I am translating code from c ++ to python, I met with such a data type:
bool b[110];
How can this be translated into Python? I tried to do something, but in my opinion something is wrong here.
b = [False] * (110)
I don't really see a difference between bool b[110]; and b = [False] * 110.
Besides that bool b[110]; only does "Garbage initialization" and python doesn't have anything even remotely similar to that.
Related
I need to give my variable a value from SQL, this part of the code it's where the compiler stops, aparently it's maybe a convertion problem. In my database that column is designed as float.
float presion = safe_cast<float>(data[4]);
Here is the code, every value gets to his variable until it reaches the floats
This is my database, the value of 1.5 should go to the variable
safe_cast is not a standard c++ type casting.
If you change your code to:
float presion = dynamic_cast<double>(data[4]);
it should work.
I solved the issue, I'm gonna post it here anyway for other people with the same problem.
Just change, float for double:
float presion = safe_cast<float>(data[4]);
For :
float presion = safe_cast<double>(data[4]);
Hi i'm using c python api.
I want to extract function object's source code.
I want pure python code (like def func: ... ) But if it is hard then, i want to
get py byte codes at least.
Here is my c++ code that i get PyCodeObject from PyFunctionObject.
//pObject is PyFunctionObject which is python standard lib's function.
PyFunctionObject* pFunctionObject = (PyFunctionObject*)pObject;
PyCodeObject* codeObject = (PyCodeObject*)pFunctionObject->func_code;
PyObject* strObject = codeObject->co_code; //get code from code object
char * sourceCode = PyString_AsString(strObject); //convert to string
But sourceCode variable(char*) always show only 1 byte.
How should i gonna get this?
There is a lot of ways to do this in python code side, like just use 'dis' or 'inspect' module.
But i want to do this by c python api.
P.S
I guess that the PyCodeObject's co_code member is an byte array.
I used visual studio debug memory view and saw co_code member's adjoined memory byte but it seems like a byte code array(just maybe).
Firstly, PyCodeObject->co_code is the generated byte code, not pure Python source code.
In Python, we could use inspect.getsource to get pure Python source code. And in C, you could also use it via PyObject_CallMethod
I am new to Python, coming from MATLAB, and long ago from C. I have written a script in MATLAB which simulates sediment transport in rivers as a Markov Process. The code randomly places circles of a random diameter within a rectangular area of a specified dimension. The circles are non-uniform is size, drawn randomly from a specified range of sizes. I do not know how many times I will step through the circle placement operation so I use a while loop to complete the process. In an attempt to be more community oriented, I am translating the MATLAB script to Python. I used the online tool OMPC to get started, and have been working through it manually from the auto-translated version (was not that helpful, which is not surprising). To debug the code as I go, I use the
MATLAB generated results to generally compare and contrast against results in Python. It seems clear to me that I have declared variables in a way that introduces problems as calculations proceed in the script. Here are two examples of consistent problems between different instances of code execution. First, the code generated what I think are arrays within arrays because the script is returning results which look like:
array([[ True]
[False]], dtype=bool)
This result was generated for the following code snippet at the overlap_logix operation:
CenterCoord_Array = np.asarray(CenterCoordinates)
Diameter_Array = np.asarray(Diameter)
dist_check = ((CenterCoord_Array[:,0] - x_Center) ** 2 + (CenterCoord_Array[:,1] - y_Center) ** 2) ** 0.5
radius_check = (Diameter_Array / 2) + radius
radius_check_update = np.reshape(radius_check,(len(radius_check),1))
radius_overlap = (radius_check_update >= dist_check)
# Now actually check the overalp condition.
if np.sum([radius_overlap]) == 0:
# The new circle does not overlap so proceed.
newCircle_Found = 1
debug_value = 2
elif np.sum([radius_overlap]) == 1:
# The new circle overlaps with one other circle
overlap = np.arange(0,len(radius_overlap), dtype=int)
overlap_update = np.reshape(overlap,(len(overlap),1))
overlap_logix = (radius_overlap == 1)
idx_true = overlap_update[overlap_logix]
radius = dist_check(idx_true,1) - (Diameter(idx_true,1) / 2)
A similar result for the same run was produced for variables:
radius_check_update
radius_overlap
overlap_update
Here is the same code snippet for the working MATLAB version (as requested):
distcheck = ((Circles.CenterCoordinates(1,:)-x_Center).^2 + (Circles.CenterCoordinates(2,:)-y_Center).^2).^0.5;
radius_check = (Circles.Diameter ./ 2) + radius;
radius_overlap = (radius_check >= distcheck);
% Now actually check the overalp condition.
if sum(radius_overlap) == 0
% The new circle does not overlap so proceed.
newCircle_Found = 1;
debug_value = 2;
elseif sum(radius_overlap) == 1
% The new circle overlaps with one other circle
temp = 1:size(radius_overlap,2);
idx_true = temp(radius_overlap == 1);
radius = distcheck(1,idx_true) - (Circles.Diameter(1,idx_true)/2);
In the Python version I have created arrays from lists to more easily operate on the contents (the first two lines of the code snippet). The array within array result and creating arrays to access data suggests to me that I have incorrectly declared variable types, but I am not sure. Furthermore, some variables have a size, for example, (2L,) (the numerical dimension will change as circles are placed) where there is no second dimension. This produces obvious problems when I try to use the array in an operation with another array with a size (2L,1L). Because of these problems I started reshaping arrays, and then I stopped because I decided these were hacks because I had declared one, or more than one variable incorrectly. Second, for the same run I encountered the following error:
TypeError: 'numpy.ndarray' object is not callable
for the operation:
radius = dist_check(idx_true,1) - (Diameter(idx_true,1) / 2)
which occurs at the bottom of the above code snippet. I have posted the entire script at the following link because it is probably more useful to execute the script for oneself:
https://github.com/smchartrand/MarkovProcess_Bedload
I have set-up the code to run with some initial parameter values so decisions do not need to be made; these parameter values produce the expected results in the MATLAB-based script, which look something like this when plotted:
So, I seem to specifically be having issues with operations on lines 151-165, depending on the test value np.sum([radius_overlap]) and I think it is because I incorrectly declared variable types, but I am really not sure. I can say with confidence that the Python version and the MATLAB version are consistent in output through the first step of the while loop, and code line 127 which is entering the second step of the while loop. Below this point in the code the above documented issues eventually cause the script to crash. Sometimes the script executes to 15% complete, and sometimes it does not make it to 5% - this is due to the random nature of circle placement. I am preparing the code in the Spyder (Python 2.7) IDE and will share the working code publicly as a part of my research. I would greatly appreciate any help that can be offered to identify my mistakes and misapplications of python coding practice.
I believe I have answered my own question, and maybe it will be of use for someone down the road. The main sources of instruction for me can be found at the following three web pages:
Stackoverflow Question 176011
SciPy FAQ
SciPy NumPy for Matlab users
The third web page was very helpful for me coming from MATLAB. Here is the modified and working python code snippet which relates to the original snippet provided above:
dist_check = ((CenterCoordinates[0,:] - x_Center) ** 2 + (CenterCoordinates[1,:] - y_Center) ** 2) ** 0.5
radius_check = (Diameter / 2) + radius
radius_overlap = (radius_check >= dist_check)
# Now actually check the overalp condition.
if np.sum([radius_overlap]) == 0:
# The new circle does not overlap so proceed.
newCircle_Found = 1
debug_value = 2
elif np.sum([radius_overlap]) == 1:
# The new circle overlaps with one other circle
overlap = np.arange(0,len(radius_overlap[0]), dtype=int).reshape(1, len(radius_overlap[0]))
overlap_logix = (radius_overlap == 1)
idx_true = overlap[overlap_logix]
radius = dist_check[idx_true] - (Diameter[0,idx_true] / 2)
In the end it was clear to me that it was more straightforward for this example to use numpy arrays vs. lists to store results for each iteration of filling the rectangular area. For the corrected code snippet this means I initialized the variables:
CenterCoordinates, and
Diameter
as numpy arrays whereas I initialized them as lists in the posted question. This made a few mathematical operations more straightforward. I was also incorrectly indexing into variables with parentheses () as opposed to the correct method using brackets []. Here is an example of a correction I made which helped the code execute as envisioned:
Incorrect: radius = dist_check(idx_true,1) - (Diameter(idx_true,1) / 2)
Correct: radius = dist_check[idx_true] - (Diameter[0,idx_true] / 2)
This example also shows that I had issues with array dimensions which I corrected variable by variable. I am still not sure if my working code is the most pythonic or most efficient way to fill a rectangular area in a random fashion, but I have tested it about 100 times with success. The revised and working code can be downloaded here:
Working Python Script to Randomly Fill Rectangular Area with Circles
Here is an image of a final results for a successful run of the working code:
The main lessons for me were (1) numpy arrays are more efficient for repetitive numerical calculations, and (2) dimensionality of arrays which I created were not always what I expected them to be and care must be practiced when establishing arrays. Thanks to those who looked at my question and asked for clarification.
I am using python wrappings for VTK. I want my script to let the user pick three arbitrary points and return a triangle with its normal information. In VTK VTK Triangle reference there is vtkTriangle::ComputeNormal (double v1[3], double v2[3],double v3[3],double n[3]).
I checked Cxx implementation examples about vtkTriangle but, I don't understand how to implement this in Python. Does n[3] stand for the normal? If so what it should be as an input parameter?
#g.stevo I understand that. However, when I give a random value the method ComputeNormal returns None. To be more clear you can find the snippet of related code below:
`p0 = trianglePolyData.GetPoints().GetPoint(0)
p1 = trianglePolyData.GetPoints().GetPoint(1)
p2 = trianglePolyData.GetPoints().GetPoint(2)
print vtk.vtkTriangle().TriangleArea(p0,p1,p2)
n=[0.0,0.0,0.0]
print vtk.vtkTriangle().ComputeNormal(p0,p1,p2,n)`
Your code is working. The result you are looking for is in the array n. The function ComputeNormal returns void, according to the documentation.
Try this:
n=[0.0,0.0,0.0]
vtk.vtkTriangle().ComputeNormal(p0,p1,p2,n)
print n
I have a C++ program that keeps generating data. I have a python class that process these data. I want to use this python class to process the data: when each time a data point is generated, I can use this python script to process the data. But this python script must be "stateful", i.e. it should be able to remember what it did before this data point.
One super basic example is, my C++ program just generates numbers, and my python class calculates the cumulative sums of the numbers generated:
Python:
class CumSum:
def addone(x):
self._cumsum += x;
print self._cumsum;
C++
[Somehow construct a CumSum instance, say c]
for (int i=0; i<100000; i++) {
int x = rand() % 1000;
[Call c.addone(x)]
}
I heard boost::python is a good way to handle this. Can anyone sketch out how to do it? I tried to read boost documents but they were too huge for me to digest.
I appreciate your help.
For basic information about how to execute your python script:
http://www.boost.org/doc/libs/1_47_0/libs/python/doc/tutorial/doc/html/python/embedding.html
For details on manipulating python objects in C++
http://www.boost.org/doc/libs/1_47_0/libs/python/doc/tutorial/doc/html/python/object.html
Much of boost-python is concerned with exporting your C++ classes to python but you aren't doing that so you can ignore it.
You may be better off using a simpler wrapper like SCXX
http://davidf.sjsoft.com/mirrors/mcmillan-inc/scxx.html