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output whole list in C#

in python language i can easily do this and output is whole list:
import random
list = [random.randrange(150) for i in range(10)]
print(list)
Can i do this thing in C# language without for cycle like this? Because output seperates my list's elements.
List<int> list = new List<int> ();
Random rnd = new Random();
for (int i = 0; i < 10; i++){
list.Add(rnd.Next (150));
}
for(int i = 0; i < list.Count; i++){
Console.WriteLine(list[i]);
}

Well, we can do it in one line if you want as well. This code is also thread-safe but requires .NET 6.0 or higher due to the use of Random.Shared.
Console.WriteLine(string.Join(",", Enumerable.Range(0, 10).Select(_ => Random.Shared.Next(150))));
This generates an IEnumerable<int> with random integers from 0 to 149 and then writes them to the Console separated by commas.

As far as I know, there is not a method generating a list of random integers in .NET, but why won't you write your own? For example:
public static class MyEnumerable
{
public static IEnumerable<int> RandomEnumerable(int maxValue, int count, Random random = default)
{
if (count < 0)
{
throw new ArgumentOutOfRangeException(nameof(count));
}
if (maxValue < 0)
{
throw new ArgumentOutOfRangeException(nameof(maxValue));
}
random ??= Random.Shared;
for (int i = 0; i < count; i++)
{
yield return random.Next(maxValue);
}
}
}
Now you can do your task in two lines like in phyton:
var randomList = MyEnumerable.RandomEnumerable(150, 10).ToList();
Console.WriteLine($"[{string.Join(", ", randomList)}]");

Utilising methods on a list object

I've had a gander and haven't found a satisfactory answer. In JavaScript it's possible to simply add a method to an array alla:
const makeGrid = (size) => {
let grid = [];
for (let i = 0; i < size; i++) {
let row = new Array(size).fill(0);
grid.push(row);
}
grid.toggleChunkPlaced = (x, y) => {
grid[y][x] === 0 ? (grid[y][x] = 1) : (grid[y][x] = 0);
};
return grid
};
However I'm not sure if it is possible (or recommended) to do in Python - my best guess is something like:
def make_grid(size):
grid = []
for i in size:
row = [0]*size
grid.append(row)
grid.toggle_chunk_placed = lambda x, y: grid[y][x] = 1 if grid[y][x] == 0 else grid[y][x] = 0
return grid

Nope. You can't do that with a list. It is possible if you really badly want it to be possible but I don't think that's the case. If you do want it, I'll have to write an answer that would probably only get downvotes 😅. What I mean is, there is a better way. – Diptangsu Goswami

Optimize cython functions operating on python lists

I am currently migrating to Cython a set of functions that are currently implemented in C++ through scipy.weave (now deprecated).
These functions operate on timeseries points that are 2D-lists (eg. [[17100, 19.2], [17101, 20.7], [17102, 20.3], ...]) both in input and in output. A sample function is subtract that accepts two timeseries and calculates a new timeserie as subtraction of the two inputs going date-by-date.
The structure and the interfaces have to be mantained for retrocompatibility, but my profiling trials show that Cython porting is about 30%-40% slower than the original scipy.weave implementation.
I have tried many ways to optimize (inner conversions to Numpy arrays and memoryviews, C pointers, ...), but the conversion time required lenghtens the overall execution time. Even trying to define input and output as C++ vectors, leveraging on Cython implicit conversions doesn't seem to be effective in order to mantain scipy.weave speed. I have also used the various hints on boundscheck, wraparound, division, ...
The highest slow-downs seem to be on functions that uses nested loops and I've seen that a little gain can be predefining the list size (cdef list target = [[-1, float('nan')]]*size).
I am aware that Cython can't be so much performing on Python structures, especially lists, but are there any other tricks or techniques with which a speedup can be obtained?
=== EDIT - ADD CODE EXAMPLE ===
A good example of the typology of functions is the following.
The function takes a 2-D list of dates/prices and a 2-D list of dates/decimal factors and searches matching dates between the two lists, calculating the output on the corresponding price/factor by multiplying or dividing (that is a third input parameter).
My best-performing cython code:
#cython.cdivision(True)
#cython.boundscheck(False)
#cython.wraparound(False)
cpdef apply_conversion(list original_timeserie, list factor_timeserie, int divide_or_multiply=False):
cdef:
Py_ssize_t i, j = 0, size = len(original_timeserie), size2 = len(factor_timeserie)
long original_date, factor_date
double original_price, factor_price, conv_price
list result = []
for i in range(size):
original_date = original_timeserie[i][0]
for j in range(j, size2):
factor_date = factor_timeserie[j][0]
if original_date == factor_date:
original_price = original_timeserie[i][1]
factor_price = factor_timeserie[j][1]
if divide_or_multiply:
if factor_price != 0:
conv_price = original_price / factor_price
else:
conv_price = float('inf')
else:
conv_price = original_price * factor_price
result.append([original_date, conv_price])
break
return result
The original scipy function:
int len = original_timeserie.length();
int len2 = factor_timeserie.length();
PyObject* py_serieconv = PyList_New(len);
PyObject* original_item = NULL;
PyObject* factor_item = NULL;
PyObject* date = NULL;
PyObject* value = NULL;
long original_date = 0;
long factor_date = 0;
double original_price = 0;
double factor_price = 0;
int j = 0;
for(int i=0;i<len;i++) {
original_item = PyList_GetItem(original_timeserie, i);
date = PyList_GetItem(original_item, 0);
original_date = PyInt_AsLong(date);
original_price = PyFloat_AsDouble( PyList_GetItem(original_item, 1) );
factor_item = NULL;
for(;j<len2;) {
factor_item = PyList_GetItem(factor_timeserie, j++);
factor_date = PyInt_AsLong(PyList_GetItem(factor_item, 0));
if (factor_date == original_date) {
factor_price = PyFloat_AsDouble(PyList_GetItem(factor_item, 1));
value = PyFloat_FromDouble(original_price * (divide_or_multiply==0 ? factor_price : 1/factor_price));
PyObject* py_new_item = PyList_New(2);
Py_XINCREF(date);
PyList_SetItem(py_new_item, 0, date);
PyList_SetItem(py_new_item, 1, value);
PyList_SetItem(py_serieconv, i, py_new_item);
break;
}
}
}
return_val = py_serieconv;
Py_XDECREF(py_serieconv);

Weave Inline C++ Code in Python 2.7

I'm trying to rewrite this function:
def smoothen_fast(heightProfile, travelTime):
smoothingInterval = 30 * travelTime
heightProfile.extend([heightProfile[-1]]*smoothingInterval)
# Get the mean of first `smoothingInterval` items
first_mean = sum(heightProfile[:smoothingInterval]) / smoothingInterval
newHeightProfile = [first_mean]
for i in xrange(len(heightProfile)-smoothingInterval-1):
prev = heightProfile[i] # the item to be subtracted from the sum
new = heightProfile[i+smoothingInterval] # item to be added
# Calculate the sum of previous items by multiplying
# last mean with smoothingInterval
prev_sum = newHeightProfile[-1] * smoothingInterval
new_sum = prev_sum - prev + new
mean = new_sum / smoothingInterval
newHeightProfile.append(mean)
return newHeightProfile
as embedded C++ Code:
import scipy.weave as weave
heightProfile = [0.14,0.148,1.423,4.5]
heightProfileSize = len(heightProfile)
travelTime = 3
code = r"""
#include <string.h>
int smoothingInterval = 30 * travelTime;
double *heightProfileR = new double[heightProfileSize+smoothingInterval];
for (int i = 0; i < heightProfileSize; i++)
{
heightProfileR[i] = heightProfile[i];
}
for (int i = 0; i < smoothingInterval; i++)
{
heightProfileR[heightProfileSize+i] = -1;
}
double mean = 0;
for (int i = 0; i < smoothingInterval; i++)
{
mean += heightProfileR[i];
}
mean = mean/smoothingInterval;
double *heightProfileNew = new double[heightProfileSize-smoothingInterval];
for (int i = 0; i < heightProfileSize-smoothingInterval-1; i++)
{
double prev = heightProfileR[i];
double newp = heightProfile[i+smoothingInterval];
double prev_sum = heightProfileNew[i] * smoothingInterval;
double new_sum = prev_sum - prev + newp;
double meanp = new_sum / smoothingInterval;
heightProfileNew[i+1] = meanp;
}
return_val = Py::new_reference_to(Py::Double(heightProfileNew));
"""
d = weave.inline(code,['heightProfile','heightProfileSize','travelTime'])
As a return type i need the heightProfileNew. I need the access it like a list in Python later.
I look at these examples:
http://docs.scipy.org/doc/scipy/reference/tutorial/weave.html
He keeps telling me that he doesn't know Py::, but in the examples there are no Py-Includes?

I know, the question is old, but I think it is still interesting.
Assuming your using weave to improve computation speed and that you know the length of your output beforehand, I suggest that you create the result before calling inline. That way you can create the result variable in python (very easy). I would also suggest using a nd.ndarray as a result because it makes shure you use the right datatype. You can iterate ndarrays in python the same way you iterate lists.
import numpy as np
heightProfileArray = np.array(heightprofile)
# heightProfileArray = np.array(heightprofile, dtype = np.float32) if you want to make shure you have the right datatype. Another choice would be np.float64
resultArray = np.zeros_like(heightProfileArray) # same array size and data type but filled with zeros
[..]
weave.inline(code,['heightProfile','heightProfileSize','travelTime','resultArray'])
for element in resultArray:
print element
In your C++-code you can then just assign values to elements of that array:
[..]
resultArray[i+1] = 5.5;
[..]

Ctree Specializer is using for loop index for computation, not the actual array value

I'm implementing a simple Xor Reducer, but it is unable to return the appropriate value.
Python Code (Input):
class LazySpecializedFunctionSubclass(LazySpecializedFunction):
subconfig_type = namedtuple('subconfig',['dtype','ndim','shape','size','flags'])
def __init__(self, py_ast = None):
py_ast = py_ast or get_ast(self.kernel)
super(LazySlimmy, self).__init__(py_ast)
# [... other code ...]
def points(self, inpt):
iter = np.nditer(input, flags=['c_index'])
while not iter.finished:
yield iter.index
iter.iternext()
class XorReduction(LazySpecializedFunctionSubclass):
def kernel(self, inpt):
'''
Calculates the cumulative XOR of elements in inpt, equivalent to
Reduce with XOR
'''
result = 0
for point in self.points(inpt): # self.points is defined in LazySpecializedFunctionSubclass
result = point ^ result # notice how 'point' here is the actual element in self.points(inpt), not the index
return result
C Code (Output):
// <file: module.c>
void kernel(long* inpt, long* output) {
long result = 0;
for (int point = 0; point < 2; point ++) {
result = point ^ result; // Notice how it's using the index, point, not inpt[point].
};
* output = result;
};
Any ideas how to fix this?

The problem is that you are using point in different ways, in XorReduction kernel method you are iterating of the values in the array, but in the generated C code you are iterating over the indices of the array. Your C code xor reduction is thus done on the indices.
The generated C function should look more like
// <file: module.c>
void kernel(long* inpt, long* output) {
long result = 0;
for (int point = 0; point < 2; point ++) {
result = inpt[point] ^ result; // you did not reference your input in the question
};
* output = result;
};