- {
- “cells”: [
- {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“# Tutorial normalization with fewer ob than sample”
]
}, {
“cell_type”: “markdown”, “metadata”: {
- “run_control”: {
“frozen”: false, “read_only”: false
}
}, “source”: [
“Package to normalize data using Open Beam (OB) and, optionally Dark Field (DF).n”, “n”, “The program allows you to select a background region to allow data to be normalized by OB that do not have the same acquisition time. n”, “Cropping the image is also possible using the crop methodn”
]
}, {
“cell_type”: “markdown”, “metadata”: {
- “run_control”: {
“frozen”: false, “read_only”: false
}
}, “source”: [
“This notebook will illustrate the use of the NeuNorm library by going through a typical normalization”
]
}, {
“cell_type”: “markdown”, “metadata”: {
- “run_control”: {
“frozen”: false, “read_only”: false
}
}, “source”: [
“## Set up system”
]
}, {
“cell_type”: “code”, “execution_count”: null, “metadata”: {
- “run_control”: {
“frozen”: false, “read_only”: false
}
}, “outputs”: [], “source”: [
“import osn”, “import sysn”, “import numpy as npn”, “import matplotlib.pyplot as pltn”, “import matplotlib.patches as patchesn”, “from matplotlib import gridspecn”, “%matplotlib notebook”
]
}, {
“cell_type”: “markdown”, “metadata”: {
- “run_control”: {
“frozen”: false, “read_only”: false
}
}, “source”: [
“Add NeuNorm to python path”
]
}, {
“cell_type”: “code”, “execution_count”: 2, “metadata”: {
- “run_control”: {
“frozen”: false, “read_only”: false
}
}, “outputs”: [], “source”: [
“root_folder = os.path.dirname(os.getcwd())n”, “sys.path.append(root_folder)n”, “import NeuNorm as neunormn”, “from NeuNorm.normalization import Normalizationn”, “from NeuNorm.roi import ROI”
]
}, {
“cell_type”: “markdown”, “metadata”: {
“collapsed”: true, “run_control”: {
“frozen”: false, “read_only”: false
}
}, “source”: [
“## Data Folders”
]
}, {
“cell_type”: “markdown”, “metadata”: {
- “run_control”: {
“frozen”: false, “read_only”: false
}
}, “source”: [
“Sample data path”
]
}, {
“cell_type”: “code”, “execution_count”: 3, “metadata”: {
- “run_control”: {
“frozen”: false, “read_only”: false
}
}, “outputs”: [], “source”: [
“path_im = ‘../data/sample’n”, “assert os.path.exists(path_im)”
]
}, {
“cell_type”: “markdown”, “metadata”: {
- “run_control”: {
“frozen”: false, “read_only”: false
}
}, “source”: [
“Open Beam files”
]
}, {
“cell_type”: “code”, “execution_count”: 4, “metadata”: {
- “run_control”: {
“frozen”: false, “read_only”: false
}
}, “outputs”: [], “source”: [
“path_ob = ‘../data/ob/’n”, “ob1 = path_ob + ‘0001.tif’n”, “ob2 = path_ob + ‘0002.tif’n”, “assert os.path.exists(ob1)n”, “assert os.path.exists(ob2)”
]
}, {
“cell_type”: “markdown”, “metadata”: {
- “run_control”: {
“frozen”: false, “read_only”: false
}
}, “source”: [
“## Loading Data “
]
}, {
“cell_type”: “code”, “execution_count”: 5, “metadata”: {
- “run_control”: {
“frozen”: false, “read_only”: false
}
}, “outputs”: [], “source”: [
“o_norm = Normalization()n”, “o_norm.load(folder=path_im)n”, “o_norm.load(file=[ob1, ob2], data_type=’ob’)”
]
}, {
“cell_type”: “markdown”, “metadata”: {
- “run_control”: {
“frozen”: false, “read_only”: false
}
}, “source”: [
“## Normalization of the data “
]
}, {
“cell_type”: “markdown”, “metadata”: {
- “run_control”: {
“frozen”: false, “read_only”: false
}
}, “source”: [
“We will use a normalization ROI.n”, “
`\n", " x0 = 3\n", " y0 = 5\n", " width = 20\n", " height = 40\n", "`
”]
}, {
“cell_type”: “code”, “execution_count”: 6, “metadata”: {
- “run_control”: {
“frozen”: false, “read_only”: false
}
}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“True”
]
}, “execution_count”: 6, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“norm_roi = ROI(x0=3, y0=5, width=20, height=40)n”, “o_norm.normalization(roi=norm_roi)”
]
}, {
“cell_type”: “markdown”, “metadata”: {
- “run_control”: {
“frozen”: false, “read_only”: false
}
}, “source”: [
“## Recovering the normalized data”
]
}, {
“cell_type”: “code”, “execution_count”: 7, “metadata”: {
- “run_control”: {
“frozen”: false, “read_only”: false
}
}, “outputs”: [], “source”: [
“normalized_data = o_norm.data[‘normalized’]”
]
}, {
“cell_type”: “code”, “execution_count”: 8, “metadata”: {
- “run_control”: {
“frozen”: false, “read_only”: false
}
}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“(15, 100, 100)”
]
}, “execution_count”: 8, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“np.shape(normalized_data)”
]
}, {
“cell_type”: “markdown”, “metadata”: {
- “run_control”: {
“frozen”: false, “read_only”: false
}
}, “source”: [
“## Crop “
]
}, {
“cell_type”: “code”, “execution_count”: 9, “metadata”: {
- “run_control”: {
“frozen”: false, “read_only”: false
}
}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“(15, 3, 3)”
]
}, “execution_count”: 9, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“roi_to_keep = ROI(x0=0, y0=0, width=2, height=2)n”, “o_norm.crop(roi=roi_to_keep)n”, “n”, “norm_crop = o_norm.data[‘normalized’]n”, “np.shape(norm_crop)”
]
}, {
“cell_type”: “code”, “execution_count”: null, “metadata”: {
- “run_control”: {
“frozen”: false, “read_only”: false
}
}, “outputs”: [], “source”: []
}
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