xvdp

"""@xvdp
Fourier 1D convs are constant speed with support size increase. HW optimized Sliding W are faster for small supports <20.
Vec (conv) kernel = ifft(fft(Vec) * fft(kernel))
I used these to RIR (Room Impusle Response) to audio augmentation. 

I filed issue to pytorch https://github.com/pytorch/pytorch/issues/79222, I just noticed I had not gisted it.
"""
from typing import Optional
import time
import torch

xvdp

"""
Morton codes should never be done in python, but in cpp, as they require looping
Still, to understand them here are 2 different implementations/ They both assume 3d points of shape (...,3]
1. More general computes a pyramid, using np or torch
>> get_morton_codes()
>> get_points_from_morton()
2. easier to read, uses morton to compute knn
>> knn()

Sources and further reading, morton code based voxelization

xvdp

"""
Conversion from WGS-84 to Transverse Mercator
WGS-84 is World Geodetic System data from 1984, utilized by GPS standard.
Transverse mercator is a general Mercator projection (projection of the oblate spheroid of earth onto a cylinder) that is used in most mapping systems. 
These short codes allow user to specify center latitude and longitude.

Contains a simpler projection assuming earth is a sphere
>>> tmercator_meters_to_gps(coords, center)
>>> tmercator_gps_to_meters(coords, center)

xvdp

#!/bin/bash
# change cache paths for huggingface, torch, openai (uses ~/.cache/ by default)
# use: set_model_caches.sh /mnt/my/model/cache/path

cache_path=$1
# Check if the argument is provided
if [ -z "$cache_path" ]; then
  echo "Error: Cache path argument is missing."
  echo "Usage: $0 /path_to_cache"
  exit 1

xvdp

""" @xvdp
Simple solution  to Show frame number and frame time with VLC or other video players
Make a subtitle file

caveats:
  only tested in linux
  requires ffmpeg / only tested with ffmpeg 4.3
  only tested with limited number of .mov and .mkv files
  it may fail if ffprobe reports nb_frames, DURATION or frame_rate with keys not parsed here.
  

xvdp

"""@xvdp
reciprocals for torch.cumsum and torch.cumprod
I noticed that torch has cumsum and cumprod but not their reciprocals
even thought cumdif and cumdiv have meanings in analysis and probability
and are routinely used.

Are these interpretations correct?

> cumsum can be thought of as a discrete integral
> cumdif as discrete derivative

xvdp

xvdp

xvdp

"""
>>> import interpolation_diff as idf
>>> algs = ("pil", "tensorflow") # ("pil", "torch")
>>> save = "/home/z/share/benchestchirescat_%s_%s"%algs
>>> idf.test_dif(dtype="float32", mode="bilinear", show=True, algs=algs, crop=(170,370, 180, 380), save=save)

# re https://discuss.pytorch.org/t/nn-interpolate-function-unexpected-behaviour/106684/6
"""
import io
import os

xvdp