data filtering code
Browse files
utils/.ipynb_checkpoints/sdss_filtering-checkpoint.ipynb
ADDED
@@ -0,0 +1,6 @@
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{
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"cells": [],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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utils/eval_baselines.py
CHANGED
@@ -32,6 +32,17 @@ from imagecodecs import (
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jpegxl_encode_max_effort_preset = lambda x: jpegxl_encode(x, lossless=True, effort=9)
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jpegxl_encode_preset = lambda x: jpegxl_encode(x, lossless=True)
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def find_matching_files():
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"""
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Returns list of test set file paths.
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@@ -80,10 +91,35 @@ def main(dim):
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with fits.open(path) as hdul:
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if dim == '2d':
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arr = hdul[0].data[0][2]
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elif dim == '3dt' and len(hdul[0].data) > 2:
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arr = hdul[0].data[0:3][2]
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elif dim == '3dw' and len(hdul[0].data[0]) > 2:
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arr = hdul[0].data[0][0:3]
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else:
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continue
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@@ -92,26 +128,31 @@ def main(dim):
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print(df.mean())
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df.to_csv(save_path)
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-
for
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-
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-
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except Exception as e:
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print(f"Failed at {path} under exception {e}.")
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-
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Process some 2D or 3D data.")
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parser.add_argument(
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"dimension",
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-
choices=['2d', '3dt', '3dw'],
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help="Specify whether the data is 2d, 3dt (3d time dimension), or 3dw (3d wavelength dimension)."
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)
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args = parser.parse_args()
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@@ -119,7 +160,13 @@ if __name__ == "__main__":
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# RICE REQUIRES UNIQUE INPUT OF ARR SHAPE AND DTYPE INTO DECODER
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-
if dim == '
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ALL_CODECS = {
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"JPEG_XL_MAX_EFFORT": [jpegxl_encode_max_effort_preset, jpegxl_decode],
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"JPEG_XL": [jpegxl_encode_preset, jpegxl_decode],
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@@ -127,12 +174,6 @@ if __name__ == "__main__":
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"JPEG_LS": [jpegls_encode, jpegls_decode],
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"RICE": [rcomp_encode, rcomp_decode],
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}
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-
else:
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-
ALL_CODECS = {
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-
"JPEG_XL_MAX_EFFORT": [jpegxl_encode_max_effort_preset, jpegxl_decode],
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"JPEG_XL": [jpegxl_encode_preset, jpegxl_decode],
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"JPEG_2K": [jpeg2k_encode, jpeg2k_decode],
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-
}
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columns = []
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for algo in ALL_CODECS.keys():
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jpegxl_encode_max_effort_preset = lambda x: jpegxl_encode(x, lossless=True, effort=9)
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jpegxl_encode_preset = lambda x: jpegxl_encode(x, lossless=True)
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+
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def split_uint16_to_uint8(arr):
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# Ensure the input is of the correct type
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assert arr.dtype == np.uint16, "Input array must be of type np.uint16"
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# Compute the top 8 bits and the bottom 8 bits
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top_bits = (arr >> 8).astype(np.uint8)
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bottom_bits = (arr & 0xFF).astype(np.uint8)
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return top_bits, bottom_bits
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def find_matching_files():
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"""
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Returns list of test set file paths.
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with fits.open(path) as hdul:
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if dim == '2d':
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arr = hdul[0].data[0][2]
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arrs = [arr]
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elif dim == '2d-top':
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arr = hdul[0].data[0][2]
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arr = split_uint16_to_uint8(arr)[0]
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arrs = [arr]
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elif dim == '2d-bottom':
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arr = hdul[0].data[0][2]
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arr = split_uint16_to_uint8(arr)[1]
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arrs = [arr]
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elif dim == '3dt' and len(hdul[0].data) > 2:
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arr = hdul[0].data[0:3][2]
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arrs = [arr]
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elif dim == '3dw' and len(hdul[0].data[0]) > 2:
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arr = hdul[0].data[0][0:3]
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arrs = [arr]
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elif dim == '3dt_reshape' and len(hdul[0].data) > 2:
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arr = hdul[0].data[0:3][2].reshape((800, -1))
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arrs = [arr]
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elif dim == '3dw_reshape' and len(hdul[0].data[0]) > 2:
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arr = hdul[0].data[0][0:3].reshape((800, -1))
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arrs = [arr]
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elif dim == 'tw':
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init_arr = hdul[0].data
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def arrs_gen():
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for i in range(init_arr.shape[-2]):
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for j in range(init_arr.shape[-1]):
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yield init_arr[:, :, i, j]
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arrs = arrs_gen()
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else:
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continue
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print(df.mean())
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df.to_csv(save_path)
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for arr_idx, arr in enumerate(arrs):
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for algo in ALL_CODECS.keys():
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try:
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if algo == "JPEG_2K" and (dim == '3dt' or dim == '3dw'):
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test_results = benchmark_imagecodecs_compression_algos(arr.transpose(1, 2, 0), algo)
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else:
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test_results = benchmark_imagecodecs_compression_algos(arr, algo)
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for column, value in test_results.items():
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if column in df.columns:
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df.at[path + f"_arr_{arr_idx}", column] = value
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except Exception as e:
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print(f"Failed at {path} under exception {e}.")
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print(df.mean())
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df.to_csv(save_path)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Process some 2D or 3D data.")
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parser.add_argument(
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"dimension",
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choices=['2d', '2d-top', '2d-bottom', '3dt', '3dw', 'tw', '3dt_reshape', '3dw_reshape'],
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help="Specify whether the data is 2d, 3dt (3d time dimension), or 3dw (3d wavelength dimension)."
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)
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args = parser.parse_args()
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# RICE REQUIRES UNIQUE INPUT OF ARR SHAPE AND DTYPE INTO DECODER
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if dim == '3dw' or dim == '3dt' or dim == 'tw':
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ALL_CODECS = {
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"JPEG_XL_MAX_EFFORT": [jpegxl_encode_max_effort_preset, jpegxl_decode],
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"JPEG_XL": [jpegxl_encode_preset, jpegxl_decode],
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"JPEG_2K": [jpeg2k_encode, jpeg2k_decode],
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}
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else:
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ALL_CODECS = {
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"JPEG_XL_MAX_EFFORT": [jpegxl_encode_max_effort_preset, jpegxl_decode],
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"JPEG_XL": [jpegxl_encode_preset, jpegxl_decode],
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"JPEG_LS": [jpegls_encode, jpegls_decode],
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"RICE": [rcomp_encode, rcomp_decode],
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}
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columns = []
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for algo in ALL_CODECS.keys():
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utils/sdss_filtering.ipynb
ADDED
@@ -0,0 +1,614 @@
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1 |
+
{
|
2 |
+
"cells": [
|
3 |
+
{
|
4 |
+
"cell_type": "code",
|
5 |
+
"execution_count": 3,
|
6 |
+
"id": "c2058f8d",
|
7 |
+
"metadata": {},
|
8 |
+
"outputs": [
|
9 |
+
{
|
10 |
+
"data": {
|
11 |
+
"text/plain": [
|
12 |
+
"522"
|
13 |
+
]
|
14 |
+
},
|
15 |
+
"execution_count": 3,
|
16 |
+
"metadata": {},
|
17 |
+
"output_type": "execute_result"
|
18 |
+
}
|
19 |
+
],
|
20 |
+
"source": [
|
21 |
+
"import os\n",
|
22 |
+
"from tqdm import tqdm\n",
|
23 |
+
"import glob\n",
|
24 |
+
"from astropy.io import fits\n",
|
25 |
+
"import os\n",
|
26 |
+
"from astropy.io import fits\n",
|
27 |
+
"from astropy.wcs import WCS\n",
|
28 |
+
"from spherical_geometry.polygon import SphericalPolygon\n",
|
29 |
+
"import os\n",
|
30 |
+
"from astropy.io import fits\n",
|
31 |
+
"from astropy.wcs import WCS\n",
|
32 |
+
"from spherical_geometry.polygon import SphericalPolygon\n",
|
33 |
+
"from sklearn.cluster import AgglomerativeClustering\n",
|
34 |
+
"import matplotlib.pyplot as plt\n",
|
35 |
+
"import pandas as pd\n",
|
36 |
+
"from astropy.io import fits\n",
|
37 |
+
"import pandas as pd\n",
|
38 |
+
"import matplotlib.pyplot as plt\n",
|
39 |
+
"import numpy as np\n",
|
40 |
+
"\n",
|
41 |
+
"def get_all_fits_files(root_dir):\n",
|
42 |
+
" # Use glob to recursively find all .fits files\n",
|
43 |
+
" pattern = os.path.join(root_dir, '**', '*.fits')\n",
|
44 |
+
" fits_files = glob.glob(pattern, recursive=True)\n",
|
45 |
+
" return fits_files\n",
|
46 |
+
"\n",
|
47 |
+
"valid_fits_paths = get_all_fits_files('./data')\n",
|
48 |
+
"len(valid_fits_paths)"
|
49 |
+
]
|
50 |
+
},
|
51 |
+
{
|
52 |
+
"cell_type": "code",
|
53 |
+
"execution_count": 11,
|
54 |
+
"id": "554c2fa7",
|
55 |
+
"metadata": {},
|
56 |
+
"outputs": [
|
57 |
+
{
|
58 |
+
"name": "stderr",
|
59 |
+
"output_type": "stream",
|
60 |
+
"text": [
|
61 |
+
" 9%|███▊ | 47/522 [00:28<06:45, 1.17it/s]WARNING: FITSFixedWarning: RADECSYS= 'ICRS ' / International Celestial Reference Sys \n",
|
62 |
+
"the RADECSYS keyword is deprecated, use RADESYSa. [astropy.wcs.wcs]\n",
|
63 |
+
"100%|█████████████████████████████████████████| 522/522 [06:48<00:00, 1.28it/s]\n"
|
64 |
+
]
|
65 |
+
}
|
66 |
+
],
|
67 |
+
"source": [
|
68 |
+
"# Initialize the list of confirmed FITS paths\n",
|
69 |
+
"confirmed_fits_paths = []\n",
|
70 |
+
"\n",
|
71 |
+
"all_polys = []\n",
|
72 |
+
"\n",
|
73 |
+
"for i in tqdm(range(len(valid_fits_paths))):\n",
|
74 |
+
" path1 = valid_fits_paths[i]\n",
|
75 |
+
" try:\n",
|
76 |
+
" with fits.open(path1) as hdul:\n",
|
77 |
+
" hdul[0].data = hdul[0].data[0, 0]\n",
|
78 |
+
" wcs1a = WCS(hdul[0].header)\n",
|
79 |
+
" shape1a = sorted(tuple(wcs1a.pixel_shape))[:2]\n",
|
80 |
+
" footprint1a = wcs1a.calc_footprint(axes=shape1a)\n",
|
81 |
+
" poly1a = SphericalPolygon.from_radec(footprint1a[:, 0], footprint1a[:, 1])\n",
|
82 |
+
" all_polys.append(poly1a)\n",
|
83 |
+
" except Exception as e:\n",
|
84 |
+
" print(e)\n",
|
85 |
+
" continue"
|
86 |
+
]
|
87 |
+
},
|
88 |
+
{
|
89 |
+
"cell_type": "code",
|
90 |
+
"execution_count": 13,
|
91 |
+
"id": "c58c3c55",
|
92 |
+
"metadata": {},
|
93 |
+
"outputs": [
|
94 |
+
{
|
95 |
+
"name": "stderr",
|
96 |
+
"output_type": "stream",
|
97 |
+
"text": [
|
98 |
+
"100%|██████████████████████████████████████| 522/522 [00:00<00:00, 17983.71it/s]\n"
|
99 |
+
]
|
100 |
+
}
|
101 |
+
],
|
102 |
+
"source": [
|
103 |
+
"latitudes = []\n",
|
104 |
+
"longitudes = []\n",
|
105 |
+
"\n",
|
106 |
+
"for poly in tqdm(all_polys):\n",
|
107 |
+
" pts = list(poly.to_radec())[0]\n",
|
108 |
+
" ra = pts[0][0]\n",
|
109 |
+
" dec = pts[1][0]\n",
|
110 |
+
" \n",
|
111 |
+
" longitudes.append(ra)\n",
|
112 |
+
" latitudes.append(dec)"
|
113 |
+
]
|
114 |
+
},
|
115 |
+
{
|
116 |
+
"cell_type": "code",
|
117 |
+
"execution_count": 14,
|
118 |
+
"id": "1c83484e",
|
119 |
+
"metadata": {},
|
120 |
+
"outputs": [
|
121 |
+
{
|
122 |
+
"name": "stdout",
|
123 |
+
"output_type": "stream",
|
124 |
+
"text": [
|
125 |
+
"Symmetric?\n",
|
126 |
+
"True\n",
|
127 |
+
"(522, 522)\n"
|
128 |
+
]
|
129 |
+
}
|
130 |
+
],
|
131 |
+
"source": [
|
132 |
+
"n_points = len(latitudes)\n",
|
133 |
+
"\n",
|
134 |
+
"# Repeat each point n_points times for lat1, lon1\n",
|
135 |
+
"lat1 = np.repeat(latitudes, n_points)\n",
|
136 |
+
"lon1 = np.repeat(longitudes, n_points)\n",
|
137 |
+
"\n",
|
138 |
+
"# Tile the whole array n_points times for lat2, lon2\n",
|
139 |
+
"lat2 = np.tile(latitudes, n_points)\n",
|
140 |
+
"lon2 = np.tile(longitudes, n_points)\n",
|
141 |
+
"\n",
|
142 |
+
"# Calculates angular separation between two spherical coords\n",
|
143 |
+
"# This can be lat/lon or ra/dec\n",
|
144 |
+
"# Taken from astropy\n",
|
145 |
+
"def angular_separation_deg(lon1, lat1, lon2, lat2):\n",
|
146 |
+
" lon1 = np.deg2rad(lon1)\n",
|
147 |
+
" lon2 = np.deg2rad(lon2)\n",
|
148 |
+
" lat1 = np.deg2rad(lat1)\n",
|
149 |
+
" lat2 = np.deg2rad(lat2)\n",
|
150 |
+
" \n",
|
151 |
+
" sdlon = np.sin(lon2 - lon1)\n",
|
152 |
+
" cdlon = np.cos(lon2 - lon1)\n",
|
153 |
+
" slat1 = np.sin(lat1)\n",
|
154 |
+
" slat2 = np.sin(lat2)\n",
|
155 |
+
" clat1 = np.cos(lat1)\n",
|
156 |
+
" clat2 = np.cos(lat2)\n",
|
157 |
+
"\n",
|
158 |
+
" num1 = clat2 * sdlon\n",
|
159 |
+
" num2 = clat1 * slat2 - slat1 * clat2 * cdlon\n",
|
160 |
+
" denominator = slat1 * slat2 + clat1 * clat2 * cdlon\n",
|
161 |
+
"\n",
|
162 |
+
" return np.rad2deg(np.arctan2(np.hypot(num1, num2), denominator))\n",
|
163 |
+
"\n",
|
164 |
+
"# Compute the pairwise angular separations\n",
|
165 |
+
"angular_separations = angular_separation_deg(lon1, lat1, lon2, lat2)\n",
|
166 |
+
"\n",
|
167 |
+
"# Reshape the result into a matrix form\n",
|
168 |
+
"angular_separations_matrix = angular_separations.reshape(n_points, n_points)\n",
|
169 |
+
"\n",
|
170 |
+
"def check_symmetric(a, rtol=1e-05, atol=1e-07):\n",
|
171 |
+
" return np.allclose(a, a.T, rtol=rtol, atol=atol)\n",
|
172 |
+
"\n",
|
173 |
+
"print(\"Symmetric?\")\n",
|
174 |
+
"print(check_symmetric(angular_separations_matrix))\n",
|
175 |
+
"print(angular_separations_matrix.shape)"
|
176 |
+
]
|
177 |
+
},
|
178 |
+
{
|
179 |
+
"cell_type": "code",
|
180 |
+
"execution_count": 19,
|
181 |
+
"id": "c66e8c1e",
|
182 |
+
"metadata": {},
|
183 |
+
"outputs": [],
|
184 |
+
"source": [
|
185 |
+
"SDSS_FOV = 0.088\n",
|
186 |
+
"\n",
|
187 |
+
"THRESH = SDSS_FOV * 4\n",
|
188 |
+
"\n",
|
189 |
+
"clustering = AgglomerativeClustering(n_clusters=None, metric='precomputed', linkage='single', distance_threshold=THRESH)\n",
|
190 |
+
"labels = clustering.fit_predict(angular_separations_matrix)"
|
191 |
+
]
|
192 |
+
},
|
193 |
+
{
|
194 |
+
"cell_type": "code",
|
195 |
+
"execution_count": 24,
|
196 |
+
"id": "51da93b0",
|
197 |
+
"metadata": {},
|
198 |
+
"outputs": [
|
199 |
+
{
|
200 |
+
"name": "stderr",
|
201 |
+
"output_type": "stream",
|
202 |
+
"text": [
|
203 |
+
" 3%|█▍ | 13/377 [00:00<00:21, 16.71it/s]"
|
204 |
+
]
|
205 |
+
},
|
206 |
+
{
|
207 |
+
"name": "stdout",
|
208 |
+
"output_type": "stream",
|
209 |
+
"text": [
|
210 |
+
"FAIL label: 10, i: 1 IoU: 0.25944657797281734\n"
|
211 |
+
]
|
212 |
+
},
|
213 |
+
{
|
214 |
+
"name": "stderr",
|
215 |
+
"output_type": "stream",
|
216 |
+
"text": [
|
217 |
+
" 8%|███▏ | 29/377 [00:01<00:15, 21.76it/s]"
|
218 |
+
]
|
219 |
+
},
|
220 |
+
{
|
221 |
+
"name": "stdout",
|
222 |
+
"output_type": "stream",
|
223 |
+
"text": [
|
224 |
+
"FAIL label: 23, i: 2 IoU: 0.4090236242653364\n"
|
225 |
+
]
|
226 |
+
},
|
227 |
+
{
|
228 |
+
"name": "stderr",
|
229 |
+
"output_type": "stream",
|
230 |
+
"text": [
|
231 |
+
" 9%|███▉ | 35/377 [00:02<00:15, 22.08it/s]"
|
232 |
+
]
|
233 |
+
},
|
234 |
+
{
|
235 |
+
"name": "stdout",
|
236 |
+
"output_type": "stream",
|
237 |
+
"text": [
|
238 |
+
"FAIL label: 29, i: 2 IoU: 0.9007704140270892\n",
|
239 |
+
"FAIL label: 32, i: 2 IoU: 0.4056510771965438\n"
|
240 |
+
]
|
241 |
+
},
|
242 |
+
{
|
243 |
+
"name": "stderr",
|
244 |
+
"output_type": "stream",
|
245 |
+
"text": [
|
246 |
+
"\r",
|
247 |
+
" 10%|████▏ | 38/377 [00:02<00:18, 18.22it/s]"
|
248 |
+
]
|
249 |
+
},
|
250 |
+
{
|
251 |
+
"name": "stdout",
|
252 |
+
"output_type": "stream",
|
253 |
+
"text": [
|
254 |
+
"FAIL label: 36, i: 2 IoU: 0.5762689111619909\n"
|
255 |
+
]
|
256 |
+
},
|
257 |
+
{
|
258 |
+
"name": "stderr",
|
259 |
+
"output_type": "stream",
|
260 |
+
"text": [
|
261 |
+
" 15%|██████▏ | 55/377 [00:02<00:10, 31.49it/s]"
|
262 |
+
]
|
263 |
+
},
|
264 |
+
{
|
265 |
+
"name": "stdout",
|
266 |
+
"output_type": "stream",
|
267 |
+
"text": [
|
268 |
+
"FAIL label: 47, i: 1 IoU: 0.9250531562187404\n",
|
269 |
+
"FAIL label: 48, i: 1 IoU: 0.8360586649509192\n",
|
270 |
+
"FAIL label: 53, i: 1 IoU: 0.10008227926664898\n"
|
271 |
+
]
|
272 |
+
},
|
273 |
+
{
|
274 |
+
"name": "stderr",
|
275 |
+
"output_type": "stream",
|
276 |
+
"text": [
|
277 |
+
" 19%|████████▏ | 73/377 [00:03<00:09, 33.33it/s]"
|
278 |
+
]
|
279 |
+
},
|
280 |
+
{
|
281 |
+
"name": "stdout",
|
282 |
+
"output_type": "stream",
|
283 |
+
"text": [
|
284 |
+
"FAIL label: 68, i: 2 IoU: 0.49064401487956755\n",
|
285 |
+
"FAIL label: 69, i: 2 IoU: 0.9662692069365345\n",
|
286 |
+
"FAIL label: 71, i: 1 IoU: 0.09857753647298885\n"
|
287 |
+
]
|
288 |
+
},
|
289 |
+
{
|
290 |
+
"name": "stderr",
|
291 |
+
"output_type": "stream",
|
292 |
+
"text": [
|
293 |
+
" 22%|█████████▎ | 84/377 [00:03<00:07, 38.05it/s]"
|
294 |
+
]
|
295 |
+
},
|
296 |
+
{
|
297 |
+
"name": "stdout",
|
298 |
+
"output_type": "stream",
|
299 |
+
"text": [
|
300 |
+
"FAIL label: 74, i: 3 IoU: 0.5845239934642943\n",
|
301 |
+
"FAIL label: 81, i: 2 IoU: 0.7402716532101037\n"
|
302 |
+
]
|
303 |
+
},
|
304 |
+
{
|
305 |
+
"name": "stderr",
|
306 |
+
"output_type": "stream",
|
307 |
+
"text": [
|
308 |
+
" 28%|███████████▋ | 107/377 [00:03<00:03, 69.94it/s]"
|
309 |
+
]
|
310 |
+
},
|
311 |
+
{
|
312 |
+
"name": "stdout",
|
313 |
+
"output_type": "stream",
|
314 |
+
"text": [
|
315 |
+
"FAIL label: 91, i: 2 IoU: 0.30092583437382314\n",
|
316 |
+
"FAIL label: 106, i: 1 IoU: 0.5437761463648566\n",
|
317 |
+
"FAIL label: 110, i: 1 IoU: 0.978096219321612\n"
|
318 |
+
]
|
319 |
+
},
|
320 |
+
{
|
321 |
+
"name": "stderr",
|
322 |
+
"output_type": "stream",
|
323 |
+
"text": [
|
324 |
+
" 43%|█████████████████▎ | 163/377 [00:04<00:01, 124.92it/s]"
|
325 |
+
]
|
326 |
+
},
|
327 |
+
{
|
328 |
+
"name": "stdout",
|
329 |
+
"output_type": "stream",
|
330 |
+
"text": [
|
331 |
+
"FAIL label: 137, i: 1 IoU: 0.5768840711253176\n",
|
332 |
+
"FAIL label: 138, i: 1 IoU: 0.426858068191846\n",
|
333 |
+
"FAIL label: 142, i: 1 IoU: 1.0\n",
|
334 |
+
"FAIL label: 151, i: 1 IoU: 0.6865076393310577\n",
|
335 |
+
"FAIL label: 162, i: 1 IoU: 0.40902362440677925\n"
|
336 |
+
]
|
337 |
+
},
|
338 |
+
{
|
339 |
+
"name": "stderr",
|
340 |
+
"output_type": "stream",
|
341 |
+
"text": [
|
342 |
+
"100%|██████████████���██████████████████████████| 377/377 [00:04<00:00, 82.43it/s]"
|
343 |
+
]
|
344 |
+
},
|
345 |
+
{
|
346 |
+
"name": "stdout",
|
347 |
+
"output_type": "stream",
|
348 |
+
"text": [
|
349 |
+
"FAIL label: 166, i: 2 IoU: 0.3312197305714273\n"
|
350 |
+
]
|
351 |
+
},
|
352 |
+
{
|
353 |
+
"name": "stderr",
|
354 |
+
"output_type": "stream",
|
355 |
+
"text": [
|
356 |
+
"\n"
|
357 |
+
]
|
358 |
+
}
|
359 |
+
],
|
360 |
+
"source": [
|
361 |
+
"failed_labels = []\n",
|
362 |
+
"failed_paths = []\n",
|
363 |
+
"\n",
|
364 |
+
"for label in tqdm(np.unique(labels)):\n",
|
365 |
+
" polys = [(all_polys[i], valid_fits_paths[i]) for i in range(len(labels)) if labels[i] == label]\n",
|
366 |
+
" if len(polys) > 1:\n",
|
367 |
+
" total_poly = polys[0][0]\n",
|
368 |
+
" for i in range(1, len(polys)):\n",
|
369 |
+
" new_poly = polys[i][0]\n",
|
370 |
+
" new_path = polys[i][1]\n",
|
371 |
+
" if total_poly.intersects_poly(new_poly):\n",
|
372 |
+
" union_over_max = total_poly.intersection(new_poly).area() / new_poly.area()\n",
|
373 |
+
" print(f\"FAIL label: {label}, i: {i} IoU: {union_over_max}\")\n",
|
374 |
+
" failed_labels.append(label)\n",
|
375 |
+
" failed_paths.append(new_path)\n",
|
376 |
+
" continue\n",
|
377 |
+
" else:\n",
|
378 |
+
" total_poly = total_poly.union(new_poly)"
|
379 |
+
]
|
380 |
+
},
|
381 |
+
{
|
382 |
+
"cell_type": "code",
|
383 |
+
"execution_count": 18,
|
384 |
+
"id": "46c6217a",
|
385 |
+
"metadata": {},
|
386 |
+
"outputs": [
|
387 |
+
{
|
388 |
+
"data": {
|
389 |
+
"text/plain": [
|
390 |
+
"['./data/cube_center_run4203_camcol6_f746_73-5-800-800.fits',\n",
|
391 |
+
" './data/cube_center_run2700_camcol2_f56_86-5-800-800.fits',\n",
|
392 |
+
" './data/cube_center_run4198_camcol6_f243_3-5-800-800.fits',\n",
|
393 |
+
" './data/cube_center_run5658_camcol2_f33_25-5-800-800.fits',\n",
|
394 |
+
" './data/cube_center_run4933_camcol2_f695_81-5-800-800.fits',\n",
|
395 |
+
" './data/cube_center_run2709_camcol5_f236_72-5-800-800.fits',\n",
|
396 |
+
" './data/cube_center_run5637_camcol4_f385_1-5-800-800.fits',\n",
|
397 |
+
" './data/cube_center_run5759_camcol4_f118_72-5-800-800.fits',\n",
|
398 |
+
" './data/cube_center_run2700_camcol5_f163_86-5-800-800.fits',\n",
|
399 |
+
" './data/cube_center_run3434_camcol4_f456_34-5-800-800.fits',\n",
|
400 |
+
" './data/cube_center_run5792_camcol6_f342_73-5-800-800.fits',\n",
|
401 |
+
" './data/cube_center_run5918_camcol5_f278_69-5-800-800.fits',\n",
|
402 |
+
" './data/cube_center_run4128_camcol3_f475_1-5-800-800.fits',\n",
|
403 |
+
" './data/cube_center_run5590_camcol2_f272_56-5-800-800.fits',\n",
|
404 |
+
" './data/cube_center_run5836_camcol6_f545_52-5-800-800.fits',\n",
|
405 |
+
" './data/cube_center_run4933_camcol3_f554_85-5-800-800.fits',\n",
|
406 |
+
" './data/cube_center_run4128_camcol5_f348_8-5-800-800.fits',\n",
|
407 |
+
" './data/cube_center_run2886_camcol1_f164_78-5-800-800.fits',\n",
|
408 |
+
" './data/cube_center_run5642_camcol1_f374_80-5-800-800.fits',\n",
|
409 |
+
" './data/cube_center_run4188_camcol5_f87_42-5-800-800.fits',\n",
|
410 |
+
" './data/cube_center_run5628_camcol5_f238_69-5-800-800.fits',\n",
|
411 |
+
" './data/cube_center_run5781_camcol5_f291_76-5-800-800.fits']"
|
412 |
+
]
|
413 |
+
},
|
414 |
+
"execution_count": 18,
|
415 |
+
"metadata": {},
|
416 |
+
"output_type": "execute_result"
|
417 |
+
}
|
418 |
+
],
|
419 |
+
"source": [
|
420 |
+
"failed_paths"
|
421 |
+
]
|
422 |
+
},
|
423 |
+
{
|
424 |
+
"cell_type": "code",
|
425 |
+
"execution_count": 29,
|
426 |
+
"id": "4af5240f",
|
427 |
+
"metadata": {},
|
428 |
+
"outputs": [
|
429 |
+
{
|
430 |
+
"data": {
|
431 |
+
"text/plain": [
|
432 |
+
"['tiny_train.jsonl', 'full_train.jsonl', 'full_test.jsonl', 'tiny_test.jsonl']"
|
433 |
+
]
|
434 |
+
},
|
435 |
+
"execution_count": 29,
|
436 |
+
"metadata": {},
|
437 |
+
"output_type": "execute_result"
|
438 |
+
}
|
439 |
+
],
|
440 |
+
"source": [
|
441 |
+
"os.listdir('./splits')"
|
442 |
+
]
|
443 |
+
},
|
444 |
+
{
|
445 |
+
"cell_type": "code",
|
446 |
+
"execution_count": 42,
|
447 |
+
"id": "abce2e5a",
|
448 |
+
"metadata": {},
|
449 |
+
"outputs": [],
|
450 |
+
"source": [
|
451 |
+
"import pandas as pd\n",
|
452 |
+
"\n",
|
453 |
+
"# Path to the JSONL file\n",
|
454 |
+
"file_path = './splits/full_train.jsonl'\n",
|
455 |
+
"\n",
|
456 |
+
"# Read the JSONL file into a DataFrame\n",
|
457 |
+
"df_train = pd.read_json(file_path, lines=True)\n",
|
458 |
+
"\n",
|
459 |
+
"# Path to the JSONL file\n",
|
460 |
+
"file_path = './splits/full_test.jsonl'\n",
|
461 |
+
"\n",
|
462 |
+
"# Read the JSONL file into a DataFrame\n",
|
463 |
+
"df_test = pd.read_json(file_path, lines=True)"
|
464 |
+
]
|
465 |
+
},
|
466 |
+
{
|
467 |
+
"cell_type": "code",
|
468 |
+
"execution_count": 49,
|
469 |
+
"id": "c5844d7a",
|
470 |
+
"metadata": {},
|
471 |
+
"outputs": [],
|
472 |
+
"source": [
|
473 |
+
"df = pd.concat([df_train, df_test])\n",
|
474 |
+
"df = df[~df['image'].isin(failed_paths)]"
|
475 |
+
]
|
476 |
+
},
|
477 |
+
{
|
478 |
+
"cell_type": "code",
|
479 |
+
"execution_count": 54,
|
480 |
+
"id": "b94657a0",
|
481 |
+
"metadata": {},
|
482 |
+
"outputs": [
|
483 |
+
{
|
484 |
+
"name": "stdout",
|
485 |
+
"output_type": "stream",
|
486 |
+
"text": [
|
487 |
+
"Train and test datasets have been saved to 'train_data.csv' and 'test_data.csv'.\n"
|
488 |
+
]
|
489 |
+
}
|
490 |
+
],
|
491 |
+
"source": [
|
492 |
+
"import pandas as pd\n",
|
493 |
+
"from sklearn.model_selection import train_test_split\n",
|
494 |
+
"\n",
|
495 |
+
"# Assuming df is your DataFrame\n",
|
496 |
+
"# df = pd.DataFrame(...) # Your DataFrame should already be defined\n",
|
497 |
+
"\n",
|
498 |
+
"# Perform an 85/15 train-test split\n",
|
499 |
+
"train_df, test_df = train_test_split(df, test_size=0.15, random_state=42)\n",
|
500 |
+
"\n",
|
501 |
+
"# Save the train and test DataFrames to CSV files\n",
|
502 |
+
"train_df.to_csv('full_train.csv', index=False)\n",
|
503 |
+
"test_df.to_csv('full_test.csv', index=False)\n",
|
504 |
+
"\n",
|
505 |
+
"print(\"Train and test datasets have been saved to 'train_data.csv' and 'test_data.csv'.\")"
|
506 |
+
]
|
507 |
+
},
|
508 |
+
{
|
509 |
+
"cell_type": "code",
|
510 |
+
"execution_count": 55,
|
511 |
+
"id": "987b9fd7",
|
512 |
+
"metadata": {},
|
513 |
+
"outputs": [
|
514 |
+
{
|
515 |
+
"name": "stdout",
|
516 |
+
"output_type": "stream",
|
517 |
+
"text": [
|
518 |
+
"2\n",
|
519 |
+
"1\n",
|
520 |
+
"Train and test datasets have been saved to 'train_data.csv' and 'test_data.csv'.\n"
|
521 |
+
]
|
522 |
+
}
|
523 |
+
],
|
524 |
+
"source": [
|
525 |
+
"import pandas as pd\n",
|
526 |
+
"\n",
|
527 |
+
"# Path to the JSONL file\n",
|
528 |
+
"file_path = './splits/tiny_train.jsonl'\n",
|
529 |
+
"\n",
|
530 |
+
"# Read the JSONL file into a DataFrame\n",
|
531 |
+
"df_train = pd.read_json(file_path, lines=True)\n",
|
532 |
+
"\n",
|
533 |
+
"# Path to the JSONL file\n",
|
534 |
+
"file_path = './splits/tiny_test.jsonl'\n",
|
535 |
+
"\n",
|
536 |
+
"# Read the JSONL file into a DataFrame\n",
|
537 |
+
"df_test = pd.read_json(file_path, lines=True)\n",
|
538 |
+
"\n",
|
539 |
+
"print(len(df_train))\n",
|
540 |
+
"print(len(df_test))\n",
|
541 |
+
"\n",
|
542 |
+
"# Save the train and test DataFrames to CSV files\n",
|
543 |
+
"df_train.to_csv('tiny_train.csv', index=False)\n",
|
544 |
+
"df_test.to_csv('tiny_test.csv', index=False)\n",
|
545 |
+
"\n",
|
546 |
+
"print(\"Train and test datasets have been saved to 'train_data.csv' and 'test_data.csv'.\")"
|
547 |
+
]
|
548 |
+
},
|
549 |
+
{
|
550 |
+
"cell_type": "code",
|
551 |
+
"execution_count": 59,
|
552 |
+
"id": "dd0209ef",
|
553 |
+
"metadata": {},
|
554 |
+
"outputs": [
|
555 |
+
{
|
556 |
+
"name": "stdout",
|
557 |
+
"output_type": "stream",
|
558 |
+
"text": [
|
559 |
+
"CSV file has been converted and saved as JSONL at ./splits/tiny_train.jsonl\n",
|
560 |
+
"CSV file has been converted and saved as JSONL at ./splits/tiny_test.jsonl\n",
|
561 |
+
"CSV file has been converted and saved as JSONL at ./splits/full_train.jsonl\n",
|
562 |
+
"CSV file has been converted and saved as JSONL at ./splits/full_test.jsonl\n"
|
563 |
+
]
|
564 |
+
}
|
565 |
+
],
|
566 |
+
"source": [
|
567 |
+
"import pandas as pd\n",
|
568 |
+
"\n",
|
569 |
+
"names = [\"./splits/tiny_train\", \"./splits/tiny_test\", \"./splits/full_train\", \"./splits/full_test\"]\n",
|
570 |
+
"\n",
|
571 |
+
"for name in names:\n",
|
572 |
+
"\n",
|
573 |
+
" # Step 1: Load the CSV file into a DataFrame\n",
|
574 |
+
" csv_file_path = f'{name}.csv' # Replace with your actual CSV file path\n",
|
575 |
+
" df = pd.read_csv(csv_file_path)\n",
|
576 |
+
"\n",
|
577 |
+
" # Step 2: Save the DataFrame as a JSONL file\n",
|
578 |
+
" jsonl_file_path = f'{name}.jsonl' # Replace with your desired output file path\n",
|
579 |
+
" df.to_json(jsonl_file_path, orient='records', lines=True)\n",
|
580 |
+
"\n",
|
581 |
+
" print(f\"CSV file has been converted and saved as JSONL at {jsonl_file_path}\")"
|
582 |
+
]
|
583 |
+
},
|
584 |
+
{
|
585 |
+
"cell_type": "code",
|
586 |
+
"execution_count": null,
|
587 |
+
"id": "dfafd26c",
|
588 |
+
"metadata": {},
|
589 |
+
"outputs": [],
|
590 |
+
"source": []
|
591 |
+
}
|
592 |
+
],
|
593 |
+
"metadata": {
|
594 |
+
"kernelspec": {
|
595 |
+
"display_name": "Python 3 (ipykernel)",
|
596 |
+
"language": "python",
|
597 |
+
"name": "python3"
|
598 |
+
},
|
599 |
+
"language_info": {
|
600 |
+
"codemirror_mode": {
|
601 |
+
"name": "ipython",
|
602 |
+
"version": 3
|
603 |
+
},
|
604 |
+
"file_extension": ".py",
|
605 |
+
"mimetype": "text/x-python",
|
606 |
+
"name": "python",
|
607 |
+
"nbconvert_exporter": "python",
|
608 |
+
"pygments_lexer": "ipython3",
|
609 |
+
"version": "3.10.13"
|
610 |
+
}
|
611 |
+
},
|
612 |
+
"nbformat": 4,
|
613 |
+
"nbformat_minor": 5
|
614 |
+
}
|