add files

.gitattributes

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+plasticc_test_lightcurves_11.csv.jsonl filter=lfs diff=lfs merge=lfs -text
+plasticc_test_lightcurves_08.csv.jsonl filter=lfs diff=lfs merge=lfs -text
+plasticc_test_lightcurves_02.csv.jsonl filter=lfs diff=lfs merge=lfs -text
+plasticc_test_lightcurves_03.csv.jsonl filter=lfs diff=lfs merge=lfs -text
+plasticc_test_lightcurves_04.csv.jsonl filter=lfs diff=lfs merge=lfs -text
+plasticc_test_lightcurves_05.csv.jsonl filter=lfs diff=lfs merge=lfs -text
+plasticc_test_lightcurves_06.csv.jsonl filter=lfs diff=lfs merge=lfs -text
+plasticc_test_lightcurves_07.csv.jsonl filter=lfs diff=lfs merge=lfs -text
+plasticc_test_lightcurves_09.csv.jsonl filter=lfs diff=lfs merge=lfs -text
+plasticc_test_lightcurves_01.csv.jsonl filter=lfs diff=lfs merge=lfs -text
+plasticc_train_lightcurves.csv.jsonl filter=lfs diff=lfs merge=lfs -text
+plasticc_test_lightcurves_10.csv.jsonl filter=lfs diff=lfs merge=lfs -text

just_train_set.txt

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+/pscratch/sd/h/helenqu/plasticc/raw/plasticc_raw_examples/plasticc_train_lightcurves.csv.jsonl

plasticc_raw_examples.py

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+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# TODO: Address all TODOs and remove all explanatory comments
+"""TODO: Add a description here."""
+
+import pandas as pd
+import numpy as np
+from pathlib import Path
+import jsonlines
+from connect_later.split_dataset_into_files import split_dataset_into_files
+import torch
+import pdb
+
+import datasets
+
+DATASET_PATH = "/pscratch/sd/h/helenqu/plasticc/raw/plasticc_raw_examples"
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {A great new dataset},
+author={huggingface, Inc.
+},
+year={2020}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+"""
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class NewDataset(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        # if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+        features = datasets.Features(
+            {
+                "objid": datasets.Value("int32"),
+                "times_wv": datasets.Array2D(shape=(300, 2), dtype='float64'), # ith row is [time, central wv of band]
+                "target": datasets.Array2D(shape=(300, 2), dtype='float64'), # the time series data, ith row is [flux, flux_err]
+            }
+        )
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+        )
+
+    def _split_generators(self, dl_manager):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        dataset_path = Path(DATASET_PATH)
+        # if not (dataset_path / 'train.csv').exists():
+        #     print('Splitting dataset into files...')
+        #     split_dataset_into_files(dataset_path, "prepr*csv", 0.8, fraction=0.15, required_paths=[dataset_path / "orig_train_set.csv"]) # full dataset size is 256G, trying to keep it under 40G for now since that's the size of the GPU mem
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": self.config.data_files['train'] if self.config.data_files is not None else dataset_path.glob('*.jsonl'),
+                    "split": "train",
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        # 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.
+        for path in filepath:
+            with jsonlines.open(path) as reader:
+                for obj in reader:
+                    yield int(obj['object_id']), {
+                        "objid": int(obj['object_id']),
+                        "times_wv": obj['times_wv'],
+                        # "target": np.transpose(np.array(obj['lightcurve'], dtype='float64')),
+                        "target": obj['lightcurve'],
+                    }

plasticc_test_lightcurves_01.csv.jsonl

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plasticc_train_lightcurves.csv.jsonl

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single_test_file.txt

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