diff --git a/240103_FSC_working.ipynb b/240103_FSC_working.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7fc8135f-635d-43f0-a733-c7287d496942",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from FSC import *\n",
+    "import mrcfile\n",
+    "import matplotlib.pyplot as plt\n",
+    "import time\n",
+    "import pandas as pd\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9df44f04",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Tomogram</th>\n",
+       "      <th>Label</th>\n",
+       "      <th>PID</th>\n",
+       "      <th>EXPID</th>\n",
+       "      <th>MPID</th>\n",
+       "      <th>ccdbprod</th>\n",
+       "      <th>Thickness</th>\n",
+       "      <th>Series</th>\n",
+       "      <th>Tilts/Series</th>\n",
+       "      <th>Magnification</th>\n",
+       "      <th>Scope/Detector</th>\n",
+       "      <th>Unnamed: 11</th>\n",
+       "      <th>PIxel Size (A)</th>\n",
+       "      <th>Pixel Size bin 4 (nm)</th>\n",
+       "      <th>FSC res (vxl)</th>\n",
+       "      <th>Notes</th>\n",
+       "      <th>Unnamed: 16</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>MouseCerebellum_A6S1_SA3.3k_de64_tomo1</td>\n",
+       "      <td>A6S1#1</td>\n",
+       "      <td>20471</td>\n",
+       "      <td>5403572</td>\n",
+       "      <td>5403638</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1.5um</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>481</td>\n",
+       "      <td>3.3k</td>\n",
+       "      <td>Halo/DE64</td>\n",
+       "      <td>fpb-bin4</td>\n",
+       "      <td>12.27</td>\n",
+       "      <td>4.908</td>\n",
+       "      <td>3.75</td>\n",
+       "      <td>121 60deg a/b</td>\n",
+       "      <td>231221.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>MouseCerebellum_A2S1_SA3.3k_de64_tomo1</td>\n",
+       "      <td>A2S1#1</td>\n",
+       "      <td>20471</td>\n",
+       "      <td>5403572</td>\n",
+       "      <td>5403586</td>\n",
+       "      <td>28</td>\n",
+       "      <td>500nm</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>481</td>\n",
+       "      <td>3.3k</td>\n",
+       "      <td>Halo/DE64</td>\n",
+       "      <td>fpb-bin4</td>\n",
+       "      <td>12.27</td>\n",
+       "      <td>4.908</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 Tomogram   Label    PID    EXPID     MPID  \\\n",
+       "0  MouseCerebellum_A6S1_SA3.3k_de64_tomo1  A6S1#1  20471  5403572  5403638   \n",
+       "1  MouseCerebellum_A2S1_SA3.3k_de64_tomo1  A2S1#1  20471  5403572  5403586   \n",
+       "\n",
+       "   ccdbprod Thickness  Series  Tilts/Series Magnification Scope/Detector  \\\n",
+       "0        28     1.5um     NaN           481          3.3k      Halo/DE64   \n",
+       "1        28     500nm     NaN           481          3.3k      Halo/DE64   \n",
+       "\n",
+       "  Unnamed: 11  PIxel Size (A)  Pixel Size bin 4 (nm)  FSC res (vxl)  \\\n",
+       "0    fpb-bin4           12.27                  4.908           3.75   \n",
+       "1    fpb-bin4           12.27                  4.908            NaN   \n",
+       "\n",
+       "           Notes  Unnamed: 16  \n",
+       "0  121 60deg a/b     231221.0  \n",
+       "1            NaN          NaN  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Working with file structure to analyze multiple datasets\n",
+    "\n",
+    "data_path = '/Users/atk42/OneDrive - Yale University/Lab/Projects/TEM_tomo/tomo_data'\n",
+    "tomo_lst = 'tomograms_lst - Local Tomograms for FSC.csv'\n",
+    "tomo_lst_path = os.sep.join([data_path, tomo_lst])\n",
+    "\n",
+    "df = pd.read_csv(tomo_lst_path)\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f4eb433a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/atk42/OneDrive - Yale University/Lab/Projects/TEM_tomo/tomo_data/microscopy_5403638/processed_data/MouseCerebellum_A6S1_SA3.3k_de64_tomo1/txbr-backprojection/limited-bin4/121-limited[60.0_-60.0]_fsc-a/MouseCerebellum_A6S1_SA3.3k_de64_tomo1a_z_-185.0.out\n",
+      "/Users/atk42/OneDrive - Yale University/Lab/Projects/TEM_tomo/tomo_data/microscopy_5403638/processed_data/MouseCerebellum_A6S1_SA3.3k_de64_tomo1/txbr-backprojection/limited-bin4/121-limited[60.0_-60.0]_fsc-b/MouseCerebellum_A6S1_SA3.3k_de64_tomo1a_z_-185.0.out\n"
+     ]
+    }
+   ],
+   "source": [
+    "row = df.iloc[0]\n",
+    "proj = 'microscopy_%i' % int(row['MPID'])\n",
+    "tomo = row['Tomogram']\n",
+    "tomo_path = os.sep.join([data_path, proj, 'processed_data',tomo,'txbr-backprojection','limited-bin4'])\n",
+    "num_ang = 121\n",
+    "max_ang = 60\n",
+    "a_dir = os.sep.join([tomo_path,'%i-limited[%.1f_-%.1f]_fsc-a' % (num_ang,max_ang,max_ang)])\n",
+    "if len(os.listdir(a_dir)) == 1:\n",
+    "    a_path = os.sep.join([a_dir,os.listdir(a_dir)[0]])\n",
+    "else:\n",
+    "    print('tomo does not have exactly 1 output file: ')\n",
+    "    print(os.listdir(a_dir))\n",
+    "    a_path = None\n",
+    "print(a_path)\n",
+    "\n",
+    "b_dir = os.sep.join([tomo_path,'%i-limited[%.1f_-%.1f]_fsc-b' % (num_ang,max_ang,max_ang)])\n",
+    "if len(os.listdir(b_dir)) == 1:\n",
+    "    b_path = os.sep.join([b_dir,os.listdir(a_dir)[0]])\n",
+    "else:\n",
+    "    print('tomo dir does not have exactly 1 output file: ')\n",
+    "    print(os.listdir(b_dir))\n",
+    "    b_path = None\n",
+    "print(b_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "f5872cd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Estimating the resolution by FSC...\n",
+      "Base arguments: {'fn1': '/Users/atk42/OneDrive - Yale University/Lab/Projects/TEM_tomo/tomo_data/microscopy_5403638/processed_data/MouseCerebellum_A6S1_SA3.3k_de64_tomo1/txbr-backprojection/limited-bin4/121-limited[60.0_-60.0]_fsc-a/MouseCerebellum_A6S1_SA3.3k_de64_tomo1a_z_-185.0.out', 'fn2': '/Users/atk42/OneDrive - Yale University/Lab/Projects/TEM_tomo/tomo_data/microscopy_5403638/processed_data/MouseCerebellum_A6S1_SA3.3k_de64_tomo1/txbr-backprojection/limited-bin4/121-limited[60.0_-60.0]_fsc-b/MouseCerebellum_A6S1_SA3.3k_de64_tomo1a_z_-185.0.out', 'cube_size': 50, 'snrt': 0.2071, 'rt': 6, 'rad_apod': 60, 'ax_apod': 60, 'pixel_size': 1, 'savefig': False, 'prefix': ''}\n",
+      "Running across 4 cores\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 64/64 [05:40<00:00,  5.32s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Outputting to test_FSC_1.5um.csv\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from resolution_measure_mrc import *\n",
+    "ofn = 'test_FSC_1.5um.csv'\n",
+    "resolution_measure(a_path, b_path, 4, 50, pixel_size = 1, sub_region = 200, ofn=ofn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "463e340a-c286-4852-9dd9-ee73379f7e8c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/atk42/OneDrive - Yale University/Lab/Projects/TEM_tomo/tomo_data/microscopy_5403586/processed_data/MouseCerebellum_A2S1_SA3.3k_de64_tomo1/txbr-backprojection/limited-bin4/121-limited[60.0_-60.0]_fsc-a/MouseCerebellum_A2S1_SA3.3k_de64_tomo1a_z_-69.0.out\n",
+      "/Users/atk42/OneDrive - Yale University/Lab/Projects/TEM_tomo/tomo_data/microscopy_5403586/processed_data/MouseCerebellum_A2S1_SA3.3k_de64_tomo1/txbr-backprojection/limited-bin4/121-limited[60.0_-60.0]_fsc-b/MouseCerebellum_A2S1_SA3.3k_de64_tomo1a_z_-69.0.out\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_path = '/Users/atk42/OneDrive - Yale University/Lab/Projects/TEM_tomo/tomo_data/'\n",
+    "\n",
+    "a_path = data_path+'microscopy_5403586'+'/'+'processed_data'+'/'+'MouseCerebellum_A2S1_SA3.3k_de64_tomo1'+ \\\n",
+    "    '/' +'txbr-backprojection/limited-bin4'+'/'+'121-limited[60.0_-60.0]_fsc-a'+'/'+'MouseCerebellum_A2S1_SA3.3k_de64_tomo1a_z_-69.0.out'\n",
+    "b_path = data_path+'microscopy_5403586'+'/'+'processed_data'+'/'+'MouseCerebellum_A2S1_SA3.3k_de64_tomo1'+ \\\n",
+    "    '/' +'txbr-backprojection/limited-bin4'+'/'+'121-limited[60.0_-60.0]_fsc-b'+'/'+'MouseCerebellum_A2S1_SA3.3k_de64_tomo1a_z_-69.0.out'\n",
+    "print(a_path)\n",
+    "print(b_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "c4c15808-4cc7-44f8-9e75-509c916124d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(130, 2065, 2067) (130, 2065, 2067)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#mrcfile.validate(path)\n",
+    "with mrcfile.open(a_path) as mrc:\n",
+    "    a = mrc.data\n",
+    "with mrcfile.open(b_path) as mrc:\n",
+    "    b = mrc.data\n",
+    "print(a.shape, b.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "650f02c3-8456-4045-950e-e438888a5975",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- 3.0877089500427246 seconds ---\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "start_time = time.time()\n",
+    "\n",
+    "a_crop = a[:199,:199,:199]\n",
+    "b_crop = b[:199,:199,:199]\n",
+    "#plt.imshow(xy_img)\n",
+    "fsc_vol = FSCPlot(a_crop,b_crop)\n",
+    "fsc_vol.plot()\n",
+    "\n",
+    "print(\"--- %s seconds ---\" % (time.time() - start_time))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "a7b86c90-ba44-4570-834f-86c6bbbd0fd8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- 1.2701420783996582 seconds ---\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "start_time = time.time()\n",
+    "\n",
+    "xy_img_a = a[:,1000,:]\n",
+    "xy_img_b = b[:,1000,:]\n",
+    "#plt.imshow(xy_img)\n",
+    "fsc_vol = FSCPlot(xy_img_a, xy_img_b)\n",
+    "fsc_vol.plot()\n",
+    "\n",
+    "print(\"--- %s seconds ---\" % (time.time() - start_time))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "0cdb69af-baea-40cf-af25-635c253b33fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xz_img_a = a[:,:,1002]\n",
+    "xz_img_b = b[:,:,1002]\n",
+    "#plt.imshow(xy_img)\n",
+    "fsc_vol = FSCPlot(xz_img_a, xz_img_b)\n",
+    "fsc_vol.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "3fd6e94a-c306-4847-a2f6-c444868a2448",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "yz_img_a = a[70,:,:]\n",
+    "yz_img_b = b[70,:,:]\n",
+    "#plt.imshow(xy_img)\n",
+    "fsc_vol = FSCPlot(yz_img_a, yz_img_b)\n",
+    "fsc_vol.plot()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/FSC.py b/FSC.py
new file mode 100755
index 0000000..d765406
--- /dev/null
+++ b/FSC.py
@@ -0,0 +1,342 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Feb 19 15:33:00 2015
+
+FOURIER SHELL CORRELATION
+FSC3D(image1, image2, SNRt, ring_thick)
+
+Computes the Fourier Shell Correlation between image1 and image2, and computes
+the threshold funcion T of 1 or 1/2 bit.
+
+"""
+from __future__ import division, print_function
+import numpy as np
+from numpy import meshgrid
+import matplotlib.pyplot as plt
+
+__all__ = ['FourierShellCorr', 'FSCPlot', 'HannApod']
+
+def printv(txt):
+    #printvtxt)
+    return
+
+def _radtap(X,Y,tappix,zerorad):
+    """
+    Creates a central cosine tapering.
+    It receives the X and Y coordinates, tappix is the extent of
+    tapering, zerorad is the radius with no data (zeros).
+    """
+    tau = 2*tappix # period of cosine function (only half a period is used)
+
+    R = np.sqrt(X**2+Y**2)
+    taperfunc = 0.5*(1+np.cos(2*np.pi*(R-zerorad-tau/2.)/tau))
+    taperfunc = (R>zerorad+tau/2.)*1.0 + taperfunc*(R<=zerorad+tau/2)
+    taperfunc = taperfunc*(R>=zerorad)
+    return taperfunc
+
+class HannApod:
+    def __init__(self,outputdim,filterdim,unmodsize):
+        printv('Calling the class HannApod')
+        self.outputdim = outputdim
+        self.unmodsize = unmodsize
+        self.filterdim = filterdim
+
+    def fract_hanning(self):#outputdim,unmodsize):
+        """
+        fract_hanning(outputdim,unmodsize)
+        out = Square array containing a fractional separable Hanning window with
+        DC in upper left corner.
+        outputdim = size of the output array
+        unmodsize = Size of the central array containing no modulation.
+        Creates a square hanning window if unmodsize = 0 (or ommited), otherwise the output array
+        will contain an array of ones in the center and cosine modulation on the
+        edges, the array of ones will have DC in upper left corner.
+        """
+        printv('Calling fract_hanning')
+        N = np.arange(0,self.outputdim)
+        Nc,Nr = np.meshgrid(N,N)
+        if self.unmodsize == 0:
+            out = (1.+np.cos(2*np.pi*Nc/self.outputdim))*(1.+np.cos(2*np.pi*Nr/self.outputdim))/4.
+        else:
+            #columns modulation
+            outc = (1.+np.cos(2*np.pi*(Nc-np.floor((self.unmodsize-1)/2))/(self.outputdim+1-self.unmodsize)))/2.
+            if np.floor((self.unmodsize-1)/2.)>0:
+                outc[:,:int(np.floor((self.unmodsize-1)/2.))]=1
+            outc[:,int(np.floor((self.unmodsize-1)/2)+self.outputdim+3-self.unmodsize):len(N)] = 1
+            #row modulation
+            outr = (1.+np.cos(2*np.pi*(Nr-np.floor((self.unmodsize-1)/2))/(self.outputdim+1-self.unmodsize)))/2.
+            if np.floor((self.unmodsize-1)/2.)>0:
+                outr[:int(np.floor((self.unmodsize-1)/2.)),:]=1
+            outr[int(np.floor((self.unmodsize-1)/2)+self.outputdim+3-self.unmodsize):len(N),:] = 1
+
+            out=outc*outr
+
+        return out
+
+    def fract_hanning_pad(self):#outputdim,filterdim,unmodsize):#(N,N,np.round(N*(1-filtertomo))):
+        """
+        fract_hanning_pad(outputdim,filterdim,unmodsize)
+        out = Square array containing a fractional separable Hanning window with
+        DC in upper left corner.
+        outputdim = size of the output array
+        filterdim = size of filter (it will zero pad if filterdim<outputdim)
+        unmodsize = Size of the central array containing no modulation.
+        Creates a square hanning window if unmodsize = 0 (or ommited), otherwise the output array
+        will contain an array of ones in the center and cosine modulation on the
+        edges, the array of ones will have DC in upper left corner.
+        """
+        if self.outputdim < self.unmodsize:
+            raise SystemExit('Output dimension must be smaller or equal to size of unmodulated window')
+        if self.outputdim < self.filterdim:
+            raise SystemExit('Filter cannot be larger than output size')
+        if self.unmodsize<0:
+            self.unmodsize = 0
+            printv('Specified unmodsize<0, setting unmodsize = 0')
+        printv('Calling fract_hanning_pad')
+        out = np.zeros((self.outputdim,self.outputdim))
+        auxindini = int(np.round(self.outputdim/2.-self.filterdim/2.))
+        auxindend = int(np.round(self.outputdim/2.+self.filterdim/2.))
+        hanning_window = self.fract_hanning()
+        out[auxindini:auxindend, auxindini:auxindend]=np.fft.fftshift(hanning_window)
+        #out[auxindini:auxindend, auxindini:auxindend]=np.fft.fftshift(self.fract_hanning(filterdim,unmodsize))
+        #return np.fft.fftshift(out)
+        return out
+
+class FourierShellCorr(HannApod):
+    """
+    FOURIER SHELL CORRELATION
+    FSC3D(image1, image2, SNRt, ring_thick)
+
+    Computes the Fourier Shell Correlation between image1 and image2, and computes
+    the threshold funcion T of 1 or 1/2 bit.
+    It can handle non-cube arrays, but it assumes that the voxel is isotropic.
+    It applies a Hanning window of the size of the data to the data before the
+    Fourier transform calculations to attenuate the border effects.
+
+    INPUTS:
+        image1 = image 1
+        image2 = image 2
+        SNRt = power SNR for threshold computation. Options:
+                SNRt = 0.5 -> 1 bit threshold for average
+                SNRt = 0.2071 -> 1/2 bit threshold for average
+        ring_thick = thickness of the frequency rings.
+                    Normally the pixels get assined to the closest integer pixel ring
+                    in Fourier Domain. With ring_thick, each ring gets more pixels and
+                    more statistics.
+    Reference: M. van Heel, M. Schatzb, "Fourier shell correlation threshold
+    criteria," Journal of Structural Biology 151, 250-262 (2005)
+
+    @author: Julio Cesar da Silva (jdasilva@esrf.fr)
+    default rad_apod 60 axial_apod 20
+    """
+    def __init__(self,img1,img2,snrt=0.2071,ring_thick=0,rad_apod=300,axial_apod=100):
+        printv('Calling the class FourierShellCorr')
+        self.snrt = snrt
+        self.ring_thick = ring_thick
+        self.img1 = np.array(img1)
+        self.img2 = np.array(img2)
+        self.rad_apod = rad_apod
+        self.axial_apod = axial_apod
+        printv('Input images have {} dimensions'.format(self.img1.ndim))
+        if self.img1.shape != self.img2.shape:
+            printv("Images must have the same size")
+            raise SystemExit
+        if ring_thick !=0:
+            printv('Using ring_thick = {}'.format(ring_thick))
+        printv('Using SNRt = %g' %snrt)
+
+    def nyquist(self):
+        """
+        Evaluate the Nyquist Frequency
+        """
+        nmax = np.max(self.img1.shape)
+        fnyquist = np.floor(nmax/2.0)
+        f = np.arange(0,fnyquist+1)
+        return f, fnyquist
+
+    def ringthickness(self):
+        """
+        Define ring_thick
+        """
+        n = self.img1.shape
+        nmax = np.max(n)
+        x = np.arange(-np.fix(n[1]/2.0),np.ceil(n[1]/2.0))*np.floor(nmax/2.0)/np.floor(n[1]/2.0)
+        y = np.arange(-np.fix(n[0]/2.0),np.ceil(n[0]/2.0))*np.floor(nmax/2.0)/np.floor(n[0]/2.0)
+        if self.img1.ndim==3:
+            z = np.arange(-np.fix(n[2]/2.0),np.ceil(n[2]/2.0))*np.floor(nmax/2.0)/np.floor(n[2]/2.0)
+            X = meshgrid(x,y,z)
+        elif self.img1.ndim==2:
+            X = np.meshgrid(x,y)
+        else:
+            printv('Number of dimensions is different from 2 or 3.Exiting...')
+            raise SystemExit('Number of dimensions is different from 2 or 3.Exiting...')
+        sumsquares = np.zeros_like(X[-1])
+        for ii in np.arange(0,self.img1.ndim):
+            sumsquares += X[ii]**2
+        index = np.round(np.sqrt(sumsquares))
+        return index
+
+    def apodization(self):
+        """
+        Compute the Hanning window of the size of the data for the apodization
+        """
+        n = self.img1.shape
+        if self.img1.ndim==2:
+            window = np.outer(np.hanning(n[0]),np.hanning(n[1]))
+        elif self.img1.ndim==3:
+            window1 = np.hanning(n[0])
+            window2 = np.hanning(n[1])
+            window3 = np.hanning(n[2])
+            windowaxial = np.outer(window2,window3)
+            windowsag = np.array([window1 for ii in range(n[1])]).swapaxes(0,1)
+            #win2d = np.rollaxis(np.array([np.tile(windowaxial,(1,1)) for ii in range(n[0])]),1).swapaxes(1,2)
+            win2d = np.array([np.tile(windowaxial,(1,1)) for ii in range(n[0])])
+            window = np.array([np.squeeze(win2d[:,:,ii])*windowsag for ii in range(n[2])]).swapaxes(0,1).swapaxes(1,2)
+        else:
+            printv('Number of dimensions is different from 2 or 3. Exiting...')
+            raise SystemExit('Number of dimensions is different from 2 or 3. Exiting...')
+        return window
+
+    def circle(self):
+        printv('Calculating the axial apodization')
+        if self.img1.ndim ==2:
+            shape_x = self.img1.shape[1]
+            shape_y = self.img1.shape[0]
+        elif self.img1.ndim ==3:
+            shape_x = self.img1.shape[2]
+            shape_y = self.img1.shape[1]
+        x_array = np.arange(0,shape_x)
+        y_array = np.arange(0,shape_y)
+        self.X,self.Y = np.meshgrid(x_array-np.round(shape_x/2.),y_array-np.round(shape_y/2.))
+        circular_region=1-_radtap(self.X,self.Y,self.rad_apod,np.round(shape_x/2.)-self.rad_apod)
+        return circular_region
+
+    def transverse_apodization(self):
+        """
+        Compute the Hanning window of the size of the data for the apodization
+        """
+        printv('Calculating the transverse apodization')
+        n = self.img1.shape
+        HannApod.__init__(self,n[0],n[0],n[0]-2*self.axial_apod)
+        filters=HannApod.fract_hanning_pad(self)
+        window1d=filters[:,int(filters.shape[0]/2)]
+        window2d=np.array([window1d for ii in range(n[1])]).swapaxes(0,1)
+        return window2d
+
+    def fouriercorr(self):
+        """
+        Compute FSC and threshold
+        """
+        # Apodization
+        n = self.img1.shape
+        circular_region = self.circle()
+        if self.img1.ndim ==2:
+            self.window = circular_region
+        elif self.img1.ndim==3:
+            window2D = self.transverse_apodization()
+            circle3D = np.asarray([circular_region for ii in range(n[0])])
+            self.window = np.array([np.squeeze(circle3D[:,:,ii])*window2D for ii in range(n[2])]).swapaxes(0,1).swapaxes(1,2)
+            printv('Apodization in 3D')
+
+        # FSC computation
+        F1 = np.fft.ifftshift(np.fft.fftn(np.fft.fftshift(self.img1*self.window)))
+        #F1 = np.fft.ifftshift(np.fft.fftn(np.fft.fftshift(self.img1)))
+        #printvF1.shape)
+        F2 = np.fft.ifftshift(np.fft.fftn(np.fft.fftshift(self.img2*self.window)))
+        #F2 = np.fft.ifftshift(np.fft.fftn(np.fft.fftshift(self.img2)))
+
+        C,C1,C2,npts = [[],[],[],[]]
+        printv('Calling method fouriercorr from the class FourierShellCorr')
+        index = self.ringthickness()
+        f,fnyquist = self.nyquist()
+        for ii in f:
+            if self.ring_thick ==0:
+                auxF1 = F1[np.where(index==ii)]
+                auxF2 = F2[np.where(index==ii)]
+            else:
+                auxF1 = F1[(np.where( (index>=(ii-self.ring_thick//2)) & (index<=(ii+self.ring_thick//2)) ))]
+                auxF2 = F2[(np.where( (index>=(ii-self.ring_thick//2)) & (index<=(ii+self.ring_thick//2)) ))]
+            C.append(np.sum(auxF1*np.conj(auxF2)))
+            C1.append(np.sum(auxF1*np.conj(auxF1)))
+            C2.append(np.sum(auxF2*np.conj(auxF2)))
+            npts.append(auxF1.shape[0])
+            # The correlation
+        FSC = np.abs(np.asarray(C))/(np.sqrt(np.asarray(C1)*np.asarray(C2)))
+
+        npts = np.asarray(npts)
+        # Threshold computation
+        Tnum = (self.snrt + (2*np.sqrt(self.snrt)/np.sqrt(npts+np.spacing(1)))+1/np.sqrt(npts))
+        Tden = (self.snrt + (2*np.sqrt(self.snrt)/np.sqrt(npts+np.spacing(1)))+1)
+        T= Tnum/Tden
+
+        return FSC, T
+
+class FSCPlot(FourierShellCorr):
+    """
+    Plot the FSC and threshold curves
+    """
+    def __init__(self,img1,img2,snrt=0.2071,ring_thick=0,rad_apod=300,axial_apod=100):
+        printv('calling the class FSCplot')
+        FourierShellCorr.__init__(self, img1, img2, snrt, ring_thick,rad_apod,axial_apod)
+        self.FSC, self.T = FourierShellCorr.fouriercorr(self)
+        self.f, self.fnyquist = FourierShellCorr.nyquist(self)
+    def plot(self):
+        printv('calling method plot from the class FSCplot')
+        plt.close()
+        plt.figure("FSC %s %s %s"%(self.ring_thick, self.rad_apod, self.axial_apod))
+        plt.clf()
+        plt.plot(self.f/self.fnyquist,self.FSC.real,'-b', label='FSC')
+        plt.legend()
+        i =self.get_intersect()
+        plt.plot([i,i],[0,1],label="%s"%i)
+        if self.snrt == 0.2071:
+            plt.plot(self.f/self.fnyquist, self.T, '--r',label='1/2 bit threshold')
+            plt.legend()
+        elif self.snrt == 0.5:
+            plt.plot(self.f/self.fnyquist, self.T, '--r',label='1 bit threshold')
+            plt.legend()
+        else:
+            plotT = plt.plot(self.f/self.fnyquist, self.T)
+            plt.legend(plotT,'Threshold SNR = %g ' %self.snrt, loc='center')
+        plt.xlim(0,1)
+        plt.ylim(0,1.1)
+        plt.xlabel('Spatial frequency/Nyquist')
+        plt.ylabel('Magnitude')
+        #plt.show()
+        if self.img1.ndim==2:
+            plt.savefig('FSC_2D.png', bbox_inches='tight')
+        elif self.img1.ndim==2:
+            plt.savefig('FSC_3D_%s_%s_%s.png'%(self.ring_thick, self.rad_apod, self.axial_apod), bbox_inches='tight')
+
+    def plot_curfig(self,name=""):
+        printv('calling method plot from the class FSCplot')
+        if name == "":
+            name='FSC %s %s %s'%(self.ring_thick, self.rad_apod, self.axial_apod)
+        plt.plot(self.f/self.fnyquist,self.FSC.real, label=name)
+        plt.legend()
+    def get_intersect(self):
+        ro = np.argmax(self.FSC.real < self.T)
+        dr = self.FSC.real - self.T
+        frac = dr[ro-1]/(dr[ro-1] - dr[ro])
+        df = (self.f/self.fnyquist)[ro] - (self.f/self.fnyquist)[ro-1]
+        return (self.f/self.fnyquist)[ro-1] + frac * df
+
+    def plot_nyquist(self):
+        if self.snrt == 0.2071:
+            plt.plot(self.f/self.fnyquist, self.T, '--r',label='1/2 bit threshold')
+            plt.legend()
+        elif self.snrt == 0.5:
+            plt.plot(self.f/self.fnyquist, self.T, '--r',label='1 bit threshold')
+            plt.legend()
+        else:
+            plotT = plt.plot(self.f/self.fnyquist, self.T)
+            plt.legend(plotT,'Threshold SNR = %g ' %self.snrt, loc='center')
+        plt.xlim(0,1)
+        plt.ylim(0,1.1)
+        plt.xlabel('Spatial frequency/Nyquist')
+        plt.ylabel('Magnitude')
+    def save_fig(self,prefix):
+        if self.img1.ndim==2:
+            plt.savefig('FSC_2D.png', bbox_inches='tight')
+        elif self.img1.ndim==3:
+            plt.savefig('%sFSC_3D_%s_%s_%s.png'%(prefix,self.ring_thick, self.rad_apod, self.axial_apod), bbox_inches='tight')
diff --git a/README.md b/README.md
new file mode 100755
index 0000000..06342ce
--- /dev/null
+++ b/README.md
@@ -0,0 +1,19 @@
+Xray tools
+
+add this repo to your system $PATH and $PYTHONPATH
+
+convert volumes to zarr files
+
+vol_to_zarr.py <fn.vol> z x y chunk
+
+e.g.
+
+vol_to_zarr.py volume_1.vol 200 3000 3000 100
+
+vol_to_zarr.py volume_2.vol 200 3000 3000 100
+
+run resolution estimation on the volumes
+
+resolution_measure.py <zarr file1> <zarr file2> <ncores> <cube size>[ -ps <pixel_size=50> --snrt <snrt value=.2071>]
+
+resolution_measure.py jaspersLegCryo_r1_50nm_rec_cone_01799_10001800.zarr jaspersLegCryo_r1_50nm_rec_cone_12000_10001800.zarr 8 200 --snrt 0.143
diff --git a/resolution_measure_mrc.py b/resolution_measure_mrc.py
new file mode 100644
index 0000000..516a257
--- /dev/null
+++ b/resolution_measure_mrc.py
@@ -0,0 +1,256 @@
+#!/usr/bin/env python
+
+from __future__ import division, print_function
+#import zarr
+from scipy import *
+import numpy as np
+import os, sys
+import multiprocessing
+#import h5py
+import tqdm
+import json
+from scipy.ndimage import fourier_shift
+from scipy.special import erf
+from time import time
+from FSC import *
+import mrcfile
+import argparse
+
+
+
+
+
+# this will be the function the worker threads run to analyze one block
+def parallel_FSC_worker(a):
+    #f1t = zarr.open(a['fn1'],'r')
+    #f2t = zarr.open(a['fn2'],'r')
+    #v1 = f1t['data']
+    #v2 = f2t['data']
+    with mrcfile.open(a['fn1']) as mrc:
+        v1 = mrc.data
+    with mrcfile.open(a['fn2']) as mrc:
+        v2 = mrc.data
+    s = a['cube_size']
+    z,x,y = a['top_left']
+    a['center'] = (z+s//2,x+s//2,y+s//2)
+    v1c=v1[z:z+s,x:x+s,y:y+s]
+    v2c=v2[z:z+s,x:x+s,y:y+s]
+    a['mean_pix'] = (v1c.mean() + v2c.mean())/2.
+    if( v1c.max() > 0 and v2c.max() > 0):
+        FSC3Dvol = FSCPlot(v1[z:z+s,x:x+s,y:y+s],
+                           v2[z:z+s,x:x+s,y:y+s],
+                           a['snrt'],a['rt'],a['rad_apod'],a['ax_apod'])
+        a['resolution'] = a['pixel_size']/FSC3Dvol.get_intersect()
+        if a['savefig']:
+            FSC3Dvol.plot()
+            FSC3Dvol.save_fig(a['prefix'])
+    else:
+        a['resolution'] = -1
+        
+    return a.copy()
+    
+def sweep_param(param_name, r, base_args,proj_name):
+    makedir("%s/param_sweep"%proj_name)
+    tmpd = base_args.copy()
+    tmpd['savefig']=True
+    par_args = []
+    fp = param_name
+    if type(param_name)==list:
+       fp = param_name[0]
+    print("Sweeping %s"%fp)
+    makedir("%s/param_sweep/%s"%(proj_name,fp))
+    for i in r:
+        if type(param_name) == list:
+            for p in param_name:
+                tmpd[p] = i
+        else:
+            tmpd[param_name] = i
+        cs = tmpd['cube_size']
+        tmpd['top_left'] = (z_size//2 -cs//2, x_size//2 - cs//2,y_size//2 - cs//2)
+        tmpd['prefix'] = "%s/param_sweep/%s/%s_"%(proj_name,fp,i)
+        par_args.append(tmpd.copy())       
+    return par_args
+
+def makedir(dn):
+    if os.path.exists(dn):
+        if os.path.isdir(dn):
+            return
+        else:
+            raise Exception("Dir %s exists as a file"%dn)
+    else:
+        os.makedirs(dn)
+
+def resolution_measure(vol1, vol2, num_cores, cube_size, \
+    project_name='FSC', sub_region=-1, use_json=False, \
+    snrt = 0.2071, pixel_size = 1, param_sweep=False, \
+    ofn = None):
+    makedir(project_name)
+    
+    z_st,x_st,y_st = (0,0,0)
+    with mrcfile.open(vol1) as mrc:
+        z_size,x_size,y_size = mrc.data.shape
+
+    if sub_region > 0:
+        z_st = (z_size - sub_region)//2
+        x_st = (x_size - sub_region)//2
+        y_st = (y_size - sub_region)//2
+        z_size = sub_region
+        x_size = sub_region
+        y_size = sub_region
+    
+    tmp = dict()
+    
+    if os.path.exists("%s/default.json"%project_name) and use_json:
+        tmp = json.load(open("%s/default.json"%project_name))
+    else:
+        tmp['fn1']=vol1
+        tmp['fn2']=vol2
+        tmp['cube_size'] = cube_size
+        tmp['snrt'] = snrt
+        tmp['rt'] = 6
+        tmp['rad_apod'] = 60   
+        tmp['ax_apod'] = 60
+        tmp['pixel_size'] = pixel_size # nm
+        tmp['savefig']=False
+        tmp['prefix'] =""
+        json.dump(tmp,open("%s/default.json"%project_name,'w'))
+    
+    print("Estimating the resolution by FSC...")
+    startfsc = time()
+    
+    # prepare the pool
+    pool = multiprocessing.Pool(num_cores)
+    par_args = []
+    
+    if not param_sweep:
+        print("Base arguments: %s"%tmp)
+        for i in range( x_st, x_st + x_size - cube_size+1, cube_size):
+            for j in range( y_st, y_st + y_size - cube_size+1, cube_size):
+                for k in range(z_st,z_st + z_size-cube_size+1,cube_size):
+                    tmp['top_left'] = (k,i,j) 
+                    par_args.append(tmp.copy())
+    
+        #run 
+        print("Running across %s cores"%num_cores)
+    
+        ret = list(tqdm.tqdm(pool.imap(parallel_FSC_worker, par_args), total=len(par_args)))
+    
+        if ofn is None:
+            ofn = "%s/FSC_%s.csv"%(project_name,cube_size)
+        print("Outputting to %s"%ofn)
+        of = open(ofn,'w')
+        for r in ret:
+            tl = "%s, %s, %s"%r['center']
+            of.write("%s, %s %s\n"%(tl,r['resolution'],r['mean_pix']))
+    else:
+        print("Running parameter sweep")
+        par_args = []
+        par_args.extend(sweep_param('cube_size',range(50,800,100),tmp,project_name))
+        par_args.extend(sweep_param(['ax_apod','rad_apod'],range(10,1000,100),tmp,project_name))
+        par_args.extend(sweep_param('rt',range(2,14,2),tmp,project_name))
+        print("Running across %s cores"%num_cores)
+        pool.map(parallel_FSC_worker, par_args)
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument("vol1",help="fn of first mrc volume")
+    parser.add_argument("vol2",help="fn of second mrc volume")
+    parser.add_argument("num_cores",default=1,type=int,help="number of cores to par over")
+    parser.add_argument("cube_size",default=200,type=int,help="len of a side of the cube to partiton dataset")
+    parser.add_argument("-ps","--pixel_size",default=50,type=int,help="resolution in nm of dataset")
+    parser.add_argument("-sn","--snrt",default=0.2071,type=float,help="snrt value to use, .2071 is default however, 1/7 is 0.143") 
+    parser.add_argument("--param_sweep",action="store_true",help="flag to do an initial parameter sweep")
+    parser.add_argument("--use_json",action="store_true",help="flag to set using the default.json")
+    parser.add_argument("-sub","--sub_region",default=-1, type=int, help="size of the subvolume to run on, will take a centered cube of side length specified")
+    args = parser.parse_args()
+
+    project_name = ""
+    for ind in range(len(args.vol1)):
+        if args.vol1[ind] == args.vol2[ind]:
+            project_name += args.vol1[ind]
+        else:
+            if project_name == "":
+                project_name = "resolution_test"
+            break
+    
+    project_name = "%s_resolution"%project_name
+    print("Project name: %s"%project_name)
+    makedir(project_name)
+    
+    
+    
+    #confirm these are h5py files before launching par jobs
+    #f1 = zarr.open(args.vol1,'r')
+    #f2 = zarr.open(args.vol2,'r')
+    
+    z_st,x_st,y_st = (0,0,0)
+    with mrcfile.open(args.vol1) as mrc:
+        z_size,x_size,y_size = mrc.data.shape
+    #z_size,x_size,y_size = f1['data'].shape
+    if args.sub_region > 0:
+        z_st = (z_size - args.sub_region)//2
+        x_st = (x_size - args.sub_region)//2
+        y_st = (y_size - args.sub_region)//2
+        z_size = args.sub_region
+        x_size = args.sub_region
+        y_size = args.sub_region
+    
+    
+    # TODO assert here if f2 dosnt have same shape?
+    # prepare the dictionary to be passed to worker threads
+    
+    
+    tmp = dict()
+    
+    if os.path.exists("%s/default.json"%project_name) and args.use_json:
+        tmp = json.load(open("%s/default.json"%project_name))
+    else:
+        tmp['fn1']=args.vol1
+        tmp['fn2']=args.vol2
+        tmp['cube_size'] = args.cube_size
+        tmp['snrt'] = args.snrt
+        tmp['rt'] = 6
+        tmp['rad_apod'] = 60   
+        tmp['ax_apod'] = 60
+        tmp['pixel_size'] = args.pixel_size # nm
+        tmp['savefig']=False
+        tmp['prefix'] =""
+        json.dump(tmp,open("%s/default.json"%project_name,'w'))
+    
+    print("Estimating the resolution by FSC...")
+    startfsc = time()
+    
+    
+    # prepare the pool
+    pool = multiprocessing.Pool(args.num_cores)
+    par_args = []
+    
+    if not args.param_sweep:
+        print("Base arguments: %s"%tmp)
+        for i in range( x_st, x_st + x_size - args.cube_size+1, args.cube_size):
+            for j in range( y_st, y_st + y_size - args.cube_size+1, args.cube_size):
+                for k in range(z_st,z_st + z_size-args.cube_size+1,args.cube_size):
+                    tmp['top_left'] = (k,i,j) 
+                    par_args.append(tmp.copy())
+    
+        #run 
+        print("Running across %s cores"%args.num_cores)
+    
+        ret = list(tqdm.tqdm(pool.imap(parallel_FSC_worker, par_args), total=len(par_args)))
+    
+        ofn = "%s/FSC_%s.csv"%(project_name,args.cube_size)
+        print("Outputting to %s"%ofn)
+        of = open(ofn,'w')
+        for r in ret:
+            tl = "%s, %s, %s"%r['center']
+            of.write("%s, %s %s\n"%(tl,r['resolution'],r['mean_pix']))
+    else:
+        print("Running parameter sweep")
+        par_args = []
+        par_args.extend(sweep_param('cube_size',range(50,800,100),tmp,project_name))
+        par_args.extend(sweep_param(['ax_apod','rad_apod'],range(10,1000,100),tmp,project_name))
+        par_args.extend(sweep_param('rt',range(2,14,2),tmp,project_name))
+        print("Running across %s cores"%args.num_cores)
+        pool.map(parallel_FSC_worker, par_args)
+
+