Add 2.0 dev version to try version switcher

Author: ericpre

dev/.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 60e0ddb440842457518e04499e7322f0
+tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/.nojekyll

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+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# EELS curve fitting\n\nPerforms curve fitting to an EELS spectrum, plots the result and saves it as\npng file.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import hyperspy.api as hs\n\ns = hs.load(\"coreloss_spectrum.msa\", signal_type=\"EELS\")\nll = hs.load(\"lowloss_spectrum.msa\", signal_type=\"EELS\")\n\ns.add_elements((\"Mn\", \"O\"))\ns.set_microscope_parameters(\n    beam_energy=300,\n    convergence_angle=24.6,\n    collection_angle=13.6\n    )\n\nm = s.create_model(ll=ll)\nm.enable_fine_structure()\nm.multifit(kind=\"smart\")\nm.plot()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "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": "3.9.13"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

dev/_downloads/037b5aa4752b41748abf69bba9d1cbe4/EELS_curve_fitting.ipynb

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+"""
+Creates a spectrum image and plots it
+=====================================
+
+This example creates a spectrum image, i.e. navigation dimension 2 and
+signal dimension 1, and plots it
+"""
+
+import numpy as np
+import hyperspy.api as hs
+import matplotlib.pyplot as plt
+
+# Create a spectrum image with random data
+s = hs.signals.Signal1D(np.random.random((64, 64, 1024)))
+
+# Define the axis properties
+s.axes_manager.signal_axes[0].name = 'Energy'
+s.axes_manager.signal_axes[0].units = 'eV'
+s.axes_manager.signal_axes[0].scale = 0.3
+s.axes_manager.signal_axes[0].offset = 100
+
+s.axes_manager.navigation_axes[0].name = 'X'
+s.axes_manager.navigation_axes[0].units = 'nm'
+s.axes_manager.navigation_axes[0].scale = 0.1
+s.axes_manager.navigation_axes[0].offset = 100
+
+s.axes_manager.navigation_axes[1].name = 'Y'
+s.axes_manager.navigation_axes[1].units = 'nm'
+s.axes_manager.navigation_axes[1].scale = 0.1
+s.axes_manager.navigation_axes[1].offset = 100
+
+# Give a title
+s.metadata.General.title = 'Random spectrum image'
+
+# Plot it
+s.plot()
+
+plt.show()  # No necessary when running in the HyperSpy's IPython profile

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

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+"""
+Simple simulation (2 Gaussians)
+===============================
+
+Creates a 2D hyperspectrum consisting of two Gaussians and plots it.
+
+This example can serve as starting point to test other functionalities on the
+simulated hyperspectrum.
+
+"""
+import numpy as np
+import hyperspy.api as hs
+import matplotlib.pyplot as plt
+
+
+# Create an empty spectrum
+s = hs.signals.Signal1D(np.zeros((32, 32, 1024)))
+
+# Generate some simple data: two Gaussians with random centers and area
+
+# First we create a model
+m = s.create_model()
+
+# Define the first gaussian
+gs1 = hs.model.components1D.Gaussian()
+# Add it to the model
+m.append(gs1)
+
+# Set the parameters
+gs1.sigma.value = 10
+# Make the center vary in the -5,5 range around 128
+gs1.centre.map['values'][:] = 256 + (np.random.random((32, 32)) - 0.5) * 10
+gs1.centre.map['is_set'][:] = True
+
+# Make the area vary between 0 and 10000
+gs1.A.map['values'][:] = 10000 * np.random.random((32, 32))
+gs1.A.map['is_set'][:] = True
+
+# Second gaussian
+gs2 = hs.model.components1D.Gaussian()
+# Add it to the model
+m.append(gs2)
+
+# Set the parameters
+gs2.sigma.value = 20
+
+# Make the center vary in the -10,10 range around 768
+gs2.centre.map['values'][:] = 768 + (np.random.random((32, 32)) - 0.5) * 20
+gs2.centre.map['is_set'][:] = True
+
+# Make the area vary between 0 and 20000
+gs2.A.map['values'][:] = 20000 * np.random.random((32, 32))
+gs2.A.map['is_set'][:] = True
+
+# Create the dataset
+s_model = m.as_signal()
+
+# Add noise
+s_model.set_signal_origin("simulation")
+s_model.add_poissonian_noise()
+
+# Plot the result
+s_model.plot()
+
+plt.show()

dev/_downloads/145e22613cb65a105733e549bc4d2937/spectrum_image.py

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+"""
+Simple arctan fit
+=================
+
+Fit an arctan function.
+
+"""
+
+import numpy as np
+import hyperspy.api as hs
+
+# Generate the data and make the spectrum
+data = np.arctan(np.arange(-500, 500))
+s = hs.signals.Signal1D(data)
+s.axes_manager[0].offset = -500
+s.axes_manager[0].units = ""
+s.axes_manager[0].name = "x"
+s.metadata.General.title = "Simple arctan fit"
+s.set_signal_origin("simulation")
+
+s.add_gaussian_noise(0.1)
+
+# Make the arctan component for use in the model
+arctan_component = hs.model.components1D.Arctan()
+
+# Create the model and add the arctan component
+m = s.create_model()
+m.append(arctan_component)
+
+# Fit the arctan component to the spectrum
+m.fit()
+
+# Print the result of the fit
+m.print_current_values()
+
+# Plot the spectrum and the model fitting
+m.plot()

dev/_downloads/27e577ab3ef879ffbd7ffe80e1c1f4d8/two_gaussians.py

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+"""
+Creates a 4D image and plots it
+===============================
+
+This example creates a 4D dataset, i.e. 2 navigation dimension and
+2 signal dimension and plots it
+"""
+
+import numpy as np
+import hyperspy.api as hs
+
+# Create a 2D image stack with random data
+im = hs.signals.Signal2D(np.random.random((16, 16, 32, 32)))
+
+# Define the axis properties
+im.axes_manager.signal_axes[0].name = ''
+im.axes_manager.signal_axes[0].units = '1/nm'
+im.axes_manager.signal_axes[0].scale = 0.1
+im.axes_manager.signal_axes[0].offset = 0
+
+im.axes_manager.signal_axes[1].name = ''
+im.axes_manager.signal_axes[1].units = '1/nm'
+im.axes_manager.signal_axes[1].scale = 0.1
+im.axes_manager.signal_axes[1].offset = 0
+
+im.axes_manager.navigation_axes[0].name = 'X'
+im.axes_manager.navigation_axes[0].units = 'nm'
+im.axes_manager.navigation_axes[0].scale = 0.3
+im.axes_manager.navigation_axes[0].offset = 100
+
+im.axes_manager.navigation_axes[1].name = 'Y'
+im.axes_manager.navigation_axes[1].units = 'nm'
+im.axes_manager.navigation_axes[1].scale = 0.3
+im.axes_manager.navigation_axes[1].offset = 100
+
+# Give a title
+im.metadata.General.title = 'Random 2D image stack'
+
+im.plot()

dev/_downloads/445849846330eaa629bd4d1a1ffc767c/simple_arctan_fit.py

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+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Creates a line spectrum\n\nThis example creates a line spectrum and plots it\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\nimport hyperspy.api as hs\n\n# Create a line spectrum with random data\ns = hs.signals.Signal1D(np.random.random((100, 1024)))\n\n# Define the axis properties\ns.axes_manager.signal_axes[0].name = 'Energy'\ns.axes_manager.signal_axes[0].units = 'eV'\ns.axes_manager.signal_axes[0].scale = 0.3\ns.axes_manager.signal_axes[0].offset = 100\n\ns.axes_manager.navigation_axes[0].name = 'time'\ns.axes_manager.navigation_axes[0].units = 'fs'\ns.axes_manager.navigation_axes[0].scale = 0.3\ns.axes_manager.navigation_axes[0].offset = 100\n\n# Give a title\ns.metadata.General.title = 'Random line spectrum'\n\n# Plot it\ns.plot()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "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": "3.9.13"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}