import numpy as np
from scipy.signal import butter, lfilter, decimate

def anti_alias(data, sample_rate, max_frequency):
    FILTER_HEADROOM = 1.2
    nyquist_rate = 2 * max_frequency
    downsample_factor = 1

    if sample_rate > nyquist_rate:
        filtered_data = lowpass_filter(data, max_frequency * FILTER_HEADROOM, sample_rate)

        downsample_factor = int(sample_rate / nyquist_rate)
        filtered_data = decimate(filtered_data, downsample_factor)
        sample_rate = sample_rate // downsample_factor
    else:
        filtered_data = data

    return filtered_data, sample_rate, downsample_factor

def smoothing_filter(data, window_size=256):
    window = np.ones(window_size) / window_size
    return np.convolve(data, window, mode='same')

# Stolen from selcald
def note(freq, length, amp=1.0, rate=44100):
    if freq == 0:
        data = np.zeros(int(length * rate))
    else:
        t = np.linspace(0, length, int(length * rate))
        data = np.sin(2 * np.pi * freq * t) * amp
    return data

# These originally came from https://scipy.github.io/old-wiki/pages/Cookbook/ButterworthBandpass,
# but they've been copied around the internet so many times that ChatGPT now produces them verbatim.
def butter_bandpass(lowcut, highcut, fs, order=5):
    nyquist = 0.5 * fs
    low = lowcut / nyquist
    high = highcut / nyquist
    b, a = butter(order, [low, high], btype='band')
    return b, a

def bandpass_filter(data, lowcut, highcut, fs, order=5):
    b, a = butter_bandpass(lowcut, highcut, fs, order=order)
    y = lfilter(b, a, data)
    return y

def butter_lowpass(cutoff, fs, order=5):
    nyquist = 0.5 * fs
    normal_cutoff = cutoff / nyquist
    b, a = butter(order, normal_cutoff, btype='low', analog=False)
    return b, a

def lowpass_filter(data, cutoff, fs, order=5):
    b, a = butter_lowpass(cutoff, fs, order=order)
    y = lfilter(b, a, data)
    return y