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import numpy as np
import matplotlib.pyplot as plt

import tensorflow as tf
from tensorflow.keras.layers import Dense, Activation, Input
from tensorflow.keras import Model
from tensorflow.keras.optimizers import SGD

# Implement activation function
def dCaAP_activation(x):

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# Standard imports
import numpy as np
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

# Keras
from keras.layers import Dense
from keras.models import Model, Input

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def transform_to_template(fn_func, fn_template, save=False):
    """
    Reads (4D)-functional images and transforms directly to template image
    Inputs: 
        - fn_func : Path to functional images
        - fn_template : Path to template brain, e.g. MNI template
        - save : Whether to save transformed image to same folder as functional
    Outputs:
        - func_template : functional images transformed to template brain
    """

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# Imports
import numpy as np
from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt
plt.xkcd();
import seaborn as sns
sns.set_style('white')
# Set transparency off when exporting / Only if you want to save the figure
from matplotlib import patheffects, rcParams
rcParams['path.effects'] = [patheffects.withStroke(linewidth=0)]

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"""Creates a 3D surface plot, can be used as an example for demonstrating gradient descent
Author: Markus Frey
E-mail: [email protected]
"""

import numpy as np
import pandas as pd
import os

import matplotlib.pyplot as plt