automatic import of python-pywmi

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+/pywmi-0.7.15.tar.gz
diff --git a/python-pywmi.spec b/python-pywmi.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-pywmi
+Version:	0.7.15
+Release:	1
+Summary:	Essential tools and interfaces for WMI solving.
+License:	MIT
+URL:		http://github.com/samuelkolb/pywmi
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/57/ee/e6c5a32908092872b7e0f410da1d9fc989c10f6193af70a091bdc18ecdf2/pywmi-0.7.15.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-pysmt
+Requires:	python3-numpy
+Requires:	python3-future
+Requires:	python3-matplotlib
+Requires:	python3-pillow
+Requires:	python3-polytope
+Requires:	python3-tabulate
+Requires:	python3-graphviz
+Requires:	python3-sympy
+Requires:	python3-scipy
+Requires:	python3-autodora
+Requires:	python3-deprecated
+Requires:	python3-networkx
+Requires:	python3-antlr4-python3-runtime
+Requires:	python3-pysdd
+
+%description
+# pywmi [![Build Status](https://travis-ci.org/weighted-model-integration/pywmi.svg?branch=master)](https://travis-ci.org/weighted-model-integration/pywmi)
+## Installation
+
+    pip install pywmi
+
+pywmi offers various services and engines that require additional installation steps.
+
+### SMT solvers
+pywmi relies upon pysmt to interface with SMT solvers. If you want to benefit from functionality relying on SMT solvers
+please install an SMT solver through the pysmt-install tool that comes as part of your pywmi installation.
+
+    pysmt-install --msat  # example to install mathsat, more solvers are available
+
+For older versions of PySMT (older than version 0.8), you have to make sure that when you use pywmi, the SMT solvers are on your path.
+The pysmt-install tool can show you the necessary commands.
+
+    pysmt-install --env
+
+### XADD engine
+The XADD engine performs WMI using XADDs as described in [Kolb et al., 2018](https://www.ijcai.org/proceedings/2018/698).
+To use this engine you need [Java](https://www.oracle.com/technetwork/java/javase/downloads/index.html), [Gurobi](https://www.gurobi.com) and the xadd library JAR file.
+The pywmi-install tool that comes with your pywmi installation can automatically download and install the JAR file,
+however, you need to install Java and Gurobi manually. Once you did that, just call:
+
+    pywmi-install xadd
+    pywmi-install xadd --force  # To download a new version
+
+
+### Predicate abstraction engine
+The predicate abstraction engine (short PA engine) uses MathSAT and Latte to solve WMI using predicate abstraction, as 
+described in [Morettin et al., 2017](https://www.ijcai.org/proceedings/2017/0100.pdf).
+In order to use the PA engine, you need to install the MathSAT SMT solver (see instructions above),
+Latte (see instructions below) and the [wmipa library](https://github.com/unitn-sml/wmi-pa). You can use the
+`pysmt-install` utility to download the library.
+
+    pywmi-install pa
+    pywmi-install pa --force  # To download a new version
+
+**Manual installation**
+
+You can also download or clone the library manually and add it to your `PYTHONPATH`
+1. Download / clone the [wmipa library](https://github.com/unitn-sml/wmi-pa)
+2. Add the directory containing the library to your `PYTHONPATH`
+
+### Native XSDD engine
+The native XSDD engine (and the PiecewiseXSDD class for representing piecewise functions) are implemented using the
+[PySDD](https://github.com/wannesm/PySDD) library. The PySDD package can be installed as follows:
+
+    pip install git+https://github.com/wannesm/PySDD.git#egg=PySDD
+
+### External XSDD engine
+WMI using XSDD inference is also supported by pywmi. To use the XSDD engine you need to install
+[HAL-ProbLog](https://bitbucket.org/pedrozudo/hal_problog) by following the instructions provided in the README file.
+
+**Summary**
+1. Install the [dmd compiler v2.078.3](http://downloads.dlang.org/releases/2.x/2.078.3/)
+2. `git clone https://github.com/ariovistus/pyd.git`
+3. `cd pyd`
+4. `python setup.py install`
+5. `cd ../`
+6. `git clone --recursive https://github.com/ML-KULeuven/psipy.git`
+7. `cd psypi`
+8. `python psipy/build_psi.py`
+9. `python setup.py install`
+10. Add the psi library to your path (command printed during the previous step)
+11. `cd ../`
+12. `git clone https://bitbucket.org/pedrozudo/hal_problog.git`
+13. `cd hal_problog`
+14. `python setup.py install`
+
+Take care that your code does not run in the same directory as the one you cloned the libraries, as they will pollute
+your namespace.
+
+### Latte
+The Latte integration backend as well as the predicate abstraction solver require
+[Latte](https://www.math.ucdavis.edu/~latte/software.php) to be installed. You can find the latest releases on their
+[GitHub releases page](https://github.com/latte-int/latte/releases). You'll probably want the bundle: latte-integrale.
+
+**Summary**
+1. `wget "https://github.com/latte-int/latte/releases/download/version_1_7_5/latte-integrale-1.7.5.tar.gz"`
+2. `tar -xvzf latte-integrale-1.7.5.tar.gz`
+3. `cd latte-integrale-1.7.5`
+4. `./configure`
+5. `make`
+
+
+## Usage
+### Calling pywmi
+
+**Setup density and query**
+
+    import pysmt.shortcuts as smt
+
+    # Create a "domain" with boolean variables a and b and real variables x, y (both between 0 and 1)
+    domain = Domain.make(["a", "b"], ["x", "y"], [(0, 1), (0, 1)])
+
+    a, b = domain.get_bool_symbols()  # Get PySMT symbols for the boolean variables
+    x, y = domain.get_real_symbols()  # Get PySMT variables for the continuous variables
+
+    # Create support
+    support = (a | b) & (~a | ~b) & (x <= y) & domain.get_bounds()
+
+    # Create weight function (PySMT requires constants to be wrapped, e.g., smt.Real(0.2))
+    weight_function = smt.Ite(a, smt.Real(0.2), smt.Real(0.8)) * (smt.Ite(x <= 0.5, smt.Real(0.2), 0.2 + y) + smt.Real(0.1))
+
+    # Create query
+    query = x <= y / 2
+
+**Use engine to perform inference**
+
+    # Create rejection-sampling based engine (no setup required)
+    rejection_engine = RejectionEngine(domain, support, weight_function, sample_count=100000)
+
+    print("Volume (Rejection):           ", rejection_engine.compute_volume())  # Compute the weighted model integral
+    print("Query probability (Rejection):", rejection_engine.compute_probability(query))  # Compute query probability
+
+ **Use XADD engine (make sure you have installed the prerequisites)**
+
+    # Create XADD engine (mode resolve refers to the integration algorithm described in
+    # Kolb et al. Efficient symbolic integration for probabilistic inference. IJCAI 2018)
+    # !! Requires XADD solver to be setup (see above) !!
+    xadd_engine = XaddEngine(domain, support, weight_function, mode="resolve")
+
+    print("Volume (XADD):                ", xadd_engine.compute_volume())  # Compute the weighted model integral
+    print("Query probability (XADD):     ", xadd_engine.compute_probability(query))  # Compute query probability
+
+**Generating uniform samples and their labels**
+
+    from pywmi.sample import uniform
+    # n: Required number of samples
+    # domain, support: Domain and support defined as above
+    samples = uniform(domain, n)
+    labels = evaluate(samples, support, samples)
+
+**Generating weighted positive samples**
+
+    from pywmi.sample import positive
+    # n: Required number of samples
+    # domain, support, weight: Defining the density as above
+    # Optional:
+    #   sample_pool_size: The number of uniformly sampled positive samples to weight and select the samples from
+    #   sample_count: The number of samples to draw initially, from which to build the positive pool
+    #   max_samples: The maximum number of uniformly sampled samples (positive or negative) to generate before failing
+    #                => If max_samples is exceeded a SamplingError will be raised
+    samples, positive_ratio = positive(n, domain, support, weight)
+
+**Handle densities and write to files**
+
+    # Wrap support and weight function (and optionally queries) in a density object
+    density = Density(domain, support, weight_function, [query])
+
+    # Density object can be saved to and loaded from files
+    filename = "my_density.json"
+    density.to_file(filename)  # Save to file
+    density = Density.from_file(filename)  # Load from file
+
+**Work from command line**
+
+    # Compare engines from command line
+    python -m pywmi my_density.json compare rej:n100000 xadd:mresolve  # Compute the weighted model integral
+    python -m pywmi my_density.json compare rej:n100000 xadd:mresolve -q 0  # Compute query probability (query at index 0)
+
+    # Compute volumes and probabilities from command line
+    # You can provide multiple engines and the result of the first engine not to fail will be returned
+    python -m pywmi my_density.json volume rej:n100000  # Compute weighted model integral
+    python -m pywmi my_density.json prob rej:n100000  # Compute all query probabilities
+
+    # Plot 2-D support
+    python -m pywmi my_density.json plot -o my_density.png
+
+Find the complete running example in [pywmi/tests/running_example.py](pywmi/tests/running_example.py)
+
+
+
+
+%package -n python3-pywmi
+Summary:	Essential tools and interfaces for WMI solving.
+Provides:	python-pywmi
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-pywmi
+# pywmi [![Build Status](https://travis-ci.org/weighted-model-integration/pywmi.svg?branch=master)](https://travis-ci.org/weighted-model-integration/pywmi)
+## Installation
+
+    pip install pywmi
+
+pywmi offers various services and engines that require additional installation steps.
+
+### SMT solvers
+pywmi relies upon pysmt to interface with SMT solvers. If you want to benefit from functionality relying on SMT solvers
+please install an SMT solver through the pysmt-install tool that comes as part of your pywmi installation.
+
+    pysmt-install --msat  # example to install mathsat, more solvers are available
+
+For older versions of PySMT (older than version 0.8), you have to make sure that when you use pywmi, the SMT solvers are on your path.
+The pysmt-install tool can show you the necessary commands.
+
+    pysmt-install --env
+
+### XADD engine
+The XADD engine performs WMI using XADDs as described in [Kolb et al., 2018](https://www.ijcai.org/proceedings/2018/698).
+To use this engine you need [Java](https://www.oracle.com/technetwork/java/javase/downloads/index.html), [Gurobi](https://www.gurobi.com) and the xadd library JAR file.
+The pywmi-install tool that comes with your pywmi installation can automatically download and install the JAR file,
+however, you need to install Java and Gurobi manually. Once you did that, just call:
+
+    pywmi-install xadd
+    pywmi-install xadd --force  # To download a new version
+
+
+### Predicate abstraction engine
+The predicate abstraction engine (short PA engine) uses MathSAT and Latte to solve WMI using predicate abstraction, as 
+described in [Morettin et al., 2017](https://www.ijcai.org/proceedings/2017/0100.pdf).
+In order to use the PA engine, you need to install the MathSAT SMT solver (see instructions above),
+Latte (see instructions below) and the [wmipa library](https://github.com/unitn-sml/wmi-pa). You can use the
+`pysmt-install` utility to download the library.
+
+    pywmi-install pa
+    pywmi-install pa --force  # To download a new version
+
+**Manual installation**
+
+You can also download or clone the library manually and add it to your `PYTHONPATH`
+1. Download / clone the [wmipa library](https://github.com/unitn-sml/wmi-pa)
+2. Add the directory containing the library to your `PYTHONPATH`
+
+### Native XSDD engine
+The native XSDD engine (and the PiecewiseXSDD class for representing piecewise functions) are implemented using the
+[PySDD](https://github.com/wannesm/PySDD) library. The PySDD package can be installed as follows:
+
+    pip install git+https://github.com/wannesm/PySDD.git#egg=PySDD
+
+### External XSDD engine
+WMI using XSDD inference is also supported by pywmi. To use the XSDD engine you need to install
+[HAL-ProbLog](https://bitbucket.org/pedrozudo/hal_problog) by following the instructions provided in the README file.
+
+**Summary**
+1. Install the [dmd compiler v2.078.3](http://downloads.dlang.org/releases/2.x/2.078.3/)
+2. `git clone https://github.com/ariovistus/pyd.git`
+3. `cd pyd`
+4. `python setup.py install`
+5. `cd ../`
+6. `git clone --recursive https://github.com/ML-KULeuven/psipy.git`
+7. `cd psypi`
+8. `python psipy/build_psi.py`
+9. `python setup.py install`
+10. Add the psi library to your path (command printed during the previous step)
+11. `cd ../`
+12. `git clone https://bitbucket.org/pedrozudo/hal_problog.git`
+13. `cd hal_problog`
+14. `python setup.py install`
+
+Take care that your code does not run in the same directory as the one you cloned the libraries, as they will pollute
+your namespace.
+
+### Latte
+The Latte integration backend as well as the predicate abstraction solver require
+[Latte](https://www.math.ucdavis.edu/~latte/software.php) to be installed. You can find the latest releases on their
+[GitHub releases page](https://github.com/latte-int/latte/releases). You'll probably want the bundle: latte-integrale.
+
+**Summary**
+1. `wget "https://github.com/latte-int/latte/releases/download/version_1_7_5/latte-integrale-1.7.5.tar.gz"`
+2. `tar -xvzf latte-integrale-1.7.5.tar.gz`
+3. `cd latte-integrale-1.7.5`
+4. `./configure`
+5. `make`
+
+
+## Usage
+### Calling pywmi
+
+**Setup density and query**
+
+    import pysmt.shortcuts as smt
+
+    # Create a "domain" with boolean variables a and b and real variables x, y (both between 0 and 1)
+    domain = Domain.make(["a", "b"], ["x", "y"], [(0, 1), (0, 1)])
+
+    a, b = domain.get_bool_symbols()  # Get PySMT symbols for the boolean variables
+    x, y = domain.get_real_symbols()  # Get PySMT variables for the continuous variables
+
+    # Create support
+    support = (a | b) & (~a | ~b) & (x <= y) & domain.get_bounds()
+
+    # Create weight function (PySMT requires constants to be wrapped, e.g., smt.Real(0.2))
+    weight_function = smt.Ite(a, smt.Real(0.2), smt.Real(0.8)) * (smt.Ite(x <= 0.5, smt.Real(0.2), 0.2 + y) + smt.Real(0.1))
+
+    # Create query
+    query = x <= y / 2
+
+**Use engine to perform inference**
+
+    # Create rejection-sampling based engine (no setup required)
+    rejection_engine = RejectionEngine(domain, support, weight_function, sample_count=100000)
+
+    print("Volume (Rejection):           ", rejection_engine.compute_volume())  # Compute the weighted model integral
+    print("Query probability (Rejection):", rejection_engine.compute_probability(query))  # Compute query probability
+
+ **Use XADD engine (make sure you have installed the prerequisites)**
+
+    # Create XADD engine (mode resolve refers to the integration algorithm described in
+    # Kolb et al. Efficient symbolic integration for probabilistic inference. IJCAI 2018)
+    # !! Requires XADD solver to be setup (see above) !!
+    xadd_engine = XaddEngine(domain, support, weight_function, mode="resolve")
+
+    print("Volume (XADD):                ", xadd_engine.compute_volume())  # Compute the weighted model integral
+    print("Query probability (XADD):     ", xadd_engine.compute_probability(query))  # Compute query probability
+
+**Generating uniform samples and their labels**
+
+    from pywmi.sample import uniform
+    # n: Required number of samples
+    # domain, support: Domain and support defined as above
+    samples = uniform(domain, n)
+    labels = evaluate(samples, support, samples)
+
+**Generating weighted positive samples**
+
+    from pywmi.sample import positive
+    # n: Required number of samples
+    # domain, support, weight: Defining the density as above
+    # Optional:
+    #   sample_pool_size: The number of uniformly sampled positive samples to weight and select the samples from
+    #   sample_count: The number of samples to draw initially, from which to build the positive pool
+    #   max_samples: The maximum number of uniformly sampled samples (positive or negative) to generate before failing
+    #                => If max_samples is exceeded a SamplingError will be raised
+    samples, positive_ratio = positive(n, domain, support, weight)
+
+**Handle densities and write to files**
+
+    # Wrap support and weight function (and optionally queries) in a density object
+    density = Density(domain, support, weight_function, [query])
+
+    # Density object can be saved to and loaded from files
+    filename = "my_density.json"
+    density.to_file(filename)  # Save to file
+    density = Density.from_file(filename)  # Load from file
+
+**Work from command line**
+
+    # Compare engines from command line
+    python -m pywmi my_density.json compare rej:n100000 xadd:mresolve  # Compute the weighted model integral
+    python -m pywmi my_density.json compare rej:n100000 xadd:mresolve -q 0  # Compute query probability (query at index 0)
+
+    # Compute volumes and probabilities from command line
+    # You can provide multiple engines and the result of the first engine not to fail will be returned
+    python -m pywmi my_density.json volume rej:n100000  # Compute weighted model integral
+    python -m pywmi my_density.json prob rej:n100000  # Compute all query probabilities
+
+    # Plot 2-D support
+    python -m pywmi my_density.json plot -o my_density.png
+
+Find the complete running example in [pywmi/tests/running_example.py](pywmi/tests/running_example.py)
+
+
+
+
+%package help
+Summary:	Development documents and examples for pywmi
+Provides:	python3-pywmi-doc
+%description help
+# pywmi [![Build Status](https://travis-ci.org/weighted-model-integration/pywmi.svg?branch=master)](https://travis-ci.org/weighted-model-integration/pywmi)
+## Installation
+
+    pip install pywmi
+
+pywmi offers various services and engines that require additional installation steps.
+
+### SMT solvers
+pywmi relies upon pysmt to interface with SMT solvers. If you want to benefit from functionality relying on SMT solvers
+please install an SMT solver through the pysmt-install tool that comes as part of your pywmi installation.
+
+    pysmt-install --msat  # example to install mathsat, more solvers are available
+
+For older versions of PySMT (older than version 0.8), you have to make sure that when you use pywmi, the SMT solvers are on your path.
+The pysmt-install tool can show you the necessary commands.
+
+    pysmt-install --env
+
+### XADD engine
+The XADD engine performs WMI using XADDs as described in [Kolb et al., 2018](https://www.ijcai.org/proceedings/2018/698).
+To use this engine you need [Java](https://www.oracle.com/technetwork/java/javase/downloads/index.html), [Gurobi](https://www.gurobi.com) and the xadd library JAR file.
+The pywmi-install tool that comes with your pywmi installation can automatically download and install the JAR file,
+however, you need to install Java and Gurobi manually. Once you did that, just call:
+
+    pywmi-install xadd
+    pywmi-install xadd --force  # To download a new version
+
+
+### Predicate abstraction engine
+The predicate abstraction engine (short PA engine) uses MathSAT and Latte to solve WMI using predicate abstraction, as 
+described in [Morettin et al., 2017](https://www.ijcai.org/proceedings/2017/0100.pdf).
+In order to use the PA engine, you need to install the MathSAT SMT solver (see instructions above),
+Latte (see instructions below) and the [wmipa library](https://github.com/unitn-sml/wmi-pa). You can use the
+`pysmt-install` utility to download the library.
+
+    pywmi-install pa
+    pywmi-install pa --force  # To download a new version
+
+**Manual installation**
+
+You can also download or clone the library manually and add it to your `PYTHONPATH`
+1. Download / clone the [wmipa library](https://github.com/unitn-sml/wmi-pa)
+2. Add the directory containing the library to your `PYTHONPATH`
+
+### Native XSDD engine
+The native XSDD engine (and the PiecewiseXSDD class for representing piecewise functions) are implemented using the
+[PySDD](https://github.com/wannesm/PySDD) library. The PySDD package can be installed as follows:
+
+    pip install git+https://github.com/wannesm/PySDD.git#egg=PySDD
+
+### External XSDD engine
+WMI using XSDD inference is also supported by pywmi. To use the XSDD engine you need to install
+[HAL-ProbLog](https://bitbucket.org/pedrozudo/hal_problog) by following the instructions provided in the README file.
+
+**Summary**
+1. Install the [dmd compiler v2.078.3](http://downloads.dlang.org/releases/2.x/2.078.3/)
+2. `git clone https://github.com/ariovistus/pyd.git`
+3. `cd pyd`
+4. `python setup.py install`
+5. `cd ../`
+6. `git clone --recursive https://github.com/ML-KULeuven/psipy.git`
+7. `cd psypi`
+8. `python psipy/build_psi.py`
+9. `python setup.py install`
+10. Add the psi library to your path (command printed during the previous step)
+11. `cd ../`
+12. `git clone https://bitbucket.org/pedrozudo/hal_problog.git`
+13. `cd hal_problog`
+14. `python setup.py install`
+
+Take care that your code does not run in the same directory as the one you cloned the libraries, as they will pollute
+your namespace.
+
+### Latte
+The Latte integration backend as well as the predicate abstraction solver require
+[Latte](https://www.math.ucdavis.edu/~latte/software.php) to be installed. You can find the latest releases on their
+[GitHub releases page](https://github.com/latte-int/latte/releases). You'll probably want the bundle: latte-integrale.
+
+**Summary**
+1. `wget "https://github.com/latte-int/latte/releases/download/version_1_7_5/latte-integrale-1.7.5.tar.gz"`
+2. `tar -xvzf latte-integrale-1.7.5.tar.gz`
+3. `cd latte-integrale-1.7.5`
+4. `./configure`
+5. `make`
+
+
+## Usage
+### Calling pywmi
+
+**Setup density and query**
+
+    import pysmt.shortcuts as smt
+
+    # Create a "domain" with boolean variables a and b and real variables x, y (both between 0 and 1)
+    domain = Domain.make(["a", "b"], ["x", "y"], [(0, 1), (0, 1)])
+
+    a, b = domain.get_bool_symbols()  # Get PySMT symbols for the boolean variables
+    x, y = domain.get_real_symbols()  # Get PySMT variables for the continuous variables
+
+    # Create support
+    support = (a | b) & (~a | ~b) & (x <= y) & domain.get_bounds()
+
+    # Create weight function (PySMT requires constants to be wrapped, e.g., smt.Real(0.2))
+    weight_function = smt.Ite(a, smt.Real(0.2), smt.Real(0.8)) * (smt.Ite(x <= 0.5, smt.Real(0.2), 0.2 + y) + smt.Real(0.1))
+
+    # Create query
+    query = x <= y / 2
+
+**Use engine to perform inference**
+
+    # Create rejection-sampling based engine (no setup required)
+    rejection_engine = RejectionEngine(domain, support, weight_function, sample_count=100000)
+
+    print("Volume (Rejection):           ", rejection_engine.compute_volume())  # Compute the weighted model integral
+    print("Query probability (Rejection):", rejection_engine.compute_probability(query))  # Compute query probability
+
+ **Use XADD engine (make sure you have installed the prerequisites)**
+
+    # Create XADD engine (mode resolve refers to the integration algorithm described in
+    # Kolb et al. Efficient symbolic integration for probabilistic inference. IJCAI 2018)
+    # !! Requires XADD solver to be setup (see above) !!
+    xadd_engine = XaddEngine(domain, support, weight_function, mode="resolve")
+
+    print("Volume (XADD):                ", xadd_engine.compute_volume())  # Compute the weighted model integral
+    print("Query probability (XADD):     ", xadd_engine.compute_probability(query))  # Compute query probability
+
+**Generating uniform samples and their labels**
+
+    from pywmi.sample import uniform
+    # n: Required number of samples
+    # domain, support: Domain and support defined as above
+    samples = uniform(domain, n)
+    labels = evaluate(samples, support, samples)
+
+**Generating weighted positive samples**
+
+    from pywmi.sample import positive
+    # n: Required number of samples
+    # domain, support, weight: Defining the density as above
+    # Optional:
+    #   sample_pool_size: The number of uniformly sampled positive samples to weight and select the samples from
+    #   sample_count: The number of samples to draw initially, from which to build the positive pool
+    #   max_samples: The maximum number of uniformly sampled samples (positive or negative) to generate before failing
+    #                => If max_samples is exceeded a SamplingError will be raised
+    samples, positive_ratio = positive(n, domain, support, weight)
+
+**Handle densities and write to files**
+
+    # Wrap support and weight function (and optionally queries) in a density object
+    density = Density(domain, support, weight_function, [query])
+
+    # Density object can be saved to and loaded from files
+    filename = "my_density.json"
+    density.to_file(filename)  # Save to file
+    density = Density.from_file(filename)  # Load from file
+
+**Work from command line**
+
+    # Compare engines from command line
+    python -m pywmi my_density.json compare rej:n100000 xadd:mresolve  # Compute the weighted model integral
+    python -m pywmi my_density.json compare rej:n100000 xadd:mresolve -q 0  # Compute query probability (query at index 0)
+
+    # Compute volumes and probabilities from command line
+    # You can provide multiple engines and the result of the first engine not to fail will be returned
+    python -m pywmi my_density.json volume rej:n100000  # Compute weighted model integral
+    python -m pywmi my_density.json prob rej:n100000  # Compute all query probabilities
+
+    # Plot 2-D support
+    python -m pywmi my_density.json plot -o my_density.png
+
+Find the complete running example in [pywmi/tests/running_example.py](pywmi/tests/running_example.py)
+
+
+
+
+%prep
+%autosetup -n pywmi-0.7.15
+
+%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-pywmi -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 31 2023 Python_Bot <Python_Bot@openeuler.org> - 0.7.15-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..ff70184
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+43e12e8076fc7c1178d5ffc503d9cd91  pywmi-0.7.15.tar.gz