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+/bicm-3.0.3.tar.gz
diff --git a/python-bicm.spec b/python-bicm.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-bicm
+Version:	3.0.3
+Release:	1
+Summary:	Package for bipartite configuration model
+License:	MIT License
+URL:		https://github.com/mat701/BiCM
+Source0:	https://mirrors.aliyun.com/pypi/web/packages/3c/96/85d6a05d20c225a0d970ca29265f4d02b52d6981fb917822bf7bff7f2899/bicm-3.0.3.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-scipy
+Requires:	python3-tqdm
+Requires:	python3-numba
+
+%description
+## BiCM package
+
+This is a Python package for the computation of the maximum entropy bipartite configuration model (BiCM) and the projection of bipartite networks on one layer. It was developed with Python 3.5.
+
+You can install this package via pip: 
+
+    pip install bicm
+
+Documentation is available at https://bipartite-configuration-model.readthedocs.io/en/latest/ .
+
+This package is also a module of NEMtropy that you can find at https://github.com/nicoloval/NEMtropy .
+
+For more solvers of maximum entropy configuration models visit https://meh.imtlucca.it/ .
+
+
+## Basic functionalities
+
+To install:
+    
+    pip install bicm
+
+To import the module:
+    
+    import bicm
+
+To generate a Graph object and initialize it (with a biadjacency matrix, edgelist or degree sequences):
+    
+    from bicm import BipartiteGraph
+    myGraph = BipartiteGraph()
+    myGraph.set_biadjacency_matrix(my_biadjacency_matrix)
+    myGraph.set_adjacency_list(my_adjacency_list)
+    myGraph.set_edgelist(my_edgelist)
+    myGraph.set_degree_sequences((first_degree_sequence, second_degree_sequence))
+
+Or alternatively, with the respective data structure as input:
+    
+    from bicm import BipartiteGraph
+    myGraph = BipartiteGraph(biadjacency=my_biadjacency_matrix, adjacency_list=my_adjacency_list, edgelist=my_edgelist, degree_sequences=((first_degree_sequence, second_degree_sequence)))
+
+To compute the BiCM probability matrix of the graph or the relative fitnesses coefficients as dictionaries containing the nodes names as keys:
+
+    my_probability_matrix = myGraph.get_bicm_matrix()
+    my_x, my_y = myGraph.get_bicm_fitnesses()
+
+This will solve the bicm using recommended settings for the solver. 
+To customize the solver you can alternatively use (in advance) the following method:
+    
+    myGraph.solve_tool(light_mode=False, method='newton', initial_guess=None, tolerance=1e-8, max_steps=None, verbose=False, linsearch=True, regularise=False, print_error=True, exp=False)
+
+To get the rows or columns projection of the graph:
+
+    myGraph.get_rows_projection()
+    myGraph.get_cols_projection()
+
+Alternatively, to customize the projection:
+
+    myGraph.compute_projection(rows=True, alpha=0.05, method='poisson', threads_num=4, progress_bar=True)
+    
+Now version 3.0.0 is online, and you can use the package with weighted networks as well using the BiWCM models!
+
+See a more detailed walkthrough in **tests/bicm_test** or **tests/biwcm_test** notebooks, or check out the API in the documentation.
+
+## How to cite
+
+If you use the `bicm` module, please cite its location on Github
+[https://github.com/mat701/BiCM](https://github.com/mat701/BiCM) and the
+original articles [Vallarano2021], [Saracco2015] and [Saracco2017].
+
+If you use the weighted models BiWCM_c or BiMCM you might consider citing also the following paper introducing the solvers of this package:
+
+* Bruno, M., Mazzilli, D., Patelli, A., Squartini, T., and Saracco, F. \
+    *Inferring comparative advantage via entropy maximization.* \
+    In preparation
+
+### References
+
+[Vallarano2021] [N. Vallarano, M. Bruno, E. Marchese, G. Trapani, F. Saracco, T. Squartini, G. Cimini, M. Zanon, Fast and scalable likelihood maximization for Exponential Random Graph Models with local constraints, Nature Scientific Reports](https://doi.org/10.1038/s41598-021-93830-4)
+
+[Saracco2015] [F. Saracco, R. Di Clemente, A. Gabrielli, T. Squartini, Randomizing bipartite networks: the case of the World Trade Web, Scientific Reports 5, 10595 (2015)](http://www.nature.com/articles/srep10595).
+
+[Saracco2017] [F. Saracco, M. J. Straka, R. Di Clemente, A. Gabrielli, G. Caldarelli, and T. Squartini, Inferring monopartite projections of bipartite networks: an entropy-based approach, New J. Phys. 19, 053022 (2017)](http://stacks.iop.org/1367-2630/19/i=5/a=053022)
+
+
+_Author_:
+
+[Matteo Bruno](https://csl.sony.it/member/matteo-bruno/) (BiCM) (a.k.a. [mat701](https://github.com/mat701))
+
+
+
+
+%package -n python3-bicm
+Summary:	Package for bipartite configuration model
+Provides:	python-bicm
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-bicm
+## BiCM package
+
+This is a Python package for the computation of the maximum entropy bipartite configuration model (BiCM) and the projection of bipartite networks on one layer. It was developed with Python 3.5.
+
+You can install this package via pip: 
+
+    pip install bicm
+
+Documentation is available at https://bipartite-configuration-model.readthedocs.io/en/latest/ .
+
+This package is also a module of NEMtropy that you can find at https://github.com/nicoloval/NEMtropy .
+
+For more solvers of maximum entropy configuration models visit https://meh.imtlucca.it/ .
+
+
+## Basic functionalities
+
+To install:
+    
+    pip install bicm
+
+To import the module:
+    
+    import bicm
+
+To generate a Graph object and initialize it (with a biadjacency matrix, edgelist or degree sequences):
+    
+    from bicm import BipartiteGraph
+    myGraph = BipartiteGraph()
+    myGraph.set_biadjacency_matrix(my_biadjacency_matrix)
+    myGraph.set_adjacency_list(my_adjacency_list)
+    myGraph.set_edgelist(my_edgelist)
+    myGraph.set_degree_sequences((first_degree_sequence, second_degree_sequence))
+
+Or alternatively, with the respective data structure as input:
+    
+    from bicm import BipartiteGraph
+    myGraph = BipartiteGraph(biadjacency=my_biadjacency_matrix, adjacency_list=my_adjacency_list, edgelist=my_edgelist, degree_sequences=((first_degree_sequence, second_degree_sequence)))
+
+To compute the BiCM probability matrix of the graph or the relative fitnesses coefficients as dictionaries containing the nodes names as keys:
+
+    my_probability_matrix = myGraph.get_bicm_matrix()
+    my_x, my_y = myGraph.get_bicm_fitnesses()
+
+This will solve the bicm using recommended settings for the solver. 
+To customize the solver you can alternatively use (in advance) the following method:
+    
+    myGraph.solve_tool(light_mode=False, method='newton', initial_guess=None, tolerance=1e-8, max_steps=None, verbose=False, linsearch=True, regularise=False, print_error=True, exp=False)
+
+To get the rows or columns projection of the graph:
+
+    myGraph.get_rows_projection()
+    myGraph.get_cols_projection()
+
+Alternatively, to customize the projection:
+
+    myGraph.compute_projection(rows=True, alpha=0.05, method='poisson', threads_num=4, progress_bar=True)
+    
+Now version 3.0.0 is online, and you can use the package with weighted networks as well using the BiWCM models!
+
+See a more detailed walkthrough in **tests/bicm_test** or **tests/biwcm_test** notebooks, or check out the API in the documentation.
+
+## How to cite
+
+If you use the `bicm` module, please cite its location on Github
+[https://github.com/mat701/BiCM](https://github.com/mat701/BiCM) and the
+original articles [Vallarano2021], [Saracco2015] and [Saracco2017].
+
+If you use the weighted models BiWCM_c or BiMCM you might consider citing also the following paper introducing the solvers of this package:
+
+* Bruno, M., Mazzilli, D., Patelli, A., Squartini, T., and Saracco, F. \
+    *Inferring comparative advantage via entropy maximization.* \
+    In preparation
+
+### References
+
+[Vallarano2021] [N. Vallarano, M. Bruno, E. Marchese, G. Trapani, F. Saracco, T. Squartini, G. Cimini, M. Zanon, Fast and scalable likelihood maximization for Exponential Random Graph Models with local constraints, Nature Scientific Reports](https://doi.org/10.1038/s41598-021-93830-4)
+
+[Saracco2015] [F. Saracco, R. Di Clemente, A. Gabrielli, T. Squartini, Randomizing bipartite networks: the case of the World Trade Web, Scientific Reports 5, 10595 (2015)](http://www.nature.com/articles/srep10595).
+
+[Saracco2017] [F. Saracco, M. J. Straka, R. Di Clemente, A. Gabrielli, G. Caldarelli, and T. Squartini, Inferring monopartite projections of bipartite networks: an entropy-based approach, New J. Phys. 19, 053022 (2017)](http://stacks.iop.org/1367-2630/19/i=5/a=053022)
+
+
+_Author_:
+
+[Matteo Bruno](https://csl.sony.it/member/matteo-bruno/) (BiCM) (a.k.a. [mat701](https://github.com/mat701))
+
+
+
+
+%package help
+Summary:	Development documents and examples for bicm
+Provides:	python3-bicm-doc
+%description help
+## BiCM package
+
+This is a Python package for the computation of the maximum entropy bipartite configuration model (BiCM) and the projection of bipartite networks on one layer. It was developed with Python 3.5.
+
+You can install this package via pip: 
+
+    pip install bicm
+
+Documentation is available at https://bipartite-configuration-model.readthedocs.io/en/latest/ .
+
+This package is also a module of NEMtropy that you can find at https://github.com/nicoloval/NEMtropy .
+
+For more solvers of maximum entropy configuration models visit https://meh.imtlucca.it/ .
+
+
+## Basic functionalities
+
+To install:
+    
+    pip install bicm
+
+To import the module:
+    
+    import bicm
+
+To generate a Graph object and initialize it (with a biadjacency matrix, edgelist or degree sequences):
+    
+    from bicm import BipartiteGraph
+    myGraph = BipartiteGraph()
+    myGraph.set_biadjacency_matrix(my_biadjacency_matrix)
+    myGraph.set_adjacency_list(my_adjacency_list)
+    myGraph.set_edgelist(my_edgelist)
+    myGraph.set_degree_sequences((first_degree_sequence, second_degree_sequence))
+
+Or alternatively, with the respective data structure as input:
+    
+    from bicm import BipartiteGraph
+    myGraph = BipartiteGraph(biadjacency=my_biadjacency_matrix, adjacency_list=my_adjacency_list, edgelist=my_edgelist, degree_sequences=((first_degree_sequence, second_degree_sequence)))
+
+To compute the BiCM probability matrix of the graph or the relative fitnesses coefficients as dictionaries containing the nodes names as keys:
+
+    my_probability_matrix = myGraph.get_bicm_matrix()
+    my_x, my_y = myGraph.get_bicm_fitnesses()
+
+This will solve the bicm using recommended settings for the solver. 
+To customize the solver you can alternatively use (in advance) the following method:
+    
+    myGraph.solve_tool(light_mode=False, method='newton', initial_guess=None, tolerance=1e-8, max_steps=None, verbose=False, linsearch=True, regularise=False, print_error=True, exp=False)
+
+To get the rows or columns projection of the graph:
+
+    myGraph.get_rows_projection()
+    myGraph.get_cols_projection()
+
+Alternatively, to customize the projection:
+
+    myGraph.compute_projection(rows=True, alpha=0.05, method='poisson', threads_num=4, progress_bar=True)
+    
+Now version 3.0.0 is online, and you can use the package with weighted networks as well using the BiWCM models!
+
+See a more detailed walkthrough in **tests/bicm_test** or **tests/biwcm_test** notebooks, or check out the API in the documentation.
+
+## How to cite
+
+If you use the `bicm` module, please cite its location on Github
+[https://github.com/mat701/BiCM](https://github.com/mat701/BiCM) and the
+original articles [Vallarano2021], [Saracco2015] and [Saracco2017].
+
+If you use the weighted models BiWCM_c or BiMCM you might consider citing also the following paper introducing the solvers of this package:
+
+* Bruno, M., Mazzilli, D., Patelli, A., Squartini, T., and Saracco, F. \
+    *Inferring comparative advantage via entropy maximization.* \
+    In preparation
+
+### References
+
+[Vallarano2021] [N. Vallarano, M. Bruno, E. Marchese, G. Trapani, F. Saracco, T. Squartini, G. Cimini, M. Zanon, Fast and scalable likelihood maximization for Exponential Random Graph Models with local constraints, Nature Scientific Reports](https://doi.org/10.1038/s41598-021-93830-4)
+
+[Saracco2015] [F. Saracco, R. Di Clemente, A. Gabrielli, T. Squartini, Randomizing bipartite networks: the case of the World Trade Web, Scientific Reports 5, 10595 (2015)](http://www.nature.com/articles/srep10595).
+
+[Saracco2017] [F. Saracco, M. J. Straka, R. Di Clemente, A. Gabrielli, G. Caldarelli, and T. Squartini, Inferring monopartite projections of bipartite networks: an entropy-based approach, New J. Phys. 19, 053022 (2017)](http://stacks.iop.org/1367-2630/19/i=5/a=053022)
+
+
+_Author_:
+
+[Matteo Bruno](https://csl.sony.it/member/matteo-bruno/) (BiCM) (a.k.a. [mat701](https://github.com/mat701))
+
+
+
+
+%prep
+%autosetup -n bicm-3.0.3
+
+%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-bicm -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 3.0.3-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..e9a58df
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+ad5e4a6b356b9b2c11c3e1f56accc2d9  bicm-3.0.3.tar.gz