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+%global _empty_manifest_terminate_build 0
+Name:		python-immuneML
+Version:	2.2.5
+Release:	1
+Summary:	immuneML is a software platform for machine learning analysis of immune receptor repertoires.
+License:	GNU Affero General Public License v3
+URL:		https://github.com/uio-bmi/immuneML
+Source0:	https://mirrors.aliyun.com/pypi/web/packages/78/0a/0f6dd04d3ac651f2b7d1669b486940f0ce5066612f8177642a35839bdb0a/immuneML-2.2.5.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-pytest
+Requires:	python3-pandas
+Requires:	python3-PyYAML
+Requires:	python3-scikit-learn
+Requires:	python3-gensim
+Requires:	python3-matplotlib
+Requires:	python3-editdistance
+Requires:	python3-regex
+Requires:	python3-tzlocal
+Requires:	python3-airr
+Requires:	python3-fishersapi
+Requires:	python3-pystache
+Requires:	python3-torch
+Requires:	python3-dill
+Requires:	python3-tensorboard
+Requires:	python3-plotly
+Requires:	python3-logomaker
+Requires:	python3-matplotlib-venn
+Requires:	python3-scipy
+Requires:	python3-Cython
+Requires:	python3-parasail
+Requires:	python3-tcrdist3
+
+%description
+# immuneML
+
+![Python application](https://github.com/uio-bmi/immuneML/workflows/Python%20application/badge.svg?branch=master)
+![Docker](https://github.com/uio-bmi/immuneML/workflows/Docker/badge.svg?branch=master)
+[![](https://img.shields.io/static/v1?label=AIRR-C%20sw-tools%20v1&message=compliant&color=008AFF&labelColor=000000&style=plastic)](https://docs.airr-community.org/en/stable/swtools/airr_swtools_standard.html)
+
+
+immuneML is a platform for machine learning-based analysis and 
+classification of adaptive immune receptors and repertoires (AIRR).
+
+It supports the analyses of experimental B- and T-cell receptor data,
+as well as synthetic data for benchmarking purposes.
+
+In immuneML, users can define flexible workflows supporting different
+machine learning libraries (such as scikit-learn or PyTorch), benchmarking of different approaches, numerous reports
+of data characteristics, ML algorithms and their predictions, and
+visualizations of results.
+
+Additionally, users can extend the platform by defining their own data 
+representations, ML models, reports and visualizations.
+
+
+Useful links:
+- Main website: https://immuneml.uio.no
+- Documentation: https://docs.immuneml.uio.no
+- Galaxy web interface: https://galaxy.immuneml.uiocloud.no
+
+
+
+## Installation
+
+immuneML can be installed directly [using pip](<https://pypi.org/project/immuneML/>).
+immuneML uses Python 3.7 or 3.8, we recommend installing immuneML inside a virtual environment 
+with one of these Python versions. 
+
+For more detailed instructions (virtual environment, troubleshooting, Docker, developer installation), please see the [installation documentation](https://docs.immuneml.uio.no/installation/install_with_package_manager.html).
+
+### Installation using pip
+
+
+To install the immuneML core package, run:
+
+```bash
+pip install immuneML
+```
+
+Alternatively, to use the TCRdistClassifier ML method and corresponding TCRdistMotifDiscovery report, install immuneML with the optional TCRdist extra:
+
+```bash
+pip install immuneML[TCRdist]
+```
+
+Optionally, if you want to use the DeepRC ML method and and corresponding DeepRCMotifDiscovery report, you also
+have to install DeepRC dependencies using the [requirements_DeepRC.txt](https://raw.githubusercontent.com/uio-bmi/immuneML/master/requirements_DeepRC.txt) file.
+Important note: DeepRC uses PyTorch functionalities that depend on GPU. Therefore, DeepRC does not work on a CPU.
+To install the DeepRC dependencies, run:
+
+```bash
+pip install -r requirements_DeepRC.txt --no-dependencies
+```
+
+### Validating the installation
+
+To validate the installation, run:
+
+```bash
+immune-ml -h
+```
+
+This should display a help message explaining immuneML usage.
+
+To quickly test out whether immuneML is able to run, try running the quickstart command:
+
+```bash
+immune-ml-quickstart ./quickstart_results/
+```
+
+This will generate a synthetic dataset and run a simple machine machine learning analysis 
+on the generated data. The results folder will contain two sub-folders: one for the generated dataset (`synthetic_dataset`) 
+and one for the results of the machine learning analysis (`machine_learning_analysis`). 
+The files named `specs.yaml` are the input files for immuneML that describe how to generate 
+the dataset and how to do the machine learning analysis. The `index.html` files can be used 
+to navigate through all the results that were produced.
+
+## Usage 
+
+### Quickstart
+
+The quickest way to familiarize yourself with immuneML usage is to follow
+one of the [Quickstart tutorials](https://docs.immuneml.uio.no/quickstart.html).
+These tutorials provide a step-by-step guide on how to use immuneML for a 
+simple machine learning analysis on an adaptive immune receptor repertoire (AIRR) dataset,
+using either the command line tool or the [Galaxy web interface](https://galaxy.immuneml.uiocloud.no). 
+
+
+### Overview of input, analyses and results
+
+The figure below shows an overview of immuneML usage. 
+All parameters for an immuneML analysis are defined in the a YAML specification file. 
+In this file, the settings of the analysis components are defined (also known as `definitions`, 
+shown in six different colors in the figure). 
+Additionally, the YAML file describes one or more `instructions`, which are workflows that are
+applied to the defined analysis components. 
+Each instruction uses at least a dataset component, and optionally additional components.
+AIRR datasets may either be [imported from files](https://docs.immuneml.uio.no/tutorials/how_to_import_the_data_to_immuneML.html), 
+or [generated synthetically](https://docs.immuneml.uio.no/tutorials/how_to_generate_a_random_repertoire_dataset.html) during runtime.
+
+Each instruction produces different types of results, including trained ML models, 
+ML model predictions on a given dataset, plots or other reports describing the 
+dataset or trained models, and modified datasets. 
+To navigate over the results, immuneML generates a summary HTML file. 
+
+
+![image info](https://docs.immuneml.uio.no/latest/_images/definitions_instructions_overview.png)
+
+For a detailed explanation of the YAML specification file, see the tutorial [How to specify an analysis with YAML](https://docs.immuneml.uio.no/tutorials/how_to_specify_an_analysis_with_yaml.html).
+
+See also the following tutorials for specific instructions:
+- [Training ML models](https://docs.immuneml.uio.no/tutorials/how_to_train_and_assess_a_receptor_or_repertoire_classifier.html) for repertoire classification (e.g., disease prediction) or receptor sequence classification (e.g., antigen binding prediction). In immuneML, the performance of different machine learning (ML) settings can be compared by nested cross-validation. These ML settings consist of data preprocessing steps, encodings and ML models and their hyperparameters.
+- [Exploratory analysis](https://docs.immuneml.uio.no/tutorials/how_to_perform_exploratory_analysis.html) of datasets by applying preprocessing and encoding, and plotting descriptive statistics without training ML models.
+- [Simulating](https://docs.immuneml.uio.no/tutorials/how_to_simulate_antigen_signals_in_airr_datasets.html) immune events, such as disease states, into experimental or synthetic repertoire datasets. By implanting known immune signals into a given dataset, a ground truth benchmarking dataset is created. Such a dataset can be used to test the performance of ML settings under known conditions.
+- [Applying trained ML models](https://docs.immuneml.uio.no/tutorials/how_to_apply_to_new_data.html) to new datasets with unknown class labels.
+- And [other tutorials](https://docs.immuneml.uio.no/tutorials.html)
+
+
+### Command line usage 
+
+The `immune-ml` command takes only two parameters: the YAML specification file and a result path. 
+An example is given here:
+
+```bash
+immune-ml path/to/specification.yaml result/folder/path/
+```
+
+For each instruction specified in the YAML specification file, a subfolder is created in the 
+`result/folder/path`. Each subfolder will contain:
+- An `index.html` file which shows an overview of the results produced by that instruction. Inspecting the results of an immuneML analysis typically starts here. 
+- A copy of the used YAML specification (`full_specification.yaml`) with all default parameters explicitly set.
+- A folder containing all raw results produced by the instruction.
+- A folder containing the imported dataset(s) in optimized binary (Pickle) format.
+
+## Support
+
+We will prioritize fixing important bugs, and try to answer any questions as soon as possible. We may implement suggested features and enhancements as time permits. 
+
+If you run into problems when using immuneML, please see [the documentation](https://docs.immuneml.uio.no/latest/). In particular, we recommend you check out:
+- The [Quickstart tutorial](https://docs.immuneml.uio.no/latest/quickstart.html) for new users
+- The [Troubleshooting](https://docs.immuneml.uio.no/latest/troubleshooting.html) page
+
+If this does not answer your question, you can contact us via:
+- Twitter [`@immuneml`](https://twitter.com/immuneml)
+- Email [`contact@immuneml.uio.no`](mailto:contact@immuneml.uio.no)
+
+To report a potential bug or suggest new features, please [submit an issue on GitHub](https://github.com/uio-bmi/immuneML/issues).
+
+If you would like to make contributions, for example by adding a new ML method, encoding, report or preprocessing, please [see our developer documentation](https://docs.immuneml.uio.no/latest/developer_docs.html) and [submit a pull request](https://github.com/uio-bmi/compairr/pulls).
+
+## Requirements
+
+- [Python 3.7 or 3.8](https://www.python.org/)
+- Python packages:
+   - [airr](https://pypi.org/project/airr/) (1 or higher)
+   - [dill](https://pypi.org/project/dill/) (0.3 or higher)
+   - [editdistance](https://pypi.org/project/editdistance/) (0.5.3 or higher)
+   - [fishersapi](https://pypi.org/project/fishersapi/)
+   - [gensim](https://pypi.org/project/gensim/) (3.8 or higher, < 4)
+   - [logomaker](https://pypi.org/project/logomaker/) (0.8 or higher)
+   - [matplotlib](https://matplotlib.org) (3.1 or higher)
+   - [matplotlib-venn](https://pypi.org/project/matplotlib-venn/) (0.11 or higher)
+   - [numpy](https://www.numpy.org/) (1.18 or higher, but at most 1.23.5)
+   - [pandas](https://pandas.pydata.org/) (1 or higher)
+   - [plotly](https://plotly.com/python/) (4 or higher)
+   - [pystache](https://pypi.org/project/pystache/) (0.5.4)
+   - [Pytorch](https://pytorch.org/) (1.5.1 or higher)
+   - [PyYAML](https://pyyaml.org) (5.3 or higher)
+   - [regex](https://pypi.org/project/regex/) 
+   - [scikit-learn](https://scikit-learn.org/) (0.23 or higher)
+   - [scipy](https://www.scipy.org)
+   - [tensorboard](https://www.tensorflow.org/tensorboard) (1.14.0 or higher)
+   - [tzlocal](https://pypi.org/project/tzlocal/) 
+- Optional dependencies when using DeepRC:
+   - [DeepRC](https://github.com/ml-jku/DeepRC) (0.0.1)
+   - [widis-lstm-tools](https://github.com/widmi/widis-lstm-tools) (0.4)
+   - [tqdm](https://tqdm.github.io/) (0.24 or higher)
+   - [h5py](https://www.h5py.org/) (2.10.0 or lower when using DeepRC 0.0.1)
+- Optional dependencies when using TCRdist:
+   - [parasail](https://pypi.org/project/parasail/) (1.2)
+   - [tcrdist3](https://github.com/kmayerb/tcrdist3) (0.1.6 or higher)
+
+# Citing immuneML
+
+If you are using immuneML in any published work, please cite:
+
+Pavlović, M., Scheffer, L., Motwani, K. et al. The immuneML ecosystem for machine learning analysis of adaptive immune 
+receptor repertoires. Nat Mach Intell 3, 936–944 (2021). https://doi.org/10.1038/s42256-021-00413-z
+
+
+
+<hr>
+
+
+© Copyright 2021-2022, Milena Pavlovic, Lonneke Scheffer, Keshav Motwani, Victor Greiff, Geir Kjetil Sandve
+
+
+
+
+
+
+%package -n python3-immuneML
+Summary:	immuneML is a software platform for machine learning analysis of immune receptor repertoires.
+Provides:	python-immuneML
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-immuneML
+# immuneML
+
+![Python application](https://github.com/uio-bmi/immuneML/workflows/Python%20application/badge.svg?branch=master)
+![Docker](https://github.com/uio-bmi/immuneML/workflows/Docker/badge.svg?branch=master)
+[![](https://img.shields.io/static/v1?label=AIRR-C%20sw-tools%20v1&message=compliant&color=008AFF&labelColor=000000&style=plastic)](https://docs.airr-community.org/en/stable/swtools/airr_swtools_standard.html)
+
+
+immuneML is a platform for machine learning-based analysis and 
+classification of adaptive immune receptors and repertoires (AIRR).
+
+It supports the analyses of experimental B- and T-cell receptor data,
+as well as synthetic data for benchmarking purposes.
+
+In immuneML, users can define flexible workflows supporting different
+machine learning libraries (such as scikit-learn or PyTorch), benchmarking of different approaches, numerous reports
+of data characteristics, ML algorithms and their predictions, and
+visualizations of results.
+
+Additionally, users can extend the platform by defining their own data 
+representations, ML models, reports and visualizations.
+
+
+Useful links:
+- Main website: https://immuneml.uio.no
+- Documentation: https://docs.immuneml.uio.no
+- Galaxy web interface: https://galaxy.immuneml.uiocloud.no
+
+
+
+## Installation
+
+immuneML can be installed directly [using pip](<https://pypi.org/project/immuneML/>).
+immuneML uses Python 3.7 or 3.8, we recommend installing immuneML inside a virtual environment 
+with one of these Python versions. 
+
+For more detailed instructions (virtual environment, troubleshooting, Docker, developer installation), please see the [installation documentation](https://docs.immuneml.uio.no/installation/install_with_package_manager.html).
+
+### Installation using pip
+
+
+To install the immuneML core package, run:
+
+```bash
+pip install immuneML
+```
+
+Alternatively, to use the TCRdistClassifier ML method and corresponding TCRdistMotifDiscovery report, install immuneML with the optional TCRdist extra:
+
+```bash
+pip install immuneML[TCRdist]
+```
+
+Optionally, if you want to use the DeepRC ML method and and corresponding DeepRCMotifDiscovery report, you also
+have to install DeepRC dependencies using the [requirements_DeepRC.txt](https://raw.githubusercontent.com/uio-bmi/immuneML/master/requirements_DeepRC.txt) file.
+Important note: DeepRC uses PyTorch functionalities that depend on GPU. Therefore, DeepRC does not work on a CPU.
+To install the DeepRC dependencies, run:
+
+```bash
+pip install -r requirements_DeepRC.txt --no-dependencies
+```
+
+### Validating the installation
+
+To validate the installation, run:
+
+```bash
+immune-ml -h
+```
+
+This should display a help message explaining immuneML usage.
+
+To quickly test out whether immuneML is able to run, try running the quickstart command:
+
+```bash
+immune-ml-quickstart ./quickstart_results/
+```
+
+This will generate a synthetic dataset and run a simple machine machine learning analysis 
+on the generated data. The results folder will contain two sub-folders: one for the generated dataset (`synthetic_dataset`) 
+and one for the results of the machine learning analysis (`machine_learning_analysis`). 
+The files named `specs.yaml` are the input files for immuneML that describe how to generate 
+the dataset and how to do the machine learning analysis. The `index.html` files can be used 
+to navigate through all the results that were produced.
+
+## Usage 
+
+### Quickstart
+
+The quickest way to familiarize yourself with immuneML usage is to follow
+one of the [Quickstart tutorials](https://docs.immuneml.uio.no/quickstart.html).
+These tutorials provide a step-by-step guide on how to use immuneML for a 
+simple machine learning analysis on an adaptive immune receptor repertoire (AIRR) dataset,
+using either the command line tool or the [Galaxy web interface](https://galaxy.immuneml.uiocloud.no). 
+
+
+### Overview of input, analyses and results
+
+The figure below shows an overview of immuneML usage. 
+All parameters for an immuneML analysis are defined in the a YAML specification file. 
+In this file, the settings of the analysis components are defined (also known as `definitions`, 
+shown in six different colors in the figure). 
+Additionally, the YAML file describes one or more `instructions`, which are workflows that are
+applied to the defined analysis components. 
+Each instruction uses at least a dataset component, and optionally additional components.
+AIRR datasets may either be [imported from files](https://docs.immuneml.uio.no/tutorials/how_to_import_the_data_to_immuneML.html), 
+or [generated synthetically](https://docs.immuneml.uio.no/tutorials/how_to_generate_a_random_repertoire_dataset.html) during runtime.
+
+Each instruction produces different types of results, including trained ML models, 
+ML model predictions on a given dataset, plots or other reports describing the 
+dataset or trained models, and modified datasets. 
+To navigate over the results, immuneML generates a summary HTML file. 
+
+
+![image info](https://docs.immuneml.uio.no/latest/_images/definitions_instructions_overview.png)
+
+For a detailed explanation of the YAML specification file, see the tutorial [How to specify an analysis with YAML](https://docs.immuneml.uio.no/tutorials/how_to_specify_an_analysis_with_yaml.html).
+
+See also the following tutorials for specific instructions:
+- [Training ML models](https://docs.immuneml.uio.no/tutorials/how_to_train_and_assess_a_receptor_or_repertoire_classifier.html) for repertoire classification (e.g., disease prediction) or receptor sequence classification (e.g., antigen binding prediction). In immuneML, the performance of different machine learning (ML) settings can be compared by nested cross-validation. These ML settings consist of data preprocessing steps, encodings and ML models and their hyperparameters.
+- [Exploratory analysis](https://docs.immuneml.uio.no/tutorials/how_to_perform_exploratory_analysis.html) of datasets by applying preprocessing and encoding, and plotting descriptive statistics without training ML models.
+- [Simulating](https://docs.immuneml.uio.no/tutorials/how_to_simulate_antigen_signals_in_airr_datasets.html) immune events, such as disease states, into experimental or synthetic repertoire datasets. By implanting known immune signals into a given dataset, a ground truth benchmarking dataset is created. Such a dataset can be used to test the performance of ML settings under known conditions.
+- [Applying trained ML models](https://docs.immuneml.uio.no/tutorials/how_to_apply_to_new_data.html) to new datasets with unknown class labels.
+- And [other tutorials](https://docs.immuneml.uio.no/tutorials.html)
+
+
+### Command line usage 
+
+The `immune-ml` command takes only two parameters: the YAML specification file and a result path. 
+An example is given here:
+
+```bash
+immune-ml path/to/specification.yaml result/folder/path/
+```
+
+For each instruction specified in the YAML specification file, a subfolder is created in the 
+`result/folder/path`. Each subfolder will contain:
+- An `index.html` file which shows an overview of the results produced by that instruction. Inspecting the results of an immuneML analysis typically starts here. 
+- A copy of the used YAML specification (`full_specification.yaml`) with all default parameters explicitly set.
+- A folder containing all raw results produced by the instruction.
+- A folder containing the imported dataset(s) in optimized binary (Pickle) format.
+
+## Support
+
+We will prioritize fixing important bugs, and try to answer any questions as soon as possible. We may implement suggested features and enhancements as time permits. 
+
+If you run into problems when using immuneML, please see [the documentation](https://docs.immuneml.uio.no/latest/). In particular, we recommend you check out:
+- The [Quickstart tutorial](https://docs.immuneml.uio.no/latest/quickstart.html) for new users
+- The [Troubleshooting](https://docs.immuneml.uio.no/latest/troubleshooting.html) page
+
+If this does not answer your question, you can contact us via:
+- Twitter [`@immuneml`](https://twitter.com/immuneml)
+- Email [`contact@immuneml.uio.no`](mailto:contact@immuneml.uio.no)
+
+To report a potential bug or suggest new features, please [submit an issue on GitHub](https://github.com/uio-bmi/immuneML/issues).
+
+If you would like to make contributions, for example by adding a new ML method, encoding, report or preprocessing, please [see our developer documentation](https://docs.immuneml.uio.no/latest/developer_docs.html) and [submit a pull request](https://github.com/uio-bmi/compairr/pulls).
+
+## Requirements
+
+- [Python 3.7 or 3.8](https://www.python.org/)
+- Python packages:
+   - [airr](https://pypi.org/project/airr/) (1 or higher)
+   - [dill](https://pypi.org/project/dill/) (0.3 or higher)
+   - [editdistance](https://pypi.org/project/editdistance/) (0.5.3 or higher)
+   - [fishersapi](https://pypi.org/project/fishersapi/)
+   - [gensim](https://pypi.org/project/gensim/) (3.8 or higher, < 4)
+   - [logomaker](https://pypi.org/project/logomaker/) (0.8 or higher)
+   - [matplotlib](https://matplotlib.org) (3.1 or higher)
+   - [matplotlib-venn](https://pypi.org/project/matplotlib-venn/) (0.11 or higher)
+   - [numpy](https://www.numpy.org/) (1.18 or higher, but at most 1.23.5)
+   - [pandas](https://pandas.pydata.org/) (1 or higher)
+   - [plotly](https://plotly.com/python/) (4 or higher)
+   - [pystache](https://pypi.org/project/pystache/) (0.5.4)
+   - [Pytorch](https://pytorch.org/) (1.5.1 or higher)
+   - [PyYAML](https://pyyaml.org) (5.3 or higher)
+   - [regex](https://pypi.org/project/regex/) 
+   - [scikit-learn](https://scikit-learn.org/) (0.23 or higher)
+   - [scipy](https://www.scipy.org)
+   - [tensorboard](https://www.tensorflow.org/tensorboard) (1.14.0 or higher)
+   - [tzlocal](https://pypi.org/project/tzlocal/) 
+- Optional dependencies when using DeepRC:
+   - [DeepRC](https://github.com/ml-jku/DeepRC) (0.0.1)
+   - [widis-lstm-tools](https://github.com/widmi/widis-lstm-tools) (0.4)
+   - [tqdm](https://tqdm.github.io/) (0.24 or higher)
+   - [h5py](https://www.h5py.org/) (2.10.0 or lower when using DeepRC 0.0.1)
+- Optional dependencies when using TCRdist:
+   - [parasail](https://pypi.org/project/parasail/) (1.2)
+   - [tcrdist3](https://github.com/kmayerb/tcrdist3) (0.1.6 or higher)
+
+# Citing immuneML
+
+If you are using immuneML in any published work, please cite:
+
+Pavlović, M., Scheffer, L., Motwani, K. et al. The immuneML ecosystem for machine learning analysis of adaptive immune 
+receptor repertoires. Nat Mach Intell 3, 936–944 (2021). https://doi.org/10.1038/s42256-021-00413-z
+
+
+
+<hr>
+
+
+© Copyright 2021-2022, Milena Pavlovic, Lonneke Scheffer, Keshav Motwani, Victor Greiff, Geir Kjetil Sandve
+
+
+
+
+
+
+%package help
+Summary:	Development documents and examples for immuneML
+Provides:	python3-immuneML-doc
+%description help
+# immuneML
+
+![Python application](https://github.com/uio-bmi/immuneML/workflows/Python%20application/badge.svg?branch=master)
+![Docker](https://github.com/uio-bmi/immuneML/workflows/Docker/badge.svg?branch=master)
+[![](https://img.shields.io/static/v1?label=AIRR-C%20sw-tools%20v1&message=compliant&color=008AFF&labelColor=000000&style=plastic)](https://docs.airr-community.org/en/stable/swtools/airr_swtools_standard.html)
+
+
+immuneML is a platform for machine learning-based analysis and 
+classification of adaptive immune receptors and repertoires (AIRR).
+
+It supports the analyses of experimental B- and T-cell receptor data,
+as well as synthetic data for benchmarking purposes.
+
+In immuneML, users can define flexible workflows supporting different
+machine learning libraries (such as scikit-learn or PyTorch), benchmarking of different approaches, numerous reports
+of data characteristics, ML algorithms and their predictions, and
+visualizations of results.
+
+Additionally, users can extend the platform by defining their own data 
+representations, ML models, reports and visualizations.
+
+
+Useful links:
+- Main website: https://immuneml.uio.no
+- Documentation: https://docs.immuneml.uio.no
+- Galaxy web interface: https://galaxy.immuneml.uiocloud.no
+
+
+
+## Installation
+
+immuneML can be installed directly [using pip](<https://pypi.org/project/immuneML/>).
+immuneML uses Python 3.7 or 3.8, we recommend installing immuneML inside a virtual environment 
+with one of these Python versions. 
+
+For more detailed instructions (virtual environment, troubleshooting, Docker, developer installation), please see the [installation documentation](https://docs.immuneml.uio.no/installation/install_with_package_manager.html).
+
+### Installation using pip
+
+
+To install the immuneML core package, run:
+
+```bash
+pip install immuneML
+```
+
+Alternatively, to use the TCRdistClassifier ML method and corresponding TCRdistMotifDiscovery report, install immuneML with the optional TCRdist extra:
+
+```bash
+pip install immuneML[TCRdist]
+```
+
+Optionally, if you want to use the DeepRC ML method and and corresponding DeepRCMotifDiscovery report, you also
+have to install DeepRC dependencies using the [requirements_DeepRC.txt](https://raw.githubusercontent.com/uio-bmi/immuneML/master/requirements_DeepRC.txt) file.
+Important note: DeepRC uses PyTorch functionalities that depend on GPU. Therefore, DeepRC does not work on a CPU.
+To install the DeepRC dependencies, run:
+
+```bash
+pip install -r requirements_DeepRC.txt --no-dependencies
+```
+
+### Validating the installation
+
+To validate the installation, run:
+
+```bash
+immune-ml -h
+```
+
+This should display a help message explaining immuneML usage.
+
+To quickly test out whether immuneML is able to run, try running the quickstart command:
+
+```bash
+immune-ml-quickstart ./quickstart_results/
+```
+
+This will generate a synthetic dataset and run a simple machine machine learning analysis 
+on the generated data. The results folder will contain two sub-folders: one for the generated dataset (`synthetic_dataset`) 
+and one for the results of the machine learning analysis (`machine_learning_analysis`). 
+The files named `specs.yaml` are the input files for immuneML that describe how to generate 
+the dataset and how to do the machine learning analysis. The `index.html` files can be used 
+to navigate through all the results that were produced.
+
+## Usage 
+
+### Quickstart
+
+The quickest way to familiarize yourself with immuneML usage is to follow
+one of the [Quickstart tutorials](https://docs.immuneml.uio.no/quickstart.html).
+These tutorials provide a step-by-step guide on how to use immuneML for a 
+simple machine learning analysis on an adaptive immune receptor repertoire (AIRR) dataset,
+using either the command line tool or the [Galaxy web interface](https://galaxy.immuneml.uiocloud.no). 
+
+
+### Overview of input, analyses and results
+
+The figure below shows an overview of immuneML usage. 
+All parameters for an immuneML analysis are defined in the a YAML specification file. 
+In this file, the settings of the analysis components are defined (also known as `definitions`, 
+shown in six different colors in the figure). 
+Additionally, the YAML file describes one or more `instructions`, which are workflows that are
+applied to the defined analysis components. 
+Each instruction uses at least a dataset component, and optionally additional components.
+AIRR datasets may either be [imported from files](https://docs.immuneml.uio.no/tutorials/how_to_import_the_data_to_immuneML.html), 
+or [generated synthetically](https://docs.immuneml.uio.no/tutorials/how_to_generate_a_random_repertoire_dataset.html) during runtime.
+
+Each instruction produces different types of results, including trained ML models, 
+ML model predictions on a given dataset, plots or other reports describing the 
+dataset or trained models, and modified datasets. 
+To navigate over the results, immuneML generates a summary HTML file. 
+
+
+![image info](https://docs.immuneml.uio.no/latest/_images/definitions_instructions_overview.png)
+
+For a detailed explanation of the YAML specification file, see the tutorial [How to specify an analysis with YAML](https://docs.immuneml.uio.no/tutorials/how_to_specify_an_analysis_with_yaml.html).
+
+See also the following tutorials for specific instructions:
+- [Training ML models](https://docs.immuneml.uio.no/tutorials/how_to_train_and_assess_a_receptor_or_repertoire_classifier.html) for repertoire classification (e.g., disease prediction) or receptor sequence classification (e.g., antigen binding prediction). In immuneML, the performance of different machine learning (ML) settings can be compared by nested cross-validation. These ML settings consist of data preprocessing steps, encodings and ML models and their hyperparameters.
+- [Exploratory analysis](https://docs.immuneml.uio.no/tutorials/how_to_perform_exploratory_analysis.html) of datasets by applying preprocessing and encoding, and plotting descriptive statistics without training ML models.
+- [Simulating](https://docs.immuneml.uio.no/tutorials/how_to_simulate_antigen_signals_in_airr_datasets.html) immune events, such as disease states, into experimental or synthetic repertoire datasets. By implanting known immune signals into a given dataset, a ground truth benchmarking dataset is created. Such a dataset can be used to test the performance of ML settings under known conditions.
+- [Applying trained ML models](https://docs.immuneml.uio.no/tutorials/how_to_apply_to_new_data.html) to new datasets with unknown class labels.
+- And [other tutorials](https://docs.immuneml.uio.no/tutorials.html)
+
+
+### Command line usage 
+
+The `immune-ml` command takes only two parameters: the YAML specification file and a result path. 
+An example is given here:
+
+```bash
+immune-ml path/to/specification.yaml result/folder/path/
+```
+
+For each instruction specified in the YAML specification file, a subfolder is created in the 
+`result/folder/path`. Each subfolder will contain:
+- An `index.html` file which shows an overview of the results produced by that instruction. Inspecting the results of an immuneML analysis typically starts here. 
+- A copy of the used YAML specification (`full_specification.yaml`) with all default parameters explicitly set.
+- A folder containing all raw results produced by the instruction.
+- A folder containing the imported dataset(s) in optimized binary (Pickle) format.
+
+## Support
+
+We will prioritize fixing important bugs, and try to answer any questions as soon as possible. We may implement suggested features and enhancements as time permits. 
+
+If you run into problems when using immuneML, please see [the documentation](https://docs.immuneml.uio.no/latest/). In particular, we recommend you check out:
+- The [Quickstart tutorial](https://docs.immuneml.uio.no/latest/quickstart.html) for new users
+- The [Troubleshooting](https://docs.immuneml.uio.no/latest/troubleshooting.html) page
+
+If this does not answer your question, you can contact us via:
+- Twitter [`@immuneml`](https://twitter.com/immuneml)
+- Email [`contact@immuneml.uio.no`](mailto:contact@immuneml.uio.no)
+
+To report a potential bug or suggest new features, please [submit an issue on GitHub](https://github.com/uio-bmi/immuneML/issues).
+
+If you would like to make contributions, for example by adding a new ML method, encoding, report or preprocessing, please [see our developer documentation](https://docs.immuneml.uio.no/latest/developer_docs.html) and [submit a pull request](https://github.com/uio-bmi/compairr/pulls).
+
+## Requirements
+
+- [Python 3.7 or 3.8](https://www.python.org/)
+- Python packages:
+   - [airr](https://pypi.org/project/airr/) (1 or higher)
+   - [dill](https://pypi.org/project/dill/) (0.3 or higher)
+   - [editdistance](https://pypi.org/project/editdistance/) (0.5.3 or higher)
+   - [fishersapi](https://pypi.org/project/fishersapi/)
+   - [gensim](https://pypi.org/project/gensim/) (3.8 or higher, < 4)
+   - [logomaker](https://pypi.org/project/logomaker/) (0.8 or higher)
+   - [matplotlib](https://matplotlib.org) (3.1 or higher)
+   - [matplotlib-venn](https://pypi.org/project/matplotlib-venn/) (0.11 or higher)
+   - [numpy](https://www.numpy.org/) (1.18 or higher, but at most 1.23.5)
+   - [pandas](https://pandas.pydata.org/) (1 or higher)
+   - [plotly](https://plotly.com/python/) (4 or higher)
+   - [pystache](https://pypi.org/project/pystache/) (0.5.4)
+   - [Pytorch](https://pytorch.org/) (1.5.1 or higher)
+   - [PyYAML](https://pyyaml.org) (5.3 or higher)
+   - [regex](https://pypi.org/project/regex/) 
+   - [scikit-learn](https://scikit-learn.org/) (0.23 or higher)
+   - [scipy](https://www.scipy.org)
+   - [tensorboard](https://www.tensorflow.org/tensorboard) (1.14.0 or higher)
+   - [tzlocal](https://pypi.org/project/tzlocal/) 
+- Optional dependencies when using DeepRC:
+   - [DeepRC](https://github.com/ml-jku/DeepRC) (0.0.1)
+   - [widis-lstm-tools](https://github.com/widmi/widis-lstm-tools) (0.4)
+   - [tqdm](https://tqdm.github.io/) (0.24 or higher)
+   - [h5py](https://www.h5py.org/) (2.10.0 or lower when using DeepRC 0.0.1)
+- Optional dependencies when using TCRdist:
+   - [parasail](https://pypi.org/project/parasail/) (1.2)
+   - [tcrdist3](https://github.com/kmayerb/tcrdist3) (0.1.6 or higher)
+
+# Citing immuneML
+
+If you are using immuneML in any published work, please cite:
+
+Pavlović, M., Scheffer, L., Motwani, K. et al. The immuneML ecosystem for machine learning analysis of adaptive immune 
+receptor repertoires. Nat Mach Intell 3, 936–944 (2021). https://doi.org/10.1038/s42256-021-00413-z
+
+
+
+<hr>
+
+
+© Copyright 2021-2022, Milena Pavlovic, Lonneke Scheffer, Keshav Motwani, Victor Greiff, Geir Kjetil Sandve
+
+
+
+
+
+
+%prep
+%autosetup -n immuneML-2.2.5
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-immuneML -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri Jun 09 2023 Python_Bot <Python_Bot@openeuler.org> - 2.2.5-1
+- Package Spec generated