- {% endif %}
-
-{% endblock %}
\ No newline at end of file
diff --git a/docs/conf.py b/docs/conf.py
deleted file mode 100644
index d35d82f..0000000
--- a/docs/conf.py
+++ /dev/null
@@ -1,84 +0,0 @@
-# Configuration file for the Sphinx documentation builder.
-#
-# This file only contains a selection of the most common options. For a full
-# list see the documentation:
-# https://www.sphinx-doc.org/en/master/usage/configuration.html
-
-# -- Path setup --------------------------------------------------------------
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-#
-import os
-import sys
-sys.path.insert(0, os.path.abspath('../src'))
-
-# -- Project information -----------------------------------------------------
-
-project = 'TemGym'
-copyright = '2022, David Landers'
-author = 'David Landers'
-
-# -- General configuration ---------------------------------------------------
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
-# ones.
-extensions = [
- 'sphinx.ext.autodoc',
- 'sphinx.ext.mathjax',
- 'sphinx.ext.todo',
- 'sphinx.ext.autosummary',
- 'sphinx.ext.napoleon',
- 'sphinx.ext.viewcode',
- 'sphinx_licenseinfo']
-
-templates_path = ["_templates"]
-html_context = {
- "sidebar_external_links_caption": "Links",
- "sidebar_external_links": [
- (
- ' Source code',
- "https://github.com/AMCLab/TemGymBasic",
- ),
- (
- ' Zenodo',
- "https://zenodo.org/communities/amcl/?page=1&size=20",
- )
- ],
-}
-
-# Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-# This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = []
-
-# The suffix of source filenames.
-source_suffix = '.rst'
-
-# The master toctree document.
-master_doc = 'index'
-
-# -- Options for HTML output -------------------------------------------------
-
-# The theme to use for HTML and HTML Help pages. See the documentation for
-# a list of builtin themes.
-#
-html_theme = 'sphinx_rtd_theme'
-
-napoleon_numpy_docstring = True
-
-# on_rtd is whether we are on readthedocs.org
-on_rtd = os.environ.get('READTHEDOCS', None) == 'True'
-
-if not on_rtd: # only import and set the theme if we're building docs locally
- import sphinx_rtd_theme
- html_theme = 'sphinx_rtd_theme'
- html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
-
-# Output file base name for HTML help builder.
-htmlhelp_basename = project+'doc'
\ No newline at end of file
diff --git a/docs/img/GUI_graphic.svg b/docs/img/GUI_graphic.svg
deleted file mode 100644
index 6da119c..0000000
--- a/docs/img/GUI_graphic.svg
+++ /dev/null
@@ -1,186 +0,0 @@
-
-
-
-
diff --git a/docs/img/all_components_example.png b/docs/img/all_components_example.png
deleted file mode 100644
index 29dc36d..0000000
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diff --git a/docs/img/beam_shift_basic.svg b/docs/img/beam_shift_basic.svg
deleted file mode 100644
index 6f6879d..0000000
--- a/docs/img/beam_shift_basic.svg
+++ /dev/null
@@ -1,2687 +0,0 @@
-
-
-
diff --git a/docs/img/beam_tilt_basic.svg b/docs/img/beam_tilt_basic.svg
deleted file mode 100644
index 74c0af2..0000000
--- a/docs/img/beam_tilt_basic.svg
+++ /dev/null
@@ -1,2678 +0,0 @@
-
-
-
diff --git a/docs/img/beam_tilt_shift_base.png b/docs/img/beam_tilt_shift_base.png
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diff --git a/docs/img/beam_tilt_shift_wobble_circled.png b/docs/img/beam_tilt_shift_wobble_circled.png
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index e99e65d..0000000
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diff --git a/docs/img/biprism_model.png b/docs/img/biprism_model.png
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diff --git a/docs/img/condenser_aperture_before_lens_adjust.png b/docs/img/condenser_aperture_before_lens_adjust.png
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diff --git a/docs/img/condenser_asigmatism_base.png b/docs/img/condenser_asigmatism_base.png
deleted file mode 100644
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diff --git a/docs/img/condenser_asigmatism_corrected.png b/docs/img/condenser_asigmatism_corrected.png
deleted file mode 100644
index 674fb47..0000000
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diff --git a/docs/img/model_sem_example.png b/docs/img/model_sem_example.png
deleted file mode 100644
index b81cf53..0000000
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diff --git a/docs/img/model_tem.svg b/docs/img/model_tem.svg
deleted file mode 100644
index 309b388..0000000
--- a/docs/img/model_tem.svg
+++ /dev/null
@@ -1,6787 +0,0 @@
-
-
-
diff --git a/docs/img/model_tem_example.png b/docs/img/model_tem_example.png
deleted file mode 100644
index 85a3dc9..0000000
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diff --git a/docs/img/simple_lens.png b/docs/img/simple_lens.png
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deleted file mode 100644
index 86891d8..0000000
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diff --git a/docs/img/simple_lens_annotated.png b/docs/img/simple_lens_annotated.png
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diff --git a/docs/img/simple_lens_annotated.pptx b/docs/img/simple_lens_annotated.pptx
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index c945afd..0000000
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index 5f75769..0000000
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diff --git a/docs/img/simple_lens_wobble_on.png b/docs/img/simple_lens_wobble_on.png
deleted file mode 100644
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diff --git a/docs/img/simple_lens_wobble_on_top_of_sinewave.png b/docs/img/simple_lens_wobble_on_top_of_sinewave.png
deleted file mode 100644
index e36faca..0000000
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diff --git a/docs/index.rst b/docs/index.rst
deleted file mode 100644
index 4724f96..0000000
--- a/docs/index.rst
+++ /dev/null
@@ -1,99 +0,0 @@
-.. TemGym documentation master file, created by
- sphinx-quickstart on Thu Sep 22 15:08:17 2022.
- You can adapt this file completely to your liking, but it should at least
- contain the root `toctree` directive.
-
-=============
-TemGym Basic
-=============
-
-TemGym Basic is a ray-tracing software that models and visualises the first order behaviour of
-components inside a transmission electron microscope.
-
-.. image:: /img/GUI_graphic.svg
- :width: 900px
-
-The interactive models we generate are designed with a focus
-on educating new users on how the basic alignments work inside a TEM.
-Our code is also capable of producing publication
-quality ray diagrams with a single function call.
-
-Features
---------
-
-* Interactive TEM models generated with pyqtgraph & PyQt5.
-
-* Ready-made example direct alignment models that enable users to learn at their own pace in an offline manner.
-
-* Generate interactive models of a transmission & scanning electron microscope.
-
-* Easy to use python code which allows users to create their own models by naming components inside a python list.
-
-* Generate publication quality ray diagrams of electron microscope experimental setups with one function call.
-
-Component Overview
-------------------
-Our python code consists of 8 different electron microscope components which can be combined
-to create a model microscope.
-
-* Lens
-* Astigmatic Lens
-* Deflector
-* Double Deflector
-* Aperture
-* Stigmator
-* Biprism
-* Sample
-
-We can easily create an example model containing all of these components by first importing the required packages
-into a python script.
-
-.. literalinclude:: /../pyqtgraph_examples/all_components_example_pyqt.py
- :language: python
- :lines: 1-6
-
-Then we add the components into a list, and specify their position inside the microscope on the z-axis.
-
-.. literalinclude:: /../pyqtgraph_examples/all_components_example_pyqt.py
- :language: python
- :lines: 8-19
-
-Then input the model into our pyqt function that creates the interactive 3D viewer and automatically populates
-the GUI.
-
-.. literalinclude:: /../pyqtgraph_examples/all_components_example_pyqt.py
- :language: python
- :lines: 21-22
-
-which generates an interactive window on your PC.
-
-.. image:: /img/all_components_example.png
-
-Contents
---------
-
-.. toctree::
- :maxdepth: 2
- :caption: Tutorials
-
- source/usage
-
-.. toctree::
- :maxdepth: 2
- :caption: Examples
-
- source/tutorials
- source/showcases
- source/matplotlib
-
-.. toctree::
- :maxdepth: 1
- :caption: Reference
-
- source/documentation
- source/bibliography
- source/license
-
-
-
-
\ No newline at end of file
diff --git a/docs/make.bat b/docs/make.bat
deleted file mode 100644
index dc1312a..0000000
--- a/docs/make.bat
+++ /dev/null
@@ -1,35 +0,0 @@
-@ECHO OFF
-
-pushd %~dp0
-
-REM Command file for Sphinx documentation
-
-if "%SPHINXBUILD%" == "" (
- set SPHINXBUILD=sphinx-build
-)
-set SOURCEDIR=source
-set BUILDDIR=build
-
-%SPHINXBUILD% >NUL 2>NUL
-if errorlevel 9009 (
- echo.
- echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
- echo.installed, then set the SPHINXBUILD environment variable to point
- echo.to the full path of the 'sphinx-build' executable. Alternatively you
- echo.may add the Sphinx directory to PATH.
- echo.
- echo.If you don't have Sphinx installed, grab it from
- echo.https://www.sphinx-doc.org/
- exit /b 1
-)
-
-if "%1" == "" goto help
-
-%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-goto end
-
-:help
-%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-
-:end
-popd
diff --git a/docs/source/bibliography.rst b/docs/source/bibliography.rst
deleted file mode 100644
index 47db1b2..0000000
--- a/docs/source/bibliography.rst
+++ /dev/null
@@ -1,13 +0,0 @@
-============
-Bibliography
-============
-
-Papers
-^^^^^^
-
-Papers mentioned accross the documentation are referenced here.
-
-- Toshiaki Tanigaki, Yoshikatsu Inada, Shinji Aizawa, Takahiro Suzuki, Hyun Soon Park,
- Tsuyoshi Matsuda, Akira Taniyama, Daisuke Shindo, and Akira Tonomura , "Split-illumination electron holography",
- Appl. Phys. Lett. 101, 043101 (2012) https://doi.org/10.1063/1.4737152
-
diff --git a/docs/source/documentation.rst b/docs/source/documentation.rst
deleted file mode 100644
index fde0036..0000000
--- a/docs/source/documentation.rst
+++ /dev/null
@@ -1,43 +0,0 @@
-=============
-Documentation
-=============
-
-model.py
---------
-.. automodule:: temgymbasic.model
- :members:
- :special-members: __init__
-
-components.py
--------------
-.. automodule:: temgymbasic.components
- :members:
- :special-members: __init__
-
-run.py
-------
-.. automodule:: temgymbasic.run
- :members:
- :special-members: __init__
-
-shapes.py
----------
-.. automodule:: temgymbasic.shapes
- :members:
- :special-members: __init__
-
-
-functions.py
-------------
-.. automodule:: temgymbasic.functions
- :members:
- :special-members: __init__
-
-gui.py
-------
-.. automodule:: temgymbasic.gui
- :members:
- :special-members: __init__
-
-
-
diff --git a/docs/source/license.rst b/docs/source/license.rst
deleted file mode 100644
index cda884c..0000000
--- a/docs/source/license.rst
+++ /dev/null
@@ -1,10 +0,0 @@
-=======
-License
-=======
-
-``TemGym Basic`` is licensed under the :choosealicense:`gpl-3.0`
-
-.. license-info:: GPL-3.0
-
-.. license::
- :file: ../LICENSE.md
\ No newline at end of file
diff --git a/docs/source/matplotlib.rst b/docs/source/matplotlib.rst
deleted file mode 100644
index fa7d9b8..0000000
--- a/docs/source/matplotlib.rst
+++ /dev/null
@@ -1,20 +0,0 @@
-============================
-Ray Diagrams with Matplotlib
-============================
-
-Model TEM
----------
-
-The same code which creates an interactive model via pyqtgraph of the specified microscope components
-can also be used to create a static ray diagram in matplotlib. The highlighted lines show the changes that need
-to me made to create a matplotlib diagram rather than in an interactive pyqtgraph model.
-
-.. literalinclude:: /../matplotlib_examples/model_tem_example_matplotlib.py
- :language: python
- :emphasize-lines: 31-34
-
-which produces this model:
-
-.. image:: /img/model_tem.svg
- :width: 600px
-
diff --git a/docs/source/showcases.rst b/docs/source/showcases.rst
deleted file mode 100644
index 71b8c68..0000000
--- a/docs/source/showcases.rst
+++ /dev/null
@@ -1,37 +0,0 @@
-===========================
-Showcase Interactive Models
-===========================
-
-The models presented below showcase some of the other modelling capabilities of our software.
-
-Biprism
--------
-We can combine three biprism components created in our software to make an interactive
-version of the microscope setup presented in this paper from tonamura in 2012 (See Bibliography).
-
-.. image:: /img/biprism_model.png
- :width: 600px
-
-Model TEM
----------
-Adopting a qualitative schematic found here for a JEOL 2010F Transmission Electron Microscope,
-we can recreate an interactive alignment model of this microscope using the components we have created.
-The code which creates this model is located in the python script ''model_tem_example_pyqt.py''
-
-.. literalinclude:: /../pyqtgraph_examples/model_tem_example_pyqt.py
- :language: python
-
-.. image:: /img/model_tem_example.png
- :width: 600px
- :alt: project
-
-Model SEM
----------
-Also simply using a SEM schematic found on wikipedia, we can also easily recreate an alignment model of
-showing how the beam inside a scanning electron microscope operates.
-
-.. literalinclude:: /../pyqtgraph_examples/model_sem_example_pyqt.py
- :language: python
-
-.. image:: /img/model_sem_example.png
- :width: 600px
diff --git a/docs/source/tutorials.rst b/docs/source/tutorials.rst
deleted file mode 100644
index d112c12..0000000
--- a/docs/source/tutorials.rst
+++ /dev/null
@@ -1,147 +0,0 @@
-==================
-Example Alignments
-==================
-
-Lens & Rotation Centre Alignment
---------------------------------
-Let's begin with a tutorial using the simplest model our programme can produce: A beam, a single lens and a detector.
-Run the model "lens_example_pyqt" (via the `.exe `_ , or the `python script `_), and become familiar with the interface.
-The important gui components are annotated in the image below.
-
-.. image:: /img/simple_lens_annotated.png
- :width: 600px
-
-
-With this basic model, we can demonstrate
-the principle of one of the direct alignments of a TEM, the rotation centre alignment. This alignment
-oscillates the current to the objective lens in a TEM, which in turn oscillates the focal length of the
-objective lens.
-
-Using the information of how the spot responds, we can then adjust the tilt of the beam before the lens
-so that it lies paralell to the optic axis. In a real TEM, this is performed with an image of a sample or
-grid inside the TEM. We cannot easily model a sample in our model, but by visualising the beam spot we
-can recreate the behaviour of this alignment.
-
-Tilt the beam off axis and turn on the lens wobble by ticking the check box "Wobble Lens Current".
-
-.. figure:: /img/simple_lens_wobble_on.png
- :width: 600px
-
- Rotation centre is misaligned, as the beam is away from the optic axis.
-
-
-Notice how the beam moves accross the screen as the focal length changes. We wish to align the rotation centre
-by tilting the beam so that the spot no longer moves, and so that it only contracts in and out when the
-focal length changes. Bring the beam back onto the centre of the lens and thus paralell to the optic axis
-by adjusting the tilt of the beam. When the beam spot stops moving, the rotation centre is now aligned.
-
-.. figure:: /img/simple_lens_aligned.png
- :width: 600px
-
- Rotation centre aligned.
-
-
-Beam Tilt/Shift Alignment
--------------------------
-In this basic model of the beam shift/tilt alignment, we use a pair of deflectors, a lens, and a detector
-to make the model interactive. For the beam shift and beam tilt alignment, the goal is to find the
-"deflector ratio" setting such that the beam purely shifts or purely tilts in the detector plane.
-The deflector ratio value is a multiplier which dictates how the lower deflector responds to a deflection
-provided by the upper deflector.
-
-For example, if the upper deflector adds a deflection of 0.5 radians to the beam, and the deflector ratio
-is set to -1, the lower deflector will add a deflection of -0.5 radians to the beam, cancelling out the
-deflection from the upper deflector. This will then shift the beam over the sample and keep the beam paralell to
-the optic axis.
-
-Another layer of complication is added because alignment manuals typically explain this alignment
-in terms of "Pivot Points", and there is a seperate pivot point for both beam tilt and beam shift.
-Pivot points are simply where the beam pivots as a result of the settings of the deflector, and the
-location and focal length of a lens after it.
-
-|pic1| |pic2|
-
-.. |pic1| image:: /img/beam_shift_basic.svg
- :width: 45%
- :class: with-border
-.. |pic2| image:: /img/beam_tilt_basic.svg
- :class: with-border
- :width: 45%
-
-For a pure beam shift setting, the beam needs to go over the sample and into the lens paralell to the optic axis,
-and this will cause all rays to converge or "pivot" on the focal point of this lens (a.k.a the back focal plane).
-For beam tilt, the beam needs to pivot about a point on the sample before the lens, and this requires that
-our deflector ratio is set so that all rays go through the front focal plane, which is where the pivot point
-for beam tilt is located.
-
-Also note that the deflector ratio that we need to find is a
-function of the distances between each component and of the focal length of the lens.
-When creating this model, we placed the components at convenient distances so that the deflector ratio for
-beam shift and beam tilt are convenient values.
-
-Beam Shift Alignment
-^^^^^^^^^^^^^^^^^^^^
-Run the basic Beam Shift/Tilt model and click the "Wobble Upper Deflector X" checkbox.
-
-.. image:: /img/beam_tilt_shift_wobble_circled.png
- :width: 500px
-
-Set the deflector ratio to -1 and see that the spot on the detector is now stationary.
-You have correctly aligned the beam shift pivot point.
-
-Beam Tilt Alignment
-^^^^^^^^^^^^^^^^^^^
-Now adjust the deflector ratio so that it is set to -2, and see that the beam tilts about a single point
-in the sample plane on the 3D viewer. Note that the beam will still oscillate on the detector, as another lens needs
-to be added to the model to image the beam tilt pivot point.
-
-Condenser Astigmatism Alignment
--------------------------------
-In this alignment we introduce two new components, an astigmatic lens, and a stigmator. In our model, an
-astigmatic lens is simply a lens where the focal length can be adjusted on each axis. This captures the behaviour
-of a real lens in a TEM, which cannot perfectly focus in both x & y. This is because a real lens cannot be
-manufactured to be perfectly circular, and will thus have two different focal lengths
-on each axis. The component which is used to correct for this is a stigmator. This is composed of two
-quadrupole magnets which when the current to each is adjusted, can correct for astigmatism in a lens.
-
-.. figure:: /img/condenser_asigmatism_base.png
- :width: 500px
-
- Elliptical beam indicates condenser asigmatism is misaligned
-
-
-Load the "Condenser Asigmatism" alignment and adjust the axial width of the beam to make the spot larger.
-You can also adjust the number of rays so that the beam spot appears filled in, if your PC can handle a larger
-amount of rays. Adjust the focal length of the astigmatic lens until the beam appears elliptical. Note that in practise
-you as a user would not have control of the astigmatism of the lens!
-Use the condenser astigmatism sliders to correct for the astigmatism in the lens by making the beam
-appear round again.
-
-.. figure:: /img/condenser_asigmatism_corrected.png
- :width: 500px
-
- Circular beam means we have corrected the condenser astigmatism
-
-Condenser Aperture Alignment
-----------------------------
-Similar to almost every other alignment in the microscope,
-this alignment requires that the aperture is centred on the optic axis. Run the alignment "Condenser Aperture",
-and adjust the strength of the condenser lens focal length.
-
-.. figure:: /img/condenser_aperture_before_lens_adjust.png
- :width: 500px
-
- Beamspot position before adjusting focal length.
-
-Notice how as you do this, the beam spot appears
-to move accross the screen.
-
-.. figure:: /img/condenser_aperture_after_lens_adjust.png
- :width: 500px
-
- Beamspot position after adjusting focal length.
-
-This happens because the aperture is not centred on the optic axis. Adjust the position
-of the aperture so that it is centred on the column, and adjust the focal length once more. Notice that the beam
-only changes size, and does not move accross the screen.
-
diff --git a/docs/source/usage.rst b/docs/source/usage.rst
deleted file mode 100644
index ab24234..0000000
--- a/docs/source/usage.rst
+++ /dev/null
@@ -1,40 +0,0 @@
-============
-Installation
-============
-
-Quickstart Executables
-----------------------
-Download and run the example executable for your plafrom (Windows, MACOS or Ubuntu) from `Zenodo `_ to
-instantly access example models of microscope alignments without any coding knowledge.
-
-Python
-------
-To run our interactive models via python, we recommend you use `miniconda `_ .
-
-After downloading and installing, create a new environment in a terminal::
-
- $ conda create --name temgymbasic python=3.9.6
-
-activate the environment, and install pip::
-
- $ conda activate temgymbasic
-
- $ conda install pip
-
-then install `temgymbasic `_::
-
- $ pip install temgymbasic
-
-To download and run the python code of examples, see our `GitHub `_ and the folder `pyqtgraph_examples `_.
-
-Creating Executables
---------------------
-
-To create the .exe files that we have shared in the zenodo,
-install the package pyinstaller inside your temgymbasic environment::
-
- $ pip install pyinstaller
-
-then run the appropiate terminal script, make_exe.bat (Windows) or make_exe.sh (Linux, MACOS).
-This process may take some time to create the executables for all 9 examples (~30 minutes)
-
diff --git a/fourdstem_overfocused.zip b/fourdstem_overfocused.zip
deleted file mode 100644
index 3920ed7..0000000
Binary files a/fourdstem_overfocused.zip and /dev/null differ
diff --git a/live_calibration_examples/fourdstem_example_no_gui.py b/live_calibration_examples/fourdstem_example_no_gui.py
deleted file mode 100644
index 6093900..0000000
--- a/live_calibration_examples/fourdstem_example_no_gui.py
+++ /dev/null
@@ -1,213 +0,0 @@
-
-from temgymbasic import components as comp
-from temgymbasic.model import Model
-from temgymbasic.functions import make_test_sample, get_image_from_rays
-
-import matplotlib.pyplot as plt
-import numpy as np
-
-#Load the fourdstem_overfocused sample to use as comparison
-fourdstem_overfocused = np.load(r"C:\Users\User\Documents\Code\src\temgymbasic\fourdstem_overfocused.npz")['arr_0']
-sample = make_test_sample()
-
-#Set number of detector pixels and detector size. For now keeping it simple
-#and assuming a square detector.
-detector_pixels = 256 #Detector pixels
-detector_pixel_size = 0.000050 #pixel size in metres
-detector_width = detector_pixels * detector_pixel_size #detector size
-
-#We need to estimate the size of the sample in the sample plane,
-#so we've used the scan pixel size and the number of pixels in the sample to do so.
-#If we want to sub sample the scan at a lower number of position while keeping the same sample size,
-#we can easily change the number of scan pixels later before running an experiment.
-scan_pixels = sample.shape[0] #for now we match the number of pixels in the sample image
-scan_pixel_size = 0.000001 #m
-sample_width = scan_pixels * scan_pixel_size
-
-#Set experiment parameters
-camera_length = 0.15
-overfocus = 0.001
-semiconv = 0.020
-scan_rotation = 0
-
-#Create a list of components to model a simplified 4DSTEM experiment
-components = [comp.DoubleDeflector(name = 'Scan Coils', z_up = 0.3, z_low = 0.25),
- comp.Lens(name = 'Lens', z = 0.20),
- comp.Sample(name = 'Sample', sample = sample, z = camera_length, width = sample_width),
- comp.DoubleDeflector(name = 'Descan Coils', z_up = 0.1, z_low = 0.05, scan_rotation = scan_rotation)
- ]
-
-#Create the model Electron microscope. Initially we can create a parallel circular beam
-#with 32000 rays to view a sample image
-model = Model(components, beam_z = 0.4, beam_type = 'paralell', num_rays = 2**15,
- experiment = '4DSTEM', detector_pixels = detector_pixels,
- detector_size = detector_width)
-
-
-'''Now we run the first experiment, which is to try and recreate a single 4DSTEM image in the
-fourdstem_overfocused dataset obtained in the adjustment -- screen capture state.ipynb
-Note we have performed this calculation with 0 scan rotation firstly to simplify comparison'''
-
-#Set the scan_pixel_size and scan_pixels of the model experiment
-model.scan_pixel_size = scan_pixel_size
-
-#Since the parameters such as overfocus and semi convergence angle are derived parameters from the settings
-#of the components inside the electron microscope, we need to update the parameters of the model with the following functions
-model.set_obj_lens_f_from_overfocus(overfocus)
-model.set_beam_radius_from_semiconv(semiconv)
-
-#For a basic circular beam use this function to make the model rays: note that the beam_type = 'parallel' initialised in the creation of the
-#model class triggers the creation of a circular beam (parallel is not a great name I know). The number of rays that fill this
-#circular beam is defined by num_rays also initialised in the "Model" class.
-model.generate_rays()
-
-#model.step() is where the matrix multiplication between the position matrix of each ray, stored in the variable (model.r),
-#and the component transfer matrix occurs.
-model.step()
-
-#Note below: model.r is the ray matrix that stores positions and slopes of each ray.
-#This matrix is of shape (steps, 5, num rays), where
-#steps is defined by the number of components. The middle shape index of 5 is so because each ray position matrix
-#is of shape (5, 1): [x, slope_x, y, slope_y, 1]. The 1 is neccessary at the end to facilitate the addition of a "slope"
-#to the ray which can be performed by a deflector component.
-#We can index the positions of rays at each component if we know the index of that component in the ray matrix.
-#We have created a special variable to index the ray positions of the sample called model.sample_r_idx
-sample_rays_x = model.r[model.sample_r_idx, 0, :]
-sample_rays_y = model.r[model.sample_r_idx, 2, :]
-
-#Detector ray positions. Detector is always at the bottom, so -1 as the component index finds it's
-#ray positions.
-detector_rays_x = model.r[-1, 0, :]
-detector_rays_y = model.r[-1, 2, :]
-
-#Function to obtain the sample_image
-detector_ray_image, detector_sample_image, sample_pixel_coords, _ = get_image_from_rays(
- detector_rays_x, detector_rays_y,
- sample_rays_x, sample_rays_y,
- model.detector_size,
- model.detector_pixels,
- model.components[model.sample_idx].sample_size,
- model.components[model.sample_idx].sample_pixels,
- model.components[model.sample_idx].sample
-)
-
-#Show results
-plt.figure()
-plt.imshow(detector_sample_image)
-
-plt.figure()
-plt.imshow(fourdstem_overfocused[64, 64, :, :])
-
-plt.figure()
-plt.imshow(abs(fourdstem_overfocused[64, 64, :, :] - detector_sample_image))
-
-#Comparison between two is nearly right, but it's not exact yet. This could be because of how my circular beam is created
-#which tries to evenly fill a radius with the chosen amount of rays, thus I may not be sampling the same pixels perfectly
-#like in Dieter's code. I also worry that there might be an accidental sampling error
-#of pixels in the sample (maybe rounding the ray coordinate to the nearest pixel is wrong?. Will investigate further soon.
-
-
-'''For a custom number of rays (say 3), we can use the following code. Note, that in order to
-achieve the desired semi convergence angle before the sample, we have called the function
-"model.set_beam_radius_from_semiconv" earlier, which will set the correct beam_radius at the "gun" (initial ray position)
-. The beam radius this function finds should be used to
-set the outer radius of the ray bundle, which will achieve the desired semi_angle of the beam at crossover after the lens.'''
-
-#Creation of the ray matrix which is of shape (steps in model, 5, number of rays)
-model.r = np.zeros((model.steps, 5, 3))
-
-#Due to how we have set up the model to add a deflection, the 5th entry in each
-#ray matrix should be 1
-model.r[:, 4, :] = np.ones(3)
-
-#Create a ray that starts with a position of 45 degrees above the x-axis, and
-#a radius of the model.beam_radius that gives the desired semi convergence angle
-model.r[:, 0, 1] = model.beam_radius*np.cos(np.pi/4) #x position
-model.r[:, 2, 1] = model.beam_radius*np.sin(np.pi/4) #y position
-
-#Create a ray that starts with a position of -135 degrees below the x-axis, and
-#a radius of the model.beam_radius that gives the desired semi convergence angle
-model.r[:, 0, 2] = model.beam_radius*np.cos(-3*np.pi/4) #x position
-model.r[:, 2, 2] = model.beam_radius*np.sin(-3*np.pi/4) #y position
-
-#Note that this time we don't call model.generate_rays() because
-#that would overwrite out custom ray matrix of 3 rays
-model.step()
-
-sample_rays_x = model.r[model.sample_r_idx, 0, :]
-sample_rays_y = model.r[model.sample_r_idx, 2, :]
-
-detector_rays_x = model.r[-1, 0, :]
-detector_rays_y = model.r[-1, 2, :]
-
-detector_ray_image, detector_sample_image, sample_pixel_coords, _ = get_image_from_rays(
- detector_rays_x, detector_rays_y,
- sample_rays_x, sample_rays_y,
- model.detector_size,
- model.detector_pixels,
- model.components[model.sample_idx].sample_size,
- model.components[model.sample_idx].sample_pixels,
- model.components[model.sample_idx].sample
-)
-
-#Plot an image of 3 rays
-plt.figure()
-plt.imshow(detector_sample_image)
-
-'''We can also plot a number of scan positions on the sample, to see which parts of the sample
-are projected forward to the detector'''
-#We can set a lower amount of scan_pixels if we want to sample at a different spacing
-model.scan_pixels = 4
-num_sample_positions = model.scan_pixels**2
-
-#go back to the circular beam:
-model.generate_rays()
-
-fig, ax = plt.subplots()
-
-for _ in range(num_sample_positions):
-
- #We update the scan coil ratio to index the first scan position. This function
- #finds the deflector settings which will go through the beam pivot point that will perform perfect
- #beam_shift on the sample. Initial model.scan_pixel_x and model.scan_pixel_y are both 0, which means that the first beam
- #position is in the top left corner of the sample
- model.update_scan_coil_ratio()
- model.step()
-
- #Obtain the scan position of the beam in pixel coordinates on the sample
- px_x = scan_pixels/(2*model.scan_pixels)+(model.scan_pixel_x/model.scan_pixels)*sample.shape[0]-1
- px_y = scan_pixels/(2*model.scan_pixels)+(model.scan_pixel_y/model.scan_pixels)*sample.shape[0]-1
-
- #Move to the next scan position
- model.update_scan_position()
-
- sample_rays_x = model.r[model.sample_r_idx, 0, :]
- sample_rays_y = model.r[model.sample_r_idx, 2, :]
-
- #Detector ray positions
- detector_rays_x = model.r[-1, 0, :]
- detector_rays_y = model.r[-1, 2, :]
-
- #Create detector image of rays: get_image_from_rays converts sample and detector positions
- #to sample and detector coordinates
- detector_ray_image, detector_sample_image, sample_pixel_coords, _ = get_image_from_rays(
- detector_rays_x, detector_rays_y,
- sample_rays_x, sample_rays_y,
- model.detector_size,
- model.detector_pixels,
- model.components[model.sample_idx].sample_size,
- model.components[model.sample_idx].sample_pixels,
- model.components[model.sample_idx].sample
- )
-
- #Plot the beam pixel coordinates on the sample to see which part of the sample we are imaging.
- ax.plot(sample_pixel_coords[:, 1], sample_pixel_coords[:, 0], '.r', alpha = 0.1, zorder = 1)
- ax.plot(px_y, px_x, '.b', zorder = 2)
-
-ax.imshow(sample, zorder = 0)
-plt.show()
-
-
-
-
-
diff --git a/live_calibration_examples/fourdstem_example_no_gui_notebook.ipynb b/live_calibration_examples/fourdstem_example_no_gui_notebook.ipynb
deleted file mode 100644
index 6916edc..0000000
--- a/live_calibration_examples/fourdstem_example_no_gui_notebook.ipynb
+++ /dev/null
@@ -1,414 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [],
- "source": [
- "from temgymbasic import components as comp\n",
- "from temgymbasic.model import Model\n",
- "from temgymbasic.functions import make_test_sample, get_image_from_rays\n",
- "\n",
- "import matplotlib.pyplot as plt\n",
- "import numpy as np"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Load the fourdstem_overfocused sample to use as comparison to temgymbasic's forward model. Note that I have zipped up the 4GB file that is created from this 4DSTEM result and it is available in the \n",
- "temgymbasic repo as a 50mb file - see the file \"fourdstem_overfocused.zip\". "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [],
- "source": [
- "file = r\"C:\\Users\\User\\Documents\\Code\\src\\temgymbasic\\fourdstem_overfocused.npz\"\n",
- "\n",
- "fourdstem_overfocused = np.load(file)['arr_0']\n",
- "sample = make_test_sample()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [],
- "source": [
- "#Set number of detector pixels and detector size. For now keeping it simple \n",
- "#and assuming a square detector. \n",
- "detector_pixels = 256 #Detector pixels\n",
- "detector_pixel_size = 0.000050 #pixel size in metres\n",
- "detector_width = detector_pixels * detector_pixel_size #detector size\n",
- "\n",
- "#We need to estimate the size of the sample in the sample plane,\n",
- "#so we've used the scan pixel size and the number of pixels in the sample to do so. \n",
- "#If we want to sub sample the scan at a lower number of position while keeping the same sample size, \n",
- "#we can easily change the number of scan pixels later before running an experiment.\n",
- "scan_pixels = sample.shape[0] #for now we match the number of pixels in the sample image\n",
- "scan_pixel_size = 0.000001 #m\n",
- "sample_width = scan_pixels * scan_pixel_size\n",
- "\n",
- "#Set experiment parameters\n",
- "camera_length = 0.15\n",
- "overfocus = 0.001\n",
- "semiconv = 0.020\n",
- "scan_rotation = 0"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [],
- "source": [
- "#Create a list of components to model a simplified 4DSTEM experiment\n",
- "components = [comp.DoubleDeflector(name = 'Scan Coils', z_up = 0.3, z_low = 0.25),\n",
- " comp.Lens(name = 'Lens', z = 0.20),\n",
- " comp.Sample(name = 'Sample', sample = sample, z = camera_length, width = sample_width),\n",
- " comp.DoubleDeflector(name = 'Descan Coils', z_up = 0.1, z_low = 0.05, scan_rotation = scan_rotation)\n",
- " ]\n",
- "\n",
- "#Create the model Electron microscope. Initially we create a parallel circular beam leaving the \"gun\"\n",
- "#where ~ 32000 rays can be used to view a sample image\n",
- "model = Model(components, beam_z = 0.4, beam_type = 'paralell', num_rays = 2**15, \n",
- " experiment = '4DSTEM', detector_pixels = detector_pixels, \n",
- " detector_size = detector_width)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now we run the first experiment, which is to try and recreate a single 4DSTEM image in the \n",
- "fourdstem_overfocused dataset obtained in the adjustment -- screen capture state.ipynb. Note we have performed this calculation with 0 scan rotation to simplify the comparison."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [],
- "source": [
- "#Set the scan_pixel_size and scan_pixels of the model experiment\n",
- "model.scan_pixel_size = scan_pixel_size\n",
- "\n",
- "#Since the parameters such as overfocus and semi convergence angle are derived parameters from the settings\n",
- "#of the components inside the electron microscope, we need to update the parameters of the model with the following functions\n",
- "model.set_obj_lens_f_from_overfocus(overfocus)\n",
- "model.set_beam_radius_from_semiconv(semiconv)\n",
- "\n",
- "#For a basic circular beam use this function to make the model rays: note that the beam_type = 'parallel' initialised in the creation of the \n",
- "#model class triggers the creation of a circular beam (parallel is not a great name I know). The number of rays that fill this\n",
- "#circular beam is defined by num_rays also initialised in the \"Model\" class. \n",
- "model.generate_rays()\n",
- "\n",
- "#model.step() is where the matrix multiplication between the position matrix of each ray, stored in the variable (model.r),\n",
- "#and the component transfer matrix occurs. \n",
- "_ = model.step()\n",
- "\n"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Note below: model.r is the ray matrix that stores positions and slopes of each ray.\n",
- "This matrix is of shape (steps, 5, num rays), where\n",
- "steps is defined by the number of components. The middle shape index of 5 is so because each ray position matrix\n",
- "is of shape (5, 1): [x, slope_x, y, slope_y, 1]. The 1 is neccessary at the end to facilitate the addition of a \"slope\"\n",
- "to the ray which can be performed by a deflector component.\n",
- "We can index the positions of rays at each component if we know the index of that component in the ray matrix.\n",
- "We have created a special variable to index the ray positions of the sample called model.sample_r_idx"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [],
- "source": [
- "\n",
- "sample_rays_x = model.r[model.sample_r_idx, 0, :]\n",
- "sample_rays_y = model.r[model.sample_r_idx, 2, :]\n",
- "\n",
- "#Detector ray positions. Detector is always at the bottom, so -1 as the component index finds it's \n",
- "#ray positions.\n",
- "detector_rays_x = model.r[-1, 0, :]\n",
- "detector_rays_y = model.r[-1, 2, :]\n",
- "\n",
- "#Function to obtain the sample_image\n",
- "detector_ray_image, detector_sample_image, sample_pixel_coords, _ = get_image_from_rays(\n",
- " detector_rays_x, detector_rays_y,\n",
- " sample_rays_x, sample_rays_y,\n",
- " model.detector_size,\n",
- " model.detector_pixels,\n",
- " model.components[model.sample_idx].sample_size,\n",
- " model.components[model.sample_idx].sample_pixels,\n",
- " model.components[model.sample_idx].sample\n",
- ")\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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",
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- "output_type": "display_data"
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- {
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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#Show results\n",
- "plt.figure()\n",
- "plt.title('TEMGYMBasic Forward Projection')\n",
- "plt.imshow(detector_sample_image)\n",
- "\n",
- "plt.figure()\n",
- "plt.title('Original Forward Projection (Dieter\\'s code)')\n",
- "plt.imshow(fourdstem_overfocused[64, 64, :, :])\n",
- "\n",
- "plt.figure()\n",
- "plt.title('Difference')\n",
- "plt.imshow(abs(fourdstem_overfocused[64, 64, :, :] - detector_sample_image))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Difference between two is nearly right, but it's not exact yet. This could be because of how my circular beam is created\n",
- "which tries to evenly fill a radius with the chosen amount of rays, thus I may not be sampling the same pixels perfectly\n",
- "like in Dieter's code. I also worry that there might be an accidental sampling error\n",
- "of pixels in the sample (maybe rounding the ray coordinate to the nearest pixel is causing this). Will investigate further. "
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "To create a custom number of rays (say 3) with specific positions, we can use the following code. Note, that in order to\n",
- "achieve the desired semi convergence angle before the sample, we have called the function\n",
- "\"model.set_beam_radius_from_semiconv\" earlier, which will set the correct beam_radius at the \"gun\" (initial ray position)\n",
- ". The beam radius this function finds should be used to \n",
- "set the outer radius of the ray bundle, which will achieve the desired semi_angle of the beam at crossover after the lens."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#Creation of the ray matrix which is of shape (steps in model, 5, number of rays)\n",
- "model.r = np.zeros((model.steps, 5, 3))\n",
- "\n",
- "#Due to how we have set up the model to add a deflection, the 5th entry in each\n",
- "#ray matrix should be 1\n",
- "model.r[:, 4, :] = np.ones(3)\n",
- "\n",
- "#Create a ray that starts with a position of 45 degrees above the x-axis, and \n",
- "#a radius of the model.beam_radius that gives the desired semi convergence angle\n",
- "model.r[:, 0, 1] = model.beam_radius*np.cos(np.pi/4) #x position\n",
- "model.r[:, 2, 1] = model.beam_radius*np.sin(np.pi/4) #y position\n",
- "\n",
- "#Create a ray that starts with a position of -135 degrees below the x-axis, and \n",
- "#a radius of the model.beam_radius that gives the desired semi convergence angle\n",
- "model.r[:, 0, 2] = model.beam_radius*np.cos(-3*np.pi/4) #x position\n",
- "model.r[:, 2, 2] = model.beam_radius*np.sin(-3*np.pi/4) #y position\n",
- "\n",
- "#Note that this time we don't call model.generate_rays() because \n",
- "#that would overwrite out custom ray matrix of 3 rays\n",
- "model.step()\n",
- "\n",
- "sample_rays_x = model.r[model.sample_r_idx, 0, :]\n",
- "sample_rays_y = model.r[model.sample_r_idx, 2, :]\n",
- "\n",
- "detector_rays_x = model.r[-1, 0, :]\n",
- "detector_rays_y = model.r[-1, 2, :]\n",
- "\n",
- "detector_ray_image, detector_sample_image, sample_pixel_coords, _ = get_image_from_rays(\n",
- " detector_rays_x, detector_rays_y,\n",
- " sample_rays_x, sample_rays_y,\n",
- " model.detector_size,\n",
- " model.detector_pixels,\n",
- " model.components[model.sample_idx].sample_size,\n",
- " model.components[model.sample_idx].sample_pixels,\n",
- " model.components[model.sample_idx].sample\n",
- ")\n",
- "\n",
- "#Plot an image of 3 rays\n",
- "plt.figure()\n",
- "plt.imshow(detector_sample_image)\n"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can also plot a number of scan positions on the sample, to see which parts of the sample \n",
- "are projected forward to the detector"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#We can set a lower amount of scan_pixels if we want to sample at a different spacing\n",
- "model.scan_pixels = 4 #Determines number scan_pixels in one axis.\n",
- "num_sample_positions = model.scan_pixels**2\n",
- "\n",
- "#go back to the circular beam: \n",
- "model.generate_rays()\n",
- "\n",
- "fig, ax = plt.subplots()\n",
- "\n",
- "for _ in range(num_sample_positions):\n",
- " \n",
- " #We update the scan coil ratio to index the first scan position. This function\n",
- " #finds the deflector settings which will go through the beam pivot point that will perform perfect\n",
- " #beam_shift on the sample. Initial model.scan_pixel_x and model.scan_pixel_y are both 0, which means that the first beam \n",
- " #position is in the top left corner of the sample\n",
- " model.update_scan_coil_ratio()\n",
- " model.step()\n",
- " \n",
- " #Obtain the scan position of the beam in pixel coordinates on the sample\n",
- " px_x = scan_pixels/(2*model.scan_pixels)+(model.scan_pixel_x/model.scan_pixels)*sample.shape[0]-1\n",
- " px_y = scan_pixels/(2*model.scan_pixels)+(model.scan_pixel_y/model.scan_pixels)*sample.shape[0]-1\n",
- "\n",
- " #Move to the next scan position\n",
- " model.update_scan_position()\n",
- " \n",
- " sample_rays_x = model.r[model.sample_r_idx, 0, :]\n",
- " sample_rays_y = model.r[model.sample_r_idx, 2, :]\n",
- " \n",
- " #Detector ray positions\n",
- " detector_rays_x = model.r[-1, 0, :]\n",
- " detector_rays_y = model.r[-1, 2, :]\n",
- " \n",
- " #Create detector image of rays: get_image_from_rays converts sample and detector positions \n",
- " #to sample and detector coordinates\n",
- " detector_ray_image, detector_sample_image, sample_pixel_coords, _ = get_image_from_rays(\n",
- " detector_rays_x, detector_rays_y,\n",
- " sample_rays_x, sample_rays_y,\n",
- " model.detector_size,\n",
- " model.detector_pixels,\n",
- " model.components[model.sample_idx].sample_size,\n",
- " model.components[model.sample_idx].sample_pixels,\n",
- " model.components[model.sample_idx].sample\n",
- " )\n",
- " \n",
- " #Plot the beam pixel coordinates on the sample to see which part of the sample we are imaging. \n",
- " ax.plot(sample_pixel_coords[:, 1], sample_pixel_coords[:, 0], '.r', alpha = 0.1, zorder = 1)\n",
- " ax.plot(px_y, px_x, '.b', zorder = 2)\n",
- "\n",
- "ax.imshow(sample, zorder = 0)\n",
- "plt.show()\n"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "temgymbasic",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.6"
- },
- "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/live_calibration_examples/fourdstem_example_pyqt_large_scale.py b/live_calibration_examples/fourdstem_example_pyqt_large_scale.py
deleted file mode 100644
index a6002ed..0000000
--- a/live_calibration_examples/fourdstem_example_pyqt_large_scale.py
+++ /dev/null
@@ -1,33 +0,0 @@
-
-from temgymbasic import components as comp
-from temgymbasic.model import Model
-from temgymbasic.run import run_pyqt
-from temgymbasic.functions import make_test_sample
-from PyQt5.QtWidgets import QApplication
-import os
-import sys
-
-def main():
- sample = make_test_sample()
-
- camera_length = 0.15
-
- components = [comp.DoubleDeflector(name = 'Scan Coils', z_up = 0.8, z_low = 0.7),
- comp.Lens(name = 'Lens', z = 0.30, f = -0.05),
- comp.Sample(name = 'Sample', sample = sample, z = camera_length),
- comp.DoubleDeflector(name = 'Descan Coils', z_up = 0.1, z_low = 0.09)
- ]
-
- model = Model(components, beam_z = 1, beam_type = 'paralell', num_rays = 2**12,
- experiment = '4DSTEM')
-
- viewer = run_pyqt(model)
-
- return viewer
-
-if __name__ == '__main__':
- AppWindow = QApplication(sys.argv)
- viewer = main()
- viewer.show()
-
- AppWindow.exec_()
diff --git a/live_calibration_examples/fourdstem_example_pyqt_micron_scale.py b/live_calibration_examples/fourdstem_example_pyqt_micron_scale.py
deleted file mode 100644
index 1cbb761..0000000
--- a/live_calibration_examples/fourdstem_example_pyqt_micron_scale.py
+++ /dev/null
@@ -1,47 +0,0 @@
-
-from temgymbasic import components as comp
-from temgymbasic.model import Model
-from temgymbasic.run import run_pyqt
-from temgymbasic.functions import make_test_sample
-from PyQt5.QtWidgets import QApplication
-import os
-import sys
-
-def main():
- sample = make_test_sample()
-
- detector_pixels = 256
- detector_pixel_size = 0.000050 #pixel size in metres
- detector_width = detector_pixels * detector_pixel_size
-
- scan_pixels = 256
- scan_pixel_size = 0.000001 #for now assume square sample
- sample_width = scan_pixels * scan_pixel_size
-
- camera_length = 0.15
- overfocus = 0.001
- semiconv = 0.020
-
- components = [comp.DoubleDeflector(name = 'Scan Coils', z_up = 0.3, z_low = 0.25),
- comp.Lens(name = 'Lens', z = 0.20, f = -0.05),
- comp.Sample(name = 'Sample', sample = sample, z = camera_length, width = sample_width),
- comp.DoubleDeflector(name = 'Descan Coils', z_up = 0.1, z_low = 0.09)
- ]
-
- model = Model(components, beam_z = 0.4, beam_type = 'paralell', num_rays = 2**12,
- experiment = '4DSTEM', detector_pixels = detector_pixels,
- detector_size = detector_width)
-
- model.set_beam_radius_from_semiconv(semiconv)
- model.set_obj_lens_f_from_overfocus(overfocus)
-
- viewer = run_pyqt(model)
-
- return viewer
-
-if __name__ == '__main__':
- AppWindow = QApplication(sys.argv)
- viewer = main()
- viewer.show()
-
- AppWindow.exec_()
diff --git a/src/temgymbasic/components.py b/src/temgymbasic/components.py
index 0d0fce6..2184056 100644
--- a/src/temgymbasic/components.py
+++ b/src/temgymbasic/components.py
@@ -1,3 +1,5 @@
+# To make analtyical, need to define distance units?
+
import temgymbasic.shapes as geom
from temgymbasic.gui import *
import pyqtgraph.opengl as gl
@@ -132,7 +134,8 @@ def update_parameters_from_gui(self):
class AstigmaticLens():
- '''Creates an Astigmatic lens component and handles calls to GUI creation, updates to GUI
+ '''
+ Creates an Astigmatic lens component and handles calls to GUI creation, updates to GUI
and stores the component matrix.
'''
def __init__(self, z, name = '', fx = -0.5, fy = -0.5, label_radius = 0.3, radius = 0.25, num_points = 50):
@@ -421,7 +424,8 @@ def update_parameters_from_gui(self):
self.set_matrix()
class Sample():
- '''Creates a sample component which serves only as a visualisation on the 3D model.
+ '''
+ Creates a sample component which serves only as a visualisation on the 3D model.
'''
def __init__(self, z = 0., sample = None, name = '', label_radius = 0.3, width = 0.25, num_points = 50, x = 0., y = 0.):
'''
@@ -430,6 +434,8 @@ def __init__(self, z = 0., sample = None, name = '', label_radius = 0.3, width =
----------
z : float
Position of component in optic axis
+ sample : 2D or 3D array
+ Array representing sample.
name : str, optional
Name of this component which will be displayed by GUI, by default ''
label_radius : float, optional
@@ -444,6 +450,14 @@ def __init__(self, z = 0., sample = None, name = '', label_radius = 0.3, width =
Y position of sample model, by default 0.
width : float, optional
Width of the edges of the square sample, by default 0.25
+
+ Warnings:
+ ------
+ TypeError: data must be 2D or 3D
+ Caused by passing a 1D array to the sample kwarg.
+
+ AttributeError: 'NoneType' object has no attribute 'shape'
+ Caused by not passing an array to the sample kwarg.
'''
self.type = 'Sample'
@@ -579,8 +593,9 @@ def update_parameters_from_gui(self):
self.set_slabel()
class Deflector():
- '''Creates a single deflector component and handles calls to GUI creation, updates to GUI
- and stores the component matrix. See Double Deflector component for a more useful version
+ '''
+ Creates a single deflector component and handles calls to GUI creation, updates to GUI
+ and stores the component matrix. See Double Deflector component for a more useful version
'''
def __init__(self, z, name = '', defx = 0.5, defy = 0.5, label_radius = 0.3, radius = 0.25, num_points = 50):
'''_summary_
@@ -638,11 +653,11 @@ def deflector_matrix(self, def_x, def_y):
Output ray transfer matrix
'''
- matrix = np.array([[1, 0, 0, 0, 0],
+ matrix = np.array([[1, 0, 0, 0, 0],
[0, 1, 0, 0, def_x],
- [0, 0, 1, 0, 0],
+ [0, 0, 1, 0, 0],
[0, 0, 0, 1, def_y],
- [0, 0, 0, 0, 1]])
+ [0, 0, 0, 0, 1]])
return matrix
diff --git a/src/temgymbasic/functions.py b/src/temgymbasic/functions.py
index 1b1435e..3fc5351 100644
--- a/src/temgymbasic/functions.py
+++ b/src/temgymbasic/functions.py
@@ -1,4 +1,3 @@
-
import numpy as np
@@ -51,7 +50,7 @@ def make_test_sample(size=256):
# FIXME resolve code duplication between circular_beam() and point_beam()
def circular_beam(r, outer_radius):
- '''Generates a circular paralell initial beam
+ '''Generates a circular parallel initial beam
Parameters
----------
diff --git a/src/temgymbasic/gui.py b/src/temgymbasic/gui.py
index 3bd04ee..8b55d1f 100644
--- a/src/temgymbasic/gui.py
+++ b/src/temgymbasic/gui.py
@@ -652,7 +652,7 @@ def __init__(self, num_rays, beam_type, gun_beam_semi_angle, beam_tilt_x, beam_t
num_rays : int
Number of rays in the model
beam_type : str
- Type of initial beam: Axial, paralell of point.
+ Type of initial beam: Axial, parallel of point.
gun_beam_semi_angle : float
Semi angle of the beam
beam_tilt_x : float
@@ -696,7 +696,7 @@ def __init__(self, num_rays, beam_type, gun_beam_semi_angle, beam_tilt_x, beam_t
self.beamanglelabel = QLabel(str(round(gun_beam_semi_angle, 2)))
self.beamanglelabel.setMinimumWidth(80)
- self.modelbeamanglelabel = QLabel('Axial/Paralell Beam Semi Angle')
+ self.modelbeamanglelabel = QLabel('Axial/parallel Beam Semi Angle')
hbox = QHBoxLayout()
hbox.addWidget(self.beamangleslider)
@@ -717,7 +717,7 @@ def __init__(self, num_rays, beam_type, gun_beam_semi_angle, beam_tilt_x, beam_t
self.beamwidthlabel = QLabel('0')
self.beamwidthlabel.setMinimumWidth(80)
- self.modelbeamwidthlabel = QLabel('Paralell Beam Width')
+ self.modelbeamwidthlabel = QLabel('parallel Beam Width')
hbox = QHBoxLayout()
hbox.addWidget(self.beamwidthslider)
@@ -733,10 +733,10 @@ def __init__(self, num_rays, beam_type, gun_beam_semi_angle, beam_tilt_x, beam_t
self.checkBoxPoint = QCheckBox("Point Beam")
- self.checkBoxParalell = QCheckBox("Paralell Beam")
+ self.checkBoxParallel = QCheckBox("parallel Beam")
- self.checkBoxParalell.stateChanged.connect(
- partial(self.uncheck, self.checkBoxParalell))
+ self.checkBoxParallel.stateChanged.connect(
+ partial(self.uncheck, self.checkBoxParallel))
self.checkBoxPoint.stateChanged.connect(
partial(self.uncheck, self.checkBoxPoint))
self.checkBoxAxial.stateChanged.connect(
@@ -744,12 +744,12 @@ def __init__(self, num_rays, beam_type, gun_beam_semi_angle, beam_tilt_x, beam_t
hbox.addWidget(self.checkBoxAxial)
hbox.addWidget(self.checkBoxPoint)
- hbox.addWidget(self.checkBoxParalell)
+ hbox.addWidget(self.checkBoxParallel)
if beam_type == 'axial':
self.checkBoxAxial.setChecked(True)
- elif beam_type == 'paralell':
- self.checkBoxParalell.setChecked(True)
+ elif beam_type == 'parallel':
+ self.checkBoxParallel.setChecked(True)
elif beam_type == 'point':
self.checkBoxPoint.setChecked(True)
@@ -818,11 +818,11 @@ def uncheck(self, btn):
if btn == self.checkBoxAxial:
# making other check box to uncheck
- self.checkBoxParalell.setChecked(False)
+ self.checkBoxParallel.setChecked(False)
self.checkBoxPoint.setChecked(False)
# if second check box is selected
- elif btn == self.checkBoxParalell:
+ elif btn == self.checkBoxParallel:
# making other check box to uncheck
self.checkBoxAxial.setChecked(False)
@@ -833,7 +833,7 @@ def uncheck(self, btn):
# making other check box to uncheck
self.checkBoxAxial.setChecked(False)
- self.checkBoxParalell.setChecked(False)
+ self.checkBoxParallel.setChecked(False)
class ApertureGui():
diff --git a/src/temgymbasic/model.py b/src/temgymbasic/model.py
index 3a88c82..ef92c1c 100644
--- a/src/temgymbasic/model.py
+++ b/src/temgymbasic/model.py
@@ -1,10 +1,13 @@
+'''
+This class create the model composed of the specified components,
+and handles all of the computation that transmits the rays through each component.
+'''
+
import numpy as np
from temgymbasic.functions import circular_beam, point_beam, axial_point_beam, x_axial_point_beam
from temgymbasic.gui import ModelGui, ExperimentGui
-'''This class create the model composed of the specified components, and handles all of the computation
-that transmits the rays through each component.'''
class Model():
'''Generates a model electron microscope. This class generates performs the matrix
@@ -28,7 +31,7 @@ def __init__(self, components, beam_z=1, num_rays=16, beam_type='point',
Choose the type of beam:
-'point' beam creates a set of rays that start from a single point and
spread out like a cone.
- -'paralell' beam creates a set of rays that start from the same position, but
+ -'parallel' beam creates a set of rays that start from the same position, but
each have the same angle.
- 'axial' creates a beam which is only visible on the x and y axis.
- 'x_axial' creates a beam which is only visible on the x-axis. This is only used
@@ -40,7 +43,7 @@ def __init__(self, components, beam_z=1, num_rays=16, beam_type='point',
beam_tilt_y : int, optional
Set the tilt of the beam in the y direction, by default 0
beam_radius : float, optional
- Set the width of the beam - only matters if "paralell" beam type is selected
+ Set the width of the beam - only matters if "parallel" beam type is selected
detector_size : float, optional
Set the size of the detector, by default 0.5
detector_pixels : int, optional
@@ -159,7 +162,8 @@ def set_beam_radius_from_semiconv(self, semiconv):
#Create the ModelGUI if we are running pyqtgraph
def create_gui(self):
- '''Create the GUI
+ '''
+ Create the GUI
'''
self.gui = ModelGui(self.num_rays, self.beam_type,
self.gun_beam_semi_angle, self.beam_tilt_x, self.beam_tilt_y, self.beam_radius)
@@ -169,10 +173,12 @@ def create_gui(self):
self.scan_pixels = self.experiment_gui.scanpixelsslider.value()
def generate_rays(self):
- '''Generate electron rays
- '''
- #Make our 3D matrix of rays. This matrix is of shape (steps, 5, num rays), where
- #steps is defined by the number of components.
+ '''
+ Generate electron rays.
+
+ Make our 3D matrix of rays. This matrix is of shape (steps, 5, num rays), where
+ steps is defined by the number of components.
+ '''
self.steps = len(self.z_positions)
@@ -181,7 +187,7 @@ def generate_rays(self):
self.r[:, 4, :] = np.ones(self.num_rays)
- if self.beam_type == 'paralell':
+ if self.beam_type == 'parallel':
self.r, self.spot_indices = circular_beam(self.r, self.beam_radius)
elif self.beam_type == 'point':
self.r, self.spot_indices = point_beam(self.r, self.gun_beam_semi_angle)
@@ -195,7 +201,8 @@ def generate_rays(self):
#Add the matrices of each component to a list
def update_component_matrix(self):
- '''Update the list of all component matrices, each matrix of which has
+ '''
+ Update the list of all component matrices, each matrix of which has
been set by the component upon it's creation.
'''
self.components_matrix = []
@@ -208,7 +215,8 @@ def update_component_matrix(self):
#Perform the matrix multiplication of the rays with each component in the model
def update_rays_stepwise(self):
- '''Perform the neccessary matrix multiplications and function multiplications
+ '''
+ Perform the neccessary matrix multiplications and function multiplications
to propagate the beam through the column
'''
#Do the matrix multiplication of the first rays with the distance between the initial beam z
@@ -270,7 +278,8 @@ def update_rays_stepwise(self):
idx += 1
def update_parameters_from_gui(self):
- '''Update the GUI
+ '''
+ Update the GUI
'''
#This code updates the GUI sliders if it exists
self.num_rays = 2**(self.gui.rayslider.value())
@@ -285,8 +294,8 @@ def update_parameters_from_gui(self):
if self.gui.checkBoxAxial.isChecked():
self.beam_type = 'axial'
- if self.gui.checkBoxParalell.isChecked():
- self.beam_type = 'paralell'
+ if self.gui.checkBoxparallel.isChecked():
+ self.beam_type = ''
if self.gui.checkBoxPoint.isChecked():
self.beam_type = 'point'
@@ -346,7 +355,8 @@ def update_scan_position(self):
self.scan_pixel_y = 0
def set_model_labels(self):
- '''Set labels of the model inside the GUI
+ '''
+ Set labels of the model inside the GUI
'''
self.gui.raylabel.setText(
str(self.num_rays))
@@ -363,7 +373,8 @@ def set_model_labels(self):
)
def set_experiment_labels(self):
- '''Set labels of the model inside the GUI
+ '''
+ Set labels of the model inside the GUI
'''
self.experiment_gui.scanpixelslabel.setText(
str(self.scan_pixels))
@@ -393,7 +404,8 @@ def step(self):
#Propagation matrix used by the model to propagate rays between components
def propagate(self, z):
- '''Propagation matrix
+ '''
+ Propagation matrix
Parameters
----------
diff --git a/src/temgymbasic/run.py b/src/temgymbasic/run.py
index ceea2a0..c5689f5 100644
--- a/src/temgymbasic/run.py
+++ b/src/temgymbasic/run.py
@@ -269,7 +269,7 @@ def connectSignals(self):
if self.model.experiment != None:
self.model.experiment_gui.scanpixelsslider.valueChanged.connect(self.update)
- self.model.gui.checkBoxParalell.stateChanged.connect(self.update)
+ self.model.gui.checkBoxParallel.stateChanged.connect(self.update)
self.model.gui.checkBoxPoint.stateChanged.connect(self.update)
self.model.gui.checkBoxAxial.stateChanged.connect(self.update)
self.model.gui.beamangleslider.valueChanged.connect(self.update)
@@ -464,7 +464,7 @@ def run_pyqt(model):
return viewer
#Example code to make a matplotlib plot
-def show_matplotlib(model, name = 'model.svg', component_lw = 4, edge_lw = 1, label_fontsize = 20):
+def show_matplotlib(model, name = 'model.svg', component_lw = 4, edge_lw = 1, label_fontsize = 20, **kwargs):
'''Code to show a matplotlib model
Parameters
@@ -479,6 +479,10 @@ def show_matplotlib(model, name = 'model.svg', component_lw = 4, edge_lw = 1, la
Linewidth of highlight to edges, by default 1
label_fontsize : int, optional
Fontsize of labels, by default 20
+ fig : class, optional
+ Matplotlib figure object
+ ax : class, optional
+ Matplotlib axis object of the figure
Returns
-------
@@ -486,7 +490,21 @@ def show_matplotlib(model, name = 'model.svg', component_lw = 4, edge_lw = 1, la
Matplotlib figure object
ax : class
Matplotlib axis object of the figure
- '''
+ '''
+
+ # kwargs
+ fig = kwargs.get('figure', None)
+ ax = kwargs.get('axis', None)
+ is_labels = kwargs.get('labels', True)
+ figsize = kwargs.get('figsize', [12, 20])
+ plot_rays = kwargs.get('plot_rays', True)
+ label_x = kwargs.get('label_x', 0.3)
+
+
+ rcol = kwargs.get('rcol', 'dimgray')
+ fcol = kwargs.get('rcol', 'aquamarine')
+ fcolpair = kwargs.get('rcol', ['khaki', 'deepskyblue'])
+
#Step the rays through the model to get the ray positions throughout the column
rays = model.step()
@@ -494,7 +512,12 @@ def show_matplotlib(model, name = 'model.svg', component_lw = 4, edge_lw = 1, la
x, y, z = rays[:, 0, :], rays[:, 2, :], model.z_positions
#Create a figure
- fig, ax = plt.subplots(figsize=(12, 20))
+ if (fig == None and ax == None):
+ fig, ax = plt.subplots(figsize=(figsize[0], figsize[1]))
+ elif (fig == None and ax != None):
+ fig, _ = plt.subplots(figsize=(figsize[0], figsize[1]))
+ elif (fig != None and ax == None):
+ _, ax = plt.subplots(figsize=(figsize[0], figsize[1]))
ax.tick_params(axis='both', which='major', labelsize=14)
ax.tick_params(axis='both', which='minor', labelsize=12)
@@ -521,21 +544,20 @@ def show_matplotlib(model, name = 'model.svg', component_lw = 4, edge_lw = 1, la
allowed_rays = range(model.num_rays)
#Set colors of rays
- ray_color = 'dimgray'
- fill_color = 'aquamarine'
- fill_color_pair = ['khaki', 'deepskyblue']
+ ray_color = rcol
+ fill_color = fcol
+ fill_color_pair = fcolpair
fill_alpha = 1
ray_alpha = 1
+ # Width of rays in diagram.
ray_lw = 0.25
- plot_rays = True
highlight_edges = True
fill_between = True
edge_rays = [0, model.num_rays-1]
- label_x = 0.30
#Loop through components, and for each type of component plot rays in the correct ray,
#and increment the index correctly
@@ -559,8 +581,9 @@ def show_matplotlib(model, name = 'model.svg', component_lw = 4, edge_lw = 1, la
color=ray_color, linewidth=ray_lw, alpha=ray_alpha, zorder=1)
if component.type == 'Biprism':
- ax.text(label_x, component.z-0.01,
- component.name, fontsize=label_fontsize, zorder = 1000)
+ if is_labels == True:
+ ax.text(label_x, component.z-0.01,
+ component.name, fontsize=label_fontsize, zorder = 1000)
if model.beam_type == 'x_axial' and component.theta == 0:
ax.plot(component.points[0, :], component.points[2,
@@ -572,8 +595,9 @@ def show_matplotlib(model, name = 'model.svg', component_lw = 4, edge_lw = 1, la
idx += 1
elif component.type == 'Quadrupole':
r = component.radius
- ax.text(label_x, component.z-0.01, 'Upper ' +
- component.name, fontsize=label_fontsize, zorder = 1000)
+ if is_labels == True:
+ ax.text(label_x, component.z-0.01, 'Upper ' +
+ component.name, fontsize=label_fontsize, zorder = 1000)
ax.plot([-r, -r/2], [z[idx], z[idx]],
color='lightcoral', alpha=1, linewidth=component_lw, zorder=999)
ax.plot([-r/2, 0], [z[idx], z[idx]],
@@ -587,8 +611,9 @@ def show_matplotlib(model, name = 'model.svg', component_lw = 4, edge_lw = 1, la
idx += 1
elif component.type == 'Aperture':
- ax.text(label_x, component.z-0.01,
- component.name, fontsize=label_fontsize, zorder = 1000)
+ if is_labels == True:
+ ax.text(label_x, component.z-0.01,
+ component.name, fontsize=label_fontsize, zorder = 1000)
ri = component.aperture_radius_inner
ro = component.aperture_radius_outer
@@ -604,8 +629,9 @@ def show_matplotlib(model, name = 'model.svg', component_lw = 4, edge_lw = 1, la
idx += 1
elif component.type == 'Double Deflector':
r = component.radius
- ax.text(label_x, component.z_up-0.01, 'Upper ' +
- component.name, fontsize=label_fontsize, zorder = 1000)
+ if is_labels == True:
+ ax.text(label_x, component.z_up-0.01, 'Upper ' +
+ component.name, fontsize=label_fontsize, zorder = 1000)
ax.plot([-r, 0], [z[idx], z[idx]],
color='lightcoral', alpha=1, linewidth=component_lw, zorder=999)
ax.plot([0, r], [z[idx], z[idx]],
@@ -632,8 +658,9 @@ def show_matplotlib(model, name = 'model.svg', component_lw = 4, edge_lw = 1, la
ax.plot(x[idx-1:idx+1, allowed_rays], z[idx-1:idx+1],
color=ray_color, linewidth=ray_lw, alpha=ray_alpha, zorder=1)
- ax.text(label_x, component.z_low-0.01,
- 'Lower ' + component.name, fontsize=label_fontsize, zorder = 1000)
+ if is_labels == True:
+ ax.text(label_x, component.z_low-0.01,
+ 'Lower ' + component.name, fontsize=label_fontsize, zorder = 1000)
ax.plot([-r, 0], [z[idx], z[idx]],
color='lightcoral', alpha=1, linewidth=component_lw, zorder=999)
ax.plot([0, r], [z[idx], z[idx]],
@@ -643,8 +670,9 @@ def show_matplotlib(model, name = 'model.svg', component_lw = 4, edge_lw = 1, la
idx += 1
elif component.type == 'Lens':
- ax.text(label_x, component.z-0.01,
- component.name, fontsize=label_fontsize, zorder = 1000)
+ if is_labels == True:
+ ax.text(label_x, component.z-0.01,
+ component.name, fontsize=label_fontsize, zorder = 1000)
ax.add_patch(mpl.patches.Arc((0, component.z), component.radius*2, height=0.05,
theta1=0, theta2=180, linewidth=1, fill=False, zorder=-1, edgecolor='k'))
ax.add_patch(mpl.patches.Arc((0, component.z), component.radius*2, height=0.05,
@@ -653,8 +681,9 @@ def show_matplotlib(model, name = 'model.svg', component_lw = 4, edge_lw = 1, la
idx += 1
elif component.type == 'Astigmatic Lens':
- ax.text(label_x, component.z-0.01,
- component.name, fontsize=label_fontsize, zorder = 1000)
+ if is_labels == True:
+ ax.text(label_x, component.z-0.01,
+ component.name, fontsize=label_fontsize, zorder = 1000)
ax.add_patch(mpl.patches.Arc((0, component.z), component.radius*2, height=0.05,
theta1=0, theta2=180, linewidth=1, fill=False, zorder=-1, edgecolor='k'))
ax.add_patch(mpl.patches.Arc((0, component.z), component.radius*2, height=0.05,
@@ -663,8 +692,9 @@ def show_matplotlib(model, name = 'model.svg', component_lw = 4, edge_lw = 1, la
idx += 1
elif component.type == 'Deflector':
r = component.radius
- ax.text(label_x, component.z-0.01,
- component.name, fontsize=label_fontsize, zorder = 1000)
+ if is_labels == True:
+ ax.text(label_x, component.z-0.01,
+ component.name, fontsize=label_fontsize, zorder = 1000)
ax.plot([-r, 0], [z[idx], z[idx]],
color='lightcoral', alpha=1, linewidth=component_lw, zorder=999)
ax.plot([0, r], [z[idx], z[idx]],
@@ -674,8 +704,9 @@ def show_matplotlib(model, name = 'model.svg', component_lw = 4, edge_lw = 1, la
idx += 1
elif component.type == 'Sample':
- ax.text(label_x, component.z-0.01,
- component.name, fontsize=label_fontsize, zorder = 1000)
+ if is_labels == True:
+ ax.text(label_x, component.z-0.01,
+ component.name, fontsize=label_fontsize, zorder = 1000)
w = component.width
ax.plot([component.x-w/2, component.x+w/2], [z[idx], z[idx]],
color='dimgrey', alpha=0.8, linewidth=3)
@@ -719,7 +750,8 @@ def show_matplotlib(model, name = 'model.svg', component_lw = 4, edge_lw = 1, la
color=ray_color, linewidth=ray_lw, alpha=ray_alpha, zorder=1)
#Create the final labels and plot the detector shape
- ax.text(label_x, -0.01, 'Detector', fontsize=label_fontsize, zorder = 1000)
+ if is_labels == True:
+ ax.text(label_x, -0.01, 'Detector', fontsize=label_fontsize, zorder = 1000)
ax.plot([-model.detector_size/2, model.detector_size/2],
[0, 0], color='dimgrey', alpha=1, linewidth=component_lw)
diff --git a/src/temgymbasic/shapes.py b/src/temgymbasic/shapes.py
index de44873..41ca920 100644
--- a/src/temgymbasic/shapes.py
+++ b/src/temgymbasic/shapes.py
@@ -1,3 +1,6 @@
+'''
+Shapes of components for visualisations.
+'''
import numpy as np
import triangle as tr