Hello I am using the following code to try to use scipy's interpolation.
I am getting the following error:
Traceback (most recent call last):
File "C:\Program Files (x86)\JetBrains\PyCharm Community Edition 3.4.1\helpers\pydev\pydevd.py", line 1733, in <module>
debugger.run(setup['file'], None, None)
File "C:\Program Files (x86)\JetBrains\PyCharm Community Edition 3.4.1\helpers\pydev\pydevd.py", line 1226, in run
pydev_imports.execfile(file, globals, locals) # execute the script
File "C:/Users/%user%/PycharmProjects/price_options_multithread/curve_interpolation.py", line 81, in <module>
tck = interpolate.PiecewisePolynomial(moneyness_,vols_, orders = 2)
File "C:\Users\%user%\Miniconda\lib\site-packages\scipy\interpolate\polyint.py", line 731, in __init__
self._set_yi(yi0)
File "C:\Users\%user%\Miniconda\lib\site-packages\scipy\interpolate\polyint.py", line 131, in _set_yi
self._y_axis = (axis % yi.ndim)
ZeroDivisionError: integer division or modulo by zero
With the code to call the interpolation reading as:
tck = interpolate.PiecewisePolynomial(X,Y, orders = 2)
Where Y is:
[0.2015, 0.3469, 0.2985, 0.2113, 0.19989999999999997, 0.4262, 0.21355000000000002, 0.22260000000000002, 0.21194999999999997, 0.1846, 0.2627, 0.2058, 0.2276, 0.21715, 0.23099999999999998, 0.20235, 0.2165, 0.21165, 0.3836, 0.19594999999999999, 0.20450000000000002, 0.20375, 0.20145000000000002, 0.23525000000000001, 0.2242, 0.22645, 0.27455, 0.22425, 0.2232, 0.1977, 0.19635000000000002, 0.21995, 0.30325, 0.22565]
And X is:
[0.91157701638785, 0.586013796249332, 0.820419314749065, 0.937622073998931, 1.17202759249866, 0.520901152221628, 0.872509429971228, 0.989712189221094, 1.0548248332488, 1.02877977563772, 0.807396785943524, 0.92459954519339, 1.30225288055407, 1.01575724683218, 0.859486901165687, 0.976689660415553, 1.00273471802663, 1.23714023652637, 0.455788508193925, 1.10691494847096, 0.846464372360146, 0.963667131610013, 1.09389241966542, 0.781351728332443, 0.898554487582309, 1.08086989085988, 0.716239084304739, 0.833441843554605, 0.950644602804472, 1.04180230444326, 1.1199374772765, 1.06784736205434, 0.651126440277035, 0.885531958776768]
type(X) and type(Y) give: <type 'list'>
If I do a polynomial interpolation in excel it looks like the following:
Any pointers on what is generating this error would be super helpful.
Thank you!
The parallel fit using python would be best accomplished using numpy polyfit.
Hope this helps someone else in the future.
This is unrelated to the source error - but I will leave to hopefully help someone else.
Related
I attempted to solve this differential equation by adapting the code found here.
import sympy as sp
t = sp.symbols('t', real=True)
x = sp.Function('x', real=True)
diffeq = sp.Eq(x(t).diff(t), sp.sqrt(1 - x(t)**2))
res = sp.dsolve(diffeq, ics={x(0): 0})
The math.se post suggests that a piecewise function satisfies the equation, but perhaps my code does not add all the necessary assumptions. I get the following traceback:
Traceback (most recent call last):
File "/usr/lib/python3.10/idlelib/run.py", line 578, in runcode
exec(code, self.locals)
File "/home/galen/testing.py", line 7, in <module>
res = sp.dsolve(diffeq, ics={x(0): 0})
File "/usr/lib/python3/dist-packages/sympy/solvers/ode/ode.py", line 639, in dsolve
return _helper_simplify(eq, hint, hints, simplify, ics=ics)
File "/usr/lib/python3/dist-packages/sympy/solvers/ode/ode.py", line 694, in _helper_simplify
solved_constants = solve_ics([rv], [r['func']], cons(rv), ics)
File "/usr/lib/python3/dist-packages/sympy/solvers/ode/ode.py", line 808, in solve_ics
raise NotImplementedError("Initial conditions produced too many solutions for constants")
NotImplementedError: Initial conditions produced too many solutions for constants
How do I change my code to correctly solve the ODE?
First, the solver treats the equation as algebraic, that is, it solves x'²=1-x², and it does not get the piecewise solution that are possible, only the fully analytical.
For some reason returning a tuple of possible solutions is not implemented, you can do that manually, see https://stackoverflow.com/a/46979535/3088138
res = sp.dsolve(diffeq)
sol = res.rhs
print([ sol.subs(coeffs) for coeffs in sp.solve(sol.subs(t,0), dict=True) ])
which returns [sin(t), -sin(t)]. The second is not compatible with the ODE in its given form.
I am new to numpy and use a lab instrument that returns data as a numpy array, which I am in turn storing as part of a list with the structure
raw_data = [[int, numpy.ndarray, int, int]]
I want to grab the biggest number from the second column of that numpy array (many rows and 2 columns), so I wrote this code:
…code that collects data and writes it to the raw_data list…
max_ccounts = raw_data[1][1].max(axis=0)[1]
print(max_ccounts)
But I constantly get the following error:
File "/usr/local/lib/python3.9/runpy.py", line 197, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/usr/local/lib/python3.9/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/home/kimfook-lee/Desktop/automated_TPI.py", line 75, in <module>
max_ccounts = raw_data[1][1].max(axis=0)[1]
KeyError: '__builtins__'
I know that raw_data[1][1] points to a numpy array because I printed it. I wasn’t sure if I was accessing the list elements properly so I tried to do it on my own in the terminal, using the exact same numpy array that was printed:
>>> raw_data1 = np.array([[7200, 876],[8000,840271]])
>>> raw_data = [[0,raw_data1,10,10]]
>>> max_ccounts = raw_data[0][1].max(axis=0)[1]
>>> print(max_ccounts)
840271
Whenever I search for KeyError, I only find things related to dictionaries, and can’t find any information related to builtins or numpy. What is causing this error and how can it be resolved?
EDIT:
Following the advice of ForceBru I went ahead and added some temp variables and printed them to find the error.
…code that collects data and writes it to the raw_data list…
tmp1 = raw_data[1]
print('tmp1:', tmp1)
tmp2 = tmp1[1]
print('tmp2', tmp2)
tmp3=tmp2.max(axis=0)
print('tmp3', tmp3)
tmp4 = tmp3[1]
print('tmp4', tmp4)
Now there is the same error with a different traceback.
KeyError: '__builtins__'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/usr/local/lib/python3.9/runpy.py", line 197, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/usr/local/lib/python3.9/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/home/kimfook-lee/Desktop/automated_TPI.py", line 75, in <module>
print('tmp1:', tmp1)
RuntimeError: Unable to configure default ndarray.__repr__
cant figure out the question , but to me
import numpy as np
raw_data1 = np.array([[7200, 876],[8000,840271]])
raw_data = [[0,raw_data1,10,10]]
max_ccounts = raw_data[0][1].max(axis=0)[1]
print(max_ccounts)
output
840271
I'm learning how to use Numba to speed up functions with jit and vectorize. I didn't have any issues with the jit version of this code, but I am getting an index error with vectorize. I suspect this question's answer is getting at the right idea that there is a type error, but I'm not confident on which direction to take on changing the indexing. Included below is the function I've been playing around with, which outputs the Fibonacci numbers up to a chosen index of the sequence. What is going wrong with the indexing, and how I can correct my code to account for it?
from numba import vectorize
import numpy as np
from timeit import timeit
#vectorize
def fib(n):
'''
Adjusted from:
https://lectures.quantecon.org/py/numba.html
https://en.wikipedia.org/wiki/Fibonacci_number
https://www.geeksforgeeks.org/program-for-nth-fibonacci-number/
'''
if n == 1:
return np.ones(1)
elif n > 1:
x = np.empty(n)
x[0] = 1
x[1] = 1
for i in range(2,n):
x[i] = x[i-1] + x[i-2]
return x
else:
print('WARNING: Check validity of input.')
print(timeit('fib(10)', globals={'fib':fib}))
Which results in the following error output.
Traceback (most recent call last):
File "/usr/local/lib/python3.6/dist-packages/llvmlite/ir/instructions.py", line 619, in __init__
typ = typ.elements[i]
AttributeError: 'PointerType' object has no attribute 'elements'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/galen/Projects/myjekyllblog/test_code/quantecon_2.py", line 27, in <module>
print(timeit('fib(10)', globals={'fib':fib}))
File "/usr/lib/python3.6/timeit.py", line 233, in timeit
return Timer(stmt, setup, timer, globals).timeit(number)
File "/usr/lib/python3.6/timeit.py", line 178, in timeit
timing = self.inner(it, self.timer)
File "<timeit-src>", line 6, in inner
File "/usr/local/lib/python3.6/dist-packages/numba/npyufunc/dufunc.py", line 166, in _compile_for_args
return self._compile_for_argtys(tuple(argtys))
File "/usr/local/lib/python3.6/dist-packages/numba/npyufunc/dufunc.py", line 188, in _compile_for_argtys
cres, actual_sig)
File "/usr/local/lib/python3.6/dist-packages/numba/npyufunc/ufuncbuilder.py", line 157, in _build_element_wise_ufunc_wrapper
cres.objectmode, cres)
File "/usr/local/lib/python3.6/dist-packages/numba/npyufunc/wrappers.py", line 220, in build_ufunc_wrapper
env=envptr)
File "/usr/local/lib/python3.6/dist-packages/numba/npyufunc/wrappers.py", line 130, in build_fast_loop_body
env=env)
File "/usr/local/lib/python3.6/dist-packages/numba/npyufunc/wrappers.py", line 23, in _build_ufunc_loop_body
store(retval)
File "/usr/local/lib/python3.6/dist-packages/numba/npyufunc/wrappers.py", line 126, in store
out.store_aligned(retval, ind)
File "/usr/local/lib/python3.6/dist-packages/numba/npyufunc/wrappers.py", line 276, in store_aligned
self.context.pack_value(self.builder, self.fe_type, value, ptr)
File "/usr/local/lib/python3.6/dist-packages/numba/targets/base.py", line 482, in pack_value
dataval = self.data_model_manager[ty].as_data(builder, value)
File "/usr/local/lib/python3.6/dist-packages/numba/datamodel/models.py", line 558, in as_data
elems = self._as("as_data", builder, value)
File "/usr/local/lib/python3.6/dist-packages/numba/datamodel/models.py", line 530, in _as
self.get(builder, value, i)))
File "/usr/local/lib/python3.6/dist-packages/numba/datamodel/models.py", line 558, in as_data
elems = self._as("as_data", builder, value)
File "/usr/local/lib/python3.6/dist-packages/numba/datamodel/models.py", line 530, in _as
self.get(builder, value, i)))
File "/usr/local/lib/python3.6/dist-packages/numba/datamodel/models.py", line 624, in get
name="extracted." + self._fields[pos])
File "/usr/local/lib/python3.6/dist-packages/llvmlite/ir/builder.py", line 911, in extract_value
instr = instructions.ExtractValue(self.block, agg, idx, name=name)
File "/usr/local/lib/python3.6/dist-packages/llvmlite/ir/instructions.py", line 622, in __init__
% (list(indices), agg.type))
TypeError: Can't index at [0] in i8*
The error is because you are trying to vectorize a function which you can say is essentially not vectorizable. I think you are confusing the functionality of how #jit and #vectorize work. In order to speed up your functions, you use #jit, while #vectorize is used to create numpy universal functions. See the official documentation here :
Using vectorize(), you write your function as operating over input
scalars, rather than arrays. Numba will generate the surrounding loop
(or kernel) allowing efficient iteration over the actual inputs.
So it is essentially not possible to create a numpy universal function which has the same functionality as your fibonacci function. Here is the link for official documentation on universal functions if you are interested.
So in order to use #vectorize, you need to create a function which can be essentially used as a numpy universal function. For your purpose of speeding up your code, you simply need to use #jit.
I have written this code for vector addition using numba.SmartArrays. I am using this numba.SmartArrays for the first time. I am not sure how to use that.
This code is not working and it is throwing errors.
import numpy as np
from numba import SmartArray,cuda, jit, uint32
li1=np.uint32([1,2,3,4])
li=np.uint32([1,2,3,4])
b=SmartArray(li,where="host",copy=True)
a=SmartArray(li1,where="host",copy=True)
c=np.uint32([1,1,1,1])
print type(li)
print type(a)
#cuda.jit('void(uint32[:],uint32[:],uint32[:])',type="gpu")
def additionG(c,a,b):
idx=cuda.threadIdx.x+cuda.blockDim.x*cuda.blockIdx.x
if idx< len(a):
a[idx]=c[idx]+b[idx]
dA=cuda.to_device(a)
dB=cuda.to_device(b)
dC=cuda.to_device(c)
additionG[1, 128](c,a,b)
print a.__array__()
Errors:
<type 'numpy.ndarray'>
<class 'numba.smartarray.SmartArray'>
Traceback (most recent call last):
File "C:\Users\hp-pc\My Documents\LiClipse Workspace\cuda\blowfishgpu_smart_arrays.py", line 20, in <module>
dA=cuda.to_device(a)
File "C:\Anaconda\lib\site-packages\numba\cuda\cudadrv\devices.py", line 257, in _require_cuda_context
return fn(*args, **kws)
File "C:\Anaconda\lib\site-packages\numba\cuda\api.py", line 55, in to_device
to, new = devicearray.auto_device(obj, stream=stream, copy=copy)
File "C:\Anaconda\lib\site-packages\numba\cuda\cudadrv\devicearray.py", line 403, in auto_device
devobj.copy_to_device(obj, stream=stream)
File "C:\Anaconda\lib\site-packages\numba\cuda\cudadrv\devicearray.py", line 148, in copy_to_device
sz = min(_driver.host_memory_size(ary), self.alloc_size)
File "C:\Anaconda\lib\site-packages\numba\cuda\cudadrv\driver.py", line 1348, in host_memory_size
s, e = host_memory_extents(obj)
File "C:\Anaconda\lib\site-packages\numba\cuda\cudadrv\driver.py", line 1333, in host_memory_extents
return mviewbuf.memoryview_get_extents(obj)
TypeError: expected a readable buffer object
Its been a while since I posted this question. Still posting the answer so that someone may find it helpful in future.
import numpy as np
from numba import SmartArray,cuda, jit, uint32,autojit
li1=np.uint32([6,7,8,9])
li=np.uint32([1,2,3,4])
a=SmartArray(li1,where='host',copy=True)
b=SmartArray(li,where="host",copy=True)
c=np.uint32([1,1,1,1])
def additionG(a,c):
idx=cuda.threadIdx.x+cuda.blockDim.x*cuda.blockIdx.x
if idx < len(c):
a[idx]=a[idx]+c[idx]
cuda.syncthreads()
bpg=1
tpb=128
dC=cuda.to_device(c)
cfunc = cuda.jit()(additionG)
cfunc[bpg, tpb](a,dC)
print a.__array__()
It looks to me like cuda.to_device doesn't handle smart arrays, which would sort of make sense, because smart arrays are supposed to do away with explicit copy management.
If my reading of the documentation is correct (I have never tried SmartArray before), you should just be able to change this
dA=cuda.to_device(a)
dB=cuda.to_device(b)
dC=cuda.to_device(c)
additionG[1, 128](c,a,b)
to just
dC=cuda.to_device(c)
additionG[1, 128](dC,a.gpu(),b.gpu())
The .gpu() method should return a GPU resident object that the kernel can understand and access.
I'm using numba to speed up my code which is working fine without numba. But after using #jit, it crashes with this error:
Traceback (most recent call last):
File "C:\work_asaaki\code\gbc_classifier_train_7.py", line 54, in <module>
gentlebooster.train(X_train, y_train, boosting_rounds)
File "C:\work_asaaki\code\gentleboost_c_class_jit_v7_nolimit.py", line 298, in train
self.g_per_round, self.g = train_function(X, y, H)
File "C:\Anaconda\lib\site-packages\numba\dispatcher.py", line 152, in _compile_for_args
return self.jit(sig)
File "C:\Anaconda\lib\site-packages\numba\dispatcher.py", line 143, in jit
return self.compile(sig, **kws)
File "C:\Anaconda\lib\site-packages\numba\dispatcher.py", line 250, in compile
locals=self.locals)
File "C:\Anaconda\lib\site-packages\numba\compiler.py", line 183, in compile_bytecode
flags.no_compile)
File "C:\Anaconda\lib\site-packages\numba\compiler.py", line 323, in native_lowering_stage
lower.lower()
File "C:\Anaconda\lib\site-packages\numba\lowering.py", line 219, in lower
self.lower_block(block)
File "C:\Anaconda\lib\site-packages\numba\lowering.py", line 254, in lower_block
raise LoweringError(msg, inst.loc)
numba.lowering.LoweringError: Internal error:
NotImplementedError: ('cast', <llvm.core.Instruction object at 0x000000001801D320>, slice3_type, int64)
File "gentleboost_c_class_jit_v7_nolimit.py", line 103
Line 103 is below, in a loop:
weights = np.empty([n,m])
for curr_n in range(n):
weights[curr_n,:] = 1.0/(n) # this is line 103
where n is a constant already defined somewhere above in my code.
How can I remove the error? What "lowering" is going on? I'm using Anaconda 2.0.1 with Numba 0.13.x and Numpy 1.8.x on a 64-bit machine.
Based on this: https://gist.github.com/cc7768/bc5b8b7b9052708f0c0a,
I figured out what to do to avoid the issue. Instead of using the colon : to refer to any row/column, I just opened up the loop into two loops to explicitly refer to the indices in each dimension of the array:
weights = np.empty([n,m])
for curr_n in range(n):
for curr_m in range (m):
weights[curr_n,curr_m] = 1.0/(n)
There were other instances in my code after this where I used the colon, but they didn't cause errors further down, not sure why.