Updating tutorial

Jupyter/.ipynb_checkpoints/spatiotemporal-colab-checkpoint.ipynb

@@ -134,7 +134,7 @@
    "metadata": {},
    "source": [
     "From the output of `prepare_protein_library`, we see that there are 3 heterogeneity models at each time point (it is possible to have more heterogeneity models than copy numbers if multiple copies of the protein exist in the complex). For each heterogeneity model, we see 2 files:\n",
-    "- *.config, a file with a list of proteins represented in the heterogeneity model\n",
+    "- *.config, a file with a list of proteins represented in the heterogeneity model.\n",
     "- *_topol.txt, a topology file for snapshot modeling corresponding to this heterogeneity model.\n",
     "\n",
     "# Heterogeneity modeling step 3: assessment<a id=\"notebook_heterogeneity_assess\"></a>\n",
@@ -194,7 +194,7 @@
     "|E3-ubi-RING2|green|3rpg.fasta.txt|E3-ubi-RING2|3rpg.pdb|C|45,116|-15|1|10|6|3||\n",
     "```\n",
     "\n",
-    "Next, we must prepare `static_snapshot.py` to read in this topology file. We begin by defining the input variables, `state` and `time`, which define which topology to use, as well as the paths to other pieces of input information.\n",
+    "Next, we must prepare `static_snapshot.py` to read in this topology file. We begin by defining the input variables, `state` and `time`, which define which heterogeneity model to use, as well as the paths to other pieces of input information.\n",
     "\n",
     "```python\n",
     "# Running parameters to access correct path of ET_data for EM restraint\n",
@@ -698,7 +698,7 @@
     "- `dag_heatmap.eps` and `dag_heatmap`: image of the directed acyclic graph from the set of models.\n",
     "- `path*.txt`: files where each row includes a `{state}_{time}` string, so that rows correspond to the states visited over that trajectory. Files are numbered from the most likely path to the least likely path.\n",
     "\n",
-    "Now that we have a trajectory model, we can plot the directed acyclic graph (left) and the series of centroid models from each snapshot model along the most likely trajectory (right). Each row corresponds to a different time point in the assembly process (0 min, 1 min, and 2 min). Each node is shaded according to its weight in the final model ($W(X_{N,t}N_{t})$). Proteins are colored as A - blue, B - orange, and C - purple.\n",
+    "Now that we have a trajectory model, we can plot the directed acyclic graph (left) and the series of centroid models from each snapshot model along the most likely trajectory (right). Each row corresponds to a different time point in the assembly process (0 min, 1 min, and 2 min). Each node is shaded according to its weight in the final model ($W(X_{t})$). Proteins are colored as A - blue, B - orange, and C - purple.\n",
     "\n",
     "<img src=\"images/Spatiotemporal_Model.png\" width=\"300px\" />\n"
    ]

Jupyter/spatiotemporal-colab.ipynb

@@ -134,7 +134,7 @@
    "metadata": {},
    "source": [
     "From the output of `prepare_protein_library`, we see that there are 3 heterogeneity models at each time point (it is possible to have more heterogeneity models than copy numbers if multiple copies of the protein exist in the complex). For each heterogeneity model, we see 2 files:\n",
-    "- *.config, a file with a list of proteins represented in the heterogeneity model\n",
+    "- *.config, a file with a list of proteins represented in the heterogeneity model.\n",
     "- *_topol.txt, a topology file for snapshot modeling corresponding to this heterogeneity model.\n",
     "\n",
     "# Heterogeneity modeling step 3: assessment<a id=\"notebook_heterogeneity_assess\"></a>\n",
@@ -194,7 +194,7 @@
     "|E3-ubi-RING2|green|3rpg.fasta.txt|E3-ubi-RING2|3rpg.pdb|C|45,116|-15|1|10|6|3||\n",
     "```\n",
     "\n",
-    "Next, we must prepare `static_snapshot.py` to read in this topology file. We begin by defining the input variables, `state` and `time`, which define which topology to use, as well as the paths to other pieces of input information.\n",
+    "Next, we must prepare `static_snapshot.py` to read in this topology file. We begin by defining the input variables, `state` and `time`, which define which heterogeneity model to use, as well as the paths to other pieces of input information.\n",
     "\n",
     "```python\n",
     "# Running parameters to access correct path of ET_data for EM restraint\n",