# module.py

import numpy as np
import tensorflow as tf

# NumPy Functions
def matrix(data):
    return np.array(data)

def ghost(shape):
    return np.zeros(shape)

def squad(shape):
    return np.ones(shape)

def swipe(start, stop, step):
    return np.arange(start, stop, step)

def drip(start, stop, num):
    return np.linspace(start, stop, num)

def remaster(data, shape):
    return np.reshape(data, shape)

def flip(data):
    return np.transpose(data)

def connect(a, b):
    return np.dot(a, b)

def add(a, b):
    return np.add(a, b)

def subtract(a, b):
    return np.subtract(a, b)

def mult(a, b):
    return np.multiply(a, b)

def slice(a, b):
    return np.divide(a, b)

def stack(data, axis=None):
    return np.sum(data, axis=axis)

def average(data, axis=None):
    return np.mean(data, axis=axis)

def peak(data):
    return np.max(data)

def low(data):
    return np.min(data)

def scatter(data):
    return np.std(data)

def vibes(data):
    return np.var(data)

def merge(arrays, axis=0):
    return np.concatenate(arrays, axis=axis)

def break_array(data, indices_or_sections, axis=0):
    return np.split(data, indices_or_sections, axis=axis)

# TensorFlow Functions
def random_uniform(shape, minval=0, maxval=1):
    return tf.random.uniform(shape, minval=minval, maxval=maxval)

def random_normal(shape, mean=0.0, stddev=1.0):
    return tf.random.normal(shape, mean=mean, stddev=stddev)

def drop(data, rate, training=None):
    return tf.nn.dropout(data, rate=rate, training=training)

def pick(data, num_samples, replace=False):
    return tf.random.shuffle(data)[:num_samples]

def rank(data, axis=-1):
    return np.argsort(data, axis=axis)

def lowkey(data):
    return np.argmin(data)

def highkey(data):
    return np.argmax(data)

def anchor(value, dtype=None, shape=None, name=None):
    return tf.constant(value, dtype=dtype, shape=shape, name=name)

def flex(initial_value, trainable=True, dtype=None, name=None):
    return tf.Variable(initial_value, trainable=trainable, dtype=dtype, name=name)

def spot(dtype, shape=None, name=None):
    return tf.placeholder(dtype, shape=shape, name=name)

def studio(graph=None):
    return tf.Session(graph=graph)

def prime():
    return tf.global_variables_initializer()

def boost(learning_rate=0.001):
    return tf.train.AdamOptimizer(learning_rate)

def ignite(data):
    return tf.nn.relu(data)

def smooth(data):
    return tf.nn.softmax(data)

def curve(data):
    return tf.nn.sigmoid(data)

def vibe(data):
    return tf.nn.tanh(data)

def scan(input, filter, strides, padding):
    return tf.nn.conv2d(input, filter, strides, padding)

def extract(value, ksize, strides, padding):
    return tf.nn.max_pool(value, ksize, strides, padding)

def chill(value, ksize, strides, padding):
    return tf.nn.avg_pool(value, ksize, strides, padding)
