I inherited a project in the middle of pandemonium and to makes matters worse I am just learning python.
I managed to implement a polynomial function into my code and the results
are the same as the ones posted in the examples of this web page.
[z = numpy.polyfit(x, y, 5)]
However, I will like to know how to modify the program so I can insert an input of one of the know values of y to find x.
In other words. I have x and y arrays where:
- array x holds values for know kilograms weights (0.0, 0.5, 1.0, 1.5, 2.0)
- array y (0.074581967, 0.088474754, 0.106797419, 0.124461935, 0.133726833)
I have a program who reads a load of weight and the tension created by a phidget and generates the array to be used by this program.
What I need to accomplish is to read the next value from the phidget and be able to convert the reading into kilograms, from within the provided array.
Is there a way to do this? I feel I am only missing a line of code but I don't know how to implement the results of the returned values from the formula. (z)
Thanks in advance.
CODE ADDED AS REQUESTED
from Phidgets.PhidgetException import PhidgetException
from Phidgets.Devices.Bridge import Bridge, BridgeGain
import datetime
import os
import re
import sys
import time
import numpy
wgh = list()
avg = list()
buf = list()
x = []
y = []
def nonlinear_regression(): # reads the calibration mapping and generates the conversion coefficients
fd = open("calibration.csv", "r")
for line in fd:
[v0, v1] = line.split(",")
x.append(float(v0))
y.append(float(v1[:len(v1) - 1]))
xdata = numpy.array(x)
ydata = numpy.array(y)
z = numpy.polyfit(x, y, 5)
return z
def create_data_directory(): # create the data directory
if not os.path.exists("data"):
os.makedirs("data")
def parse_config(): # get config-file value
v = int()
config = open("config.properties", "r")
for line in config:
toks = re.split(r"[\n= ]+", line)
if toks[0] == "record_interval":
v = int(toks[1])
return v
def read_bridge_data(event): # read the data
buf.append(event.value)
def record_data(f_name, date, rms):
if not os.path.isfile(f_name):
fd = open(f_name, "w")
fd.write("time,weight\n")
fd.write(datetime.datetime.strftime(date, "%H:%M"))
fd.write(",")
fd.write(str(rms) + "\n")
fd.close()
else:
fd = open(f_name, "a")
fd.write(datetime.datetime.strftime(date, "%H:%M"))
fd.write(",")
fd.write(str(rms) + "\n")
fd.close()
print("Data recorded.")
def release_bridge(event): # release the phidget device
try:
event.device.closePhidget()
except:
print("Phidget bridge could not be released properly.")
sys.exit(1)
def main():
create_data_directory()
RECORD_INTERVAL = parse_config() # get the config-file value
calibrate = nonlinear_regression() # get calibration function; use like: calibrate(some_input)
bridge = Bridge()
try:
bridge.setOnBridgeDataHandler(read_bridge_data)
bridge.setOnDetachHandler(release_bridge) # when the phidget gets physically detached
bridge.setOnErrorhandler(release_bridge) # asynchronous exception (i.e. keyboard interrupt)
except:
print("Phidget bridge event binding failed.")
sys.exit(1)
try:
bridge.openPhidget()
bridge.waitForAttach(3000)
except:
print("Phidget bridge opening failed.")
sys.exit(1)
last_record = int()
while (True):
date = datetime.datetime.now()
f_name = "data\\" + datetime.datetime.strftime(date, "%B_%d_%Y") + ".csv"
curr = time.time() * 1000
if (curr - last_record) > (RECORD_INTERVAL * 1000):
try:
bridge.setDataRate(10)
last = time.time() * 1000
bridge.setEnabled(0, True)
while (time.time() * 1000 - last) < 1000: # collects over 1 sec
pass
bridge.setEnabled(0, False)
except:
print("Phidget bridge data reading error.")
bridge.setEnabled(0, False)
bridge.closePhidget()
sys.exit(1)
vol = sum(buf) / len(buf)
del buf[:]
last_record = curr
record_data(f_name, date, vol) # replace curr with calibrated data
#THIS IS WHERE I WILL LIKE TO INCORPORATE THE CHANGES TO SAVE THE WEIGHT
#record_data(f_name, date, conversion[0] * vol + conversion[1]) # using the linear conversion function
else:
time.sleep(RECORD_INTERVAL - 1) # to reduce the CPU's busy-waiting
if __name__ == "__main__":
main()
The linear conversion function from the calibrations is returned by numpy.polyfit as an array of coefficients. Since you passed 5 for the degree argument of polyfit, you will get an array of six coefficients:
f(x) = ax5 + bx4 + cx3 + dx2 + ex + f
Where a, b, c, d, e, and f are the elements of the z array returned by nonlinear_regression.
To implement the linear conversion formula, simply use the power operator **, the elements of z, and the value of vol:
vol_calibrated = vol**5 * z[0] + vol**4 * z[1] + vol**3 * z[2] + vol**2 * z[3] + vol * z[4] + z[5]
Or more generally:
degree = len(z) - 1
vol_calibrated = sum(vol**(degree-i) * coeff for i, coeff in enumerate(z))
Related
I am trying to print the mean and standard deviation however in its current form it doesnt recognize anything inside the loop. How would I go about correcting this to properly display what is intended. When i try to print the mean it says ex not defined.
import numpy as np
p = 0.44
q = 0.56
mu_1 = 26.5
sigma = 4.3
mu_2 = 76.4
n = 7
print( 'total number of jobs =', n)
lst_times = []
j = 0
def calc_avg_std(n):
while j < 100:
m = np.random.binomial(n,p)
easy_jobs = np.random.normal(mu_1,sigma,m)
n_chall = n-m
chall_jobs = np.random.exponential(mu_2,n_chall)
totalTime = sum(easy_jobs) + sum(chall_jobs)
lst_times.append(totalTime)
j = j + 1
ex = (mu_1 * p) + (mu_2 * q)
ex2 = (p *((mu_1**2)))+ (q*(mu_2**2)*2)
var = ex2-(ex**2)
stdev = np.sqrt(var)
return [ex , stdev]
print(' mean is',ex)
I tried this code without the def and return and runs properly but the professor insists that it should be implemented.
def is used to create a function. When you use return you return the values to the caller.
Replace your last prin witht the following lines:
call the function and keep the return values
print the returned values
mean, stdev = calc_avg_std(n)
print(mean)
I am trying to read a file using Python. I want to take the values that follow 'Average Value' and 'Standard Deviation' after the term 'DISTRIBUTION OF OFFSET_0' in a list that can be put in CSV file for analysis.
A part of the file is given below.
DISTRIBUTION OF BLB_FLIP_0
* NOMINAL VALUE = 5.0000E-01
* AVERAGE VALUE = 5.0000E-01
* STANDARD DEVIATION = 0.0 ( 0.0%)
* STANDARD DEVIATION BASED ON NOMINAL RUN = 0.0 ( 0.0%)
DISTRIBUTION OF OFFSET_0
* NOMINAL VALUE = 4.0000E-03
* AVERAGE VALUE = 1.4000E-02
* STANDARD DEVIATION = 1.9987E-02 (142.8%)
* STANDARD DEVIATION BASED ON NOMINAL RUN = 2.0484E-02 (512.1%)
EXTRACT for TRANSIENT ANALYSIS
PARAM TEMP = -4.0000E+01
PARAM VH = 1.3200E+00
TEMPERATURE = -4.0000E+01 Celsius
*T_OUT_F10_0 = 4.0302E-04
*BL_FLIP_0 = 1.1210E+00
*BLB_FLIP_0 = 1.1200E+00
*OFFSET_0 = 1.0000E-03
DISTRIBUTION OF T_OUT_F10_0
* NOMINAL VALUE = 4.0302E-04
* AVERAGE VALUE = 4.3982E-04
* STANDARD DEVIATION = 3.5741E-05 ( 8.1%)
* STANDARD DEVIATION BASED ON NOMINAL RUN = 4.8746E-05 (12.1%)
DISTRIBUTION OF BL_FLIP_0
* NOMINAL VALUE = 1.1210E+00
* AVERAGE VALUE = 1.1394E+00
* STANDARD DEVIATION = 1.7869E-02 ( 1.6%)
* STANDARD DEVIATION BASED ON NOMINAL RUN = 2.4372E-02 ( 2.2%)
DISTRIBUTION OF BLB_FLIP_0
* NOMINAL VALUE = 1.1200E+00
* AVERAGE VALUE = 1.1200E+00
* STANDARD DEVIATION = 0.0 ( 0.0%)
* STANDARD DEVIATION BASED ON NOMINAL RUN = 0.0 ( 0.0%)
DISTRIBUTION OF OFFSET_0
* NOMINAL VALUE = 1.0000E-03
* AVERAGE VALUE = 1.9400E-02
* STANDARD DEVIATION = 1.7869E-02 (92.1%)
* STANDARD DEVIATION BASED ON NOMINAL RUN = 2.4372E-02 (2437.2%)
However, when I run the following python code, I realised the value is stuck at the first time the loop finds 'DISTRIBUTION OF OFFSET_0' that is the index does not change as I read the file array. I am unable to determine the error. Any lead appreciated.
import csv
import numpy as np
i_file = open ("setup.aex", "r")
file_matrix = i_file.readlines()
#print (file_matrix)
mean = []
sd = []
for i in file_matrix:
print(file_matrix.index (i))
if ('DISTRIBUTION OF OFFSET_0' in i) & ('AVERAGE VALUE' in (file_matrix.index(i)+2)):
print(file_matrix.index(i))
mean_loc = file_matrix[(file_matrix.index(i))+2]
mean_el = float(mean_loc.split ("AVERAGE VALUE = ")[1])
print (mean_el)
mean.append(mean_el)
sd_loc = file_matrix[(file_matrix.index(i))+3]
sd_inter =sd_loc.split (" * STANDARD DEVIATION = ")[1]
sd_el = float (sd_inter.split (" (")[0])
print (sd_el)
sd.append(sd_el)
print (mean)
print (sd)
Thanks in Advance.
you can fix your code according to the comments. In case you want to take this project any further, I've made a more streamlined version to get the data. It gradually consumes the input and returns you a dictionary with all the data in it.
import json
i_file = open ("log.log", "r")
data = {}
def linevalue(line):
name, value = line.split("=")
name = name.replace("*", "").strip()
value = value.strip()
return name, value
try:
while line := next(i_file):
indent = len(line) - len(line.lstrip())
line = line.strip()
is_value = "=" in line
if indent == 0 and len(line.strip()) > 0:
title = line
data[title] = {}
elif (indent == 2 or indent == 4) and is_value:
name, value = linevalue(line)
data[title][name] = value
elif indent == 4: #is a title
name = line
data[title][name] = {}
for i in range(4):
line = next(i_file)
sub_name, sub_value = linevalue(line)
data[title][name][sub_name] = sub_value
except StopIteration:
pass
finally:
del i_file
with open("output.json", "w") as fp:
json.dump(data, fp)
I am trying to create a script to compare two audio samples and determine if they are similar or not (factoring in things like background noise would mean a lower correlation, so a "pass" would be arbitrarily determined by some rule of thumb). I have a piece of code written but I am producing errors that I am not sure how to debug.
Specifically, the code generates the following error, in the correlation function: ValueError: object of too small depth for the desired array.
I am not sure if this is because I am passing too few arguments to the function, but I really can't source the problem. The code appears below and should be relatively complete.
# compare.py
import argparse
from numpy import correlate
def initialize():
parser = argparse.ArgumentParser()
parser.add_argument("-i ", "--source-file", help="source file")
parser.add_argument("-o ", "--target-file", help="target file")
args = parser.parse_args()
SOURCE_FILE = args.source_file if args.source_file else None
TARGET_FILE = args.target_file if args.target_file else None
SOURCE_FILE = "Comparison1.wav"
TARGET_FILE = "Comparison2.wav"
if not SOURCE_FILE or not TARGET_FILE:
raise Exception("Source or Target files not specified.")
return SOURCE_FILE, TARGET_FILE
if __name__ == "__main__":
SOURCE_FILE, TARGET_FILE = initialize()
correlate(SOURCE_FILE, TARGET_FILE)
# correlation.py
import commands
import numpy
# seconds to sample audio file for
sample_time = 500
# number of points to scan cross correlation over
span = 150
# step size (in points) of cross correlation
step = 1
# minimum number of points that must overlap in cross correlation
# exception is raised if this cannot be met
min_overlap = 20
# report match when cross correlation has a peak exceeding threshold
threshold = 0.5
# calculate fingerprint
def calculate_fingerprints(filename):
fpcalc_out = commands.getoutput('fpcalc -raw -length %i %s' % (sample_time, filename))
fingerprint_index = fpcalc_out.find('FINGERPRINT=') + 12
# convert fingerprint to list of integers
fingerprints = map(int, fpcalc_out[fingerprint_index:].split(','))
return fingerprints
# returns correlation between lists
def correlation(listx, listy):
if len(listx) == 0 or len(listy) == 0:
# Error checking in main program should prevent us from ever being
# able to get here.
raise Exception('Empty lists cannot be correlated.')
if len(listx) > len(listy):
listx = listx[:len(listy)]
elif len(listx) < len(listy):
listy = listy[:len(listx)]
covariance = 0
for i in range(len(listx)):
covariance += 32 - bin(listx[i] ^ listy[i]).count("1")
covariance = covariance / float(len(listx))
return covariance / 32
# return cross correlation, with listy offset from listx
def cross_correlation(listx, listy, offset):
if offset > 0:
listx = listx[offset:]
listy = listy[:len(listx)]
elif offset < 0:
offset = -offset
listy = listy[offset:]
listx = listx[:len(listy)]
if min(len(listx), len(listy)) < min_overlap:
# Error checking in main program should prevent us from ever being
# able to get here.
return
#raise Exception('Overlap too small: %i' % min(len(listx), len(listy)))
return correlation(listx, listy)
# cross correlate listx and listy with offsets from -span to span
def compare(listx, listy, span, step):
if span > min(len(listx), len(listy)):
# Error checking in main program should prevent us from ever being
# able to get here.
raise Exception('span >= sample size: %i >= %i\n' % (span, min(len(listx), len(listy))) +
'Reduce span, reduce crop or increase sample_time.')
corr_xy = []
for offset in numpy.arange(-span, span + 1, step):
corr_xy.append(cross_correlation(listx, listy, offset))
return corr_xy
# return index of maximum value in list
def max_index(listx):
max_index = 0
max_value = listx[0]
for i, value in enumerate(listx):
if value > max_value:
max_value = value
max_index = i
return max_index
def get_max_corr(corr, source, target):
max_corr_index = max_index(corr)
max_corr_offset = -span + max_corr_index * step
print("max_corr_index = ", max_corr_index, "max_corr_offset = ", max_corr_offset)
# report matches
if corr[max_corr_index] > threshold:
print('%s and %s match with correlation of %.4f at offset %i' %
(source, target, corr[max_corr_index], max_corr_offset))
def correlate(source, target):
fingerprint_source = calculate_fingerprints(source)
fingerprint_target = calculate_fingerprints(target)
corr = compare(fingerprint_source, fingerprint_target, span, step)
max_corr_offset = get_max_corr(corr, source, target)
You have a little error in your code, more specifically:
SOURCE_FILE = "Comparison1.wav"
TARGET_FILE = "Comparison2.wav"
Here you assign this variables to a string (not files). And so from then they are processed as "Comparison1.wav" and "Comparison2.wav" strings respectively.
I have a fairly long code that processes spectra, and along the way I need an interpolation of some points. I used to have all this code written line-by-line without any functions, and it all worked properly, but now I'm converting it to two large functions so that I can call it on other models more easily in the future. Below is my code (I have more code after the last line here that plots some things, but that's not relevant to my issue, since I've tested this with a bunch of print lines and learned that my issue arises when I call the interpolation function inside my process function.
import re
import numpy as np
import scipy.interpolate
# Required files and lists
filename = 'bpass_spectra.txt' # number of columns = 4
extinctionfile = 'ExtinctionLawPoints.txt' # R_V = 4.0
datalist = []
if filename == 'bpass_spectra.txt':
filetype = 4
else:
filetype = 1
if extinctionfile == 'ExtinctionLawPoints.txt':
R_V = 4.0
else:
R_V = 1.0 #to be determined
# Constants
h = 4.1357e-15 # Planck's constant [eV s]
c = float(3e8) # speed of light [m/s]
# Inputs
beta = 2.0 # power used in extinction law
R = 1.0 # star formation rate [Msun/yr]
z = 1.0 # redshift
M_gas = 1.0 # mass of gas
M_halo = 2e41 # mass of dark matter halo
# Read spectra file
f = open(filename, 'r')
rawlines = f.readlines()
met = re.findall('Z\s=\s(\d*\.\d+)', rawlines[0])
del rawlines[0]
for i in range(len(rawlines)):
newlist = rawlines[i].split(' ')
datalist.append(newlist)
# Read extinction curve data file
rawpoints = open(extinctionfile, 'r').readlines()
def interpolate(R_V, rawpoints, Elist, i):
pointslist = []
if R_V == 4.0:
for i in range(len(rawpoints)):
newlst = re.split('(?!\S)\s(?=\S)|(?!\S)\s+(?=\S)', rawpoints[i])
pointslist.append(newlst)
pointslist = pointslist[3:]
lambdalist = [float(item[0]) for item in pointslist]
k_abslist = [float(item[4]) for item in pointslist]
xvallist = [(c*h)/(lamb*1e-6) for lamb in lambdalist]
k_interp = scipy.interpolate.interp1d(xvallist, k_abslist)
return k_interp(Elist[i])
# Processing function
def process(interpolate, filetype, datalist, beta, R, z, M_gas, M_halo, met):
speclist = []
if filetype == 4:
metallicity = float(met[0])
Elist = [float(item[0]) for item in datalist]
speclambdalist = [h*c*1e9/E for E in Elist]
met1list = [float(item[1]) for item in datalist]
speclist.extend(met1list)
klist, Tlist = [None]*len(speclist), [None]*len(speclist)
if metallicity > 0.0052:
DGRlist = [50.0*np.exp(-2.21)*metallicity]*len(speclist) # dust to gas ratio
elif metallicity <= 0.0052:
DGRlist = [((50.0*metallicity)**3.15)*np.exp(-0.96)]*len(speclist)
for i in range(len(speclist)):
if Elist[i] <= 4.1357e-3: # frequencies <= 10^12 Hz
klist[i] = 0.1*(float(Elist[i])/(1000.0*h))**beta # extinction law [cm^2/g]
elif Elist[i] > 4.1357e-3: # frequencies > 10^12 Hz
klist[i] = interpolate(R_V, rawpoints, Elist, i) # interpolated function's value at Elist[i]
print "KLIST (INTERPOLATION) ELEMENTS 0 AND 1000:", klist[0], klist[1000]
return
The output from the print line is KLIST (INTERPOLATION) ELEMENTS 0 AND 1000: 52167.31734159269 52167.31734159269.
When I run my old code without functions, I print klist[0] and klist[1000] like I do here and get different values for each. In this new code, I get back two values that are the same from this line. This shouldn't be the case, so it must not be interpolating correctly inside my function (maybe it's not performing it on each point correctly in the loop?). Does anyone have any insight? It would be unreasonable to post my entire code with all the used text files here (they're very large), so I'm not expecting anyone to run it, but rather examine how I use and call my functions.
Edit: Below is the original version of my code up to the interpolation point without the functions (which works).
import re
import numpy as np
import scipy.interpolate
filename = 'bpass_spectra.txt'
extinctionfile = 'ExtinctionLawPoints.txt' # from R_V = 4.0
pointslist = []
datalist = []
speclist = []
# Constants
h = 4.1357e-15 # Planck's constant [eV s]
c = float(3e8) # speed of light [m/s]
# Read spectra file
f = open(filename, 'r')
rawspectra = f.readlines()
met = re.findall('Z\s=\s(\d*\.\d+)', rawspectra[0])
del rawspectra[0]
for i in range(len(rawspectra)):
newlist = rawspectra[i].split(' ')
datalist.append(newlist)
# Read extinction curve data file
rawpoints = open(extinctionfile, 'r').readlines()
for i in range(len(rawpoints)):
newlst = re.split('(?!\S)\s(?=\S)|(?!\S)\s+(?=\S)', rawpoints[i])
pointslist.append(newlst)
pointslist = pointslist[3:]
lambdalist = [float(item[0]) for item in pointslist]
k_abslist = [float(item[4]) for item in pointslist]
xvallist = [(c*h)/(lamb*1e-6) for lamb in lambdalist]
k_interp = scipy.interpolate.interp1d(xvallist, k_abslist)
# Create new lists
Elist = [float(item[0]) for item in datalist]
speclambdalist = [h*c*1e9/E for E in Elist]
z1list = [float(item[1]) for item in datalist]
speclist.extend(z1list)
met = met[0]
klist = [None]*len(speclist)
Loutlist = [None]*len(speclist)
Tlist = [None]*len(speclist)
# Define parameters
b = 2.0 # power used in extinction law (beta)
R = 1.0 # star formation ratw [Msun/yr]
z = 1.0 # redshift
Mgas = 1.0 # mass of gas
Mhalo = 2e41 # mass of dark matter halo
if float(met) > 0.0052:
DGRlist = [50.0*np.exp(-2.21)*float(met)]*len(speclist)
elif float(met) <= 0.0052:
DGRlist = [((50.0*float(met))**3.15)*np.exp(-0.96)]*len(speclist)
for i in range(len(speclist)):
if float(Elist[i]) <= 4.1357e-3: # frequencies <= 10^12 Hz
klist[i] = 0.1*(float(Elist[i])/(1000.0*h))**b # extinction law [cm^2/g]
elif float(Elist[i]) > 4.1357e-3: # frequencies > 10^12 Hz
klist[i] = k_interp(Elist[i]) # interpolated function's value at Elist[i]
print "KLIST (INTERPOLATION) ELEMENTS 0 AND 1000:", klist[0], klist[1000]
The output from this print line is KLIST (INTERPOLATION) ELEMENTS 0 AND 1000 7779.275435560996 58253.589270674354.
You are passing i as an argument to interpolate, and then also using i in a loop within interpolate. Once i is used within the for i in range(len(rawpoints)) loop in interpolate, it will be set to some value: len(rawpoints)-1. The interpolate function will then always return the same value k_interp(Elist[i]), which is equivalent to k_interp(Elist[len(rawpoints)-1]). You will need to either define a new variable within your loop (e.g. for not_i in range(len(rawpoints))), or use a different variable for the Elist argument. Consider the following change to interpolate:
def interpolate(R_V, rawpoints, Elist, j):
pointslist = []
if R_V == 4.0:
for i in range(len(rawpoints)):
newlst = re.split('(?!\S)\s(?=\S)|(?!\S)\s+(?=\S)', rawpoints[i])
pointslist.append(newlst)
pointslist = pointslist[3:]
lambdalist = [float(item[0]) for item in pointslist]
k_abslist = [float(item[4]) for item in pointslist]
xvallist = [(c*h)/(lamb*1e-6) for lamb in lambdalist]
k_interp = scipy.interpolate.interp1d(xvallist, k_abslist)
return k_interp(Elist[j])
There is a code:
from arcpy.sa import *
arcpy.CheckOutExtension("Spatial")
print "Creating and defining X and Y coordinates:" #set env properties
env.workspace = r"C:\Users\Desktop\data\new"
env.overwriteOutput = 1
theme = '/watersheds_3D.shp' #polygons in vector format
demName = '/demlab4' # raster of DEM
rasObject = Raster(demName)
my_extent = rasObject.extent #find a grid cell size:
my_cellsize = (rasObject.meanCellHeight + rasObject.meanCellWidth)/2
print my_cellsize
slope_deg = Slope(demName)
slope_deg.save('/demlab4_slope')
for row in arcpy.da.SearchCursor(theme, ["FID","SHAPE#"]): # inside shp file -
print ("Polygon # {}: ".format(row[0]))
for part in row[1]: # Inside the polygon
xCoords = []
yCoords = []
zCoords = []
for pnt in part:
print (" {}, {}, {} ".format(pnt.X, pnt.Y, pnt.Z))
xCoords.append(pnt.X)
yCoords.append(pnt.Y)
zCoords.append(pnt.Z)
area3D = lab4_arcpy_module.define3Darea(my_cellsize, slope_deg)
and function to calculate this:
def define3Darea(my_cellsize, slope_deg):
a = my_cellsize
slope_rad = math.pi * slope_deg / 180 # return in radiant
c = math.sqrt(a**2 + (math.tan(slope_rad)*a)**2)
area3D = a*c
print "Total 3D Area is: ", area3D, "m^2"
return area3D
it gives: TypeError: a float is required.
What can be a problem?
Also need to calculate 3D Area inside the each polygon (4). How t do it?
Not sure what Raster and Slope are doing, but this TypeError: a float is required comes when you try to some float operations on a variable that is not of float type. My guess would be that problem is with : slope_deg variable. See here :
slope_deg = Slope(demName)
slope_deg.save('/demlab4_slope')
You are declaring and doing some operation with slope_deg variable. And now in this statement:
area3D = lab4_arcpy_module.define3Darea(my_cellsize, slope_deg)
you are using same variable which is an instance of Slope class and not a float variable. Either there has to be some attribute of this(Slope) which is float or you have to use correct variable.
i found that theres is some error trying to print loss so:
print ("Model paramters:" )
print ("Weight:%f" %sess.run(W))
print ("bias:%f" %sess.run(b))
#print ("loss:%f" %(loss)) <------
make the error disapear, still don't understand well why is this.