8qOjB/72OqrXoLZ5L+tuudDPwyz8/xy/y8rznoN4iIRRFxU5NVj4/nHxF/Bmrj+dvYsx+wOCJuiYjVwDmk79/GsIi4FLiv4eUjSfNykP9/RVczNQIc9MuVjOc/Vr1N0jX58rfvL28LVPm7rhfAxZKuzMOQV9G2EXEnQP5/Ro/zs9FG3cxZ3TCC4/n3paE+D9KsYx8hvdePAJ8F/r57ueuJMftdt2n/iFgqaQawQNKNuTRsfaySQX8Ex/PvS6Wfh6TTgZ+NcHZGgzH7XbcjIpbm/5dJOp9U7VW1oH+3pO0i4k5J2wHLep2hjeXqnXLDjuc/FuUfes1RpBvdY93lwO6SniJpIukG/rwe56mrJE2WtGXtMXAI1fjuG80jzcsBY2R+jkqW9Ici6SjgS8B00nj+CyPipRFxvaTaeP5rqRvPf4z7lKRZpOqNW4G39DY7Iy8i1kp6G3ARMB44I8/nUCXbAudLghQnvhcRF/Y2SyNL0tnAAcC0PKfHB4FPAD+QdCJwG/Dq3uWwMzwMg5lZhbh6x8ysQhz0zcwqxEHfzKxCHPTNzCrEQd/MrEIc9M3MKsRB38ysQhz0rdIkfVzSO0do33+Q9MyR2LfZhnLnLKssSdOBhcBuEfHICOz/aOA1EfF3nd632YZySd+q7ARg/kgE/GwecGDD+EVmPeWgb2OKpC0kDdQHWkl7SbqzNoBYncOA/27Y/n2Svlr3fGtJayRNkvQmSRdJ+qqk+yX9UdKekt4h6TZJyyW9srZtRDwKXEkarMxsVHDQtzElIh4EbgT2rXv5E8DHImJVQ/K9gcZZ0vYmVfnUzAJuygF8H2A2cC4wDbgWuCCnexppvoH3N+xvETBzg96M2Qhw0Lex6HJy0Jf0QtI8t19rkm4q0OxE0Bj0r86PZwIfj4hf5hFWbwCujYgvRsQa0tDDjSPXrsrHMRsVHPRtLHo86AOfAv49z3Xb6H7g8SqfPHb+00gl+JqZPHES2Id1J5HZs8nzGxuOsSWwos38m40YB30biy4H9pX0d8BmwNkt0l0D7FH3fE/gjoh4GEBpMPkDgKsl7QJMBP5Yl34W614V7NPwHOAZPHGlYNZzDvo2Fl1NmvP3s8DJETHYIt184EV1z/cGZkh6mqTNSHX0u5Amj5lJqsoZBJC0VV53Td32M6kL8JI2BZ4NLOjAezLrCAd9G3Mi4jFSFc2tEXHBEEm/DRyeAzykoH8R6ebsYuBu4BbSBPH11TyQSvWL664KxgF7NaQ5Avh1ba5Zs9HAnbNszMl184uBoyPi98Ok/RiwLCK+IOkC4OsR8aMO5eMy4MSIqOLcsjZKOejbmCPpP4GnRsSxbW63BDgkIm4YmZyZ9Z6rd2zMkLSvpJXAC4F/bnPbrYEZwM0jkTez0cIlfTOzCnFJ38ysQhz0zcwqxEHfzKxCHPTNzCrEQd/MrEIc9M3MKsRB38ysQv4fyivYF7m3K+MAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x114d59780>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Slice across z, very close to zmin.\n",
     "Ez2, info_Ez2 = ts_3d.get_field( field='E', coord='z', iteration=500, \n",
     "                                slicing_dir='z', slicing=-0.9, plot=True )"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When passing `slicing=None`, `get_field` returns a full 3D Cartesian array. This can be useful for further analysis by hand, with `numpy` (e.g. calculating the total energy in the field)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the full 3D Cartesian array\n",
+    "Ez_3d, info_Ez_3d = ts_3d.get_field( field='E', coord='z', iteration=500, slicing=None )\n",
+    "print( Ez_3d.ndim )"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -193,20 +178,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x114d695c0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "Ey, info_Ey = ts_circ.get_field( field='E', coord='y', iteration=500, m=0, \n",
     "                              plot=True, theta=0.5)"
@@ -221,20 +195,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x115141b38>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "Ey, info_Ey = ts_circ.get_field( field='E', coord='y', iteration=500, m='all', \n",
     "                              plot=True, theta=0.5)"
@@ -246,28 +209,25 @@
    "source": [
     "The argument `theta` (in radians) selects the plane of observation: this plane contains the $z$ axis and has an angle `theta` with respect to the $x$ axis.\n",
     "\n",
-    "In addition, in cylindrical geometry, the users can also choose the coordinates `r` and `t` for the radial and azimuthal components of the fields. For instance:"
+    "When passing `slicing=None`, `get_field` returns a full 3D Cartesian array. This can be useful for further analysis by hand, with `numpy` (e.g. calculating the total energy in the field), or for comparison with Cartesian simulations."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x114bf5cf8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "Er, info_Er = ts_circ.get_field( field='E', coord='r', iteration=500, m=0, \n",
-    "                              plot=True, theta=0.5)"
+    "# Get the full 3D Cartesian array\n",
+    "Ey_3d, info_Ey3d = ts_circ.get_field( field='E', coord='y', iteration=500, theta=None )\n",
+    "print( Ey_3d.ndim )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Finally, in cylindrical geometry, the users can also choose the coordinates `r` and `t` for the radial and azimuthal components of the fields. For instance:"
    ]
   },
   {
@@ -275,7 +235,10 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "Er, info_Er = ts_circ.get_field( field='E', coord='r', iteration=500, m=0, \n",
+    "                              plot=True, theta=0.5)"
+   ]
   }
  ],
  "metadata": {
@@ -294,7 +257,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.8"
   }
  },
  "nbformat": 4,