Vis timeline

.github/workflows/unit-tests.yaml

@@ -12,9 +12,9 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest]
-        python-version: ["3.7", "3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
         include:
-        - python-version: "3.6"
+        - python-version: "3.7"
           os: ubuntu-20.04
 
     steps:
@@ -26,7 +26,7 @@ jobs:
       - name: Install Python packages
         run: |
           pip install --upgrade pip
-          pip install --upgrade numpy pandas pytest otf2
+          pip install --upgrade numpy pandas pytest otf2 bokeh datashader
 
       - name: Lint and format check with flake8 and black
         if: ${{ matrix.python-version == 3.9 }}
@@ -54,7 +54,7 @@ jobs:
       - name: Install Python packages
         run: |
           pip install --upgrade pip
-          pip install --upgrade numpy pandas pytest otf2
+          pip install --upgrade numpy pandas pytest otf2 bokeh datashader
 
       - name: Basic test with pytest
         run: |

.gitignore

@@ -2,3 +2,4 @@
 .cache
 .pytest_cache
 .ipynb_checkpoints
+env

docs/examples/vis.ipynb

@@ -0,0 +1,90 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e5841460",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "import sys\n",
+    "\n",
+    "sys.path.append(\"../../\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "da5ebec2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pipit as pp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7070057d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ping_pong = pp.Trace.from_otf2(\"../../pipit/tests/data/ping-pong-otf2\")\n",
+    "ping_pong"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2476f1f3-a6cf-49f2-8e3e-0e004f088504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ping_pong.plot_comm_matrix()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a4aff933",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ping_pong.plot_message_histogram()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d7318b31-b9bf-4a28-ac44-b75614d2ddf4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ping_pong.plot_timeline()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

pipit/readers/otf2_reader.py

@@ -316,9 +316,9 @@ def events_reader(self, rank_size):
                                 if value is not None and key != "time":
                                     # uses field_to_val to convert all data types
                                     # and ensure that there are no pickling errors
-                                    attributes_dict[
-                                        self.field_to_val(key)
-                                    ] = self.handle_data(value)
+                                    attributes_dict[self.field_to_val(key)] = (
+                                        self.handle_data(value)
+                                    )
                             event_attributes.append(attributes_dict)
                         else:
                             # nan attributes for leave rows

pipit/readers/projections_reader.py

@@ -513,10 +513,10 @@ def _read_log_file(self, rank_size) -> pd.DataFrame:
                     pe = int(line_arr[5])
                     msglen = int(line_arr[6])
                     send_time = int(line_arr[7]) * 1000
-                    numPEs = int(line_arr[8])
-                    destPEs = []
-                    for i in (0, numPEs):
-                        destPEs.append(int(line_arr[9 + i]))
+                    num_procs = int(line_arr[8])
+                    dest_procs = []
+                    for i in (0, num_procs):
+                        dest_procs.append(int(line_arr[9 + i]))
 
                     details = {
                         "From PE": pe,
@@ -525,7 +525,7 @@ def _read_log_file(self, rank_size) -> pd.DataFrame:
                         "Message Length": msglen,
                         "Event ID": event,
                         "Send Time": send_time,
-                        "Destinatopn PEs": destPEs,
+                        "Destinatopn PEs": dest_procs,
                     }
 
                     _add_to_trace_dict(
@@ -534,7 +534,7 @@ def _read_log_file(self, rank_size) -> pd.DataFrame:
                         "Instant",
                         time,
                         pe_num,
-                        "To " + str(numPEs) + "processors",
+                        "To " + str(num_procs) + "processors",
                     )
 
                 # Processing of chare (i.e. execution) ?