Robert Knight robertknight

## hypothesis-export.py
import getpass
import json
import re
import sys

import requests

GROUP_URL_PATTERN = "https://hypothes.is/groups/([^/]+)/.*"

api_token = getpass.getpass(

## find_pyramid_routes.py
# Analysis of Hypothesis Python app routes as part of
# https://github.com/hypothesis/playbook/issues/1797#issuecomment-2397242000.

import os
import ast
import sys


def find_py_files(root_dir):
    excluded_dirs = ["node_modules", "build", "test", "tests", "third_party"]

## youtube_captions_api_test.py
# Script to fetch and print the transcript for a YouTube video using the
# YouTube Data API v3.
#
# 1. Create a project in the Google API Console
# 2. Enable the YouTube Data API v3 for the new project
# 3. Create credentials for a "Desktop" OAuth client. Download the JSON file
#    containing the credentials at the end of the setup process.
# 4. Create a new virtualenv and install dependencies with:
#
#    ```

## accelerate_gemv.cpp
#include <algorithm>
#include <chrono>
#include <iostream>
#include <iterator>
#include <random>
#include <vector>

#include <Accelerate/Accelerate.h>
#include <cblas.h>

## rectify.py
import cv2
import numpy as np
import argparse


def warp_quadrilateral(
    input_path: str,
    src_points: list[tuple[int, int]],
    output_path: str,
    output_size: tuple[int, int],

## rten_ndarray_conv.rs
use ndarray::{Array, Array2, ArrayView, Dim, Dimension, Ix, StrideShape};

use rten_tensor::prelude::*;
use rten_tensor::{NdTensor, NdTensorView};

/// Convert an N-dimensional ndarray view to an [NdTensorView].
///
/// Returns `None` if the view is not in the standard layout (see
/// [ArrayView::is_standard_layout]).
fn as_ndtensor_view<'a, T, const N: usize>(

## avx-512-gemm-kernel.s
# At entry params are:
#
# tile_ptr (rdi)
# tile_row_stride (rsi)
# a (rdx, rcx)
# b (r8, r9)
# depth (stack)
# alpha (xmm0)
# beta (xmm1)
.section __TEXT,__text,regular,pure_instructions

## gemm_bench.rs
//! Port of https://github.com/danieldk/gemm-benchmark/ for comparison of
//! matrix multiplication performance against other popular libraries.

use std::time::Duration;

use wasnn::gemm;
use wasnn_tensor::prelude::*;
use wasnn_tensor::NdTensor;

use rayon::iter::IntoParallelIterator;

## fetch-annotation-pages.py
import os

import requests

search_after = ""
page_size = 200
group = os.environ["HYPOTHESIS_GROUP"]
access_token = os.environ["HYPOTHESIS_ACCESS_TOKEN"]
max_to_fetch = 10_000  # Max annotations to fetch before we stop

## hypothesis-canonical-links.md

      
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                robertknight
                / hypothesis-canonical-links.md
            
            
              Created
              July 7, 2022 10:37
            
              
                How Hypothesis interacts with canonical links (`<link rel=canonical>`)
              
          
    Hypothesis and canonical links

What are canonical links?

A <link rel="canonical" href="{url}"> tag in the <head> of a page specifies the canonical/"main" URL for the current page. When the same content can be accessed at different URLs, it specifies the preferred URL for the page. It was originally introduced for use with search engines.
For example, https://www.smore.com/577k7, https://www.smore.com/577k7-whatever and https://www.smore.com/577k7-the-edtech-monthly all return the same page. That page has a <link rel=canonical> field which specifies https://www.smore.com/577k7-the-edtech-monthly as the preferred URL.
See also https://en.wikipedia.org/wiki/Canonical_link_element.
	import getpass
	import json
	import re
	import sys

	import requests

	GROUP_URL_PATTERN = "https://hypothes.is/groups/([^/]+)/.*"

	api_token = getpass.getpass(
	# Analysis of Hypothesis Python app routes as part of
	# https://github.com/hypothesis/playbook/issues/1797#issuecomment-2397242000.

	import os
	import ast
	import sys


	def find_py_files(root_dir):
	excluded_dirs = ["node_modules", "build", "test", "tests", "third_party"]
	# Script to fetch and print the transcript for a YouTube video using the
	# YouTube Data API v3.
	#
	# 1. Create a project in the Google API Console
	# 2. Enable the YouTube Data API v3 for the new project
	# 3. Create credentials for a "Desktop" OAuth client. Download the JSON file
	# containing the credentials at the end of the setup process.
	# 4. Create a new virtualenv and install dependencies with:
	#
	# ```
	#include <algorithm>
	#include <chrono>
	#include <iostream>
	#include <iterator>
	#include <random>
	#include <vector>

	#include <Accelerate/Accelerate.h>
	#include <cblas.h>
	import cv2
	import numpy as np
	import argparse


	def warp_quadrilateral(
	input_path: str,
	src_points: list[tuple[int, int]],
	output_path: str,
	output_size: tuple[int, int],
	use ndarray::{Array, Array2, ArrayView, Dim, Dimension, Ix, StrideShape};

	use rten_tensor::prelude::*;
	use rten_tensor::{NdTensor, NdTensorView};

	/// Convert an N-dimensional ndarray view to an [NdTensorView].
	///
	/// Returns `None` if the view is not in the standard layout (see
	/// [ArrayView::is_standard_layout]).
	fn as_ndtensor_view<'a, T, const N: usize>(
	# At entry params are:
	#
	# tile_ptr (rdi)
	# tile_row_stride (rsi)
	# a (rdx, rcx)
	# b (r8, r9)
	# depth (stack)
	# alpha (xmm0)
	# beta (xmm1)
	.section __TEXT,__text,regular,pure_instructions
	//! Port of https://github.com/danieldk/gemm-benchmark/ for comparison of
	//! matrix multiplication performance against other popular libraries.

	use std::time::Duration;

	use wasnn::gemm;
	use wasnn_tensor::prelude::*;
	use wasnn_tensor::NdTensor;

	use rayon::iter::IntoParallelIterator;
	import os

	import requests

	search_after = ""
	page_size = 200
	group = os.environ["HYPOTHESIS_GROUP"]
	access_token = os.environ["HYPOTHESIS_ACCESS_TOKEN"]
	max_to_fetch = 10_000 # Max annotations to fetch before we stop