Assessing network performance#
Author: Amanda M. Thomas
Goal: This notebook computes some metrics commonly used to assess network performance.
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchsummary import summary
import matplotlib.pyplot as plt
import numpy as np
import time
import seisbench.data as sbd
import seisbench.models as sbm
from torch.utils.data import random_split, DataLoader
import seisbench.generate as sbg
import pandas as pd
from scipy.signal import find_peaks
from obspy import Trace, Stream
# Set seeds
np.random.seed(42)
torch.manual_seed(42)
<torch._C.Generator at 0x11a6c79f0>
1. Load the U-Net Architecture#
We need to first load in the network architecture.
# Cell 2: U-Net Building Blocks
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, padding=1):
super().__init__()
self.conv = nn.Sequential(
nn.Conv1d(in_channels, out_channels, kernel_size, padding=padding),
nn.ReLU(),
nn.Conv1d(out_channels, out_channels, kernel_size, padding=padding),
nn.ReLU()
)
def forward(self, x):
return self.conv(x)
class UNet1D(nn.Module):
def __init__(self, in_channels=3, out_channels=3, features=[16, 32, 64, 128]):
super().__init__()
self.downs = nn.ModuleList() # Encoder blocks (downsampling path)
self.ups = nn.ModuleList() # Decoder blocks (upsampling path)
# ----- Encoder: Downsampling Path -----
# Each ConvBlock halves the temporal resolution via pooling (done in forward),
# and increases the number of feature channels.
for feat in features:
self.downs.append(ConvBlock(in_channels, feat)) # ConvBlock: Conv + ReLU + Conv + ReLU
in_channels = feat # Update in_channels for the next block
# ----- Bottleneck -----
# Deepest layer in the U-Net, connects encoder and decoder
self.bottleneck = ConvBlock(features[-1], features[-1]*2)
# ----- Decoder: Upsampling Path -----
# Reverse features list for symmetrical decoder
rev_feats = features[::-1]
for feat in rev_feats:
# First upsample (via transposed convolution)
self.ups.append(
nn.ConvTranspose1d(feat*2, feat, kernel_size=2, stride=2)
)
# Then apply ConvBlock: input has double channels due to skip connection
self.ups.append(ConvBlock(feat*2, feat))
# ----- Final Output Convolution -----
# 1x1 convolution to map to desired output channels (e.g., P, S, noise)
self.final_conv = nn.Conv1d(features[0], out_channels, kernel_size=1)
def forward(self, x):
skip_connections = []
for down in self.downs:
x = down(x)
skip_connections.append(x)
x = F.max_pool1d(x, kernel_size=2)
x = self.bottleneck(x)
skip_connections = skip_connections[::-1]
for idx in range(0, len(self.ups), 2):
x = self.ups[idx](x)
skip_conn = skip_connections[idx//2]
if x.shape[-1] != skip_conn.shape[-1]:
x = F.pad(x, (0, skip_conn.shape[-1] - x.shape[-1]))
x = torch.cat((skip_conn, x), dim=1)
x = self.ups[idx+1](x)
x = self.final_conv(x)
return F.softmax(x, dim=1)
We then intialize the model and load in the model weights.
model = UNet1D()
# This cell loads the saved weights into the model.
model.load_state_dict(torch.load("../Loic/UNet/model_weights_eq_only_v2.pt",weights_only=True, map_location=torch.device('cpu')))
model.eval()
UNet1D(
(downs): ModuleList(
(0): ConvBlock(
(conv): Sequential(
(0): Conv1d(3, 16, kernel_size=(3,), stride=(1,), padding=(1,))
(1): ReLU()
(2): Conv1d(16, 16, kernel_size=(3,), stride=(1,), padding=(1,))
(3): ReLU()
)
)
(1): ConvBlock(
(conv): Sequential(
(0): Conv1d(16, 32, kernel_size=(3,), stride=(1,), padding=(1,))
(1): ReLU()
(2): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))
(3): ReLU()
)
)
(2): ConvBlock(
(conv): Sequential(
(0): Conv1d(32, 64, kernel_size=(3,), stride=(1,), padding=(1,))
(1): ReLU()
(2): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,))
(3): ReLU()
)
)
(3): ConvBlock(
(conv): Sequential(
(0): Conv1d(64, 128, kernel_size=(3,), stride=(1,), padding=(1,))
(1): ReLU()
(2): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))
(3): ReLU()
)
)
)
(ups): ModuleList(
(0): ConvTranspose1d(256, 128, kernel_size=(2,), stride=(2,))
(1): ConvBlock(
(conv): Sequential(
(0): Conv1d(256, 128, kernel_size=(3,), stride=(1,), padding=(1,))
(1): ReLU()
(2): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))
(3): ReLU()
)
)
(2): ConvTranspose1d(128, 64, kernel_size=(2,), stride=(2,))
(3): ConvBlock(
(conv): Sequential(
(0): Conv1d(128, 64, kernel_size=(3,), stride=(1,), padding=(1,))
(1): ReLU()
(2): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,))
(3): ReLU()
)
)
(4): ConvTranspose1d(64, 32, kernel_size=(2,), stride=(2,))
(5): ConvBlock(
(conv): Sequential(
(0): Conv1d(64, 32, kernel_size=(3,), stride=(1,), padding=(1,))
(1): ReLU()
(2): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))
(3): ReLU()
)
)
(6): ConvTranspose1d(32, 16, kernel_size=(2,), stride=(2,))
(7): ConvBlock(
(conv): Sequential(
(0): Conv1d(32, 16, kernel_size=(3,), stride=(1,), padding=(1,))
(1): ReLU()
(2): Conv1d(16, 16, kernel_size=(3,), stride=(1,), padding=(1,))
(3): ReLU()
)
)
)
(bottleneck): ConvBlock(
(conv): Sequential(
(0): Conv1d(128, 256, kernel_size=(3,), stride=(1,), padding=(1,))
(1): ReLU()
(2): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,))
(3): ReLU()
)
)
(final_conv): Conv1d(16, 3, kernel_size=(1,), stride=(1,))
)
2. Load the dataset from the current directory#
Loads the “meso” PNW dataset.
#data = sbd.WaveformDataset("../data/waveform_dataset/", component_order="ENZ")
data = sbd.WaveformDataset("../../../shared/shortcourses/crescent_ml_2025/miniseed/", component_order="ENZ")
data._metadata = data.metadata[data.metadata.source_type == "earthquake"].reset_index(drop=True)
print(data.metadata["source_type"].value_counts())
2025-05-09 22:15:33,007 | seisbench | WARNING | Dimension order not specified in data set. Assuming CW.
source_type
earthquake 17960
Name: count, dtype: int64
Define the test dataset and the labels.
test = data.test()
phase_dict = {"trace_P_arrival_sample": "P",
"trace_S_arrival_sample": "S"}
model_labels = ["P", "S", "noise"]
3. Set up and test the generator#
Set up generator.
test_generator = sbg.GenericGenerator(test)
augmentations = [
sbg.WindowAroundSample(
list(phase_dict.keys()), samples_before=3000, windowlen=6000,
selection="random", strategy="variable"
),
sbg.RandomWindow(windowlen=3001, strategy="pad"),
sbg.Normalize(demean_axis=-1, amp_norm_axis=-1, amp_norm_type="peak"),
sbg.ChangeDtype(np.float32),
sbg.ProbabilisticLabeller(label_columns=phase_dict, model_labels=model_labels, sigma=30, dim=0)
]
test_generator.add_augmentations(augmentations)
Plot generator output.
sample_id = 4
sample = test_generator[sample_id]
# Plot timeseries
fig = plt.figure(figsize=(15, 10))
axs = fig.subplots(2, 1, sharex=True, gridspec_kw={"hspace": 0, "height_ratios": [3, 1]})
axs[0].plot(sample["X"][0].T,label='E',color="tab:red")
axs[0].plot(sample["X"][1].T,label='N',color="tab:blue")
axs[0].plot(sample["X"][2].T,label='Z',color="tab:grey")
axs[0].legend()
axs[1].plot(sample["y"][0].T,label='P-wave',color="tab:red")
axs[1].plot(sample["y"][1].T,label='S-wave',color="tab:blue")
axs[1].plot(sample["y"][2].T,label='Noise',color="tab:grey")
axs[1].set_xlabel('Time (s)',fontsize=14)
axs[1].set_ylabel('Target Amplitude',fontsize=14)
axs[0].set_ylabel('Amplitude',fontsize=14)
axs[1].set_xlim((0,3000))
axs[1].legend()
<matplotlib.legend.Legend at 0x314937f50>
4. Make test predictions#
Next we make predictions on the test set.
# seisbench prediction?
seisbench_pred=True
if seisbench_pred:
pn_model = sbm.PhaseNet.from_pretrained("original")
# Define a lambda that returns the index of where the timeseries is maximum
def idx_max(ts):
indices = np.where(ts == np.max(ts))[0]
return indices[0], ts[indices[0]]
# Generate augmented samples and log their stats
targets =[]
predictions = []
for ii in range(len(test)):
# Initialize variables
p_idx_max, p_val_max, s_idx_max, s_val_max, p_pred_idx_max, s_pred_idx_max, largest_ppeak_idx, largest_speak_idx = -1, -1, -1, -1, -1, -1, -1, -1
# Get the sample
sample = test_generator[ii]
if seisbench_pred:
st=Stream()
for cc, ch in enumerate(['E', 'N', 'Z']):
trace = Trace(data=np.pad(sample['X'][cc], (250, 250), mode='constant', constant_values=0))
trace.stats.sampling_rate = 100
trace.stats.channel = ch
st += trace
preds = pn_model.annotate(st)
noise_pred = preds[0].data
p_pred = preds[1].data
s_pred = preds[2].data
else:
with torch.no_grad():
pred = model(torch.tensor(sample["X"], device=torch.device('cpu')).unsqueeze(0)) # Add a fake batch dimension
p_pred, s_pred, noise_pred = pred[0].cpu().numpy()
# Find the index of the maximum value in the analyist target
p_idx_max, p_val_max = idx_max(sample["y"][0])
s_idx_max, s_val_max = idx_max(sample["y"][1])
# Find the index of the maximum value in the prediction
p_pred_idx_max, _ = find_peaks(p_pred, height=0.05, distance=100)
s_pred_idx_max, _ = find_peaks(s_pred, height=0.05, distance=100)
# ------------ There is a P-wave prediction and a P-wave target ------------
if len(p_pred_idx_max) > 0 and p_val_max == 1:
largest_ppeak_idx = p_pred_idx_max[np.argmax(p_pred[p_pred_idx_max])]
predictions.append({"trace_id": ii,
"pred_max_idx": largest_ppeak_idx,
"pred_val": p_pred[largest_ppeak_idx],
"phase": "P"})
targets.append({"trace_id": ii,
"max_idx": p_idx_max,
"phase": "P"})
# There is a P-wave prediction and no P-wave target --> false positive > dt / true negative if < dt
elif len(p_pred_idx_max) > 0 and p_val_max != 1:
largest_ppeak_idx = p_pred_idx_max[np.argmax(p_pred[p_pred_idx_max])]
predictions.append({"trace_id": ii,
"pred_max_idx": largest_ppeak_idx,
"pred_val": p_pred[largest_ppeak_idx],
"phase": "P"})
targets.append({"trace_id": ii,
"max_idx": np.nan,
"phase": "P"})
# There is no P-wave prediction and a P-wave target --> false negative
elif len(p_pred_idx_max) == 0 and p_val_max == 1:
predictions.append({"trace_id": ii,
"pred_max_idx": np.nan,
"pred_val": np.nan,
"phase": "P"})
targets.append({"trace_id": ii,
"max_idx": p_idx_max,
"phase": "P"})
# There is no P-wave prediction and no P-wave target --> true negative
else:
predictions.append({"trace_id": ii,
"pred_max_idx": np.nan,
"pred_val": np.nan,
"phase": "P"})
targets.append({"trace_id": ii,
"max_idx": np.nan,
"phase": "P"})
# ------------ There is a S-wave prediction and a S-wave target ------------
if len(s_pred_idx_max) > 0 and s_val_max == 1:
targets.append({"trace_id": ii,
"max_idx": s_idx_max,
"phase": "S"})
largest_speak_idx = s_pred_idx_max[np.argmax(s_pred[s_pred_idx_max])]
predictions.append({"trace_id": ii,
"pred_max_idx": largest_speak_idx,
"pred_val": s_pred[largest_speak_idx],
"phase": "S"})
# There is a S-wave prediction and no S-wave target --> false positive > dt / true negative if < dt
elif len(s_pred_idx_max) > 0 and s_val_max != 1:
largest_speak_idx = s_pred_idx_max[np.argmax(s_pred[s_pred_idx_max])]
predictions.append({"trace_id": ii,
"pred_max_idx": largest_speak_idx,
"pred_val": s_pred[largest_speak_idx],
"phase": "S"})
targets.append({"trace_id": ii,
"max_idx": np.nan,
"phase": "S"})
# There is no S-wave prediction and a S-wave target --> false negative
elif len(s_pred_idx_max) == 0 and s_val_max == 1:
predictions.append({"trace_id": ii,
"pred_max_idx": np.nan,
"pred_val": np.nan,
"phase": "S"})
targets.append({"trace_id": ii,
"max_idx": s_idx_max,
"phase": "S"})
# There is no S-wave prediction and no S-wave target --> true negative
else:
predictions.append({"trace_id": ii,
"pred_max_idx": np.nan,
"pred_val": np.nan,
"phase": "S"})
targets.append({"trace_id": ii,
"max_idx": np.nan,
"phase": "S"})
# Plot some examples
if ii<10:
# Plot waveforms
fig = plt.figure(figsize=(15, 10))
axs = fig.subplots(2, 1, sharex=True, gridspec_kw={"hspace": 0, "height_ratios": [3, 1]})
axs[0].plot(sample["X"][0].T,label='E',color="tab:red")
axs[0].plot(sample["X"][1].T,label='N',color="tab:blue")
axs[0].plot(sample["X"][2].T,label='Z',color="tab:grey")
axs[0].text(20,-0.83, f"P-wave target: {targets[-2]["max_idx"]}", fontsize=10)
axs[0].text(20,-0.88, f"P-wave prediction: {predictions[-2]["pred_max_idx"]}", fontsize=10)
axs[0].text(20,-0.93, f"S-wave target: {targets[-1]["max_idx"]}", fontsize=10)
axs[0].text(20,-0.98, f"S-wave prediction: {predictions[-1]["pred_max_idx"]}", fontsize=10)
axs[0].set_ylim((-1,1))
# Plot target timeseries
axs[1].plot(sample["y"][0].T,label='P-wave target', color="tab:red")
axs[1].plot(sample["y"][1].T,label='S-wave target', color='tab:blue')
if p_val_max == 1:
axs[1].plot(targets[-2]["max_idx"], sample["y"][0][targets[-2]["max_idx"]], 'o', label='Analyst P-wave', color="tab:red")
if s_val_max == 1:
axs[1].plot(targets[-1]["max_idx"], sample["y"][1][targets[-1]["max_idx"]], 'o', label='Analyst S-wave', color='tab:blue')
axs[1].plot(p_pred,label='P-wave Prediction', color="tab:red", linestyle='-.')
axs[1].plot(s_pred,label='S-wave Predition', color='tab:blue', linestyle='-.')
if largest_ppeak_idx > 0:
axs[1].plot(predictions[-2]["pred_max_idx"],p_pred[predictions[-2]["pred_max_idx"]], 'D', label='Predicted P-wave', color="tab:red")
if largest_speak_idx > 0:
axs[1].plot(predictions[-1]["pred_max_idx"],s_pred[predictions[-1]["pred_max_idx"]], 'D', label='Predicted S-wave', color="tab:blue")
axs[1].set_xlabel('Time (s)',fontsize=14)
axs[1].set_ylabel('Target Amplitude',fontsize=14)
axs[0].set_ylabel('Amplitude',fontsize=14)
# axs[1].plot(noise_pred,label='Noise Prediction', color="tab:green", linestyle='-.')
axs[1].legend()
axs[0].legend()
axs[0].set_xlim((0,3000))
axs[1].set_xlim((0,3000))
axs[0].set_title(f"Trace ID: {ii}", fontsize=16)
targets = pd.DataFrame(targets)
predictions = pd.DataFrame(predictions)
/Users/amt/anaconda3/envs/pytorch/lib/python3.12/site-packages/seisbench/models/base.py:489: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
model_weights = torch.load(f"{path_pt}")
targets
| trace_id | max_idx | phase | |
|---|---|---|---|
| 0 | 0 | 1870.0 | P |
| 1 | 0 | 2890.0 | S |
| 2 | 1 | 1362.0 | P |
| 3 | 1 | 2286.0 | S |
| 4 | 2 | 1762.0 | P |
| ... | ... | ... | ... |
| 17935 | 8967 | 2031.0 | S |
| 17936 | 8968 | 809.0 | P |
| 17937 | 8968 | 1738.0 | S |
| 17938 | 8969 | 1506.0 | P |
| 17939 | 8969 | 2310.0 | S |
17940 rows × 3 columns
predictions
| trace_id | pred_max_idx | pred_val | phase | |
|---|---|---|---|---|
| 0 | 0 | 1873.0 | 0.436259 | P |
| 1 | 0 | NaN | NaN | S |
| 2 | 1 | 1361.0 | 0.791852 | P |
| 3 | 1 | 2282.0 | 0.720918 | S |
| 4 | 2 | 1764.0 | 0.932841 | P |
| ... | ... | ... | ... | ... |
| 17935 | 8967 | 2038.0 | 0.472659 | S |
| 17936 | 8968 | 813.0 | 0.908409 | P |
| 17937 | 8968 | 1738.0 | 0.755865 | S |
| 17938 | 8969 | 1511.0 | 0.946008 | P |
| 17939 | 8969 | 2293.0 | 0.575749 | S |
17940 rows × 4 columns
5. Common Performance Metrics#
The Confusion Matrix#
The confusion matrix is a tool used to evaluate the performance of a model. It provides insight into the model’s performance, errors, and weaknesses which will allow us to further improve our model through fine-tuning.
The four categories in the confusion matrix are:
True positive (TP) - The model correctly (true) predicts the positive class (positive).
False positive (FP) - The model incorrectly (false) predicts the positive class (positive).
False negative (FN) - The model incorrectly (false) predicts the negative class (negative).
True negative (TN) - The model correctly (true) predicts the negative class (negative).
Using these categories, there are multiple different metrics one can use to assess network perfomance. For demonstration purposes, lets say we have a dataset of 100 seismograms. 5 of them contain earthquakes, and 95 of them contain noise. Lets also say of the 5 earthquakes, the model correctly predicts 4 of them and misses 1. For the 95 noise samples, the model correctly predicts 90 of them as noise and the other 5 it calls earthquakes. In this scenario, TP = 4, TN = 90, FP = 5, FN = 1.
Accuracy#
Accuracy shows how often a model is correct overall. It is defined as the total number of true predictions (i.e. TP + TN) divided by the total number of predictions.
\(\huge Accuracy = \frac{TP + TN}{TP + FP + FN + TN} \)
In our example above, TP + TN = 94 and the total number of predictions is 100 so we have 94% accuracy overall – woo hoo!
Accuracy is widely used but its not always useful because it treats all classes as equally important. If a dataset is well balanced, meaning it has equal or nearly equal representation in the training data of all classes, accuracy can be a helpful metric. However, many real-world applications have a high imbalance of classes. These are the cases when one category has significantly more frequent occurrences than the other. In seismology, this would happen if you were to just estimate whether each section of a seismogram had an earthquake or recorded only noise. In scenarios like this, you are typically interested in predicting the events that rarely occur (i.e. earthquakes). It is easy to “game” the accuracy metric when making predictions for a dataset like this. To do that, you simply need to predict that every waveform is noise. If only 5 in 100 waveforms actually contain an earthquake, a model predicting noise all the time will mostly be right, leading to very high accuracy. In this particular situaion, your model would be 95% accurate however you’d miss every earthquake – so high accuracy models can be useless. Overall accuracy is informative but using this metric alone is not advisable.
Precision#
Precision is a metric that measures how often a machine learning model is correct when it predicts the positive class (in our case, earthquakes). You can calculate precision by dividing the number of true positive predictions (TPs) by the total number of instances the model predicted as positive (TP + FP).
\(\huge Precision = \frac{TP}{TP + FP} \)
In our example above, TP = 4 and FP = 5 so the precition is 4/9 or 44%.
Precision works well for problems with imbalanced classes since it shows the model correctness in identifying the positive class. It’s important to note that precision does not consider false negatives meaning it does not account for the cases when we miss earthquakes.
Recall#
Recall is a metric that measures how often a machine learning model correctly identifies positive instances (TP) from all the actual positive samples in the dataset (TP + FN).
\(\huge Recall = \frac{TP}{TP + FN} \)
From our example above, TP = 4 and FN = 1, so our recall is 4/5 or 80%.
Recall works well for problems with imbalanced classes since it is focused on the model’s ability to find earthquakes. Recall is useful when the cost of false negatives is high. In this case, you typically want to find all earthquakes, even if this results in some false positives (predicting an earthquake when it is actually noise). A downside is that recall does not account for the cost of these false positives.
F1 score#
If precision is high but recall is low, it means you’re cautious and only pick when confident — but miss many real picks. If recall is high but precision is low, it means you’re aggressive, finding most real picks — but also making lots of false ones. The F1 score is a metric that combines precision and recall into a single number. It’s especially useful when you want a balance between these two, particularly in imbalanced datasets — like seismic phase picking, where picks are rare compared to the number of time steps.
\(\huge F1 = \frac{2*precision*recall}{precision + recall}\)
F1 gives a harmonic mean, so it heavily penalizes large gaps between precision and recall.
6. Compute UNet Performance metrics#
Looking back at the original Zhu and Beroza [2019] paper that introduced PhaseNet, they have a little bit of a problem. They’ve set up the earthquake detection problem as a regression where the network outputs a “probability” between 0 and 1 to identify noise (0) vs. a phase arrival (1). So how do we translate this into a classicifcation problem so that we can use the swanky metrics we introduced above? Well they did it as follows (and so will we!):
True positive - peak amplitude is above decision threshold and < 0.1s from true arrival
False positive - peak amplitude is above decision threshold and >= 0.1s from true arrival
False negative - peak amplitude is below decision threshold and < 0.1s from true arrival
True negative - peak amplitude is below decision threshold and >= 0.1s
This next block includes masks that deal with missing targets or predictions.
merged = pd.merge(
predictions,
targets,
how="inner",
on=["trace_id", "phase"],
suffixes=('_pred', '_true')
)
# Delete for speed
del predictions
del targets
# Dealing with the NaNs
mask=False
if mask:
no_pred_and_no_target = (
merged["pred_max_idx"].isna() &
merged["max_idx"].isna() &
merged["pred_val"].isna()
)
target_no_pred = (
merged["pred_max_idx"].isna() &
merged["max_idx"].notna() &
merged["pred_val"].isna()
)
pred_no_target = (
merged["pred_max_idx"].notna() &
merged["max_idx"].isna() &
merged["pred_val"].notna()
)
# Apply masks
merged.loc[no_pred_and_no_target, ["pred_max_idx", "max_idx", "pred_val"]] = [0, 100, 0]
merged.loc[target_no_pred, ["pred_max_idx", "max_idx", "pred_val"]] = [0, 0, 0]
merged.loc[pred_no_target, ["pred_max_idx", "max_idx"]] = [0, 100]
else:
merged=merged.dropna()
with pd.option_context('display.max_rows', None, 'display.max_columns', None):
print(merged)
# Check for NaN values in the merged DataFrame
merged[merged.isna().any(axis=1)]
trace_id pred_max_idx pred_val phase max_idx
0 0 1873.0 0.436259 P 1870.0
2 1 1361.0 0.791852 P 1362.0
3 1 2282.0 0.720918 S 2286.0
4 2 1764.0 0.932841 P 1762.0
6 3 866.0 0.971795 P 865.0
7 3 1495.0 0.822210 S 1495.0
9 4 88.0 0.334481 S 81.0
10 5 1314.0 0.864006 P 1315.0
11 5 2141.0 0.608934 S 2141.0
13 6 610.0 0.777767 S 609.0
15 7 164.0 0.082916 S 147.0
16 8 900.0 0.952481 P 896.0
17 8 2048.0 0.378576 S 2045.0
18 9 2545.0 0.748032 P 2541.0
21 10 711.0 0.654785 S 700.0
22 11 2528.0 0.876624 P 2526.0
24 12 957.0 0.931530 P 953.0
25 12 2419.0 0.600804 S 2425.0
26 13 220.0 0.261616 P 214.0
27 13 2020.0 0.081804 S 2025.0
29 14 274.0 0.790355 S 266.0
33 16 128.0 0.134234 S 121.0
34 17 2372.0 0.402990 P 2354.0
36 18 789.0 0.749376 P 786.0
38 19 937.0 0.733322 P 910.0
39 19 2413.0 0.608579 S 2400.0
40 20 1906.0 0.755071 P 1887.0
42 21 398.0 0.848328 P 388.0
43 21 1725.0 0.756060 S 1723.0
45 22 864.0 0.569663 S 861.0
46 23 665.0 0.756814 P 663.0
47 23 1522.0 0.425100 S 1522.0
48 24 2228.0 0.941068 P 2225.0
50 25 751.0 0.911382 P 747.0
51 25 2074.0 0.709062 S 2088.0
52 26 1.0 0.935530 P 541.0
53 26 1369.0 0.675218 S 1415.0
54 27 1657.0 0.956999 P 1657.0
55 27 1986.0 0.587287 S 1979.0
57 28 1881.0 0.549673 S 1871.0
58 29 696.0 0.379802 P 694.0
59 29 1496.0 0.317812 S 1500.0
61 30 233.0 0.154976 S 223.0
62 31 2016.0 0.458405 P 2008.0
63 31 2496.0 0.141618 S 2799.0
64 32 264.0 0.732779 P 262.0
65 32 685.0 0.294195 S 683.0
66 33 2031.0 0.975519 P 2031.0
67 33 2545.0 0.833583 S 2545.0
68 34 1734.0 0.183986 P 1725.0
71 35 658.0 0.680583 S 657.0
72 36 2406.0 0.978647 P 2405.0
73 36 2666.0 0.832650 S 2663.0
74 37 2204.0 0.868407 P 2209.0
76 38 2234.0 0.932393 P 2237.0
78 39 1063.0 0.962008 P 1059.0
79 39 2500.0 0.105669 S 2508.0
80 40 944.0 0.919282 P 938.0
81 40 2940.0 0.688067 S 2927.0
82 41 435.0 0.964710 P 432.0
83 41 1102.0 0.761894 S 1102.0
84 42 973.0 0.940258 P 973.0
85 42 1482.0 0.383651 S 1517.0
86 43 1168.0 0.951275 P 1167.0
87 43 1840.0 0.333522 S 1838.0
88 44 2611.0 0.747099 P 2609.0
90 45 668.0 0.766872 P 664.0
91 45 687.0 0.076164 S 2728.0
92 46 725.0 0.920820 P 722.0
93 46 1108.0 0.475360 S 1110.0
94 47 816.0 0.375220 P 818.0
95 47 2330.0 0.231823 S 2327.0
96 48 588.0 0.244103 P 590.0
97 48 2232.0 0.068660 S 2221.0
98 49 2498.0 0.956155 P 2498.0
100 50 218.0 0.977179 P 215.0
101 50 1253.0 0.861259 S 1249.0
102 51 269.0 0.071597 P 269.0
103 51 1073.0 0.439838 S 1068.0
104 52 1.0 0.822320 P 159.0
105 52 1437.0 0.759692 S 1430.0
106 53 346.0 0.090355 P 340.0
107 53 916.0 0.415582 S 910.0
108 54 61.0 0.951506 P 42.0
109 54 318.0 0.766135 S 310.0
110 55 2498.0 0.969449 P 2498.0
112 56 885.0 0.942668 P 882.0
113 56 2133.0 0.281005 S 2130.0
118 59 2137.0 0.951296 P 2137.0
120 60 2437.0 0.465447 P 2437.0
123 61 1080.0 0.226933 S 1077.0
124 62 708.0 0.716536 P 722.0
125 62 2045.0 0.445983 S 2044.0
126 63 2460.0 0.961358 P 2460.0
127 63 2836.0 0.707845 S 2832.0
128 64 1818.0 0.937934 P 1817.0
129 64 2819.0 0.917913 S 2819.0
130 65 1185.0 0.729063 P 1181.0
134 67 309.0 0.849408 P 307.0
136 68 384.0 0.850559 P 375.0
137 68 1352.0 0.310292 S 1357.0
139 69 1025.0 0.760093 S 1019.0
141 70 1480.0 0.464614 S 1471.0
142 71 741.0 0.942778 P 740.0
143 71 1509.0 0.850226 S 1504.0
144 72 375.0 0.971249 P 374.0
145 72 1594.0 0.786541 S 1592.0
146 73 2617.0 0.234605 P 320.0
147 73 2746.0 0.574445 S 2744.0
148 74 1232.0 0.820585 P 1232.0
150 75 2292.0 0.547189 P 2291.0
152 76 2348.0 0.721229 P 2352.0
154 77 1741.0 0.221363 P 1743.0
155 77 2428.0 0.452230 S 2422.0
156 78 636.0 0.963283 P 634.0
157 78 998.0 0.910853 S 1002.0
158 79 395.0 0.944034 P 395.0
159 79 1371.0 0.763683 S 1369.0
160 80 1878.0 0.356815 P 1864.0
162 81 513.0 0.954230 P 511.0
163 81 1209.0 0.617484 S 1206.0
164 82 677.0 0.948704 P 677.0
165 82 1367.0 0.754227 S 1364.0
166 83 1232.0 0.491531 P 1234.0
167 83 2306.0 0.566176 S 2302.0
168 84 1732.0 0.152655 P 1740.0
169 84 2369.0 0.286120 S 2359.0
170 85 1051.0 0.941743 P 1046.0
171 85 2155.0 0.614426 S 2146.0
173 86 569.0 0.598641 S 568.0
174 87 904.0 0.258406 P 900.0
175 87 1926.0 0.920896 S 1924.0
176 88 354.0 0.838605 P 346.0
177 88 1270.0 0.703574 S 1267.0
178 89 2497.0 0.974181 P 2495.0
180 90 2601.0 0.054598 P 2600.0
183 91 669.0 0.580520 S 673.0
184 92 1893.0 0.468988 P 1891.0
186 93 202.0 0.864333 P 201.0
187 93 1366.0 0.713817 S 1364.0
188 94 1857.0 0.894451 P 1853.0
189 94 2173.0 0.602558 S 2171.0
191 95 500.0 0.372934 S 489.0
192 96 2349.0 0.403948 P 2341.0
194 97 1048.0 0.590777 P 1045.0
196 98 285.0 0.928876 P 283.0
197 98 1021.0 0.607062 S 1014.0
198 99 89.0 0.194524 P 89.0
199 99 669.0 0.217902 S 663.0
202 101 642.0 0.428881 P 635.0
203 101 1346.0 0.730971 S 1337.0
205 102 85.0 0.648900 S 81.0
206 103 483.0 0.945899 P 480.0
207 103 1617.0 0.708863 S 1612.0
208 104 1568.0 0.951864 P 1562.0
209 104 2469.0 0.893829 S 2466.0
210 105 1085.0 0.664361 P 1078.0
211 105 1374.0 0.639726 S 1366.0
212 106 551.0 0.982304 P 548.0
213 106 814.0 0.933171 S 809.0
214 107 1392.0 0.893014 P 1389.0
215 107 1981.0 0.655784 S 1984.0
216 108 1469.0 0.982738 P 1470.0
217 108 2121.0 0.878151 S 2120.0
218 109 1732.0 0.939903 P 1733.0
219 109 2542.0 0.565120 S 2548.0
221 110 948.0 0.391936 S 944.0
222 111 1319.0 0.971296 P 1318.0
223 111 1893.0 0.520942 S 1905.0
224 112 672.0 0.438210 P 673.0
225 112 1841.0 0.521932 S 1846.0
226 113 902.0 0.117412 P 385.0
228 114 2375.0 0.473664 P 2373.0
231 115 2053.0 0.357832 S 2035.0
232 116 2216.0 0.455601 P 2217.0
235 117 450.0 0.144893 S 443.0
236 118 800.0 0.902181 P 800.0
237 118 1509.0 0.597703 S 1428.0
242 121 849.0 0.919781 P 848.0
243 121 2045.0 0.441881 S 2044.0
244 122 408.0 0.529562 P 410.0
245 122 2842.0 0.390609 S 2840.0
248 124 484.0 0.871282 P 484.0
249 124 1479.0 0.699570 S 1473.0
250 125 1840.0 0.238697 P 185.0
251 125 1852.0 0.067949 S 1842.0
252 126 2363.0 0.923130 P 2361.0
254 127 250.0 0.627578 P 256.0
256 128 2518.0 0.195126 P 2531.0
259 129 251.0 0.720574 S 203.0
261 130 724.0 0.203853 S 723.0
262 131 513.0 0.880401 P 515.0
263 131 1042.0 0.392533 S 1016.0
264 132 932.0 0.756047 P 926.0
266 133 2201.0 0.407875 P 2203.0
269 134 86.0 0.479798 S 74.0
276 138 487.0 0.060229 P 43.0
277 138 500.0 0.521410 S 476.0
278 139 440.0 0.717462 P 435.0
279 139 2104.0 0.059430 S 2093.0
281 140 1238.0 0.111519 S 1232.0
284 142 2388.0 0.333436 P 2394.0
287 143 2010.0 0.209286 S 2008.0
288 144 704.0 0.914006 P 700.0
290 145 337.0 0.548587 P 334.0
291 145 1001.0 0.464785 S 994.0
292 146 457.0 0.620944 P 434.0
294 147 1245.0 0.956491 P 1242.0
295 147 1968.0 0.951865 S 1964.0
296 148 841.0 0.953956 P 840.0
297 148 1658.0 0.951304 S 1654.0
298 149 1669.0 0.929181 P 1668.0
299 149 2387.0 0.717616 S 2375.0
300 150 101.0 0.406456 P 99.0
301 150 841.0 0.547475 S 835.0
302 151 2328.0 0.525904 P 2325.0
306 153 2764.0 0.581323 P 2755.0
308 154 1498.0 0.850033 P 1500.0
310 155 2465.0 0.485875 P 2463.0
312 156 1749.0 0.931955 P 1747.0
313 156 2085.0 0.603565 S 2080.0
314 157 124.0 0.889495 P 125.0
315 157 1198.0 0.664188 S 1190.0
319 159 633.0 0.508033 S 632.0
320 160 1131.0 0.908914 P 1133.0
321 160 2602.0 0.690637 S 2601.0
323 161 1095.0 0.819913 S 1094.0
324 162 1884.0 0.963091 P 1885.0
325 162 2970.0 0.136057 S 2963.0
327 163 1179.0 0.604860 S 1177.0
328 164 2149.0 0.634542 P 2147.0
329 164 2834.0 0.121270 S 2835.0
334 167 107.0 0.091163 P 109.0
336 168 1657.0 0.108713 P 1658.0
337 168 2174.0 0.235620 S 2163.0
342 171 2381.0 0.136681 P 2379.0
344 172 1517.0 0.405326 P 1516.0
345 172 2233.0 0.510860 S 2227.0
351 175 298.0 0.521215 S 297.0
352 176 1856.0 0.391807 P 1855.0
353 176 2369.0 0.599569 S 2365.0
354 177 976.0 0.383919 P 974.0
355 177 1326.0 0.055372 S 1318.0
356 178 581.0 0.605198 P 576.0
357 178 2180.0 0.250528 S 2170.0
358 179 1409.0 0.346202 P 1411.0
359 179 1925.0 0.532528 S 1919.0
360 180 1933.0 0.582570 P 1932.0
361 180 2266.0 0.490411 S 2260.0
362 181 1149.0 0.694404 P 1147.0
363 181 2289.0 0.499271 S 2286.0
364 182 1069.0 0.961472 P 1069.0
365 182 1567.0 0.886550 S 1565.0
368 184 153.0 0.876243 P 151.0
369 184 500.0 0.286771 S 496.0
372 186 1219.0 0.964393 P 1221.0
373 186 2103.0 0.853219 S 2104.0
374 187 2297.0 0.438424 P 2297.0
376 188 165.0 0.651871 P 163.0
377 188 625.0 0.175589 S 631.0
380 190 2005.0 0.956166 P 2004.0
381 190 2505.0 0.910007 S 2504.0
382 191 1759.0 0.503255 P 1757.0
384 192 1993.0 0.134982 P 1992.0
386 193 74.0 0.538592 P 80.0
387 193 2044.0 0.052031 S 2043.0
388 194 1773.0 0.956558 P 1773.0
389 194 2333.0 0.662966 S 2328.0
390 195 85.0 0.911979 P 81.0
391 195 478.0 0.579596 S 479.0
392 196 1365.0 0.751873 P 1365.0
393 196 2037.0 0.540685 S 2032.0
400 200 192.0 0.973738 P 191.0
401 200 457.0 0.701410 S 453.0
403 201 1368.0 0.219844 S 1369.0
404 202 733.0 0.955732 P 728.0
405 202 1657.0 0.701650 S 1652.0
407 203 177.0 0.725934 S 161.0
408 204 697.0 0.394339 P 695.0
409 204 1193.0 0.514132 S 1192.0
412 206 2153.0 0.655434 P 2149.0
413 206 2413.0 0.571867 S 2411.0
415 207 265.0 0.797802 S 263.0
416 208 1129.0 0.214362 P 1127.0
417 208 1437.0 0.207603 S 1432.0
418 209 262.0 0.663903 P 262.0
419 209 589.0 0.380523 S 590.0
420 210 1709.0 0.542315 P 1709.0
421 210 2580.0 0.073150 S 2581.0
422 211 1561.0 0.152188 P 1560.0
423 211 2118.0 0.216456 S 2113.0
424 212 1341.0 0.158465 P 1338.0
425 212 1933.0 0.396244 S 1934.0
426 213 556.0 0.795878 P 556.0
427 213 1753.0 0.652975 S 1749.0
428 214 457.0 0.977061 P 454.0
429 214 1577.0 0.785107 S 1573.0
430 215 224.0 0.343186 P 226.0
431 215 1059.0 0.064704 S 1063.0
435 217 1373.0 0.168111 S 1375.0
436 218 2127.0 0.519982 P 2127.0
438 219 2177.0 0.446718 P 2175.0
440 220 1126.0 0.954156 P 1125.0
441 220 1485.0 0.427106 S 1484.0
442 221 2275.0 0.490966 P 2272.0
444 222 193.0 0.900512 P 192.0
445 222 656.0 0.877337 S 657.0
446 223 706.0 0.200393 P 694.0
447 223 1108.0 0.170497 S 1108.0
448 224 413.0 0.913310 P 411.0
449 224 769.0 0.610617 S 761.0
450 225 1881.0 0.453554 P 1878.0
451 225 2280.0 0.608847 S 2276.0
459 229 1896.0 0.273110 S 1865.0
460 230 2133.0 0.266663 P 2128.0
461 230 2500.0 0.220329 S 2501.0
462 231 892.0 0.940696 P 891.0
463 231 1295.0 0.836180 S 1296.0
469 234 509.0 0.248064 S 508.0
474 237 2149.0 0.428576 P 2150.0
475 237 2495.0 0.483433 S 2486.0
476 238 1589.0 0.913357 P 1589.0
477 238 2122.0 0.675887 S 2116.0
482 241 242.0 0.867756 P 240.0
483 241 1828.0 0.139070 S 1833.0
484 242 845.0 0.236304 P 844.0
485 242 1512.0 0.334797 S 1509.0
487 243 1792.0 0.068054 S 1791.0
488 244 1956.0 0.072300 P 1962.0
489 244 2369.0 0.235971 S 2367.0
490 245 1764.0 0.501729 P 1762.0
492 246 1.0 0.935251 P 295.0
493 246 586.0 0.819496 S 593.0
494 247 1561.0 0.169809 P 1554.0
495 247 2052.0 0.183885 S 2047.0
500 250 4.0 0.189416 P 7.0
501 250 614.0 0.427114 S 603.0
504 252 1557.0 0.513365 P 1555.0
505 252 2347.0 0.385654 S 2342.0
506 253 932.0 0.267335 P 920.0
507 253 1620.0 0.256833 S 1612.0
508 254 1641.0 0.733000 P 1636.0
509 254 2502.0 0.628159 S 2501.0
512 256 2.0 0.190279 P 97.0
513 256 1178.0 0.360958 S 1173.0
514 257 909.0 0.730937 P 907.0
515 257 1197.0 0.351896 S 1190.0
518 259 2293.0 0.438729 P 2287.0
519 259 2676.0 0.075505 S 2667.0
524 262 1369.0 0.963404 P 1369.0
525 262 1763.0 0.913601 S 1757.0
527 263 1320.0 0.164220 S 1317.0
529 264 2173.0 0.448580 S 2172.0
530 265 1041.0 0.262259 P 1042.0
532 266 607.0 0.988247 P 605.0
533 266 889.0 0.850488 S 885.0
534 267 1788.0 0.969314 P 1786.0
535 267 2514.0 0.864872 S 2523.0
536 268 2164.0 0.118443 P 2177.0
538 269 1413.0 0.900383 P 1413.0
539 269 1859.0 0.816044 S 1860.0
540 270 1964.0 0.629417 P 1967.0
541 270 2317.0 0.499192 S 2316.0
548 274 1313.0 0.186931 P 1301.0
549 274 1867.0 0.053323 S 1844.0
550 275 1450.0 0.496418 P 1445.0
551 275 1770.0 0.338406 S 1765.0
553 276 909.0 0.330855 S 906.0
554 277 1456.0 0.780600 P 1451.0
555 277 2019.0 0.642629 S 2007.0
556 278 1609.0 0.287715 P 1604.0
557 278 2009.0 0.374694 S 2005.0
560 280 1973.0 0.379885 P 1965.0
564 282 1957.0 0.086155 P 1953.0
565 282 2400.0 0.073533 S 2333.0
566 283 1489.0 0.312914 P 1488.0
567 283 1857.0 0.247832 S 1855.0
568 284 2533.0 0.806239 P 2534.0
569 284 2912.0 0.184764 S 2911.0
574 287 4.0 0.672200 P 64.0
575 287 1053.0 0.773065 S 1054.0
584 292 2745.0 0.090416 P 2745.0
586 293 1060.0 0.141269 P 1056.0
587 293 2290.0 0.229398 S 2279.0
590 295 1146.0 0.613270 P 1145.0
591 295 2136.0 0.516647 S 2124.0
592 296 1988.0 0.490640 P 1985.0
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595 297 808.0 0.265918 S 807.0
596 298 2149.0 0.495980 P 2148.0
597 298 2441.0 0.395829 S 2439.0
600 300 1517.0 0.552243 P 1516.0
601 300 2005.0 0.414834 S 2002.0
603 301 852.0 0.184411 S 848.0
604 302 2617.0 0.966504 P 2616.0
608 304 469.0 0.969226 P 467.0
609 304 998.0 0.462226 S 1000.0
610 305 1333.0 0.911929 P 1332.0
611 305 1897.0 0.792077 S 1899.0
613 306 676.0 0.428887 S 677.0
616 308 1101.0 0.839964 P 1099.0
617 308 1409.0 0.715977 S 1408.0
618 309 2317.0 0.357390 P 2316.0
619 309 2630.0 0.214509 S 2623.0
620 310 1017.0 0.623564 P 1014.0
621 310 1533.0 0.613668 S 1531.0
622 311 2500.0 0.190758 P 2513.0
625 312 860.0 0.189537 S 858.0
626 313 799.0 0.889619 P 799.0
627 313 1316.0 0.495006 S 1314.0
629 314 852.0 0.174153 S 835.0
632 316 2413.0 0.763169 P 2412.0
633 316 2770.0 0.430005 S 2765.0
634 317 1897.0 0.710514 P 1883.0
635 317 2247.0 0.804349 S 2238.0
636 318 2376.0 0.506269 P 2377.0
638 319 5.0 0.062481 P 34.0
639 319 334.0 0.274183 S 323.0
640 320 104.0 0.362486 P 100.0
641 320 414.0 0.461181 S 397.0
642 321 409.0 0.225166 P 409.0
643 321 890.0 0.293110 S 878.0
644 322 2237.0 0.489497 P 2237.0
646 323 750.0 0.055290 P 739.0
647 323 1678.0 0.094376 S 1671.0
649 324 155.0 0.050599 S 155.0
650 325 521.0 0.267702 P 518.0
651 325 1036.0 0.232233 S 1032.0
652 326 1085.0 0.509569 P 1080.0
653 326 1674.0 0.736907 S 1669.0
654 327 654.0 0.961704 P 652.0
655 327 1643.0 0.733677 S 1642.0
656 328 1181.0 0.585993 P 1181.0
657 328 2256.0 0.407714 S 2242.0
658 329 877.0 0.142394 P 872.0
659 329 1262.0 0.205066 S 1251.0
660 330 14.0 0.485550 P 53.0
661 330 928.0 0.339338 S 925.0
664 332 761.0 0.144601 P 757.0
665 332 1130.0 0.178081 S 1125.0
670 335 2256.0 0.909226 P 2255.0
671 335 2960.0 0.232356 S 2958.0
672 336 845.0 0.707683 P 842.0
673 336 1379.0 0.595400 S 1381.0
674 337 6.0 0.244308 P 105.0
675 337 1313.0 0.314320 S 1313.0
676 338 1069.0 0.967910 P 1065.0
677 338 1956.0 0.488238 S 1975.0
678 339 2295.0 0.482202 P 2293.0
680 340 2380.0 0.470786 P 2377.0
681 340 2714.0 0.058391 S 2720.0
684 342 157.0 0.915290 P 155.0
685 342 1037.0 0.796737 S 1031.0
686 343 932.0 0.148475 P 936.0
687 343 1936.0 0.534987 S 1972.0
692 346 308.0 0.898762 P 308.0
695 347 612.0 0.211492 S 607.0
696 348 657.0 0.175351 P 655.0
697 348 1036.0 0.339120 S 1032.0
698 349 521.0 0.179226 P 517.0
699 349 1178.0 0.068314 S 1164.0
700 350 163.0 0.957952 P 159.0
701 350 443.0 0.945381 S 441.0
702 351 1857.0 0.422535 P 1856.0
703 351 2137.0 0.401779 S 2138.0
704 352 1924.0 0.849821 P 1921.0
705 352 2673.0 0.318504 S 2685.0
709 354 2942.0 0.130995 S 2943.0
710 355 1385.0 0.975849 P 1383.0
711 355 1723.0 0.815693 S 1720.0
713 356 1784.0 0.285860 S 1773.0
715 357 231.0 0.136194 S 234.0
718 359 334.0 0.192969 P 338.0
719 359 874.0 0.256214 S 875.0
723 361 279.0 0.799164 S 254.0
724 362 1969.0 0.942800 P 1970.0
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1537 768 2704.0 0.315114 S 2694.0
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1557 778 152.0 0.780371 S 148.0
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1600 800 1749.0 0.464487 P 1748.0
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1602 801 2500.0 0.390831 P 1528.0
1603 801 2504.0 0.531825 S 2502.0
1604 802 796.0 0.054989 P 793.0
1606 803 2376.0 0.449626 P 2371.0
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1612 806 1721.0 0.850721 P 1720.0
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1615 807 1226.0 0.296486 S 1201.0
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1627 813 814.0 0.399850 S 797.0
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1634 817 444.0 0.822080 P 442.0
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1776 888 1669.0 0.450052 P 1668.0
1778 889 669.0 0.848687 P 668.0
1779 889 1205.0 0.880599 S 1202.0
1780 890 1808.0 0.972060 P 1805.0
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1782 891 1614.0 0.479460 P 1615.0
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1797 898 2419.0 0.056273 S 2407.0
1800 900 97.0 0.456097 P 96.0
1801 900 410.0 0.108480 S 405.0
1802 901 1540.0 0.405057 P 1538.0
1803 901 1940.0 0.495929 S 1937.0
1805 902 845.0 0.354121 S 838.0
1806 903 1749.0 0.188447 P 1746.0
1807 903 2333.0 0.396900 S 2328.0
1808 904 2340.0 0.580218 P 2339.0
1809 904 2919.0 0.059167 S 2911.0
1810 905 537.0 0.855565 P 534.0
1811 905 949.0 0.236360 S 947.0
1820 910 1424.0 0.872038 P 1423.0
1821 910 1866.0 0.484861 S 1858.0
1822 911 505.0 0.927882 P 494.0
1823 911 960.0 0.793453 S 956.0
1825 912 663.0 0.534947 S 651.0
1826 913 1397.0 0.324119 P 1395.0
1827 913 1765.0 0.477927 S 1759.0
1832 916 881.0 0.350369 P 883.0
1833 916 1242.0 0.190014 S 1236.0
1835 917 1086.0 0.160400 S 1078.0
1838 919 10.0 0.225525 P 47.0
1839 919 540.0 0.167067 S 528.0
1840 920 801.0 0.526022 P 800.0
1841 920 1199.0 0.638893 S 1196.0
1842 921 1653.0 0.963756 P 1651.0
1843 921 2049.0 0.903360 S 2045.0
1846 923 864.0 0.723285 P 860.0
1847 923 1270.0 0.622758 S 1265.0
1848 924 572.0 0.977723 P 572.0
1849 924 1233.0 0.730339 S 1242.0
1855 927 352.0 0.290399 S 339.0
1856 928 605.0 0.845057 P 599.0
1857 928 2052.0 0.143603 S 2053.0
1858 929 1784.0 0.352322 P 1782.0
1860 930 297.0 0.374420 P 293.0
1861 930 1537.0 0.741529 S 1535.0
1862 931 1146.0 0.887541 P 1132.0
1863 931 1597.0 0.826179 S 1587.0
1868 934 1581.0 0.731755 P 1582.0
1869 934 2375.0 0.784909 S 2368.0
1870 935 1517.0 0.803533 P 1522.0
1871 935 2354.0 0.402744 S 2344.0
1874 937 1777.0 0.394921 P 1775.0
1875 937 2768.0 0.052007 S 2764.0
1879 939 1464.0 0.125468 S 1460.0
1884 942 115.0 0.952326 P 113.0
1885 942 562.0 0.777690 S 547.0
1886 943 515.0 0.910314 P 506.0
1887 943 1234.0 0.822132 S 1234.0
1888 944 1991.0 0.487478 P 1988.0
1889 944 2772.0 0.149238 S 2777.0
1891 945 1486.0 0.152684 S 1477.0
1892 946 2516.0 0.081501 P 2520.0
1895 947 209.0 0.732043 S 204.0
1898 949 197.0 0.554771 P 192.0
1899 949 1464.0 0.142011 S 1463.0
1900 950 1137.0 0.892426 P 1134.0
1901 950 1641.0 0.515547 S 1622.0
1910 955 1625.0 0.803457 P 1620.0
1911 955 2500.0 0.352423 S 2511.0
1912 956 2442.0 0.723285 P 2440.0
1914 957 1793.0 0.868324 P 1791.0
1915 957 2415.0 0.716064 S 2407.0
1916 958 1308.0 0.214440 P 1307.0
1917 958 2201.0 0.598123 S 2199.0
1918 959 1561.0 0.573016 P 1549.0
1919 959 2060.0 0.481749 S 2053.0
1923 961 732.0 0.226693 S 727.0
1926 963 917.0 0.368268 P 916.0
1927 963 1194.0 0.129771 S 1189.0
1928 964 2109.0 0.577037 P 2107.0
1930 965 1909.0 0.715539 P 1907.0
1931 965 2279.0 0.452630 S 2208.0
1940 970 472.0 0.268692 P 470.0
1941 970 749.0 0.411092 S 749.0
1942 971 1104.0 0.608302 P 1092.0
1943 971 1955.0 0.861920 S 1952.0
1944 972 470.0 0.093680 P 471.0
1945 972 1014.0 0.447725 S 1010.0
1948 974 1401.0 0.843736 P 1397.0
1950 975 1581.0 0.288601 P 1576.0
1952 976 2192.0 0.131355 P 2196.0
1953 976 2436.0 0.118763 S 2458.0
1954 977 285.0 0.757475 P 284.0
1955 977 1320.0 0.485499 S 1319.0
1957 978 509.0 0.441737 S 506.0
1958 979 374.0 0.737620 P 371.0
1959 979 1126.0 0.305471 S 1116.0
1960 980 1332.0 0.668373 P 1331.0
1961 980 1920.0 0.682243 S 1917.0
1963 981 972.0 0.050165 S 973.0
1966 983 2361.0 0.352106 P 2359.0
1967 983 2670.0 0.133291 S 2663.0
1968 984 1053.0 0.456369 P 1050.0
1969 984 1421.0 0.700788 S 1409.0
1972 986 1877.0 0.437149 P 1878.0
1974 987 1821.0 0.762648 P 1821.0
1975 987 2414.0 0.748187 S 2410.0
1976 988 1402.0 0.977242 P 1400.0
1977 988 2128.0 0.636392 S 2118.0
1978 989 2246.0 0.234808 P 2243.0
1979 989 2609.0 0.222167 S 2601.0
1980 990 1207.0 0.827218 P 1205.0
1981 990 1639.0 0.688377 S 1632.0
1985 992 2194.0 0.070159 S 2181.0
1986 993 2273.0 0.453398 P 2272.0
1988 994 808.0 0.946666 P 810.0
1989 994 1497.0 0.851509 S 1492.0
1990 995 1408.0 0.507713 P 1403.0
1991 995 1904.0 0.356785 S 1894.0
1993 996 1096.0 0.180578 S 1088.0
1994 997 859.0 0.276652 P 861.0
1995 997 1357.0 0.445790 S 1351.0
1998 999 2082.0 0.510593 P 2081.0
1999 999 2495.0 0.484449 S 2491.0
2001 1000 549.0 0.107333 S 548.0
2003 1001 295.0 0.653775 S 292.0
2006 1003 889.0 0.197948 P 888.0
2008 1004 993.0 0.956671 P 991.0
2009 1004 1718.0 0.590153 S 1715.0
2010 1005 626.0 0.058344 P 615.0
2012 1006 948.0 0.738960 P 2329.0
2013 1006 1936.0 0.152245 S 2965.0
2014 1007 2500.0 0.079687 P 2504.0
2016 1008 2299.0 0.597972 P 2298.0
2017 1008 2572.0 0.174353 S 2572.0
2018 1009 2405.0 0.425616 P 2401.0
2024 1012 414.0 0.467338 P 446.0
2025 1012 1230.0 0.798035 S 1232.0
2026 1013 116.0 0.089980 P 117.0
2031 1015 1064.0 0.243324 S 1053.0
2034 1017 2584.0 0.055279 P 2576.0
2036 1018 1645.0 0.559067 P 1645.0
2037 1018 2437.0 0.459482 S 2429.0
2040 1020 209.0 0.953556 P 209.0
2041 1020 732.0 0.309937 S 721.0
2042 1021 1723.0 0.938115 P 1722.0
2043 1021 2290.0 0.421328 S 2286.0
2050 1025 377.0 0.898929 P 372.0
2051 1025 915.0 0.947907 S 908.0
2052 1026 860.0 0.860439 P 852.0
2053 1026 1945.0 0.631558 S 1938.0
2058 1029 516.0 0.971393 P 514.0
2059 1029 1335.0 0.806403 S 1332.0
2064 1032 1301.0 0.874672 P 1301.0
2066 1033 446.0 0.072409 P 454.0
2067 1033 1044.0 0.342919 S 1036.0
2068 1034 513.0 0.106447 P 511.0
2069 1034 806.0 0.050433 S 799.0
2071 1035 916.0 0.306373 S 911.0
2072 1036 134.0 0.695611 P 132.0
2075 1037 1432.0 0.137526 S 1427.0
2076 1038 269.0 0.727380 P 265.0
2077 1038 593.0 0.775822 S 585.0
2078 1039 1759.0 0.482547 P 1754.0
2079 1039 2500.0 0.083837 S 2514.0
2080 1040 1997.0 0.921748 P 1995.0
2081 1040 2653.0 0.694736 S 2641.0
2082 1041 493.0 0.568613 P 490.0
2083 1041 1048.0 0.592810 S 1044.0
2084 1042 1173.0 0.568421 P 1173.0
2085 1042 1557.0 0.091415 S 1552.0
2086 1043 676.0 0.531393 P 673.0
2087 1043 1392.0 0.470163 S 1387.0
2092 1046 881.0 0.551696 P 882.0
2093 1046 1200.0 0.406150 S 1199.0
2094 1047 2064.0 0.062317 P 2068.0
2095 1047 2358.0 0.138526 S 2349.0
2102 1051 1268.0 0.593837 P 1264.0
2103 1051 1753.0 0.454026 S 1751.0
2104 1052 1966.0 0.141259 P 1957.0
2105 1052 2221.0 0.263745 S 2218.0
2107 1053 775.0 0.068002 S 770.0
2112 1056 165.0 0.377028 P 163.0
2113 1056 1238.0 0.205276 S 1242.0
2114 1057 435.0 0.916337 P 427.0
2115 1057 1002.0 0.845375 S 997.0
2116 1058 537.0 0.276470 P 540.0
2118 1059 417.0 0.170488 P 418.0
2119 1059 836.0 0.395444 S 831.0
2122 1061 750.0 0.771109 P 748.0
2123 1061 1747.0 0.472819 S 1744.0
2127 1063 298.0 0.838435 S 292.0
2128 1064 2299.0 0.253856 P 2298.0
2130 1065 2045.0 0.101342 P 2043.0
2131 1065 2356.0 0.139403 S 2354.0
2134 1067 2013.0 0.253410 P 2011.0
2135 1067 2409.0 0.335498 S 2403.0
2136 1068 1633.0 0.477220 P 1632.0
2137 1068 2025.0 0.341006 S 2023.0
2138 1069 1888.0 0.100337 P 1891.0
2139 1069 2233.0 0.586069 S 2231.0
2148 1074 1281.0 0.868731 P 1276.0
2150 1075 2297.0 0.785055 P 2298.0
2154 1077 377.0 0.437367 P 379.0
2155 1077 1177.0 0.582846 S 1170.0
2156 1078 1697.0 0.763258 P 1696.0
2157 1078 2226.0 0.566972 S 2232.0
2158 1079 1873.0 0.516300 P 1866.0
2161 1080 822.0 0.501673 S 824.0
2164 1082 114.0 0.865835 P 112.0
2165 1082 1510.0 0.520537 S 1494.0
2166 1083 505.0 0.275608 P 499.0
2167 1083 2136.0 0.241080 S 2127.0
2170 1085 652.0 0.957491 P 651.0
2171 1085 1172.0 0.186444 S 1179.0
2174 1087 843.0 0.964524 P 841.0
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2176 1088 12.0 0.165574 P 453.0
2177 1088 1821.0 0.732186 S 1808.0
2179 1089 721.0 0.586877 S 719.0
2181 1090 399.0 0.833473 S 387.0
2182 1091 1006.0 0.942488 P 1003.0
2183 1091 2566.0 0.643472 S 2569.0
2184 1092 372.0 0.945241 P 366.0
2185 1092 1327.0 0.488653 S 1320.0
2186 1093 1991.0 0.940368 P 1986.0
2187 1093 2409.0 0.924507 S 2408.0
2188 1094 703.0 0.962673 P 698.0
2189 1094 1877.0 0.929204 S 1876.0
2190 1095 1053.0 0.382249 P 1049.0
2191 1095 1601.0 0.594434 S 1596.0
2192 1096 232.0 0.877645 P 232.0
2193 1096 1073.0 0.485607 S 1071.0
2194 1097 1709.0 0.977099 P 1708.0
2195 1097 2780.0 0.613690 S 2774.0
2196 1098 1509.0 0.613900 P 1508.0
2197 1098 2068.0 0.486060 S 2066.0
2198 1099 960.0 0.971237 P 957.0
2199 1099 2133.0 0.805344 S 2130.0
2201 1100 918.0 0.689535 S 915.0
2202 1101 2004.0 0.665402 P 2002.0
2203 1101 2817.0 0.054177 S 2813.0
2204 1102 682.0 0.576201 P 680.0
2205 1102 1268.0 0.435927 S 1263.0
2206 1103 437.0 0.985398 P 437.0
2207 1103 998.0 0.928082 S 989.0
2208 1104 1609.0 0.614410 P 1609.0
2209 1104 2666.0 0.136615 S 2665.0
2210 1105 453.0 0.958416 P 453.0
2211 1105 1546.0 0.833706 S 1544.0
2213 1106 189.0 0.663677 S 187.0
2214 1107 1848.0 0.576080 P 1847.0
2215 1107 2408.0 0.519642 S 2407.0
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2223 1111 2458.0 0.686319 S 2459.0
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2225 1112 1860.0 0.277602 S 1860.0
2227 1113 902.0 0.598619 S 934.0
2228 1114 2265.0 0.532870 P 2261.0
2230 1115 852.0 0.874227 P 840.0
2231 1115 2003.0 0.654181 S 2006.0
2232 1116 1836.0 0.959835 P 1831.0
2233 1116 2247.0 0.909354 S 2241.0
2234 1117 704.0 0.868035 P 706.0
2235 1117 1866.0 0.570976 S 1864.0
2238 1119 1985.0 0.657915 P 1981.0
2242 1121 144.0 0.072818 P 154.0
2243 1121 1717.0 0.476855 S 1717.0
2244 1122 1014.0 0.828333 P 990.0
2245 1122 2040.0 0.506014 S 2043.0
2246 1123 551.0 0.951391 P 546.0
2247 1123 1431.0 0.766293 S 1418.0
2248 1124 1033.0 0.653352 P 1014.0
2252 1126 678.0 0.962014 P 676.0
2253 1126 1414.0 0.574964 S 1404.0
2254 1127 12.0 0.454732 P 50.0
2255 1127 1213.0 0.655154 S 1206.0
2256 1128 1120.0 0.397214 P 1115.0
2259 1129 977.0 0.161306 S 972.0
2260 1130 1101.0 0.941926 P 1100.0
2261 1130 1564.0 0.561765 S 1569.0
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2984 1492 180.0 0.973016 P 177.0
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2989 1494 2153.0 0.696466 S 2151.0
2990 1495 1128.0 0.982563 P 1126.0
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2992 1496 985.0 0.965357 P 984.0
2993 1496 1539.0 0.911390 S 1536.0
2994 1497 1118.0 0.955051 P 1116.0
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2996 1498 94.0 0.974934 P 93.0
2997 1498 659.0 0.770356 S 659.0
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3007 1503 954.0 0.849043 S 954.0
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3064 1532 333.0 0.314232 P 332.0
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3596 1798 1168.0 0.983519 P 1166.0
3597 1798 1673.0 0.905888 S 1673.0
3598 1799 1195.0 0.753550 P 1194.0
3599 1799 1641.0 0.802713 S 1639.0
3602 1801 365.0 0.828957 P 364.0
3603 1801 1142.0 0.609081 S 1136.0
3605 1802 568.0 0.702885 S 566.0
3607 1803 130.0 0.101194 S 120.0
3619 1809 160.0 0.579304 S 157.0
3621 1810 401.0 0.109332 S 404.0
3622 1811 1663.0 0.974293 P 1661.0
3623 1811 2433.0 0.641719 S 2433.0
3628 1814 1352.0 0.754375 P 1350.0
3629 1814 2260.0 0.314667 S 2259.0
3630 1815 173.0 0.976559 P 173.0
3631 1815 533.0 0.961376 S 533.0
3632 1816 318.0 0.436711 P 326.0
3633 1816 1357.0 0.814061 S 1358.0
3634 1817 865.0 0.506077 P 861.0
3635 1817 1234.0 0.547526 S 1228.0
3637 1818 2942.0 0.122845 S 2938.0
3641 1820 2088.0 0.271931 S 2087.0
3642 1821 2385.0 0.610549 P 2382.0
3643 1821 2746.0 0.183764 S 2745.0
3644 1822 2135.0 0.438276 P 2126.0
3645 1822 2771.0 0.494098 S 2773.0
3646 1823 1124.0 0.826034 P 1118.0
3647 1823 2183.0 0.885091 S 2178.0
3649 1824 27.0 0.237633 S 19.0
3650 1825 1373.0 0.153989 P 1363.0
3651 1825 2002.0 0.358947 S 2001.0
3652 1826 1997.0 0.513765 P 1993.0
3654 1827 975.0 0.927056 P 968.0
3655 1827 1889.0 0.742633 S 1887.0
3656 1828 105.0 0.598677 P 105.0
3657 1828 935.0 0.596609 S 932.0
3658 1829 1.0 0.774717 P 332.0
3659 1829 887.0 0.912152 S 886.0
3660 1830 1759.0 0.952039 P 1752.0
3661 1830 2214.0 0.769522 S 2198.0
3662 1831 1844.0 0.446112 P 1848.0
3664 1832 349.0 0.532394 P 348.0
3665 1832 660.0 0.334031 S 670.0
3668 1834 1087.0 0.658184 P 1087.0
3669 1834 2138.0 0.886734 S 2135.0
3671 1835 797.0 0.240191 S 800.0
3674 1837 1429.0 0.417254 P 1429.0
3675 1837 1818.0 0.249041 S 1818.0
3676 1838 944.0 0.914317 P 941.0
3677 1838 1803.0 0.772284 S 1800.0
3679 1839 158.0 0.477583 S 146.0
3680 1840 70.0 0.653578 P 72.0
3681 1840 500.0 0.370343 S 488.0
3684 1842 848.0 0.889978 P 844.0
3685 1842 1898.0 0.698650 S 1894.0
3686 1843 2344.0 0.947954 P 2339.0
3687 1843 2801.0 0.908943 S 2798.0
3688 1844 151.0 0.955480 P 146.0
3689 1844 713.0 0.889675 S 692.0
3690 1845 489.0 0.972342 P 488.0
3691 1845 1749.0 0.405613 S 1739.0
3692 1846 1.0 0.894343 P 539.0
3693 1846 1138.0 0.897036 S 1137.0
3694 1847 227.0 0.932894 P 227.0
3695 1847 1040.0 0.809639 S 1036.0
3696 1848 1298.0 0.402171 P 1300.0
3697 1848 1716.0 0.725619 S 1713.0
3698 1849 281.0 0.144451 P 281.0
3700 1850 1280.0 0.431939 P 1287.0
3701 1850 2500.0 0.319584 S 2506.0
3702 1851 1577.0 0.976293 P 1576.0
3703 1851 2317.0 0.866506 S 2315.0
3707 1853 342.0 0.687838 S 333.0
3708 1854 1085.0 0.321064 P 1086.0
3709 1854 2339.0 0.351408 S 2339.0
3710 1855 2283.0 0.112733 P 2280.0
3712 1856 536.0 0.828487 P 537.0
3713 1856 948.0 0.377692 S 946.0
3714 1857 2374.0 0.106593 P 2367.0
3718 1859 641.0 0.409547 P 662.0
3719 1859 1266.0 0.711644 S 1264.0
3721 1860 759.0 0.676121 S 747.0
3724 1862 835.0 0.954922 P 835.0
3725 1862 1601.0 0.838714 S 1596.0
3726 1863 1820.0 0.490211 P 1818.0
3727 1863 2588.0 0.090562 S 2589.0
3728 1864 52.0 0.499989 P 50.0
3729 1864 593.0 0.731115 S 590.0
3732 1866 761.0 0.409140 P 759.0
3733 1866 1044.0 0.283490 S 1041.0
3736 1868 1229.0 0.192850 P 1226.0
3737 1868 1629.0 0.723958 S 1623.0
3744 1872 2757.0 0.140365 P 2756.0
3746 1873 29.0 0.783454 P 28.0
3747 1873 775.0 0.747820 S 771.0
3748 1874 1713.0 0.054320 P 1716.0
3749 1874 2149.0 0.447935 S 2145.0
3754 1877 1667.0 0.950093 P 1663.0
3755 1877 2544.0 0.287686 S 2541.0
3756 1878 2181.0 0.572204 P 2180.0
3758 1879 2473.0 0.499430 P 2472.0
3764 1882 540.0 0.927212 P 537.0
3765 1882 1254.0 0.598561 S 1257.0
3766 1883 744.0 0.959686 P 742.0
3767 1883 1835.0 0.821416 S 1833.0
3769 1884 286.0 0.524724 S 285.0
3770 1885 892.0 0.735879 P 890.0
3771 1885 1157.0 0.663738 S 1153.0
3772 1886 2499.0 0.485958 P 2496.0
3775 1887 532.0 0.319411 S 528.0
3776 1888 325.0 0.922540 P 323.0
3777 1888 1218.0 0.820930 S 1215.0
3778 1889 2282.0 0.571470 P 2283.0
3779 1889 2666.0 0.075808 S 2666.0
3780 1890 2343.0 0.517539 P 2342.0
3781 1890 2626.0 0.051672 S 2628.0
3786 1893 1744.0 0.979648 P 1745.0
3787 1893 2121.0 0.923801 S 2121.0
3790 1895 104.0 0.985326 P 102.0
3791 1895 417.0 0.318277 S 419.0
3793 1896 208.0 0.871330 S 202.0
3794 1897 273.0 0.932465 P 273.0
3795 1897 664.0 0.957559 S 661.0
3796 1898 248.0 0.842082 P 245.0
3797 1898 850.0 0.346622 S 837.0
3800 1900 337.0 0.844190 P 328.0
3801 1900 624.0 0.927567 S 620.0
3802 1901 331.0 0.615206 P 330.0
3803 1901 620.0 0.446580 S 618.0
3804 1902 709.0 0.389084 P 713.0
3807 1903 637.0 0.807401 S 630.0
3808 1904 1281.0 0.819937 P 1283.0
3812 1906 384.0 0.759986 P 379.0
3813 1906 1022.0 0.687256 S 1025.0
3817 1908 337.0 0.912423 S 334.0
3818 1909 2104.0 0.575001 P 2103.0
3821 1910 398.0 0.602743 S 392.0
3822 1911 1997.0 0.305425 P 1994.0
3823 1911 2324.0 0.273134 S 2330.0
3825 1912 634.0 0.443866 S 636.0
3826 1913 1929.0 0.980655 P 1929.0
3827 1913 2329.0 0.921485 S 2329.0
3828 1914 1176.0 0.977913 P 1175.0
3829 1914 1845.0 0.899129 S 1841.0
3830 1915 1.0 0.476073 P 136.0
3831 1915 644.0 0.603144 S 639.0
3832 1916 645.0 0.804965 P 642.0
3833 1916 1149.0 0.866597 S 1147.0
3840 1920 456.0 0.132477 P 430.0
3841 1920 1078.0 0.460339 S 1075.0
3842 1921 224.0 0.923159 P 226.0
3843 1921 1119.0 0.869601 S 1114.0
3844 1922 1190.0 0.889601 P 1188.0
3845 1922 1581.0 0.924144 S 1582.0
3847 1923 2200.0 0.201986 S 2197.0
3848 1924 1160.0 0.129997 P 1220.0
3852 1926 105.0 0.639473 P 110.0
3853 1926 704.0 0.938227 S 686.0
3854 1927 2061.0 0.474935 P 2060.0
3855 1927 2387.0 0.445743 S 2385.0
3856 1928 2047.0 0.475328 P 2046.0
3861 1930 652.0 0.126266 S 653.0
3862 1931 8.0 0.733560 P 7.0
3863 1931 358.0 0.174184 S 360.0
3864 1932 241.0 0.678845 P 243.0
3865 1932 557.0 0.393164 S 557.0
3869 1934 221.0 0.374374 S 217.0
3870 1935 597.0 0.442224 P 594.0
3871 1935 894.0 0.086388 S 891.0
3877 1938 266.0 0.112536 S 268.0
3878 1939 1384.0 0.975641 P 1382.0
3879 1939 2082.0 0.911556 S 2078.0
3880 1940 1393.0 0.775855 P 1391.0
3881 1940 2121.0 0.731845 S 2117.0
3882 1941 2026.0 0.465566 P 2026.0
3883 1941 2542.0 0.078572 S 2545.0
3885 1942 1191.0 0.866332 S 1186.0
3886 1943 1277.0 0.717974 P 1274.0
3887 1943 1646.0 0.481505 S 1637.0
3888 1944 611.0 0.938074 P 613.0
3889 1944 1673.0 0.913274 S 1663.0
3892 1946 380.0 0.884794 P 378.0
3893 1946 1483.0 0.694141 S 1482.0
3895 1947 62.0 0.222365 S 60.0
3897 1948 1390.0 0.316092 S 1386.0
3902 1951 1394.0 0.750969 P 1376.0
3903 1951 2645.0 0.058707 S 2635.0
3904 1952 2024.0 0.057150 P 2029.0
3905 1952 2542.0 0.192100 S 2546.0
3906 1953 377.0 0.909182 P 377.0
3907 1953 1064.0 0.277738 S 1063.0
3909 1954 626.0 0.479748 S 622.0
3910 1955 876.0 0.857777 P 868.0
3911 1955 2110.0 0.508909 S 2108.0
3913 1956 269.0 0.856400 S 265.0
3914 1957 590.0 0.828890 P 590.0
3915 1957 1328.0 0.601056 S 1325.0
3917 1958 940.0 0.072801 S 941.0
3922 1961 2652.0 0.478002 P 2650.0
3925 1962 697.0 0.330299 S 700.0
3926 1963 150.0 0.827190 P 150.0
3927 1963 924.0 0.328021 S 918.0
3929 1964 779.0 0.067567 S 778.0
3931 1965 1011.0 0.499990 S 1006.0
3932 1966 2495.0 0.488691 P 2493.0
3937 1968 501.0 0.377976 S 491.0
3938 1969 1225.0 0.968422 P 1224.0
3939 1969 2000.0 0.493569 S 2009.0
3941 1970 744.0 0.327116 S 746.0
3942 1971 2489.0 0.297052 P 2486.0
3944 1972 817.0 0.911719 P 815.0
3945 1972 1539.0 0.785510 S 1544.0
3950 1975 101.0 0.623534 P 99.0
3951 1975 868.0 0.406477 S 879.0
3952 1976 1697.0 0.384140 P 1701.0
3953 1976 2461.0 0.296668 S 2468.0
3955 1977 870.0 0.566140 S 871.0
3956 1978 1357.0 0.211801 P 1355.0
3957 1978 1938.0 0.484151 S 1929.0
3958 1979 409.0 0.115986 P 413.0
3959 1979 817.0 0.242567 S 814.0
3962 1981 1316.0 0.961291 P 1316.0
3963 1981 2125.0 0.829836 S 2124.0
3965 1982 1076.0 0.434905 S 1077.0
3972 1986 50.0 0.521135 P 39.0
3973 1986 886.0 0.460263 S 884.0
3974 1987 253.0 0.254114 P 259.0
3975 1987 590.0 0.291242 S 586.0
3976 1988 2.0 0.715325 P 34.0
3977 1988 473.0 0.777581 S 482.0
3978 1989 2063.0 0.462561 P 2061.0
3984 1992 1.0 0.253015 P 53.0
3985 1992 503.0 0.831792 S 495.0
3986 1993 2173.0 0.841638 P 2168.0
3987 1993 2504.0 0.949829 S 2504.0
3988 1994 759.0 0.948953 P 760.0
3989 1994 1095.0 0.952838 S 1094.0
3990 1995 2190.0 0.366132 P 2188.0
3993 1996 46.0 0.597166 S 10.0
3998 1999 2626.0 0.262408 P 2626.0
4000 2000 2792.0 0.140752 P 2794.0
4002 2001 1281.0 0.920542 P 1280.0
4003 2001 1860.0 0.859427 S 1859.0
4004 2002 364.0 0.238114 P 362.0
4005 2002 788.0 0.356933 S 787.0
4006 2003 1951.0 0.501476 P 1948.0
4007 2003 2914.0 0.063496 S 2911.0
4008 2004 1145.0 0.423083 P 1143.0
4009 2004 1672.0 0.234685 S 1671.0
4010 2005 12.0 0.265086 P 211.0
4011 2005 1069.0 0.747251 S 1054.0
4012 2006 237.0 0.957326 P 235.0
4013 2006 1390.0 0.752897 S 1390.0
4014 2007 673.0 0.270377 P 671.0
4015 2007 1040.0 0.362752 S 1041.0
4017 2008 298.0 0.053348 S 293.0
4018 2009 2715.0 0.064485 P 2705.0
4020 2010 1601.0 0.885790 P 1600.0
4021 2010 2365.0 0.356267 S 2361.0
4024 2012 1309.0 0.573000 P 1311.0
4025 2012 2178.0 0.393679 S 2171.0
4026 2013 2029.0 0.095216 P 2025.0
4027 2013 2379.0 0.403413 S 2376.0
4028 2014 1737.0 0.653062 P 1738.0
4029 2014 2206.0 0.379373 S 2206.0
4032 2016 329.0 0.764612 P 326.0
4033 2016 1465.0 0.719391 S 1466.0
4034 2017 2827.0 0.104267 P 2822.0
4036 2018 1745.0 0.633929 P 1743.0
4037 2018 2114.0 0.659210 S 2109.0
4038 2019 318.0 0.090163 P 322.0
4039 2019 638.0 0.457160 S 638.0
4042 2021 589.0 0.525948 P 587.0
4043 2021 1021.0 0.407475 S 1018.0
4044 2022 1352.0 0.065477 P 1357.0
4045 2022 1674.0 0.463561 S 1668.0
4047 2023 2062.0 0.389874 S 2056.0
4050 2025 1060.0 0.316857 P 1062.0
4051 2025 1488.0 0.527497 S 1482.0
4052 2026 1033.0 0.889013 P 1031.0
4053 2026 1707.0 0.888612 S 1702.0
4054 2027 14.0 0.403196 P 22.0
4055 2027 386.0 0.374109 S 383.0
4057 2028 993.0 0.225355 S 987.0
4059 2029 226.0 0.148581 S 225.0
4060 2030 2134.0 0.079396 P 2127.0
4061 2030 2442.0 0.576073 S 2437.0
4062 2031 964.0 0.939926 P 960.0
4063 2031 2027.0 0.761879 S 2018.0
4064 2032 1325.0 0.432616 P 1322.0
4065 2032 1693.0 0.667578 S 1681.0
4066 2033 353.0 0.816740 P 346.0
4067 2033 1028.0 0.299145 S 1058.0
4070 2035 1525.0 0.769851 P 1526.0
4071 2035 1886.0 0.374989 S 1884.0
4072 2036 1441.0 0.866663 P 1441.0
4073 2036 1923.0 0.648190 S 1917.0
4074 2037 2364.0 0.475242 P 2364.0
4076 2038 1721.0 0.328745 P 1720.0
4080 2040 229.0 0.729893 P 225.0
4081 2040 573.0 0.665653 S 570.0
4083 2041 166.0 0.870943 S 163.0
4086 2043 4.0 0.215260 P 181.0
4087 2043 869.0 0.681573 S 857.0
4088 2044 762.0 0.259699 P 761.0
4089 2044 1608.0 0.383309 S 1600.0
4090 2045 612.0 0.608025 P 611.0
4091 2045 1553.0 0.134144 S 1553.0
4092 2046 673.0 0.218792 P 672.0
4093 2046 1066.0 0.364154 S 1057.0
4094 2047 661.0 0.748059 P 659.0
4095 2047 1157.0 0.796379 S 1149.0
4096 2048 221.0 0.955843 P 218.0
4097 2048 488.0 0.941837 S 484.0
4098 2049 2183.0 0.184022 P 1034.0
4099 2049 2182.0 0.386721 S 2176.0
4100 2050 429.0 0.899442 P 427.0
4101 2050 777.0 0.456889 S 772.0
4102 2051 1961.0 0.504555 P 1958.0
4103 2051 2232.0 0.396220 S 2224.0
4104 2052 1886.0 0.055603 P 1888.0
4107 2053 2037.0 0.447636 S 2029.0
4108 2054 1580.0 0.915182 P 1578.0
4109 2054 2139.0 0.855362 S 2134.0
4110 2055 1005.0 0.693579 P 1002.0
4111 2055 1540.0 0.729817 S 1538.0
4112 2056 2325.0 0.487964 P 2322.0
4114 2057 565.0 0.946619 P 560.0
4115 2057 1165.0 0.737536 S 1161.0
4116 2058 1627.0 0.981852 P 1619.0
4117 2058 1896.0 0.970299 S 1895.0
4118 2059 132.0 0.155340 P 137.0
4119 2059 762.0 0.066778 S 752.0
4120 2060 932.0 0.625758 P 931.0
4121 2060 1770.0 0.755558 S 1766.0
4122 2061 185.0 0.059942 P 192.0
4123 2061 1021.0 0.356544 S 1020.0
4125 2062 50.0 0.250469 S 41.0
4129 2064 128.0 0.495009 S 123.0
4132 2066 1749.0 0.131476 P 1748.0
4133 2066 2704.0 0.083591 S 2710.0
4134 2067 1392.0 0.283151 P 1388.0
4135 2067 2433.0 0.370088 S 2429.0
4147 2073 826.0 0.486879 S 833.0
4148 2074 1247.0 0.889492 P 1243.0
4149 2074 1757.0 0.671317 S 1743.0
4150 2075 1001.0 0.843143 P 1000.0
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4155 2077 345.0 0.487117 S 345.0
4158 2079 365.0 0.676652 P 365.0
4159 2079 1145.0 0.403451 S 1147.0
4162 2081 16.0 0.090719 P 47.0
4163 2081 529.0 0.380346 S 526.0
4164 2082 1917.0 0.985196 P 1915.0
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4166 2083 2173.0 0.336663 P 2174.0
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4172 2086 1043.0 0.965586 P 1040.0
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4174 2087 1655.0 0.935181 P 1649.0
4175 2087 2277.0 0.409143 S 2276.0
4176 2088 1729.0 0.086679 P 1733.0
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4179 2089 2480.0 0.429357 S 2476.0
4180 2090 5.0 0.332167 P 15.0
4181 2090 442.0 0.074303 S 441.0
4182 2091 2727.0 0.084265 P 2725.0
4186 2093 2097.0 0.512535 P 2093.0
4188 2094 1005.0 0.933549 P 1004.0
4189 2094 1565.0 0.886687 S 1562.0
4191 2095 1657.0 0.429388 S 1649.0
4194 2097 2333.0 0.968242 P 2331.0
4195 2097 2674.0 0.676585 S 2673.0
4196 2098 1865.0 0.584438 P 1866.0
4197 2098 2301.0 0.481326 S 2298.0
4198 2099 1481.0 0.965781 P 1480.0
4199 2099 1800.0 0.538616 S 1807.0
4200 2100 1275.0 0.928132 P 1276.0
4201 2100 2001.0 0.910602 S 2002.0
4204 2102 2101.0 0.354870 P 2088.0
4211 2105 188.0 0.115517 S 172.0
4212 2106 353.0 0.421114 P 347.0
4213 2106 1066.0 0.299546 S 1069.0
4214 2107 929.0 0.256856 P 928.0
4215 2107 1412.0 0.188316 S 1410.0
4218 2109 1778.0 0.438002 P 1776.0
4219 2109 2801.0 0.618947 S 2798.0
4222 2111 569.0 0.944124 P 569.0
4223 2111 1187.0 0.759582 S 1173.0
4224 2112 1794.0 0.052431 P 1784.0
4228 2114 1933.0 0.358345 P 1933.0
4230 2115 1880.0 0.974234 P 1879.0
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4232 2116 1194.0 0.063463 P 1194.0
4233 2116 1552.0 0.761349 S 1547.0
4234 2117 1982.0 0.695695 P 1984.0
4235 2117 2444.0 0.882913 S 2441.0
4236 2118 2478.0 0.318839 P 2475.0
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4239 2119 1719.0 0.779264 S 1709.0
4240 2120 2127.0 0.466451 P 2118.0
4242 2121 90.0 0.826535 P 90.0
4243 2121 1262.0 0.687496 S 1256.0
4244 2122 537.0 0.354221 P 537.0
4245 2122 1018.0 0.423917 S 1023.0
4246 2123 1255.0 0.953076 P 1252.0
4247 2123 1703.0 0.902955 S 1699.0
4248 2124 917.0 0.409067 P 915.0
4249 2124 1414.0 0.717947 S 1409.0
4250 2125 1255.0 0.975616 P 1252.0
4251 2125 2057.0 0.579381 S 2055.0
4252 2126 2509.0 0.977219 P 2508.0
4254 2127 2181.0 0.473473 P 2179.0
4255 2127 2451.0 0.845594 S 2452.0
4256 2128 1033.0 0.974421 P 1032.0
4257 2128 1365.0 0.884779 S 1363.0
4259 2129 1181.0 0.060196 S 1177.0
4260 2130 1520.0 0.700109 P 1527.0
4264 2132 209.0 0.580472 P 208.0
4265 2132 932.0 0.313055 S 958.0
4266 2133 49.0 0.235559 P 48.0
4267 2133 773.0 0.660875 S 769.0
4268 2134 688.0 0.832021 P 686.0
4269 2134 1429.0 0.910831 S 1427.0
4274 2137 1147.0 0.884132 P 1146.0
4275 2137 2026.0 0.564893 S 2024.0
4276 2138 856.0 0.776409 P 864.0
4277 2138 1733.0 0.720982 S 1730.0
4278 2139 132.0 0.966135 P 132.0
4279 2139 873.0 0.798452 S 870.0
4280 2140 1133.0 0.688760 P 1134.0
4281 2140 1620.0 0.525113 S 1622.0
4282 2141 2.0 0.439585 P 44.0
4283 2141 1284.0 0.430553 S 1281.0
4284 2142 1428.0 0.386638 P 1422.0
4285 2142 2106.0 0.340834 S 2104.0
4287 2143 137.0 0.148415 S 137.0
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17813 8906 1616.0 0.697353 S 1610.0
17814 8907 2164.0 0.929048 P 2160.0
17815 8907 2672.0 0.407627 S 2697.0
17816 8908 2405.0 0.493660 P 2403.0
17819 8909 454.0 0.104338 S 446.0
17821 8910 88.0 0.321007 S 77.0
17824 8912 501.0 0.932249 P 501.0
17825 8912 1024.0 0.486563 S 1046.0
17826 8913 1601.0 0.871462 P 1598.0
17827 8913 1991.0 0.775475 S 1988.0
17832 8916 897.0 0.821189 P 887.0
17833 8916 1318.0 0.568378 S 1315.0
17834 8917 1945.0 0.818902 P 1939.0
17835 8917 2797.0 0.137649 S 2804.0
17836 8918 1.0 0.918597 P 289.0
17837 8918 593.0 0.822582 S 594.0
17844 8922 2181.0 0.466426 P 2179.0
17845 8922 2651.0 0.082069 S 2666.0
17846 8923 978.0 0.727301 P 961.0
17847 8923 2208.0 0.463433 S 2196.0
17848 8924 2235.0 0.497240 P 2234.0
17849 8924 2570.0 0.537339 S 2565.0
17850 8925 700.0 0.918104 P 694.0
17851 8925 1090.0 0.807647 S 1087.0
17854 8927 1229.0 0.368421 P 1260.0
17855 8927 2164.0 0.416741 S 2164.0
17856 8928 978.0 0.957647 P 976.0
17857 8928 1285.0 0.940094 S 1286.0
17862 8931 13.0 0.815553 P 2884.0
17864 8932 773.0 0.767944 P 746.0
17865 8932 1523.0 0.826864 S 1496.0
17866 8933 969.0 0.969533 P 967.0
17867 8933 1728.0 0.850846 S 1728.0
17868 8934 336.0 0.717368 P 302.0
17869 8934 1085.0 0.871307 S 1077.0
17870 8935 34.0 0.549849 P 24.0
17871 8935 609.0 0.681850 S 598.0
17875 8937 1517.0 0.177591 S 1508.0
17876 8938 577.0 0.063143 P 572.0
17877 8938 838.0 0.102852 S 831.0
17878 8939 2129.0 0.451502 P 2128.0
17882 8941 2309.0 0.320329 P 2305.0
17886 8943 1249.0 0.908084 P 1242.0
17887 8943 1808.0 0.929549 S 1802.0
17888 8944 14.0 0.380269 P 30.0
17889 8944 610.0 0.535015 S 593.0
17890 8945 1653.0 0.806691 P 1651.0
17891 8945 2913.0 0.725327 S 2905.0
17892 8946 1081.0 0.708287 P 1078.0
17893 8946 1978.0 0.487086 S 1979.0
17894 8947 665.0 0.332335 P 664.0
17895 8947 968.0 0.158652 S 965.0
17902 8951 2396.0 0.866268 P 2361.0
17903 8951 2772.0 0.419173 S 2769.0
17904 8952 12.0 0.051401 P 2799.0
17906 8953 284.0 0.963664 P 283.0
17907 8953 649.0 0.869811 S 648.0
17908 8954 1969.0 0.610528 P 1967.0
17909 8954 2532.0 0.804504 S 2529.0
17912 8956 1717.0 0.637780 P 1711.0
17913 8956 1968.0 0.439360 S 1971.0
17914 8957 601.0 0.952805 P 596.0
17915 8957 1927.0 0.648681 S 1927.0
17916 8958 944.0 0.393326 P 935.0
17918 8959 1673.0 0.943336 P 1671.0
17919 8959 2715.0 0.852722 S 2714.0
17920 8960 2195.0 0.941716 P 2194.0
17921 8960 2581.0 0.819055 S 2584.0
17923 8961 728.0 0.943743 S 727.0
17925 8962 526.0 0.772542 S 525.0
17927 8963 33.0 0.738730 S 21.0
17928 8964 1561.0 0.864198 P 1558.0
17929 8964 2102.0 0.412373 S 2091.0
17930 8965 888.0 0.975232 P 886.0
17931 8965 1647.0 0.564629 S 1704.0
17934 8967 1105.0 0.949671 P 1104.0
17935 8967 2038.0 0.472659 S 2031.0
17936 8968 813.0 0.908409 P 809.0
17937 8968 1738.0 0.755865 S 1738.0
17938 8969 1511.0 0.946008 P 1506.0
17939 8969 2293.0 0.575749 S 2310.0
| trace_id | pred_max_idx | pred_val | phase | max_idx |
|---|
decision_thresholds = np.linspace(0.05, 0.95, num=19)
pprecision = np.zeros(len(decision_thresholds))
precall = np.zeros(len(decision_thresholds))
sprecision = np.zeros(len(decision_thresholds))
srecall = np.zeros(len(decision_thresholds))
for phase in ["P", "S"]:
phase_data = merged[merged["phase"] == phase]
for ii, decision_threshold in enumerate(decision_thresholds):
above_thresh = phase_data["pred_val"] >= decision_threshold
close_enough = (phase_data["pred_max_idx"] - phase_data["max_idx"]).abs() < 10
TP = ((above_thresh) & (close_enough)).sum()
FP = ((above_thresh) & (~close_enough)).sum()
FN = ((~above_thresh) & (close_enough)).sum()
TN = ((~above_thresh) & (~close_enough)).sum()
# Save or print results here as before
print(f"Decision Threshold: {decision_threshold}, TP: {TP}, FP: {FP}, FN: {FN}, TN: {TN}, Total: {TN+FN+TP+FP}")
if phase == "P":
pprecision[ii] = TP / (TP + FP) if (TP + FP) > 0 else 0
precall[ii] = TP / (TP + FN) if (TP + FN) > 0 else 0
elif phase == "S":
sprecision[ii] = TP / (TP + FP) if (TP + FP) > 0 else 0
srecall[ii] = TP / (TP + FN) if (TP + FN) > 0 else 0
pF1=2*pprecision*precall/(pprecision+precall)
sF1=2*sprecision*srecall/(sprecision+srecall)
Decision Threshold: 0.05, TP: 5116, FP: 710, FN: 0, TN: 0, Total: 5826
Decision Threshold: 0.1, TP: 4940, FP: 638, FN: 176, TN: 72, Total: 5826
Decision Threshold: 0.15, TP: 4795, FP: 585, FN: 321, TN: 125, Total: 5826
Decision Threshold: 0.2, TP: 4642, FP: 542, FN: 474, TN: 168, Total: 5826
Decision Threshold: 0.25, TP: 4510, FP: 499, FN: 606, TN: 211, Total: 5826
Decision Threshold: 0.3, TP: 4366, FP: 471, FN: 750, TN: 239, Total: 5826
Decision Threshold: 0.35, TP: 4241, FP: 439, FN: 875, TN: 271, Total: 5826
Decision Threshold: 0.39999999999999997, TP: 4087, FP: 406, FN: 1029, TN: 304, Total: 5826
Decision Threshold: 0.44999999999999996, TP: 3885, FP: 376, FN: 1231, TN: 334, Total: 5826
Decision Threshold: 0.49999999999999994, TP: 3529, FP: 348, FN: 1587, TN: 362, Total: 5826
Decision Threshold: 0.5499999999999999, TP: 3316, FP: 327, FN: 1800, TN: 383, Total: 5826
Decision Threshold: 0.6, TP: 3139, FP: 302, FN: 1977, TN: 408, Total: 5826
Decision Threshold: 0.65, TP: 2989, FP: 278, FN: 2127, TN: 432, Total: 5826
Decision Threshold: 0.7, TP: 2854, FP: 245, FN: 2262, TN: 465, Total: 5826
Decision Threshold: 0.75, TP: 2708, FP: 210, FN: 2408, TN: 500, Total: 5826
Decision Threshold: 0.7999999999999999, TP: 2498, FP: 174, FN: 2618, TN: 536, Total: 5826
Decision Threshold: 0.85, TP: 2185, FP: 113, FN: 2931, TN: 597, Total: 5826
Decision Threshold: 0.9, TP: 1790, FP: 70, FN: 3326, TN: 640, Total: 5826
Decision Threshold: 0.95, TP: 941, FP: 20, FN: 4175, TN: 690, Total: 5826
Decision Threshold: 0.05, TP: 4828, FP: 1006, FN: 0, TN: 0, Total: 5834
Decision Threshold: 0.1, TP: 4600, FP: 891, FN: 228, TN: 115, Total: 5834
Decision Threshold: 0.15, TP: 4386, FP: 824, FN: 442, TN: 182, Total: 5834
Decision Threshold: 0.2, TP: 4193, FP: 767, FN: 635, TN: 239, Total: 5834
Decision Threshold: 0.25, TP: 3985, FP: 721, FN: 843, TN: 285, Total: 5834
Decision Threshold: 0.3, TP: 3785, FP: 654, FN: 1043, TN: 352, Total: 5834
Decision Threshold: 0.35, TP: 3543, FP: 595, FN: 1285, TN: 411, Total: 5834
Decision Threshold: 0.39999999999999997, TP: 3299, FP: 541, FN: 1529, TN: 465, Total: 5834
Decision Threshold: 0.44999999999999996, TP: 3021, FP: 499, FN: 1807, TN: 507, Total: 5834
Decision Threshold: 0.49999999999999994, TP: 2765, FP: 431, FN: 2063, TN: 575, Total: 5834
Decision Threshold: 0.5499999999999999, TP: 2534, FP: 370, FN: 2294, TN: 636, Total: 5834
Decision Threshold: 0.6, TP: 2272, FP: 330, FN: 2556, TN: 676, Total: 5834
Decision Threshold: 0.65, TP: 2022, FP: 278, FN: 2806, TN: 728, Total: 5834
Decision Threshold: 0.7, TP: 1772, FP: 215, FN: 3056, TN: 791, Total: 5834
Decision Threshold: 0.75, TP: 1475, FP: 156, FN: 3353, TN: 850, Total: 5834
Decision Threshold: 0.7999999999999999, TP: 1156, FP: 110, FN: 3672, TN: 896, Total: 5834
Decision Threshold: 0.85, TP: 802, FP: 55, FN: 4026, TN: 951, Total: 5834
Decision Threshold: 0.9, TP: 417, FP: 18, FN: 4411, TN: 988, Total: 5834
Decision Threshold: 0.95, TP: 79, FP: 3, FN: 4749, TN: 1003, Total: 5834
7. Plot precision, recall, and F1#
fig = plt.figure()
fig, axs = plt.subplots(
1, 2, # 1 row, 2 columns
figsize=(15, 6), # overall figure size
sharey=True, # share y-axis
gridspec_kw={"width_ratios": [1, 1], "wspace": 0.1} # control layout
)
decision_thresholds = np.linspace(0.05, 0.95, num=19)
axs[0].plot(decision_thresholds, pprecision, label='P Precision', color="tab:red")
axs[0].plot(decision_thresholds, precall, label='P Recall', color="tab:blue")
axs[0].plot(decision_thresholds, pF1, label='P F1', color="tab:green")
axs[0].set_xlim(0,1)
axs[0].set_ylim(0,1)
axs[0].legend()
axs[0].set_title("P-wave", fontsize=14)
axs[1].set_title("S-wave", fontsize=14)
axs[1].plot(decision_thresholds, sprecision, label='S Precision', color="tab:red")
axs[1].plot(decision_thresholds, srecall, label='S Recall', color="tab:blue")
axs[1].plot(decision_thresholds, sF1, label='S F1', color="tab:green")
axs[1].set_xlim(0,1)
axs[1].set_ylim(0,1)
axs[0].set_xlabel('Decision Threshold', fontsize=14)
axs[1].set_xlabel('Decision Threshold', fontsize=14)
axs[1].legend()
ind=9
print("P Precision = "+str(pprecision[ind]))
print("P Recall = "+str(precall[ind]))
print("S Precision = "+str(sprecision[ind]))
print("S Recall = "+str(srecall[ind]))
P Precision = 0.9102398761929327
P Recall = 0.6897967161845192
S Precision = 0.8651439299123905
S Recall = 0.5727009113504556
<Figure size 640x480 with 0 Axes>
from IPython.display import Image, display display(Image(filename=’./table1.png’))
For reference here are the specs from the original PhaseNet paper. How do they compare?
8. Compute and plot the pick distribution#
decision_thresholds=[0.5,0.7,0.9]
# Plot the histograms
colors = [[0.5,0,0],[0,0,0.5],[0,0.5,0]]
fig = plt.figure()
fig, axs = plt.subplots(
1, 2, # 1 row, 2 columns
figsize=(15, 6), # overall figure size
sharey=True, # share y-axis
gridspec_kw={"width_ratios": [1, 1], "wspace": 0.1} # control layout
)
for count, decision_threshold in enumerate(decision_thresholds):
# Decision threshold
phase="P"
phase_data = merged[(merged["phase"] == phase) & (merged["pred_val"] >= decision_threshold) & (merged["max_idx"] != -100)]
p_pick_diff = phase_data["pred_max_idx"] - phase_data["max_idx"]
phase="S"
phase_data = merged[(merged["phase"] == phase) & (merged["pred_val"] >= decision_threshold) & (merged["max_idx"] != -100)]
s_pick_diff = phase_data["pred_max_idx"] - phase_data["max_idx"]
axs[0].hist(p_pick_diff, bins=np.arange(-30.5,30.5,1), rwidth=0.8, color=colors[count], alpha=0.5, edgecolor='black', label="decision threshold ="+'% 6.2f' % decision_threshold)
axs[1].hist(s_pick_diff, bins=np.arange(-30.5,30.5,1), rwidth=0.8, color=colors[count], alpha=0.5, edgecolor='black', label="decision threshold ="+'% 6.2f' % decision_threshold)
plt.legend()
axs[0].set_xlabel("Residual [samples]",fontsize=14)
axs[0].set_title("P wave",fontsize=14)
axs[1].set_title("S wave",fontsize=14)
axs[0].set_ylabel("Frequency",fontsize=14)
axs[1].set_xlabel("Residual [samples]",fontsize=14)
Text(0.5, 0, 'Residual [samples]')
<Figure size 640x480 with 0 Axes>
We will leave it as an exercise for the reader compute the mean and standard deviation of the residuals (t < 0.5 s) whose distributions are shown above and compare those to the values reported in table 1.
9. Other metrics?#
In some cases performance metrics are straightforward and in other cases they are subjective. The onus is on the researcher to select or design appropriate metrics.
References#
Zhu, W., & Beroza, G. C. (2019). PhaseNet: A deep-neural-network-based seismic arrival-time picking method. Geophysical Journal International, 216(1), 261–273. https://doi.org/10.1093/gji/ggy423