readme and cleaning code

Lenze_dataset.py

@@ -19,43 +19,16 @@ _HOMEPAGE = "https://zenodo.org/records/11162448"
 
 _LICENSE = ""
 
-# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-# _URLS = {
-#     "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
-#     "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
-# }
-
-
-# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
 class LenzeDataset(datasets.GeneratorBasedBuilder):
 
 	VERSION = datasets.Version("1.0.0")
 
-	# This is an example of a dataset with multiple configurations.
-	# If you don't want/need to define several sub-sets in your dataset,
-	# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
-
-	# If you need to make complex sub-parts in the datasets with configurable options
-	# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-	# BUILDER_CONFIG_CLASS = MyBuilderConfig
-
-	# You will be able to load one or the other configurations in the following list with
-	# data = datasets.load_dataset('my_dataset', 'first_domain')
-	# data = datasets.load_dataset('my_dataset', 'second_domain')
 	folders = os.listdir("data") 
 	BUILDER_CONFIGS = []
 	for folder in folders:
 		BUILDER_CONFIGS.append(datasets.BuilderConfig(name=folder, version=VERSION))
 
-	
-
-	# BUILDER_CONFIGS = [
-	# 	datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"),
-	# 	datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
-	# ]
 
-	# DEFAULT_CONFIG_NAME = folders[0] # It's not mandatory to have a default configuration. Just use one if it make sense.
 
 	def _info(self):
 		features = datasets.Features(
@@ -121,8 +94,6 @@ class LenzeDataset(datasets.GeneratorBasedBuilder):
 
 	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
 	def _generate_examples(self, data_dir, id_start, id_end):
-		# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
-		# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
 
 		data_path = Path.Path(data_dir)	
 		meta_path = data_path / self.config.name / "Meta_Data.pickle"

README.md

@@ -0,0 +1,10 @@
+# overview
+- the purpose of this repository is to load a certain dataset
+- the dataset can be found in https://zenodo.org/records/11162448
+- this repository contains a small sample of data from the original dataset
+
+# how to load dataset
+- download the dataset from https://zenodo.org/records/11162448. the folder data should contain this data
+- define train, test and validation splits by redefining *id_start* and *id_end* in the loading script (Lenze_dataset.py)
+- you should define a config_name when calling the function *load_dataset*. the config_name is the name of a folder like H045_I_ccw_withoutLoad
+	example: *dataset = load_dataset("Lenze_dataset.py","H045_I_ccw_withoutLoad",trust_remote_code=True)*

notebook.ipynb

@@ -2,181 +2,25 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import os \n",
-    "import pathlib as Path"
+    "import pathlib as Path\n",
+    "from datasets import load_dataset"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  <thead>\n",
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-       "      <th></th>\n",
-       "      <th>Ch1</th>\n",
-       "      <th>Ch2</th>\n",
-       "      <th>Ch3</th>\n",
-       "      <th>Ch4</th>\n",
-       "      <th>Ch5</th>\n",
-       "      <th>Ch6</th>\n",
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-       "      <th>Ch8</th>\n",
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-       "      <th>2</th>\n",
-       "      <td>[-113038606.0, -113189450.0, -113324950.0, -11...</td>\n",
-       "      <td>[103214543.0, 102753899.0, 102391051.0, 102286...</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>[-113094566.0, -112948690.0, -112728272.0, -11...</td>\n",
-       "      <td>[102569207.0, 102932501.0, 103309646.0, 103446...</td>\n",
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-       "      <th>4</th>\n",
-       "      <td>[-111870924.0, -112523505.0, -113160018.0, -11...</td>\n",
-       "      <td>[102843637.0, 102631651.0, 102524970.0, 102459...</td>\n",
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-       "      <td>[351167548.0, 351167548.0, 351167548.0, 353956...</td>\n",
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-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>[-113644003.0, -113838059.0, -114077127.0, -11...</td>\n",
-       "      <td>[102545197.0, 102503508.0, 102137408.0, 102081...</td>\n",
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-       "    </tr>\n",
-       "  </tbody>\n",
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-       "</div>"
-      ],
-      "text/plain": [
-       "                                                 Ch1  \\\n",
-       "1  [-111321426.0, -111654495.0, -112009801.0, -11...   \n",
-       "2  [-113038606.0, -113189450.0, -113324950.0, -11...   \n",
-       "3  [-113094566.0, -112948690.0, -112728272.0, -11...   \n",
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-       "5  [-113644003.0, -113838059.0, -114077127.0, -11...   \n",
-       "\n",
-       "                                                 Ch2  \\\n",
-       "1  [103018081.0, 103132762.0, 103205819.0, 103230...   \n",
-       "2  [103214543.0, 102753899.0, 102391051.0, 102286...   \n",
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-       "\n",
-       "                                                 Ch3  \\\n",
-       "1  [151607336.0, 151918362.0, 152218710.0, 152309...   \n",
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-       "5  [152958508.0, 153071616.0, 153006696.0, 153096...   \n",
-       "\n",
-       "                                                 Ch4  \\\n",
-       "1  [2317.0, 2317.0, 2317.0, 2317.0, 2317.0, 2317....   \n",
-       "2  [2316.0, 2316.0, 2316.0, 2316.0, 2316.0, 2317....   \n",
-       "3  [2317.0, 2317.0, 2317.0, 2317.0, 2317.0, 2317....   \n",
-       "4  [2315.0, 2315.0, 2315.0, 2315.0, 2315.0, 2315....   \n",
-       "5  [2316.0, 2316.0, 2316.0, 2316.0, 2316.0, 2316....   \n",
-       "\n",
-       "                                                 Ch5  \\\n",
-       "1  [351167548.0, 351167548.0, 351167548.0, 351167...   \n",
-       "2  [351167548.0, 351167548.0, 353956162.0, 353956...   \n",
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-       "5  [351167548.0, 351167548.0, 353956162.0, 353956...   \n",
-       "\n",
-       "                                                 Ch6  \\\n",
-       "1  [-135673176.0, -138442132.0, -140947490.0, -14...   \n",
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-       "5  [-147935998.0, -149386470.0, -150441358.0, -15...   \n",
-       "\n",
-       "                                                 Ch7  \\\n",
-       "1  [9631514.0, 15436964.0, 21242286.0, 27047610.0...   \n",
-       "2  [-132467908.0, -134974806.0, -137349636.0, -13...   \n",
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-       "5  [40109746.0, 45783126.0, 51060680.0, 56602246....   \n",
-       "\n",
-       "                                                 Ch8  \n",
-       "1  [126522230.0, 123491662.0, 120329330.0, 116640...  \n",
-       "2  [294276.0, 5828356.0, 11494072.0, 17291554.0, ...  \n",
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-       "5  [107943786.0, 103859108.0, 99642792.0, 9516282...  "
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "folders = os.listdir(\"data\")\n",
-    "data_path = Path.Path(\"data\")\n",
-    "path = data_path / folders[0]\n",
-    "\n",
-    "meta_df = pd.read_pickle(path / \"Meta_Data.pickle\") \n",
-    "signal_df = pd.read_pickle(path / \"Signal_Data.pickle\")"
+    "# TODO make sure to add config_name (like H045_I_ccw_withoutLoad), which is the name of the folder in data folder\n",
+    "dataset = load_dataset(\"Lenze_dataset.py\",\"H045_I_ccw_withoutLoad\",trust_remote_code=True)\n",
+    "dataset"
    ]
   },
   {
@@ -185,6 +29,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# mapping for channels defined in the dataset\n",
     "Ch_Mapper = {\"Ch1\": \"Direct Current\",\n",
     "             \"Ch2\": \"Quadrature Current\",\n",
     "             \"Ch3\": \"Effective Current\",\n",
@@ -199,7 +44,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "creating a small dataset"
+    "creating a small dataset out of the original dataset"
    ]
   },
   {