I have been practicing python to learn how to code and I'm making python program that calculates averages from numbers which are put in by users.
Here is codes
avg_input=0.0
import sys
input_list=sys.argv[1:]
for avg in input_list :
avg_input += avg
avg_input /= len(input_list)
avg_input
and this is error messages
Traceback (most recent call last):
File "exfor3.py", line 30, in <module>
avg_input += avg
TypeError: unsupported operand type(s) for +=: 'float' and 'str'
sys.argv is a list of strings. You need to convert these to numbers before adding them. Use:
avg_input += float(avg)
It seems you are trying to add a float value to a string. You will either need to convert the variable avg into a float using the float()function or change the variable type for avg
Related
For a project i am working on i need the derivative of a function against wrt cos(theta) but when using Sympy v1.5.1 get an error message stating non-symbols cannot be used as a derivative. This was no problem up to Sympy v1.3 but later versions give this error.
>>> l=1
>>> theta = symbols('theta')
>>> eq=diff((cos(theta)**2-1)**l,cos(theta),l)
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/base/data/home/apps/s~sympy-live-
hrd/20200105t193609.423659059328302322/sympy/sympy/core/function.py", line 2446, in diff
return f.diff(*symbols, **kwargs)
File "/base/data/home/apps/s~sympy-live-
hrd/20200105t193609.423659059328302322/sympy/sympy/core/expr.py", line 3352, in diff
return Derivative(self, *symbols, **assumptions)
File "/base/data/home/apps/s~sympy-live-
hrd/20200105t193609.423659059328302322/sympy/sympy/core/function.py", line 1343, in __new__
__)))
ValueError:
Can't calculate derivative wrt cos(theta).
According to the Sympy documentation (https://docs.sympy.org/latest/modules/core.html#sympy.core.function.Derivative) i may be able to solve this using:
>>> from sympy.abc import t
>>> F = Function('F')
>>> U = f(t)
>>> V = U.diff(t)
>>> direct = F(t, U, V).diff(U)
Unfortunately i can't get this to work with this equation in Sympy v1.5.1.
Suggestions/help are much appreciated.
derivative of a function against wrt cos(theta)
Did this really work before in sympy? i.e. you were able to differentiate w.r.t cos(theta)? This should not work as differentiation is w.r.t to a symbol. For example Maple also gives error
diff( 1+cos(theta)^2,cos(theta))
Error, invalid input: diff received cos(theta), which is not valid for its 2nd argument
Strange that Mathematica does allow this. But I think this is not good behavior. May be that is why sympy no longer allows it.
But you can do this in sympy
from sympy import *
theta,x = symbols('theta x')
eq = (cos(theta)**2-1)**2
result = diff( eq.subs(cos(theta),x) ,x)
result.subs(x,cos(theta))
Which gives
4*(cos(theta)**2 - 1)*cos(theta)
In Mathematica (which allows this)
D[(Cos[theta]^2 - 1)^2, Cos[theta]]
gives
4 Cos[theta] (-1 + Cos[theta]^2)
Perhaps SymPy over-corrected. If the expression has a single generator matching the function of interest then the substitution-equivalent differentiation could take place. Cases which shouldn't (probably) be allowed are (x + 1).diff(cos(x)), sin(x).diff(cos(x)), etc... But (cos(x)**2 - 1).diff(cos(x)) should (probably) be ok. As #Nasser has indicated, a simple substitution/differentiation/backsubstitution will work.
I have a pyomo ConcreteModel() which I solve repeatedly within another stochastic optimization process whereas one or more parameters are changed on the model.
The basic process can be described as follows:
# model is created as a pyomo.ConcreteModel()
for i in range(0, 10):
# change some parameter on the model
opt = SolverFactory('gurobi', solver_io='lp')
# how can I check here if the changed model/lp-file is valid?
results = opt.solve(model)
Now I get an error for some cases where the model and LP file (see gist) seems to contain NaN values:
ERROR: Solver (gurobi) returned non-zero return code (1)
ERROR: Solver log: Academic license - for non-commercial use only Error
reading LP format file /tmp/tmp8agg07az.pyomo.lp at line 1453 Unrecognized
constraint RHS or sense Neighboring tokens: " <= nan c_u_x1371_: +1 x434
<= nan "
Unable to read file Traceback (most recent call last):
File "<stdin>", line 5, in <module> File
"/home/cord/.anaconda3/lib/python3.6/site-
packages/pyomo/solvers/plugins/solvers/GUROBI_RUN.py", line 61, in
gurobi_run
model = read(model_file)
File "gurobi.pxi", line 2652, in gurobipy.read
(../../src/python/gurobipy.c:127968) File "gurobi.pxi", line 72, in
gurobipy.gurobi.read (../../src/python/gurobipy.c:125753)
gurobipy.GurobiError: Unable to read model Freed default Gurobi
environment
Of course, the first idea would be to prevent setting these NaN-values. But I don't know why they occur anyhow and want to figure out when the model breaks due to a wrong structure caused by NaNs.
I know that I can catch the solver status and termination criterion from the SolverFactory() object. But the error obviously occurs somewhere before the solving process due to the invalid changed values.
How can I can catch these kinds of errors for different solvers before solving i. e. check if the model/lp-file is valid before applying a solver? Is there some method e.g. check_model() which delivers True or False if the model is (not) valid or something similar?
Thanks in advance!
If you know that the error is taking place when the parameter values are being changed, then you could test to see whether the sum of all relevant parameter values is a valid number. After all, NaN + 3 = NaN.
Since you are getting NaN, I am going to guess that you are importing parameter values using Pandas from an Excel spreadsheet? There is a way to convert all the NaNs to a default number.
Code example for parameter check:
>>> from pyomo.environ import *
>>> m = ConcreteModel()
>>> m.p1 = Param(initialize=1)
>>> m.p2 = Param(initialize=2)
>>> for p in m.component_data_objects(ctype=Param):
... print(p.name)
...
p1
p2
>>> import numpy
>>> m.p3 = Param(initialize=numpy.nan)
>>> import math
>>> math.isnan(value(sum(m.component_data_objects(ctype=Param))))
True
Indexed, Mutable Parameters:
>>> from pyomo.environ import *
>>> m = ConcreteModel()
>>> m.i = RangeSet(2)
>>> m.p = Param(m.i, initialize={1: 1, 2:2}, mutable=True)
>>> import math
>>> import numpy
>>> math.isnan(value(sum(m.component_data_objects(ctype=Param))))
False
>>> m.p[1] = numpy.nan
>>> math.isnan(value(sum(m.component_data_objects(ctype=Param))))
True
I have a code that opens a file, calculates the median value and writes that value to a separate file. Some of the files maybe empty so I wrote the following loop to check it the file is empty and if so skip it, increment the count and go back to the loop. It does what is expected for the first empty file it finds ,but not the second. The loop is below
t = 15.2
while t>=11.4:
if os.stat(r'C:\Users\Khary\Documents\bin%.2f.txt'%t ).st_size > 0:
print("All good")
F= r'C:\Users\Documents\bin%.2f.txt'%t
print(t)
F= np.loadtxt(F,skiprows=0)
LogMass = F[:,0]
LogRed = F[:,1]
value = np.median(LogMass)
filesave(*find_nearest(LogMass,LogRed))
t -=0.2
else:
t -=0.2
print("empty file")
The output is as follows
All good
15.2
All good
15.0
All good
14.8
All good
14.600000000000001
All good
14.400000000000002
All good
14.200000000000003
All good
14.000000000000004
All good
13.800000000000004
All good
13.600000000000005
All good
13.400000000000006
empty file
All good
13.000000000000007
Traceback (most recent call last):
File "C:\Users\Documents\Codes\Calculate Bin Median.py", line 35, in <module>
LogMass = F[:,0]
IndexError: too many indices
A second issue is that t somehow goes from one decimal place to 15 and the last place seems to incrementing whats with that?
Thanks for any and all help
EDIT
The error IndexError: too many indices only seems to apply to files with only one line example...
12.9982324 0.004321374
If I add a second line I no longer get the error can someone explain why this is? Thanks
EDIT
I tried a little experiment and it seems numpy does not like extracting a column if the array only has one row.
In [8]: x = np.array([1,3])
In [9]: y=x[:,0]
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-9-50e27cf81d21> in <module>()
----> 1 y=x[:,0]
IndexError: too many indices
In [10]: y=x[:,0].shape
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-10-e8108cf30e9a> in <module>()
----> 1 y=x[:,0].shape
IndexError: too many indices
In [11]:
You should be using try/except blocks. Something like:
t = 15.2
while t >= 11.4:
F= r'C:\Users\Documents\bin%.2f.txt'%t
try:
F = np.loadtxt(F,skiprows=0)
LogMass = F[:,0]
LogRed = F[:,1]
value = np.median(LogMass)
filesave(*find_nearest(LogMass,LogRed))
except IndexError:
print("bad file: {}".format(F))
else:
print("file worked!")
finally:
t -=0.2
Please refer to the official tutorial for more details about exception handling.
The issue with the last digit is due to how floats work they can not represent base10 numbers exactly. This can lead to fun things like:
In [13]: .3 * 3 - .9
Out[13]: -1.1102230246251565e-16
To deal with the one line file case, add the ndmin parameter to np.loadtxt (review its doc):
np.loadtxt('test.npy',ndmin=2)
# array([[ 1., 2.]])
With the help of a user named ajcr, found the problem was that ndim=2 should have been used in numpy.loadtxt() to insure that the array always 2 has dimensions.
Python uses indentation to define if while and for blocks.
It doesn't look like your if else statement is fully indented from the while.
I usually use a full 'tab' keyboard key to indent instead of 'spaces'
I am writing a program to print the sum of multiple of 3 or 5 less than 1000. I am using an arithmetic progression to do it. My code is:
def multiple(x,y):
a=(1000-(1000%x) - x)/x +1
b=(995-y)/y +1
c=(1000-(1000%x*y)-x*y)/x*y +1
Sa=int(a/2(2*x+(a-1)*a))
Sb=b/2(2*y+(b-1)*b)
Sc=c/2(2*x*y+(c-1)*x*y)
Sd=Sa+Sb-Sc
print Sd
When I call the function I get the error:
Traceback (most recent call last):
File "<interactive input>", line 1, in <module>
File "C:\Python27\swampy-2.1.7\MULTIPLE.py", line 23, in multiple
Sa=int(a/2(2*x+(a-1)*a))
TypeError: 'int' object is not callable
Please point out the mistake in my code. Thanks.
P.S. Please forgive my "art" of question asking. I am new to Python and StackOverflow, so please bear with me. Thanks!
In Sa=int(a/2(2*x+(a-1)*a)) you forgot a * to multiply between a/2 and (2*x+(a-1)*a) You should have Sa=int(a/2*(2*x+(a-1)*a)).
Also, the same on Sb and Sc.
Using the python 2.7 shell on osx lion. The .csv file has 12 columns by 892 rows.
import csv as csv
import numpy as np
# Open up csv file into a Python object
csv_file_object = csv.reader(open('/Users/scdavis6/Documents/Kaggle/train.csv', 'rb'))
header = csv_file_object.next()
data=[]
for row in csv_file_object:
data.append(row)
data = np.array(data)
# Convert to float for numerical calculations
number_passengers = np.size(data[0::,0].astype(np.float))
And this is the error I get:
Traceback (most recent call last):
File "pyshell#5>", line 1, in <module>
number_passengers = np.size(data[0::,0].astype(np.float))
TypeError: list indices must be integers, not tuple
What am I doing wrong.
Don't use csv to read the data into a NumPy array. Use numpy.genfromtxt; using dtype=None will cause genfromtxt to make an intelligent guess at the dtypes for you. By doing it this way you won't have to manually convert strings to floats.
data[0::, 0] just gives you the first column of data.
data[:, 0] would give you the same result.
The error message
TypeError: list indices must be integers, not tuple
suggests that for some reason your data variable might be holding a list rather than a ndarray. For example, the same Exception can produced like this:
In [73]: data = [1,2,3]
In [74]: data[1,2]
TypeError: list indices must be integers, not tuple
I don't know why that is happening, but if you post a sample of your CSV we should be able to help fix that.
Using np.genfromtxt, your current code could be simplified to:
import numpy as np
filename = '/Users/scdavis6/Documents/Kaggle/train.csv'
data = np.genfromtxt(filename, delimiter=',', skiprows=1, dtype=None)
number_passengers = np.size(data, axis=0)