!pip install -q git+https://github.com/MideTechnology/endaq-python.git@development
exit()  #forces a restart, it will make Colab crash - that is okay! just go on to run all the cells below
        #Note that this is only needed in colab and not if running locally

  Installing build dependencies ... done
  Getting requirements to build wheel ... done
    Preparing wheel metadata ... done
     |████████████████████████████████| 63 kB 1.1 MB/s
     |████████████████████████████████| 93 kB 879 kB/s
     |████████████████████████████████| 38.1 MB 1.3 MB/s
     |████████████████████████████████| 83 kB 1.2 MB/s
  Building wheel for endaq (PEP 517) ... done
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.27.1 which is incompatible.
datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.
albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.

Quick Start Guide

Introduction

This example script will use the enDAQ Python library to do the following:

• Load open-source libraries

• Load data directly from an IDE file recorded from an enDAQ sensor

  • Provide meta data about the sensor which recorded the data

  • Summary the contents of the file as a table of all sensor channels

  • Summarize all data in a dashboard

• Get the accelerometer data

  • Apply a high pass filter

  • Plot the full time history

  • Plot the time history around the peak

  • Plot the time history in a specific time range

• Shock & Vibration Analysis

  • Linear PSD

  • Octave spaced PSD

  • FFT

  • Shock response spectrum

Load Libraries

import endaq

endaq.plot.utilities.set_theme('endaq_light')

import plotly.express as px
import pandas as pd
import numpy as np
import scipy

Load Data

Using the endaq.ide.get_doc function, load in the contents of an IDE file. Note that this doesn’t load all data into memory yet.

doc = endaq.ide.get_doc('https://info.endaq.com/hubfs/data/Mining-Hammer/LOC__3__DAQ41551_11_01_02.ide')

Display meta data

print(f"Serial Number: {doc.recorderInfo['RecorderSerial']}")
print(f"Part Number: {doc.recorderInfo['PartNumber']}")
print(f"Recording Finished at {pd.to_datetime(doc.lastUtcTime,unit='s')}")

Serial Number: 9680
Part Number: S3-E2000D40
Recording Finished at 2021-03-25 03:32:32

Table of file contents

This uses the function endaq.ide.get_channel_table.

endaq.ide.get_channel_table(doc)

	channel	name	type	units	start	end	duration	samples	rate
0	8.0	X (2000g)	Acceleration	g	34:01.0868	34:15.0561	00:13.0693	273867	20000.07 Hz
1	8.1	Y (2000g)	Acceleration	g	34:01.0868	34:15.0561	00:13.0693	273867	20000.07 Hz
2	8.2	Z (2000g)	Acceleration	g	34:01.0868	34:15.0561	00:13.0693	273867	20000.07 Hz
3	80.0	X (40g)	Acceleration	g	34:01.0717	34:15.0531	00:13.0814	55510	4018.33 Hz
4	80.1	Y (40g)	Acceleration	g	34:01.0717	34:15.0531	00:13.0814	55510	4018.33 Hz
5	80.2	Z (40g)	Acceleration	g	34:01.0717	34:15.0531	00:13.0814	55510	4018.33 Hz
6	36.0	Pressure/Temperature:00	Pressure	Pa	33:47.0105	34:12.0130	00:25.0025	26	1.04 Hz
7	36.1	Pressure/Temperature:01	Temperature	°C	33:47.0105	34:12.0130	00:25.0025	26	1.04 Hz
8	47.0	X	Rotation	dps	34:00.0947	34:14.0633	00:13.0685	2765	202.04 Hz
9	47.1	Y	Rotation	dps	34:00.0947	34:14.0633	00:13.0685	2765	202.04 Hz
10	47.2	Z	Rotation	dps	34:00.0947	34:14.0633	00:13.0685	2765	202.04 Hz

Dashboard of all data

Note this requires first loading in all data into memory. Then it will display bars of all data plotted from min to max.

channel_dict = {doc.channels[ch].name: endaq.ide.to_pandas(doc.channels[ch]) for ch in doc.channels}
fig = endaq.plot.dashboards.rolling_enveloped_dashboard(channel_dict, plot_as_bars=True, num_rows=1, num_cols=None)
fig.show()

Get accelerometer data

accel = endaq.ide.get_primary_sensor_data(doc=doc,measurement_type='accel',time_mode='seconds')
accel

	X (2000g)	Y (2000g)	Z (2000g)
timestamp
2041.868560	-24.046861	14.828584	-9.588154
2041.868610	-7.205801	14.828584	-11.630245
2041.868660	14.496595	19.594306	20.532687
2041.868710	55.123480	13.240010	77.541059
2041.868760	83.944262	1.766976	84.518203
...	...	...	...
2055.561659	50.956620	13.946043	5.387179
2055.561709	62.068247	14.828584	7.939793
2055.561759	40.713089	14.828584	5.217005
2055.561809	2.864111	6.709206	3.855611
2055.561859	-21.616192	-9.176533	3.004740

273867 rows × 3 columns

Apply High Pass Filter

Filter away the DC offset, there are more filtering options at endaq.calc.filters.

accel = endaq.calc.filters.butterworth(accel,low_cutoff=1)

Plot Full Time History

endaq.plot.rolling_min_max_envelope() will reduce a large time history into a bar chart that plots from the min to the max so that it appears identical to the full time history plot yet is fast to generate and responsive.

fig_full_accel = endaq.plot.rolling_min_max_envelope(
    accel,
    desired_num_points=1000,
    plot_as_bars=True,
    opacity=0.7
).update_layout(
    yaxis_title_text='Acceleration (g)',
    xaxis_title_text='Time (s)',
    title_text='Full Time History of Acceleration Data'
)
fig_full_accel.show()

Plot Time History Around Peak

endaq.plot.around_peak() finds and plots around the maximum value.

fig_peak = endaq.plot.around_peak(accel, num=1000).update_layout(
    yaxis_title_text='Acceleration (g)',
    xaxis_title_text='Time (s)',
    title_text='Time History Around Peak',
    legend_title_text=''
)
fig_peak.show()

Time History at Specific Time

fig_time = px.line(accel[2044.6:2044.7]).update_layout(
    yaxis_title_text='Acceleration (g)',
    xaxis_title_text='Time (s)',
    title_text='Time History at Specific Time',
    legend_title_text=''
)
fig_time.show()

Analysis

Linear PSD

Calculate the PSD using endaq.calc.psd.welch()

#Calculate PSD
psd = endaq.calc.psd.welch(accel, bin_width=2)

fig_psd = px.line(
    psd,
    log_x=True,
    log_y=True
).update_layout(
    xaxis_title='Frequency (Hz)',
    yaxis_title='Acceleration (g^2/Hz)',
    title_text='PSD with 2 Hz Bin Width',
    legend_title_text=''
)

fig_psd.show()

Octave PSD

Now convert to octave using endaq.calc.psd.to_octave()

#Calculate PSD with a finer bin width to convert to octave
psd = endaq.calc.psd.welch(accel, bin_width=0.1)

#Convert to Octave
oct_psd = endaq.calc.psd.to_octave(psd,octave_bins=6)

fig_psd_oct = px.line(
    oct_psd,
    log_x=True,
    log_y=True
).update_layout(
    xaxis_title='Frequency (Hz)',
    yaxis_title='Acceleration (g^2/Hz)',
    title_text='1/6 Octave PSD',
    legend_title_text=''
)

fig_psd_oct.show()

/usr/local/lib/python3.7/dist-packages/endaq/calc/psd.py:172: RuntimeWarning:

empty frequency bins in re-binned PSD; original PSD's frequency spacing is too coarse

FFT

See endaq.calc.fft for a full list of FFT functions.

#Calculate FFT
fft = endaq.calc.fft.rfft(accel[2044.6:2044.7])

#Plot
fig_fft = px.line(fft).update_layout(
    xaxis_title='Frequency (Hz)',
    yaxis_title='Acceleration (g)',
    title_text='FFT of Time Range 2044.6 to 2044.7 Seconds',
    legend_title_text=''
)

fig_fft.show()

Shock Response Spectrum

This uses endaq.calc.utils.logfreqs() to define log spaced frequencies to use, then passes those to endaq.calc.shock.shock_spectrum() along with the time history data to compute the shock spectrum.

#Calculate for reduced time series
accel_zoomed = accel[2044.6:2044.7]

#Define frequencies to calculate responses at
freqs = endaq.calc.utils.logfreqs(accel_zoomed, bins_per_octave=12, init_freq=0.5)

#Calculate pseudo velocity shock spectrum
pvss = endaq.calc.shock.shock_spectrum(accel_zoomed, freqs=freqs, damp=0.05, mode='pvss')

#Convert to in/s (currently in g/s)
pvss = pvss * 9.81 * 39.37

#Plot
fig_pvss = px.line(
    pvss,
    log_x=True,
    log_y=True
).update_layout(
    xaxis_title='Natural Frequency (Hz)',
    yaxis_title='Peak Pseudo Velocity (in/s)',
    legend_title_text='',
    title_text='Psuedo Velocity Shock Spectrum'
)

fig_pvss.show()

/usr/local/lib/python3.7/dist-packages/endaq/calc/utils.py:61: RuntimeWarning:

the data's duration is too short to accurately represent an initial frequency of 0.500 Hz

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enDAQ Custom Analysis