| from numpy import linalg as la | |
| import numpy as np | |
| def nearestPD(A): | |
| """Find the nearest positive-definite matrix to input | |
| A Python/Numpy port of John D'Errico's `nearestSPD` MATLAB code [1], which | |
| credits [2]. |
Get the following:
brew upgrade youtube-dl),cookies.txt file.Then,
Overview Nayr's Japanese Core5000 Anki deck (discussion) contains pronunciations of all five thousand or so sentences in A Frequency Dictionary of Japanese by Yukio Tono, Makoto Yamazaki, and Kikuo Maekawa (2013), which contains the top five thousand words in Japanese according to the latest corpus research. I analyzed these sentences to make a histogram table of hiragana occurrences, including dipthongs like きゃ, ちょ, etc. The attached two tables show the results in modern hiragana order, and sorted order.
Technical notes I parsed a file containing those sentences (with annotated readings in hiragana, in core5k-sentences.md) using the following script and helper file (in kana.txt):
cp core5k-sentences.md sacrifice.md;
sed '/^$/d' kana.txt | while read i; do
echo -n $i " : " ;
sed -n "s/$i/$i\n/gp"
| import numpy as np | |
| from datetime import datetime, timedelta | |
| import pylab as plt | |
| from numpy.polynomial import Polynomial | |
| plt.style.use("ggplot") | |
| plt.ion() | |
| # this is the min/max points in my line chart on my profile page | |
| rawMin = 4.9e3 |
| import pandas as pd | |
| import seaborn as sns | |
| import matplotlib.pyplot as plt | |
| plt.ion() | |
| # URL for the data | |
| url = "https://data.bls.gov/timeseries/JTS510000000000000LDL" | |
| # Creating a date range from 2001 to 2023 |
Very tall image: "If only [blank] voted" with image caption
| const sleep = (t: number) => new Promise((rs) => setTimeout(rs, t)); | |
| /** | |
| * This function will return an array of the same length as the input, | |
| * but not in the same order. It's straightforward to extend it to | |
| * preserve order, but I left it like this because this way the timing | |
| * is clearly visible. This helped me confirm that it works. | |
| */ | |
| async function processArray(input: number[], numWorkers: number) { | |
| const ret: { date: number; description: string }[] = []; |
I’m not very familiar with the aviation jargon (see FAA’s ADS-B FAQ), but ADS-B is a next-gen system where aircraft are equipped with transponders that periodically broadcast their own positions and receive the reports from both other aircraft (direct air-to-air) as well as air-traffic control (ATC) ground transmitters.
There are two separate ADS-B radio bands: the commercial aviation (CA) is at 1090 MHz while the general aviation (GA) is at 978 MHz. If I can be permitted a gross generalization—the former corresponds to big commercial jets and the latter to small private aircraft.
Because ADS-B is designed to democratize airspace situational awareness (in contrast to the older setup, like from films, where a central air-traffic controller is coordinating all these aircraft that can’t see each other), we can buy cheap RF receivers to pick up and decode the messages being broadcast by aircraft and ground towers to get our own picture of the
| import numpy as np | |
| from numpy.fft import fft, ifft | |
| def fftUpsample(a, factor): | |
| a_f = fft(a) | |
| n = len(a) | |
| assert n % 2 == 0, "odd case is harder" | |
| padded = np.zeros(int(len(a) * factor), dtype=a_f.dtype) | |
| padded[:n // 2 + 1] = a_f[:n // 2 + 1] |