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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE

# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER

# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,

# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN

# THE SOFTWARE.

import array

import math

import audiobusio

import board

import neopixel

# Exponential scaling factor.

# Should probably be in range -10 .. 10 to be reasonable.

CURVE = 2

SCALE_EXPONENT = math.pow(10, CURVE * -0.1)

PEAK_COLOR = (100, 0, 255)

NUM_PIXELS = 10

# Number of samples to read at once.

NUM_SAMPLES = 160

# Restrict value to be between floor and ceiling.

def constrain(value, floor, ceiling):

return max(floor, min(value, ceiling))

# Scale input_value between output_min and output_max, exponentially.

def log_scale(input_value, input_min, input_max, output_min, output_max):

normalized_input_value = (input_value - input_min) / \

(input_max - input_min)

return output_min + \

math.pow(normalized_input_value, SCALE_EXPONENT) \

* (output_max - output_min)

# Remove DC bias before computing RMS.

def normalized_rms(values):

minbuf = int(mean(values))

samples_sum = sum(

float(sample - minbuf) * (sample - minbuf)

for sample in values

)

return math.sqrt(samples_sum / len(values))

def mean(values):

return sum(values) / len(values)