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a/light_curves/ML_AGNzoo.html b/light_curves/ML_AGNzoo.html index b76ea63b..84c1ab50 100644 --- a/light_curves/ML_AGNzoo.html +++ b/light_curves/ML_AGNzoo.html @@ -47,7 +47,7 @@ - + @@ -535,9 +535,9 @@

1) Loading data 
Downloading...
 From (original): https://drive.google.com/uc?id=1gb2vWn0V2unstElGTTrHIIWIftHbXJvz
-From (redirected): https://drive.google.com/uc?id=1gb2vWn0V2unstElGTTrHIIWIftHbXJvz&confirm=t&uuid=f56b1ed7-ed80-4832-9303-7357e7745f18
+From (redirected): https://drive.google.com/uc?id=1gb2vWn0V2unstElGTTrHIIWIftHbXJvz&confirm=t&uuid=1fb3bd30-b745-405a-97cf-bd180673aada
 To: /home/runner/work/fornax-demo-notebooks/fornax-demo-notebooks/light_curves/data/df_lc_020724.parquet.gzip
-100%|██████████| 243M/243M [00:04<00:00, 54.6MB/s]
+100%|██████████| 243M/243M [00:03<00:00, 78.2MB/s]
@@ -824,7 +824,7 @@

2) Preprocess data for ML (ZTF bands) -../_images/5508df58bb316bce61f77fbb0d2035e416d9d3dabc0e329b71ef47c8defd8275.png +../_images/5508df58bb316bce61f77fbb0d2035e416d9d3dabc0e329b71ef47c8defd8275.png ../_images/27132b4030ea2249b6b4b8b94da40fe294f95e8153e595a7c89a8181a3cc4cd2.png ../_images/18c6537b1d3dcdd27572533eff55d9b6ce1b76f0fb91a22d4b0fb1d7beecc822.png ../_images/12c760252dd26270ecee641b82ee2be229f7c1e096dd1e913dc79f2c8563d71c.png @@ -858,7 +858,7 @@

2) Preprocess data for ML (ZTF bands) -../_images/65eb4e3fa037aae435ec89ce691efa7fda8ae63ce20e3aa7b3c64fcb93e9dbf9.png +../_images/8870019439db16e7139fc41e894aa94bea3a87fbbe9126f01f68cef0cd803da1.png @@ -913,7 +913,7 @@

3) Learn the Manifold -../_images/46d8c94a36620f14175ac85a20dfd8783327fe8a2a49c0a8e1c79ee16152875f.png +../_images/c91361cd46421f1b910d0c3ce6e1891b61a67565cf225140e9b98b9f711adf52.png

The left panel is colorcoded by the origin of the sample. The middle panel shows the sum of mean brightnesses in three bands (arbitrary unit) demonstrating that after normalization we see no correlation with brightness. The panel on the right is color coded by a statistical measure of variability (i.e. the fractional variation see here). As with the plotting above it is not easy to see all the data points and correlations in the next two cells measure probability of belonging to each original sample as well as the mean statistical property on an interpolated grid on this reduced 2D projected surface.

@@ -964,10 +964,10 @@

3) Learn the Manifold
-
<matplotlib.colorbar.Colorbar at 0x7f36bef20210>
+
<matplotlib.colorbar.Colorbar at 0x7f35f1d06490>
 

 

-../_images/4c1206aaf3a7462bf1b8ade81dc798560ba4d06721beae22b23dac83419aafbc.png
+../_images/c7ece1dc707c4bf2023e8f199b4514787bde498ab609c706185a63db68abc28c.png
@@ -999,11 +999,11 @@

3.1) Sample comparison on the UMAP -
/tmp/ipykernel_2023/3793995111.py:10: RuntimeWarning: invalid value encountered in divide
+
/tmp/ipykernel_1896/3793995111.py:10: RuntimeWarning: invalid value encountered in divide
   prob = hist_per_cluster / hist
 

 

-../_images/da318e3ea101f5c93a00cf3c377c97beb1ca6d42d2653cc38f6b4730bd900e06.png
+../_images/5c143963ce9f4832683bada6c16f9f2836587dd0cb39a9b737cc83701b84615e.png

Figure above shows how with ZTF light curves alone we can separate some of these AGN samples, where they have overlaps. We can do a similar exercise with other dimensionality reduction techniques. Below we show two SOMs one with normalized and another with no normalization. The advantage of Umaps to SOMs is that in practice you may change the parameters to separate classes of vastly different data points, as distance is preserved on a umap. On a SOM however only topology of higher dimensions is preserved and not distance hence, the change on the 2d grid does not need to be smooth and from one cell to next there might be larg jumps. On the other hand, an advantage of the SOM is that by definition it has a grid and no need for a posterior interpolation (as we did above) is needed to map more data or to measure probabilities, etc.

@@ -1021,24 +1021,24 @@

3.2) Reduced dimensions on a SOM grid 
Training...
-pca_linear_initialization took: 0.020000 seconds
+pca_linear_initialization took: 0.028000 seconds
 Rough training...
 radius_ini: 2.000000 , radius_final: 1.000000, trainlen: 5
-epoch: 1 ---> elapsed time:  0.102000, quantization error: 7.891746
-epoch: 2 ---> elapsed time:  0.017000, quantization error: 7.278950
+epoch: 1 ---> elapsed time:  0.089000, quantization error: 7.891746
+epoch: 2 ---> elapsed time:  0.016000, quantization error: 7.278950
 epoch: 3 ---> elapsed time:  0.016000, quantization error: 7.065468
-epoch: 4 ---> elapsed time:  0.031000, quantization error: 6.941469
+epoch: 4 ---> elapsed time:  0.016000, quantization error: 6.941469
 epoch: 5 ---> elapsed time:  0.016000, quantization error: 6.804933
 Finetune training...
 radius_ini: 1.000000 , radius_final: 1.000000, trainlen: 6
-epoch: 1 ---> elapsed time:  0.016000, quantization error: 6.635077
+epoch: 1 ---> elapsed time:  0.031000, quantization error: 6.635077
 epoch: 2 ---> elapsed time:  0.016000, quantization error: 6.586020
 epoch: 3 ---> elapsed time:  0.016000, quantization error: 6.569531
 epoch: 4 ---> elapsed time:  0.016000, quantization error: 6.555973
-epoch: 5 ---> elapsed time:  0.019000, quantization error: 6.548818
-epoch: 6 ---> elapsed time:  0.016000, quantization error: 6.545620
+epoch: 5 ---> elapsed time:  0.016000, quantization error: 6.548818
+epoch: 6 ---> elapsed time:  0.015000, quantization error: 6.545620
 Final quantization error: 6.543132
-train took: 0.359000 seconds
+train took: 0.356000 seconds

@@ -1270,24 +1270,24 @@

3.2) Reduced dimensions on a SOM grid 
Training...
-pca_linear_initialization took: 0.145000 seconds
+pca_linear_initialization took: 0.044000 seconds
 Rough training...
 radius_ini: 2.000000 , radius_final: 1.000000, trainlen: 5
-epoch: 1 ---> elapsed time:  0.033000, quantization error: 5.114529
-epoch: 2 ---> elapsed time:  0.066000, quantization error: 2.607937
+epoch: 1 ---> elapsed time:  0.072000, quantization error: 5.114529
+epoch: 2 ---> elapsed time:  0.016000, quantization error: 2.607937
 epoch: 3 ---> elapsed time:  0.016000, quantization error: 1.832338
 epoch: 4 ---> elapsed time:  0.016000, quantization error: 1.587327
-epoch: 5 ---> elapsed time:  0.015000, quantization error: 1.480731
+epoch: 5 ---> elapsed time:  0.016000, quantization error: 1.480731
 Finetune training...
 radius_ini: 1.000000 , radius_final: 1.000000, trainlen: 6
 epoch: 1 ---> elapsed time:  0.016000, quantization error: 1.405660
-epoch: 2 ---> elapsed time:  0.022000, quantization error: 1.394511
+epoch: 2 ---> elapsed time:  0.016000, quantization error: 1.394511
 epoch: 3 ---> elapsed time:  0.016000, quantization error: 1.391247
 epoch: 4 ---> elapsed time:  0.016000, quantization error: 1.385959
-epoch: 5 ---> elapsed time:  0.015000, quantization error: 1.381555
-epoch: 6 ---> elapsed time:  0.015000, quantization error: 1.380592
+epoch: 5 ---> elapsed time:  0.016000, quantization error: 1.381555
+epoch: 6 ---> elapsed time:  0.019000, quantization error: 1.380592
 Final quantization error: 1.377762
-train took: 0.441000 seconds
+train took: 0.340000 seconds
@@ -1418,7 +1418,7 @@

4) Repeating the above, this time with ZTF + WISE manifold -../_images/1e3c537201968f3473502fb729f77ca2a7eeb91c4a353fe31a6c31ad4c0bfd11.png +../_images/1e3c537201968f3473502fb729f77ca2a7eeb91c4a353fe31a6c31ad4c0bfd11.png ../_images/62ced5210f0a62098de705b0e23755e4823b7b452e5b8a07ab46ca4b0b35bb5a.png ../_images/38d17bdd5558e170ffa755a5d0fa803442260ec6b77cc677dde6530f6259528f.png 
/home/runner/work/fornax-demo-notebooks/fornax-demo-notebooks/light_curves/code_src/ML_utils.py:279: RuntimeWarning: invalid value encountered in sqrt
@@ -1476,7 +1476,7 @@ 
4) Repeating the above, this time with ZTF + WISE manifold
+../_images/9a7cc8db93a7fc9168cc9b117129158ab3b8bccb2382138bfb32adfbe4d93c36.png
@@ -1506,11 +1506,11 @@

4) Repeating the above, this time with ZTF + WISE manifold -
/tmp/ipykernel_2023/3793995111.py:10: RuntimeWarning: invalid value encountered in divide
+
/tmp/ipykernel_1896/3793995111.py:10: RuntimeWarning: invalid value encountered in divide
   prob = hist_per_cluster / hist
 

 

-../_images/ba6ed5c419ec3e97ebc9d810ab38d6672a2011aadd3e53391212cc0cfd83d189.png
+../_images/5fe4107afa929aedbc1356db6269e52468a890a2f11af714395aeb81cd9aae83.png
@@ -1541,7 +1541,7 @@

5) Wise bands alone -../_images/1e3c537201968f3473502fb729f77ca2a7eeb91c4a353fe31a6c31ad4c0bfd11.png +../_images/1e3c537201968f3473502fb729f77ca2a7eeb91c4a353fe31a6c31ad4c0bfd11.png 
/home/runner/work/fornax-demo-notebooks/fornax-demo-notebooks/light_curves/code_src/ML_utils.py:279: RuntimeWarning: invalid value encountered in sqrt
   fvar = (np.sqrt(varf-deltaf))/meanf
@@ -1595,7 +1595,7 @@

5) Wise bands alone +../_images/0cf6c7f457a0a021bff3864bf46a040ae3a883c1f22e4d1d45e7960ff15c9af5.png

@@ -1625,11 +1625,11 @@

5) Wise bands alone -
/tmp/ipykernel_2023/3463858432.py:10: RuntimeWarning: invalid value encountered in divide
+
/tmp/ipykernel_1896/3463858432.py:10: RuntimeWarning: invalid value encountered in divide
   prob = hist_per_cluster / hist
 

 

-../_images/b5b6039d950c7f1ccdb1a1a325a219b0f47f9d765448e02da87d130f9bbde069.png
+../_images/88537cede31988344ab4f2f814ba6f172dc0b07bc3203cf1a26e658ddc9f912a.png
@@ -1688,10 +1688,13 @@

6) UMAP with different metrics/distances on ZTF+WISE

-
(0.17634897232055657, 15.66729040145874, 8.039368104934692, 13.325207281112672)
+
(1.7352550029754639,
+ 18.437179803848267,
+ 10.636931610107421,
+ 14.886763381958009)
 

 

-../_images/2ddaa17ca0050a7d46468f65aea5e6870eb5bff71e14ed4b8d46358fd1b980fe.png
+../_images/aa1ebb095a9150fa95f7a960a64db2a64cf70e5e6f26dbf809526960b975e5b5.png
diff --git a/light_curves/light_curve_classifier.html b/light_curves/light_curve_classifier.html index a87cc70d..4e00b1c2 100644 --- a/light_curves/light_curve_classifier.html +++ b/light_curves/light_curve_classifier.html @@ -1680,7 +1680,7 @@

4.1 A single Classifier 
Accuracy of Random Interval Classifier: 0.7444444444444445
-CPU times: user 2.21 s, sys: 1.76 s, total: 3.97 s
+CPU times: user 2.26 s, sys: 1.73 s, total: 3.99 s
 Wall time: 1min 16s
diff --git a/light_curves/scale_up.html b/light_curves/scale_up.html index bfa03a8b..58c34788 100644 --- a/light_curves/scale_up.html +++ b/light_curves/scale_up.html @@ -47,7 +47,7 @@ - + @@ -782,59 +782,59 @@

top - 2024-03-01 21:22:27+00:00 - 1.05 - 0.71 - 0.76 + 2024-03-01 22:55:14+00:00 + 1.94 + 2.45 + 2.33 61.8 - 45.9 - 7.0 - 54.4 - 0 days 00:50:59 + 31.6 + 21.3 + 40.3 + 0 days 02:23:46 - 2024-03-01 23:06:32+00:00 - 1.40 - 1.87 - 2.14 + 2024-03-01 23:01:21+00:00 + 1.93 + 2.36 + 2.35 61.8 - 17.4 - 30.8 - 30.4 - 0 days 02:35:04 + 27.4 + 20.9 + 40.3 + 0 days 02:29:53 - 2024-03-01 21:21:06+00:00 - 0.52 - 0.59 - 0.72 + 2024-03-01 20:53:38+00:00 + 0.85 + 1.00 + 0.94 61.8 - 48.9 - 4.0 - 57.1 - 0 days 00:49:38 + 47.8 + 5.6 + 55.5 + 0 days 00:22:10 - 2024-03-01 21:31:26+00:00 - 2.41 + 2024-03-01 23:03:23+00:00 + 1.81 + 2.17 2.28 - 1.54 61.8 - 41.0 - 11.9 - 49.7 - 0 days 00:59:58 + 34.3 + 13.9 + 47.2 + 0 days 02:31:55 - 2024-03-01 20:38:34+00:00 - 1.92 - 1.14 - 0.62 + 2024-03-01 21:22:31+00:00 + 1.04 + 0.71 + 0.76 61.8 - 38.3 - 15.1 46.0 - 0 days 00:07:06 + 6.9 + 54.4 + 0 days 00:51:03 @@ -903,94 +903,94 @@

top - 2024-03-01 23:02:00+00:00 + 2024-03-01 22:36:56+00:00 wise wise 3980 jovyan 20 0 - 11.8 - 3.1 - 0.1 - R - 106.7 - 5.1 - 77:17.96 - python - 0 days 02:30:32 - - - 2024-03-01 20:42:31+00:00 - wise - wise - 3980 - jovyan - 20 - 0 - 8.8 - 2.2 + 11.6 + 3.0 0.1 R 100.0 - 3.6 - 5:03.47 + 4.8 + 64:38.24 python - 0 days 00:11:03 + 0 days 02:05:28 - 2024-03-01 22:56:45+00:00 + 2024-03-01 22:59:17+00:00 ztf ztf-worker - 13393 + 13391 jovyan 20 0 - 13.0 - 1.6 + 11.0 + 1.1 0.0 S 13.3 - 2.5 - 20:24.42 + 1.9 + 20:32.76 python - 0 days 02:25:17 + 0 days 02:27:49 - 2024-03-01 21:21:26+00:00 + 2024-03-01 21:56:49+00:00 wise wise 3980 jovyan 20 0 - 10.0 - 2.4 + 11.5 + 2.9 0.1 - S - 0.0 - 3.9 - 24:06.77 + R + 100.0 + 4.7 + 44:11.18 python - 0 days 00:49:58 + 0 days 01:25:21 - 2024-03-01 22:55:14+00:00 + 2024-03-01 21:07:45+00:00 ztf ztf 3984 jovyan 20 0 - 12.4 - 9.5 + 3.1 + 0.5 0.1 S 0.0 - 15.3 - 1:50.72 + 0.9 + 0:36.01 python - 0 days 02:23:46 + 0 days 00:36:17 + + + 2024-03-01 20:37:38+00:00 + wise + wise + 3980 + jovyan + 20 + 0 + 3.3 + 0.3 + 0.1 + R + 100.0 + 0.5 + 0:39.63 + python + 0 days 00:06:10 @@ -1047,7 +1047,7 @@

top

-
2024/12/04 02:07:46 UTC | starting explode
+
2024/12/04 03:31:47 UTC | starting explode
@@ -1187,53 +1187,53 @@

Example 2: Parallelizing the light_curve_generator notebook -
2024/12/04 02:07:47 UTC | [pid=2403] Starting build=sample
+
2024/12/04 03:31:48 UTC | [pid=2355] Starting build=sample
 Building object sample from: ['yang']
 Changing Look AGN- Yang et al:  31
 Object sample size, after duplicates removal: 30
 Object sample saved to: /home/runner/work/fornax-demo-notebooks/fornax-demo-notebooks/light_curves/output/lightcurves-demo-Yang-sample/object_sample.ecsv
-2024/12/04 02:07:50 UTC
-2024/12/04 02:07:50 UTC | [pid=2452] Starting build=lightcurves, archive=hcv
-2024/12/04 02:07:50 UTC | [pid=2454] Starting build=lightcurves, archive=icecube
-2024/12/04 02:07:50 UTC | [pid=2451] Starting build=lightcurves, archive=gaia
-2024/12/04 02:07:50 UTC | [pid=2455] Starting build=lightcurves, archive=panstarrs
-2024/12/04 02:07:50 UTC | [pid=2458] Starting build=lightcurves, archive=ztf
-2024/12/04 02:07:50 UTC | [pid=2453] Starting build=lightcurves, archive=heasarc
-2024/12/04 02:07:50 UTC | [pid=2457] Starting build=lightcurves, archive=wise
-2024/12/04 02:07:50 UTC | [pid=2456] Starting build=lightcurves, archive=tess_kepler
+2024/12/04 03:31:49 UTC
+2024/12/04 03:31:49 UTC | [pid=2405] Starting build=lightcurves, archive=icecube
+2024/12/04 03:31:49 UTC | [pid=2404] Starting build=lightcurves, archive=heasarc
+2024/12/04 03:31:49 UTC | [pid=2402] Starting build=lightcurves, archive=gaia
+2024/12/04 03:31:49 UTC | [pid=2403] Starting build=lightcurves, archive=hcv
+2024/12/04 03:31:49 UTC | [pid=2406] Starting build=lightcurves, archive=panstarrs
+2024/12/04 03:31:49 UTC | [pid=2408] Starting build=lightcurves, archive=wise
+2024/12/04 03:31:49 UTC | [pid=2409] Starting build=lightcurves, archive=ztf
+2024/12/04 03:31:49 UTC | [pid=2407] Starting build=lightcurves, archive=tess_kepler
 working on mission
 FERMIGTRIG
-INFO
-: Query finished. [astroquery.utils.tap.core]
 working on mission
 SAXGRBMGRB
+INFO
+: Query finished. [astroquery.utils.tap.core]
 Light curves saved to:
 	parquet_dir=/home/runner/work/fornax-demo-notebooks/fornax-demo-notebooks/light_curves/output/lightcurves-demo-Yang-sample/lightcurves.parquet
 	file=archive=gaia/part0.snappy.parquet
-2024/12/04 02:07:54 UTC
+2024/12/04 03:31:54 UTC
 Light curves saved to:
 	parquet_dir=/home/runner/work/fornax-demo-notebooks/fornax-demo-notebooks/light_curves/output/lightcurves-demo-Yang-sample/lightcurves.parquet
-	file=archive=heasarc/part0.snappy.parquet
-2024/12/04 02:07:55 UTC
+	file=archive=icecube/part0.snappy.parquet
+2024/12/04 03:31:56 UTC
 Light curves saved to:
 	parquet_dir=/home/runner/work/fornax-demo-notebooks/fornax-demo-notebooks/light_curves/output/lightcurves-demo-Yang-sample/lightcurves.parquet
-	file=archive=icecube/part0.snappy.parquet
-2024/12/04 02:07:56 UTC
+	file=archive=heasarc/part0.snappy.parquet
+2024/12/04 03:31:56 UTC
 No light curve data was returned from hcv.
 Light curves saved to:
 	parquet_dir=/home/runner/work/fornax-demo-notebooks/fornax-demo-notebooks/light_curves/output/lightcurves-demo-Yang-sample/lightcurves.parquet
 	file=archive=wise/part0.snappy.parquet
-2024/12/04 02:10:02 UTC
+2024/12/04 03:33:43 UTC
 No light curve data was returned from tess_kepler.
-CPU times: user 594 ms, sys: 203 ms, total: 797 ms
-Wall time: 3min 2s
+CPU times: user 585 ms, sys: 227 ms, total: 811 ms
+Wall time: 2min 26s
 

 

 
0%|          | 0/1 [00:00<?, ?it/s]
-100%|██████████| 1/1 [00:03<00:00,  3.09s/it]
-100%|██████████| 1/1 [00:03<00:00,  3.09s/it]
 No data found for target "<SkyCoord (ICRS): (ra, dec) in deg
     (0.28136, -0.09792)>".
+100%|██████████| 1/1 [00:07<00:00,  7.43s/it]
+100%|██████████| 1/1 [00:07<00:00,  7.43s/it]
 No data found for target "<SkyCoord (ICRS): (ra, dec) in deg
     (21.70042, -8.66336)>".
 No data found for target "<SkyCoord (ICRS): (ra, dec) in deg
@@ -1294,7 +1294,7 @@ 
Example 2: Parallelizing the light_curve_generator notebook{"version_major": 2, "version_minor": 0, "model_id": "ee0b3a1fa4b140c1a3874b30ff0d2583"}

+

The light curves are saved in the “parquet_dir” directory. Each archive call writes data to a parquet file in its own subdirectory. @@ -1347,93 +1347,91 @@

Example 2: Parallelizing the light_curve_generator notebook21 + Yang 18 + RP + 57053.684797 + 0.553946 + 0.009845 + gaia - 24 - Yang 18 - BP - 57334.277500 - 0.298424 - 0.006802 - gaia + W1 + 56980.258961 + 1.073977 + 0.005672 + wise - 26 + 10 Yang 18 W2 - 57235.536349 - 1.652875 - 0.013787 + 58957.736549 + 1.526785 + 0.015576 wise - 5 + 20 Yang 18 - BP - 57838.742103 - 0.080389 - 0.004695 + RP + 56944.492058 + 0.306885 + 0.011099 gaia - 3 + 13 Yang 18 - W1 - 55574.769430 - 0.301494 - 0.005878 - wise + IceCube + 55188.490795 + 4.160000 + 0.000000 + icecube 1 Yang 18 W1 - 58452.657742 - 0.279057 - 0.007431 + 59183.926889 + 0.302206 + 0.006442 wise - 19 + 10 Yang 18 - W1 - 57001.694249 - 0.594351 - 0.006677 - wise + RP + 57002.682038 + 0.530202 + 0.011801 + gaia - 29 + 21 Yang 18 RP - 57868.797124 - 0.317232 - 0.008274 + 57113.131033 + 0.510727 + 0.009771 gaia - 10 + 29 Yang 18 RP - 57175.727067 - 0.556527 - 0.011795 + 57759.803797 + 0.362867 + 0.008676 gaia @@ -1693,7 +1691,7 @@

Using a yaml file -
2024/12/04 02:10:49 UTC | [pid=2403] Starting build=sample
+
2024/12/04 03:34:14 UTC | [pid=2355] Starting build=sample
 Building object sample from: ['sdss', 'ztf_objectid', 'green', 'papers_list', 'ruan']
 SDSS Quasar: 10
 number of ztf coords added by Objectname: 4
@@ -1703,7 +1701,7 @@ 
Using a yaml file
WARNING: OverflowError converting to IntType in column specObjID, reverting to String. [astropy.io.ascii.fastbasic]
diff --git a/searchindex.js b/searchindex.js
index 4358e473..f425f404 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles": {"1) Loading data": [[4, "loading-data"]], "1. Define the sample": [[7, "define-the-sample"]], "1. Get an account on the Fornax Science Console": [[0, "get-an-account-on-the-fornax-science-console"]], "1. Read in a dataset of archival light curves": [[6, "read-in-a-dataset-of-archival-light-curves"]], "1. Retrieve Initial Catalog from IRSA": [[2, "retrieve-initial-catalog-from-irsa"]], "1.1 Build your own sample": [[7, "build-your-own-sample"]], "1.1) What is in this sample": [[4, "what-is-in-this-sample"]], "1.2 Write out your sample to disk": [[7, "write-out-your-sample-to-disk"]], "1.3 Load the sample table from disk": [[7, "load-the-sample-table-from-disk"]], "1.4 Initialize data structure to hold the light curves": [[7, "initialize-data-structure-to-hold-the-light-curves"]], "1a. Filter Catalog": [[2, "a-filter-catalog"]], "2) Preprocess data for ML (ZTF bands)": [[4, "preprocess-data-for-ml-ztf-bands"]], "2. Data Prep": [[6, "data-prep"]], "2. Find light curves for these targets in NASA catalogs": [[7, "find-light-curves-for-these-targets-in-nasa-catalogs"]], "2. Log into the Fornax Science Console": [[0, "log-into-the-fornax-science-console"]], "2. Retrieve Image Datasets from the Cloud": [[2, "retrieve-image-datasets-from-the-cloud"]], "2.1 HEASARC: FERMI & Beppo SAX": [[7, "heasarc-fermi-beppo-sax"]], "2.1 Remove bands with not enough data": [[6, "remove-bands-with-not-enough-data"]], "2.2 Combine Labels for a Simpler Classification": [[6, "combine-labels-for-a-simpler-classification"]], "2.2 IRSA: ZTF": [[7, "irsa-ztf"]], "2.3 Data Visualization": [[6, "data-visualization"]], "2.3 IRSA: WISE": [[7, "irsa-wise"]], "2.4 Clean the dataset of unwanted data": [[6, "clean-the-dataset-of-unwanted-data"]], "2.4 MAST: Pan-STARRS": [[7, "mast-pan-starrs"]], "2.5 MAST: TESS, Kepler and K2": [[7, "mast-tess-kepler-and-k2"]], "2.5 Missing Data": [[6, "missing-data"]], "2.6  Make all objects and bands have identical time arrays (uniform length and spacing)": [[6, "make-all-objects-and-bands-have-identical-time-arrays-uniform-length-and-spacing"]], "2.6 MAST: Hubble Catalog of Variables (HCV)": [[7, "mast-hubble-catalog-of-variables-hcv"]], "2.7  Restructure dataframe": [[6, "restructure-dataframe"]], "2.8 Normalize": [[6, "normalize"]], "2.9 Cleanup": [[6, "cleanup"]], "3) Learn the Manifold": [[4, "learn-the-manifold"]], "3.  Run Forced Photometry": [[2, "run-forced-photometry"]], "3. Find light curves for these targets in relevant, non-NASA catalogs": [[7, "find-light-curves-for-these-targets-in-relevant-non-nasa-catalogs"]], "3. Prep for ML algorithms": [[6, "prep-for-ml-algorithms"]], "3. Start a new notebook.": [[0, "start-a-new-notebook"]], "3.1 Gaia": [[7, "gaia"]], "3.1 Train test split": [[6, "train-test-split"]], "3.1) Sample comparison on the UMAP": [[4, "sample-comparison-on-the-umap"]], "3.2) Reduced dimensions on a SOM grid": [[4, "reduced-dimensions-on-a-som-grid"]], "3.3 IceCube neutrinos": [[7, "icecube-neutrinos"]], "3a. Setup": [[2, "a-setup"]], "3b. Main Function to do the Forced Photometry": [[2, "b-main-function-to-do-the-forced-photometry"]], "3c. Calculate Forced Photometry with Straightforward but Slow Method": [[2, "c-calculate-forced-photometry-with-straightforward-but-slow-method"]], "3d. Calculate Forced Photometry - Parallelization": [[2, "d-calculate-forced-photometry-parallelization"]], "3e. Cleanup": [[2, "e-cleanup"]], "3f. Plot to Confirm our Photometry Results": [[2, "f-plot-to-confirm-our-photometry-results"]], "4) Repeating the above, this time with ZTF + WISE manifold": [[4, "repeating-the-above-this-time-with-ztf-wise-manifold"]], "4. Cross Match our New Photometry Catalog with an X-ray archival Catalog": [[2, "cross-match-our-new-photometry-catalog-with-an-x-ray-archival-catalog"]], "4. End your JupyterHub session.": [[0, "end-your-jupyterhub-session"]], "4. Parallel processing the archive calls": [[7, "parallel-processing-the-archive-calls"]], "4. Run sktime algorithms on the light curves": [[6, "run-sktime-algorithms-on-the-light-curves"]], "4.1 A single Classifier": [[6, "a-single-classifier"]], "4.1 Check that the data types are ok for sktime": [[6, "check-that-the-data-types-are-ok-for-sktime"]], "4.2 Loop over a bunch of classifiers": [[6, "loop-over-a-bunch-of-classifiers"]], "4a. Retrieve the HEASARC Catalog": [[2, "a-retrieve-the-heasarc-catalog"]], "4b. Run nway to do the Cross-Match": [[2, "b-run-nway-to-do-the-cross-match"]], "5) Wise bands alone": [[4, "wise-bands-alone"]], "5. Make plots of luminosity as a function of time": [[7, "make-plots-of-luminosity-as-a-function-of-time"]], "5. Plot Final Results": [[2, "plot-final-results"]], "5.0 Create a candidate list": [[6, "create-a-candidate-list"]], "6) UMAP with different metrics/distances on ZTF+WISE": [[4, "umap-with-different-metrics-distances-on-ztf-wise"]], "AGN Zoo: Comparison of AGN selected with different metrics": [[4, null]], "About this Notebook": [[4, "about-this-notebook"]], "About this notebook": [[8, "about-this-notebook"]], "Acknowledgements": [[6, "acknowledgements"]], "Acknowledgements:": [[2, "acknowledgements"], [7, "acknowledgements"]], "Additional Resources": [[0, "additional-resources"]], "Advice for the user": [[8, "advice-for-the-user"]], "Appendix: What to expect": [[8, "appendix-what-to-expect"]], "Authors": [[6, "authors"]], "Authors:": [[2, "authors"], [7, "authors"]], "Automated Multiband Forced Photometry on Large Datasets": [[2, null]], "Bash script flags": [[8, "bash-script-flags"]], "Can I install my own software on the Fornax Science Console?": [[0, "can-i-install-my-own-software-on-the-fornax-science-console"]], "Cancel": [[8, "cancel"]], "Challenges of large-scale runs": [[8, "challenges-of-large-scale-runs"]], "Citations": [[4, "citations"]], "Cloud": [[0, "cloud"]], "Conclusions": [[6, "conclusions"]], "Cross matching two large catalogs": [[0, "cross-matching-two-large-catalogs"]], "Data Management": [[0, "data-management"]], "Data access within the Fornax Science Console": [[0, "data-access-within-the-fornax-science-console"]], "Distributed processing with Dask DataFrames \u2013 basic concepts": [[0, "distributed-processing-with-dask-dataframes-basic-concepts"]], "Does work persist between sessions?": [[0, "does-work-persist-between-sessions"]], "Example 1: Multi-wavelength light curves for 500,000 SDSS AGN": [[8, "example-1-multi-wavelength-light-curves-for-500-000-sdss-agn"]], "Example 2: Parallelizing the light_curve_generator notebook": [[8, "example-2-parallelizing-the-light-curve-generator-notebook"]], "Example 3: Keyword arguments and script flags": [[8, "example-3-keyword-arguments-and-script-flags"]], "Examples and Tutorials": [[0, "examples-and-tutorials"]], "Fornax Science Console": [[0, null], [8, "fornax-science-console"]], "Fornax Science Console basic capabilities and selection guidelines": [[0, "fornax-science-console-basic-capabilities-and-selection-guidelines"]], "Fully worked science use cases": [[0, "fully-worked-science-use-cases"]], "Getting Help": [[0, "getting-help"]], "HEASARC sciserver_cookbooks": [[0, "heasarc-sciserver-cookbooks"]], "How can I access data that has been hosted on the cloud by the Fornax archives?": [[0, "how-can-i-access-data-that-has-been-hosted-on-the-cloud-by-the-fornax-archives"]], "How can I contribute to existing Open-Source Fornax notebook tutorials?": [[0, "how-can-i-contribute-to-existing-open-source-fornax-notebook-tutorials"]], "How can I get a list of what software is pre-installed on the Fornax Science Console?": [[0, "how-can-i-get-a-list-of-what-software-is-pre-installed-on-the-fornax-science-console"]], "How can I open a plot (e.g. png, pdf) that I generated in a notebook or uploaded?": [[0, "how-can-i-open-a-plot-e-g-png-pdf-that-i-generated-in-a-notebook-or-uploaded"]], "How can I run a notebook non-interactively?": [[0, "how-can-i-run-a-notebook-non-interactively"]], "How can I save my notebook as a Python script?": [[0, "how-can-i-save-my-notebook-as-a-python-script"]], "How can I tell if I am close to using up my allocation of compute and storage resources?": [[0, "how-can-i-tell-if-i-am-close-to-using-up-my-allocation-of-compute-and-storage-resources"]], "How can I tell which computing resources are available on the Fornax Science Console?": [[0, "how-can-i-tell-which-computing-resources-are-available-on-the-fornax-science-console"]], "How can I upload my own data for use with compute provided by the Fornax Science Console?": [[0, "how-can-i-upload-my-own-data-for-use-with-compute-provided-by-the-fornax-science-console"]], "How do I download data from the Fornax Science Console to my local machine?": [[0, "how-do-i-download-data-from-the-fornax-science-console-to-my-local-machine"]], "How do I share data from inside Fornax with collaborators?": [[0, "how-do-i-share-data-from-inside-fornax-with-collaborators"]], "How will my analysis be affected by memory limitations?": [[0, "how-will-my-analysis-be-affected-by-memory-limitations"]], "Imports": [[2, "imports"], [4, "imports"], [6, "imports"], [8, "imports"]], "Imports:": [[7, "imports"]], "Input": [[6, "input"]], "Input:": [[2, "input"], [7, "input"]], "Interpreting top": [[8, "interpreting-top"]], "Introduction": [[4, "introduction"], [6, "introduction"], [8, "introduction"]], "Introduction:": [[2, "introduction"], [7, "introduction"]], "Is it possible to bring my own docker image?": [[0, "is-it-possible-to-bring-my-own-docker-image"]], "Is it possible to do code development in emacs or vi or some other IDE?": [[0, "is-it-possible-to-do-code-development-in-emacs-or-vi-or-some-other-ide"]], "Is it possible to launch apps from icons? Like MOPEX or SPICE": [[0, "is-it-possible-to-launch-apps-from-icons-like-mopex-or-spice"]], "Is it possible to run licensed software (IDL) in Fornax?": [[0, "is-it-possible-to-run-licensed-software-idl-in-fornax"]], "Is it possible to run the container from Fornax on my own personal computer/laptop?": [[0, "is-it-possible-to-run-the-container-from-fornax-on-my-own-personal-computer-laptop"]], "Is there a way access data in a Box account from the Fornax Science Console?": [[0, "is-there-a-way-access-data-in-a-box-account-from-the-fornax-science-console"]], "Is there a way to access data from an AWS bucket?": [[0, "is-there-a-way-to-access-data-from-an-aws-bucket"]], "Launch the run": [[8, "launch-the-run"]], "Learning Goals": [[4, "learning-goals"], [6, "learning-goals"], [7, "learning-goals"], [8, "learning-goals"]], "Learning Goals:": [[2, "learning-goals"]], "Light Curve Classifier": [[6, null]], "Logs": [[8, "logs"]], "MAST science examples": [[0, "mast-science-examples"]], "Make 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+Search.setIndex({"alltitles": {"1) Loading data": [[4, "loading-data"]], "1. Define the sample": [[7, "define-the-sample"]], "1. Get an account on the Fornax Science Console": [[0, "get-an-account-on-the-fornax-science-console"]], "1. Read in a dataset of archival light curves": [[6, "read-in-a-dataset-of-archival-light-curves"]], "1. Retrieve Initial Catalog from IRSA": [[2, "retrieve-initial-catalog-from-irsa"]], "1.1 Build your own sample": [[7, "build-your-own-sample"]], "1.1) What is in this sample": [[4, "what-is-in-this-sample"]], "1.2 Write out your sample to disk": [[7, "write-out-your-sample-to-disk"]], "1.3 Load the sample table from disk": [[7, "load-the-sample-table-from-disk"]], "1.4 Initialize data structure to hold the light curves": [[7, "initialize-data-structure-to-hold-the-light-curves"]], "1a. Filter Catalog": [[2, "a-filter-catalog"]], "2) Preprocess data for ML (ZTF bands)": [[4, "preprocess-data-for-ml-ztf-bands"]], "2. Data Prep": [[6, "data-prep"]], "2. Find light curves for these targets in NASA catalogs": [[7, "find-light-curves-for-these-targets-in-nasa-catalogs"]], "2. Log into the Fornax Science Console": [[0, "log-into-the-fornax-science-console"]], "2. Retrieve Image Datasets from the Cloud": [[2, "retrieve-image-datasets-from-the-cloud"]], "2.1 HEASARC: FERMI & Beppo SAX": [[7, "heasarc-fermi-beppo-sax"]], "2.1 Remove bands with not enough data": [[6, "remove-bands-with-not-enough-data"]], "2.2 Combine Labels for a Simpler Classification": [[6, "combine-labels-for-a-simpler-classification"]], "2.2 IRSA: ZTF": [[7, "irsa-ztf"]], "2.3 Data Visualization": [[6, "data-visualization"]], "2.3 IRSA: WISE": [[7, "irsa-wise"]], "2.4 Clean the dataset of unwanted data": [[6, "clean-the-dataset-of-unwanted-data"]], "2.4 MAST: Pan-STARRS": [[7, "mast-pan-starrs"]], "2.5 MAST: TESS, Kepler and K2": [[7, "mast-tess-kepler-and-k2"]], "2.5 Missing Data": [[6, "missing-data"]], "2.6  Make all objects and bands have identical time arrays (uniform length and spacing)": [[6, "make-all-objects-and-bands-have-identical-time-arrays-uniform-length-and-spacing"]], "2.6 MAST: Hubble Catalog of Variables (HCV)": [[7, "mast-hubble-catalog-of-variables-hcv"]], "2.7  Restructure dataframe": [[6, "restructure-dataframe"]], "2.8 Normalize": [[6, "normalize"]], "2.9 Cleanup": [[6, "cleanup"]], "3) Learn the Manifold": [[4, "learn-the-manifold"]], "3.  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Calculate Forced Photometry - Parallelization": [[2, "d-calculate-forced-photometry-parallelization"]], "3e. Cleanup": [[2, "e-cleanup"]], "3f. Plot to Confirm our Photometry Results": [[2, "f-plot-to-confirm-our-photometry-results"]], "4) Repeating the above, this time with ZTF + WISE manifold": [[4, "repeating-the-above-this-time-with-ztf-wise-manifold"]], "4. Cross Match our New Photometry Catalog with an X-ray archival Catalog": [[2, "cross-match-our-new-photometry-catalog-with-an-x-ray-archival-catalog"]], "4. End your JupyterHub session.": [[0, "end-your-jupyterhub-session"]], "4. Parallel processing the archive calls": [[7, "parallel-processing-the-archive-calls"]], "4. Run sktime algorithms on the light curves": [[6, "run-sktime-algorithms-on-the-light-curves"]], "4.1 A single Classifier": [[6, "a-single-classifier"]], "4.1 Check that the data types are ok for sktime": [[6, "check-that-the-data-types-are-ok-for-sktime"]], "4.2 Loop over a bunch of classifiers": [[6, "loop-over-a-bunch-of-classifiers"]], "4a. Retrieve the HEASARC Catalog": [[2, "a-retrieve-the-heasarc-catalog"]], "4b. Run nway to do the Cross-Match": [[2, "b-run-nway-to-do-the-cross-match"]], "5) Wise bands alone": [[4, "wise-bands-alone"]], "5. Make plots of luminosity as a function of time": [[7, "make-plots-of-luminosity-as-a-function-of-time"]], "5. 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