csxtools.image.rotate90(a, sense='ccw')[source]¶Rotate a stack of images by 90 degrees
This routine rotates a stack of images by 90. The rotation is performed on the last two axes. i.e. For a stack of images of shape (N, y, x) N rotations of the image of size (y, x) are performed.
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| Returns: | Rotated stack of images of shape (N, x, y) |
| Return type: |
csxtools.image.stackmean(array)[source]¶Cacluate the mean of a stack
This function calculates the mean of a stack of images (or any array). It ignores values that are np.NAN and does not include them in the mean calculation. It assumes an array of shape (.. i, j, x, y) where x and y are the size of the returned array (x, y).
| Parameters: | array (array_like) – Input array of at least 3 dimensions. |
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| Returns: | 2D Array of mean of stack. |
| Return type: | array |
csxtools.image.stacksum(array)[source]¶Cacluate the sum of a stack
This function calculates the sum of a stack of images (or any array). It ignores values that are np.NAN and does not include them in the sum calculation. It assumes an array of shape (.. i, j, x, y) where x and y are the size of the returned array (x, y).
| Parameters: | array (array_like) – Input array of at least 3 dimensions. |
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| Returns: | tuple of 2 arrays of the sum and number of points in the sum |
| Return type: | tuple |
csxtools.image.stackvar(array)[source]¶Cacluate the varience of a stack
This function calculates the variance of a stack of images (or any array). It ignores values that are np.NAN and does not include them in the calculation. It assumes an array of shape (.. i, j, x, y) where x and y are the size of the returned array (x, y).
| Parameters: | array (array_like) – Input array of at least 3 dimensions. |
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| Returns: | tuple of 2 arrays of the varience and number of points in the calculation |
| Return type: | tuple |
csxtools.image.stackstderr(array)[source]¶Cacluate the standard error of a stack
This function calculates the standard error of a stack of images (or any array). It ignores values that are np.NAN and does not include them in the calculation. It assumes an array of shape (.. i, j, x, y) where x and y are the size of the returned array (x, y).
| Parameters: | array (array_like) – Input array of at least 3 dimensions. |
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| Returns: | tuple of 2 arrays of the standard error and number of points in the calculation |
| Return type: | tuple |
csxtools.image.stackstd(array)[source]¶Cacluate the standard deviation of a stack
This function calculates the standard deviation of a stack of images It ignores values that are np.NAN and does not include them in the sum calculation. It assumes an array of shape (.. i, j, x, y) where x and y are the size of the returned array (x, y).
| Parameters: | array (array_like) – Input array of at least 3 dimensions. |
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| Returns: | tuple of 2 arrays of the standard deviation and number of points in the calculation |
| Return type: | tuple |
csxtools.image.images_mean(images)[source]¶Cacluate the mean ccd counts per event
This function calculates the mean of ccd counts for each event. The input is a “slicerator” object returned by get_fastccd_images.
| Parameters: | object (slicerator) – This is the output of get_fastccd_images |
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| Returns: | array |
| Return type: | 1D numpy array |
csxtools.image.images_sum(images)[source]¶Cacluate the total ccd counts per event
This function calculates the sum of ccd counts for each event. The input is a “slicerator” object returned by get_fastccd_images.
| Parameters: | object (slicerator) – This is the output of get_fastccd_images |
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| Returns: | array |
| Return type: | 1D numpy array |