Fix some typos. Change the version number

stdogpkg · Sep 17, 2019 · 1bd46c2 · 1bd46c2
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diff --git a/MANIFEST.in b/MANIFEST.in
@@ -1 +1,3 @@
-include README.md
+include README.md
+include docs/source/imgs/kpm_dos.png
+include docs/source/imgs/stdog.png
diff --git a/README b/README
diff --git 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
@@ -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
@@ -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
@@ -7,5 +7,5 @@ The package is available as as pypi repository
 
     $ pip install stdog
 
-The source code is available at <http://github.com/stdogpkg>`.
+The source code is available at http://github.com/stdogpkg.
 
diff --git 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="[email protected]",
     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
diff --git a/stdog/__init__.py b/stdog/__init__.py
@@ -3,7 +3,7 @@
 from stdog import utils
 
 
-__version__ = "1.0.3"
+__version__ = "1.0.4"
 __license__ = "MIT"
 __author__ = "Bruno Messias, Thomas K. Peron"
 __author_email__ = "[email protected]"
Original file line number	Diff line number	Diff line change
Expand Up		@@ -7,5 +7,5 @@ The package is available as as pypi repository

		$ pip install stdog

		The source code is available at <http://github.com/stdogpkg>`.
		The source code is available at http://github.com/stdogpkg.