In mid-2018 I started learning Python by reading textbooks and watching online tutorials. I had absolutely zero background in computer science, but it seemed interesting so I continued to try. At some point, I decided I wanted to do a master’s degree in statistics, so I began to work on more statistics-based programming. That’s when I found SciPy. I became (and still am) fascinated by the idea of open-source software that is completely free to use and supported by a community of diligent programmers.
Posts with #scipy
I was selected as an intern to work on SciPy build system. In this blog post, I will be describing my journey of this 10-months long internship at SciPy. I worked on a variety of topics starting from migrating the SciPy build system to Meson, cleaning up the public API namespaces and adding Uarray support to SciPy submodules.
Experience Meson Build System The main reasons for switching to Meson include (in addition to distutils being deprecated):
April 4, 2022
At the end of this article, my goal is to convince you that: if you need to use random numbers, you should consider using scipy.stats.qmc instead of np.random.
In the following, we assume that SciPy, NumPy and Matplotlib are installed and imported:
import numpy as np from scipy.stats import qmc import matplotlib.pyplot as plt Note that no seeding is used in these examples. This will be the topic of another article: seeding should only be used for testing purposes.
At the end of this article, my goal is to convince you that: if you need to use random numbers, you should consider using scipy.stats.qmc instead of np.random.
In the following, we assume that SciPy, NumPy and Matplotlib are installed and imported:
import numpy as np from scipy.stats import qmc import matplotlib.pyplot as plt Note that no seeding is used in these examples. This will be the topic of another article: seeding should only be used for testing purposes.At the end of this article, my goal is to convince you that: if you need to use random numbers, you should consider using scipy.stats.qmc instead of np.random.
In the following, we assume that SciPy, NumPy and Matplotlib are installed and imported:
import numpy as np from scipy.stats import qmc import matplotlib.pyplot as plt Note that no seeding is used in these examples. This will be the topic of another article: seeding should only be used for testing purposes.