diff --git a/.ipynb_checkpoints/movie_writer-checkpoint.ipynb b/.ipynb_checkpoints/movie_writer-checkpoint.ipynb
new file mode 100755
index 0000000000000000000000000000000000000000..ffd5cf3592da95abceb2ac83326bddc2882c1a6c
--- /dev/null
+++ b/.ipynb_checkpoints/movie_writer-checkpoint.ipynb
@@ -0,0 +1,144 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import imageio\n",
+    "import os\n",
+    "import cv2\n",
+    "from datetime import datetime\n",
+    "from tqdm import tqdm_notebook as tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "in_dir = r'C:\\Users\\Wheeler\\Desktop\\LCL_software\\Experiments\\experiment_18_07_2018___12.20.17.722253'\n",
+    "out_loc = r'C:\\Users\\Wheeler\\Desktop\\LCL_software\\Experiments\\experiment_18_07_2018___12.20.17.avi'\n",
+    "images = []\n",
+    "clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))\n",
+    "files = [os.path.join(in_dir, f) for f in os.listdir(in_dir) if '.tif' in f] \n",
+    "\n",
+    "\n",
+    "def get_time(file_name):\n",
+    "#     t = file_name.split('_')[-2].split('_')[0]\n",
+    "    t=file_name.split('_')[-1].split('.tif')[0]\n",
+    "    t = datetime.strptime(t, '%H.%M.%S.%f')\n",
+    "    t = t.hour*3600 + t.minute*60 + t.second + t.microsecond/10**6\n",
+    "    return t\n",
+    " \n",
+    "\n",
+    "files.sort(key=get_time,reverse = False)\n",
+    "# for file in files:\n",
+    "#     print('processing...',file.split('/')[-1])\n",
+    "#     img = cv2.imread(file,0)\n",
+    "#     img = cv2.resize(img,(int(img.shape[1]/3),int(img.shape[0]/3)),interpolation = cv2.INTER_CUBIC)\n",
+    "#     img = clahe.apply(img)\n",
+    "#     images.append(img)\n",
+    "# images = [images[0]] * 2 + images\n",
+    "# imageio.mimsave(out_loc, images,duration=.2)\n",
+    "# print('done')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(822, 1024, 3)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a9c244c465bc40b8bc8e603e2ef92fb0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "done\n"
+     ]
+    }
+   ],
+   "source": [
+    "img = cv2.imread(files[0],1)\n",
+    "width = img.shape[1]\n",
+    "height = img.shape[0]\n",
+    "print(img.shape)\n",
+    "use_clahe = False\n",
+    "fourcc = cv2.VideoWriter_fourcc(*'MJPG') # Be sure to use lower case\n",
+    "out = cv2.VideoWriter(out_loc, fourcc, 14.0, (int(width/1), int(height/1)))\n",
+    "clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))\n",
+    "for file in tqdm(files):\n",
+    "    img = cv2.imread(file,1)\n",
+    "    img = cv2.resize(img,(int(width/1),int(height/1)))\n",
+    "    if use_clahe == True:\n",
+    "        r,g,b = img[:,:,0],img[:,:,1],img[:,:,2]\n",
+    "        r = clahe.apply(r)\n",
+    "        g = clahe.apply(g)\n",
+    "        b = clahe.apply(b)\n",
+    "        img[:,:,0] = r\n",
+    "        img[:,:,1] = g\n",
+    "        img[:,:,2] = b\n",
+    "    out.write(img)\n",
+    "out.release()\n",
+    "print('done')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/__pycache__/localizer.cpython-36.pyc b/__pycache__/localizer.cpython-36.pyc
index 984146bd28f5c34bc3b87411c54223841883672b..14f3529b29e51701748e8410b3fa0ba409fae41d 100755
Binary files a/__pycache__/localizer.cpython-36.pyc and b/__pycache__/localizer.cpython-36.pyc differ
diff --git a/localizer.py b/localizer.py
index c8e678e7663db470e00fef8670d7379cce12210c..ac578097e6d8263119b71235b0b6e115ef3bc9ad 100755
--- a/localizer.py
+++ b/localizer.py
@@ -30,15 +30,16 @@ class wellStitcher():
 		self.curr_y = self.center
 		# initialize the image and show it
 		self.img_x,self.img_y = int(1024),int(822)
-		self.well_img = np.zeros((self.img_y*self.box_size,self.img_x*self.box_size,3))
-
+		self.well_img = np.zeros((self.img_y*self.box_size,self.img_x*self.box_size,3))		
 		self.stitch_img(initial_img)
 
+	def manage_zoom(self,pos):
+		print('trackbar at',pos)
 
 	def stitch_img(self,img):
 		self.well_img[self.curr_y*self.img_y:(self.curr_y+1)*self.img_y, self.curr_x*self.img_x:(self.curr_x+1)*self.img_x,:] = img
-		self.resized_img = cv2.resize(self.well_img,(1024,822))
-		cv2.imshow('Stitch',self.resized_img)
+		self.resized_img = cv2.resize(self.well_img,(int(1024*.8),int(822*.8)),interpolation = cv2.INTER_AREA)
+		cv2.imshow('Stitch',self.resized_img)		
 
 	def add_img(self,let,img):
 		if let == 'u': self.curr_y -= 1
@@ -52,7 +53,7 @@ class wellStitcher():
 		experiment_folder_location = os.path.join(os.path.dirname(os.path.abspath(__file__)),'well_images')
 		cv2.imwrite(os.path.join(experiment_folder_location,
 				'{}___{}.tif'.format('well_image',now())),self.well_img)
-		
+		cv2.createTrackbar('Zoom (2^x)','Stitch',1,6,self.manage_zoom)
 
 class Localizer(QtCore.QObject):
 	localizer_move_signal = QtCore.pyqtSignal('PyQt_PyObject','PyQt_PyObject','PyQt_PyObject','PyQt_PyObject')
@@ -168,7 +169,8 @@ class Localizer(QtCore.QObject):
 		self.localizer_move_signal.emit(position,False,False,False)
 
 	@QtCore.pyqtSlot()
-	def tile_well(self):
+	def localize2(self):
+		box_size = 5
 		self.well_center = self.get_stage_position()		
 		stitcher = wellStitcher(box_size,self.image)		
 		directions = self.get_spiral_directions(box_size)	
@@ -194,9 +196,9 @@ class Localizer(QtCore.QObject):
 		self.auto_lysis = True
 		self.well_center = self.get_stage_position()		
 		# now start moving and lysing all in view
-		self.lyse_all_in_view()
+		# self.lyse_all_in_view()
 		box_size = 5
-		# stitcher = wellStitcher(box_size,self.image)		
+		stitcher = wellStitcher(box_size,self.image)		
 		directions = self.get_spiral_directions(box_size)		
 		self.get_well_center = False
 		for num,let in directions:
@@ -214,10 +216,10 @@ class Localizer(QtCore.QObject):
 				QApplication.processEvents()
 				self.delay()
 				QApplication.processEvents()
-				# stitcher.add_img(let,self.image)
-				self.lyse_all_in_view()
+				stitcher.add_img(let,self.image)
+				# self.lyse_all_in_view()
 		comment('lysis completed!')
-		# stitcher.write_well_img()
+		stitcher.write_well_img()
 		# self.return_to_original_position(self.well_center)
 
 
diff --git a/movie_writer.ipynb b/movie_writer.ipynb
new file mode 100755
index 0000000000000000000000000000000000000000..a4413561a15c2901e7540da1fe46e598397aa1c0
--- /dev/null
+++ b/movie_writer.ipynb
@@ -0,0 +1,144 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import imageio\n",
+    "import os\n",
+    "import cv2\n",
+    "from datetime import datetime\n",
+    "from tqdm import tqdm_notebook as tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "in_dir = r'C:\\Users\\Wheeler\\Desktop\\LCL_software\\Experiments\\experiment_20_09_2018___10.08.42.888353'\n",
+    "out_loc = r'C:\\Users\\Wheeler\\Desktop\\LCL_software\\Experiments\\experiment_20_09_2018___10.08.42.avi'\n",
+    "images = []\n",
+    "clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))\n",
+    "files = [os.path.join(in_dir, f) for f in os.listdir(in_dir) if '.tif' in f] \n",
+    "\n",
+    "\n",
+    "def get_time(file_name):\n",
+    "#     t = file_name.split('_')[-2].split('_')[0]\n",
+    "    t=file_name.split('_')[-1].split('.tif')[0]\n",
+    "    t = datetime.strptime(t, '%H.%M.%S.%f')\n",
+    "    t = t.hour*3600 + t.minute*60 + t.second + t.microsecond/10**6\n",
+    "    return t\n",
+    " \n",
+    "\n",
+    "files.sort(key=get_time,reverse = False)\n",
+    "# for file in files:\n",
+    "#     print('processing...',file.split('/')[-1])\n",
+    "#     img = cv2.imread(file,0)\n",
+    "#     img = cv2.resize(img,(int(img.shape[1]/3),int(img.shape[0]/3)),interpolation = cv2.INTER_CUBIC)\n",
+    "#     img = clahe.apply(img)\n",
+    "#     images.append(img)\n",
+    "# images = [images[0]] * 2 + images\n",
+    "# imageio.mimsave(out_loc, images,duration=.2)\n",
+    "# print('done')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(822, 1024, 3)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "09d2a745ee4b4b68bd1b8fe19bdc89be",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "done\n"
+     ]
+    }
+   ],
+   "source": [
+    "img = cv2.imread(files[0],1)\n",
+    "width = img.shape[1]\n",
+    "height = img.shape[0]\n",
+    "print(img.shape)\n",
+    "use_clahe = False\n",
+    "fourcc = cv2.VideoWriter_fourcc(*'MJPG') # Be sure to use lower case\n",
+    "out = cv2.VideoWriter(out_loc, fourcc, 14.0, (int(width/1), int(height/1)))\n",
+    "clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))\n",
+    "for file in tqdm(files):\n",
+    "    img = cv2.imread(file,1)\n",
+    "    img = cv2.resize(img,(int(width/1),int(height/1)))\n",
+    "    if use_clahe == True:\n",
+    "        r,g,b = img[:,:,0],img[:,:,1],img[:,:,2]\n",
+    "        r = clahe.apply(r)\n",
+    "        g = clahe.apply(g)\n",
+    "        b = clahe.apply(b)\n",
+    "        img[:,:,0] = r\n",
+    "        img[:,:,1] = g\n",
+    "        img[:,:,2] = b\n",
+    "    out.write(img)\n",
+    "out.release()\n",
+    "print('done')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/zoom_gui_test.py b/zoom_gui_test.py
new file mode 100755
index 0000000000000000000000000000000000000000..17488a5e2cff63278f252e9a057153cd9486ff74
--- /dev/null
+++ b/zoom_gui_test.py
@@ -0,0 +1,53 @@
+import numpy
+import cv2
+
+img_loc = r'C:\Users\Wheeler\Desktop\LCL_software\well_images\well_image___20_09_2018___12.18.52.033870.tif'
+
+
+class Stitcher():
+
+	def __init__(self):
+		self.img = cv2.imread(img_loc)
+
+		# the values we will use to resize the image at the end to fit the screen
+		self.user_view_x = int(1024*.78)
+		self.user_view_y = int(822*.78)
+
+		# the center point of our image (will be updated when we move along the image)
+		self.x = int(self.img.shape[0]/2)
+		self.y = int(self.img.shape[1]/2)
+
+		# the number of pixels the unaltered image has (will be updated with zoom)
+		self.px_x = int(self.img.shape[0])
+		self.px_y = int(self.img.shape[1])
+		
+
+
+		self.img_resized = cv2.resize(self.img,(self.user_view_x,self.user_view_y),cv2.INTER_AREA)
+		cv2.imshow('Stitch',self.img_resized)
+		cv2.createTrackbar('Zoom','Stitch',0,6,self.manage_zoom)
+		cv2.waitKey(0)
+		cv2.destroyAllWindows()
+
+
+	def zoom(self,amt):
+		if amt != 0:
+			self.px_x = int(self.px_x/amt) 
+			self.px_y = int(self.px_y/amt)
+			# now we need to update based on our location and the new unaltered img
+			# values
+			cropped_img = self.img[self.x:self.px_x,self.y:self.px_y]
+			img_resized = cv2.resize(cropped_img,
+				(self.user_view_x,self.user_view_y),
+				cv2.INTER_AREA)
+			cv2.imshow('Stitch',img_resized)
+	
+	def manage_zoom(self,pos):
+		print('trackbar at',pos)
+		self.zoom(pos)
+
+s = Stitcher()
+
+
+
+