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@etale-cohomology
etale-cohomology / calculus0.py
Created Oct 11, 2017
The fundamental theorem of calculus, in discrete version!
View calculus0.py
# The fundamental theorem of calculus, in discrete version!
import numpy as np
np.set_printoptions(linewidth=200, precision=2)
print('This Python snippet shows how the sum of many differences is one difference of endpoinds!')
print('You can consider this the "discrete fundamental theorem of calculus"!')
# ----------------------------------------------------------------------
N_VALUES = 16
@etale-cohomology
etale-cohomology / add1.cu
Last active Oct 12, 2017
A minimal CUDA program that naively performs vector addition, component-wise!
View add1.cu
// Compile and run with:
// nvcc add1.cu -o add1 && ./add1
// This minimal CUDA program performs vector addition, component-wise!
#include <stdio.h>
#define N_ELEMENTS 8
@etale-cohomology
etale-cohomology / load_ndarray.py
Created Mar 11, 2016
Load a NumPy array from disk, either from a .npy file or from a .7z file. No need to include extension
View load_ndarray.py
def load_ndarray(filename):
"""Load a NumPy array from disk into memory, with extension .npy or .7z. If no extension is
included in the argument, first assume it's .npy, then .7z.
Args:
filename (str): Name of the NumPy array (in disk).
Returns:
ndarray
"""
@etale-cohomology
etale-cohomology / save_ndarray.py
Created Mar 11, 2016
Save a NumPy array to disk with optional compression using 7z
View save_ndarray.py
def save_ndarray(ndarray, filename, compress=False):
"""Store a single NumPy array on an .npy binary file. Tries to keep allow_pickle at False.
Optional multithreaded compression using 7-zip (needs to be in the PATH).
Args:
ndarray (ndarray): Array to be stored on disk.
filename (str): The file's name on disk. No need for .npy extension
compress (bool, optional): Set to True to save array as .npy and then compress it to .7z
Returns:
@etale-cohomology
etale-cohomology / vispy0.py
Created Mar 11, 2016
A small working example of vispy, along with some notes
View vispy0.py
"""Vispy is based on OpenGL ES 2.0.
Vispy offers a Pythonic object-oriented interface to OpenGL, useful to those
who know OpenGL.
It is critical to use as few OpenGL draw calls as possible. Every draw incurs
a significant overhead. High performance is achieved by rendering all similar
primitive types at once (batch rendering).
The vertex shader is executed for EACH vertex that is given to the rendering
View timeit.py
@contextlib.contextmanager # Create factory function for *with* context managers
def timeit():
"""Time execution of code, based on time.clock(). Call using a *with* statement!
Examples:
with timeit():
a @ a
"""
start = time.clock()
yield # The decorated func must return a generator-iterator
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