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Bootleg_Dropview_9_24_23.py
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import numpy
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pathlib import Path
import math
from math import pi
from math import sqrt
import shutil
# SETUP VARIABLES - USER INPUTS
BD = 3 #Bluedrop file is from
fileNum = '00A0'
# write the bin file number for the earliest drop in the folder (do not include 'bLog' or '.bin')
numFiles = 20 #write the number of bin files in the folder of interest (if only looking at 1 drop, write 2)
offset = 0 # this value is subtracted from the accelerometer readings
# paste the filepath to the folder where the BD data is stored
binFilepath = Path("C:/Users/elise/Desktop/Sequim_2021/")
#paste the filepath that you want the files containing drops copied to
outputPath = Path("C:/Users/elise/Desktop/Sequim_2021/Drops Only")
plotPath = Path("C:/Users/elise/Desktop/Sequim_2021/Drops Only/Dec-Time Plots")
def overviewplot(): #Plot showing all accelerometers and pore pressure readings
fig, (ax1) = plt.subplots(1)
fig.set_size_inches(4, 4)
ax1.plot(time, g250g, label="250g")
ax1.plot(time, g50g, label="50g")
ax1.plot(time, g18g, label="18g")
ax1.plot(time, g2g, label="2g")
ax1.legend(loc = "upper right")
ax1.set(ylabel="Deceleration (g)")
ax1.set_title("BD file "+ newName)
ax1.grid()
plt.draw()
print("Is there a drop? y/n")
plt.waitforbuttonpress(2)
drop_input=input()
if drop_input=='y':
origFile = 'bLog'+newName+".bin"
origFilePath = binFilepath / origFile
newFilePath = outputPath / origFile
plotFilePath = plotPath / newName
shutil.copyfile(origFilePath, newFilePath)
plt.savefig(plotPath / newName)
plt.close(fig)
def newNamefun(): #find the next sequential bin name a=existing last variable, b=new last variable
global newName
a = name[3]
c = name[2]
e = name[1]
g = name[0]
if a == "A":
b = "B"
elif a == "B":
b = "C"
elif a == "C":
b = "D"
elif a == "D":
b = "E"
elif a == "E":
b = "F"
elif a == "F":
b = "0"
if c == "A":
d = "B"
elif c == "B":
d = "C"
elif c == "C":
d = "D"
elif c == "D":
d = "E"
elif c == "E":
d = "F"
elif c == "F":
d = "0"
if e == "A":
f = "B"
elif e == "B":
f = "C"
elif e == "C":
f = "D"
elif e == "D":
f = "E"
elif e == "E":
f = "F"
elif e == "F":
f = "0"
if g == "A":
h = "B"
elif g == "B":
h = "C"
elif g == "C":
h = "D"
elif g == "D":
h = "E"
elif g == "E":
h = "F"
elif g == "F":
h = "0"
f = "0"
d = "0"
b = "0"
elif g == "9":
h = "A"
else: #a <=8:
g = int(name[0]) #the only other options are numbers
h = g + 1
h = str(b)
elif e == "9":
f = "A"
else: #a <=8:
e = int(name[1]) #the only other options are numbers
f = e + 1
f = str(f)
elif c == "9":
d = "A"
else: #a <=8:
c = int(name[2]) #the only other options are numbers
d = c + 1
d = str(d)
elif a == "9":
b = "A"
else: #a <=8:
a = int(name[3]) #the only other options are numbers
b = a + 1
b = str(b)
newNameList = name
newNameList[3] = b #b always will change
if a == "F":
newNameList[2] = d
if c == "F":
newNameList[1] = f
if e == "F":
newNameList[0] = h
else:
newNameList[2] = c
newNameList[1] = e
newNameList[0] = g
print(newNameList)
newName = ""
newName = newName.join(newNameList)
print(newName)
newName = fileNum #for the first drop, select the earliest drop defined earlier
for i in range(1, numFiles):
# READ BD DATA IN
data_array = [] # creates an empty array for us to fill with bd data
fileName = 'bLog'+newName+".bin"
# print(fileName)
newPath = binFilepath / fileName
#print(newPath)
file = open(newPath, 'rb') # read file
element = file.read(3) # create a byte list with each element having 3 bytes
while element:
# Convert to signed integer before adding to data array
iVAl = int.from_bytes(element, byteorder='big', signed=True)
data_array.append(iVAl) # adds the reshaped data from the bd file to the data frame
element = file.read(3)
np_array = np.array(data_array) # create numpy array from the list
np_array = np.reshape(np_array, (-1, 10)) # convert the 1d array to 2d array with 10 cols
df = pd.DataFrame(np_array) # Creates a Dataframe in pandas from the bd data
df.columns = ['Count', 'no clue', 'g2g', 'g18g', 'g50g', 'ppm', 'g200g', 'gX55g', 'gY55g', 'g250g'] # names columns
# APPLY CALIBRATION FACTORS
if BD == 3: # calibration factors from March 2023
g2g = (df['g2g']-33570.1)/1614577.9 - offset# accelerometers are in g
g18g = (df['g18g']+13495)/163387.2 - offset
g50g = (df['g50g']-238817.4)/63779.5 - offset
ppm = ((df['ppm']-139040.1)/20705) * 6.89475729 # converts to kPa
g200g = ((df['g200g'] -262332.4/38888.7)) - offset
gX55g = (df['gX55g']-70406)/59754.3
gY55g = (df['gY55g']-69421.1)/141871.5
g250g = (df['g250g']-39077.4)/13746.9 - offset
if BD == 2: # calibration factors from Aug 26, 2021
g2g = (df['g2g']+37242.2)/1639250.2 - offset# accelerometers are in g
g18g = (df['g18g']-26867.0)/160460.5 - offset
g50g = (df['g50g']-213923.3)/64080.7- offset
ppm = ((df['ppm']+55518.9)/18981.7) * 6.89475729 # converts to kPa
g200g = (df['g200g']-171448.6)/30334.2 - offset
gX55g = (df['gX55g']-54242.6)/64767.7
gY55g = (df['gY55g']-40574.2)/66343.1
g250g = (df['g250g']-40614.9)/13654.6 - offset
if BD == 1: # calibration factors from July 2020
g2g = (df['g2g']-42590.9)/1626361.1 - offset # accelerometers are in g
g18g = (df['g18g']-44492.9)/161125.5 - offset
g50g = (df['g50g']-171656.1)/64020.3 - offset
ppm = ((df['ppm']+31776.1)/20679.7) * 6.89475729 # this is kPa
g200g = (df['g200g'] -723404.8)/32209.7 - offset
gX55g = (df['gX55g'] -54881.1)/64858.6
gY55g = (df['gY55g']-28735.5)/63839.9
g250g = (df['g250g']+13299.7)/13697.1 - offset
time = (df['Count']-df['Count'].iloc[0]+1)/2000 # gives time in s
count = df["Count"]-df['Count'].iloc[0]
# make a new dataframe of the calibrated values in units of g
dfCalg = pd.DataFrame([count, time, g2g, g18g, g50g, g200g, g250g, gX55g, gY55g, ppm])
dfCalg = dfCalg.T
dfCalg.columns = ['Count', 'Time (s)', '2g (g)', '18g (g)', '50g (g)', '200g (g)', '250g (g)', 'X55g (g)', 'Y55g (g)', 'Pore Pressure (kPa)'] # names columns
#print(dfCalg)
#make a new dataframe of the calibrated values in units of m/s^2
dfCal = pd.DataFrame([count, time, g2g, g18g, g50g, g200g, g250g, gX55g, gY55g, ppm])
dfCal = dfCal.T
dfCal.columns = ['Count','Time (s)', '2g (m/s^2)', '18g (m/s^2)', '50g (m/s^2)', '200g (m/s^2)', '250g (m/s^2)', 'X55g (m/s^2)', 'Y55g (m/s^2)', 'Pore Pressure (kPa)'] # names columns
dfCal['2g (m/s^2)'] = dfCal['2g (m/s^2)'] * 9.80665
dfCal['18g (m/s^2)'] = dfCal['18g (m/s^2)'] * 9.80665
dfCal['50g (m/s^2)'] = dfCal['50g (m/s^2)'] * 9.80665
dfCal['200g (m/s^2)'] = dfCal['200g (m/s^2)'] * 9.80665
dfCal['250g (m/s^2)'] = dfCal['250g (m/s^2)'] * 9.80665
dfCal['X55g (m/s^2)'] = dfCal['X55g (m/s^2)'] * 9.80665
dfCal['Y55g (m/s^2)'] = dfCal['Y55g (m/s^2)'] * 9.80665
#print(dfCal)
overviewplot()
name = [*newName]
newNamefun()
print("all files checked")