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# Copyright 2014 Diamond Light Source Ltd. |
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# |
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# Licensed under the Apache License, Version 2.0 (the "License"); |
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# you may not use this file except in compliance with the License. |
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# You may obtain a copy of the License at |
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# |
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# http://www.apache.org/licenses/LICENSE-2.0 |
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# |
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# Unless required by applicable law or agreed to in writing, software |
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# distributed under the License is distributed on an "AS IS" BASIS, |
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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# See the License for the specific language governing permissions and |
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# limitations under the License. |
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""" |
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.. module:: min_and_max |
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:platform: Unix |
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:synopsis: A plugin to calculate the min and max of each frame |
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.. moduleauthor:: Nicola Wadeson <[email protected]> |
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""" |
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import logging |
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import numpy as np |
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from scipy.ndimage import gaussian_filter |
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from savu.plugins.plugin import Plugin |
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from savu.plugins.utils import register_plugin |
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from savu.plugins.driver.cpu_plugin import CpuPlugin |
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import savu.core.utils as cu |
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@register_plugin |
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class MinAndMax(Plugin, CpuPlugin): |
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""" |
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A plugin to calculate the min and max values of each slice (as determined \ |
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by the pattern parameter) |
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:u*param pattern: How to slice the data. Default: 'VOLUME_XZ'. |
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:param smoothing: Apply a smoothing filter or not. Default: True. |
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:u*param masking: Apply a circular mask or not. Default: True. |
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:u*param ratio: Used to calculate the circular mask. If not provided, \ |
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it is calculated using the center of rotation. Default: None. |
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:u*param method: Method to find the global min and the global max. \ |
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Available options: 'extrema', 'percentile'. Default: 'percentile'. |
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:u*param p_range: Percentage range if use the 'percentile' method. \ |
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Default: [0.0, 100.0]. |
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:param out_datasets: The default names. Default: ['the_min','the_max']. |
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""" |
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def __init__(self): |
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super(MinAndMax, self).__init__("MinAndMax") |
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def circle_mask(self, width, ratio): |
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# Create a circle mask. |
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mask = np.zeros((width, width), dtype=np.float32) |
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center = width // 2 |
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radius = ratio * center |
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y, x = np.ogrid[-center:width - center, -center:width - center] |
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mask_check = x * x + y * y <= radius * radius |
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mask[mask_check] = 1.0 |
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return mask |
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def pre_process(self): |
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in_pData = self.get_plugin_in_datasets()[0] |
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in_meta_data = self.get_in_meta_data()[0] |
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data = self.get_in_datasets()[0] |
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data_shape = data.get_shape() |
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width = data_shape[0] |
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self.use_mask = self.parameters['masking'] |
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self.data_pattern = self.parameters['pattern'] |
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self.mask = np.ones((width, width), dtype=np.float32) |
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if self.use_mask is True: |
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ratio = self.parameters['ratio'] |
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if ratio is None: |
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try: |
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cor = np.min(in_meta_data.get('centre_of_rotation')) |
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ratio = (min(cor, abs(width - cor))) / (width * 0.5) |
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except KeyError: |
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ratio = 1.0 |
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self.mask = self.circle_mask(width, ratio) |
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self.method = self.parameters['method'] |
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if not (self.method == 'percentile' or self.method == 'extrema'): |
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msg = "\n***********************************************\n"\ |
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"!!! ERROR !!! -> Wrong method. Please use only one of "\ |
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"the provided options \n"\ |
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"***********************************************\n" |
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logging.warning(msg) |
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cu.user_message(msg) |
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raise ValueError(msg) |
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self.p_min, self.p_max = np.sort(np.clip(np.asarray( |
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self.parameters['p_range'], dtype=np.float32), 0.0, 100.0)) |
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def process_frames(self, data): |
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use_filter = self.parameters['smoothing'] |
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frame = np.nan_to_num(data[0]) |
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if use_filter is True: |
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frame = gaussian_filter(frame, (3, 3)) |
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if (self.use_mask is True) and (self.data_pattern == 'VOLUME_XZ')\ |
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and (self.mask.shape == frame.shape): |
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frame = frame * self.mask |
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if self.method == 'percentile': |
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list_out = [np.array( |
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[np.percentile(frame, self.p_min)], dtype=np.float32), |
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np.array([np.percentile(frame, self.p_max)], dtype=np.float32)] |
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else: |
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list_out = [np.array([np.min(frame)], dtype=np.float32), |
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np.array([np.max(frame)], dtype=np.float32)] |
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return list_out |
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def post_process(self): |
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in_datasets, out_datasets = self.get_datasets() |
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the_min = np.squeeze(out_datasets[0].data[...]) |
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the_max = np.squeeze(out_datasets[1].data[...]) |
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pattern = self._get_pattern() |
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in_datasets[0].meta_data.set(['stats', 'min', pattern], the_min) |
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in_datasets[0].meta_data.set(['stats', 'max', pattern], the_max) |
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def setup(self): |
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in_dataset, out_datasets = self.get_datasets() |
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in_pData, out_pData = self.get_plugin_datasets() |
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try: |
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in_pData[0].plugin_data_setup(self._get_pattern(), 'single') |
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except: |
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msg = "\n***************************************************"\ |
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"**********\n"\ |
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"Can't find the data pattern: {}.\nThe pattern parameter of " \ |
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"this plugin must be relevant to its \nprevious plugin" \ |
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"\n*************************************************************"\ |
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"\n".format(self._get_pattern()) |
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logging.warning(msg) |
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cu.user_message(msg) |
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raise ValueError(msg) |
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slice_dirs = list(in_dataset[0].get_slice_dimensions()) |
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orig_shape = in_dataset[0].get_shape() |
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new_shape = (np.prod(np.array(orig_shape)[slice_dirs]), 1) |
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labels = ['x.pixels', 'y.pixels'] |
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for i in range(len(out_datasets)): |
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out_datasets[i].create_dataset(shape=new_shape, axis_labels=labels, |
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remove=True, transport='hdf5') |
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out_datasets[i].add_pattern( |
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"METADATA", core_dims=(1,), slice_dims=(0,)) |
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out_pData[i].plugin_data_setup('METADATA', 'single') |
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def _get_pattern(self): |
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return self.parameters['pattern'] |
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def nOutput_datasets(self): |
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return 2 |
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DiamondLightSource /
Savu
