TimSchmeier

import numpy as np


def normalize(arr):
    # normalize a 1d to [0,1] using
    # empirical cdf
    idx = np.argsort(arr)
    cdf = 1 - (idx / arr.size)
    return cdf[idx]

TimSchmeier

import scipy.special
import numpy as np
import matplotlib.pyplot as plt


x = np.linspace(-10, 10, 200)
y = 4*(x**3) - 2*(x**2) + x
y += np.random.normal(size=y.shape[0])*500
plt.plot(x, y, 'ro')
plt.show()

TimSchmeier

"Plot a PNG using matplotlib in a web request, using Flask."

# Install dependencies, preferably in a virtualenv:
#
#     pip install flask matplotlib
#
# Run the development server:
#
#     python app.py
#

TimSchmeier

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

TimSchmeier

# Mostly taken from: http://nbviewer.ipython.org/github/bmcfee/librosa/blob/master/examples/LibROSA%20demo.ipynb

import librosa
import matplotlib.pyplot as plt

# Load sound file
y, sr = librosa.load("filename.mp3")

# Let's make and display a mel-scaled power (energy-squared) spectrogram
S = librosa.feature.melspectrogram(y, sr=sr, n_mels=128)

TimSchmeier

import numpy as np


def freq2mel(freq):
    return 1127.01048 * np.log(1 + freq / 700.0)

def mel2freq(mel):
    return (np.exp(mel / 1127.01048) - 1) * 700

def mel_binning_matrix(specgram_window_size, sample_frequency, num_mel_bands):