Updates notebooks.

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

upload/upload_layer_frames.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pickle\n",
+    "\n",
+    "from datasets import Dataset, Split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"../../NIST-In-Situ-IN625-LPBF-Overhangs/layer/base/1.pkl\", \"rb\") as f:\n",
+    "    layer = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "build_time (3, 377)\n",
+      "build_time (377, 3)\n",
+      "raw_frame_number (1, 377)\n",
+      "raw_frame_number (377, 1)\n",
+      "resolution [[49.8]\n",
+      " [33.5]]\n",
+      "resolution [49.8 33.5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "layer_transposed = {}\n",
+    "for key, value in layer.items():\n",
+    "    if (key in [\"build_time\", \"raw_frame_number\"]):\n",
+    "        print(key, value.shape)\n",
+    "        value_transposed = value.transpose(1, 0)\n",
+    "        print(key, value_transposed.shape)\n",
+    "        if (key == \"raw_frame_number\"):\n",
+    "            layer_transposed[key] = value_transposed.flatten()\n",
+    "        else:\n",
+    "            layer_transposed[key] = value_transposed\n",
+    "    elif (key == \"resolution\"):\n",
+    "        print(key, value)\n",
+    "        value_flatten = value.flatten()\n",
+    "        print(key, value_flatten)\n",
+    "        layer_transposed[key] = value_flatten\n",
+    "    else:\n",
+    "        layer_transposed[key] = value\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(377, 126, 360)\n",
+      "377\n"
+     ]
+    }
+   ],
+   "source": [
+    "frames = layer_transposed[\"radiant_temp\"]\n",
+    "print(frames.shape)\n",
+    "print(len(frames))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'folder_layer_range': '001-005', 'part': 'OverhangPart', 'part_section': 'BASE', 'process': 'LPBFthermography', 'source': 'NIST', 'supports': 'N/A', 'layer_number': 1, 'build_time': array([0., 0., 0.], dtype=float32), 'contact_email': 'jarred.heigel@nist.gov', 'file_name': '20180801_OverhangStudy_Layer01.mat', 'hatch_spacing': 100, 'laser_power': 195, 'layer_thickness': 20, 'material': 'IN625', 'radiant_temp': array([[0, 0, 0, ..., 0, 0, 0],\n",
+      "       [0, 0, 0, ..., 0, 0, 0],\n",
+      "       [0, 0, 0, ..., 0, 0, 0],\n",
+      "       ...,\n",
+      "       [0, 0, 0, ..., 0, 0, 0],\n",
+      "       [0, 0, 0, ..., 0, 0, 0],\n",
+      "       [0, 0, 0, ..., 0, 0, 0]], dtype=uint16), 'raw_frame_number': 5024, 'resolution': array([49.8, 33.5], dtype=float32), 's_hvariable__a': 2.655, 's_hvariable__b': -800.7, 's_hvariable__c': 1940000.0, 'scan_speed': 800, 'website': 'nist.gov/el/lpbf-thermography/3D-part-builds/OverhangPart-IN625'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "frames_list = []\n",
+    "for frame_index, frame in enumerate(frames):\n",
+    "    frame_dict = {}\n",
+    "    for key, value in layer_transposed.items():\n",
+    "        if (key in [\"radiant_temp\", \"build_time\", \"raw_frame_number\"]):\n",
+    "            frame_dict[key] = value[frame_index]\n",
+    "        else:\n",
+    "            frame_dict[key] = value\n",
+    "    \n",
+    "    frames_list.append(frame_dict)\n",
+    "\n",
+    "print(frames_list[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = Dataset.from_list(frames_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Creating parquet from Arrow format: 100%|██████████| 1/1 [00:00<00:00,  1.45ba/s]\n",
+      "Uploading the dataset shards: 100%|██████████| 1/1 [00:01<00:00,  1.33s/it]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "CommitInfo(commit_url='https://huggingface.co/datasets/ppak10/Melt-Pool-Thermal-Images/commit/7012f060899646abef1d05262df89ba69167e4b7', commit_message='Upload dataset', commit_description='', oid='7012f060899646abef1d05262df89ba69167e4b7', pr_url=None, pr_revision=None, pr_num=None)"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.push_to_hub(\n",
+    "    \"ppak10/Melt-Pool-Thermal-Images\",\n",
+    "    config_name = \"nist_overhangs_layer_1\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(layer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "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": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

upload/upload_part_section_frames.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/media/ppak/Storage/HuggingFace/Datasets/Melt-Pool-Thermal-Images/venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import os\n",
+    "import pickle\n",
+    "import re\n",
+    "\n",
+    "from datasets import Dataset, concatenate_datasets\n",
+    "from tqdm import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dir = \"../../NIST-In-Situ-IN625-LPBF-Overhangs/layer/base/\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def numerical_sort(value):\n",
+    "    # Extract numerical part from the string\n",
+    "    numbers = re.compile(r'(\\d+)')\n",
+    "    parts = numbers.split(value)\n",
+    "    parts[1::2] = map(int, parts[1::2])\n",
+    "    return parts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 99/99 [03:09<00:00,  1.91s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset = None\n",
+    "layer_frames_list = []\n",
+    "# for layer_file in sorted(os.listdir(data_dir), key=numerical_sort)[0:2]:\n",
+    "for layer_file in tqdm(sorted(os.listdir(data_dir), key=numerical_sort)):\n",
+    "    with open(f\"{data_dir}/{layer_file}\", \"rb\") as f:\n",
+    "        layer = pickle.load(f)\n",
+    "\n",
+    "    # Transpose specific layer values\n",
+    "    layer_transposed = {}\n",
+    "    for key, value in layer.items():\n",
+    "        if (key in [\"build_time\", \"raw_frame_number\"]):\n",
+    "            # print(key, value.shape)\n",
+    "            value_transposed = value.transpose(1, 0)\n",
+    "            # print(key, value_transposed.shape)\n",
+    "            if (key == \"raw_frame_number\"):\n",
+    "                layer_transposed[key] = value_transposed.flatten()\n",
+    "            else:\n",
+    "                layer_transposed[key] = value_transposed\n",
+    "        elif (key == \"resolution\"):\n",
+    "            # print(key, value)\n",
+    "            value_flatten = value.flatten()\n",
+    "            # print(key, value_flatten)\n",
+    "            layer_transposed[key] = value_flatten\n",
+    "        else:\n",
+    "            layer_transposed[key] = value\n",
+    "\n",
+    "    frames_list = []\n",
+    "    for frame_index, frame in enumerate(layer_transposed[\"radiant_temp\"]):\n",
+    "        frame_dict = {}\n",
+    "        frame_dict[\"frame_index\"] = frame_index\n",
+    "        for key, value in layer_transposed.items():\n",
+    "            if (key in [\"radiant_temp\", \"build_time\", \"raw_frame_number\"]):\n",
+    "                frame_dict[key] = value[frame_index]\n",
+    "            else:\n",
+    "                frame_dict[key] = value\n",
+    "        \n",
+    "        frames_list.append(frame_dict)\n",
+    "    \n",
+    "    layer_dataset = Dataset.from_list(frames_list)\n",
+    "    if dataset == None:\n",
+    "        dataset = layer_dataset\n",
+    "    else:\n",
+    "        dataset = concatenate_datasets([dataset, layer_dataset])\n",
+    "\n",
+    "    # layer_frames_list += frames_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Creating parquet from Arrow format: 100%|██████████| 6/6 [00:07<00:00,  1.25s/ba]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 6/6 [00:07<00:00,  1.18s/ba]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 6/6 [00:07<00:00,  1.25s/ba]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 6/6 [00:07<00:00,  1.30s/ba]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 6/6 [00:08<00:00,  1.34s/ba]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 6/6 [00:06<00:00,  1.10s/ba]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 6/6 [00:09<00:00,  1.52s/ba]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 6/6 [00:07<00:00,  1.32s/ba]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 6/6 [00:06<00:00,  1.03s/ba]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 6/6 [00:06<00:00,  1.04s/ba]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 6/6 [00:07<00:00,  1.25s/ba]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 6/6 [00:07<00:00,  1.22s/ba]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 6/6 [00:08<00:00,  1.45s/ba]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 6/6 [00:10<00:00,  1.71s/ba]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 6/6 [00:07<00:00,  1.17s/ba]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 6/6 [00:08<00:00,  1.37s/ba]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 6/6 [00:07<00:00,  1.26s/ba]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 6/6 [00:08<00:00,  1.46s/ba]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 6/6 [00:06<00:00,  1.08s/ba]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 6/6 [00:07<00:00,  1.32s/ba]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 6/6 [00:09<00:00,  1.61s/ba]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 6/6 [00:08<00:00,  1.37s/ba]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 6/6 [00:07<00:00,  1.28s/ba]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 6/6 [00:08<00:00,  1.43s/ba]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 6/6 [00:09<00:00,  1.65s/ba]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 6/6 [00:08<00:00,  1.49s/ba]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 6/6 [00:09<00:00,  1.57s/ba]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 6/6 [00:06<00:00,  1.12s/ba]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 6/6 [00:07<00:00,  1.25s/ba]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 6/6 [00:07<00:00,  1.31s/ba]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 6/6 [00:07<00:00,  1.18s/ba]\n",
+      "Uploading the dataset shards: 100%|██████████| 31/31 [05:00<00:00,  9.70s/it]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "CommitInfo(commit_url='https://huggingface.co/datasets/ppak10/Melt-Pool-Thermal-Images/commit/3252ef78aceb07c7bbea05c43eda0f5c7070f869', commit_message='Upload dataset', commit_description='', oid='3252ef78aceb07c7bbea05c43eda0f5c7070f869', pr_url=None, pr_revision=None, pr_num=None)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.push_to_hub(\n",
+    "    \"ppak10/Melt-Pool-Thermal-Images\",\n",
+    "    config_name = \"nist_overhangs_base_frame_data\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "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": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}