diff --git a/file_utility.py b/file_utility.py
new file mode 100644
index 0000000..0316bc2
--- /dev/null
+++ b/file_utility.py
@@ -0,0 +1,27 @@
+from PIL import Image
+
+def openImageList(list, resize: bool = True):
+    stack = []
+    for path in list:
+        img = Image.open(path)
+        if resize == True:
+            img = img.resize((1280, 720))
+        stack.append(img)
+    return stack
+
+def saveStacktoFile(stack, quality: int = 75):
+    '''Saves the arrays in the stack returned by the get exposure stack function to files'''
+
+    print("Saving..")
+    i = 0
+    path = []
+    for array in stack:
+        i += 1
+        image = Image.fromarray(array)
+        image.save(f"image_nr{i}.jpg", quality=quality)
+        path.append(f"image_nr{i}.jpg")
+    return path
+
+def saveResultToFile(hdr_image , output_path: str = '/', quality: int = 75):
+    image = Image.fromarray(hdr_image)
+    image.save(f"{output_path}_hdr.jpg", quality=quality)
\ No newline at end of file
diff --git a/main.py b/main.py
index b8ce720..7632025 100644
--- a/main.py
+++ b/main.py
@@ -1,9 +1,16 @@
-import numpyHDR
+import numpyHDR as hdr
+import picamburst as pcb
+import file_utility as file
+
+'''Example of a complete HDR process starting with raspicam '''
+
+#Get sequence from raspicam
+stack = pcb.get_exposure_stack()
+
+#Process HDR with mertens fusion and post effects
+result = hdr.process(stack, 1, 1, 1, True)
+
+#Save Result to File
+file = file.saveResultToFile(result, 'hdr/', 75)
+
 
-#Testfile
-hdr = numpyHDR.NumpyHDR()
-liste = ['test_hdr0.jpg','test_hdr1.jpg', 'test_hdr2.jpg']
-hdr.input_image = liste
-hdr.output_path = 'hdr/fused_merten17'
-hdr.compress_quality = 75
-hdr.sequence(1, 1, 1, True)
diff --git a/numpyHDR.py b/numpyHDR.py
index fef70c6..8bfad19 100644
--- a/numpyHDR.py
+++ b/numpyHDR.py
@@ -1,244 +1,218 @@
 import numpy as np
-from PIL import Image
 
 
-#import matplotlib.pyplot as plt
 
-class NumpyHDR:
-    '''Numpy and PIL implementation of a Mertens Fusion alghoritm
-    Usage: Instantiate then set attributes:
-        input_image = List containing path strings including .jpg Extension
-        output_path = String ot Output without jpg ending
-        compress_quality = 0-100 Jpeg compression level defaults to 75
+'''Numpy and PIL implementation of a Mertens Fusion alghoritm
+Usage: Instantiate then set attributes:
+    input_image = List containing path strings including .jpg Extension
+    output_path = String ot Output without jpg ending
+    compress_quality = 0-100 Jpeg compression level defaults to 75
 
-        Run function sequence() to start processing.
-        Example:
+    Run function sequence() to start processing.
+    Example:
 
-            hdr = numpyHDR.NumpyHDR()
+        hdr = numpyHDR.NumpyHDR()
 
-            hdr.input_image = photos/EV- stages/
-            hdr.compress_quality = 50
-            hdr.output_path = photos/result/
-            hdr.sequence()
+        hdr.input_image = photos/EV- stages/
+        hdr.compress_quality = 50
+        hdr.output_path = photos/result/
+        hdr.sequence()
+    returns: Nothing    
+'''
+
+def simple_clip(fused,gamma):
+    # Apply gamma correction
+    #fused = np.clip(fused, 0, 1)
+    fused = np.power(fused, 1.0 / gamma)
+    #hdr_8bit = np.clip(res_mertens * 255, 0, 255).astype('uint8')
+    fused = (255.0 * fused).astype(np.uint8)
+    #fused = Image.fromarray(fused)
+
+    return fused
+def convolve2d(image, kernel):
+    """Perform a 2D convolution on the given image with the given kernel.
+
+    Args:
+        image: The input image to convolve.
+        kernel: The kernel to convolve the image with.
+
+    Returns:
+        The convolved image.
+    """
+    # Get the dimensions of the image and kernel.
+    image_height, image_width = image.shape[:2]
+    kernel_height, kernel_width = kernel.shape[:2]
+
+    # Compute the amount of padding to add to the image.
+    pad_height = kernel_height // 2
+    pad_width = kernel_width // 2
+
+    # Pad the image with zeros.
+    padded_image = np.zeros(
+        (image_height + 2 * pad_height, image_width + 2 * pad_width),
+        dtype=np.float32,
+    )
+    padded_image[pad_height:-pad_height, pad_width:-pad_width] = image
+
+    # Flip the kernel horizontally and vertically.
+    flipped_kernel = np.flipud(np.fliplr(kernel))
+
+    # Convolve the padded image with the flipped kernel.
+    convolved_image = np.zeros_like(image, dtype=np.float32)
+    for row in range(image_height):
+        for col in range(image_width):
+            patch = padded_image[
+                row : row + kernel_height, col : col + kernel_width
+            ]
+            product = patch * flipped_kernel
+            convolved_image[row, col] = product.sum()
+
+    return convolved_image
+
+def mask(img, center=50, width=20, threshold=0.2):
+    '''Mask with sigmoid smooth'''
+    mask = 1 / (1 + np.exp((center - img) / width))  # Smooth gradient mask
+    mask = np.where(img > threshold, mask, 1)  # Apply threshold to the mask
+    mask =  img * mask
+    #plot_histogram(mask, title="mask")
+    return mask
+
+def highlightsdrop(img, center=0.7, width=0.2, threshold=0.6, amount=0.08):
+    '''Mask with sigmoid smooth targets bright sections'''
+    mask = 1 / (1 + np.exp((center - img) / width))  # Smooth gradient mask
+    mask = np.where(img > threshold, mask, 0)  # Apply threshold to the mask
+    mask = mask.reshape((img.shape))
+    print(np.max(mask))
+    img_adjusted  = img - (mask * amount)   # Adjust the image with a user-specified amount
+    img_adjusted = np.clip(img_adjusted, 0, 1)
+
+    return img_adjusted
+
+def shadowlift(img, center=0.2, width=0.1, threshold=0.2, amount= 0.05):
+    '''Mask with sigmoid smooth targets bright sections'''
+    mask = 1 / (1 + np.exp((center - img) / width))  # Smooth gradient mask
+    mask = np.where(img < threshold, mask, 0)  # Apply threshold to the mask
+    mask = mask.reshape((img.shape))
+    print(np.max(mask))
+    img_adjusted  = (mask * amount) + img   # Adjust the image with a user-specified amount
+    img_adjusted = np.clip(img_adjusted, 0, 1)
+
+    return img_adjusted
+
+def mertens_fusion(stack, gamma=1, contrast_weight=1):
+    """Fuse multiple exposures into a single HDR image using the Mertens algorithm.
+
+    Args:
+        image_paths: A list of paths to input images.
+        gamma: The gamma correction value to apply to the input images.
+        contrast_weight: The weight of the local contrast term in the weight map computation.
+
+    Returns:
+        The fused HDR image.
+    """
+
+    images = []
+    for array in stack:
+        img = np.array(array).astype(np.float32) / 255.0
+        img = np.power(img, gamma)
+        images.append(img)
+
+    # Compute the weight maps for each input image based on the local contrast.
+    weight_maps = []
+
+    for img in images:
+        gray = np.dot(img, [0.2989, 0.5870, 0.1140])
+        kernel = np.array([[-1, -1, -1], [-1, 7, -1], [-1, -1, -1]])
+        laplacian = np.abs(convolve2d(gray, kernel))
+        weight = np.power(laplacian, contrast_weight)
+        weight_maps.append(weight)
+
+    # Normalize the weight maps.
+    total_weight = sum(weight_maps)
+    weight_maps = [w / total_weight for w in weight_maps]
+
+    # Compute the fused HDR image by computing a weighted sum of the input images.
+    fused = np.zeros(images[0].shape, dtype=np.float32)
+    for i, img in enumerate(images):
+        fused += weight_maps[i][:, :, np.newaxis] * img
+    #print(fused)
+
+    return fused
+
+def compress_dynamic_range(image):
+    '''Compress dynamic range based on percentile'''
+    # Find the 1st and 99th percentiles of the image
+    p1, p99 = np.percentile(image, (0, 99))
+
+    # Calculate the range of the image
+    img_range = p99 - p1
+
+    # Calculate the compression factor required to fit the image into 8-bit range
+    c = 1 / img_range
+
+    # Subtract the 1st percentile from the image and clip it to the [0, 1] range
+    new_image = np.clip((image - p1) * c, 0, 1)
+
+    return new_image
+
+def compress_dynamic_range_histo(image, new_min=0.01, new_max=0.99):
+    """Compress the dynamic range of an image using histogram stretching.
+
+    Args:
+        image: A numpy array representing an image.
+        new_min: The minimum value of the new range.
+        new_max: The maximum value of the new range.
+    Returns:
+        The compressed image.
+    """
+    # Calculate the histogram of the image.
+    hist, bins = np.histogram(image.ravel(), bins=256, range=(0, 1))
+
+    # Calculate the cumulative distribution function (CDF) of the histogram.
+    cdf = hist.cumsum()
+    cdf = (cdf - cdf.min()) / (cdf.max() - cdf.min())  # normalize to [0, 1]
+
+    # Interpolate the CDF to get the new pixel values.
+    new_pixels = np.interp(image.ravel(), bins[:-1], cdf * (new_max - new_min) + new_min)
+
+    # Reshape the new pixel values to the shape of the original image.
+    new_image = new_pixels.reshape((image.shape[0], image.shape[1], image.shape[2]))
+
+    return new_image
+
+def process(stack, gain: float = 1, weight: float = 1, gamma: float = 1, post: bool = True):
+    '''Processes the stack that contains a list of arrays form the camera into a PIL compatible clipped output array
+    Args:
+        stack : input list with arrays
+        gain : low value low contrast, high value high contrast and brightness
+        weight: How much the extracted portions of each image gets allpied to to the result "HDR effect intensity"
+        gamma: Post fusion adjustment of the gamma.
+        post: Enable or disable effects applied after the fusion True or False, default True
+            shadowlift = slightly lifts the shadows
+            Args:
+                center: position of the filter dropoff
+                width: range of the gradient, softness
+                threshold: sets the threshhold form 0 to 1 0.1= lowest blacks....
+                amount: How much the shadows should be lifted. Values under 0.1 seem to be good.
+            returns:
+                Hdr image with lifted blacks clipped to 0,1 range
+
+            compress dynamic range:
+                Tries to fit the image better into the available range. Less loggy image.
+    Returns:
+        HDR Image as PIL compatible array.
 
-        returns: Nothing
     '''
 
-    def __init__(self):
-        self.input_image: list = []
-        self.output_path: str = '/'
-        self.compress_quality: int = 75
+    hdr_image = mertens_fusion(stack ,gain, weight)
+    if post == True:
+        #hdr_image = self.highlightsdrop(hdr_image)
+        hdr_image = shadowlift(hdr_image)
+        hdr_image = compress_dynamic_range(hdr_image)
+        #hdr_image = self.compress_dynamic_range_histo(hdr_image)
+
+    hdr_image = simple_clip(hdr_image,gamma)
+    return hdr_image
 
-    def plot_histogram(self, image, title="Histogram", bins=256):
-        """Plot the histogram of an image.
-
-        Args:
-            image: A numpy array representing an image.
-            title: The title of the plot.
-            bins: The number of bins in the histogram.
-        """
-        fig, ax = plt.subplots()
-        ax.hist(image.ravel(), bins=bins, color='gray', alpha=0.7)
-        ax.set_title(title)
-        ax.set_xlabel('Pixel value')
-        ax.set_ylabel('Frequency')
-        plt.show()
-    ### Experimental functions above this line. chatGPT sketches
-
-    def simple_clip(self, fused,gamma):
-        # Apply gamma correction
-        #fused = np.clip(fused, 0, 1)
-        fused = np.power(fused, 1.0 / gamma)
-        #hdr_8bit = np.clip(res_mertens * 255, 0, 255).astype('uint8')
-        fused = (255.0 * fused).astype(np.uint8)
-        #fused = Image.fromarray(fused)
-
-        return fused
-
-    def convolve2d(self, image, kernel):
-        """Perform a 2D convolution on the given image with the given kernel.
-
-        Args:
-            image: The input image to convolve.
-            kernel: The kernel to convolve the image with.
-
-        Returns:
-            The convolved image.
-        """
-        # Get the dimensions of the image and kernel.
-        image_height, image_width = image.shape[:2]
-        kernel_height, kernel_width = kernel.shape[:2]
-
-        # Compute the amount of padding to add to the image.
-        pad_height = kernel_height // 2
-        pad_width = kernel_width // 2
-
-        # Pad the image with zeros.
-        padded_image = np.zeros(
-            (image_height + 2 * pad_height, image_width + 2 * pad_width),
-            dtype=np.float32,
-        )
-        padded_image[pad_height:-pad_height, pad_width:-pad_width] = image
-
-        # Flip the kernel horizontally and vertically.
-        flipped_kernel = np.flipud(np.fliplr(kernel))
-
-        # Convolve the padded image with the flipped kernel.
-        convolved_image = np.zeros_like(image, dtype=np.float32)
-        for row in range(image_height):
-            for col in range(image_width):
-                patch = padded_image[
-                    row : row + kernel_height, col : col + kernel_width
-                ]
-                product = patch * flipped_kernel
-                convolved_image[row, col] = product.sum()
-
-        return convolved_image
-
-    def mask(self, img, center=50, width=20, threshold=0.2):
-        '''Mask with sigmoid smooth'''
-        mask = 1 / (1 + np.exp((center - img) / width))  # Smooth gradient mask
-        mask = np.where(img > threshold, mask, 1)  # Apply threshold to the mask
-        mask =  img * mask
-        #plot_histogram(mask, title="mask")
-        return mask
-
-    def highlightsdrop(self, img, center=0.7, width=0.2, threshold=0.6, amount=0.08):
-        '''Mask with sigmoid smooth targets bright sections'''
-        mask = 1 / (1 + np.exp((center - img) / width))  # Smooth gradient mask
-        mask = np.where(img > threshold, mask, 0)  # Apply threshold to the mask
-        mask = mask.reshape((img.shape))
-        print(np.max(mask))
-        img_adjusted  = img - (mask * amount)   # Adjust the image with a user-specified amount
-        img_adjusted = np.clip(img_adjusted, 0, 1)
-
-        return img_adjusted
-
-    def shadowlift(self, img, center=0.2, width=0.1, threshold=0.2, amount= 0.05):
-        '''Mask with sigmoid smooth targets bright sections'''
-        mask = 1 / (1 + np.exp((center - img) / width))  # Smooth gradient mask
-        mask = np.where(img < threshold, mask, 0)  # Apply threshold to the mask
-        mask = mask.reshape((img.shape))
-        print(np.max(mask))
-        img_adjusted  = (mask * amount) + img   # Adjust the image with a user-specified amount
-        img_adjusted = np.clip(img_adjusted, 0, 1)
-
-        return img_adjusted
-
-    def mertens_fusion(self, image_paths, gamma=2.2, contrast_weight=0.2):
-        """Fuse multiple exposures into a single HDR image using the Mertens algorithm.
-
-        Args:
-            image_paths: A list of paths to input images.
-            gamma: The gamma correction value to apply to the input images.
-            contrast_weight: The weight of the local contrast term in the weight map computation.
-
-        Returns:
-            The fused HDR image.
-        """
-        # Load the input images and convert them to floating-point format.
-        images = []
-        for path in image_paths:
-            img = Image.open(path)
-            img = img.resize((1280, 720))
-            img = np.array(img).astype(np.float32) / 255.0
-            img = np.power(img, gamma)
-
-            images.append(img)
-
-        # Compute the weight maps for each input image based on the local contrast.
-        weight_maps = []
-
-        for img in images:
-            gray = np.dot(img, [0.2989, 0.5870, 0.1140])
-            kernel = np.array([[-1, -1, -1], [-1, 7, -1], [-1, -1, -1]])
-            laplacian = np.abs(self.convolve2d(gray, kernel))
-            weight = np.power(laplacian, contrast_weight)
-            weight_maps.append(weight)
-
-        # Normalize the weight maps.
-        total_weight = sum(weight_maps)
-        weight_maps = [w / total_weight for w in weight_maps]
-
-        # Compute the fused HDR image by computing a weighted sum of the input images.
-        fused = np.zeros(images[0].shape, dtype=np.float32)
-        for i, img in enumerate(images):
-            fused += weight_maps[i][:, :, np.newaxis] * img
-        #print(fused)
-
-        return fused
-
-    def compress_dynamic_range(self, image):
-        # Find the 1st and 99th percentiles of the image
-        p1, p99 = np.percentile(image, (0, 99))
-
-        # Calculate the range of the image
-        img_range = p99 - p1
-
-        # Calculate the compression factor required to fit the image into 8-bit range
-        c = 1 / img_range
-
-        # Subtract the 1st percentile from the image and clip it to the [0, 1] range
-        new_image = np.clip((image - p1) * c, 0, 1)
-
-        return new_image
-
-    def compress_dynamic_range_histo(self, image, new_min=0.01, new_max=0.99):
-        """Compress the dynamic range of an image using histogram stretching.
-
-        Args:
-            image: A numpy array representing an image.
-            new_min: The minimum value of the new range.
-            new_max: The maximum value of the new range.
-        Returns:
-            The compressed image.
-        """
-        # Calculate the histogram of the image.
-        hist, bins = np.histogram(image.ravel(), bins=256, range=(0, 1))
-
-        # Calculate the cumulative distribution function (CDF) of the histogram.
-        cdf = hist.cumsum()
-        cdf = (cdf - cdf.min()) / (cdf.max() - cdf.min())  # normalize to [0, 1]
-
-        # Interpolate the CDF to get the new pixel values.
-        new_pixels = np.interp(image.ravel(), bins[:-1], cdf * (new_max - new_min) + new_min)
-
-        # Reshape the new pixel values to the shape of the original image.
-        new_image = new_pixels.reshape((image.shape[0], image.shape[1], image.shape[2]))
-
-        return new_image
-
-    def open_image(filename):
-        # Open the image file in binary mode
-        with open(filename, 'rb') as f:
-            # Read the binary data from the file
-            binary_data = f.read()
-
-        # Convert the binary data to a 1D numpy array of uint8 type
-        image_array = np.frombuffer(binary_data, dtype=np.uint8)
-
-        # Reshape the 1D array into a 2D array with the correct image shape
-        # (Assuming a 3-channel RGB image with shape (height, width))
-        height = int.from_bytes(binary_data[16:20], byteorder='big')
-        width = int.from_bytes(binary_data[20:24], byteorder='big')
-        image_array = image_array[24:].reshape((height, width, 3))
-
-        return image_array
-
-    def sequence(self, gain: float = 0.8, weight: float = 0.5, gamma: float = 1, post: bool = True):
-        '''gain setting : the higher the darker, good range from 0.4- 1.0'''
-        print(self.input_image)
-        hdr_image = self.mertens_fusion(self.input_image ,gain, weight)
-
-        if post == True:
-
-            #hdr_image = self.highlightsdrop(hdr_image)
-            hdr_image = self.shadowlift(hdr_image)
-            hdr_image = self.compress_dynamic_range(hdr_image)
-            #hdr_image = self.compress_dynamic_range_histo(hdr_image)
-
-        hdr_image = self.simple_clip(hdr_image,gamma)
-        image = Image.fromarray(hdr_image)
-        image.save(f"{self.output_path}_hdr.jpg", quality=self.compress_quality)
 
 
diff --git a/picamburst.py b/picamburst.py
index a58d841..05eb031 100644
--- a/picamburst.py
+++ b/picamburst.py
@@ -13,60 +13,66 @@ picam2.set_controls({"AwbEnable": 1})
 picam2.set_controls({"AeEnable": 1})
 picam2.set_controls({"AfMode": controls.AfModeEnum.Manual })
 picam2.set_controls({"LensPosition": 0.0 })
-#picam2.set_controls({"AnalogueGain": 1.0})
-picam2.start()
-time.sleep(1)
 
-print(picam2.capture_metadata())
-start = picam2.capture_metadata()
-exposure_start = start["ExposureTime"]
-gain_start = start["AnalogueGain"]
+def get_exposure_stack(factor: int = 2):
+    '''Returns a list with arrays that contain different exposures controlled by the factor.'''
+    '''The Autoamtically set exposure of the first frame is saved and multiplied or divided ba the factor to get the above or under epxosures.'''
 
-picam2.set_controls({"AeEnable": 0})
-confirmed = picam2.capture_metadata()["AeLocked"]
-while confirmed != True:
-	confimed = picam2.capture_metadata()["AeLocked"]
-	time.sleep(.1)
+    picam2.start()
+    time.sleep(1)
 
-picam2.set_controls({"AnalogueGain": gain_start})
-confirmed = picam2.capture_metadata()["AnalogueGain"]
-while confirmed != gain_start in range(gain_start -0.1, gain_start +0.1):
+    print(picam2.capture_metadata())
+    start = picam2.capture_metadata()
+    exposure_start = start["ExposureTime"]
+    gain_start = start["AnalogueGain"]
+
+    picam2.set_controls({"AeEnable": 0})
+    confirmed = picam2.capture_metadata()["AeLocked"]
+    while confirmed != True:
+        confimed = picam2.capture_metadata()["AeLocked"]
+        time.sleep(.1)
+
+    picam2.set_controls({"AnalogueGain": gain_start})
+    confirmed = picam2.capture_metadata()["AnalogueGain"]
+    while confirmed != gain_start in range(gain_start -0.1, gain_start +0.1):
         confimed = picam2.capture_metadata()["AnalogueGain"]
         time.sleep(.1)
 
-ev1 = picam2.capture_array()
-#print("Picture one is done")
+    ev1 = picam2.capture_array()
+    #print("Picture one is done")
 
-ev_low = int(exposure_start / 2)
-picam2.set_controls({"ExposureTime": ev_low})
-confirmed = picam2.capture_metadata()["ExposureTime"]
-while confirmed not in range(ev_low -100, ev_low + 100 ):
+    ev_low = int(exposure_start / factor)
+    picam2.set_controls({"ExposureTime": ev_low})
+    confirmed = picam2.capture_metadata()["ExposureTime"]
+    while confirmed not in range(ev_low -100, ev_low + 100 ):
         confirmed = picam2.capture_metadata()["ExposureTime"]
         time.sleep(.01)
 
-#print("2",confirmed)
-ev2 = picam2.capture_array()
-#print("Picture 2 is captured to array")
+    #print("2",confirmed)
+    ev2 = picam2.capture_array()
+    #print("Picture 2 is captured to array")
 
-ev_high = int(exposure_start * 2)
-picam2.set_controls({"ExposureTime": ev_high})
-confirmed = picam2.capture_metadata()["ExposureTime"]
-while confirmed not in range(ev_high -100, ev_high + 100 ):
+    ev_high = int(exposure_start * factor)
+    picam2.set_controls({"ExposureTime": ev_high})
+    confirmed = picam2.capture_metadata()["ExposureTime"]
+    while confirmed not in range(ev_high -100, ev_high + 100 ):
         confirmed = picam2.capture_metadata()["ExposureTime"]
         time.sleep(.01)
 
-#print("3",confirmed)
-ev3 = picam2.capture_array()
-#print("Picture 3 is captured")
-print("Saving..")
+    #print("3",confirmed)
+    ev3 = picam2.capture_array()
+    #print("Picture 3 is captured")
+
+    picam2.stop()
+    stack = [ev1,ev2,ev3]
+
+    return stack
+
+
+
+
+
 
-image = Image.fromarray(ev1)
-image.save(f"test_hdr0.jpg", quality=50)
 
-image = Image.fromarray(ev2)
-image.save(f"test_hdr1.jpg", quality=50)
 
-image = Image.fromarray(ev3)
-image.save(f"test_hdr2.jpg", quality=50)
 
-picam2.stop()