From 86445bab0c40d6490ecc3ce20665eb3949124097 Mon Sep 17 00:00:00 2001 From: Alberto Soutullo Date: Wed, 22 May 2024 13:02:24 +0200 Subject: [PATCH] Alberto/graph visualization into main (#25) * WIP: Create plot using qt5 in jupyter. Select from first timestamp to given one, and message hash. * WIP: First approach efficiently replot subgraph saving positions * WIP: Maintain ax limits between plots for easier visualization * WIP: Plot elapsed time on edges with msg hash * WIP: Correctly calculate elapsed time based on msg hash. * WIP: Change multiindex order. Now dropdown only shows timestamps related to msg hash * WIP: Moved functions to external files. Figure crashes with weird behavior. * WIP: Fix Python crashing with jupyter backend --- src/mesh_analysis/tracers/waku_tracer.py | 3 +- src/mesh_analysis/visualizer/__init__.py | 0 src/mesh_analysis/visualizer/visualizer.ipynb | 254 ++++++++++++++++++ .../visualizer/waku_visualizer.py | 77 ++++++ 4 files changed, 333 insertions(+), 1 deletion(-) create mode 100644 src/mesh_analysis/visualizer/__init__.py create mode 100644 src/mesh_analysis/visualizer/visualizer.ipynb create mode 100644 src/mesh_analysis/visualizer/waku_visualizer.py diff --git a/src/mesh_analysis/tracers/waku_tracer.py b/src/mesh_analysis/tracers/waku_tracer.py index bf40f9a..3096dd9 100644 --- a/src/mesh_analysis/tracers/waku_tracer.py +++ b/src/mesh_analysis/tracers/waku_tracer.py @@ -30,7 +30,8 @@ class WakuTracer(MessageTracer): df = pd.DataFrame(res, columns=['timestamp', 'msg_hash', 'sender_peer_id', 'receiver_peer_id']) df['receiver_peer_id'] = df['receiver_peer_id'].apply(lambda x: x.split('/')[-1]) - df.set_index(['timestamp', 'msg_hash'], inplace=True) + df['timestamp'] = pd.to_datetime(df['timestamp']) + df.set_index(['msg_hash', 'timestamp'], inplace=True) df.sort_index(inplace=True) return df diff --git a/src/mesh_analysis/visualizer/__init__.py b/src/mesh_analysis/visualizer/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/mesh_analysis/visualizer/visualizer.ipynb b/src/mesh_analysis/visualizer/visualizer.ipynb new file mode 100644 index 0000000..448e870 --- /dev/null +++ b/src/mesh_analysis/visualizer/visualizer.ipynb @@ -0,0 +1,254 @@ +{ + "cells": [ + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-05-17T15:35:32.591535Z", + "start_time": "2024-05-17T15:35:31.824829Z" + } + }, + "cell_type": "code", + "source": [ + "# Python Imports\n", + "import ipywidgets as widgets\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Project Imports\n", + "from src.mesh_analysis.readers.file_reader import FileReader\n", + "from src.mesh_analysis.tracers.waku_tracer import WakuTracer\n", + "from src.mesh_analysis.visualizer.waku_visualizer import add_elapsed_time\n", + "from src.mesh_analysis.visualizer.waku_visualizer import prepare_dropdowns\n", + "from src.mesh_analysis.visualizer.waku_visualizer import get_node_position\n", + "from src.mesh_analysis.visualizer.waku_visualizer import display_msg_trace\n" + ], + "id": "b364992afb5a097b", + "outputs": [], + "execution_count": 1 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-05-17T15:35:33.125098Z", + "start_time": "2024-05-17T15:35:32.592554Z" + } + }, + "cell_type": "code", + "source": [ + "tracer = WakuTracer()\n", + "reader = FileReader(\"../../../test/log_test/\", tracer)\n", + "\n", + "df = reader.read()\n", + "df = add_elapsed_time(df)" + ], + "id": "initial_id", + "outputs": [], + "execution_count": 2 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-05-17T15:35:33.134743Z", + "start_time": "2024-05-17T15:35:33.126102Z" + } + }, + "cell_type": "code", + "source": "df", + "id": "43e4f1618d751b7d", + "outputs": [ + { + "data": { + "text/plain": [ + " sender_peer_id receiver_peer_id \\\n", + "msg_hash timestamp \n", + "0x1 2024-04-22 14:06:58.001000+00:00 A B \n", + " 2024-04-22 14:06:58.002000+00:00 B D \n", + " 2024-04-22 14:06:58.003000+00:00 A C \n", + " 2024-04-22 14:06:58.003000+00:00 D E \n", + " 2024-04-22 14:06:58.006000+00:00 A F \n", + "0x2 2024-04-22 14:06:59.001000+00:00 A B \n", + "\n", + " elapsed_time \n", + "msg_hash timestamp \n", + "0x1 2024-04-22 14:06:58.001000+00:00 0 \n", + " 2024-04-22 14:06:58.002000+00:00 1 \n", + " 2024-04-22 14:06:58.003000+00:00 2 \n", + " 2024-04-22 14:06:58.003000+00:00 2 \n", + " 2024-04-22 14:06:58.006000+00:00 5 \n", + "0x2 2024-04-22 14:06:59.001000+00:00 0 " + ], + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "interactive(children=(SelectMultiple(description='Msg hash', layout=Layout(height='100px', width='100%'), opti…" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "38f07c0d8e664e75b7464183cbc4c3e1" + } + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 4 + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "", + "id": "f057ce5d030cc16f" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/mesh_analysis/visualizer/waku_visualizer.py b/src/mesh_analysis/visualizer/waku_visualizer.py new file mode 100644 index 0000000..57360a0 --- /dev/null +++ b/src/mesh_analysis/visualizer/waku_visualizer.py @@ -0,0 +1,77 @@ +import pandas as pd +import networkx as nx +import ipywidgets as widgets +import matplotlib.pyplot as plt +from datetime import timedelta + + +def add_elapsed_time(df: pd.DataFrame): + first_timestamps = df.groupby('msg_hash').apply( + lambda x: x.index.get_level_values('timestamp').min()) + df['elapsed_time'] = df.index.get_level_values('timestamp') - df.index.get_level_values( + 'msg_hash').map(first_timestamps) + df['elapsed_time'] = df['elapsed_time'] // timedelta(milliseconds=1) + + return df + + +def prepare_dropdowns(dataf: pd.DataFrame): + msg_dropdown = widgets.SelectMultiple( + options=dataf.index.get_level_values(0).drop_duplicates(), + description='Msg hash', + disabled=False, + layout={'height': '100px', 'width': '100%'}) + + timestamp_dropdown = widgets.SelectMultiple( + options=[], + description='Timestamp', + disabled=False, + layout={'height': '100px', 'width': '100%'}) + + def update_timestamps(change): + selected_hashes = change['new'] + timestamps = dataf.loc[selected_hashes[0]].index.get_level_values( + 'timestamp').drop_duplicates() + timestamp_dropdown.options = timestamps + + msg_dropdown.observe(update_timestamps, names='value') + + return msg_dropdown, timestamp_dropdown + + +def get_node_position(df: pd.DataFrame): + data_prepared = df.reset_index() + complete_graph = nx.from_pandas_edgelist(data_prepared, 'sender_peer_id', 'receiver_peer_id', + edge_attr=['timestamp', 'msg_hash', 'elapsed_time'], + create_using=nx.DiGraph) + g_pos_layout = nx.kamada_kawai_layout(complete_graph) + + return g_pos_layout + + +def display_msg_trace(x, y, g_pos_layout, data_, ax_): + # todo maintain limits between display and clear? + ax_.clear() + ax_.set_xlim(-2, 2) + ax_.set_ylim(-2, 2) + + data_ = data_[data_.index.get_level_values(0) == x[0]] + first_timestamp = data_.index.get_level_values(1)[0] + df_ = data_.loc[(x[0], first_timestamp,):(x[0], y[0])] + df_.reset_index(inplace=True) + + selected_graph = nx.from_pandas_edgelist(df_, 'sender_peer_id', 'receiver_peer_id', + edge_attr=['timestamp', 'msg_hash', + 'elapsed_time'], + create_using=nx.DiGraph) + + g_pos_layout_selected = {key: g_pos_layout[key] for key in list(selected_graph.nodes)} + + nx.draw(selected_graph, g_pos_layout_selected, ax=ax_, with_labels=True) + edge_labels = {} + for u, v, data in selected_graph.edges(data=True): + edge_labels[u, v] = f"{data['elapsed_time']}ms" + + nx.draw_networkx_edge_labels(selected_graph, g_pos_layout_selected, edge_labels=edge_labels, + ax=ax_) + plt.show()