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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:: rotate_90 |
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:platform: Unix |
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:synopsis: (Change this) A template to create a simple plugin that takes |
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one dataset as input and returns a similar dataset as output. |
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.. moduleauthor:: Jacob Williamson <[email protected]> |
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""" |
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from copy import deepcopy |
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from savu.plugins.utils import register_plugin |
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from savu.plugins.plugin import Plugin |
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from savu.plugins.driver.cpu_plugin import CpuPlugin |
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from savu.core.iterate_plugin_group_utils import enable_iterative_loop, \ |
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check_if_end_plugin_in_iterate_group, setup_extra_plugin_data_padding |
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from savu.data.data_structures.data_types.data_plus_darks_and_flats \ |
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import ImageKey |
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import numpy as np |
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import h5py |
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@register_plugin |
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class Rotate90(Plugin, CpuPlugin): |
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# Each class must inherit from the Plugin class and a driver |
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def __init__(self): |
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super(Rotate90, self).__init__("Rotate90") |
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def nInput_datasets(self): |
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return 1 |
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def nOutput_datasets(self): |
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return 1 |
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def setup(self): |
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# assumes 3D data and 2D frames |
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if self.exp.meta_data.get("pre_run"): |
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self.stats_obj.calc_stats = False |
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in_dataset, out_dataset = self.get_datasets() |
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pattern = self.parameters["pattern"] |
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data_info = in_dataset[0].data_info |
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core_dims = data_info["data_patterns"][pattern]["core_dims"] |
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c0, c1 = core_dims[0], core_dims[1] # core dimensions |
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s0 = data_info["data_patterns"][pattern]["slice_dims"][0] # slice dimension |
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# swapping round core dimensions in the shape due to rotation |
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new_shape = list(data_info["shape"]) |
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new_shape[c0], new_shape[c1] = data_info["shape"][c1], data_info["shape"][c0] |
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if self.exp.meta_data.get("pre_run"): |
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new_shape[0] = in_dataset[0].data.image_key.shape[0] |
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new_shape = tuple(new_shape) |
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# swapping round core dimensions in axis labels |
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new_axis_labels = deepcopy(data_info["axis_labels"]) |
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new_axis_labels[c0], new_axis_labels[c1] = data_info["axis_labels"][c1], data_info["axis_labels"][c0] |
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# swapping round core dimensions in data patterns |
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new_data_patterns = deepcopy(data_info["data_patterns"]) |
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for pattern in new_data_patterns: |
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for dims in new_data_patterns[pattern]: |
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if dims != "main_dir": # not sure what main_dir is ( = slice dim?) |
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dims_list = list(new_data_patterns[pattern][dims]) |
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for i, dim in enumerate(dims_list): |
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if dim == c0: |
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dims_list[i] = c1 |
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elif dim == c1: |
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dims_list[i] = c0 |
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new_data_patterns[pattern][dims] = tuple(dims_list) |
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dtype = in_dataset[0].dtype |
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if dtype is None: |
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dtype = in_dataset[0].data.data.dtype |
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# creating output dataset with new axis, shape and data patterns to reflect rotated image |
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if dtype: |
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out_dataset[0].create_dataset(shape=new_shape, axis_labels=new_axis_labels, dtype=dtype) |
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else: |
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out_dataset[0].create_dataset(shape=new_shape, axis_labels=new_axis_labels) |
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out_dataset[0].data_info.set("data_patterns", new_data_patterns) |
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in_pData, out_pData = self.get_plugin_datasets() |
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in_pData[0].plugin_data_setup(self.parameters['pattern'], 'single') |
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out_pData[0].plugin_data_setup(self.parameters['pattern'], 'single') |
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def pre_process(self): |
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if self.exp.meta_data.get("pre_run"): |
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in_dataset, out_dataset = self.get_datasets() |
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dark = in_dataset[0].data.dark() |
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flat = in_dataset[0].data.flat() |
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if dark.size: |
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in_dataset[0].data.update_dark(self.process_frames_3d(dark)) |
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if flat.size: |
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in_dataset[0].data.update_flat(self.process_frames_3d(flat)) |
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out_dataset[0].data.image_key = in_dataset[0].data.image_key |
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def process_frames(self, data): |
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# assumes 2D frame |
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if self.parameters["direction"] == "ACW": |
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data[0] = np.rot90(data[0], axes=(0, 1)) |
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elif self.parameters["direction"] == "CW": |
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data[0] = np.rot90(data[0], axes=(1, 0)) |
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return data[0] |
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def process_frames_3d(self, data): |
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# assumes 3D frame |
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if self.parameters["direction"] == "ACW": |
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data = np.rot90(data, axes=(1, 2)) |
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elif self.parameters["direction"] == "CW": |
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data = np.rot90(data, axes=(2, 1)) |
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return data |
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def post_process(self): |
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if self.exp.meta_data.get("pre_run"): |
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in_dataset, out_dataset = self.get_datasets() |
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image_key = in_dataset[0].data.image_key |
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dark = in_dataset[0].data.dark_updated |
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flat = in_dataset[0].data.flat_updated |
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new_image_key = np.array([0.] * len(image_key)) |
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new_image_key[- len(dark):] = [2.] * len(dark) |
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new_image_key[- len(dark) - len(flat): - len(dark)] = [1.] * len(flat) |
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out_dataset[0].data[- len(dark):] = dark |
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out_dataset[0].data[- len(dark) - len(flat): - len(dark)] = flat |
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out_dataset[0].data.image_key = new_image_key |
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out_dataset[0].data = ImageKey(out_dataset[0], new_image_key, 0) |
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DiamondLightSource /
Savu
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