drscotthawley

diff --git a/src/core/lib/gpr/log_linux.cc b/src/core/lib/gpr/log_linux.cc
index 561276f0c2..1af0935e1f 100644
--- a/src/core/lib/gpr/log_linux.cc
+++ b/src/core/lib/gpr/log_linux.cc
@@ -40,7 +40,7 @@
 #include <time.h>
 #include <unistd.h>
 
-static long gettid(void) { return syscall(__NR_gettid); }
+static long sys_gettid(void) { return syscall(__NR_sys_gettid); }

drscotthawley

#! /usr/bin/env python3
# Test script for Phase 'Unrolling' / Instantaneous frequency
#
# See Jesse Engel's "rainbowgrams" script, https://gist.github.com/jesseengel/e223622e255bd5b8c9130407397a0494
#
# Modifications by Scott H. Hawley, @drscotthawley and Billy Mitchell
# These modified versions seem to be both more accurate (~2000x less reconstruction error)
# and faster (>20%)
#
# note, to really see/hear the difference, change dtypes to np.float16!

drscotthawley

#! /usr/env/python3
#
#  FMA conversion script,  genre classification
#  Author: Scott Hawley
#  License: Do as you like
#
# For FMA dataset https://github.com/mdeff/fma
# to be used with panotti https://github.com/drscotthawley/panotti
#
# This will create a directory called Samples/

drscotthawley

#! /usr/bin/env python3
'''
osc2wek.py
Author: Scott Hawley

This listens for incoming OSC messages and sends them on to Wekinator

Steps to get running (in Terminal):
0. First you need Mercurial "hg".  It might be installed by default.
1. Use hg to clone the grail osc code:

drscotthawley

import numpy as np

def get_1cycle_schedule(lr_max=1e-3, n_data_points=8000, epochs=200, batch_size=40, verbose=0):          
  """
  Creates a look-up table of learning rates for 1cycle schedule with cosine annealing
  See @sgugger's & @jeremyhoward's code in fastai library: https://github.com/fastai/fastai/blob/master/fastai/train.py
  Wrote this to use with my Keras and (non-fastai-)PyTorch codes.
  Note that in Keras, the LearningRateScheduler callback (https://keras.io/callbacks/#learningratescheduler) only operates once per epoch, not per batch
      So see below for Keras callback
      

drscotthawley

drscotthawley

#!/usr/bin/env python

# Replaces lengthy words/phrases with shorter variants

# Author: Scott Hawley

import pandas as pd
import re
import os

drscotthawley

#! /usr/bin/env python3
# -*- coding: utf-8 -*-
__author__ = 'Scott H. Hawley'
__copyright__ = 'Scott H. Hawley'
__license__ = "MIT Licence (do what you want, don't blame me)"

import numpy as np
import cv2
import soundcard as sc  # https://github.com/bastibe/SoundCard
from scipy.ndimage.interpolation import shift

drscotthawley

import numpy as np
import cv2
import soundcard as sc        # Get it from https://github.com/bastibe/SoundCard
imWidth, imHeight = 1024, 512                       # screen size
def draw_wave(screen, mono_audio, xs, title="oscilloscope", gain=5):
    screen *= 0                                     # clear the screen
    ys = imHeight/2*(1 - np.clip( gain * mono_audio[0:len(xs)], -1, 1))  # the y-values of the waveform
    pts = np.array(list(zip(xs,ys))).astype(np.int) # pair up xs & ys
    cv2.polylines(screen,[pts],False,(0,255,0))     # connect points w/ lines
    cv2.imshow(title, screen)                       # show what we've got

drscotthawley

import pysox
import librosa
import numpy as np

def apply_sox_effect(signal, sr, fxstr):
    # This writes signal to a .wav file, processes it sox to another file, loads that and returns it.
    #
    # signal:  a numpy list of numbers; the audio signal
    # sr:      the sample rate in Hz, must be an integer
    # fxstr:   a semicolon-separated string starting with the effect name followed by parameter values in order