diff --git a/.gitattributes b/.gitattributes index 55cab133643a2a73e083373d2106533678d0edd5..8055c9301bccc1997abc19b33726c5c3e96133ba 100644 --- a/.gitattributes +++ b/.gitattributes @@ -56,3 +56,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text # Video files - compressed *.mp4 filter=lfs diff=lfs merge=lfs -text *.webm filter=lfs diff=lfs merge=lfs -text +*.fits filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..bb58f88f4d47cf6dd68679e7ecba705194508c1e --- /dev/null +++ b/README.md @@ -0,0 +1,119 @@ +--- +license: cc-by-4.0 +pretty_name: Space-based (JWST) 3d data cubes +tags: +- astronomy +- compression +- images +dataset_info: + config_name: tiny + features: + - name: image + dtype: + array3_d: + shape: + - 2048 + - 2048 + dtype: uint8 + - name: ra + dtype: float64 + - name: dec + dtype: float64 + - name: pixscale + dtype: float64 + - name: ntimes + dtype: int64 + - name: image_id + dtype: string + splits: + - name: train + num_bytes: 100761802 + num_examples: 2 + - name: test + num_bytes: 75571313 + num_examples: 1 + download_size: 201496920 + dataset_size: 176333115 +--- + +# SBI-16-3D Dataset + +SBI-16-3D is a dataset which is part of the AstroCompress project. It contains data assembled from the James Webb Space Telescope (JWST). Describe data format + +# Usage + +You first need to install the `datasets` and `astropy` packages: + +```bash +pip install datasets astropy +``` + +There are two datasets: `tiny` and `full`, each with `train` and `test` splits. The `tiny` dataset has 2 4D images in the `train` and 1 in the `test`. The `full` dataset contains all the images in the `data/` directory. + + +## Local Use (RECOMMENDED) + +You can clone this repo and use directly without connecting to hf: + +```bash +git clone https://huggingface.co/datasets/AstroCompress/SBI-16-3D +``` + +```bash +git lfs pull +``` + +Then `cd SBI-16-3D` and start python like: + +```python +from datasets import load_dataset +import numpy +dataset = load_dataset("./SBI-16-3D.py", "tiny", data_dir="./data/", writer_batch_size=1, trust_remote_code=True) +ds = dataset.with_format("np", dtype=numpy.uint16) +``` + +Now you should be able to use the `ds` variable like: + +```python +ds["test"][0]["image"].shape # -> (5, 2048, 2048) +``` + +Note of course that it will take a long time to download and convert the images in the local cache for the `full` dataset. Afterward, the usage should be quick as the files are memory-mapped from disk. + + + +## Use from Huggingface Directly + +This method may only be an option when trying to access the "tiny" version of the dataset. + +To directly use from this data from Huggingface, you'll want to log in on the command line before starting python: + +```bash +huggingface-cli login +``` + +or + +``` +import huggingface_hub +huggingface_hub.login(token=token) +``` + +Then in your python script: + +```python +from datasets import load_dataset +import numpy +dataset = load_dataset("AstroCompress/SBI-16-3D", "tiny", writer_batch_size=1, trust_remote_code=True) +ds = dataset.with_format("np", columns=["image"], dtype=numpy.uint16) + +# or torch +import torch +dst = dataset.with_format("torch", columns=["image"], dtype=torch.uint16) + +# or pandas +dsp = dataset.with_format("pandas", columns=["image"], dtype=numpy.uint16) +``` + +## Utils scripts +Note that utils scripts such as `eval_baselines.py` must be run from the parent directory of `utils`, i.e. `python utils/eval_baselines.py`. diff --git a/SBI-16-3D.py b/SBI-16-3D.py new file mode 100644 index 0000000000000000000000000000000000000000..4804a7981f9bfb4e2dbd3cb456c7788fb1548f9a --- /dev/null +++ b/SBI-16-3D.py @@ -0,0 +1,259 @@ +import os +import random +from glob import glob +import json +from huggingface_hub import hf_hub_download +from tqdm import tqdm +import numpy as np + +from astropy.io import fits +from astropy.wcs import WCS +import datasets +from datasets import DownloadManager +from fsspec.core import url_to_fs + +_DESCRIPTION = ( + """SBI-16-3D is a dataset which is part of the AstroCompress project. """ + """It contains data assembled from the James Webb Space Telescope (JWST). """ + """Describe data format""" +) + +_HOMEPAGE = "https://google.github.io/AstroCompress" + +_LICENSE = "CC BY 4.0" + +_URL = "https://huggingface.co/datasets/AstroCompress/SBI-16-3D/resolve/main/" + +_URLS = { + "tiny": { + "train": "./splits/tiny_train.jsonl", + "test": "./splits/tiny_test.jsonl", + }, + "full": { + "train": "./splits/full_train.jsonl", + "test": "./splits/full_test.jsonl", + }, +} + +_REPO_ID = "AstroCompress/SBI-16-3D" + + +class SBI_16_3D(datasets.GeneratorBasedBuilder): + """SBI-16-3D Dataset""" + + VERSION = datasets.Version("1.0.3") + + BUILDER_CONFIGS = [ + datasets.BuilderConfig( + name="tiny", + version=VERSION, + description="A small subset of the data, to test downsteam workflows.", + ), + datasets.BuilderConfig( + name="full", + version=VERSION, + description="The full dataset", + ), + ] + + DEFAULT_CONFIG_NAME = "tiny" + + def __init__(self, **kwargs): + super().__init__(version=self.VERSION, **kwargs) + + def _info(self): + return datasets.DatasetInfo( + description=_DESCRIPTION, + features=datasets.Features( + { + "image": datasets.Array3D(shape=(None, 2048, 2048), dtype="uint16"), + "ra": datasets.Value("float64"), + "dec": datasets.Value("float64"), + "pixscale": datasets.Value("float64"), + "ntimes": datasets.Value("int64"), + "image_id": datasets.Value("string"), + } + ), + supervised_keys=None, + homepage=_HOMEPAGE, + license=_LICENSE, + citation="TBD", + ) + + def _split_generators(self, dl_manager: DownloadManager): + + ret = [] + base_path = dl_manager._base_path + locally_run = not base_path.startswith(datasets.config.HF_ENDPOINT) + _, path = url_to_fs(base_path) + + for split in ["train", "test"]: + if locally_run: + split_file_location = os.path.normpath( + os.path.join(path, _URLS[self.config.name][split]) + ) + split_file = dl_manager.download_and_extract(split_file_location) + else: + split_file = hf_hub_download( + repo_id=_REPO_ID, + filename=_URLS[self.config.name][split], + repo_type="dataset", + ) + with open(split_file, encoding="utf-8") as f: + data_filenames = [] + data_metadata = [] + for line in f: + item = json.loads(line) + data_filenames.append(item["image"]) + data_metadata.append( + { + "ra": item["ra"], + "dec": item["dec"], + "pixscale": item["pixscale"], + "ntimes": item["ntimes"], + "image_id": item["image_id"], + } + ) + if locally_run: + data_urls = [ + os.path.normpath(os.path.join(path, data_filename)) + for data_filename in data_filenames + ] + data_files = [ + dl_manager.download(data_url) for data_url in data_urls + ] + else: + data_urls = data_filenames + data_files = [ + hf_hub_download( + repo_id=_REPO_ID, filename=data_url, repo_type="dataset" + ) + for data_url in data_urls + ] + ret.append( + datasets.SplitGenerator( + name=( + datasets.Split.TRAIN + if split == "train" + else datasets.Split.TEST + ), + gen_kwargs={ + "filepaths": data_files, + "split_file": split_file, + "split": split, + "data_metadata": data_metadata, + }, + ), + ) + return ret + + def _generate_examples(self, filepaths, split_file, split, data_metadata): + """Generate GBI-16-4D examples""" + + for idx, (filepath, item) in enumerate(zip(filepaths, data_metadata)): + task_instance_key = f"{self.config.name}-{split}-{idx}" + with fits.open(filepath, memmap=False) as hdul: + # the first axis is integrations one, so we take the first element + # the second axis is the groups (time) axis and varies between images + image_data = hdul["SCI"].data[0, :, :, :] # .tolist() + yield task_instance_key, {**{"image": image_data}, **item} + + +def get_fits_footprint(fits_path): + """ + Process a FITS file to extract WCS information and calculate the footprint. + + Parameters: + fits_path (str): Path to the FITS file. + + Returns: + tuple: A tuple containing the WCS footprint coordinates. + """ + with fits.open(fits_path) as hdul: + hdul[1].data = hdul[1].data[0, 0] + wcs = WCS(hdul[1].header) + shape = sorted(tuple(wcs.pixel_shape))[:2] + footprint = wcs.calc_footprint(axes=shape) + coords = list(footprint.flatten()) + return coords + + +def calculate_pixel_scale(header): + """ + Calculate the pixel scale in arcseconds per pixel from a FITS header. + + Parameters: + header (astropy.io.fits.header.Header): The FITS header containing WCS information. + + Returns: + Mean of the pixel scales in x and y. + """ + + # Calculate the pixel scales in arcseconds per pixel + pixscale_x = header.get("CDELT1", np.nan) + pixscale_y = header.get("CDELT2", np.nan) + + return np.mean([pixscale_x, pixscale_y]) + + +def make_split_jsonl_files( + config_type="tiny", data_dir="./data", outdir="./splits", seed=42 +): + """ + Create jsonl files for the SBI-16-3D dataset. + + config_type: str, default="tiny" + The type of split to create. Options are "tiny" and "full". + data_dir: str, default="./data" + The directory where the FITS files are located. + outdir: str, default="./splits" + The directory where the jsonl files will be created. + seed: int, default=42 + The seed for the random split. + """ + random.seed(seed) + os.makedirs(outdir, exist_ok=True) + + fits_files = glob(os.path.join(data_dir, "*.fits")) + random.shuffle(fits_files) + if config_type == "tiny": + train_files = fits_files[:2] + test_files = fits_files[2:3] + elif config_type == "full": + split_idx = int(0.8 * len(fits_files)) + train_files = fits_files[:split_idx] + test_files = fits_files[split_idx:] + else: + raise ValueError("Unsupported config_type. Use 'tiny' or 'full'.") + + def create_jsonl(files, split_name): + output_file = os.path.join(outdir, f"{config_type}_{split_name}.jsonl") + with open(output_file, "w") as out_f: + for file in tqdm(files): + # print(file, flush=True, end="...") + with fits.open(file, memmap=False) as hdul: + image_id = os.path.basename(file).split(".fits")[0] + ra = hdul["SCI"].header.get("CRVAL1", 0) + dec = hdul["SCI"].header.get("CRVAL2", 0) + pixscale = calculate_pixel_scale(hdul["SCI"].header) + footprint = get_fits_footprint(file) + # get the number of groups per int + ntimes = hdul["SCI"].data.shape[1] + item = { + "image_id": image_id, + "image": file, + "ra": ra, + "dec": dec, + "pixscale": pixscale, + "ntimes": ntimes, + "footprint": footprint, + } + out_f.write(json.dumps(item) + "\n") + + create_jsonl(train_files, "train") + create_jsonl(test_files, "test") + + +if __name__ == "__main__": + make_split_jsonl_files("tiny") + make_split_jsonl_files("full") diff --git a/baseline_results_2d.csv 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"jw03368115001_02101_00008_nrcb4_uncal", "image": "./data/jw03368115001_02101_00008_nrcb4_uncal.fits", "ra": 138.38517267702798, "dec": -10.34188505176674, "pixscale": 8.702037499999995e-06, "ntimes": 6, "read_pattern": "RAPID", "footprint": [138.37542300347644, -10.350040674049035, 138.3934164997972, -10.351530528091551, 138.39492184407794, -10.3337291365046, 138.37692936076033, -10.332239365977268]} diff --git a/splits/tiny_test.jsonl b/splits/tiny_test.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..45c261145b237e72d17245eb0d254d02d4375a27 --- /dev/null +++ b/splits/tiny_test.jsonl @@ -0,0 +1 @@ +{"image_id": "jw01181006001_18201_00001_nrcb3_uncal", "image": "./data/jw01181006001_18201_00001_nrcb3_uncal.fits", "ra": 189.29522579682242, "dec": 62.170810657813895, "pixscale": 8.575886111111111e-06, "ntimes": 5, "read_pattern": "DEEP8", "footprint": [189.30240387763766, 62.182778659588344, 189.26965845442572, 62.17416743800172, 189.2880533944291, 62.15884228507122, 189.32078746079904, 62.167449168520186]} diff --git a/splits/tiny_train.jsonl b/splits/tiny_train.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..f8b00b58bada103aed0091babbf892951833f5b9 --- /dev/null +++ b/splits/tiny_train.jsonl @@ -0,0 +1,2 @@ +{"image_id": "jw02736001001_02105_00002_nrcb2_uncal", "image": "./data/jw02736001001_02105_00002_nrcb2_uncal.fits", "ra": 110.83192206560962, "dec": -73.43953078693673, "pixscale": 8.66568472222222e-06, "ntimes": 8, "read_pattern": "MEDIUM8", "footprint": [110.78883025365087, -73.43716349365775, 110.82367419417301, -73.45188632192357, 110.87502583164303, -73.44188922346261, 110.840157982959, -73.42717492804437]} +{"image_id": "jw02128002001_04201_00001_nrcb3_uncal", "image": "./data/jw02128002001_04201_00001_nrcb3_uncal.fits", "ra": 24.094268349401542, "dec": 30.671409367815368, "pixscale": 8.575886111111111e-06, "ntimes": 2, "read_pattern": "SHALLOW2", "footprint": [24.098127118715173, 30.683386215331247, 24.08038220809025, 30.674736549930312, 24.09041053675658, 30.659432406304237, 24.10815353404423, 30.668080709211786]} diff --git a/utils/__init__.py b/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..21a29ec0e60065cb27c27c5cf7604918f5e3a169 --- /dev/null +++ b/utils/__init__.py @@ -0,0 +1 @@ +from .create_splits import * \ No newline at end of file diff --git a/utils/create_splits.py b/utils/create_splits.py new file mode 100644 index 0000000000000000000000000000000000000000..420c9c16040065326444782a9de42e0864b460e7 --- /dev/null +++ b/utils/create_splits.py @@ -0,0 +1,106 @@ +import os +import random +from glob import glob +import json +from huggingface_hub import hf_hub_download +from tqdm import tqdm +import numpy as np + +from astropy.io import fits +from astropy.wcs import WCS +import datasets +from datasets import DownloadManager +from fsspec.core import url_to_fs + + +def get_fits_footprint(fits_path): + """ + Process a FITS file to extract WCS information and calculate the footprint. + + Parameters: + fits_path (str): Path to the FITS file. + + Returns: + tuple: A tuple containing the WCS footprint coordinates. + """ + with fits.open(fits_path) as hdul: + hdul[1].data = hdul[1].data[0, 0] + wcs = WCS(hdul[1].header) + shape = sorted(tuple(wcs.pixel_shape))[:2] + footprint = wcs.calc_footprint(axes=shape) + coords = list(footprint.flatten()) + return coords + + +def calculate_pixel_scale(header): + """ + Calculate the pixel scale in arcseconds per pixel from a FITS header. + + Parameters: + header (astropy.io.fits.header.Header): The FITS header containing WCS information. + + Returns: + Mean of the pixel scales in x and y. + """ + + # Calculate the pixel scales in arcseconds per pixel + pixscale_x = header.get('CDELT1', np.nan) + pixscale_y = header.get('CDELT2', np.nan) + + return np.mean([pixscale_x, pixscale_y]) + + +def make_split_jsonl_files(config_type="tiny", data_dir="./data", + outdir="./splits", seed=42): + """ + Create jsonl files for the SBI-16-3D dataset. + + config_type: str, default="tiny" + The type of split to create. Options are "tiny" and "full". + data_dir: str, default="./data" + The directory where the FITS files are located. + outdir: str, default="./splits" + The directory where the jsonl files will be created. + seed: int, default=42 + The seed for the random split. + """ + random.seed(seed) + os.makedirs(outdir, exist_ok=True) + + fits_files = glob(os.path.join(data_dir, "*.fits")) + random.shuffle(fits_files) + if config_type == "tiny": + train_files = fits_files[:2] + test_files = fits_files[2:3] + elif config_type == "full": + split_idx = int(0.8 * len(fits_files)) + train_files = fits_files[:split_idx] + test_files = fits_files[split_idx:] + else: + raise ValueError("Unsupported config_type. Use 'tiny' or 'full'.") + + def create_jsonl(files, split_name): + output_file = os.path.join(outdir, f"{config_type}_{split_name}.jsonl") + with open(output_file, "w") as out_f: + for file in tqdm(files): + #print(file, flush=True, end="...") + with fits.open(file, memmap=False) as hdul: + image_id = os.path.basename(file).split(".fits")[0] + ra = hdul["SCI"].header.get('CRVAL1', 0) + dec = hdul["SCI"].header.get('CRVAL2', 0) + pixscale = calculate_pixel_scale(hdul["SCI"].header) + footprint = get_fits_footprint(file) + read_pattern = hdul[0].header.get('READPATT', 0) + # get the number of groups per int + ntimes = hdul["SCI"].data.shape[1] + item = {"image_id": image_id, "image": file, "ra": ra, "dec": dec, + "pixscale": pixscale, "ntimes": ntimes, "read_pattern": read_pattern, "footprint": footprint} + out_f.write(json.dumps(item) + "\n") + + create_jsonl(train_files, "train") + create_jsonl(test_files, "test") + + +if __name__ == "__main__": + make_split_jsonl_files("tiny") + make_split_jsonl_files("full") \ No newline at end of file diff --git a/utils/eval_baselines.py b/utils/eval_baselines.py new file mode 100644 index 0000000000000000000000000000000000000000..9817324534404b5eab4e8d0b3b841ac924c6dbae --- /dev/null +++ b/utils/eval_baselines.py @@ -0,0 +1,144 @@ +""" +Runs several baseline compression algorithms and stores results for each FITS file in a csv. +This code is written functionality-only and cleaning it up is a TODO. +""" + + +import os +import re +from pathlib import Path +import argparse +import os.path +from astropy.io import fits +import numpy as np +from time import time +import pandas as pd +from tqdm import tqdm + +from astropy.io.fits import CompImageHDU +from imagecodecs import ( + jpeg2k_encode, + jpeg2k_decode, + jpegls_encode, + jpegls_decode, + jpegxl_encode, + jpegxl_decode, + rcomp_encode, + rcomp_decode, +) + +# Functions that require some preset parameters. All others default to lossless. + +jpegxl_encode_max_effort_preset = lambda x: jpegxl_encode(x, lossless=True, effort=9) +jpegxl_encode_preset = lambda x: jpegxl_encode(x, lossless=True) + +def find_matching_files(): + """ + Returns list of test set file paths. + """ + df = pd.read_json("./splits/full_test.jsonl", lines=True) + return list(df['image']) + +def benchmark_imagecodecs_compression_algos(arr, compression_type): + + encoder, decoder = ALL_CODECS[compression_type] + + write_start_time = time() + encoded = encoder(arr) + write_time = time() - write_start_time + + read_start_time = time() + if compression_type == "RICE": + decoded = decoder(encoded, shape=arr.shape, dtype=np.uint16) + else: + decoded = decoder(encoded) + read_time = time() - read_start_time + + assert np.array_equal(arr, decoded) + + buflength = len(encoded) + + return {compression_type + "_BPD": buflength / arr.size, + compression_type + "_WRITE_RUNTIME": write_time, + compression_type + "_READ_RUNTIME": read_time, + #compression_type + "_TILE_DIVISOR": np.nan, + } + +def main(dim): + + save_path = f"baseline_results_{dim}.csv" + + file_paths = find_matching_files() + + df = pd.DataFrame(columns=columns, index=[str(p) for p in file_paths]) + + print(f"Number of files to be tested: {len(file_paths)}") + + ct = 0 + + for path in tqdm(file_paths): + with fits.open(path) as hdul: + if dim == '2d': # run on just 2d arrays of the first timestep frame + arrs = [hdul[1].data[0][0]] + elif dim == '2d_diffs' and len(hdul[1].data[0]) > 1: # run on ALL residual frame images + arrs = [hdul[1].data[0][i + 1] - hdul[1].data[0][i] for i in range(len(hdul[1].data[0]) - 1)] + elif dim == '3dt' and len(hdul[1].data[0]) > 2: # compress the first 3 timestep frames as a 3D tensor + arrs = [hdul[1].data[0][0:3]] + else: + continue + + ct += 1 + if ct % 10 == 0: + print(df.mean()) + df.to_csv(save_path) + for group, arr in enumerate(arrs): + for algo in ALL_CODECS.keys(): + try: + if algo == "JPEG_2K" and dim == '3dt': + test_results = benchmark_imagecodecs_compression_algos(arr.transpose(1, 2, 0), algo) + else: + test_results = benchmark_imagecodecs_compression_algos(arr, algo) + + for column, value in test_results.items(): + if column in df.columns: + df.at[path + f"_{group}", column] = value + + except Exception as e: + print(f"Failed at {path} under exception {e}.") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Process some 2D or 3D data.") + parser.add_argument( + "dimension", + choices=['2d', '2d_diffs', '3dt'], + help="Specify whether the data is 2d, 2d_diffs (compressing residuals between second and first exposures), or 3dt (3d time dimension)." + ) + args = parser.parse_args() + dim = args.dimension.lower() + + # RICE REQUIRES UNIQUE INPUT OF ARR SHAPE AND DTYPE INTO DECODER + + if dim == '2d' or dim == '2d_diffs': + ALL_CODECS = { + "JPEG_XL_MAX_EFFORT": [jpegxl_encode_max_effort_preset, jpegxl_decode], + "JPEG_XL": [jpegxl_encode_preset, jpegxl_decode], + "JPEG_2K": [jpeg2k_encode, jpeg2k_decode], + "JPEG_LS": [jpegls_encode, jpegls_decode], + "RICE": [rcomp_encode, rcomp_decode], + } + else: + ALL_CODECS = { + "JPEG_XL_MAX_EFFORT": [jpegxl_encode_max_effort_preset, jpegxl_decode], + "JPEG_XL": [jpegxl_encode_preset, jpegxl_decode], + "JPEG_2K": [jpeg2k_encode, jpeg2k_decode], + } + + columns = [] + for algo in ALL_CODECS.keys(): + columns.append(algo + "_BPD") + columns.append(algo + "_WRITE_RUNTIME") + columns.append(algo + "_READ_RUNTIME") + #columns.append(algo + "_TILE_DIVISOR") + + main(dim) \ No newline at end of file diff --git a/utils/jwst_downloading.ipynb b/utils/jwst_downloading.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..ba9a30eba915238336a98b68bd48cc9ae459e3f9 --- /dev/null +++ b/utils/jwst_downloading.ipynb @@ -0,0 +1,1105 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "07b57859", + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "\n", + "Contains the necessary scripts to actually download the FITS files that are in your JWST csv.\n", + "\n", + "\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "240cc56f-e1b6-47f7-93ee-dc1a0918f5af", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from astropy.coordinates import SkyCoord\n", + "from astropy import units as u\n", + "from sklearn.cluster import AgglomerativeClustering\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as patches\n", + "import os\n", + "import numpy as np\n", + "from astropy.io import fits\n", + "from astropy.wcs import WCS\n", + "from tqdm import tqdm\n", + "\n", + "df = pd.read_csv(\"jwst_FINAL.csv\")\n", + "\n", + "df = df.rename(columns={'sci_data_set_name': 'obs_id'})\n", + "\n", + "# Effective integration time should be more than 30 seconds\n", + "df = df[df['effinttm'] > 30]\n", + "\n", + "df = df[df['exp_type'] == \"NRC_IMAGE\"]\n", + "\n", + "\"\"\"\n", + "The data downloading process looks like the following:\n", + "\n", + "1. Use MastMissions to query the list of observations and their metadata, like ra/dec\n", + "\n", + "2. Filtering process to make sure there are no overlapping observations.\n", + "\n", + "3. Use Observations to pull the names of the data files associated with each observation.\n", + "\n", + "4. Pull the data by wget all those file links.\n", + "\n", + "5. Preprocess.\n", + "\n", + "Note that the data file names use the first 6 chars of obs_id from this observations array\n", + "that we have created. That's why we create the shortened identifier, to match\n", + "observations to product file names. This will be used later.\n", + "\"\"\"\n", + "\n", + "df['obs_id_short'] = df['obs_id'].str[:6]\n", + "\n", + "RA_NAME = 'targ_ra'\n", + "DEC_NAME = 'targ_dec'\n", + "\n", + "assert df[RA_NAME].isna().sum() < 10\n", + "assert df[DEC_NAME].isna().sum() < 10\n", + "\n", + "df = df.dropna(subset=[RA_NAME, DEC_NAME])\n", + "\n", + "df = df.groupby([RA_NAME, DEC_NAME]).apply(lambda x: x.drop_duplicates(subset='detector', keep='first'))\n", + "\n", + "multi_index_df = df.index.to_frame().groupby(level=0).first().reset_index(drop=True)\n", + "multi_index_df = multi_index_df.drop(columns=[2])\n", + "\n", + "df = df.reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "id": "cd89a849-6ef4-493a-910f-0b385e254eb2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████| 117/117 [13:48<00:00, 7.08s/it]\n" + ] + } + ], + "source": [ + "import requests\n", + "import csv\n", + "\n", + "# Function to search datasets using the given endpoint\n", + "def search_datasets(dataset_ids):\n", + " # Base URL for the search API\n", + " base_url = 'https://mast.stsci.edu/search/jwst/api/v0.1/list_products'\n", + " \n", + " # List to store search results\n", + " search_results = []\n", + " \n", + " ids_str = ','.join(dataset_ids)\n", + "\n", + " # Construct the search URL\n", + " search_url = f\"{base_url}?dataset_ids={ids_str}\"\n", + "\n", + " # Make the API request\n", + " response = requests.get(search_url)\n", + "\n", + " # Check if the request was successful\n", + " if response.status_code == 200:\n", + " # Parse the JSON response\n", + " data = response.json()\n", + " search_results.append(data)\n", + " else:\n", + " # Handle errors\n", + " print(f\"Error: Unable to fetch data for dataset ID {dataset_id}\")\n", + " \n", + " return search_results\n", + "\n", + "# Example usage\n", + "dataset_ids_csv = list(df['fileSetName'])\n", + "\n", + "sz_chunk = 10\n", + "chunks = [dataset_ids_csv[i:i+sz_chunk] for i in range(0,len(dataset_ids_csv), sz_chunk)]\n", + "\n", + "all_results = []\n", + "\n", + "for chunk in tqdm(chunks):\n", + " all_results.append(search_datasets(chunk))" + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "id": "b6aad3d4-fdd0-4799-a84c-692d1c94463d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████| 117/117 [00:00<00:00, 3803.87it/s]\n" + ] + } + ], + "source": [ + "new_all_results = []\n", + "\n", + "for result in tqdm(all_results):\n", + " l = result[0]['products']\n", + " new_all_results.extend(l)" + ] + }, + { + "cell_type": "code", + "execution_count": 181, + "id": "0c2caafa-d649-4e2a-92a6-24680ee06cb7", + "metadata": {}, + "outputs": [], + "source": [ + "new_all_results_df = pd.DataFrame(new_all_results)" + ] + }, + { + "cell_type": "code", + "execution_count": 184, + "id": "e189e901-dbad-4689-8454-ee9e1ccab09a", + "metadata": {}, + "outputs": [], + "source": [ + "detectors = ['NRCA1_FULL', 'NRCA2_FULL', 'NRCA3_FULL', 'NRCA4_FULL', 'NRCB1_FULL', 'NRCB2_FULL', 'NRCB3_FULL', 'NRCB4_FULL']\n", + "\n", + "\n", + "resultsdf = new_all_results_df[new_all_results_df['category'] == '1b']\n", + "resultsdf = resultsdf[resultsdf['filters'].isin(detectors)]" + ] + }, + { + "cell_type": "code", + "execution_count": 189, + "id": "1f46cbf4-5b53-437d-8e9b-f7d126839ccc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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product_keyaccessdatasetinstrument_namefiltersfilenameuriauthz_primary_identifierauthz_secondary_identifierfile_suffixcategorysizetype
28jw02561001002_06101_00001_jw02561001002_06101_...PUBLICjw02561001002_06101_00001NIRCAMNRCA3_FULLjw02561001002_06101_00001_nrca3_uncal.fitsjw02561001002_06101_00001/jw02561001002_06101_...jw02561001002_06101_00001_nrca3_uncal.fitsjw02561001002_06101_00001_nrca3_uncal.fits_uncal1b75553920science
50jw02561001002_06101_00001_jw02561001002_06101_...PUBLICjw02561001002_06101_00001NIRCAMNRCB3_FULLjw02561001002_06101_00001_nrcb3_uncal.fitsjw02561001002_06101_00001/jw02561001002_06101_...jw02561001002_06101_00001_nrcb3_uncal.fitsjw02561001002_06101_00001_nrcb3_uncal.fits_uncal1b75553920science
72jw02561001002_06101_00001_jw02561001002_06101_...PUBLICjw02561001002_06101_00001NIRCAMNRCA2_FULLjw02561001002_06101_00001_nrca2_uncal.fitsjw02561001002_06101_00001/jw02561001002_06101_...jw02561001002_06101_00001_nrca2_uncal.fitsjw02561001002_06101_00001_nrca2_uncal.fits_uncal1b75553920science
93jw02561001002_06101_00001_jw02561001002_06101_...PUBLICjw02561001002_06101_00001NIRCAMNRCB2_FULLjw02561001002_06101_00001_nrcb2_uncal.fitsjw02561001002_06101_00001/jw02561001002_06101_...jw02561001002_06101_00001_nrcb2_uncal.fitsjw02561001002_06101_00001_nrcb2_uncal.fits_uncal1b75553920science
114jw02561001002_06101_00001_jw02561001002_06101_...PUBLICjw02561001002_06101_00001NIRCAMNRCA4_FULLjw02561001002_06101_00001_nrca4_uncal.fitsjw02561001002_06101_00001/jw02561001002_06101_...jw02561001002_06101_00001_nrca4_uncal.fitsjw02561001002_06101_00001_nrca4_uncal.fits_uncal1b75553920science
..........................................
216718jw02130007001_03101_00002_jw02130007001_03101_...PUBLICjw02130007001_03101_00002NIRCAMNRCB1_FULLjw02130007001_03101_00002_nrcb1_uncal.fitsjw02130007001_03101_00002/jw02130007001_03101_...jw02130007001_03101_00002_nrcb1_uncal.fitsjw02130007001_03101_00002_nrcb1_uncal.fits_uncal1b75553920science
216740jw02130007001_03101_00002_jw02130007001_03101_...PUBLICjw02130007001_03101_00002NIRCAMNRCA4_FULLjw02130007001_03101_00002_nrca4_uncal.fitsjw02130007001_03101_00002/jw02130007001_03101_...jw02130007001_03101_00002_nrca4_uncal.fitsjw02130007001_03101_00002_nrca4_uncal.fits_uncal1b75553920science
216786jw02130007001_03101_00002_jw02130007001_03101_...PUBLICjw02130007001_03101_00002NIRCAMNRCA1_FULLjw02130007001_03101_00002_nrca1_uncal.fitsjw02130007001_03101_00002/jw02130007001_03101_...jw02130007001_03101_00002_nrca1_uncal.fitsjw02130007001_03101_00002_nrca1_uncal.fits_uncal1b75553920science
216808jw02130007001_03101_00002_jw02130007001_03101_...PUBLICjw02130007001_03101_00002NIRCAMNRCA3_FULLjw02130007001_03101_00002_nrca3_uncal.fitsjw02130007001_03101_00002/jw02130007001_03101_...jw02130007001_03101_00002_nrca3_uncal.fitsjw02130007001_03101_00002_nrca3_uncal.fits_uncal1b75553920science
216830jw02130007001_03101_00002_jw02130007001_03101_...PUBLICjw02130007001_03101_00002NIRCAMNRCA2_FULLjw02130007001_03101_00002_nrca2_uncal.fitsjw02130007001_03101_00002/jw02130007001_03101_...jw02130007001_03101_00002_nrca2_uncal.fitsjw02130007001_03101_00002_nrca2_uncal.fits_uncal1b75553920science
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7537 rows × 13 columns

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" + ], + "text/plain": [ + " product_key access \\\n", + "28 jw02561001002_06101_00001_jw02561001002_06101_... PUBLIC \n", + "50 jw02561001002_06101_00001_jw02561001002_06101_... PUBLIC \n", + "72 jw02561001002_06101_00001_jw02561001002_06101_... PUBLIC \n", + "93 jw02561001002_06101_00001_jw02561001002_06101_... PUBLIC \n", + "114 jw02561001002_06101_00001_jw02561001002_06101_... PUBLIC \n", + "... ... ... \n", + "216718 jw02130007001_03101_00002_jw02130007001_03101_... PUBLIC \n", + "216740 jw02130007001_03101_00002_jw02130007001_03101_... PUBLIC \n", + "216786 jw02130007001_03101_00002_jw02130007001_03101_... PUBLIC \n", + "216808 jw02130007001_03101_00002_jw02130007001_03101_... PUBLIC \n", + "216830 jw02130007001_03101_00002_jw02130007001_03101_... PUBLIC \n", + "\n", + " dataset instrument_name filters \\\n", + "28 jw02561001002_06101_00001 NIRCAM NRCA3_FULL \n", + "50 jw02561001002_06101_00001 NIRCAM NRCB3_FULL \n", + "72 jw02561001002_06101_00001 NIRCAM NRCA2_FULL \n", + "93 jw02561001002_06101_00001 NIRCAM NRCB2_FULL \n", + "114 jw02561001002_06101_00001 NIRCAM NRCA4_FULL \n", + "... ... ... ... \n", + "216718 jw02130007001_03101_00002 NIRCAM NRCB1_FULL \n", + "216740 jw02130007001_03101_00002 NIRCAM NRCA4_FULL \n", + "216786 jw02130007001_03101_00002 NIRCAM NRCA1_FULL \n", + "216808 jw02130007001_03101_00002 NIRCAM NRCA3_FULL \n", + "216830 jw02130007001_03101_00002 NIRCAM NRCA2_FULL \n", + "\n", + " filename \\\n", + "28 jw02561001002_06101_00001_nrca3_uncal.fits \n", + "50 jw02561001002_06101_00001_nrcb3_uncal.fits \n", + "72 jw02561001002_06101_00001_nrca2_uncal.fits \n", + "93 jw02561001002_06101_00001_nrcb2_uncal.fits \n", + "114 jw02561001002_06101_00001_nrca4_uncal.fits \n", + "... ... \n", + "216718 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jw02561001002_06101_00001_nrca3_uncal.fits \n", + "50 jw02561001002_06101_00001_nrcb3_uncal.fits \n", + "72 jw02561001002_06101_00001_nrca2_uncal.fits \n", + "93 jw02561001002_06101_00001_nrcb2_uncal.fits \n", + "114 jw02561001002_06101_00001_nrca4_uncal.fits \n", + "... ... \n", + "216718 jw02130007001_03101_00002_nrcb1_uncal.fits \n", + "216740 jw02130007001_03101_00002_nrca4_uncal.fits \n", + "216786 jw02130007001_03101_00002_nrca1_uncal.fits \n", + "216808 jw02130007001_03101_00002_nrca3_uncal.fits \n", + "216830 jw02130007001_03101_00002_nrca2_uncal.fits \n", + "\n", + " authz_secondary_identifier file_suffix category \\\n", + "28 jw02561001002_06101_00001_nrca3_uncal.fits _uncal 1b \n", + "50 jw02561001002_06101_00001_nrcb3_uncal.fits _uncal 1b \n", + "72 jw02561001002_06101_00001_nrca2_uncal.fits _uncal 1b \n", + "93 jw02561001002_06101_00001_nrcb2_uncal.fits _uncal 1b \n", + "114 jw02561001002_06101_00001_nrca4_uncal.fits _uncal 1b \n", + "... ... ... ... \n", + "216718 jw02130007001_03101_00002_nrcb1_uncal.fits _uncal 1b \n", + "216740 jw02130007001_03101_00002_nrca4_uncal.fits _uncal 1b \n", + "216786 jw02130007001_03101_00002_nrca1_uncal.fits _uncal 1b \n", + "216808 jw02130007001_03101_00002_nrca3_uncal.fits _uncal 1b \n", + "216830 jw02130007001_03101_00002_nrca2_uncal.fits _uncal 1b \n", + "\n", + " size type \n", + "28 75553920 science \n", + "50 75553920 science \n", + "72 75553920 science \n", + "93 75553920 science \n", + "114 75553920 science \n", + "... ... ... \n", + "216718 75553920 science \n", + "216740 75553920 science \n", + "216786 75553920 science \n", + "216808 75553920 science \n", + "216830 75553920 science \n", + "\n", + "[7537 rows x 13 columns]" + ] + }, + "execution_count": 189, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "resultsdf" + ] + }, + { + "cell_type": "code", + "execution_count": 188, + "id": "f086c0f9-9ef6-4945-a53a-f3cf051b4dee", + "metadata": {}, + "outputs": [], + "source": [ + "resultsdf.to_csv(\"all_jwst_uris.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "id": "09ca516e-3bbb-4a2b-9c6e-984c9a7e801a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Symmetric?\n", + "True\n", + "(475, 475)\n" + ] + } + ], + "source": [ + "# Array of latitudes and longitudes\n", + "# MAKE SURE TO PUT RA=LON, DEC=LAT\n", + "latitudes = np.array(multi_index_df.groupby(level=0).first()[DEC_NAME]) # Example latitudes\n", + "longitudes = np.array(multi_index_df.groupby(level=0).first()[RA_NAME]) # Example longitudes\n", + "\n", + "n_points = len(latitudes)\n", + "\n", + "# Repeat each point n_points times for lat1, lon1\n", + "lat1 = np.repeat(latitudes, n_points)\n", + "lon1 = np.repeat(longitudes, n_points)\n", + "\n", + "# Tile the whole array n_points times for lat2, lon2\n", + "lat2 = np.tile(latitudes, n_points)\n", + "lon2 = np.tile(longitudes, n_points)\n", + "\n", + "# Calculates angular separation between two spherical coords\n", + "# This can be lat/lon or ra/dec\n", + "# Taken from astropy\n", + "def angular_separation_deg(lon1, lat1, lon2, lat2):\n", + " lon1 = np.deg2rad(lon1)\n", + " lon2 = np.deg2rad(lon2)\n", + " lat1 = np.deg2rad(lat1)\n", + " lat2 = np.deg2rad(lat2)\n", + " \n", + " sdlon = np.sin(lon2 - lon1)\n", + " cdlon = np.cos(lon2 - lon1)\n", + " slat1 = np.sin(lat1)\n", + " slat2 = np.sin(lat2)\n", + " clat1 = np.cos(lat1)\n", + " clat2 = np.cos(lat2)\n", + "\n", + " num1 = clat2 * sdlon\n", + " num2 = clat1 * slat2 - slat1 * clat2 * cdlon\n", + " denominator = slat1 * slat2 + clat1 * clat2 * cdlon\n", + "\n", + " return np.rad2deg(np.arctan2(np.hypot(num1, num2), denominator))\n", + "\n", + "# Compute the pairwise angular separations\n", + "angular_separations = angular_separation_deg(lon1, lat1, lon2, lat2)\n", + "\n", + "# Reshape the result into a matrix form\n", + "angular_separations_matrix = angular_separations.reshape(n_points, n_points)\n", + "\n", + "def check_symmetric(a, rtol=1e-05, atol=1e-07):\n", + " return np.allclose(a, a.T, rtol=rtol, atol=atol)\n", + "\n", + "print(\"Symmetric?\")\n", + "print(check_symmetric(angular_separations_matrix))\n", + "print(angular_separations_matrix.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "85edd94e-5591-4c7c-8251-b8c976962f72", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "24 40\n", + "12 40\n", + "76 36\n", + "34 30\n", + "7 24\n", + " ..\n", + "221 1\n", + "166 1\n", + "139 1\n", + "176 1\n", + "20 1\n", + "Length: 311, dtype: int64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████| 311/311 [00:00<00:00, 3456.86it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Max subset with minimum distance: 321\n", + "1110\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "#HUBBLE_FOV = 0.057\n", + "JWST_FOV = 0.0366667\n", + "\n", + "THRESH = JWST_FOV\n", + "\n", + "\"\"\"\n", + "Initial clustering and filtering using single RA DEC value.\n", + "\n", + "\"\"\"\n", + "\n", + "clustering = AgglomerativeClustering(n_clusters=None, metric='precomputed', linkage='single', distance_threshold=THRESH)\n", + "labels = clustering.fit_predict(angular_separations_matrix)\n", + "\n", + "multi_index_df['label'] = labels\n", + "\n", + "print(pd.Series(labels).value_counts())\n", + "\n", + "def max_subset_with_min_distance(points, min_distance):\n", + " subset = []\n", + " for i, row in points.iterrows():\n", + " if all(angular_separation_deg(row[RA_NAME], row[DEC_NAME], existing_point[RA_NAME], existing_point[DEC_NAME]) >= min_distance for existing_point in subset):\n", + " subset.append(row)\n", + " return subset\n", + "\n", + "all_subsets = []\n", + "\n", + "for label in tqdm(np.unique(labels)):\n", + " cds = multi_index_df[multi_index_df['label'] == label]\n", + " subset = max_subset_with_min_distance(cds, THRESH)\n", + " all_subsets.extend(subset)\n", + "\n", + "print(\"Max subset with minimum distance:\", len(all_subsets))\n", + "\n", + "locations = pd.DataFrame(all_subsets)\n", + "\n", + "df = pd.merge(df, locations, on=[RA_NAME, DEC_NAME], how='right')\n", + "\n", + "print(len(df))" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "id": "55a9d951-3210-4a7f-aca8-a0a80628a8ac", + "metadata": {}, + "outputs": [], + "source": [ + "detectors = ['nrca1', 'nrca2', 'nrca3', 'nrca4', 'nrcb1', 'nrcb2', 'nrcb3', 'nrcb4']\n", + "obsids_search = []\n", + "\n", + "for fileset in df['fileSetName']:\n", + " for detector in detectors:\n", + " obsids_search.append(fileset + \"_\" + detector)" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "id": "55ae58fc-5c2f-46ec-8fa7-d7229fd61b5f", + "metadata": {}, + "outputs": [], + "source": [ + "from astroquery.mast import Observations\n", + "\n", + "# Query for data with the specified obs_id\n", + "result = Observations.query_criteria(obs_id=obsids_search)" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "id": "5a29aaf9-dd68-4fa2-910a-8d399f4580cd", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████| 108/108 [01:07<00:00, 1.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 0 unique files, which are 0.0 GB in size.\n" + ] + }, + { + "ename": "ValueError", + "evalue": "no values provided to stack.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [120]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 21\u001b[0m files[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mobs_id_short\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m files[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mobs_id\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mstr[:\u001b[38;5;241m6\u001b[39m]\n\u001b[1;32m 23\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThere are \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(files)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m unique files, which are \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28msum\u001b[39m(files[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msize\u001b[39m\u001b[38;5;124m'\u001b[39m])\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m10\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m9\u001b[39m\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.1f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m GB in size.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 25\u001b[0m manifest \u001b[38;5;241m=\u001b[39m \u001b[43mObservations\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload_products\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfiles\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mobsID\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcurl_flag\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/astroquery/mast/observations.py:715\u001b[0m, in \u001b[0;36mObservationsClass.download_products\u001b[0;34m(self, products, download_dir, cache, curl_flag, mrp_only, cloud_only, **filters)\u001b[0m\n\u001b[1;32m 712\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m oid \u001b[38;5;129;01min\u001b[39;00m products:\n\u001b[1;32m 713\u001b[0m product_lists\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_product_list(oid))\n\u001b[0;32m--> 715\u001b[0m products \u001b[38;5;241m=\u001b[39m \u001b[43mvstack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mproduct_lists\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 717\u001b[0m \u001b[38;5;66;03m# apply filters\u001b[39;00m\n\u001b[1;32m 718\u001b[0m products \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfilter_products(products, mrp_only\u001b[38;5;241m=\u001b[39mmrp_only, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfilters)\n", + "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/astropy/table/operations.py:677\u001b[0m, in \u001b[0;36mvstack\u001b[0;34m(tables, join_type, metadata_conflicts)\u001b[0m\n\u001b[1;32m 623\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 624\u001b[0m \u001b[38;5;124;03mStack tables vertically (along rows).\u001b[39;00m\n\u001b[1;32m 625\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 673\u001b[0m \u001b[38;5;124;03m 6 8\u001b[39;00m\n\u001b[1;32m 674\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 675\u001b[0m _check_join_type(join_type, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvstack\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 677\u001b[0m tables \u001b[38;5;241m=\u001b[39m \u001b[43m_get_list_of_tables\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtables\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# validates input\u001b[39;00m\n\u001b[1;32m 678\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(tables) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 679\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m tables[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;66;03m# no point in stacking a single table\u001b[39;00m\n", + "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/astropy/table/operations.py:60\u001b[0m, in \u001b[0;36m_get_list_of_tables\u001b[0;34m(tables)\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[38;5;66;03m# Make sure there is something to stack\u001b[39;00m\n\u001b[1;32m 59\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(tables) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m---> 60\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mno values provided to stack.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 62\u001b[0m \u001b[38;5;66;03m# Convert inputs (Table, Row, or anything column-like) to Tables.\u001b[39;00m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;66;03m# Special case that Quantity converts to a QTable.\u001b[39;00m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ii, val \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(tables):\n", + "\u001b[0;31mValueError\u001b[0m: no values provided to stack." + ] + } + ], + "source": [ + "from astropy.table import unique, vstack, Table\n", + "\n", + "\"\"\"\n", + "Call API that gives the file URLs for each observations.\n", + "\n", + "\"\"\"\n", + "\n", + "matched_obs = result\n", + "\n", + "# Split the observations into \"chunks\" of size five\n", + "sz_chunk = 5\n", + "chunks = [matched_obs[i:i+sz_chunk] for i in range(0,len(matched_obs), sz_chunk)]\n", + "\n", + "# Get the list of products for each chunk\n", + "t = []\n", + "for chunk in tqdm(chunks):\n", + " t.append(Observations.get_product_list(chunk))\n", + "\n", + "files = unique(vstack(t), keys='productFilename')\n", + "files = files.to_pandas()\n", + "\n", + "# Ensure we only keep raw data files\n", + "files = files[files['productSubGroupDescription'] == 'UNCAL']\n", + "\n", + "# Create a shortened identified\n", + "files['obs_id_short'] = files['obs_id'].str[:6]\n", + "\n", + "print(f\"There are {len(files)} unique files, which are {sum(files['size'])/10**9:.1f} GB in size.\")\n", + "\n", + "manifest = Observations.download_products(files['obsID'], curl_flag=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b2307af8-d6ea-4969-8b8a-4d42f21d674f", + "metadata": {}, + "outputs": [], + "source": [ + "files[['obsID']].to_csv('list_of_hubble_filenames.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ab5d1fd5-502f-4b94-a8f1-23636043f779", + "metadata": {}, + "outputs": [], + "source": [ + "manifest = Observations.download_products(files['obsID'], curl_flag=True)\n", + "# Creates .sh scripts that have to then be run to actually download data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "556fdab2-517e-466c-a9ee-c7b629df5276", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from astropy.io import fits\n", + "from astropy.table import Table\n", + "import glob\n", + "\n", + "\"\"\"\n", + "This code allows us to combine multiple exposures of the same observation into one FITS file.\n", + "\n", + "This code was NOT used; we only used a single exposure for our dataset.\n", + "\"\"\"\n", + "\n", + "def create_combined_hubble_file(short_obs_id):\n", + " \n", + " file_list = list(files[files['obs_id_short'] == short_obs_id]['productFilename'])\n", + " \n", + " ccd1_data = []\n", + " ccd2_data = []\n", + " ccd1_times = []\n", + " ccd2_times = []\n", + " ccd1_headers = []\n", + " ccd2_headers = []\n", + "\n", + " for file in file_list:\n", + " with fits.open(file) as hdul:\n", + " # Extract TIME-OBS from the primary header\n", + " time_obs = hdul[0].header['TIME-OBS']\n", + "\n", + " # Extract data and headers from HDUs\n", + " for hdu in [hdul[1], hdul[4]]:\n", + " if hdu.header['CCDCHIP'] == 1:\n", + " ccd1_data.append(hdu.data)\n", + " ccd1_times.append(time_obs)\n", + " ccd1_headers.append(hdul[0].header.copy())\n", + " elif hdu.header['CCDCHIP'] == 2:\n", + " ccd2_data.append(hdu.data)\n", + " ccd2_times.append(time_obs)\n", + " ccd2_headers.append(hdul[0].header.copy())\n", + "\n", + " # Sort the data based on TIME-OBS\n", + " ccd1_times, ccd1_data, ccd1_headers = zip(*sorted(zip(ccd1_times, ccd1_data, ccd1_headers)))\n", + " ccd2_times, ccd2_data, ccd2_headers = zip(*sorted(zip(ccd2_times, ccd2_data, ccd2_headers)))\n", + "\n", + " # Concatenate the data for each CCDCHIP\n", + " ccd1_concat = np.stack(ccd1_data)\n", + " ccd2_concat = np.stack(ccd2_data)\n", + "\n", + " # Function to create a new FITS file for a given CCDCHIP\n", + " def create_fits_file(output_file, ccd_data, ccd_headers, ccd_chip):\n", + "\n", + " primary_hdu = fits.PrimaryHDU()\n", + " primary_hdu.header['EXTEND'] = True\n", + " primary_hdu.header['CCDCHIP'] = ccd_chip\n", + "\n", + " metadata_hdus = [fits.ImageHDU(header=header) for header in ccd_headers]\n", + "\n", + " # Create ImageHDU with concatenated data\n", + " image_hdu = fits.ImageHDU(data=ccd_data, header=fits.Header({'CCDCHIP': ccd_chip}))\n", + "\n", + " # Create HDUList and write to a new FITS file\n", + " hdulist = fits.HDUList([primary_hdu] + [image_hdu] + metadata_hdus)\n", + " hdulist.writeto(output_file, overwrite=True)\n", + "\n", + " # Create FITS files for CCDCHIP 1 and 2\n", + " create_fits_file(f'{short_obs_id}_ccd1.fits', ccd1_concat, ccd1_headers, 1)\n", + " create_fits_file(f'{short_obs_id}_ccd2.fits', ccd2_concat, ccd2_headers, 2)\n", + "\n", + " print(\"New FITS files created successfully.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d09be044-ce2e-4cb5-ae1f-9562f9ac6fa3", + "metadata": {}, + "outputs": [], + "source": [ + "def visualize_label(df, LABEL=None):\n", + " \n", + " cds = df\n", + " \n", + " if LABEL is not None:\n", + " cds = cds[labels == LABEL]\n", + "\n", + " ras, decs = [], []\n", + " cmap = []\n", + " \n", + " print(len(cds))\n", + "\n", + " for i, cd in cds.iterrows():\n", + " ras.append(cd[RA_NAME])\n", + " decs.append(cd[DEC_NAME])\n", + "\n", + " if LABEL is None:\n", + " # No gridlines, will be overwhelming\n", + "\n", + " # Compute the 2D histogram\n", + " # Number of bins for x and y; adjust these based on your dataset\n", + " bins = (30, 30)\n", + "\n", + " # Compute the histogram\n", + " hist, xedges, yedges = np.histogram2d(ras, decs, bins=bins)\n", + " \n", + " N_cutoff = 50\n", + " hist = np.clip(hist, 0, N_cutoff)\n", + "\n", + " # Generate a 2D histogram plot\n", + " plt.figure(figsize=(8, 6))\n", + " plt.imshow(hist, interpolation='nearest', origin='lower',\n", + " extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]],\n", + " cmap='viridis') # Choose a colormap (e.g., 'viridis', 'plasma', 'inferno')\n", + "\n", + " # Add labels and title if necessary\n", + " plt.colorbar(label='Number of points in bin')\n", + " plt.xlabel('RA')\n", + " plt.ylabel('DEC')\n", + " plt.title(f'2D Histogram of Point Density, clipped to {N_cutoff} points')\n", + "\n", + " # Show the plot\n", + " plt.show()\n", + "\n", + " return\n", + " else:\n", + " fig = plt.figure()\n", + " ax = fig.gca()\n", + " ax.set_xticks(np.arange(np.min(ras), np.max(ras), THRESH))\n", + " ax.set_yticks(np.arange(np.min(decs), np.max(decs), THRESH))\n", + " plt.scatter(ras, decs, alpha=0.1)\n", + " plt.grid()\n", + " plt.show()\n", + "\n", + "visualize_label(df, 7)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35a648ab-07cd-409e-be32-125ead927bdf", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "data = list(range(len(labels)))\n", + "\n", + "# Perform the train-test split with an 80-20 ratio\n", + "train_indices, test_indices = train_test_split(data, test_size=0.2, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f6a0472-b8fc-4989-a506-39019914c853", + "metadata": {}, + "outputs": [], + "source": [ + "sdss_test = all_sdss_data[all_sdss_data['cluster_label'].isin(test_indices)]\n", + "sdss_train = all_sdss_data[all_sdss_data['cluster_label'].isin(train_indices)]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bfbe8686-e580-4285-bd8f-43154d18182b", + "metadata": {}, + "outputs": [], + "source": [ + "len(sdss_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cacb74dd-0d54-43d7-a301-ea4eb7b3e07e", + "metadata": {}, + "outputs": [], + "source": [ + "len(sdss_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f0fb157d-1307-42d9-ac57-7e6aa3cecd84", + "metadata": {}, + "outputs": [], + "source": [ + "sdss_test" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62fb160e-74e5-4f50-96ee-109a6261370f", + "metadata": {}, + "outputs": [], + "source": [ + "test_data = pd.DataFrame(test_data)\n", + "train_data = pd.DataFrame(train_data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e7b1379c-d5a9-4f3a-ac68-e8b5a46f4c8a", + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "Code to verify test/train pollution.\n", + "Prints images which are nearby and might overlap.\n", + "\"\"\"\n", + "\n", + "\n", + "import json\n", + "from math import radians, sin, cos, sqrt, atan2, degrees\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "# Function to load data from file\n", + "def load_data(file_path):\n", + " data = []\n", + " with open(file_path, 'r') as f:\n", + " for line in f:\n", + " data.append(json.loads(line))\n", + " return data\n", + "\n", + "# Load data\n", + "train_file_path = '/Users/rithwik/Desktop/full_train.jsonl.txt'\n", + "test_file_path = '/Users/rithwik/Desktop/full_test.jsonl.txt'\n", + "#train_data = sdss_train\n", + "#test_data = sdss_test\n", + "\n", + "# Define the threshold\n", + "threshold = 0.09*3\n", + "\n", + "# Find test dataset rows with a minimum great circle distance less than the threshold\n", + "close_pairs = []\n", + "\n", + "for i, test_point in test_data.iterrows():\n", + " ra_test, dec_test = test_point['ra'], test_point['dec']\n", + " distances = [(train_point, angular_separation_deg(ra_test, dec_test, train_point['ra'], train_point['dec'])) for i, train_point in train_data.iterrows()]\n", + " closest_train_point, min_distance = min(distances, key=lambda x: x[1])\n", + " if min_distance < threshold:\n", + " close_pairs.append((test_point, closest_train_point, min_distance))\n", + "\n", + "close_pairs_summary = [\n", + " {\n", + " \"test_image_id\": test_point['image_id'],\n", + " \"test_ra\": test_point['ra'],\n", + " \"test_dec\": test_point['dec'],\n", + " \"train_image_id\": closest_train_point['image_id'],\n", + " \"train_ra\": closest_train_point['ra'],\n", + " \"train_dec\": closest_train_point['dec'],\n", + " \"min_distance_deg\": min_distance\n", + " }\n", + " for test_point, closest_train_point, min_distance in close_pairs\n", + "]\n", + "\n", + "# Print the results\n", + "result = \"Success\"\n", + "for pair in close_pairs_summary:\n", + " print(pair)\n", + " result = \"FAIL\"\n", + " \n", + "print(f\"Done. result is {result}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "93cf2978-0799-4d6c-93ec-921f4f41e10c", + "metadata": {}, + "outputs": [], + "source": [ + "sdss_test.to_json('full_test1.jsonl', orient='records', lines=True)\n", + "sdss_train.to_json('full_train1.jsonl', orient='records', lines=True)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/utils/jwst_filtering.ipynb b/utils/jwst_filtering.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..c46131792e4f030835904fd08337bb50d6b87b73 --- /dev/null +++ b/utils/jwst_filtering.ipynb @@ -0,0 +1,3346 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "ebcd0eee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7537" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "from tqdm import tqdm\n", + "import glob\n", + "from astropy.io import fits\n", + "import os\n", + "from astropy.io import fits\n", + "from astropy.wcs import WCS\n", + "from spherical_geometry.polygon import SphericalPolygon\n", + "import os\n", + "from astropy.io import fits\n", + "from astropy.wcs import WCS\n", + "from spherical_geometry.polygon import SphericalPolygon\n", + "from sklearn.cluster import AgglomerativeClustering\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "from astropy.io import fits\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "def get_all_fits_files(root_dir):\n", + " # Use glob to recursively find all .fits files\n", + " pattern = os.path.join(root_dir, '**', '*.fits')\n", + " fits_files = glob.glob(pattern, recursive=True)\n", + " return fits_files\n", + "\n", + "all_fits_paths = get_all_fits_files('.')\n", + "len(all_fits_paths)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0e4504d2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/7537 [00:00 4\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m fits\u001b[38;5;241m.\u001b[39mopen(\u001b[43mpath1\u001b[49m) \u001b[38;5;28;01mas\u001b[39;00m hdul:\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m hdul[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mheader[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFILTER\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mF200W\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m 6\u001b[0m valid_fits_paths\u001b[38;5;241m.\u001b[39mappend(p)\n", + "\u001b[0;31mNameError\u001b[0m: name 'path1' is not defined" + ] + } + ], + "source": [ + "valid_fits_paths = []\n", + "\n", + "for p in tqdm(all_fits_paths):\n", + " with fits.open(path1) as hdul:\n", + " if hdul[0].header['FILTER'] == 'F200W':\n", + " valid_fits_paths.append(p)\n", + " else:\n", + " print(hdul[0].header['FILTER'])\n", + " \n", + "len(valid_fits_paths)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b22505d", + "metadata": {}, + "outputs": [], + "source": [ + "from collections import defaultdict\n", + "\n", + "def build_file_dict(file_list):\n", + " \"\"\"\n", + " Build a dictionary mapping base file paths to lists of suffixes.\n", + "\n", + " Parameters:\n", + " file_list (list of str): List of file paths.\n", + "\n", + " Returns:\n", + " dict: Dictionary where keys are base file paths and values are lists of suffixes.\n", + " \"\"\"\n", + " file_dict = defaultdict(list)\n", + "\n", + " for filepath in tqdm(file_list):\n", + " # Split the filepath to get the base and suffix\n", + " base, suffix = filepath.rsplit('_', 2)[0], '_' + '_'.join(filepath.rsplit('_', 2)[1:])\n", + " file_dict[base].append(suffix)\n", + "\n", + " return dict(file_dict)\n", + "\n", + "file_dict = build_file_dict(valid_fits_paths)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ce9e359d", + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the list of confirmed FITS paths\n", + "confirmed_fits_paths1 = []\n", + "\n", + "all_polys1 = []\n", + "\n", + "ct = 0\n", + "\n", + "for prefix, suffixes in tqdm(file_dict.items()):\n", + " total_poly = None\n", + " for suffix in suffixes:\n", + " path1 = prefix + suffix\n", + " try:\n", + " with fits.open(path1) as hdul:\n", + " print(hdul.info())\n", + " break\n", + " hdul[1].data = hdul[1].data[0, 0]\n", + " wcs1a = WCS(hdul[1].header)\n", + " shape1a = sorted(tuple(wcs1a.pixel_shape))[:2]\n", + " footprint1a = wcs1a.calc_footprint(axes=shape1a)\n", + " \n", + " plot_rectangle(footprint1a)\n", + " \n", + " poly1a = SphericalPolygon.from_radec(footprint1a[:, 0], footprint1a[:, 1])\n", + " \n", + " if total_poly is None:\n", + " total_poly = poly1a\n", + " else:\n", + " total_poly = total_poly.union(poly1a)\n", + " except Exception as e:\n", + " print(e)\n", + " continue\n", + "\n", + " all_polys.append(total_poly)\n", + " confirmed_fits_paths.append(prefix)\n", + " \n", + " ct += 1\n", + " if ct > 1:\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "4d31f039", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████| 1157/1157 [00:00<00:00, 9673.87it/s]\n" + ] + } + ], + "source": [ + "latitudes = []\n", + "longitudes = []\n", + "\n", + "for poly in tqdm(all_polys):\n", + " pts = list(poly.to_radec())[0]\n", + " ra = pts[0][0]\n", + " dec = pts[1][0]\n", + " \n", + " longitudes.append(ra)\n", + " latitudes.append(dec)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "89fbf2fc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Symmetric?\n", + "True\n", + "(1157, 1157)\n" + ] + } + ], + "source": [ + "n_points = len(latitudes)\n", + "\n", + "# Repeat each point n_points times for lat1, lon1\n", + "lat1 = np.repeat(latitudes, n_points)\n", + "lon1 = np.repeat(longitudes, n_points)\n", + "\n", + "# Tile the whole array n_points times for lat2, lon2\n", + "lat2 = np.tile(latitudes, n_points)\n", + "lon2 = np.tile(longitudes, n_points)\n", + "\n", + "# Calculates angular separation between two spherical coords\n", + "# This can be lat/lon or ra/dec\n", + "# Taken from astropy\n", + "def angular_separation_deg(lon1, lat1, lon2, lat2):\n", + " lon1 = np.deg2rad(lon1)\n", + " lon2 = np.deg2rad(lon2)\n", + " lat1 = np.deg2rad(lat1)\n", + " lat2 = np.deg2rad(lat2)\n", + " \n", + " sdlon = np.sin(lon2 - lon1)\n", + " cdlon = np.cos(lon2 - lon1)\n", + " slat1 = np.sin(lat1)\n", + " slat2 = np.sin(lat2)\n", + " clat1 = np.cos(lat1)\n", + " clat2 = np.cos(lat2)\n", + "\n", + " num1 = clat2 * sdlon\n", + " num2 = clat1 * slat2 - slat1 * clat2 * cdlon\n", + " denominator = slat1 * slat2 + clat1 * clat2 * cdlon\n", + "\n", + " return np.rad2deg(np.arctan2(np.hypot(num1, num2), denominator))\n", + "\n", + "# Compute the pairwise angular separations\n", + "angular_separations = angular_separation_deg(lon1, lat1, lon2, lat2)\n", + "\n", + "# Reshape the result into a matrix form\n", + "angular_separations_matrix = angular_separations.reshape(n_points, n_points)\n", + "\n", + "def check_symmetric(a, rtol=1e-05, atol=1e-07):\n", + " return np.allclose(a, a.T, rtol=rtol, atol=atol)\n", + "\n", + "print(\"Symmetric?\")\n", + "print(check_symmetric(angular_separations_matrix))\n", + "print(angular_separations_matrix.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "625dab48", + "metadata": {}, + "outputs": [], + "source": [ + "#JWST_FOV = 0.1\n", + "# This is 6 arcmin; both detectors are 5.1 by 2.2 arcmin\n", + "JWST_FOV = 0.085 # 5.1 arcmin\n", + "\n", + "# Initial clustering from RA DEC only\n", + "\n", + "THRESH = JWST_FOV * 2\n", + "\n", + "clustering = AgglomerativeClustering(n_clusters=None, metric='precomputed', linkage='single', distance_threshold=THRESH)\n", + "labels = clustering.fit_predict(angular_separations_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "7c8510c4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "19 89\n", + "38 49\n", + "120 41\n", + "145 40\n", + "70 40\n", + " ..\n", + "106 1\n", + "133 1\n", + "113 1\n", + "142 1\n", + "127 1\n", + "Name: count, Length: 158, dtype: int64" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series(labels).value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "bead29fc", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 1%|▎ | 1/158 [00:00<02:10, 1.20it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 1, i: 3 IoU: 0.596504425087758\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 1%|▌ | 2/158 [00:10<15:39, 6.02s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 2, i: 2 IoU: 0.34363724115294875\n", + "FAIL label: 2, i: 3 IoU: 0.9976215196100199\n", + "FAIL label: 2, i: 4 IoU: 0.049803398542476354\n", + "FAIL label: 2, i: 5 IoU: 0.34417848130128015\n", + "FAIL label: 2, i: 6 IoU: 0.050723662560985434\n", + "FAIL label: 2, i: 7 IoU: 0.9690881457893782\n", + "FAIL label: 2, i: 8 IoU: 0.016727240411126703\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 2%|▊ | 3/158 [00:13<12:23, 4.79s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 2, i: 9 IoU: 0.051363243220488235\n", + "FAIL label: 3, i: 2 IoU: 0.8772338341742306\n", + "FAIL label: 3, i: 3 IoU: 0.6033748465129958\n", + "FAIL label: 3, i: 4 IoU: 0.772891871262731\n", + "FAIL label: 3, i: 5 IoU: 0.6831373397854319\n", + "FAIL label: 3, i: 6 IoU: 0.5340221026195638\n", + "FAIL label: 3, i: 7 IoU: 0.772905655459724\n", + "FAIL label: 3, i: 8 IoU: 0.772890592692672\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 3%|█ | 4/158 [00:20<13:54, 5.42s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 3, i: 9 IoU: 0.8772302526430777\n", + "FAIL label: 4, i: 1 IoU: 0.909832585344332\n", + "FAIL label: 4, i: 4 IoU: 0.8025004233260927\n", + "FAIL label: 4, i: 5 IoU: 0.8453293017068085\n", + "FAIL label: 4, i: 6 IoU: 0.9765939688413316\n", + "FAIL label: 4, i: 7 IoU: 0.9882627477713982\n", + "FAIL label: 4, i: 8 IoU: 0.7624884879588034\n", + "FAIL label: 4, i: 9 IoU: 0.8025139110760734\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 3%|█▎ | 5/158 [00:29<17:31, 6.88s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 4, i: 10 IoU: 0.8025001441743084\n", + "FAIL label: 5, i: 2 IoU: 0.4496355511991237\n", + "FAIL label: 5, i: 3 IoU: 0.4588227417680728\n", + "FAIL label: 5, i: 4 IoU: 0.9957487200884658\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 4%|█▋ | 6/158 [00:30<12:36, 4.98s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 5, i: 5 IoU: 0.9950081446006347\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 4%|█▉ | 7/158 [00:31<08:58, 3.56s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 6, i: 1 IoU: 0.7122162230626211\n", + "FAIL label: 7, i: 1 IoU: 0.10093506292076006\n", + "FAIL label: 7, i: 2 IoU: 0.9998915615986623\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 5%|██▏ | 8/158 [00:32<06:34, 2.63s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 7, i: 3 IoU: 0.10939640800206532\n", + "FAIL label: 8, i: 2 IoU: 0.9912084265208747\n", + "FAIL label: 8, i: 3 IoU: 0.9865537737461816\n", + "FAIL label: 8, i: 4 IoU: 0.986622964141607\n", + "FAIL label: 8, i: 5 IoU: 0.9913135645293272\n", + "FAIL label: 8, i: 6 IoU: 0.9913186939395294\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 6%|██▍ | 9/158 [00:39<09:49, 3.96s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 8, i: 8 IoU: 0.9913006277472834\n", + "FAIL label: 9, i: 1 IoU: 0.7621271076354184\n", + "FAIL label: 9, i: 2 IoU: 0.8450710718376875\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 6%|██▋ | 10/158 [00:41<08:13, 3.33s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 9, i: 3 IoU: 0.8030564932324532\n", + "FAIL label: 9, i: 4 IoU: 0.999999990751099\n", + "FAIL label: 10, i: 1 IoU: 0.916000605364933\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 7%|██▉ | 11/158 [00:41<06:21, 2.60s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 10, i: 2 IoU: 0.9151461996335089\n", + "FAIL label: 11, i: 2 IoU: 0.9912637565838156\n", + "FAIL label: 11, i: 3 IoU: 0.9912612328123992\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 8%|███▏ | 12/158 [00:44<06:16, 2.58s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 11, i: 4 IoU: 0.9913025296454008\n", + "FAIL label: 12, i: 2 IoU: 0.6830654664457954\n", + "FAIL label: 12, i: 3 IoU: 0.8773594104753869\n", + "FAIL label: 12, i: 4 IoU: 0.6814276762267611\n", + "FAIL label: 12, i: 5 IoU: 0.9990068671379828\n", + "FAIL label: 12, i: 6 IoU: 0.6834578859973331\n", + "FAIL label: 12, i: 7 IoU: 0.7733918311827014\n", + "FAIL label: 12, i: 8 IoU: 0.8772340747780768\n", + "FAIL label: 12, i: 9 IoU: 0.772902072485895\n", + "FAIL label: 12, i: 10 IoU: 0.683083780992213\n", + "FAIL label: 12, i: 11 IoU: 0.603786226689533\n", + "FAIL label: 12, i: 12 IoU: 0.7734041418188575\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 8%|███▍ | 13/158 [00:53<10:33, 4.37s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 12, i: 13 IoU: 0.9990233600686207\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 9%|███▋ | 14/158 [00:53<07:29, 3.12s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 13, i: 1 IoU: 0.47216301251728043\n", + "FAIL label: 14, i: 1 IoU: 0.9169049880588698\n", + "FAIL label: 14, i: 2 IoU: 0.843307430477678\n", + "FAIL label: 14, i: 3 IoU: 0.8672320389669367\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 9%|███▉ | 15/158 [00:55<06:45, 2.83s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 14, i: 4 IoU: 0.8013347757661077\n", + "FAIL label: 15, i: 1 IoU: 0.9954379957198788\n", + "FAIL label: 15, i: 2 IoU: 0.9939489890173334\n", + "FAIL label: 15, i: 3 IoU: 0.9954511582506983\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 10%|████▎ | 16/158 [00:56<05:39, 2.39s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 15, i: 4 IoU: 0.9894314896039336\n", + "FAIL label: 16, i: 1 IoU: 0.9153973737877844\n", + "FAIL label: 16, i: 2 IoU: 0.8433466312045649\n", + "FAIL label: 16, i: 3 IoU: 0.7987078131370232\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 11%|████▌ | 17/158 [00:58<05:28, 2.33s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 16, i: 4 IoU: 0.8654809533467365\n", + "FAIL label: 17, i: 1 IoU: 0.8433790371270119\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 11%|████▊ | 18/158 [01:00<04:34, 1.96s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 17, i: 2 IoU: 0.9160603832849191\n", + "FAIL label: 18, i: 3 IoU: 0.9882684639969264\n", + "FAIL label: 18, i: 4 IoU: 0.0025188064621530465\n", + "FAIL label: 18, i: 5 IoU: 0.0484668306380221\n", + "FAIL label: 18, i: 6 IoU: 0.5848858905090375\n", + "FAIL label: 18, i: 7 IoU: 0.01365337421431782\n", + "FAIL label: 18, i: 8 IoU: 0.012351783016065904\n", + "FAIL label: 18, i: 9 IoU: 0.2416277252965741\n", + "FAIL label: 18, i: 10 IoU: 0.5837599658422793\n", + "FAIL label: 18, i: 11 IoU: 0.0032292368173571957\n", + "FAIL label: 18, i: 12 IoU: 0.045870357014619584\n", + "FAIL label: 18, i: 13 IoU: 0.025027863919334377\n", + "FAIL label: 18, i: 14 IoU: 0.04715498646147894\n", + "FAIL label: 18, i: 15 IoU: 0.014989417119491494\n", + "FAIL label: 18, i: 16 IoU: 0.9765959736164441\n", + "FAIL label: 18, i: 17 IoU: 0.5857512473047392\n", + "FAIL label: 18, i: 18 IoU: 0.24273894598992174\n", + "FAIL label: 18, i: 19 IoU: 0.02005236853676387\n", + "FAIL label: 18, i: 20 IoU: 0.027250288584163125\n", + "FAIL label: 18, i: 21 IoU: 0.025488794945425854\n", + "FAIL label: 18, i: 22 IoU: 0.025289672205581298\n", + "FAIL label: 18, i: 23 IoU: 0.02560967411746968\n", + "FAIL label: 18, i: 24 IoU: 0.026154376775870562\n", + "FAIL label: 18, i: 25 IoU: 0.9124358616477318\n", + "FAIL label: 18, i: 26 IoU: 0.9882660290563782\n", + "FAIL label: 18, i: 27 IoU: 0.018402355644192306\n", + "FAIL label: 18, i: 28 IoU: 0.025404618909271696\n", + "FAIL label: 18, i: 29 IoU: 0.021708656531527412\n", + "FAIL label: 18, i: 30 IoU: 0.24222313896520478\n", + "FAIL label: 18, i: 31 IoU: 0.9882606868585162\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 12%|█████ | 19/158 [01:20<17:14, 7.44s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 18, i: 32 IoU: 0.9557206186978192\n", + "FAIL label: 19, i: 2 IoU: 0.9774512422414808\n", + "FAIL label: 19, i: 3 IoU: 0.03800596678121059\n", + "FAIL label: 19, i: 6 IoU: 0.7829255632049799\n", + "FAIL label: 19, i: 8 IoU: 0.9746636707728144\n", + "FAIL label: 19, i: 9 IoU: 0.19587164001707222\n", + "FAIL label: 19, i: 10 IoU: 0.30380911467953886\n", + "FAIL label: 19, i: 11 IoU: 0.18641665627856108\n", + "FAIL label: 19, i: 12 IoU: 0.08236341553678125\n", + "FAIL label: 19, i: 13 IoU: 0.015784931793429914\n", + "FAIL label: 19, i: 14 IoU: 0.024861524805580473\n", + "FAIL label: 19, i: 15 IoU: 0.5423330644834442\n", + "FAIL label: 19, i: 16 IoU: 0.035131974942288896\n", + "FAIL label: 19, i: 18 IoU: 0.1809097032842005\n", + "FAIL label: 19, i: 19 IoU: 0.19562169352965575\n", + "FAIL label: 19, i: 20 IoU: 0.9826473212266222\n", + "FAIL label: 19, i: 21 IoU: 0.19587126373686517\n", + "FAIL label: 19, i: 22 IoU: 0.9883075670752045\n", + "FAIL label: 19, i: 23 IoU: 0.18152681652772515\n", + "FAIL label: 19, i: 24 IoU: 0.24652929358453682\n", + "FAIL label: 19, i: 25 IoU: 0.04213105148731472\n", + "FAIL label: 19, i: 27 IoU: 0.30823150223284135\n", + "FAIL label: 19, i: 28 IoU: 0.19602517061895727\n", + "FAIL label: 19, i: 29 IoU: 0.9819743620989128\n", + "FAIL label: 19, i: 30 IoU: 0.9883120219737941\n", + "FAIL label: 19, i: 31 IoU: 0.021760129494771994\n", + "FAIL label: 19, i: 32 IoU: 0.5460161384887331\n", + "FAIL label: 19, i: 33 IoU: 0.0032548961777994576\n", + "FAIL label: 19, i: 34 IoU: 0.03083575110478276\n", + "FAIL label: 19, i: 35 IoU: 0.2661686559792982\n", + "FAIL label: 19, i: 36 IoU: 0.7498430708208742\n", + "FAIL label: 19, i: 37 IoU: 0.9882688965002933\n", + "FAIL label: 19, i: 38 IoU: 0.9731375600337068\n", + "FAIL label: 19, i: 39 IoU: 0.09072073089008503\n", + "FAIL label: 19, i: 40 IoU: 0.9772307730362002\n", + "FAIL label: 19, i: 41 IoU: 0.9925388688106603\n", + "FAIL label: 19, i: 42 IoU: 0.7617951531147109\n", + "FAIL label: 19, i: 43 IoU: 0.7914271495695858\n", + "FAIL label: 19, i: 44 IoU: 0.7425493432961079\n", + "FAIL label: 19, i: 45 IoU: 0.01819431137920125\n", + "FAIL label: 19, i: 46 IoU: 0.05744936172437481\n", + "FAIL label: 19, i: 47 IoU: 0.18718585225129822\n", + "FAIL label: 19, i: 48 IoU: 0.9812273863119968\n", + "FAIL label: 19, i: 49 IoU: 0.16874297484134188\n", + "FAIL label: 19, i: 50 IoU: 0.02932861962348203\n", + "FAIL label: 19, i: 51 IoU: 0.36531848828883906\n", + "FAIL label: 19, i: 52 IoU: 0.9824080971985487\n", + "FAIL label: 19, i: 53 IoU: 0.09083185866147955\n", + "FAIL label: 19, i: 54 IoU: 0.03583263967140223\n", + "FAIL label: 19, i: 55 IoU: 0.8251032974630866\n", + "FAIL label: 19, i: 56 IoU: 0.5462916748253124\n", + "FAIL label: 19, i: 57 IoU: 0.09032154685015416\n", + "FAIL label: 19, i: 58 IoU: 0.03414604629117849\n", + "FAIL label: 19, i: 59 IoU: 0.9940484480804381\n", + "FAIL label: 19, i: 60 IoU: 0.9881266159429885\n", + "FAIL label: 19, i: 61 IoU: 0.04664905013690878\n", + "FAIL label: 19, i: 62 IoU: 0.7684304663319903\n", + "FAIL label: 19, i: 63 IoU: 0.023600585523854172\n", + "FAIL label: 19, i: 64 IoU: 0.3038086297883715\n", + "FAIL label: 19, i: 65 IoU: 0.9855714862090889\n", + "FAIL label: 19, i: 66 IoU: 0.36476655047851253\n", + "FAIL label: 19, i: 67 IoU: 0.9878270089955195\n", + "FAIL label: 19, i: 68 IoU: 0.5477713665524203\n", + "FAIL label: 19, i: 69 IoU: 0.02088497092889637\n", + "FAIL label: 19, i: 71 IoU: 0.7773221366636632\n", + "FAIL label: 19, i: 72 IoU: 0.5412266559368765\n", + "FAIL label: 19, i: 73 IoU: 0.4016454720730764\n", + "FAIL label: 19, i: 74 IoU: 0.9547045249754462\n", + "FAIL label: 19, i: 75 IoU: 0.7705280133900928\n", + "FAIL label: 19, i: 76 IoU: 0.7498466753257911\n", + "FAIL label: 19, i: 77 IoU: 0.5551154321510348\n", + "FAIL label: 19, i: 78 IoU: 0.8251001502072599\n", + "FAIL label: 19, i: 79 IoU: 0.04022281306730935\n", + "FAIL label: 19, i: 80 IoU: 0.9873648715233847\n", + "FAIL label: 19, i: 81 IoU: 0.4778585441598775\n", + "FAIL label: 19, i: 82 IoU: 0.9887807863494356\n", + "FAIL label: 19, i: 83 IoU: 0.1691278663406635\n", + "FAIL label: 19, i: 84 IoU: 0.9131096958085387\n", + "FAIL label: 19, i: 85 IoU: 0.03185931333172202\n", + "FAIL label: 19, i: 86 IoU: 0.917341643089878\n", + "FAIL label: 19, i: 87 IoU: 0.2000373325363423\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 13%|█████ | 20/158 [03:19<1:34:12, 40.96s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 19, i: 88 IoU: 0.790623023166806\n", + "FAIL label: 20, i: 1 IoU: 0.8434105415636716\n", + "FAIL label: 20, i: 2 IoU: 0.9161239966430783\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 13%|█████▎ | 21/158 [03:20<1:06:31, 29.14s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 20, i: 3 IoU: 0.9151125025499951\n", + "FAIL label: 21, i: 1 IoU: 0.682936365942041\n", + "FAIL label: 21, i: 3 IoU: 0.7461944940821114\n", + "FAIL label: 21, i: 4 IoU: 0.942697559584912\n", + "FAIL label: 21, i: 5 IoU: 0.773066337158479\n", + "FAIL label: 21, i: 6 IoU: 0.580660187921634\n", + "FAIL label: 21, i: 7 IoU: 0.7730732256044107\n", + "FAIL label: 21, i: 8 IoU: 0.5753414881623378\n", + "FAIL label: 21, i: 9 IoU: 0.9367130313753453\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 14%|█████▊ | 22/158 [03:28<51:19, 22.64s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 21, i: 10 IoU: 0.9209076100173705\n", + "FAIL label: 22, i: 1 IoU: 0.007972203927431606\n", + "FAIL label: 22, i: 2 IoU: 0.059654487893238524\n", + "FAIL label: 22, i: 3 IoU: 0.8292869581756113\n", + "FAIL label: 22, i: 4 IoU: 0.07132877161933554\n", + "FAIL label: 22, i: 5 IoU: 0.007594431801768884\n", + "FAIL label: 22, i: 7 IoU: 0.861914023770954\n", + "FAIL label: 22, i: 9 IoU: 0.8450643035298813\n", + "FAIL label: 22, i: 10 IoU: 0.9886794814448442\n", + "FAIL label: 22, i: 11 IoU: 0.007121621681412606\n", + "FAIL label: 22, i: 12 IoU: 0.20833300850716094\n", + "FAIL label: 22, i: 14 IoU: 0.003099958623662712\n", + "FAIL label: 22, i: 15 IoU: 0.20164529473915005\n", + "FAIL label: 22, i: 16 IoU: 0.9941267137858101\n", + "FAIL label: 22, i: 17 IoU: 0.8450583587990915\n", + "FAIL label: 22, i: 18 IoU: 0.9766066111713275\n", + "FAIL label: 22, i: 19 IoU: 0.86191648401938\n", + "FAIL label: 22, i: 20 IoU: 0.004995260150536424\n", + "FAIL label: 22, i: 21 IoU: 0.1103390137021025\n", + "FAIL label: 22, i: 22 IoU: 0.43086341739676437\n", + "FAIL label: 22, i: 23 IoU: 0.9657493686209166\n", + "FAIL label: 22, i: 24 IoU: 0.006062024964591288\n", + "FAIL label: 22, i: 25 IoU: 0.9882543078297892\n", + "FAIL label: 22, i: 27 IoU: 0.53335347397824\n", + "FAIL label: 22, i: 28 IoU: 0.8450577616390562\n", + "FAIL label: 22, i: 29 IoU: 0.8292829168637669\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 15%|██████ | 23/158 [04:02<58:20, 25.93s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 22, i: 30 IoU: 0.5257882060627843\n", + "FAIL label: 23, i: 1 IoU: 0.2443012277230269\n", + "FAIL label: 23, i: 2 IoU: 0.2629469166206281\n", + "FAIL label: 23, i: 3 IoU: 0.603125439731787\n", + "FAIL label: 23, i: 4 IoU: 0.002134816632811821\n", + "FAIL label: 23, i: 6 IoU: 0.762074868367664\n", + "FAIL label: 23, i: 7 IoU: 0.2662032622949123\n", + "FAIL label: 23, i: 8 IoU: 0.25060850712024413\n", + "FAIL label: 23, i: 9 IoU: 0.08065164804724086\n", + "FAIL label: 23, i: 10 IoU: 0.6834130250533637\n", + "FAIL label: 23, i: 11 IoU: 0.2661181797936193\n", + "FAIL label: 23, i: 12 IoU: 0.1160018663088608\n", + "FAIL label: 23, i: 13 IoU: 0.1164853470314532\n", + "FAIL label: 23, i: 14 IoU: 0.08187484726229202\n", + "FAIL label: 23, i: 15 IoU: 0.07999823123893308\n", + "FAIL label: 23, i: 16 IoU: 0.0693676865767367\n", + "FAIL label: 23, i: 17 IoU: 0.9886264186659558\n", + "FAIL label: 23, i: 18 IoU: 0.999664275566909\n", + "FAIL label: 23, i: 19 IoU: 0.08187528162852865\n", + "FAIL label: 23, i: 20 IoU: 0.8023770954029319\n", + "FAIL label: 23, i: 21 IoU: 0.845033052236314\n", + "FAIL label: 23, i: 22 IoU: 0.6092412831436991\n", + "FAIL label: 23, i: 23 IoU: 0.11647178937442185\n", + "FAIL label: 23, i: 24 IoU: 0.08125977826139838\n", + "FAIL label: 23, i: 25 IoU: 0.017783490312699293\n", + "FAIL label: 23, i: 26 IoU: 0.11794012627717237\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 15%|██████▍ | 24/158 [04:15<49:34, 22.20s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 23, i: 27 IoU: 0.03912375663760288\n", + "FAIL label: 24, i: 1 IoU: 0.9151912871209427\n", + "FAIL label: 24, i: 2 IoU: 0.4001382279773053\n", + "FAIL label: 24, i: 3 IoU: 0.40209338947818773\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 16%|██████▋ | 25/158 [04:17<35:36, 16.06s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 24, i: 4 IoU: 0.9160006872466848\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 16%|██████▉ | 26/158 [04:17<25:10, 11.44s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 25, i: 1 IoU: 0.8435441191311661\n", + "FAIL label: 26, i: 2 IoU: 0.44452034396996065\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 18%|███████▍ | 28/158 [04:20<13:28, 6.22s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 27, i: 1 IoU: 0.3780093395774429\n", + "FAIL label: 28, i: 1 IoU: 0.8435572448767735\n", + "FAIL label: 28, i: 2 IoU: 0.9158190013237093\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 18%|███████▋ | 29/158 [04:22<10:21, 4.82s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 28, i: 3 IoU: 0.9153602519939127\n", + "FAIL label: 29, i: 1 IoU: 0.31016376706269033\n", + "FAIL label: 30, i: 1 IoU: 0.20075819425965785\n", + "FAIL label: 30, i: 2 IoU: 0.23397765083248212\n", + "FAIL label: 30, i: 3 IoU: 0.36580790532882856\n", + "FAIL label: 30, i: 4 IoU: 0.1763778717696363\n", + "FAIL label: 30, i: 5 IoU: 0.4856774646168649\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 20%|████████▏ | 31/158 [04:24<06:28, 3.06s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 30, i: 6 IoU: 0.7745676323720219\n", + "FAIL label: 31, i: 1 IoU: 0.8433428092058702\n", + "FAIL label: 31, i: 3 IoU: 0.9999854732709319\n", + "FAIL label: 31, i: 4 IoU: 0.8433537580990518\n", + "FAIL label: 31, i: 5 IoU: 0.9153839553419709\n", + "FAIL label: 31, i: 6 IoU: 0.33960886624849795\n", + "FAIL label: 31, i: 7 IoU: 0.915849959665912\n", + "FAIL label: 31, i: 8 IoU: 0.8433616437543685\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 20%|████████▌ | 32/158 [04:30<07:59, 3.81s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 31, i: 9 IoU: 0.9153867620271284\n", + "FAIL label: 32, i: 3 IoU: 0.5194314897298614\n", + "FAIL label: 32, i: 5 IoU: 0.48701543222226085\n", + "FAIL label: 32, i: 6 IoU: 0.06253425454904543\n", + "FAIL label: 32, i: 8 IoU: 0.066993864512601\n", + "FAIL label: 32, i: 9 IoU: 0.47937376371056784\n", + "FAIL label: 32, i: 10 IoU: 0.9657884975091581\n", + "FAIL label: 32, i: 13 IoU: 0.5373060874256407\n", + "FAIL label: 32, i: 14 IoU: 0.07975110199098062\n", + "FAIL label: 32, i: 15 IoU: 0.521148230072425\n", + "FAIL label: 32, i: 16 IoU: 0.05295243365244401\n", + "FAIL label: 32, i: 17 IoU: 0.5183182222406223\n", + "FAIL label: 32, i: 18 IoU: 0.9694721415100868\n", + "FAIL label: 32, i: 19 IoU: 0.8855027323419071\n", + "FAIL label: 32, i: 20 IoU: 0.519910193182164\n", + "FAIL label: 32, i: 21 IoU: 0.1095659456606161\n", + "FAIL label: 32, i: 22 IoU: 0.9657965177785731\n", + "FAIL label: 32, i: 23 IoU: 0.2605878483426083\n", + "FAIL label: 32, i: 24 IoU: 0.08763282323348302\n", + "FAIL label: 32, i: 25 IoU: 0.9658142661354232\n", + "FAIL label: 32, i: 26 IoU: 0.9657912749701886\n", + "FAIL label: 32, i: 27 IoU: 0.49008342959877654\n", + "FAIL label: 32, i: 29 IoU: 0.10907825551151722\n", + "FAIL label: 32, i: 30 IoU: 0.24891678639645407\n", + "FAIL label: 32, i: 31 IoU: 0.5383520347581948\n", + "FAIL label: 32, i: 32 IoU: 0.5286559766594706\n", + "FAIL label: 32, i: 33 IoU: 0.5204336183387878\n", + "FAIL label: 32, i: 34 IoU: 0.5165831426115176\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 21%|████████▊ | 33/158 [05:59<54:11, 26.01s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 32, i: 35 IoU: 0.36685879823460277\n", + "FAIL label: 33, i: 1 IoU: 0.3850491250619594\n", + "FAIL label: 33, i: 2 IoU: 0.3898977762133232\n", + "FAIL label: 33, i: 3 IoU: 0.5955706225230821\n", + "FAIL label: 33, i: 5 IoU: 0.3892251847518628\n", + "FAIL label: 33, i: 6 IoU: 0.8245936777323138\n", + "FAIL label: 33, i: 7 IoU: 0.8033995566585375\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 22%|█████████ | 34/158 [06:04<42:08, 20.39s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 33, i: 8 IoU: 0.8028234766394551\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 22%|█████████▎ | 35/158 [06:04<30:23, 14.82s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 34, i: 1 IoU: 0.8430133082011527\n", + "FAIL label: 34, i: 2 IoU: 0.9136781914465233\n", + "FAIL label: 35, i: 1 IoU: 0.4341019720196542\n", + "FAIL label: 35, i: 2 IoU: 0.4193767602363829\n", + "FAIL label: 35, i: 3 IoU: 0.7751335307059177\n", + "FAIL label: 35, i: 4 IoU: 0.4684109741530189\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 23%|█████████▌ | 36/158 [06:06<22:38, 11.13s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 35, i: 5 IoU: 0.7930069734869111\n", + "FAIL label: 36, i: 1 IoU: 0.8026237012147692\n", + "FAIL label: 36, i: 2 IoU: 0.8030950746947939\n", + "FAIL label: 36, i: 3 IoU: 0.825649414518111\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 23%|█████████▊ | 37/158 [06:08<17:07, 8.49s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 36, i: 4 IoU: 0.9999938185126345\n", + "FAIL label: 37, i: 1 IoU: 0.46705828758092655\n", + "FAIL label: 37, i: 2 IoU: 0.6004481697553506\n", + "FAIL label: 37, i: 3 IoU: 0.9938366982602898\n", + "FAIL label: 37, i: 4 IoU: 0.46560517038867194\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 24%|██████████ | 38/158 [06:11<13:42, 6.85s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 37, i: 5 IoU: 0.6012176933923705\n", + "FAIL label: 38, i: 2 IoU: 0.512634809409788\n", + "FAIL label: 38, i: 3 IoU: 0.14561119443695505\n", + "FAIL label: 38, i: 4 IoU: 0.08635479828626166\n", + "FAIL label: 38, i: 6 IoU: 0.018696336312759188\n", + "FAIL label: 38, i: 7 IoU: 0.40038567968865635\n", + "FAIL label: 38, i: 8 IoU: 0.13205832847235766\n", + "FAIL label: 38, i: 10 IoU: 0.35086477055465815\n", + "FAIL label: 38, i: 11 IoU: 0.35354185027722945\n", + "FAIL label: 38, i: 12 IoU: 0.9656759647879102\n", + "FAIL label: 38, i: 13 IoU: 0.6683543369793867\n", + "FAIL label: 38, i: 14 IoU: 0.132343396267148\n", + "FAIL label: 38, i: 15 IoU: 0.06523118557512708\n", + "FAIL label: 38, i: 16 IoU: 0.588532582979945\n", + "FAIL label: 38, i: 17 IoU: 0.5752334732748579\n", + "FAIL label: 38, i: 18 IoU: 0.6286812854836555\n", + "FAIL label: 38, i: 19 IoU: 0.08728396674871344\n", + "FAIL label: 38, i: 20 IoU: 0.14558953986946302\n", + "FAIL label: 38, i: 21 IoU: 0.30012835610663957\n", + "FAIL label: 38, i: 22 IoU: 0.39793028068973313\n", + "FAIL label: 38, i: 23 IoU: 0.13034107831693614\n", + "FAIL label: 38, i: 24 IoU: 0.2635625309520034\n", + "FAIL label: 38, i: 25 IoU: 0.9657137409431581\n", + "FAIL label: 38, i: 26 IoU: 0.23713614655519094\n", + "FAIL label: 38, i: 27 IoU: 0.3752574769797606\n", + "FAIL label: 38, i: 28 IoU: 0.6687704078286111\n", + "FAIL label: 38, i: 29 IoU: 0.5479985262202614\n", + "FAIL label: 38, i: 30 IoU: 0.39800282059116276\n", + "FAIL label: 38, i: 31 IoU: 0.06486634339085444\n", + "FAIL label: 38, i: 32 IoU: 0.014439338424445131\n", + "FAIL label: 38, i: 33 IoU: 0.35081407610750787\n", + "FAIL label: 38, i: 34 IoU: 0.20558192399834407\n", + "FAIL label: 38, i: 35 IoU: 0.5290850207438756\n", + "FAIL label: 38, i: 36 IoU: 0.14504849181019616\n", + "FAIL label: 38, i: 37 IoU: 0.6785275962108929\n", + "FAIL label: 38, i: 38 IoU: 0.685914219790576\n", + "FAIL label: 38, i: 39 IoU: 0.540363142696505\n", + "FAIL label: 38, i: 40 IoU: 0.01275244579728981\n", + "FAIL label: 38, i: 41 IoU: 0.13647671535064115\n", + "FAIL label: 38, i: 42 IoU: 0.23147424143132347\n", + "FAIL label: 38, i: 43 IoU: 0.5565422600176584\n", + "FAIL label: 38, i: 44 IoU: 0.5711024112157487\n", + "FAIL label: 38, i: 45 IoU: 0.3980180664605629\n", + "FAIL label: 38, i: 46 IoU: 0.4182934381196125\n", + "FAIL label: 38, i: 47 IoU: 0.41525463049628736\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 25%|██████████▎ | 39/158 [06:49<31:36, 15.94s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 38, i: 48 IoU: 0.6735502456632044\n", + "FAIL label: 39, i: 1 IoU: 0.03395818425107658\n", + "FAIL label: 39, i: 3 IoU: 0.9938338904061738\n", + "FAIL label: 39, i: 4 IoU: 0.5558974851150621\n", + "FAIL label: 39, i: 5 IoU: 0.13515297477445243\n", + "FAIL label: 39, i: 6 IoU: 0.9938401186377048\n", + "FAIL label: 39, i: 7 IoU: 0.5558970571431593\n", + "FAIL label: 39, i: 8 IoU: 0.5542847032177561\n", + "FAIL label: 39, i: 9 IoU: 0.135076335623055\n", + "FAIL label: 39, i: 10 IoU: 0.5542799555295289\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 25%|██████████▋ | 40/158 [06:56<26:13, 13.33s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 39, i: 11 IoU: 0.135550007444671\n", + "FAIL label: 40, i: 1 IoU: 0.9151075686460775\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 26%|██████████▉ | 41/158 [06:57<18:53, 9.69s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 40, i: 2 IoU: 0.8436006772408544\n", + "FAIL label: 41, i: 1 IoU: 0.08982039972622885\n", + "FAIL label: 41, i: 3 IoU: 0.0900551149498885\n", + "FAIL label: 41, i: 4 IoU: 0.5601937507713781\n", + "FAIL label: 41, i: 5 IoU: 0.5934232946804547\n", + "FAIL label: 41, i: 6 IoU: 0.9095844999195681\n", + "FAIL label: 41, i: 7 IoU: 0.09971748826044609\n", + "FAIL label: 41, i: 8 IoU: 0.9628509630330621\n", + "FAIL label: 41, i: 9 IoU: 0.08910474208383451\n", + "FAIL label: 41, i: 10 IoU: 0.061510665401247944\n", + "FAIL label: 41, i: 11 IoU: 0.5981617378845803\n", + "FAIL label: 41, i: 12 IoU: 0.09851176647403143\n", + "FAIL label: 41, i: 13 IoU: 0.1461561727270611\n", + "FAIL label: 41, i: 14 IoU: 0.5498825904916589\n", + "FAIL label: 41, i: 15 IoU: 0.9690820588881474\n", + "FAIL label: 41, i: 16 IoU: 0.1447957767564122\n", + "FAIL label: 41, i: 17 IoU: 0.5550135072278303\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 27%|███████████▏ | 42/158 [07:07<18:37, 9.64s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 41, i: 18 IoU: 0.09996566539149877\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 27%|███████████▍ | 43/158 [07:07<13:04, 6.82s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 42, i: 1 IoU: 0.007356975249176217\n", + "FAIL label: 43, i: 1 IoU: 0.09066304958321912\n", + "FAIL label: 43, i: 2 IoU: 0.10004553025464684\n", + "FAIL label: 43, i: 3 IoU: 0.1979231541062441\n", + "FAIL label: 43, i: 4 IoU: 0.18354782782767767\n", + "FAIL label: 43, i: 5 IoU: 0.19149790024589028\n", + "FAIL label: 43, i: 6 IoU: 0.0963780439900404\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 28%|███████████▋ | 44/158 [07:08<09:54, 5.22s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 43, i: 7 IoU: 0.9469711995110248\n", + "FAIL label: 44, i: 1 IoU: 0.15358004784777474\n", + "FAIL label: 44, i: 2 IoU: 0.8271163487989152\n", + "FAIL label: 44, i: 3 IoU: 0.2624353114013321\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 28%|███████████▉ | 45/158 [07:09<07:34, 4.02s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 44, i: 4 IoU: 0.3661283097339448\n", + "FAIL label: 45, i: 1 IoU: 0.8239665784373518\n", + "FAIL label: 45, i: 2 IoU: 0.8258742967171462\n", + "FAIL label: 45, i: 3 IoU: 0.683772854655383\n", + "FAIL label: 45, i: 4 IoU: 0.8028207319094468\n", + "FAIL label: 45, i: 5 IoU: 0.8032681006904484\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 29%|████████████▏ | 46/158 [07:13<07:09, 3.84s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 45, i: 6 IoU: 0.8026450020965097\n", + "FAIL label: 46, i: 1 IoU: 0.8427731719641065\n", + "FAIL label: 46, i: 2 IoU: 0.9153252573917504\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 30%|████████████▍ | 47/158 [07:14<05:26, 2.94s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 46, i: 3 IoU: 0.9153096441817556\n", + "FAIL label: 46, i: 4 IoU: 0.9999925689445253\n", + "FAIL label: 47, i: 1 IoU: 0.9156245171130467\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 30%|████████████▊ | 48/158 [07:14<04:09, 2.27s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 47, i: 2 IoU: 0.8428694894095335\n", + "FAIL label: 47, i: 3 IoU: 0.9155863298771904\n", + "FAIL label: 48, i: 1 IoU: 0.9990096446001775\n", + "FAIL label: 48, i: 2 IoU: 0.6830623133189799\n", + "FAIL label: 48, i: 3 IoU: 0.5350104012408574\n", + "FAIL label: 48, i: 4 IoU: 0.031982261459943\n", + "FAIL label: 48, i: 5 IoU: 0.04068509784273374\n", + "FAIL label: 48, i: 6 IoU: 0.13466017985187076\n", + "FAIL label: 48, i: 7 IoU: 0.04040415943774762\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 31%|█████████████ | 49/158 [07:18<04:48, 2.65s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 48, i: 8 IoU: 0.5347324610899336\n", + "FAIL label: 49, i: 1 IoU: 0.7056960960939562\n", + "FAIL label: 49, i: 2 IoU: 0.2641066674154206\n", + "FAIL label: 49, i: 3 IoU: 0.7034484174264136\n", + "FAIL label: 49, i: 4 IoU: 0.223893987451369\n", + "FAIL label: 49, i: 5 IoU: 0.7097813131847331\n", + "FAIL label: 49, i: 6 IoU: 0.6999674950164896\n", + "FAIL label: 49, i: 7 IoU: 0.9683482114119665\n", + "FAIL label: 49, i: 8 IoU: 0.5964945603064827\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 32%|█████████████▎ | 50/158 [07:22<05:33, 3.09s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 49, i: 9 IoU: 0.7042520673679805\n", + "FAIL label: 50, i: 1 IoU: 0.9152080388338767\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 32%|█████████████▌ | 51/158 [07:23<04:07, 2.31s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 50, i: 2 IoU: 0.8429946283837287\n", + "FAIL label: 51, i: 1 IoU: 0.06500442154612261\n", + "FAIL label: 51, i: 2 IoU: 0.3583148252066979\n", + "FAIL label: 51, i: 3 IoU: 0.5991233570832903\n", + "FAIL label: 51, i: 4 IoU: 0.326756671506977\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 33%|█████████████▊ | 52/158 [07:25<04:02, 2.29s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 51, i: 5 IoU: 0.7141559548200982\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 34%|██████████████ | 53/158 [07:25<02:57, 1.69s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 52, i: 1 IoU: 0.8204446338202439\n", + "FAIL label: 53, i: 1 IoU: 0.9950753633270243\n", + "FAIL label: 53, i: 2 IoU: 0.9943879249206222\n", + "FAIL label: 53, i: 3 IoU: 0.9967325015603868\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 34%|██████████████▎ | 54/158 [07:26<02:44, 1.58s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 53, i: 4 IoU: 0.9982532298844378\n", + "FAIL label: 54, i: 1 IoU: 0.9153262546386316\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 35%|██████████████▌ | 55/158 [07:27<02:15, 1.32s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 54, i: 2 IoU: 0.8427723801425402\n", + "FAIL label: 54, i: 3 IoU: 0.9151308354337767\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 35%|██████████████▉ | 56/158 [07:27<01:43, 1.01s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 55, i: 1 IoU: 0.7180463047952219\n", + "FAIL label: 56, i: 1 IoU: 0.5521606561357969\n", + "FAIL label: 56, i: 2 IoU: 0.8935054774845537\n", + "FAIL label: 56, i: 3 IoU: 0.8941069837868798\n", + "FAIL label: 56, i: 4 IoU: 0.6008961092266758\n", + "FAIL label: 56, i: 5 IoU: 0.5719062333183776\n", + "FAIL label: 56, i: 6 IoU: 0.5530414235191047\n", + "FAIL label: 56, i: 7 IoU: 0.8923087105751871\n", + "FAIL label: 56, i: 8 IoU: 0.5529215413504073\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 36%|███████████████▏ | 57/158 [07:33<03:54, 2.33s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 56, i: 9 IoU: 0.8930717725268775\n", + "FAIL label: 56, i: 10 IoU: 0.9938836369672028\n", + "FAIL label: 57, i: 1 IoU: 0.996514617316769\n", + "FAIL label: 57, i: 2 IoU: 0.9952433732100152\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 37%|███████████████▍ | 58/158 [07:33<03:00, 1.80s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 57, i: 3 IoU: 0.8212562267797656\n", + "FAIL label: 58, i: 1 IoU: 0.9999929200180288\n", + "FAIL label: 58, i: 2 IoU: 0.9149357087901604\n", + "FAIL label: 58, i: 3 IoU: 0.8429114380102499\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 37%|███████████████▋ | 59/158 [07:34<02:33, 1.55s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 58, i: 4 IoU: 0.8429188483689624\n", + "FAIL label: 59, i: 1 IoU: 0.9169506782280462\n", + "FAIL label: 59, i: 2 IoU: 0.914190743487813\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 38%|███████████████▉ | 60/158 [07:36<02:30, 1.54s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 59, i: 3 IoU: 0.8433107774078706\n", + "FAIL label: 60, i: 1 IoU: 0.8434393101603221\n", + "FAIL label: 60, i: 2 IoU: 0.916144540035421\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 39%|████████████████▏ | 61/158 [07:37<02:29, 1.54s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 60, i: 3 IoU: 0.9149995049374438\n", + "FAIL label: 61, i: 1 IoU: 0.9155702465594511\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 39%|████████████████▍ | 62/158 [07:38<01:49, 1.14s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 62, i: 1 IoU: 0.842973835838857\n", + "FAIL label: 62, i: 2 IoU: 0.8429872110037067\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 40%|████████████████▋ | 63/158 [07:39<01:46, 1.12s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 62, i: 3 IoU: 0.8429822518730672\n", + "FAIL label: 62, i: 4 IoU: 0.915282352125502\n", + "FAIL label: 63, i: 1 IoU: 0.8429991739825993\n", + "FAIL label: 63, i: 2 IoU: 0.9169075551540835\n", + "FAIL label: 63, i: 3 IoU: 0.9135384482199937\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 41%|█████████████████ | 64/158 [07:40<01:42, 1.09s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 63, i: 4 IoU: 0.9135352134605788\n", + "FAIL label: 64, i: 1 IoU: 0.5012803750471599\n", + "FAIL label: 64, i: 2 IoU: 0.7063042415272666\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 41%|█████████████████▎ | 65/158 [07:41<01:59, 1.28s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 64, i: 3 IoU: 0.7002940401760164\n", + "FAIL label: 65, i: 1 IoU: 0.9155889112399954\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 42%|█████████████████▌ | 66/158 [07:42<01:36, 1.05s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 65, i: 2 IoU: 0.8429077852408408\n", + "FAIL label: 66, i: 1 IoU: 0.9954569948880397\n", + "FAIL label: 66, i: 2 IoU: 0.3441210426307689\n", + "FAIL label: 66, i: 3 IoU: 0.993949136314007\n", + "FAIL label: 66, i: 4 IoU: 0.99392939015585\n", + "FAIL label: 66, i: 5 IoU: 0.9954392336272389\n", + "FAIL label: 66, i: 6 IoU: 0.3435827422112657\n", + "FAIL label: 66, i: 7 IoU: 0.3445256526435803\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 43%|██████████████████ | 68/158 [07:45<01:51, 1.24s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 66, i: 8 IoU: 0.3454706812002677\n", + "FAIL label: 67, i: 1 IoU: 0.9614856798002672\n", + "FAIL label: 68, i: 1 IoU: 0.7002962733461816\n", + "FAIL label: 68, i: 2 IoU: 0.7000466537673152\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 44%|██████████████████▎ | 69/158 [07:47<02:08, 1.45s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 68, i: 3 IoU: 0.7000536753226033\n", + "FAIL label: 69, i: 1 IoU: 0.715045875578728\n", + "FAIL label: 69, i: 2 IoU: 0.025139729172649943\n", + "FAIL label: 69, i: 3 IoU: 0.513694252322728\n", + "FAIL label: 69, i: 4 IoU: 0.7150512250960362\n", + "FAIL label: 69, i: 5 IoU: 0.3627834420305251\n", + "FAIL label: 69, i: 6 IoU: 0.030352599133712476\n", + "FAIL label: 69, i: 7 IoU: 0.11428311941702936\n", + "FAIL label: 69, i: 8 IoU: 0.05915608347442177\n", + "FAIL label: 69, i: 9 IoU: 0.10090682407856956\n", + "FAIL label: 69, i: 10 IoU: 0.7152687491217639\n", + "FAIL label: 69, i: 11 IoU: 0.36133406428096176\n", + "FAIL label: 69, i: 12 IoU: 0.7152628244739037\n", + "FAIL label: 69, i: 13 IoU: 0.5113774295936832\n", + "FAIL label: 69, i: 14 IoU: 0.11200529289620645\n", + "FAIL label: 69, i: 15 IoU: 0.04745674778348381\n", + "FAIL label: 69, i: 16 IoU: 0.05301934410664489\n", + "FAIL label: 69, i: 17 IoU: 0.04696729971101652\n", + "FAIL label: 69, i: 18 IoU: 0.0774657504366707\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 44%|██████████████████▌ | 70/158 [07:55<05:03, 3.45s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 69, i: 19 IoU: 0.5055188273287422\n", + "FAIL label: 70, i: 1 IoU: 0.8532231099580838\n", + "FAIL label: 70, i: 2 IoU: 0.9173275535340609\n", + "FAIL label: 70, i: 3 IoU: 0.8212491506420617\n", + "FAIL label: 70, i: 4 IoU: 0.885239811569498\n", + "FAIL label: 70, i: 5 IoU: 0.7436426033143856\n", + "FAIL label: 70, i: 6 IoU: 0.7050439923513071\n", + "FAIL label: 70, i: 7 IoU: 0.8372292980016972\n", + "FAIL label: 70, i: 8 IoU: 0.6793367854872849\n", + "FAIL label: 70, i: 9 IoU: 0.8692239160602578\n", + "FAIL label: 70, i: 10 IoU: 0.9681905187615356\n", + "FAIL label: 70, i: 11 IoU: 0.32701484613707116\n", + "FAIL label: 70, i: 12 IoU: 0.3152310723328548\n", + "FAIL label: 70, i: 13 IoU: 0.3388230054889661\n", + "FAIL label: 70, i: 14 IoU: 0.44324667975293863\n", + "FAIL label: 70, i: 15 IoU: 0.4558390477857239\n", + "FAIL label: 70, i: 16 IoU: 0.32112039251888147\n", + "FAIL label: 70, i: 17 IoU: 0.40642740423461327\n", + "FAIL label: 70, i: 18 IoU: 0.3495958553200418\n", + "FAIL label: 70, i: 19 IoU: 0.4706882017483577\n", + "FAIL label: 70, i: 20 IoU: 0.4211677300975124\n", + "FAIL label: 70, i: 21 IoU: 0.3582505250842286\n", + "FAIL label: 70, i: 22 IoU: 0.4701330784308889\n", + "FAIL label: 70, i: 23 IoU: 0.4137986634838313\n", + "FAIL label: 70, i: 24 IoU: 0.39904923869562037\n", + "FAIL label: 70, i: 25 IoU: 0.4285349617326156\n", + "FAIL label: 70, i: 26 IoU: 0.30934600100091053\n", + "FAIL label: 70, i: 27 IoU: 0.7179025840120316\n", + "FAIL label: 70, i: 28 IoU: 0.6921852785642312\n", + "FAIL label: 70, i: 29 IoU: 0.9368922656083546\n", + "FAIL label: 70, i: 30 IoU: 0.7603910256489023\n", + "FAIL label: 70, i: 31 IoU: 0.8050179747987\n", + "FAIL label: 70, i: 32 IoU: 0.7859274032032877\n", + "FAIL label: 70, i: 33 IoU: 0.43589134954344944\n", + "FAIL label: 70, i: 34 IoU: 0.3329168088260736\n", + "FAIL label: 70, i: 35 IoU: 0.30346673177098205\n", + "FAIL label: 70, i: 36 IoU: 0.3607720129365899\n", + "FAIL label: 70, i: 37 IoU: 0.9012720740496553\n", + "FAIL label: 70, i: 38 IoU: 0.7307707092387052\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 46%|███████████████████▏ | 72/158 [08:03<04:41, 3.27s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 70, i: 39 IoU: 0.6664977652124676\n", + "FAIL label: 71, i: 1 IoU: 0.9821982969125563\n", + "FAIL label: 72, i: 1 IoU: 0.9152900520337373\n", + "FAIL label: 72, i: 2 IoU: 0.9158898492800907\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 46%|███████████████████▍ | 73/158 [08:04<03:55, 2.77s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 72, i: 3 IoU: 0.8433729901286159\n", + "FAIL label: 73, i: 1 IoU: 1.0000000011689651\n", + "FAIL label: 73, i: 2 IoU: 0.999992723192376\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 47%|███████████████████▋ | 74/158 [08:05<03:07, 2.23s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 73, i: 3 IoU: 0.9999980899110612\n", + "FAIL label: 73, i: 4 IoU: 0.9999997954311112\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 47%|███████████████████▉ | 75/158 [08:06<02:17, 1.65s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 74, i: 1 IoU: 0.9799420062177642\n", + "FAIL label: 74, i: 2 IoU: 0.979874347241459\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 48%|████████████████████▏ | 76/158 [08:06<01:38, 1.20s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 75, i: 1 IoU: 0.9556169247612657\n", + "FAIL label: 76, i: 1 IoU: 0.8428148299759883\n", + "FAIL label: 76, i: 2 IoU: 0.8428127917771737\n", + "FAIL label: 76, i: 3 IoU: 0.915065072149557\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 49%|████████████████████▍ | 77/158 [08:07<01:32, 1.14s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 76, i: 4 IoU: 0.9154005764996468\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 49%|████████████████████▋ | 78/158 [08:07<01:15, 1.06it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 77, i: 1 IoU: 0.9158167686218105\n", + "FAIL label: 78, i: 1 IoU: 0.9938279758250715\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 50%|█████████████████████ | 79/158 [08:08<01:05, 1.21it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 78, i: 2 IoU: 0.9168879654282615\n", + "FAIL label: 78, i: 3 IoU: 0.9141136678929097\n", + "FAIL label: 80, i: 1 IoU: 0.811686115260117\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 51%|█████████████████████▌ | 81/158 [08:09<00:47, 1.61it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 80, i: 2 IoU: 0.8116842335062312\n", + "FAIL label: 80, i: 3 IoU: 0.9999946847474193\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 53%|██████████████████████ | 83/158 [08:09<00:37, 1.98it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 81, i: 1 IoU: 0.8119647042964662\n", + "FAIL label: 82, i: 1 IoU: 0.9954887478314629\n", + "FAIL label: 83, i: 1 IoU: 0.5875644719098534\n", + "FAIL label: 83, i: 2 IoU: 0.763486517479466\n", + "FAIL label: 83, i: 3 IoU: 0.5883098292426364\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 53%|██████████████████████▎ | 84/158 [08:11<00:59, 1.25it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 83, i: 4 IoU: 0.6001201810830656\n", + "FAIL label: 84, i: 1 IoU: 0.9999895543935413\n", + "FAIL label: 84, i: 2 IoU: 0.9150862678249517\n", + "FAIL label: 84, i: 3 IoU: 0.9153853917971329\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 54%|██████████████████████▌ | 85/158 [08:12<01:00, 1.21it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 84, i: 4 IoU: 0.8430140848763782\n", + "FAIL label: 85, i: 1 IoU: 0.9827706053045338\n", + "FAIL label: 85, i: 2 IoU: 0.315454848671191\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 54%|██████████████████████▊ | 86/158 [08:12<00:49, 1.47it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 85, i: 3 IoU: 0.3163725085364393\n", + "FAIL label: 86, i: 1 IoU: 0.9151485340198658\n", + "FAIL label: 86, i: 2 IoU: 0.9999708491980615\n", + "FAIL label: 86, i: 3 IoU: 0.9151561291082108\n", + "FAIL label: 86, i: 4 IoU: 0.9151437104687598\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 55%|███████████████████████▏ | 87/158 [08:13<00:52, 1.36it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 87, i: 1 IoU: 0.8213131353735267\n", + "FAIL label: 87, i: 2 IoU: 0.9965090024896376\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 56%|███████████████████████▍ | 88/158 [08:14<00:54, 1.27it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 87, i: 3 IoU: 0.8207319285361833\n", + "FAIL label: 87, i: 4 IoU: 0.9943094943318893\n", + "FAIL label: 88, i: 2 IoU: 0.5334884719889734\n", + "FAIL label: 88, i: 3 IoU: 0.5342280152515528\n", + "FAIL label: 88, i: 4 IoU: 0.1640123309684527\n", + "FAIL label: 88, i: 5 IoU: 0.6461917152094128\n", + "FAIL label: 88, i: 6 IoU: 0.08677831082741233\n", + "FAIL label: 88, i: 7 IoU: 0.5049183533592696\n", + "FAIL label: 88, i: 8 IoU: 0.1595507733121335\n", + "FAIL label: 88, i: 9 IoU: 0.574309985514325\n", + "FAIL label: 88, i: 10 IoU: 0.29508346285602816\n", + "FAIL label: 88, i: 11 IoU: 0.08679589917744919\n", + "FAIL label: 88, i: 12 IoU: 0.10672382566136134\n", + "FAIL label: 88, i: 13 IoU: 0.8775570405305991\n", + "FAIL label: 88, i: 14 IoU: 0.8775913610590895\n", + "FAIL label: 88, i: 15 IoU: 0.5114310330853414\n", + "FAIL label: 88, i: 16 IoU: 0.602671706863789\n", + "FAIL label: 88, i: 17 IoU: 0.1964633894292634\n", + "FAIL label: 88, i: 18 IoU: 0.7153769595535522\n", + "FAIL label: 88, i: 19 IoU: 0.1197156122581966\n", + "FAIL label: 88, i: 20 IoU: 0.36209859166746006\n", + "FAIL label: 88, i: 21 IoU: 0.07058447059393874\n", + "FAIL label: 88, i: 22 IoU: 0.329309231821668\n", + "FAIL label: 88, i: 23 IoU: 0.5116932003640386\n", + "FAIL label: 88, i: 24 IoU: 0.13264927437404528\n", + "FAIL label: 88, i: 25 IoU: 0.7146042322200608\n", + "FAIL label: 88, i: 26 IoU: 0.4581336091224333\n", + "FAIL label: 88, i: 27 IoU: 0.4576428027674444\n", + "FAIL label: 88, i: 28 IoU: 0.6026680671709965\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 56%|███████████████████████▋ | 89/158 [08:31<06:16, 5.46s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 88, i: 29 IoU: 0.16446984033033477\n", + "FAIL label: 89, i: 1 IoU: 0.9162228080924544\n", + "FAIL label: 89, i: 2 IoU: 0.553103408414959\n", + "FAIL label: 89, i: 3 IoU: 0.5983941124765062\n", + "FAIL label: 89, i: 4 IoU: 0.9142743607290594\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 57%|███████████████████████▉ | 90/158 [08:33<05:16, 4.65s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 89, i: 5 IoU: 0.59658258365518\n", + "FAIL label: 90, i: 1 IoU: 0.9999858774838961\n", + "FAIL label: 90, i: 2 IoU: 0.8429871602479023\n", + "FAIL label: 90, i: 3 IoU: 0.9153866184118814\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 58%|████████████████████████▏ | 91/158 [08:34<03:56, 3.52s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 90, i: 4 IoU: 0.9150573463911605\n", + "FAIL label: 91, i: 1 IoU: 0.9954392429949537\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 58%|████████████████████████▍ | 92/158 [08:35<02:56, 2.67s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 91, i: 2 IoU: 0.9954642109307051\n", + "FAIL label: 92, i: 1 IoU: 0.9154020207316252\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 59%|████████████████████████▋ | 93/158 [08:35<02:05, 1.94s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 93, i: 1 IoU: 0.9954928165109435\n", + "FAIL label: 93, i: 2 IoU: 0.9954994565036105\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 59%|████████████████████████▉ | 94/158 [08:36<01:35, 1.49s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 93, i: 3 IoU: 0.9957445042978423\n", + "FAIL label: 94, i: 1 IoU: 0.9957442608760956\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 60%|█████████████████████████▎ | 95/158 [08:36<01:14, 1.18s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 94, i: 2 IoU: 0.9954935303733637\n", + "FAIL label: 94, i: 3 IoU: 0.9955036342819167\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 61%|█████████████████████████▌ | 96/158 [08:36<00:56, 1.09it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 95, i: 1 IoU: 0.8212196187587691\n", + "FAIL label: 96, i: 1 IoU: 0.9997068819001476\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 61%|█████████████████████████▊ | 97/158 [08:37<00:46, 1.31it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 96, i: 2 IoU: 0.8234162276160522\n", + "FAIL label: 96, i: 3 IoU: 0.22801765149584644\n", + "FAIL label: 97, i: 1 IoU: 0.8338734984128814\n", + "FAIL label: 97, i: 2 IoU: 0.8427253634277804\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 62%|██████████████████████████ | 98/158 [08:38<00:50, 1.19it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 97, i: 3 IoU: 0.8547146936751142\n", + "FAIL label: 98, i: 1 IoU: 0.9942947515859598\n", + "FAIL label: 98, i: 2 IoU: 0.7180476943863174\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 63%|██████████████████████████▎ | 99/158 [08:39<00:48, 1.23it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 98, i: 3 IoU: 0.7165738698307893\n", + "FAIL label: 99, i: 1 IoU: 0.2683337049336944\n", + "FAIL label: 99, i: 2 IoU: 0.467150338282246\n", + "FAIL label: 99, i: 3 IoU: 0.485708991037263\n", + "FAIL label: 99, i: 4 IoU: 0.8272481402601363\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 63%|█████████████████████████▉ | 100/158 [08:40<01:03, 1.09s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 99, i: 5 IoU: 0.3662484602128929\n", + "FAIL label: 100, i: 1 IoU: 0.7178588760233511\n", + "FAIL label: 100, i: 2 IoU: 0.7163824134248742\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 64%|██████████████████████████▏ | 101/158 [08:41<00:59, 1.05s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 100, i: 3 IoU: 0.7169233738628326\n", + "FAIL label: 102, i: 1 IoU: 0.9999935519503039\n", + "FAIL label: 102, i: 2 IoU: 0.8429000214681437\n", + "FAIL label: 102, i: 3 IoU: 0.9130651869153273\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 65%|██████████████████████████▋ | 103/158 [08:42<00:43, 1.27it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 102, i: 4 IoU: 0.8428987619168794\n", + "FAIL label: 103, i: 1 IoU: 0.8447026797476835\n", + "FAIL label: 103, i: 2 IoU: 0.8447279116827182\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 66%|██████████████████████████▉ | 104/158 [08:43<00:44, 1.22it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 103, i: 3 IoU: 0.7617824427756772\n", + "FAIL label: 104, i: 1 IoU: 0.9965185727447226\n", + "FAIL label: 104, i: 2 IoU: 0.8213245503609953\n", + "FAIL label: 104, i: 3 IoU: 0.8223225750436894\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 66%|███████████████████████████▏ | 105/158 [08:44<00:46, 1.14it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 104, i: 4 IoU: 0.8203057178819239\n", + "FAIL label: 107, i: 1 IoU: 0.9656931879498398\n", + "FAIL label: 107, i: 2 IoU: 0.9272372792764422\n", + "FAIL label: 107, i: 3 IoU: 0.9366623502181541\n", + "FAIL label: 107, i: 4 IoU: 0.8089187218863569\n", + "FAIL label: 107, i: 5 IoU: 0.9089682384821981\n", + "FAIL label: 107, i: 6 IoU: 0.8738736228510605\n", + "FAIL label: 107, i: 7 IoU: 0.9611892193056012\n", + "FAIL label: 107, i: 8 IoU: 0.8089193659683054\n", + "FAIL label: 107, i: 9 IoU: 0.944071178173782\n", + "FAIL label: 107, i: 10 IoU: 0.9772744164149324\n", + "FAIL label: 107, i: 11 IoU: 0.9611917752114109\n", + "FAIL label: 107, i: 12 IoU: 0.8196907850055812\n", + "FAIL label: 107, i: 13 IoU: 0.8899433932386251\n", + "FAIL label: 107, i: 14 IoU: 0.936655899679712\n", + "FAIL label: 107, i: 15 IoU: 0.9656836573946403\n", + "FAIL label: 107, i: 16 IoU: 0.8569483106054716\n", + "FAIL label: 107, i: 17 IoU: 0.889939833353467\n", + "FAIL label: 107, i: 18 IoU: 0.9440732097190173\n", + "FAIL label: 107, i: 19 IoU: 0.9060931155718118\n", + "FAIL label: 107, i: 20 IoU: 0.9779341794555424\n", + "FAIL label: 107, i: 21 IoU: 0.9440733593794752\n", + "FAIL label: 107, i: 22 IoU: 0.9698569709769141\n", + "FAIL label: 107, i: 23 IoU: 0.9138905796067834\n", + "FAIL label: 107, i: 24 IoU: 0.8741265104445205\n", + "FAIL label: 107, i: 25 IoU: 0.8899402928622709\n", + "FAIL label: 107, i: 26 IoU: 0.8558290421913679\n", + "FAIL label: 107, i: 27 IoU: 0.8196876098048149\n", + "FAIL label: 107, i: 28 IoU: 0.9374741326129168\n", + "FAIL label: 107, i: 29 IoU: 0.900623912353858\n", + "FAIL label: 107, i: 30 IoU: 0.8127016659719468\n", + "FAIL label: 107, i: 31 IoU: 0.855816712827993\n", + "FAIL label: 107, i: 32 IoU: 0.977936931871674\n", + "FAIL label: 107, i: 33 IoU: 0.8230604589547162\n", + "FAIL label: 107, i: 34 IoU: 0.8558204893663434\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 68%|████████████████████████████ | 108/158 [08:51<01:22, 1.65s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 107, i: 35 IoU: 0.9372476062499094\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 69%|████████████████████████████▎ | 109/158 [08:52<01:08, 1.41s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 108, i: 1 IoU: 0.8196080257046456\n", + "FAIL label: 108, i: 2 IoU: 0.9943122490751987\n", + "FAIL label: 109, i: 1 IoU: 0.9993161579901368\n", + "FAIL label: 109, i: 2 IoU: 0.9989257699586974\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 70%|████████████████████████████▌ | 110/158 [08:52<01:01, 1.27s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 109, i: 3 IoU: 0.8428535548403675\n", + "FAIL label: 109, i: 4 IoU: 0.9145080228723852\n", + "FAIL label: 110, i: 1 IoU: 0.8458423305585046\n", + "FAIL label: 110, i: 2 IoU: 0.8446410415391881\n", + "FAIL label: 110, i: 3 IoU: 0.8446363211184524\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 70%|████████████████████████████▊ | 111/158 [08:54<00:58, 1.25s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 110, i: 4 IoU: 0.8445268326246352\n", + "FAIL label: 111, i: 1 IoU: 0.9153160245554464\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 71%|█████████████████████████████ | 112/158 [08:54<00:48, 1.06s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 111, i: 2 IoU: 0.8430063452436769\n", + "FAIL label: 112, i: 1 IoU: 0.9976229388083134\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 73%|█████████████████████████████▊ | 115/158 [08:55<00:22, 1.95it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 112, i: 2 IoU: 0.9954903917550736\n", + "FAIL label: 114, i: 1 IoU: 0.9823037781119696\n", + "FAIL label: 115, i: 1 IoU: 0.8427372002386532\n", + "FAIL label: 115, i: 2 IoU: 0.9159599471588531\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 73%|██████████████████████████████ | 116/158 [08:55<00:25, 1.65it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 115, i: 3 IoU: 0.9145308202194943\n", + "FAIL label: 115, i: 4 IoU: 0.9159610237842082\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 74%|██████████████████████████████▎ | 117/158 [08:56<00:23, 1.76it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 116, i: 1 IoU: 0.9165528032329796\n", + "FAIL label: 118, i: 1 IoU: 0.9149094484933945\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 75%|██████████████████████████████▉ | 119/158 [08:56<00:16, 2.32it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 118, i: 2 IoU: 0.842885772933309\n", + "FAIL label: 119, i: 1 IoU: 0.5339275761090779\n", + "FAIL label: 119, i: 2 IoU: 0.010117243141230947\n", + "FAIL label: 119, i: 3 IoU: 0.8779858153241423\n", + "FAIL label: 119, i: 4 IoU: 0.008731847693585018\n", + "FAIL label: 119, i: 5 IoU: 0.6025841946309932\n", + "FAIL label: 119, i: 6 IoU: 0.7149578009042737\n", + "FAIL label: 119, i: 7 IoU: 0.05291368246003679\n", + "FAIL label: 119, i: 8 IoU: 0.01697632695848591\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 76%|███████████████████████████████▏ | 120/158 [09:00<00:41, 1.09s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 120, i: 1 IoU: 0.23368983292596965\n", + "FAIL label: 120, i: 2 IoU: 0.20409995307937817\n", + "FAIL label: 120, i: 3 IoU: 0.21704727446866512\n", + "FAIL label: 120, i: 4 IoU: 0.4117722652013406\n", + "FAIL label: 120, i: 5 IoU: 0.11634418210197194\n", + "FAIL label: 120, i: 6 IoU: 0.15453027600149247\n", + "FAIL label: 120, i: 7 IoU: 0.4218859979522584\n", + "FAIL label: 120, i: 8 IoU: 0.41101925187035865\n", + "FAIL label: 120, i: 10 IoU: 0.4488145529213503\n", + "FAIL label: 120, i: 11 IoU: 0.45516204616522016\n", + "FAIL label: 120, i: 12 IoU: 0.5449217581628534\n", + "FAIL label: 120, i: 13 IoU: 0.5631469104710431\n", + "FAIL label: 120, i: 14 IoU: 0.4948313653722433\n", + "FAIL label: 120, i: 15 IoU: 0.5546341441597754\n", + "FAIL label: 120, i: 16 IoU: 0.4376660447839837\n", + "FAIL label: 120, i: 17 IoU: 0.5805181813650909\n", + "FAIL label: 120, i: 18 IoU: 0.45464451838501874\n", + "FAIL label: 120, i: 19 IoU: 0.7391671138065253\n", + "FAIL label: 120, i: 20 IoU: 0.4680884778026279\n", + "FAIL label: 120, i: 21 IoU: 0.4443466671677894\n", + "FAIL label: 120, i: 22 IoU: 0.3887287470370856\n", + "FAIL label: 120, i: 23 IoU: 0.3342192950858604\n", + "FAIL label: 120, i: 24 IoU: 0.5012575190795365\n", + "FAIL label: 120, i: 25 IoU: 0.805560156439744\n", + "FAIL label: 120, i: 26 IoU: 0.4253834463799716\n", + "FAIL label: 120, i: 27 IoU: 0.3065807187427402\n", + "FAIL label: 120, i: 28 IoU: 0.4737747231847442\n", + "FAIL label: 120, i: 29 IoU: 0.5626538625448048\n", + "FAIL label: 120, i: 30 IoU: 0.7149681567658946\n", + "FAIL label: 120, i: 31 IoU: 0.9985374855761691\n", + "FAIL label: 120, i: 32 IoU: 0.30654795813794833\n", + "FAIL label: 120, i: 33 IoU: 0.7666179035221979\n", + "FAIL label: 120, i: 34 IoU: 0.44093593394990055\n", + "FAIL label: 120, i: 35 IoU: 0.5556291276200864\n", + "FAIL label: 120, i: 36 IoU: 0.6365187913987644\n", + "FAIL label: 120, i: 37 IoU: 0.4093629791350939\n", + "FAIL label: 120, i: 38 IoU: 0.4469352705165234\n", + "FAIL label: 120, i: 39 IoU: 0.521012391604966\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 78%|███████████████████████████████▉ | 123/158 [09:24<02:21, 4.04s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 120, i: 40 IoU: 0.3901703825755954\n", + "FAIL label: 122, i: 1 IoU: 0.9822612186849033\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 78%|████████████████████████████████▏ | 124/158 [09:24<01:47, 3.15s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 123, i: 1 IoU: 0.9952728025301164\n", + "FAIL label: 124, i: 1 IoU: 0.9144128201401666\n", + "FAIL label: 124, i: 2 IoU: 0.9161508677454102\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 79%|████████████████████████████████▍ | 125/158 [09:25<01:24, 2.55s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 124, i: 3 IoU: 0.8430426673065566\n", + "FAIL label: 125, i: 1 IoU: 0.6927775379139705\n", + "FAIL label: 125, i: 2 IoU: 0.4937069668017305\n", + "FAIL label: 125, i: 3 IoU: 0.4958794352602853\n", + "FAIL label: 125, i: 4 IoU: 0.9208116264901149\n", + "FAIL label: 125, i: 5 IoU: 0.727631515760083\n", + "FAIL label: 125, i: 6 IoU: 0.505123108517373\n", + "FAIL label: 125, i: 7 IoU: 0.9218028225046004\n", + "FAIL label: 125, i: 8 IoU: 0.7237215574906564\n", + "FAIL label: 125, i: 9 IoU: 0.5073353705114093\n", + "FAIL label: 125, i: 10 IoU: 0.9938441721857603\n", + "FAIL label: 125, i: 11 IoU: 0.6959744100195436\n", + "FAIL label: 125, i: 12 IoU: 0.7028911414289177\n", + "FAIL label: 125, i: 13 IoU: 0.6519476658770348\n", + "FAIL label: 125, i: 14 IoU: 0.6518518727145441\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 80%|████████████████████████████████▋ | 126/158 [09:28<01:28, 2.77s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 125, i: 15 IoU: 0.7025855326538669\n", + "FAIL label: 126, i: 1 IoU: 0.049127163794319556\n", + "FAIL label: 126, i: 2 IoU: 0.915084072644163\n", + "FAIL label: 126, i: 3 IoU: 0.09556899782653447\n", + "FAIL label: 126, i: 4 IoU: 0.9150825509282297\n", + "FAIL label: 126, i: 5 IoU: 0.09556903248652653\n", + "FAIL label: 126, i: 6 IoU: 0.9153660527251942\n", + "FAIL label: 126, i: 7 IoU: 0.09557193703450216\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 80%|████████████████████████████████▉ | 127/158 [09:30<01:13, 2.38s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 126, i: 8 IoU: 0.9153799035463515\n", + "FAIL label: 126, i: 9 IoU: 0.08909456313183163\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 82%|█████████████████████████████████▍ | 129/158 [09:30<00:39, 1.37s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 128, i: 1 IoU: 0.982226359386617\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 82%|█████████████████████████████████▋ | 130/158 [09:30<00:31, 1.12s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 129, i: 1 IoU: 0.8114448341555182\n", + "FAIL label: 130, i: 1 IoU: 0.5180659140717497\n", + "FAIL label: 130, i: 2 IoU: 0.7207805818113393\n", + "FAIL label: 130, i: 3 IoU: 0.3237994130965319\n", + "FAIL label: 130, i: 4 IoU: 0.7205776887020672\n", + "FAIL label: 130, i: 5 IoU: 0.323989747876956\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 83%|█████████████████████████████████▉ | 131/158 [09:34<00:48, 1.80s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 130, i: 6 IoU: 0.5178014830024038\n", + "FAIL label: 131, i: 1 IoU: 0.915022796079141\n", + "FAIL label: 131, i: 2 IoU: 0.9154273382552026\n", + "FAIL label: 131, i: 3 IoU: 0.9150398022776599\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 84%|██████████████████████████████████▎ | 132/158 [09:35<00:40, 1.56s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 131, i: 4 IoU: 0.842803123213387\n", + "FAIL label: 132, i: 1 IoU: 0.9894330408329437\n", + "FAIL label: 132, i: 2 IoU: 0.4379817934133398\n", + "FAIL label: 132, i: 3 IoU: 0.9954541777034456\n", + "FAIL label: 132, i: 4 IoU: 0.43807245074647183\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 84%|██████████████████████████████████▌ | 133/158 [09:37<00:42, 1.69s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 132, i: 5 IoU: 0.9939513667205946\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 85%|███████████████████████████████████ | 135/158 [09:37<00:23, 1.01s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 134, i: 1 IoU: 0.8429304732147149\n", + "FAIL label: 135, i: 1 IoU: 0.8603302815672506\n", + "FAIL label: 135, i: 2 IoU: 0.8798327275495212\n", + "FAIL label: 135, i: 3 IoU: 0.7703044783114071\n", + "FAIL label: 135, i: 4 IoU: 0.6808827829759239\n", + "FAIL label: 135, i: 5 IoU: 0.6909746291700263\n", + "FAIL label: 135, i: 6 IoU: 0.5317420023883429\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 86%|███████████████████████████████████▎ | 136/158 [09:41<00:38, 1.73s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 135, i: 7 IoU: 0.5992982718981124\n", + "FAIL label: 136, i: 1 IoU: 0.36351635151746037\n", + "FAIL label: 136, i: 2 IoU: 0.7150202675633894\n", + "FAIL label: 136, i: 3 IoU: 0.07744958967554705\n", + "FAIL label: 136, i: 4 IoU: 0.11432450806194011\n", + "FAIL label: 136, i: 5 IoU: 0.05300909449687853\n", + "FAIL label: 136, i: 6 IoU: 0.03029251199066223\n", + "FAIL label: 136, i: 7 IoU: 0.3607440886125655\n", + "FAIL label: 136, i: 8 IoU: 0.10074075703871165\n", + "FAIL label: 136, i: 9 IoU: 0.04681380529899493\n", + "FAIL label: 136, i: 10 IoU: 0.7153934264319413\n", + "FAIL label: 136, i: 11 IoU: 0.715388520285494\n", + "FAIL label: 136, i: 12 IoU: 0.5063673422783741\n", + "FAIL label: 136, i: 13 IoU: 0.11185871038904985\n", + "FAIL label: 136, i: 14 IoU: 0.04745089460891531\n", + "FAIL label: 136, i: 15 IoU: 0.059232227314180846\n", + "FAIL label: 136, i: 16 IoU: 0.7150401850567112\n", + "FAIL label: 136, i: 17 IoU: 0.5129987356497058\n", + "FAIL label: 136, i: 18 IoU: 0.025175038873086242\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 87%|███████████████████████████████████▌ | 137/158 [09:49<01:07, 3.22s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 136, i: 19 IoU: 0.5118227258001182\n", + "FAIL label: 137, i: 1 IoU: 0.8428077355831708\n", + "FAIL label: 137, i: 2 IoU: 0.915426453352076\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 87%|███████████████████████████████████▊ | 138/158 [09:50<00:51, 2.59s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 137, i: 3 IoU: 0.9999866100572686\n", + "FAIL label: 137, i: 4 IoU: 0.9151314697065418\n", + "FAIL label: 138, i: 1 IoU: 0.9151135600790912\n", + "FAIL label: 138, i: 2 IoU: 0.9153464113339796\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 88%|████████████████████████████████████ | 139/158 [09:50<00:39, 2.10s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 138, i: 3 IoU: 0.9151061315627995\n", + "FAIL label: 138, i: 4 IoU: 0.9999846323431932\n", + "FAIL label: 139, i: 1 IoU: 0.8430225250504098\n", + "FAIL label: 139, i: 2 IoU: 0.9149506621801816\n", + "FAIL label: 139, i: 3 IoU: 0.9155237315295471\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 89%|████████████████████████████████████▎ | 140/158 [09:51<00:32, 1.79s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 139, i: 4 IoU: 0.8430301589937165\n", + "FAIL label: 140, i: 1 IoU: 0.9957416418788235\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 89%|████████████████████████████████████▌ | 141/158 [09:52<00:23, 1.36s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 140, i: 2 IoU: 0.9950190552459726\n", + "FAIL label: 143, i: 1 IoU: 0.8381998764638381\n", + "FAIL label: 143, i: 2 IoU: 0.783331525571734\n", + "FAIL label: 143, i: 3 IoU: 0.9146774542588355\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 92%|█████████████████████████████████████▋ | 145/158 [09:53<00:08, 1.46it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 143, i: 4 IoU: 0.8427350359696034\n", + "FAIL label: 144, i: 1 IoU: 0.9957311142891224\n", + "FAIL label: 145, i: 1 IoU: 0.885795531043615\n", + "FAIL label: 145, i: 2 IoU: 0.6942266623283476\n", + "FAIL label: 145, i: 3 IoU: 0.7201137158094123\n", + "FAIL label: 145, i: 4 IoU: 0.8535772754688131\n", + "FAIL label: 145, i: 5 IoU: 0.8374888177233909\n", + "FAIL label: 145, i: 6 IoU: 0.7623645805366233\n", + "FAIL label: 145, i: 7 IoU: 0.9019241496825338\n", + "FAIL label: 145, i: 8 IoU: 0.9362875888139702\n", + "FAIL label: 145, i: 9 IoU: 0.8696779311668051\n", + "FAIL label: 145, i: 10 IoU: 0.9681175635380845\n", + "FAIL label: 145, i: 11 IoU: 0.6175942730751106\n", + "FAIL label: 145, i: 12 IoU: 0.5621359725361463\n", + "FAIL label: 145, i: 13 IoU: 0.4634076717923842\n", + "FAIL label: 145, i: 14 IoU: 0.44620209383647286\n", + "FAIL label: 145, i: 15 IoU: 0.6107508343405207\n", + "FAIL label: 145, i: 16 IoU: 0.4151648309206101\n", + "FAIL label: 145, i: 17 IoU: 0.44998512505606886\n", + "FAIL label: 145, i: 18 IoU: 0.5993712999645406\n", + "FAIL label: 145, i: 19 IoU: 0.4004857859979048\n", + "FAIL label: 145, i: 20 IoU: 0.5523973240785247\n", + "FAIL label: 145, i: 21 IoU: 0.5819295407624141\n", + "FAIL label: 145, i: 22 IoU: 0.43720323939073125\n", + "FAIL label: 145, i: 23 IoU: 0.40782295521932144\n", + "FAIL label: 145, i: 24 IoU: 0.4610541395877679\n", + "FAIL label: 145, i: 25 IoU: 0.42250440277346357\n", + "FAIL label: 145, i: 26 IoU: 0.5329168398853973\n", + "FAIL label: 145, i: 27 IoU: 0.7811937382375722\n", + "FAIL label: 145, i: 28 IoU: 0.7880384665947302\n", + "FAIL label: 145, i: 29 IoU: 0.9681195019001694\n", + "FAIL label: 145, i: 30 IoU: 0.7071662958272499\n", + "FAIL label: 145, i: 31 IoU: 0.7330659467916802\n", + "FAIL label: 145, i: 32 IoU: 0.8059637609894913\n", + "FAIL label: 145, i: 33 IoU: 0.42984047088615895\n", + "FAIL label: 145, i: 34 IoU: 0.5426627516298753\n", + "FAIL label: 145, i: 35 IoU: 0.57186140801674\n", + "FAIL label: 145, i: 36 IoU: 0.5910199453699034\n", + "FAIL label: 145, i: 37 IoU: 0.6812946267147945\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 92%|█████████████████████████████████████▉ | 146/158 [10:02<00:32, 2.69s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 145, i: 38 IoU: 0.7460291461651403\n", + "FAIL label: 145, i: 39 IoU: 0.9180689424108701\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 93%|██████████████████████████████████████▏ | 147/158 [10:03<00:23, 2.12s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 146, i: 1 IoU: 0.3782786850555426\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 94%|██████████████████████████████████████▋ | 149/158 [10:03<00:11, 1.25s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 147, i: 1 IoU: 0.9794068949678159\n", + "FAIL label: 148, i: 1 IoU: 0.9822770450776601\n", + "FAIL label: 149, i: 1 IoU: 0.9149325559511551\n", + "FAIL label: 149, i: 2 IoU: 0.8429219759987583\n", + "FAIL label: 149, i: 3 IoU: 0.9999911506076378\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 95%|██████████████████████████████████████▉ | 150/158 [10:04<00:09, 1.15s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 149, i: 4 IoU: 0.9156281004031039\n", + "FAIL label: 150, i: 1 IoU: 0.9150058487906464\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 96%|███████████████████████████████████████▏ | 151/158 [10:05<00:06, 1.03it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 150, i: 2 IoU: 0.8427616634072419\n", + "FAIL label: 151, i: 1 IoU: 0.8432881581601325\n", + "FAIL label: 151, i: 2 IoU: 0.9147592045619594\n", + "FAIL label: 151, i: 3 IoU: 0.9161807563100703\n", + "FAIL label: 151, i: 4 IoU: 0.999985471072286\n", + "FAIL label: 151, i: 5 IoU: 0.8432911678499866\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 96%|███████████████████████████████████████▍ | 152/158 [10:08<00:09, 1.56s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 151, i: 6 IoU: 0.9147537370657577\n", + "FAIL label: 152, i: 1 IoU: 0.2137188172023406\n", + "FAIL label: 152, i: 2 IoU: 0.4441660613730661\n", + "FAIL label: 152, i: 3 IoU: 0.21117384003728906\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 97%|███████████████████████████████████████▋ | 153/158 [10:08<00:06, 1.26s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 152, i: 4 IoU: 0.9799480095743638\n", + "FAIL label: 154, i: 1 IoU: 0.9134073384539741\n", + "FAIL label: 154, i: 2 IoU: 0.9134116285719752\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 98%|████████████████████████████████████████▏| 155/158 [10:09<00:02, 1.18it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 154, i: 3 IoU: 0.8429857656834076\n", + "FAIL label: 155, i: 1 IoU: 0.9153920179220861\n", + "FAIL label: 155, i: 2 IoU: 0.8428097795666694\n", + "FAIL label: 155, i: 3 IoU: 0.9999565722097601\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 99%|████████████████████████████████████████▍| 156/158 [10:10<00:01, 1.16it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 155, i: 4 IoU: 0.9150915241678299\n", + "FAIL label: 156, i: 1 IoU: 0.501137542911566\n", + "FAIL label: 156, i: 2 IoU: 0.8120051883269618\n", + "FAIL label: 156, i: 3 IoU: 0.7005037140312294\n", + "FAIL label: 156, i: 4 IoU: 0.7034773420209985\n", + "FAIL label: 156, i: 5 IoU: 0.5967053745753306\n", + "FAIL label: 156, i: 6 IoU: 0.835940421928055\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 99%|████████████████████████████████████████▋| 157/158 [10:14<00:01, 1.65s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 156, i: 7 IoU: 0.8339331580508039\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████| 158/158 [10:14<00:00, 3.89s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FAIL label: 157, i: 1 IoU: 0.842945644270333\n", + "FAIL label: 157, i: 2 IoU: 0.9149707297396615\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "failed_labels = []\n", + "failed_paths = []\n", + "\n", + "# Compute spherical polygon overlaps to verify overlap and remove ones that overlap\n", + "\n", + "for label in tqdm(np.unique(labels)):\n", + " polys = [(all_polys[i], confirmed_fits_paths[i]) for i in range(len(labels)) if labels[i] == label]\n", + " if len(polys) > 1:\n", + " total_poly = polys[0][0]\n", + " for i in range(1, len(polys)):\n", + " new_poly = polys[i][0]\n", + " new_path = polys[i][1]\n", + " if total_poly.intersects_poly(new_poly):\n", + " union_over_max = total_poly.intersection(new_poly).area() / new_poly.area()\n", + " print(f\"FAIL label: {label}, i: {i} IoU: {union_over_max}\")\n", + " failed_labels.append(label)\n", + " failed_paths.append(new_path)\n", + " continue\n", + " else:\n", + " total_poly = total_poly.union(new_poly)" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "id": "2f4a9519", + "metadata": {}, + "outputs": [], + "source": [ + "files_to_keep = set(confirmed_fits_paths) - set(failed_paths)\n", + "\n", + "all_fnames = []\n", + "\n", + "for f in files_to_keep:\n", + " fnames = [f + d for d in file_dict[f]]\n", + " all_fnames.extend(fnames)" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "id": "08558c53", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████| 1274/1274 [00:00<00:00, 3251.60it/s]\n" + ] + } + ], + "source": [ + "import shutil\n", + "\n", + "for f in tqdm(all_fnames):\n", + " try:\n", + " shutil.move(f, \"./SBI-16-3D/data\")\n", + " except Exception as e:\n", + " continue" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "3de6d9a9", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_rectangle(corners):\n", + " # Close the rectangle by repeating the first point at the end\n", + " closed_corners = np.append(corners, [corners[0]], axis=0)\n", + "\n", + " # Plot the rectangle\n", + " plt.plot(closed_corners[:, 0], closed_corners[:, 1], 'b-')\n", + " plt.scatter(corners[:, 0], corners[:, 1], color='red')\n", + " \n", + " # Annotate the points\n", + " for i, corner in enumerate(corners):\n", + " plt.annotate(f'P{i+1}', (corner[0], corner[1]), textcoords=\"offset points\", xytext=(5,5), ha='center')\n", + " \n", + " plt.xlabel('Longitude')\n", + " plt.ylabel('Latitude')\n", + " plt.title('Rectangle Plot from Given Corners')\n", + " plt.grid(True)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "2415e44e", + "metadata": {}, + "outputs": [], + "source": [ + "label = 19\n", + "paths = [confirmed_fits_paths[i] for i in range(len(labels)) if labels[i] == label]\n", + "lats = [latitudes[i] for i in range(len(labels)) if labels[i] == label]\n", + "lons = [longitudes[i] for i in range(len(labels)) if labels[i] == label]" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "40d0ae64", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(lats, lons)\n", + "\n", + "# Determine the range of the data\n", + "x_min, x_max = np.min(lats), np.max(lats)\n", + "y_min, y_max = np.min(lons), np.max(lons)\n", + "\n", + "# Set the grid with intervals of 0.1\n", + "plt.grid(True)\n", + "\n", + "# Generate ticks at intervals of 0.1 within the range of the data\n", + "x_ticks = np.arange(np.floor(x_min), np.ceil(x_max) + 0.1, 0.1)\n", + "y_ticks = np.arange(np.floor(y_min), np.ceil(y_max) + 0.1, 0.1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "28b6f4bf", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/utils/pull_jwst_csv.py b/utils/pull_jwst_csv.py new file mode 100644 index 0000000000000000000000000000000000000000..ba52f329078e08716aee9b48e521b00e3590c3b8 --- /dev/null +++ b/utils/pull_jwst_csv.py @@ -0,0 +1,280 @@ +from astroquery.mast.missions import MastMissions + +missions = MastMissions(mission='jwst') + +""" +Used to pull the list of observations that fit our filter criteria from +online JWST archives. See all filter details in the function call below. + +If your API query here times out, you may have to manually search into the UI here, and download as CSV: +https://mast.stsci.edu/search/ui + +Use the parameters outlined below when searching. +""" + +# Use query_criteria method to use selected form search conditions for making missions_mast search API call +results = missions.query_criteria(select_cols=[ + 'act_id', + 'targname', + 'ang_sep', + 'apername', + 'intarget', + 'bkgmeth', + 'bkgdtarg', + 'bartdelt', + 'cal_vcs', + 'cal_ver', + 'bkglevel', + 'bkgsub', + 'miri_cccstate', + 'cont_id', + 'crds_ver', + 'crds_ctx', + 'fileSetName', + 'date_end', + 'date_beg', + 'targ_dec', + 'miri_detmode', + 'miri_ditchdirc', + 'miri_dithopfr', + 'pixfrac', + 'effexptm', + 'effexptm', + 'effinttm', + 'intend', + 'eng_qual', + 'errtype', + 'duration', + 'exp_type', + 'expend', + 'is_psf', + 'expmid', + 'exposure', + 'expstart', + 'fastaxis', + 'fgs_focuspos', + 'filter', + 'gainfact', + 'nirspec_gwa_ytilt', + 'nirspec_gwa_xtilt', + 'gs_dec', + 'gs_udec', + 'gdstarid', + 'gs_order', + 'gs_mag', + 'gs_umag', + 'gs_dra', + 'gs_ura', + 'gsendtim', + 'gsstrttm', + 'nirspec_gwa_pxav', + 'nirspec_gwa_pyav', + 'nirspec_gwa_tilt', + 'nirspec_gwa_xp_v', + 'nirspec_gwa_yp_v', + 'helidelt', + 'hendtime', + 'hmidtime', + 'hstrtime', + 'hga_move', + 'origin', + 'instrume', + 'lamp', + 'opmode', + 'masterbg', + 'miri_coronmsk', + 'miri_mirnfrms', + 'miri_mirngrps', + 'miri_channel', + 'miri_pattnpts', + 'miri_spat_num', + 'miri_spec_num', + 'miri_band', + 'nirspec_msaconid', + 'nirspec_msametfl', + 'nirspec_msametid', + 'detector', + 'nirspec_fxd_slit', + 'nircam_smgrdpat', + 'obsfolder', + 'asntable', + 'asnpool', + 'nirspec_grating', + 'nircam_pupil', + 'nwfsest', + 'nircam_channel', + 'nircam_coronmsk', + 'nircam_module', + 'niriss_fwcpos', + 'niriss_focuspos', + 'niriss_pupil', + 'niriss_pwcpos', + 'nirspec_focuspos', + 'nirspec_is_imprt', + 'nirspec_spat_num', + 'nirspec_spec_num', + 'nirspec_nod_type', + 'miri_numdsets', + 'nsamples', + 'noutputs', + 'nextend', + 'groupgap', + 'drpfrms3', + 'drpfrms1', + 'nframes', + 'ngroups', + 'nints', + 'nirspec_nrs_norm', + 'subsize1', + 'subsize2', + 'niriss_nrimdtpt', + 'pridtpts', + 'subpxpts', + 'nirspec_nrs_ref', + 'nrststrt', + 'nresets', + 'miri_spatnstp', + 'miri_specnstp', + 'obslabel', + 'observtn', + 'oss_ver', + 'osf_file', + 'visitend', + 'opticalElements', + 'miri_mrsprchn', + 'pps_aper', + 'seq_id', + 'pi_name', + 'pxsclrt', + 'miri_dithpnts', + 'nirspec_dithpnts', + 'pcs_mode', + 'nircam_fam_la1', + 'nircam_fam_la2', + 'nircam_fam_la3', + 'patt_num', + 'datamode', + 'nirspec_preimage', + 'pwfseet', + 'pattsize', + 'patttype', + 'nircam_pridtype', + 'expripar', + 'timesys', + 'productLevel', + 'program', + 'category', + 'subcat', + 'proposal_type', + 'obs_id', + 'proposal_cycle', + 'title', + 'prop_dec', + 'prop_ra', + 'nircam_pilin', + 'targ_ra', + 'readpatt', + 'nirspec_rma_pos', + 'nirspec_fcsrlpos', + 'publicReleaseDate', + 'nircam_fa1value', + 'nircam_faphase1', + 'nircam_fastep1', + 'nircam_faunit1', + 'nircam_fa2value', + 'nircam_faphase2', + 'nircam_fastep2', + 'nircam_faunit2', + 'nircam_fa3value', + 'nircam_faphase3', + 'nircam_fastep3', + 'nircam_faunit3', + 'expcount', + 'prd_ver', + 'scicat', + 'dataprob', + 'segmfile', + 'selfref', + 'sca_num', + 'exsegnum', + 'rois', + 'roiw', + 'miri_spatstep', + 'miri_specstep', + 'slowaxis', + 'scatfile', + 'engqlptg', + 'sctarate', + 'miri_sptoffst', + 'exp_only', + 'miri_spcoffst', + 'date_obs', + 'miri_dsetstrt', + 'intstart', + 'substrt1', + 'substrt2', + 'miri_pattstrt', + 'nirspec_pattstrt', + 'nirspec_msastate', + 'access', + 'visitsta', + 'subarray', + 'nircam_subpxpat', + 'nirspec_subpxpat', + 'targcat', + 'targudec', + 'targdesc', + 'targprop', + 'mu_epoch', + 'mu_dec', + 'mu_ra', + 'targura', + 'targtype', + 'bstrtime', + 'bendtime', + 'bmidtime', + 'telescop', + 'template', + 'miri_cmd_tsel', + 'tframe', + 'tgroup', + 'tsample', + 'tsovisit', + 'texptime', + 'texptime', + 'nexposur', + 'numdthpt', + 'exsegtot', + 'tcatfile', + 'datamodl', + 'date', + 'expsteng', + 'gsendtim', + 'gsstrttm', + 'vststart', + 'gs_v3_pa', + 'frmdivsr', + 'va_dec', + 'va_ra', + 'visitgrp', + 'visid_id', + 'visit', + 'targoopp', + 'visitype', + 'wpower', + 'wtype', + 'dirimage', + 'xoffset', + 'yoffset', + 'zerofram'], exp_type='NRC_IMAGE', + instrume='NIRCAM', + opticalElements='*F200W;CLEAR*', + productLevel='*1b*', + targtype='FIXED', + visitsta='SUCCESSFUL', + access='PUBLIC', + effinttm='>30') + +df = results.to_pandas() +print(len(df)) +df.to_csv('jwst_FINAL.csv') \ No newline at end of file