Changes in utils scripts

pytest-mpi

@@ -82,7 +82,7 @@ else
   fi
 
   if [ "${ORDEROUTPUT}" -eq 1 ]; then
-    mpirun -n ${NPROCS} --output-filename outtest pytest ${PYTEST_ARGS} --only-mpi >/dev/null
+    mpirun -n ${NPROCS} --oversubscribe --output-filename outtest pytest ${PYTEST_ARGS} --only-mpi >/dev/null
     exit_code=$?
     for ((RANK=0; RANK<${NPROCS}; RANK++)); do
       RANKFILE="outtest/1/rank.${RANK}/stdout"
@@ -94,10 +94,10 @@ else
     done
     rm -r outtest
   elif [ "${UNMUTE}" -eq -1 ]; then
-    mpirun -n ${NPROCS} pytest ${PYTEST_ARGS} --only-mpi
+    mpirun -n ${NPROCS} --oversubscribe pytest ${PYTEST_ARGS} --only-mpi
     exit_code=$?
   else
-    mpirun -n ${NPROCS} utils/mute_all_ranks_except.sh ${UNMUTE} pytest ${PYTEST_ARGS} --only-mpi
+    mpirun -n ${NPROCS} --oversubscribe utils/mute_all_ranks_except.sh ${UNMUTE} pytest ${PYTEST_ARGS} --only-mpi
     exit_code=$?
   fi
 fi

utils/aggregates.py

@@ -1,3 +1,5 @@
+# PYTHON_ARGCOMPLETE_OK
+
 import MDAnalysis as mda
 from MDAnalysis.analysis import distances
 import numpy as np
@@ -6,7 +8,7 @@
 from matplotlib.ticker import MaxNLocator
 import os
 import sys
-import argparse
+import argcomplete, argparse
 from tqdm import tqdm
 import warnings
 import dask
@@ -134,6 +136,8 @@ def aggregates_clustering(
     plot_dendrograms,
     traj_in_memory,
     save_solvent,
+    font_size,
+    caption_font_size,
     summary_fig_size=(12, 8),
 ):
     u = mda.Universe(grofile, h5mdfile, in_memory=traj_in_memory)
@@ -182,21 +186,19 @@ def aggregates_clustering(
 
     n_clusters = []
     all_sizes = []
-    total_clust_by_size = {}
+    clusters_per_frame = []
     for c in clusters[0]:
         # get the number of clusters and sizes
         unique_clusts, clust_counts = np.unique(c, return_counts=True)
-        for size in clust_counts:
-            if size not in total_clust_by_size:
-                total_clust_by_size[size] = 1
-            else:
-                total_clust_by_size[size] += 1
+
         n_clusters.append(len(unique_clusts))
 
+        clusters_per_frame.append(np.sort(clust_counts))
         all_sizes += clust_counts.tolist()
 
     # based on cluster sizes get occurence of each size
     sizes, freq = np.unique(all_sizes, return_counts=True)
+    total_clust_by_size = dict(zip(sizes, freq))
     freq = freq / len(u.trajectory[skip:end:stride])
 
     # overall average number of aggregates
@@ -234,32 +236,50 @@ def aggregates_clustering(
         for s, p in prob_mol_size.items():
             of.write(f"{s}\t{p}\n")
 
+        of.write("\nClusters sizes in each analyzed frame:\n")
+        for c in clusters_per_frame:
+            for s in c:
+                of.write(f"{s}\t")
+            of.write("\n")
+
     # plot results
     _, axs = plt.subplots(2, 2, figsize=summary_fig_size)
+    plt.rcParams.update({"font.size": font_size})
+
+    axs[0, 0].text(-0.2, 1.05, "(a)", transform=axs[0, 0].transAxes, fontsize=caption_font_size, va='top', ha='right')
+    axs[0, 1].text(-0.2, 1.05, "(b)", transform=axs[0, 1].transAxes, fontsize=caption_font_size, va='top', ha='right')
+    axs[1, 0].text(-0.2, 1.05, "(c)", transform=axs[1, 0].transAxes, fontsize=caption_font_size, va='top', ha='right')
+    axs[1, 1].text(-0.2, 1.05, "(d)", transform=axs[1, 1].transAxes, fontsize=caption_font_size, va='top', ha='right')
 
     axs[0, 0].plot(frames, n_clusters)
     axs[0, 0].axhline(avg_n_aggs, linestyle="--")
-    axs[0, 0].set_ylabel("Num. of aggregates")
-    axs[0, 0].set_xlabel("Frame")
+    axs[0, 0].set_ylabel("Num. of aggregates", fontsize=font_size)
+    axs[0, 0].set_xlabel("Frame", fontsize=font_size)
+    # axs[0, 0].set_ylim([4, 6])
     axs[0, 0].yaxis.set_major_locator(MaxNLocator(integer=True))
 
     xticklabels = [f"{sizes[i]}" for i in range(len(sizes))]
     axs[0, 1].bar(xticklabels, freq, width=0.8)
-    axs[0, 1].set_ylabel("Avg. num. per snapshot")
-    axs[0, 1].set_xlabel("Aggregate size")
-    axs[0, 1].tick_params("x", labelrotation=60)
+    axs[0, 1].set_ylabel("Avg. num. per snapshot", fontsize=font_size)
+    axs[0, 1].set_xlabel("Aggregate size", fontsize=font_size)
+    axs[0, 1].tick_params("x", labelrotation=60, labelsize=font_size)
 
     xticklabels = [f"{k}" for k in prob_by_size.keys()]
     axs[1, 0].bar(xticklabels, prob_by_size.values(), width=0.8)
-    axs[1, 0].set_ylabel("Prob.")
-    axs[1, 0].set_xlabel("Aggregate size")
-    axs[1, 0].tick_params("x", labelrotation=60)
+    axs[1, 0].set_ylabel("Prob.", fontsize=font_size)
+    axs[1, 0].set_xlabel("Aggregate size", fontsize=font_size)
+    axs[1, 0].tick_params("x", labelrotation=60, labelsize=font_size)
 
     xticklabels = [f"{k}" for k in prob_mol_size.keys()]
     axs[1, 1].bar(xticklabels, prob_mol_size.values(), width=0.8)
-    axs[1, 1].set_ylabel("Prob. molecule in agg.")
-    axs[1, 1].set_xlabel("Aggregate size")
-    axs[1, 1].tick_params("x", labelrotation=60)
+    axs[1, 1].set_ylabel("Prob. molecule in agg.", fontsize=font_size)
+    axs[1, 1].set_xlabel("Aggregate size", fontsize=font_size)
+    axs[1, 1].tick_params("x", labelrotation=60, labelsize=font_size)
+
+    axs[0, 0].tick_params(axis='both', which='major', labelsize=font_size)
+    axs[0, 1].tick_params(axis='both', which='major', labelsize=font_size)
+    axs[1, 0].tick_params(axis='both', which='major', labelsize=font_size)
+    axs[1, 1].tick_params(axis='both', which='major', labelsize=font_size)
 
     plt.tight_layout()
     plt.savefig("summary_clustering.pdf", bbox_inches="tight")
@@ -351,13 +371,26 @@ def aggregates_clustering(
         default=(8, 6),
         help="two integers to define the size of the summary figure (default = 8 6)",
     )
+    parser.add_argument(
+        "--font-size",
+        type=int,
+        default=12,
+        help="font size for the summary figure (default = 12)",
+    )
+    parser.add_argument(
+        "--caption-font-size",
+        type=int,
+        default=13,
+        help="font size for the captions of summary figure (default = 13)",
+    )
     parser.add_argument(
         "--traj-in-memory",
         action="store_true",
         default=False,
         help="load the whole trajectory in memory with MDAanalysis",
     )
 
+    argcomplete.autocomplete(parser)
     args = parser.parse_args()
 
     if os.path.splitext(args.h5md_file)[1].lower() == ".h5":
@@ -404,5 +437,7 @@ def aggregates_clustering(
             args.plot_dendrograms,
             args.traj_in_memory,
             args.save_solvent,
+            args.font_size,
+            args.caption_font_size,
             tuple(args.summary_fig_size),
         )