{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import h5py\n", "from datasets import load_dataset\n", "import os" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "investigating one h5 file" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "h5_file = h5py.File(\"data/M3T9R588C3352VB152.h5\",'r' )\n", "h5_file.keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "showing all data labels" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "labels:\t\t\n", "signals_machine: \t\n", "signals_sensor: \t\n" ] } ], "source": [ "labels = h5_file['labels']\t\n", "signals_machine = h5_file['signals_machine']\n", "signals_sensor = h5_file['signals_sensor']\n", "\n", "print(f\"labels:\t\\t{labels.keys()}\")\n", "print(f\"signals_machine: \\t{signals_machine.keys()}\")\n", "print(f\"signals_sensor: \\t{signals_sensor.keys()}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "loading data using the python script" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "24920d593edf4677b685c05a34266ee6", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Generating train split: 0 examples [00:00, ? examples/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9dea1aaef7684d3cb290adbde4bf7e11", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Generating test split: 0 examples [00:00, ? examples/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5f3333148f2d4d39ab96833953e50d22", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Generating validation split: 0 examples [00:00, ? examples/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "DatasetDict({\n", " train: Dataset({\n", " features: ['cumulated_tool_contact_time', 'machine', 'run', 'tool', 'wear', 'position_control_deviation_axis_x', 'position_control_deviation_axis_y', 'time_machine', 'tool_position_x', 'tool_position_y', 'tool_position_z', 'torque_axis_x', 'torque_axis_y', 'torque_axis_z', 'torque_spindle', 'force_sensor_x', 'force_sensor_y', 'force_sensor_z', 'time_sensor'],\n", " num_rows: 5\n", " })\n", " test: Dataset({\n", " features: ['cumulated_tool_contact_time', 'machine', 'run', 'tool', 'wear', 'position_control_deviation_axis_x', 'position_control_deviation_axis_y', 'time_machine', 'tool_position_x', 'tool_position_y', 'tool_position_z', 'torque_axis_x', 'torque_axis_y', 'torque_axis_z', 'torque_spindle', 'force_sensor_x', 'force_sensor_y', 'force_sensor_z', 'time_sensor'],\n", " num_rows: 2\n", " })\n", " validation: Dataset({\n", " features: ['cumulated_tool_contact_time', 'machine', 'run', 'tool', 'wear', 'position_control_deviation_axis_x', 'position_control_deviation_axis_y', 'time_machine', 'tool_position_x', 'tool_position_y', 'tool_position_z', 'torque_axis_x', 'torque_axis_y', 'torque_axis_z', 'torque_spindle', 'force_sensor_x', 'force_sensor_y', 'force_sensor_z', 'time_sensor'],\n", " num_rows: 2\n", " })\n", "})" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset = load_dataset(\"milling_LUH_data.py\", trust_remote_code=True)\n", "dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "loading data frome hugging face" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "21a3ab53324747e5afb3348b9d13e32b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading builder script: 0%| | 0.00/7.07k [00:00