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02a-single-waveform.py
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# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.3'
# jupytext_version: 1.0.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# +
# %load_ext autoreload
# %autoreload 2
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import scipy.stats as stats
import pandas as pd
from tqdm import tqdm
import dask.dataframe as dd
from glob import glob
import icae.tools.loss
import icae.tools.loss.EMD
import icae.tools.plot_data
from icae.tools import EMD
from icae.tools import nn
from icae.tools import status_report
from icae.tools import AE_training
from icae.tools import AE_single as AEs_tools
from icae.tools import data_loader
from icae.models import single_event as AE_models
from icae.tools.config_loader import config
# -
# FIXME: deprecated references (data, optimal_NB). Does 02c-single-waveform-torch.py replace this?
df = dd.read_hdf("../" + config.data.retabled + "*.hdf", key=config.data.hdf_key)
model, encoder = AE_models.optimal_NB(3, loss_method=loss_method)
hist = AE_training.train(model, data, verbose=1, epochs=6, batch_size=1000)
status_report.init(model, "BN 1k-bx100e new preprocessor", "-")
status_report.save_plot("loss")
# data = AE_lib.preprocess(AE_lib.load_mc())
AE_training.plot_results(model, data[:100000])
status_report.save_plot("overview-no-translation")
inlier, outlier = AEs_tools.seperate_outliers(model, data)
status_report.save_plot("outlier-seperation")
status_report.save_obj(
{"inlier indices": inlier, "outlier indices": outlier}, "inlier-outlier-indices"
)
tools.plot_data.plot_latent_space(encoder, data[:10000000], log=True)
status_report.save_plot("latent-space")
def plot_loss_waveform_overview(model, data, max_loss=50, log=False):
pred = np.array(model.predict(data), dtype=float)
loss = tools.loss.EMD.numpy(data, pred).flatten()
plt.figure(figsize=[20, 20])
min_loss = loss.min()
steps = 10
window = (max_loss - min_loss) / steps
loss_intervals = np.array(
[
np.linspace(min_loss, max_loss - window, 10),
np.linspace(min_loss, max_loss - window, 10) + window,
]
).T
for col, interval in enumerate(loss_intervals):
a, b = interval
d = data[(loss > a) & (loss < b)]
l = loss[(loss > a) & (loss < b)]
for row in range(np.min([10, len(d)])):
plt.subplot(10, 10, 1 + row * 10 + col)
i = np.random.randint(0, len(d))
plt.title("loss: %d" % (l[i]))
plt.plot(d[i])
plt.axis("off")
plt.tight_layout()
plt.subplot(11, 1, 11)
plt.hist(loss, len(loss) // 100)
plt.xlabel("loss")
plt.ylabel("number of events")
if log:
plt.yscale("log")
plt.grid()
plt.xlim(min_loss, max_loss)
plot_loss_waveform_overview(model, data, 100)
status_report.save_plot("waveform-sampling-vs-loss")
# +
pred = np.array(model.predict(data), dtype=float)
loss = tools.loss.EMD.numpy(data, pred).flatten()
le_cut = 30
clean_data = data[loss <= le_cut]
clean_loss = loss[loss <= le_cut]
clean_latent = encoder.predict(clean_data)
clean_data.shape[0] / data.shape[0] * 100
# -
clean_model, clean_encoder = AE_models.optimal_simple(3, loss_method=loss_method)
AE_training.train(clean_model, clean_data, epochs=5, batch_size=10, verbose=1)
tools.plot_data.plot_latent_space(clean_encoder, clean_data, log=True)
status_report.save_plot("latent-space-clean-extra-train")
AE_training.plot_results(clean_model, clean_data)
status_report.save_plot("overview-superclean")
plot_loss_waveform_overview(clean_model, clean_data, 50)
status_report.save_plot("waveform-sampling-vs-loss-clean")
status_report.save_model(clean_model, "clean")
# +
pred = np.array(clean_model.predict(clean_data), dtype=float)
clean_loss = tools.loss.EMD.numpy(clean_data, pred).flatten()
le_cut = 12
ultraclean_data = clean_data[clean_loss <= le_cut]
ultraclean_loss = clean_loss[clean_loss <= le_cut]
ultraclean_latent = encoder.predict(ultraclean_data)
ultraclean_integral = ultraclean_data.sum(axis=1)
ultraclean_data.shape[0] / data.shape[0] * 100
# -
ultraclean_model, ultraclean_encoder = AE_models.optimal_simple(
3, loss_method=loss_method
)
AE_training.train(
ultraclean_model, ultraclean_data, epochs=10, batch_size=10, verbose=1
)
ultraclean_model.save("final model.hdf", overwrite=False)
tools.plot_data.plot_latent_space(ultraclean_encoder, ultraclean_data, log=True)
status_report.save_plot("latent-space-ultraclean-extra-train")
AE_training.plot_results(ultraclean_model, ultraclean_data)
status_report.save_plot("overview-ultraclean")
status_report.save_model(ultraclean_model, "final AE ")
status_report.save_model(ultraclean_encoder, "final encoder ")
plot_loss_waveform_overview(ultraclean_model, ultraclean_data, 30)
status_report.save_plot("waveform-sampling-vs-loss-ultraclean")
plot_loss_waveform_overview(
ultraclean_model, AE_training.preprocess(data_loader.load_raw()), 300
)
status_report.save_plot("waveform-sampling-ultraclean-training-all-data-300")
plot_loss_waveform_overview(
ultraclean_model, AE_training.preprocess(data_loader.load_raw()), 40
)
status_report.save_plot("waveform-sampling-ultraclean-training-all-data-40")
plot_loss_waveform_overview(
ultraclean_model, AE_training.preprocess(data_loader.load_raw()), 1000, log=True
)
status_report.save_plot("waveform-sampling-ultraclean-training-all-data-1k-log")
# TODO: output model?