<jupyter_start><jupyter_text>Diagnostics for PSF FFT  Configure environment<jupyter_code>import os
os.chdir("/home/hbabcock/Data/storm_analysis/sa_diagnostics/psf_fft")
print(os.getcwd())

import numpy
numpy.random.seed(1)
import storm_analysis.diagnostics.psf_fft.settings as settings
import storm_analysis.diagnostics.psf_fft.configure as configure
import storm_analysis.diagnostics.psf_fft.make_data as makeData
import storm_analysis.diagnostics.psf_fft.analyze_data as analyzeData
import storm_analysis.diagnostics.psf_fft.collate as collate

settings.photons = [[20, 500], [20, 1000]]
print(settings.photons)

settings.iterations = 20
settings.n_frames = 10
settings.peak_locations = None<jupyter_output><empty_output><jupyter_text>Configure<jupyter_code>configure.configure()<jupyter_output><empty_output><jupyter_text>Make Data<jupyter_code>makeData.makeData()<jupyter_output><empty_output><jupyter_text>Analyze data<jupyter_code>%time analyzeData.analyzeData()<jupyter_output><empty_output><jupyter_text>Collate data<jupyter_code>collate.collate()<jupyter_output><empty_output><jupyter_text>Reference results<jupyter_code>2019-06-20
commit <PASSWORD>

Processing test_01
Using max_distance 200.0 nm for error calcuations.
Processing test_02
Using max_distance 200.0 nm for error calcuations.

Analysis Summary:
Processed 2450 localizations in 8.56 seconds, 286.09/sec
Recall 0.54524
Noise 0.45476
XYZ Error Standard Deviation (nm):
test_01	34.35	36.10	59.78
test_02	20.48	20.59	33.96

XYZ RMSE Accuracy (nm):
test_01	34.35	36.10	60.27
test_02	20.48	20.59	34.55<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.09449311639549436, 'fraction_numerical': 0.07259073842302878, 'mean_word_length': 8.137142857142857, 'pattern_counts': {'":': 0, '<': 26, '<?xml version=': 0, '>': 26, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '6', 'dir': 'jupyter-structured-clean-dedup', 'id': '198339', 'license': 'CNRI-Python', 'n_tokens_mistral': 673, 'n_tokens_neox': 591, 'n_words': 107, 'path': 'storm_analysis/diagnostics/jpy_notebooks/psf_fft.ipynb', 'repo_name': 'KimLab-SNU/storm-analysis-kimlab', 'size': '1628'}