Updating tutorial

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

@@ -164,7 +164,7 @@
     "\n",
     "<img src=\"images/Input_snapshot.png\" width=\"600px\" />\n",
     "\n",
-    "The heterogeneity models inform protein copy numbers for the snapshot models. The PDB structure of the complex informs the structure of the individual proteins. The time-dependent ET data informs the size and shape of the assembling complex. physical principles inform connectivity and excluded volume.\n",
+    "The heterogeneity models inform protein copy numbers for the snapshot models. The PDB structure of the complex informs the structure of the individual proteins. The time-dependent ET data informs the size and shape of the assembling complex. Physical principles inform connectivity and excluded volume.\n",
     "\n",
     "# Snapshot modeling step 2: representation, scoring function, and search process<a id=\"notebook_snapshots2\"></a>\n",
     "\n",
@@ -303,7 +303,6 @@
     "        monte_carlo_steps=200, # Number of MC steps between writing frames.\n",
     "        number_of_best_scoring_models=0,\n",
     "        number_of_frames=500) # number of frames to be saved\n",
-    "# In our case, for each snapshot we generated 25000 frames altogether (50*500)\n",
     "rex.execute_macro()\n",
     "```\n",
     "\n",
@@ -336,7 +335,7 @@
     "\n",
     "# Snapshot modeling step 3: assessment<a id=\"notebook_snapshot_assess\"></a>\n",
     "\n",
-    "The above code would variety of alternative snapshot models. In general, we would like to assess these models in at least 4 ways: estimate the sampling precision, compare the model to data used to construct it, validate the model against data not used to construct it, and quantify the precision of the model. In this portion of the tutorial, we focus specifically on estimating the sampling precision of the model, while quantitative comparisons between the model and experimental data will be reserved for the final step, when we assess trajectories. Again, this assessment process is quite computationally intensive, so, instead of running the script explicitly, we will walk you through the `snapshot_assessment.py` script, which is located in the `modeling/Snapshots/Snapshots_Assessment` folder.\n",
+    "The above code would create a variety of alternative snapshot models. In general, we would like to assess these models in at least 4 ways: estimate the sampling precision, compare the model to data used to construct it, validate the model against data not used to construct it, and quantify the precision of the model. In this portion of the tutorial, we focus specifically on estimating the sampling precision of the model, while quantitative comparisons between the model and experimental data will be reserved for the final step, when we assess trajectories. Again, this assessment process is quite computationally intensive, so, instead of running the script explicitly, we will walk you through the `snapshot_assessment.py` script, which is located in the `modeling/Snapshots/Snapshots_Assessment` folder.\n",
     "\n",
     "## Filtering good scoring models<a id=\"notebook_snapshot_filter\"></a>\n",
     "\n",
@@ -659,7 +658,7 @@
    "metadata": {},
    "source": [
     "Next, we compute the spatiotemporal model. The inputs we included are:\n",
-    "- state_dict (dict): a dictionary that defines the spatiotemporal model. Keys are strings for each time point in the spatiotemporal process and values are integers corresponding to the number of snapshot models computed at that time point\n",
+    "- state_dict (dict): a dictionary that defines the spatiotemporal model. Keys are strings for each time point in the spatiotemporal process and values are integers corresponding to the number of snapshot models computed at that time point.\n",
     "- out_pdf (bool): whether to write the probability distribution function (pdf).\n",
     "- npaths (int): Number of states two write to a file (path*.txt).\n",
     "- input_dir (str): directory with the input information.\n",
@@ -712,7 +711,7 @@
     "\n",
     "# Trajectory modeling step 3: assessment<a id=\"notebook_trajectory_assess\"></a>\n",
     "\n",
-    "Now that the set of spatiotemporal models has been constructed, we must evaluate these models. We can evaluate these models in at least 4 ways: estimate the sampling precision, compare the model to data used to construct it, validate the model against data not used to construct it, and quantify the precision of the model.\n",
+    "Now that the set of spatiotemporal models has been constructed, we must evaluate these models. We can evaluate these models in at least 4 ways: estimating the sampling precision, comparing the model to data used to construct it, validating the model against data not used to construct it, and quantifying the precision of the model.\n",
     "\n",
     "## Sampling precision<a id=\"notebook_trajectory_sampling_precision\"></a>\n",
     "\n",

Jupyter/spatiotemporal-colab.ipynb

@@ -164,7 +164,7 @@
     "\n",
     "<img src=\"images/Input_snapshot.png\" width=\"600px\" />\n",
     "\n",
-    "The heterogeneity models inform protein copy numbers for the snapshot models. The PDB structure of the complex informs the structure of the individual proteins. The time-dependent ET data informs the size and shape of the assembling complex. physical principles inform connectivity and excluded volume.\n",
+    "The heterogeneity models inform protein copy numbers for the snapshot models. The PDB structure of the complex informs the structure of the individual proteins. The time-dependent ET data informs the size and shape of the assembling complex. Physical principles inform connectivity and excluded volume.\n",
     "\n",
     "# Snapshot modeling step 2: representation, scoring function, and search process<a id=\"notebook_snapshots2\"></a>\n",
     "\n",
@@ -303,7 +303,6 @@
     "        monte_carlo_steps=200, # Number of MC steps between writing frames.\n",
     "        number_of_best_scoring_models=0,\n",
     "        number_of_frames=500) # number of frames to be saved\n",
-    "# In our case, for each snapshot we generated 25000 frames altogether (50*500)\n",
     "rex.execute_macro()\n",
     "```\n",
     "\n",
@@ -336,7 +335,7 @@
     "\n",
     "# Snapshot modeling step 3: assessment<a id=\"notebook_snapshot_assess\"></a>\n",
     "\n",
-    "The above code would variety of alternative snapshot models. In general, we would like to assess these models in at least 4 ways: estimate the sampling precision, compare the model to data used to construct it, validate the model against data not used to construct it, and quantify the precision of the model. In this portion of the tutorial, we focus specifically on estimating the sampling precision of the model, while quantitative comparisons between the model and experimental data will be reserved for the final step, when we assess trajectories. Again, this assessment process is quite computationally intensive, so, instead of running the script explicitly, we will walk you through the `snapshot_assessment.py` script, which is located in the `modeling/Snapshots/Snapshots_Assessment` folder.\n",
+    "The above code would create a variety of alternative snapshot models. In general, we would like to assess these models in at least 4 ways: estimate the sampling precision, compare the model to data used to construct it, validate the model against data not used to construct it, and quantify the precision of the model. In this portion of the tutorial, we focus specifically on estimating the sampling precision of the model, while quantitative comparisons between the model and experimental data will be reserved for the final step, when we assess trajectories. Again, this assessment process is quite computationally intensive, so, instead of running the script explicitly, we will walk you through the `snapshot_assessment.py` script, which is located in the `modeling/Snapshots/Snapshots_Assessment` folder.\n",
     "\n",
     "## Filtering good scoring models<a id=\"notebook_snapshot_filter\"></a>\n",
     "\n",
@@ -659,7 +658,7 @@
    "metadata": {},
    "source": [
     "Next, we compute the spatiotemporal model. The inputs we included are:\n",
-    "- state_dict (dict): a dictionary that defines the spatiotemporal model. Keys are strings for each time point in the spatiotemporal process and values are integers corresponding to the number of snapshot models computed at that time point\n",
+    "- state_dict (dict): a dictionary that defines the spatiotemporal model. Keys are strings for each time point in the spatiotemporal process and values are integers corresponding to the number of snapshot models computed at that time point.\n",
     "- out_pdf (bool): whether to write the probability distribution function (pdf).\n",
     "- npaths (int): Number of states two write to a file (path*.txt).\n",
     "- input_dir (str): directory with the input information.\n",
@@ -712,7 +711,7 @@
     "\n",
     "# Trajectory modeling step 3: assessment<a id=\"notebook_trajectory_assess\"></a>\n",
     "\n",
-    "Now that the set of spatiotemporal models has been constructed, we must evaluate these models. We can evaluate these models in at least 4 ways: estimate the sampling precision, compare the model to data used to construct it, validate the model against data not used to construct it, and quantify the precision of the model.\n",
+    "Now that the set of spatiotemporal models has been constructed, we must evaluate these models. We can evaluate these models in at least 4 ways: estimating the sampling precision, comparing the model to data used to construct it, validating the model against data not used to construct it, and quantifying the precision of the model.\n",
     "\n",
     "## Sampling precision<a id=\"notebook_trajectory_sampling_precision\"></a>\n",
     "\n",

doc/snapshot.md

@@ -9,7 +9,7 @@ We begin snapshot modeling with the first step of integrative modeling, gatherin
 
 \image html Input_snapshot.png width=600px
 
-The heterogeneity models inform protein copy numbers for the snapshot models. The PDB structure of the complex informs the structure of the individual proteins. The time-dependent ET data informs the size and shape of the assembling complex. physical principles inform connectivity and excluded volume.
+The heterogeneity models inform protein copy numbers for the snapshot models. The PDB structure of the complex informs the structure of the individual proteins. The time-dependent ET data informs the size and shape of the assembling complex. Physical principles inform connectivity and excluded volume.
 
 # Snapshot modeling step 2: representation, scoring function, and search process {#snapshots2}
 
@@ -152,7 +152,6 @@ rex=IMP.pmi.macros.ReplicaExchange(mdl,
         monte_carlo_steps=200, # Number of MC steps between writing frames.
         number_of_best_scoring_models=0,
         number_of_frames=500) # number of frames to be saved
-# In our case, for each snapshot we generated 25000 frames altogether (50*500)
 rex.execute_macro()
 \endcode
 

doc/trajectory.md

@@ -68,7 +68,7 @@ nodes, graph, graph_prob, graph_scores = IMP.spatiotemporal.create_DAG(state_dic
 \endcode
 
 The inputs we included are:
-- state_dict (dict): a dictionary that defines the spatiotemporal model. Keys are strings for each time point in the spatiotemporal process and values are integers corresponding to the number of snapshot models computed at that time point
+- state_dict (dict): a dictionary that defines the spatiotemporal model. Keys are strings for each time point in the spatiotemporal process and values are integers corresponding to the number of snapshot models computed at that time point.
 - out_pdf (bool): whether to write the probability distribution function (pdf).
 - npaths (int): Number of states two write to a file (path*.txt).
 - input_dir (str): directory with the input information.
@@ -93,7 +93,7 @@ Now that we have a trajectory model, we can plot the directed acyclic graph (lef
 
 # Trajectory modeling step 3: assessment {#trajectory_assess}
 
-Now that the set of spatiotemporal models has been constructed, we must evaluate these models. We can evaluate these models in at least 4 ways: estimate the sampling precision, compare the model to data used to construct it, validate the model against data not used to construct it, and quantify the precision of the model.
+Now that the set of spatiotemporal models has been constructed, we must evaluate these models. We can evaluate these models in at least 4 ways: estimating the sampling precision, comparing the model to data used to construct it, validating the model against data not used to construct it, and quantifying the precision of the model.
 
 Navigate to `Trajectories/Trajectories_Assessment` and run `trajectories_assessment.py`. This code will perform the following steps to assess the model.