fasiha

The Indigenous Critique is a term popularized by Graeber and Wengrow’s The Dawn of Everything and it will blow your mind if you, like me, were raised in a Eurocentric universe.

In a nutshell, through the 1600s and 1700s, trenchant critiques of European society by numerous Indigenous statesmen and orators in North America were carried back to Europe, where they caused a sensation. To many Indigenous nations, the European way of life—rapacious, uncharitable, servile—was incomprehensible and obviously inferior to those Indigenous societies where starving people were invariably fed and adopted into family networks, and where no leader could tell a free human what to do let alone some military captain or priest. We have multiple independent examples of this and an oration by Kandiaronk, a famous Wendat statesman, has come down to us, and it is utterly brilliant: Barbara Mann titled her chapter translation, “‘Are you delusional?’ Kandiaronk on Christianity“ (more below).

The reason this Indigenous Critique is c

fasiha

import pandas as pd
import numpy as np
import pylab as plt
from pandas.tseries.offsets import MonthEnd

plt.style.use('ggplot')
plt.ion()

# Load the datasets
us_data = pd.read_csv('vti.txt.csv', parse_dates=['Date'], index_col='Date')

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export type Task = () => Promise<void>;

export function addTask(doTask: Task) {
  TASKS.push(doTask);
  kickOffWorkers(MAX_WORKERS);
}

const TASKS: Task[] = [];
const workerPool: number[] = []; // "number" but really doesn't matter, this is just to keep track of how many parallel workers we already have
const MAX_WORKERS = 3;

fasiha

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

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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

fasiha

Via https://old.reddit.com/r/dataisbeautiful/comments/kji3wx/oc_2020_electoral_map_if_only_voted_breakdown_by/

Very tall image: "If only [blank] voted" with image caption

• white people: trump 353, unknown 3, Biden 182
• men: trump 339, unknown 15, Biden 184
• women: 182, 6, 350 Biden
• ages 18-29: 57, 44, 437 Biden
• ages 30-44: 137, 27, 374 Biden
• ages 45-64: 333, 15, 190 Biden

fasiha

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]

fasiha

See https://octodon.social/@22/110876660994705240

fasiha

fasiha