From 1bd46c2245f3db3290b23b4b03fe228a7bd52a3d Mon Sep 17 00:00:00 2001 From: devmessias Date: Tue, 17 Sep 2019 11:32:25 -0300 Subject: [PATCH] Fix some typos. Change the version number --- MANIFEST.in | 4 +++- README | 0 README.md | 20 ++++++++++++++++---- docs/source/examples.rst | 8 ++++---- docs/source/index.rst | 2 +- docs/source/install.rst | 2 +- setup.py | 4 ++-- stdog.svg | 6 ++++-- stdog/__init__.py | 2 +- 9 files changed, 32 insertions(+), 16 deletions(-) delete mode 100644 README diff --git a/MANIFEST.in b/MANIFEST.in index 4bf4483..03db2b9 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1 +1,3 @@ -include README.md \ No newline at end of file +include README.md +include docs/source/imgs/kpm_dos.png +include docs/source/imgs/stdog.png \ No newline at end of file diff --git a/README b/README deleted file mode 100644 index e69de29..0000000 diff --git a/README.md b/README.md index 2f0f7ef..e8b560c 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ # Structure and Dynamics on Graphs (Beta) The main goal of StDoG is to provide a package which can be used to study -dynamical and structural properties (like spectra) on/off graphs with a large +dynamical and structural properties (like spectra) on graphs with a large number of vertices. The modules of StDoG are being built by combining codes written in *Tensorflow* + *CUDA* and *C++*. @@ -102,7 +102,7 @@ order_parameter_list = heuns_0.order_parameter_list # #### Spectral Density -The Kernel Polynomial Method can estimate the spectral density of large sparse Hermitan matrices with a computational cost almost linear. This method combines three key ingredients: the Chebyshev expansion + the stochastic trace estimator + kernel smoothing. +The Kernel Polynomial Method [1] can estimate the spectral density of large sparse Hermitan matrices with a computational cost almost linear. This method combines three key ingredients: the Chebyshev expansion + the stochastic trace estimator [2] + kernel smoothing. ```python import igraph as ig @@ -134,7 +134,7 @@ plt.ylim(0, 1) plt.show() ``` ![kpm](docs/imgs/kpm_dos.png) -#### Trace Functions through Stochastic Lanczos Quadrature (SLQ)[1] +#### Trace Functions through Stochastic Lanczos Quadrature (SLQ)[3] ##### Computing custom trace functions @@ -188,8 +188,20 @@ approximated_entropy, exact_entropy (-509.46283, -512.5283224633046) ``` +## References + +1 - Wang, L.W., 1994. Calculating the density of states and +optical-absorption spectra of large quantum systems by the plane-wave moments +method. Physical Review B, 49(15), p.10154. + +2 - Hutchinson, M.F., 1990. A stochastic estimator of the trace of the +influence matrix for laplacian smoothing splines. Communications in +Statistics-Simulation and Computation, 19(2), pp.433-450. + +3 - Ubaru, S., Chen, J., & Saad, Y. (2017). Fast Estimation of tr(f(A)) via Stochastic Lanczos Quadrature. +SIAM Journal on Matrix Analysis and Applications, 38(4), 1075-1099. + -[[1] Ubaru, S., Chen, J., & Saad, Y. (2017). Fast Estimation of tr(f(A)) via Stochastic Lanczos Quadrature. SIAM Journal on Matrix Analysis and Applications, 38(4), 1075-1099.](https://epubs.siam.org/doi/abs/10.1137/16M1104974) ## 3 - How to cite [Thomas Peron](https://tkdmperon.github.io/), [Bruno Messias](http://brunomessias.com/), Angélica S. Mata, [Francisco A. Rodrigues](http://conteudo.icmc.usp.br/pessoas/francisco/), and [Yamir Moreno](http://cosnet.bifi.es/people/yamir-moreno/). On the onset of synchronization of Kuramoto oscillators in scale-free networks. [arXiv:1905.02256](https://arxiv.org/abs/1905.02256) (2019). diff --git a/docs/source/examples.rst b/docs/source/examples.rst index 1acefec..b5c3a6d 100644 --- a/docs/source/examples.rst +++ b/docs/source/examples.rst @@ -47,7 +47,7 @@ Creating the data and setting the variables total_time_transient = total_time transient = False -Using a Tensorflow implementation +Tensorflow implementation ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. code-block:: python @@ -83,7 +83,7 @@ Plotting the result .. image:: imgs/heuns_tf.png -Using a pure CUDA implementation (faster) +CUDA implementation (faster) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ For that, you need to install our another package, @@ -196,7 +196,7 @@ a procedure proposed in the work made by Ubaru, S. et.al. [1] (you need to cite Spectral Entropy -^^^^^^^^^^^^^^^^ +^^^^^^^^^^^^^^^^^ .. code-block:: python @@ -242,7 +242,7 @@ The above code returns Custom Trace Function -^^^^^^^^^^^^^^^^^^^^^ +^^^^^^^^^^^^^^^^^^^^^^ .. code-block:: python diff --git a/docs/source/index.rst b/docs/source/index.rst index 473607d..92fe65b 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -13,7 +13,7 @@ Strucutre and Dyanmics on Graphs :align: center The main goal of StDoG is to provide a package which can be used to study -dynamical and structural properties (like spectra) on/off graphs with a large +dynamical and structural properties (like spectra) on graphs with a large number of vertices. The modules of StDoG are being built by combining codes written in *Tensorflow* + *CUDA* and *C++*. diff --git a/docs/source/install.rst b/docs/source/install.rst index d334a19..ad31a46 100644 --- a/docs/source/install.rst +++ b/docs/source/install.rst @@ -7,5 +7,5 @@ The package is available as as pypi repository $ pip install stdog -The source code is available at `. +The source code is available at http://github.com/stdogpkg. diff --git a/setup.py b/setup.py index 999d50f..49ba038 100644 --- a/setup.py +++ b/setup.py @@ -6,7 +6,7 @@ setup( name="stdog", - version="v1.0.3", + version="v1.0.4", packages=find_packages(exclude=["build", ]), long_description=README_TEXT, long_description_content_type="text/markdown", @@ -16,7 +16,7 @@ description="Structure and Dynamics on Graphs", author_email="messias.physics@gmail.com", author="Bruno Messias; Thomas K. Peron", - download_url="https://github.com/stdogpkg/stdog/archive/v1.0.3.tar.gz", + download_url="https://github.com/stdogpkg/stdog/archive/v1.0.4.tar.gz", keywords=[ "gpu", "science", "complex-networks", "graphs", "dynamics", "tensorflow", "kuramoto" diff --git a/stdog.svg b/stdog.svg index 0771884..6b88030 100644 --- a/stdog.svg +++ b/stdog.svg @@ -167,8 +167,8 @@ id="namedview45" showgrid="false" inkscape:zoom="0.16296571" - inkscape:cx="395.4277" - inkscape:cy="-762.21146" + inkscape:cx="-1255.2263" + inkscape:cy="-777.55211" inkscape:window-x="0" inkscape:window-y="27" inkscape:window-maximized="1" @@ -257,6 +257,7 @@ +