add usarray prediction script

[JiaoMaWHU]

Output:

Apptainer> python usarray-prediction.py 
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#                           Step 1. load manual pick                           #
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#                          Step 2. index usarray data                          #
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901 mseed files in scope
Total number of documents in mongo collection: 17773983
Index Name: _id_
 - Keys: SON([('_id', 1)])
 - Unique: False
 - Sparse: False
 - TTL: None
Index Name: sta_1
 - Keys: SON([('sta', 1)])
 - Unique: False
 - Sparse: False
 - TTL: None
Index Name: starttime_1_endtime_1
 - Keys: SON([('starttime', 1), ('endtime', 1)])
 - Unique: False
 - Sparse: False
 - TTL: None
Index Name: chan_1
 - Keys: SON([('chan', 1)])
 - Unique: False
 - Sparse: False
 - TTL: None
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#                               Step 3. Clean up                               #
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manual pick total count: 21573
manual pick 'chan' ends with Z count: 15703
Number of documents that have chan as %Z =  5901680
Number of documents that have manual_picks field: 0
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#                  Step 4. join manual pick with usarray data                  #
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Processed no. 0 manual pick, time elapsed: 0.19964170455932617 s
Processed no. 1000 manual pick, time elapsed: 12.202548027038574 s
Processed no. 2000 manual pick, time elapsed: 23.177876234054565 s
Processed no. 3000 manual pick, time elapsed: 32.27029347419739 s
Processed no. 4000 manual pick, time elapsed: 42.673574924468994 s
Processed no. 5000 manual pick, time elapsed: 55.434842348098755 s
Processed no. 6000 manual pick, time elapsed: 66.19894194602966 s
Processed no. 7000 manual pick, time elapsed: 78.07408356666565 s
Processed no. 8000 manual pick, time elapsed: 110.0684585571289 s
Processed no. 9000 manual pick, time elapsed: 122.97102999687195 s
Processed no. 10000 manual pick, time elapsed: 134.44244384765625 s
Processed no. 11000 manual pick, time elapsed: 146.75178003311157 s
Processed no. 12000 manual pick, time elapsed: 156.20526027679443 s
Processed no. 13000 manual pick, time elapsed: 170.13547205924988 s
Processed no. 14000 manual pick, time elapsed: 180.7100658416748 s
Processed no. 15000 manual pick, time elapsed: 196.51843333244324 s
manual_picks length = 3: 10 docs
manual_picks length = 2: 94 docs
manual_picks length = 1: 3224 docs
manual_picks length = 0: 17770655 docs
##########################################################################################
# Step 5. Prediction 1 - predict arrival time for timeseries with non-empty manual picks #
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2025-06-28 15:10:39,482 | seisbench | WARNING | Parts of the input stream consist of fragments shorter than the number of input samples or misaligned traces. Output might be empty.
2025-06-28 15:10:45,538 | seisbench | WARNING | Parts of the input stream consist of fragments shorter than the number of input samples or misaligned traces. Output might be empty.
2025-06-28 15:10:45,643 | seisbench | WARNING | Parts of the input stream consist of fragments shorter than the number of input samples or misaligned traces. Output might be empty.
277 out of 1114 timeseries had a prediction
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#              Step 6. Prediction 2 - group timeseries to stream               #
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[STEP 1] candidate documents with one manual_pick + BH? channel: 3,224
[STEP 2] unique sta/start/end/dfile groups formed: 3,148
[STEP 3] groups with ALL three channels: 16
         (dropped 3,132 incomplete groups)
[STEP 4] triplets dict built with 16 entries ✅
A sample of the triplets formed:

Sample key: ('MPM', 1449429792.9195, 1449429887.1945002, '/scratch1/07114/jiaoma/usarray_2015/2015/event36.mseed')
Channel IDs: [ObjectId('67fd2709b81efdb2a89ca60e'), ObjectId('67fd2709b81efdb2a89ca7e5'), ObjectId('67fd2709b81efdb2a89ca704')]
Channels in sample doc set: ['BHE', 'BHN', 'BHZ']
stream len: 16
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#           Step 7. Prediction 2 - predict arrival time for streams            #
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Over 16 streams, 6 has p wave arrival pick confidence over 0.1
Over 16 streams, 2 has p wave arrival pick confidence over 0.5
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#        Step 8. Prediction 2 - plot a prediction with high probability        #
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Figure saved ➜ /scratch1/07114/jiaoma/mspass/workdir/plots/high_prob_stream_pred.png