Standardize docs

docs/tutorials/embedding/AdjacencySpectralEmbed.ipynb

@@ -46,14 +46,14 @@
     "undirected_sbm = sbm(2 * [n_verts], P)\n",
     "heatmap(undirected_sbm, title='2-block SBM (undirected)', inner_hier_labels=labels_sbm)\n",
     "directed_sbm = sbm(2 * [n_verts], P, directed=True)\n",
-    "heatmap(directed_sbm, title='2-block SBM (directed)', inner_hier_labels=labels_sbm)"
+    "heatmap(directed_sbm, title='2-block SBM (directed)', inner_hier_labels=labels_sbm);"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can use the AdjacencySpectralEmbed class to embed the adjacency matrix as follows. If no parameters are given to the AdjacencySpectralEmbed class, it will automatically choose the number of dimensions to embed into."
+    "We can use the ``AdjacencySpectralEmbed`` class to embed the adjacency matrix as follows. If no parameters are given to the AdjacencySpectralEmbed class, it will automatically choose the number of dimensions to embed into."
    ]
   },
   {
@@ -64,7 +64,7 @@
    "source": [
     "ase = AdjacencySpectralEmbed()\n",
     "Xhat = ase.fit_transform(undirected_sbm)\n",
-    "pairplot(Xhat, title='SBM adjacency spectral embedding')"
+    "pairplot(Xhat, title='SBM adjacency spectral embedding');"
    ]
   },
   {
@@ -83,7 +83,7 @@
     "ase = AdjacencySpectralEmbed()\n",
     "Xhat, Yhat = ase.fit_transform(directed_sbm)\n",
     "pairplot(Xhat, title='SBM adjacency spectral embedding \"out\"')\n",
-    "pairplot(Yhat, title='SBM adjacency spectral embedding \"in\"')"
+    "pairplot(Yhat, title='SBM adjacency spectral embedding \"in\"');"
    ]
   },
   {
@@ -101,7 +101,7 @@
    "source": [
     "ase = AdjacencySpectralEmbed(n_components=2, algorithm='truncated')\n",
     "Xhat = ase.fit_transform(undirected_sbm)\n",
-    "pairplot(Xhat, title='2-component embedding', height=4)"
+    "pairplot(Xhat, title='2-component embedding', height=4);"
    ]
   },
   {

docs/tutorials/embedding/Omnibus.ipynb

@@ -32,11 +32,11 @@
     "\n",
     "\\begin{align*}\n",
     "P_1 = \n",
-    "\\begin{bmatrix}0.2 & 0.1\\\\\n",
+    "\\begin{bmatrix}0.3 & 0.1\\\\\n",
     "0.1 & 0.7\n",
     "\\end{bmatrix},~\n",
-    "P_2 = \\begin{bmatrix}0.2 & 0.1\\\\\n",
-    "0.1 & 0.2\n",
+    "P_2 = \\begin{bmatrix}0.3 & 0.1\\\\\n",
+    "0.1 & 0.3\n",
     "\\end{bmatrix}\n",
     "\\end{align*}\n",
     "\n",
@@ -82,7 +82,7 @@
     "from graspologic.plot import heatmap\n",
     "\n",
     "heatmap(G1, figsize=(7, 7), title='Visualization of Graph 1')\n",
-    "heatmap(G2, figsize=(7, 7), title='Visualization of Graph 2')"
+    "heatmap(G2, figsize=(7, 7), title='Visualization of Graph 2');"
    ]
   },
   {
@@ -152,7 +152,7 @@
     "for i in range(50):\n",
     "    ax.plot([Xhat1[i, 0], Xhat2[i, 0]], [Xhat1[i, 1], Xhat2[i, 1]], 'black', alpha = 0.15)\n",
     "    \n",
-    "ax.set_title('Latent Positions from Omnibus Embedding', fontsize=20)"
+    "ax.set_title('Latent Positions from Omnibus Embedding', fontsize=20);"
    ]
   }
  ],

docs/tutorials/inference/latent_distribution_test.ipynb

@@ -50,7 +50,7 @@
     "A1 = sbm(csize, P)\n",
     "X1 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A1)\n",
     "heatmap(A1, title='2-block SBM adjacency matrix')\n",
-    "pairplot(X1, title='2-block adjacency spectral embedding', height=4.5)"
+    "pairplot(X1, title='2-block adjacency spectral embedding', height=4.5);"
    ]
   },
   {
@@ -72,7 +72,7 @@
     "A1 = sbm(csize, P)\n",
     "X1 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A1)\n",
     "heatmap(A2, title='2-block SBM adjacency matrix')\n",
-    "pairplot(X2, title='2-block adjacency spectral embedding', height=4.5)"
+    "pairplot(X2, title='2-block adjacency spectral embedding', height=4.5);"
    ]
   },
   {
@@ -175,7 +175,7 @@
     "A3 = sbm(csize, P2)\n",
     "heatmap(A3, title='2-block SBM adjacency matrix A3')\n",
     "X3 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A3)\n",
-    "pairplot(X3, title='2-block adjacency spectral embedding A3', height=4.5)"
+    "pairplot(X3, title='2-block adjacency spectral embedding A3', height=4.5);"
    ]
   },
   {

docs/tutorials/inference/latent_position_test.ipynb

@@ -50,18 +50,18 @@
     "A = sbm(csize, P)\n",
     "X = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A)\n",
     "heatmap(A, title='4-block SBM adjacency matrix')\n",
-    "pairplot(X, title='4-block adjacency spectral embedding')"
+    "pairplot(X, title='4-block adjacency spectral embedding');"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the adjacency matrix above, there is a clearly defined block structrure corresponding to the 4 communities in the graph that we established. On the right, we see the **adjacency spectral embedding (ASE)** of this graph. ASE(A) recovers an estimate of the **latent positions** of $A $. Latent positions refer to the idea of a **random dot product graph (RDPG)** which can be modeled as follows:\n",
+    "In the adjacency matrix above, there is a clearly defined block structrure corresponding to the 4 communities in the graph that we established. On the right, we see the **adjacency spectral embedding (ASE)** of this graph. $\\text{ASE}(A)$ recovers an estimate of the **latent positions** of $A$. Latent positions refer to the idea of a **random dot product graph (RDPG)** which can be modeled as follows:\n",
     "\n",
-    "For an adjacency matrix $A \\in \\mathbb{R}^{n x n}$, the probability of an edge existing between node $i$ and node $j$ (aka whether or not $A_{ij}$ is a 1) is determined by the matrix $P \\in \\mathbb{R}^{n x n}$\n",
+    "For an adjacency matrix $A \\in \\mathbb{R}^{n \\times n}$, the probability of an edge existing between node $i$ and node $j$ (aka whether or not $A_{ij}$ is a 1) is determined by the matrix $P \\in \\mathbb{R}^{n \\times n}$\n",
     "\n",
-    "$P = XX^T$, where $X \\in \\mathbb{R}^{n x d} $ and is referred to as the latent positions of the graph. $X$ is referred to as the latent positions of the graph because each node $n_i$ is modeled as having a hidden, usually unobserved location in $\\mathbb{R}^d$ (we'll call it $x_i$). The probability of an edge existing between $n_i$ and $n_j$ is equal to the dot product $x_i \\cdot x_j$\n",
+    "$P = XX^T$, where $X \\in \\mathbb{R}^{n \\times d}$ and is referred to as the latent positions of the graph. $X$ is referred to as the latent positions of the graph because each node $n_i$ is modeled as having a hidden, usually unobserved location in $\\mathbb{R}^d$ (we'll call it $x_i$). The probability of an edge existing between $n_i$ and $n_j$ is equal to the dot product $x_i \\cdot x_j$\n",
     "\n",
     "ASE is one way to obtain an estimate of the latent positions of a graph, $\\hat{X}$\n",
     "\n",
@@ -73,7 +73,7 @@
    "metadata": {},
    "source": [
     "## Sample new RDPGs from this latent position\n",
-    "Given the estimate of X, we now sample two new RDPGs from the same latent position above"
+    "Given the estimate of $X$, we now sample two new RDPGs from the same latent position above"
    ]
   },
   {
@@ -97,7 +97,7 @@
     "heatmap(A1, title='Sampled RDPG 1 adjacency matrix')\n",
     "heatmap(A2, title='Sampled RDPG 2 adjacency matrix')\n",
     "pairplot(Xhat1, title='Sampled RDPG 1 adjacency spectral embedding')\n",
-    "pairplot(Xhat2, title='Sampled RDPG 2 adjacency spectral embedding')"
+    "pairplot(Xhat2, title='Sampled RDPG 2 adjacency spectral embedding');"
    ]
   },
   {
@@ -120,7 +120,7 @@
     "\n",
     "Here, R is an orthogonal rotation matrix found from solving the [orthogonal procrustes problem](https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.linalg.orthogonal_procrustes.html) (Note: this constraint can be relaxed for other versions of semipar)\n",
     "\n",
-    "Note that LatentPositionTest.fit() may take several minutes"
+    "Note that ``LatentPositionTest.fit()`` may take several minutes"
    ]
   },
   {
@@ -177,7 +177,7 @@
     "heatmap(A3, title='Sampled RDPG 3 adjacency matrix')\n",
     "heatmap(A4, title='Sampled RDPG 4 (distorted) adjacency matrix')\n",
     "pairplot(Xhat3, title='Sampled RDPG 3 adjacency spectral embedding')\n",
-    "pairplot(Xhat4, title='Sampled RDPG 4 (distorted) adjacency spectral embedding')"
+    "pairplot(Xhat4, title='Sampled RDPG 4 (distorted) adjacency spectral embedding');"
    ]
   },
   {

docs/tutorials/matching/faq.ipynb

@@ -26,7 +26,7 @@
     "\n",
     "\\begin{equation}\n",
     "\\begin{aligned}\n",
-    "\\min & {\\;-trace(APB^T P^T)}\\\\\n",
+    "\\min & {\\;-\\text{trace}(APB^T P^T)}\\\\\n",
     "\\text{s.t. } & {\\;P \\: \\epsilon \\: \\mathcal{P}} \\\\\n",
     "\\end{aligned}\n",
     "\\end{equation}\n",
@@ -44,7 +44,7 @@
     "\n",
     "\\begin{equation}\n",
     "\\begin{aligned}\n",
-    "\\min & {\\; trace(APB^T P^T)}\\\\\n",
+    "\\min & {\\; \\text{trace}(APB^T P^T)}\\\\\n",
     "\\text{s.t. } & {\\;P \\: \\epsilon \\: \\mathcal{P}} \\\\\n",
     "\\end{aligned}\n",
     "\\end{equation}\n",
@@ -110,7 +110,7 @@
    "source": [
     "from graspologic.plot import heatmap\n",
     "heatmap(G1, cbar=False, title = 'G1 [ER-NP(50, 0.3) Simulation]')\n",
-    "heatmap(G2, cbar=False, title = 'G2 [G1 Randomly Shuffled]')"
+    "heatmap(G2, cbar=False, title = 'G2 [G1 Randomly Shuffled]');"
    ]
   },
   {
@@ -141,7 +141,7 @@
    "outputs": [],
    "source": [
     "heatmap(G1, cbar=False, title = 'G1[ER-NP(50, 0.3) Simulation]')\n",
-    "heatmap(G2, cbar=False, title = 'G2[ER-NP(50, 0.3) Randomly Shuffled] unshuffled')"
+    "heatmap(G2, cbar=False, title = 'G2[ER-NP(50, 0.3) Randomly Shuffled] unshuffled');"
    ]
   }
  ],

docs/tutorials/matching/sgm.ipynb

@@ -77,7 +77,7 @@
     "fig, axs = plt.subplots(1, 3, figsize=(10, 5))\n",
     "heatmap(A1, ax=axs[0], cbar=False, title=\"Graph 1\")\n",
     "heatmap(A2, ax=axs[1], cbar=False, title=\"Graph 2\")\n",
-    "heatmap(A1 - A2, ax=axs[2], cbar=False, title=\"Diff (G1 - G2)\")"
+    "heatmap(A1 - A2, ax=axs[2], cbar=False, title=\"Diff (G1 - G2)\");"
    ]
   },
   {
@@ -116,7 +116,7 @@
     "fig, axs = plt.subplots(1, 3, figsize=(10, 5))\n",
     "heatmap(A1, ax=axs[0], cbar=False, title=\"Graph 1\")\n",
     "heatmap(A2_shuffle, ax=axs[1], cbar=False, title=\"Graph 2 shuffled\")\n",
-    "heatmap(A1 - A2_shuffle, ax=axs[2], cbar=False, title=\"Diff (G1 - G2 shuffled)\")\n"
+    "heatmap(A1 - A2_shuffle, ax=axs[2], cbar=False, title=\"Diff (G1 - G2 shuffled)\");"
    ]
   },
   {
@@ -181,7 +181,7 @@
     "heatmap(A1 - A2_unshuffle, ax=axs[2], cbar=False, title=\"Diff (G1 - G2 unshuffled)\")\n",
     "\n",
     "match_ratio = 1-(np.count_nonzero(abs(sgm.perm_inds_-node_unshuffle_input))/n_verts)\n",
-    "print(\"Match Ratio with 10 seeds: \", match_ratio)\n"
+    "print(\"Match Ratio with 10 seeds: \", match_ratio)"
    ]
   },
   {

docs/tutorials/models/models.ipynb

@@ -342,7 +342,7 @@
     "             pad=0.04,\n",
     "             shrink=0.8,\n",
     "             fraction=0.08,\n",
-    "             drawedges=False)"
+    "             drawedges=False);"
    ]
   }
  ],

docs/tutorials/simulations/corr.ipynb

@@ -82,7 +82,7 @@
    "source": [
     "from graspologic.plot import heatmap\n",
     "heatmap(G1, title = 'Correlated ER Simulation Graph 1')\n",
-    "heatmap(G2, title = 'Correlated ER Simulation Graph 2')"
+    "heatmap(G2, title = 'Correlated ER Simulation Graph 2');"
    ]
   },
   {
@@ -147,15 +147,8 @@
    "outputs": [],
    "source": [
     "heatmap(G1, title = 'Correlated SBM Simulation Graph 1')\n",
-    "heatmap(G2, title = 'Correlated SBM Simulation Graph 2')"
+    "heatmap(G2, title = 'Correlated SBM Simulation Graph 2');"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

docs/tutorials/simulations/erdos_renyi.ipynb

@@ -65,7 +65,7 @@
     "from graspologic.plot import heatmap\n",
     "\n",
     "heatmap(G1, title = 'ER-NP(50, 0.3) Simulation')\n",
-    "heatmap(G2, title = 'ER-NM(50, 250) Simulation')"
+    "heatmap(G2, title = 'ER-NM(50, 250) Simulation');"
    ]
   },
   {
@@ -74,7 +74,7 @@
    "source": [
     "## Weighted ER Graphs\n",
     "\n",
-    "Both *er_np* and *er_nm* functions provide ways to sample weights for all edges that were sampled via a probability distribution function. In order to sample with weights, we provide a probability distribution function with corresponding keyword arguments for the distribution function.\n",
+    "Both ``er_np()`` and ``er_nm()`` functions provide ways to sample weights for all edges that were sampled via a probability distribution function. In order to sample with weights, we provide a probability distribution function with corresponding keyword arguments for the distribution function.\n",
     "\n",
     "Below we sample $G_1 \\sim ER_{NP}(50, 0.2)$ where the weights are distributed normally with $\\mu = 0,~\\sigma^2 = 1$."
    ]
@@ -104,7 +104,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "heatmap(G1, title = 'ER-NP(50, 0.2) with N(0,1) Weights Simulation')"
+    "heatmap(G1, title = 'ER-NP(50, 0.2) with N(0,1) Weights Simulation');"
    ]
   }
  ],

docs/tutorials/simulations/rdpg.ipynb

@@ -71,7 +71,7 @@
    "source": [
     "from graspologic.plot import heatmap\n",
     "\n",
-    "heatmap(A, title='ER_NP(100, 0.5) Using RDPG')"
+    "heatmap(A, title='ER_NP(100, 0.5) Using RDPG');"
    ]
   },
   {
@@ -109,7 +109,7 @@
    "source": [
     "X = np.array([[0.5, 0.2, 0.2]] * 50 + [[0.1, 0.1, 0.1]] * 50)\n",
     "A_rdpg = rdpg(X, loops=False)\n",
-    "heatmap(A_rdpg, title='2-block SBM as RDPG')"
+    "heatmap(A_rdpg, title='2-block SBM as RDPG');"
    ]
   },
   {
@@ -131,7 +131,7 @@
     "p = [[0.33, 0.09], [0.09, 0.03]]\n",
     "\n",
     "A_sbm = sbm(n, p)\n",
-    "heatmap(A_sbm, title = 'SBM Simulation')"
+    "heatmap(A_sbm, title = 'SBM Simulation');"
    ]
   }
  ],

docs/tutorials/simulations/rdpg_corr.ipynb

@@ -29,7 +29,7 @@
     "\n",
     "Here, we want to generate a pair of graphs with the same latent positions but with correlation between edges. \n",
     "\n",
-    "There are several parameters in this function: \"X\" and \"Y\" are the input matrices (latent positions) which are used to generate the probability matrix; \"r\" is the correlation between the graph pair, which should be (-1,1) (note that not all values of r may be possible for a given set of latent positions).\n",
+    "There are several parameters in this function: $X$ and $Y$ are the input matrices (latent positions) which are used to generate the probability matrix; $r$ is the correlation between the graph pair, which should be (-1,1) (note that not all values of r may be possible for a given set of latent positions).\n",
     "\n",
     "Below, we sample a RDPG graph pair (undirected and no self-loops), G1 and G2, with the following parameters:\n",
     "\\begin{align*}\n",
@@ -66,7 +66,7 @@
    "metadata": {},
    "source": [
     "## Visualize the graphs using heatmap\n",
-    "Here, we define \"difference rate\" to be the number of edges between the two graphs which are not the same (exist or not exist) out of all potential edges (roughly $n^2$)"
+    "Here, we define *difference rate* to be the number of edges between the two graphs which are not the same (exist or not exist) out of all potential edges (roughly $n^2$)"
    ]
   },
   {
@@ -265,7 +265,7 @@
     "x_list = np.linspace(-0.5,0.9,15)\n",
     "plt.plot(x_list,rlist,'o-')\n",
     "plt.xlabel(\"Correlation\")\n",
-    "plt.ylabel('Difference rate')"
+    "plt.ylabel('Difference rate');"
    ]
   },
   {

docs/tutorials/simulations/sbm.ipynb

@@ -24,7 +24,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Unlike [Erdos-Renyi (ER) models](./erdos_renyi.ipynb), a Stochastic Block Model (SBM) produces graphs containing communities: disjoint subgraphs characterized by differing edge probabilities for vertices within and between communities<sup>[1](https://en.wikipedia.org/wiki/Stochastic_block_model)</sup>.\n",
+    "Unlike [Erdos-Renyi (ER) models](./erdos_renyi.ipynb), a Stochastic Block Model (SBM) produces graphs containing communities: disjoint subgraphs characterized by differing edge probabilities for vertices within and between communities [(1)](https://en.wikipedia.org/wiki/Stochastic_block_model).\n",
     "\n",
     "SBM is parametrized by the number of vertices in each community $n$, and a block probability matrix $P \\in \\mathbb{R}^{n x n}$ where each element specifies the probability of an edge in a particular block. One can think of SBM as a collection of ER graphs where each block corresponds to an ER graph.\n",
     "\n",
@@ -72,7 +72,7 @@
    "source": [
     "from graspologic.plot import heatmap\n",
     "\n",
-    "heatmap(G, title ='SBM Simulation')"
+    "heatmap(G, title ='SBM Simulation');"
    ]
   },
   {
@@ -81,7 +81,7 @@
    "source": [
     "## Weighted SBM Graphs\n",
     "\n",
-    "Similar to ER simulations, *sbm* functions provide ways to sample weights for all edges that were sampled via a probability distribution function. In order to sample with weights, you can either:\n",
+    "Similar to ER simulations, ``sbm()`` functions provide ways to sample weights for all edges that were sampled via a probability distribution function. In order to sample with weights, you can either:\n",
     "\n",
     "1. Provide a *single* probability distribution function with corresponding keyword arguments for the distribution function. All weights will be sampled using the same function.\n",
     "2. Provide a probability distribution function with corresponding keyword arguments for each block.\n",
@@ -139,7 +139,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "heatmap(G, title='Weighted SBM Simulation')"
+    "heatmap(G, title='Weighted SBM Simulation');"
    ]
   }
  ],