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Merge branch 'main' into zenodo_IO
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‎.github/workflows/analysis.yml

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outputs: # here we use the outputs from steps, and set outputs for the job `configure`
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algorithms: ${{ steps.algorithms.outputs.algorithms }}
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steps:
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- uses: actions/checkout@v3
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- uses: actions/checkout@v4
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- name: Set up Python
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id: setup_python
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uses: actions/setup-python@v4
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uses: actions/setup-python@v5
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with:
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python-version: "3.11"
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cache: 'pip'
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algorithm: ${{fromJson(needs.algorithms.outputs.algorithms).algorithms}}
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SNR: [10, 30, 50, 100, 200]
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steps:
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- uses: actions/checkout@v3
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- uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v4
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uses: actions/setup-python@v5
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with:
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python-version: "3.11"
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cache: 'pip'
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runs-on: ubuntu-latest
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needs: merge
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steps:
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- uses: actions/checkout@v3
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- uses: actions/checkout@v4
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- name: Set up R
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uses: r-lib/actions/setup-r@v2
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with:
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runs-on: ubuntu-latest
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needs: merge
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steps:
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- uses: actions/checkout@v3
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- uses: actions/checkout@v4
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- name: Set up R
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uses: r-lib/actions/setup-r@v2
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with:
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name: Comparison
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path: |
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test_reference.csv
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test_results.csv
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test_results.csv

‎.github/workflows/unit_test.yml

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# - os: windows-latest
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# python-version: "3.7"
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steps:
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- uses: actions/checkout@v3
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- uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v4
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uses: actions/setup-python@v5
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with:
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python-version: ${{ matrix.python-version }}
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cache: 'pip'

‎requirements.txt

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matplotlib
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scienceplots
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cvxpy
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zenodo-get
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zenodo-get
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pytest
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tqdm

‎src/original/TCML_TechnionIIT/SUPER-IVIM-DC/README.md

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# SUPER-IVIM-DC
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Provided here is a sample Jupyter notebook for the SUPER-IVIM-DC package.
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Full README and usage instructions can be found here: https://github.com/TechnionComputationalMRILab/SUPER-IVIM-DC or https://pypi.org/project/super-ivim-dc/0.4.0/
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Citation:
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```
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@article{
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Korngut_Rotman_Afacan_Kurugol_Zaffrani-Reznikov_Nemirovsky-Rotman_Warfield_Freiman_2022,
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title={SUPER-IVIM-DC: Intra-voxel incoherent motion based fetal lung maturity assessment from limited DWI data using supervised learning coupled with data-consistency},
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volume={13432}, DOI={10.1007/978-3-031-16434-7_71},
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journal={Lecture Notes in Computer Science},
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author={Korngut, Noam and Rotman, Elad and Afacan, Onur and Kurugol, Sila and Zaffrani-Reznikov, Yael and Nemirovsky-Rotman, Shira and Warfield, Simon and Freiman, Moti},
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year={2022},
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pages={743–752}
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}
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```
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[The paper is also available on ArXiv](https://arxiv.org/abs/2206.03820).

‎src/original/TCML_TechnionIIT/SUPER-IVIM-DC/sample.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"working_dir: str = './working_dir'\n",
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"super_ivim_dc_filename: str = 'super_ivim_dc' # do not include .pt\n",
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"ivimnet_filename: str = 'ivimnet' # do not include .pt\n",
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"\n",
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"bvalues = np.array([0,15,30,45,60,75,90,105,120,135,150,175,200,400,600,800])\n",
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"snr = 10\n",
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"sample_size = 100"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Simulate"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Run training, generate .pt files"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from super_ivim_dc.train import train\n",
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"\n",
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"train(\n",
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" SNR=snr, \n",
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" bvalues=bvalues, \n",
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" super_ivim_dc=True,\n",
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" ivimnet=True,\n",
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" work_dir=working_dir,\n",
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" super_ivim_dc_filename=super_ivim_dc_filename,\n",
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" ivimnet_filename=ivimnet_filename,\n",
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" verbose=False\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Files that will be created:\n",
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"\n",
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"- **super_ivim_dc_init.json** - contains the initial values used in the training\n",
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"- **super_ivim_dc_init_NRMSE.csv** - ???\n",
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"- **super_ivim_dc_init.pt** - the pytorch model"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Test\n",
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"\n",
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"Generate a simulated signal + ..."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from super_ivim_dc.infer import test_infer\n",
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"\n",
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"test_infer(\n",
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" SNR=snr,\n",
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" bvalues=bvalues,\n",
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" work_dir=working_dir,\n",
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" super_ivim_dc_filename=super_ivim_dc_filename,\n",
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" ivimnet_filename=ivimnet_filename,\n",
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" save_figure_to=None, # if set to None, the figure will be shown in the notebook\n",
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" sample_size=sample_size,\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Generate simulated signal"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from super_ivim_dc.IVIMNET import simulations\n",
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"\n",
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"IVIM_signal_noisy, Dt, f, Dp = simulations.sim_signal(\n",
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" SNR=snr, \n",
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" bvalues=bvalues, \n",
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" sims=sample_size\n",
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")\n",
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"\n",
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"Dt, f, Dp = np.squeeze(Dt), np.squeeze(f), np.squeeze(Dp)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Run inference on the simulated signal"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from super_ivim_dc.infer import infer_from_signal\n",
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"\n",
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"Dp_ivimnet, Dt_ivimnet, Fp_ivimnet, S0_ivimnet = infer_from_signal(\n",
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" signal=IVIM_signal_noisy, \n",
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" bvalues=bvalues,\n",
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" model_path=f\"{working_dir}/{ivimnet_filename}.pt\",\n",
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")\n",
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"\n",
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"Dp_superivimdc, Dt_superivimdc, Fp_superivimdc, S0_superivimdc = infer_from_signal(\n",
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" signal=IVIM_signal_noisy, \n",
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" bvalues=bvalues,\n",
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" model_path=f\"{working_dir}/{super_ivim_dc_filename}.pt\",\n",
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")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "super_ivim_dc",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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