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style: fix spell errors raised by codespell in basics_01_visual guide #33

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10 changes: 5 additions & 5 deletions notebooks/basics_01_visualization_and_virtualimaging.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -216,7 +216,7 @@
"source": [
"#### What's in a DataCube?\n",
"\n",
"This says that `datacube` is an object of type `DataCube`. This is py4DSTEM's containter for 4D-STEM scans. We see that it's four-dimensional, with a shape of `(100 x 84 x 125 x 125)`.\n",
"This says that `datacube` is an object of type `DataCube`. This is py4DSTEM's container for 4D-STEM scans. We see that it's four-dimensional, with a shape of `(100 x 84 x 125 x 125)`.\n",
"\n",
"What does this mean?\n",
"\n",
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],
"source": [
"# Let's start by adjusting the color axis range, aka the contrast range.\n",
"# This means specifiying which pixel intensity values we set to black (min) and which we set to white (max).\n",
"# This means specifying which pixel intensity values we set to black (min) and which we set to white (max).\n",
"\n",
"# To tell py4DSTEM we want to manually specify the color range to range from 0 to 500, we use these arguments:\n",
"\n",
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"source": [
"Logarithmic scaling is best when the features of interest have intensities which vary by multiple orders of magnitude. It is often a good place to start if you're not sure what to expect in a dataset.\n",
"\n",
"Scaling by a power law is sometimes more useful for visualization of diffraction patterns, because we can tune the power (each pixel intensity --> intensity^power) to achieve the desired scaling. This may exclude some features - and this may be desireable, for instance when extremely weak features are present which are not scientifically interesting or large enough to affect our analysis and which we don't really need to examine closely, such as detector dark current."
"Scaling by a power law is sometimes more useful for visualization of diffraction patterns, because we can tune the power (each pixel intensity --> intensity^power) to achieve the desired scaling. This may exclude some features - and this may be desirable, for instance when extremely weak features are present which are not scientifically interesting or large enough to affect our analysis and which we don't really need to examine closely, such as detector dark current."
]
},
{
Expand Down Expand Up @@ -690,7 +690,7 @@
"\n",
"The above examples look at a single diffraction pattern. Real experiments might consist of thousands or even millions of diffraction patterns. We want to evaluate the contents of the dataset as quickly as possible - is it single crystal? Polycrstalline? Amorphous? A mixture? \n",
"\n",
"To answer these questions efficiently, it's helpful to get an overview of all of the diffraction that occured in this data acquisition, all at once. The simplest way to do this is to calculate the mean diffraction pattern."
"To answer these questions efficiently, it's helpful to get an overview of all of the diffraction that occurred in this data acquisition, all at once. The simplest way to do this is to calculate the mean diffraction pattern."
]
},
{
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"source": [
"We see some interesting features, such as the rings of intensity containing some Bragg disks. However calculating the mean is good and bad: it gives us a quick overview of the most prominent features in many patterns, but it may hide diffraction features which occur in a small number of scan positions.\n",
"\n",
"For this reason, we typically also visualize the **maximum** diffraction pattern. By this, we mean the maximum signal of each pixel in diffraction space over all probe positons. This way, we see the brightest scattering from each pixel, even if it only occured in one diffraction image. This is a great way to see all of the Bragg scattering."
"For this reason, we typically also visualize the **maximum** diffraction pattern. By this, we mean the maximum signal of each pixel in diffraction space over all probe positions. This way, we see the brightest scattering from each pixel, even if it only occured in one diffraction image. This is a great way to see all of the Bragg scattering."
]
},
{
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