From 2df54e360efb3d6d70f3c57251e21cebcf2959cc Mon Sep 17 00:00:00 2001
From: Karthik <karthik@physi.uni-heidelberg.de>
Date: Thu, 4 May 2023 18:32:17 +0200
Subject: [PATCH] first working version

---
 Analyser/FitAnalyser.py      |   88 +-
 Analyser/ImagingAnalyser.py  |   14 +-
 ToolFunction/ToolFunction.py |   18 +
 test.ipynb                   | 1613 +---------------------------------
 4 files changed, 142 insertions(+), 1591 deletions(-)
 create mode 100644 ToolFunction/ToolFunction.py

diff --git a/Analyser/FitAnalyser.py b/Analyser/FitAnalyser.py
index 2eba308..52ed310 100644
--- a/Analyser/FitAnalyser.py
+++ b/Analyser/FitAnalyser.py
@@ -61,13 +61,22 @@ def expansion(x, amplitude=1.0, offset=0.0):
 def dampingOscillation(x, center=0, amplitude=1.0, frequency=1.0, decay=1.0, offset=0.0):
     return amplitude * np.exp(-decay*x)*np.sin(2*np.pi*frequency*(x-center)) + offset
 
-
-def double_structure(x, x1=0.25, x2=0.75, amplitude=1.0, center=0.0, sigma=1.0, a=-1.0, b=0, c=0):
-    
-    y = np.zeros(x.shape)
     
-    return ((amplitude/(max(tiny, s2pi*sigma)))
-            * exp(-(1.0*x-center)**2 / max(tiny, (2*sigma**2))))
+def two_gaussian2d(x, y=0.0, A_amplitude=1.0, A_centerx=0.0, A_centery=0.0, A_sigmax=1.0,
+               A_sigmay=1.0, B_amplitude=1.0, B_centerx=0.0, B_centery=0.0, B_sigmax=1.0,
+               B_sigmay=1.0):
+    """Return a 2-dimensional Gaussian function.
+
+    gaussian2d(x, y, amplitude, centerx, centery, sigmax, sigmay) =
+        amplitude/(2*pi*sigmax*sigmay) * exp(-(x-centerx)**2/(2*sigmax**2)
+                                             -(y-centery)**2/(2*sigmay**2))
+
+    """
+    z = A_amplitude*(gaussian(x, amplitude=1, center=A_centerx, sigma=A_sigmax) *
+                   gaussian(y, amplitude=1, center=A_centery, sigma=A_sigmay))
+    z += B_amplitude*(gaussian(x, amplitude=1, center=B_centerx, sigma=B_sigmax) *
+                   gaussian(y, amplitude=1, center=B_centery, sigma=B_sigmay))
+    return z
 
 
 class GaussianWithOffsetModel(Model):
@@ -178,6 +187,36 @@ class DampingOscillationModel(Model):
         return update_param_vals(pars, self.prefix, **kwargs)
 
 
+class TwoGaussian2dModel(Model):
+    
+    fwhm_factor = 2*np.sqrt(2*np.log(2))
+    height_factor = 1./2*np.pi
+
+    def __init__(self, independent_vars=['x', 'y'], prefix='', nan_policy='raise',
+                 **kwargs):
+        kwargs.update({'prefix': prefix, 'nan_policy': nan_policy,
+                       'independent_vars': independent_vars})
+        
+        self.helperModel = Gaussian2dModel()
+        
+        super().__init__(two_gaussian2d, **kwargs)
+
+    def guess(self, data, x, y, negative=False, **kwargs):
+        pars_guess = guess_from_peak2d(self.helperModel, data, x, y, negative)
+        pars = self.make_params(A_amplitude=pars_guess['amplitude'], A_centerx=pars_guess['centerx'], A_centery=pars_guess['centery'],
+                             A_sigmax=pars_guess['sigmax'], A_sigmay=pars_guess['sigmay'], 
+                             B_amplitude=pars_guess['amplitude'], B_centerx=pars_guess['centerx'], B_centery=pars_guess['centery'],
+                             B_sigmax=pars_guess['sigmax'], B_sigmay=pars_guess['sigmay'])
+        
+        pars.add(f'{self.prefix}delta', value=-1, max=0, vary=True)
+        pars[f'{self.prefix}A_sigmax'].set(expr=f'delta + {self.prefix}B_sigmax')
+        pars[f'{self.prefix}A_sigmay'].set(min=0.0)
+        pars[f'{self.prefix}B_sigmax'].set(min=0.0)
+        pars[f'{self.prefix}B_sigmay'].set(min=0.0)
+        
+        return pars
+
+
 lmfit_models = {'Constant': ConstantModel,
                 'Complex Constant': ComplexConstantModel,
                 'Linear': LinearModel,
@@ -209,7 +248,8 @@ lmfit_models = {'Constant': ConstantModel,
                 'Gaussian With Offset':GaussianWithOffsetModel,
                 'Lorentzian With Offset':LorentzianWithOffsetModel,
                 'Expansion':ExpansionModel,
-                'Damping Oscillation Model':DampingOscillationModel
+                'Damping Oscillation Model':DampingOscillationModel,
+                'Two Gaussian-2D':TwoGaussian2dModel,
                 }
 
 
@@ -224,19 +264,20 @@ class FitAnalyser():
         
         self.fitDim = fitDim
     
-    def _guess_1D(self, data, x):
-        return self.fitModel.guess(data=data, x=x)
+    def _guess_1D(self, data, x, **kwargs):
+        return self.fitModel.guess(data=data, x=x, **kwargs)
     
-    def _guess_2D(self, data, x, y):
-        return self.fitModel.guess(data=data, x=x, y=y)
+    def _guess_2D(self, data, x, y, **kwargs):
+        return self.fitModel.guess(data=data, x=x, y=y, **kwargs)
     
-    def guess(self, dataArray, x=None, y=None, input_core_dims=None, dask='parallelized', vectorize=True, **kwargs):
+    def guess(self, dataArray, x=None, y=None, guess_kwargs={}, input_core_dims=None, dask='parallelized', vectorize=True, keep_attrs=True, **kwargs):
         
         kwargs.update(
             {
                 "dask": dask,
                 "vectorize": vectorize,
-                "input_core_dims": input_core_dims
+                "input_core_dims": input_core_dims,
+                'keep_attrs': keep_attrs,
             }
         )
 
@@ -262,7 +303,13 @@ class FitAnalyser():
 
         if self.fitDim == 1:
             
-            return xr.apply_ufunc(self._guess_1D, dataArray, kwargs={'x':x},
+            guess_kwargs.update(
+                {
+                    'x':x, 
+                }
+            )
+            
+            return xr.apply_ufunc(self._guess_1D, dataArray, kwargs=guess_kwargs,
                                     output_dtypes=[type(self.fitModel.make_params())],
                                     **kwargs
                                     )
@@ -297,7 +344,14 @@ class FitAnalyser():
             
             kwargs["input_core_dims"][0] = ['_z']
 
-            return xr.apply_ufunc(self._guess_2D, dataArray, kwargs={'x':_x, 'y':_y},
+            guess_kwargs.update(
+                {
+                    'x':_x, 
+                    'y':_y,
+                }
+            )
+            
+            return xr.apply_ufunc(self._guess_2D, dataArray, kwargs=guess_kwargs,
                                     output_dtypes=[type(self.fitModel.make_params())],
                                     **kwargs
                                     )
@@ -309,13 +363,14 @@ class FitAnalyser():
     def _fit_2D(self, data, params, x, y):
         return self.fitModel.fit(data=data, x=x, y=y, params=params)
 
-    def fit(self, dataArray, paramsArray, x=None, y=None, input_core_dims=None, dask='parallelized', vectorize=True, **kwargs):
+    def fit(self, dataArray, paramsArray, x=None, y=None, input_core_dims=None, dask='parallelized', vectorize=True, keep_attrs=True, **kwargs):
         
         kwargs.update(
             {
                 "dask": dask,
                 "vectorize": vectorize,
                 "input_core_dims": input_core_dims,
+                'keep_attrs': keep_attrs,
             }
         )
 
@@ -415,7 +470,6 @@ class FitAnalyser():
                                         output_dtypes=[type(lmfit.model.ModelResult(self.fitModel, self.fitModel.make_params()))], 
                                         **kwargs)
 
-
     def _eval_1D(self, fitResult, x):
         return self.fitModel.eval(x=x, **fitResult.best_values)
     
diff --git a/Analyser/ImagingAnalyser.py b/Analyser/ImagingAnalyser.py
index 0311bc0..7e3e815 100644
--- a/Analyser/ImagingAnalyser.py
+++ b/Analyser/ImagingAnalyser.py
@@ -104,10 +104,13 @@ class ImageAnalyser():
     def get_Ncount(self, imageOD):
         return np.sum(imageOD)
 
-    def get_absorption_images(self, dataset, dask='allowed', **kwargs):
+    def get_absorption_images(self, dataset, dask='allowed', keep_attrs=True, **kwargs):
 
         kwargs.update(
-            {'dask': dask}
+            {
+                'dask': dask,
+                'keep_attrs': keep_attrs,
+            }
         )
 
         dataset = dataset.assign(
@@ -118,10 +121,13 @@ class ImageAnalyser():
 
         return dataset
 
-    def remove_background(self, dataset, dask='allowed', **kwargs):
+    def remove_background(self, dataset, dask='allowed', keep_attrs=True, **kwargs):
 
         kwargs.update(
-            {'dask': dask}
+            {
+                'dask': dask,
+                'keep_attrs': keep_attrs,
+            }
         )
 
         xr.apply_ufunc(self.get_OD, dataset[self._image_name['atoms']], dataset[self._image_name['background']], dataset[self._image_name['dark']], **kwargs)
diff --git a/ToolFunction/ToolFunction.py b/ToolFunction/ToolFunction.py
new file mode 100644
index 0000000..64c279a
--- /dev/null
+++ b/ToolFunction/ToolFunction.py
@@ -0,0 +1,18 @@
+def get_mask():
+    pass
+
+
+def remove_bad_shots():
+    pass
+
+
+def auto_rechunk():
+    pass
+
+
+def get_h5_file_path(folderpath, maxFileNum):
+    pass
+
+
+def get_folder_path():
+    pass
\ No newline at end of file
diff --git a/test.ipynb b/test.ipynb
index 39497e7..432ed41 100644
--- a/test.ipynb
+++ b/test.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,412 +36,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
-       "    <div style=\"margin-left: 48px;\">\n",
-       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
-       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-eaab97f7-e905-11ed-b318-9c7bef43b4fb</p>\n",
-       "        <table style=\"width: 100%; text-align: left;\">\n",
-       "\n",
-       "        <tr>\n",
-       "        \n",
-       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
-       "            <td style=\"text-align: left;\"><strong>Cluster type:</strong> distributed.LocalCluster</td>\n",
-       "        \n",
-       "        </tr>\n",
-       "\n",
-       "        \n",
-       "            <tr>\n",
-       "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
-       "                </td>\n",
-       "                <td style=\"text-align: left;\"></td>\n",
-       "            </tr>\n",
-       "        \n",
-       "\n",
-       "        </table>\n",
-       "\n",
-       "        \n",
-       "\n",
-       "        \n",
-       "            <details>\n",
-       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
-       "            <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
-       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\">\n",
-       "    </div>\n",
-       "    <div style=\"margin-left: 48px;\">\n",
-       "        <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
-       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">d4aeddff</p>\n",
-       "        <table style=\"width: 100%; text-align: left;\">\n",
-       "            <tr>\n",
-       "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
-       "                </td>\n",
-       "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Workers:</strong> 6\n",
-       "                </td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Total threads:</strong> 60\n",
-       "                </td>\n",
-       "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Total memory:</strong> 55.88 GiB\n",
-       "                </td>\n",
-       "            </tr>\n",
-       "            \n",
-       "            <tr>\n",
-       "    <td style=\"text-align: left;\"><strong>Status:</strong> running</td>\n",
-       "    <td style=\"text-align: left;\"><strong>Using processes:</strong> True</td>\n",
-       "</tr>\n",
-       "\n",
-       "            \n",
-       "        </table>\n",
-       "\n",
-       "        <details>\n",
-       "            <summary style=\"margin-bottom: 20px;\">\n",
-       "                <h3 style=\"display: inline;\">Scheduler Info</h3>\n",
-       "            </summary>\n",
-       "\n",
-       "            <div style=\"\">\n",
-       "    <div>\n",
-       "        <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
-       "        <div style=\"margin-left: 48px;\">\n",
-       "            <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
-       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-99210554-0c4e-4072-ad2d-4d72899b1ed8</p>\n",
-       "            <table style=\"width: 100%; text-align: left;\">\n",
-       "                <tr>\n",
-       "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Comm:</strong> tcp://127.0.0.1:49232\n",
-       "                    </td>\n",
-       "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Workers:</strong> 6\n",
-       "                    </td>\n",
-       "                </tr>\n",
-       "                <tr>\n",
-       "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
-       "                    </td>\n",
-       "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Total threads:</strong> 60\n",
-       "                    </td>\n",
-       "                </tr>\n",
-       "                <tr>\n",
-       "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Started:</strong> Just now\n",
-       "                    </td>\n",
-       "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Total memory:</strong> 55.88 GiB\n",
-       "                    </td>\n",
-       "                </tr>\n",
-       "            </table>\n",
-       "        </div>\n",
-       "    </div>\n",
-       "\n",
-       "    <details style=\"margin-left: 48px;\">\n",
-       "        <summary style=\"margin-bottom: 20px;\">\n",
-       "            <h3 style=\"display: inline;\">Workers</h3>\n",
-       "        </summary>\n",
-       "\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:49259\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 10\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:49265/status\" target=\"_blank\">http://127.0.0.1:49265/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 9.31 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:49235\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> C:\\Users\\JIANSH~1\\AppData\\Local\\Temp\\dask-worker-space\\worker-ww2ggttu\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 1</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:49262\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 10\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:49271/status\" target=\"_blank\">http://127.0.0.1:49271/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 9.31 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:49236\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> C:\\Users\\JIANSH~1\\AppData\\Local\\Temp\\dask-worker-space\\worker-5p0p884i\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 2</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:49260\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 10\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:49269/status\" target=\"_blank\">http://127.0.0.1:49269/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 9.31 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:49237\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> C:\\Users\\JIANSH~1\\AppData\\Local\\Temp\\dask-worker-space\\worker-za3qby07\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 3</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:49261\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 10\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:49267/status\" target=\"_blank\">http://127.0.0.1:49267/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 9.31 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:49238\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> C:\\Users\\JIANSH~1\\AppData\\Local\\Temp\\dask-worker-space\\worker-qkb264de\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 4</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:49264\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 10\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:49273/status\" target=\"_blank\">http://127.0.0.1:49273/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 9.31 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:49239\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> C:\\Users\\JIANSH~1\\AppData\\Local\\Temp\\dask-worker-space\\worker-schn83iz\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 5</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:49263\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 10\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:49274/status\" target=\"_blank\">http://127.0.0.1:49274/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 9.31 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:49240\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> C:\\Users\\JIANSH~1\\AppData\\Local\\Temp\\dask-worker-space\\worker-mvo1ci40\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "\n",
-       "    </details>\n",
-       "</div>\n",
-       "\n",
-       "        </details>\n",
-       "    </div>\n",
-       "</div>\n",
-       "            </details>\n",
-       "        \n",
-       "\n",
-       "    </div>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "<Client: 'tcp://127.0.0.1:49232' processes=6 threads=60, memory=55.88 GiB>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from dask.distributed import Client\n",
     "client = Client(n_workers=6, threads_per_worker=10, processes=True, memory_limit='10GB')\n",
@@ -466,23 +63,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# filepath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/04/18/0003/*.h5\"\n",
+    "# filepath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/05/03/0043/*.h5\"\n",
     "# filepath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/04/18/0003/2023-04-18_0003_Evaporative_Cooling_000.h5\"\n",
     "\n",
-    "# filepath = \"//DyLabNAS/Data/Repetition_scan/2023/04/21/0002/*.h5\"\n",
+    "filepath = \"//DyLabNAS/Data/Repetition_scan/2023/04/21/0002/*.h5\"\n",
     "\n",
-    "# filepath = r\"./testData/0003/*.h5\"\n",
+    "# filepath = r\"./testData/0000/*.h5\"\n",
     "\n",
-    "filepath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/04/18/0003/*.h5\""
+    "# filepath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/04/18/0003/*.h5\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -512,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -520,14 +117,15 @@
     "\n",
     "scanAxis = dataset.scanAxis\n",
     "\n",
-    "dataset = dataset.chunk(\n",
-    "    {\n",
-    "        \"compZ_current_sg\": \"auto\",\n",
-    "        \"runs\": 2,\n",
-    "        \"x\": \"auto\",\n",
-    "        \"y\": \"auto\",\n",
-    "    }\n",
-    ")"
+    "# dataset = dataset.chunk(\n",
+    "#     {\n",
+    "#         # \"compZ_current_sg\": \"auto\",\n",
+    "#         \"sin_mod_freq\": \"auto\",\n",
+    "#         \"runs\": 2,\n",
+    "#         \"x\": \"auto\",\n",
+    "#         \"y\": \"auto\",\n",
+    "#     }\n",
+    "# )"
    ]
   },
   {
@@ -548,1033 +146,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
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-       "\n",
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-       ".xr-header > ul {\n",
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-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
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-       ".xr-obj-type {\n",
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-       "\n",
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-       "\n",
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-       ".xr-var-data,\n",
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-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:           (compZ_current_sg: 300, runs: 2, x: 1200, y: 1920)\n",
-       "Coordinates:\n",
-       "  * compZ_current_sg  (compZ_current_sg) float64 0.175 0.176 ... 0.473 0.474\n",
-       "  * runs              (runs) float64 0.0 1.0\n",
-       "Dimensions without coordinates: x, y\n",
-       "Data variables:\n",
-       "    atoms             (compZ_current_sg, runs, x, y) uint16 dask.array&lt;chunksize=(14, 2, 1200, 1920), meta=np.ndarray&gt;\n",
-       "    background        (compZ_current_sg, runs, x, y) uint16 dask.array&lt;chunksize=(14, 2, 1200, 1920), meta=np.ndarray&gt;\n",
-       "    dark              (compZ_current_sg, runs, x, y) uint16 dask.array&lt;chunksize=(14, 2, 1200, 1920), meta=np.ndarray&gt;\n",
-       "    shotNum           (compZ_current_sg, runs) &lt;U3 dask.array&lt;chunksize=(300, 2), meta=np.ndarray&gt;\n",
-       "    OD                (compZ_current_sg, runs, x, y) float64 dask.array&lt;chunksize=(14, 2, 1200, 1920), meta=np.ndarray&gt;\n",
-       "Attributes: (12/100)\n",
-       "    TOF_free:                          0.02\n",
-       "    abs_img_freq:                      110.866\n",
-       "    absorption_imaging_flag:           True\n",
-       "    backup_data:                       True\n",
-       "    blink_off_time:                    nan\n",
-       "    blink_on_time:                     nan\n",
-       "    ...                                ...\n",
-       "    z_offset:                          compZ_current_sg\n",
-       "    z_offset_img:                      compZ_current_sg\n",
-       "    compZ_current_sg:                  [0.175 0.176 0.177 0.178 0.179 0.18  0...\n",
-       "    runs:                              [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0...\n",
-       "    scanAxis:                          [&#x27;compZ_current_sg&#x27;, &#x27;runs&#x27;]\n",
-       "    scanAxisLength:                    [600. 600.]</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-ee374266-6256-442f-8811-c9f32f92835e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ee374266-6256-442f-8811-c9f32f92835e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>compZ_current_sg</span>: 300</li><li><span class='xr-has-index'>runs</span>: 2</li><li><span>x</span>: 1200</li><li><span>y</span>: 1920</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-37c5ddc4-44ca-4a03-8260-30b174898eaf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-37c5ddc4-44ca-4a03-8260-30b174898eaf' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>compZ_current_sg</span></div><div class='xr-var-dims'>(compZ_current_sg)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.175 0.176 0.177 ... 0.473 0.474</div><input id='attrs-050c9ec6-745b-41c3-8a08-66981ce37ebb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-050c9ec6-745b-41c3-8a08-66981ce37ebb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e972803-729a-4288-aaf1-3d81c711d312' class='xr-var-data-in' type='checkbox'><label for='data-5e972803-729a-4288-aaf1-3d81c711d312' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.175, 0.176, 0.177, ..., 0.472, 0.473, 0.474])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>runs</span></div><div class='xr-var-dims'>(runs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0</div><input id='attrs-8dd34359-cae3-409c-98a1-c3b4c09b6880' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8dd34359-cae3-409c-98a1-c3b4c09b6880' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2f1ca85-e02c-448f-ac9b-d1fc0049f3aa' class='xr-var-data-in' type='checkbox'><label for='data-f2f1ca85-e02c-448f-ac9b-d1fc0049f3aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 1.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-15d1c07e-b833-47fa-a097-911e106e286a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-15d1c07e-b833-47fa-a097-911e106e286a' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>atoms</span></div><div class='xr-var-dims'>(compZ_current_sg, runs, x, y)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(14, 2, 1200, 1920), meta=np.ndarray&gt;</div><input id='attrs-bb5b80b8-ab0a-4567-8d97-6eb3029fd9a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb5b80b8-ab0a-4567-8d97-6eb3029fd9a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e215839-57b4-4dbe-8e29-a2e9dfd65758' class='xr-var-data-in' type='checkbox'><label for='data-4e215839-57b4-4dbe-8e29-a2e9dfd65758' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 2.57 GiB </td>\n",
-       "                        <td> 123.05 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (300, 2, 1200, 1920) </td>\n",
-       "                        <td> (14, 2, 1200, 1920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 22 chunks in 1804 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> uint16 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "\n",
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-       "  <text x=\"107.474299\" y=\"102.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,107.474299,102.474299)\">2</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>background</span></div><div class='xr-var-dims'>(compZ_current_sg, runs, x, y)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(14, 2, 1200, 1920), meta=np.ndarray&gt;</div><input id='attrs-269f0afe-dd66-4697-9150-feebe15ebdd2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-269f0afe-dd66-4697-9150-feebe15ebdd2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69e3c61a-3e67-4263-a8aa-6a96498033e6' class='xr-var-data-in' type='checkbox'><label for='data-69e3c61a-3e67-4263-a8aa-6a96498033e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 2.57 GiB </td>\n",
-       "                        <td> 123.05 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (300, 2, 1200, 1920) </td>\n",
-       "                        <td> (14, 2, 1200, 1920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 22 chunks in 1804 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> uint16 numpy.ndarray </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dark</span></div><div class='xr-var-dims'>(compZ_current_sg, runs, x, y)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(14, 2, 1200, 1920), meta=np.ndarray&gt;</div><input id='attrs-6bf62c54-9c5b-4c5d-8ce1-cb4cca5a8b20' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6bf62c54-9c5b-4c5d-8ce1-cb4cca5a8b20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e43a2c8-5197-41b7-b53b-2e60e250c707' class='xr-var-data-in' type='checkbox'><label for='data-6e43a2c8-5197-41b7-b53b-2e60e250c707' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
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-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
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-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 2.57 GiB </td>\n",
-       "                        <td> 123.05 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
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-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> uint16 numpy.ndarray </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shotNum</span></div><div class='xr-var-dims'>(compZ_current_sg, runs)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(300, 2), meta=np.ndarray&gt;</div><input id='attrs-3d584d73-d6d7-489a-bd51-b1a4ff77ff68' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3d584d73-d6d7-489a-bd51-b1a4ff77ff68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-470cc4a3-5088-4447-8b36-969e3dc82dab' class='xr-var-data-in' type='checkbox'><label for='data-470cc4a3-5088-4447-8b36-969e3dc82dab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 7.03 kiB </td>\n",
-       "                        <td> 7.03 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (300, 2) </td>\n",
-       "                        <td> (300, 2) </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> <U3 numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>OD</span></div><div class='xr-var-dims'>(compZ_current_sg, runs, x, y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(14, 2, 1200, 1920), meta=np.ndarray&gt;</div><input id='attrs-fcf5da1f-5026-4433-97fc-ff7868abe070' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fcf5da1f-5026-4433-97fc-ff7868abe070' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-efa6b255-ffde-4831-8d1b-1758ddb230cc' class='xr-var-data-in' type='checkbox'><label for='data-efa6b255-ffde-4831-8d1b-1758ddb230cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 10.30 GiB </td>\n",
-       "                        <td> 492.19 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (300, 2, 1200, 1920) </td>\n",
-       "                        <td> (14, 2, 1200, 1920) </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 22 chunks in 5422 graph layers </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-445d4cf1-ba85-463f-8e41-58b1d015498e' class='xr-section-summary-in' type='checkbox'  ><label for='section-445d4cf1-ba85-463f-8e41-58b1d015498e' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>compZ_current_sg</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e02b6d8e-5e4a-4c22-82f4-1269725ca7d8' class='xr-index-data-in' type='checkbox'/><label for='index-e02b6d8e-5e4a-4c22-82f4-1269725ca7d8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([              0.175,               0.176,               0.177,\n",
-       "                            0.178,               0.179,                0.18,\n",
-       "                            0.181,               0.182,               0.183,\n",
-       "                            0.184,\n",
-       "              ...\n",
-       "              0.46500000000000025, 0.46600000000000025, 0.46700000000000025,\n",
-       "              0.46800000000000025, 0.46900000000000025, 0.47000000000000025,\n",
-       "              0.47100000000000025, 0.47200000000000025, 0.47300000000000025,\n",
-       "              0.47400000000000025],\n",
-       "             dtype=&#x27;float64&#x27;, name=&#x27;compZ_current_sg&#x27;, length=300))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>runs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6cdddac2-04ae-40a6-b5ec-fc93ffd2006c' class='xr-index-data-in' type='checkbox'/><label for='index-6cdddac2-04ae-40a6-b5ec-fc93ffd2006c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([0.0, 1.0], dtype=&#x27;float64&#x27;, name=&#x27;runs&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dd5124d7-280a-42f6-b0e4-b3ea99e0b59f' class='xr-section-summary-in' type='checkbox'  ><label for='section-dd5124d7-280a-42f6-b0e4-b3ea99e0b59f' class='xr-section-summary' >Attributes: <span>(100)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>TOF_free :</span></dt><dd>0.02</dd><dt><span>abs_img_freq :</span></dt><dd>110.866</dd><dt><span>absorption_imaging_flag :</span></dt><dd>True</dd><dt><span>backup_data :</span></dt><dd>True</dd><dt><span>blink_off_time :</span></dt><dd>nan</dd><dt><span>blink_on_time :</span></dt><dd>nan</dd><dt><span>c_duration :</span></dt><dd>0.2</dd><dt><span>cmot_final_current :</span></dt><dd>0.65</dd><dt><span>cmot_hold :</span></dt><dd>0.06</dd><dt><span>cmot_initial_current :</span></dt><dd>0.18</dd><dt><span>compX_current :</span></dt><dd>0.005</dd><dt><span>compX_current_sg :</span></dt><dd>0</dd><dt><span>compX_final_current :</span></dt><dd>0.002</dd><dt><span>compX_initial_current :</span></dt><dd>0.005</dd><dt><span>compY_current :</span></dt><dd>0</dd><dt><span>compY_current_sg :</span></dt><dd>0</dd><dt><span>compY_final_current :</span></dt><dd>0</dd><dt><span>compY_initial_current :</span></dt><dd>0</dd><dt><span>compZ_current :</span></dt><dd>0</dd><dt><span>compZ_final_current :</span></dt><dd>0.286</dd><dt><span>compZ_initial_current :</span></dt><dd>0</dd><dt><span>default_camera :</span></dt><dd>0</dd><dt><span>evap_1_final_pow_1 :</span></dt><dd>4.8</dd><dt><span>evap_1_final_pow_2 :</span></dt><dd>2.2</dd><dt><span>evap_1_final_pow_3 :</span></dt><dd>1.2</dd><dt><span>evap_1_final_pow_4 :</span></dt><dd>0.526</dd><dt><span>evap_1_ramp_duration_1 :</span></dt><dd>0.15</dd><dt><span>evap_1_ramp_duration_2 :</span></dt><dd>0.3</dd><dt><span>evap_1_ramp_duration_3 :</span></dt><dd>0.2</dd><dt><span>evap_1_ramp_duration_4 :</span></dt><dd>0.5</dd><dt><span>evap_1_start_pow_1 :</span></dt><dd>7</dd><dt><span>evap_1_start_pow_2 :</span></dt><dd>4.8</dd><dt><span>evap_1_start_pow_3 :</span></dt><dd>2.2</dd><dt><span>evap_1_start_pow_4 :</span></dt><dd>1.2</dd><dt><span>evap_2_final_pow_1 :</span></dt><dd>0.35</dd><dt><span>evap_2_ramp_duration_1 :</span></dt><dd>0.5</dd><dt><span>evap_2_start_pow_1 :</span></dt><dd>0.526</dd><dt><span>evap_3_arm_1_final_pow :</span></dt><dd>0.037</dd><dt><span>evap_3_arm_1_start_pow :</span></dt><dd>0.35</dd><dt><span>evap_3_arm_2_final_pow :</span></dt><dd>0.09</dd><dt><span>evap_3_arm_2_start_pow :</span></dt><dd>5</dd><dt><span>evap_3_ramp_duration :</span></dt><dd>1.0</dd><dt><span>evap_3_rate_constant_1 :</span></dt><dd>0.37</dd><dt><span>evap_3_rate_constant_2 :</span></dt><dd>0.71</dd><dt><span>evap_4_arm_1_final_pow :</span></dt><dd>0.085</dd><dt><span>evap_4_arm_1_start_pow :</span></dt><dd>0.037</dd><dt><span>evap_ramp_4_duration :</span></dt><dd>0.1</dd><dt><span>final_amp :</span></dt><dd>0.00018</dd><dt><span>final_freq :</span></dt><dd>104</dd><dt><span>gradCoil_current :</span></dt><dd>0.18</dd><dt><span>gradCoil_current_sg :</span></dt><dd>0</dd><dt><span>imaging_method :</span></dt><dd>in_situ_absorption</dd><dt><span>imaging_pulse_duration :</span></dt><dd>2.5e-05</dd><dt><span>imaging_wavelength :</span></dt><dd>4.21291e-07</dd><dt><span>initial_amp :</span></dt><dd>0.62</dd><dt><span>initial_freq :</span></dt><dd>101.8</dd><dt><span>mod_depth_1_fin :</span></dt><dd>0.5</dd><dt><span>mod_depth_1_ini :</span></dt><dd>1</dd><dt><span>mod_depth_2_fin :</span></dt><dd>0.2</dd><dt><span>mod_depth_2_ini :</span></dt><dd>0.5</dd><dt><span>mod_depth_3_fin :</span></dt><dd>0.15</dd><dt><span>mod_depth_3_ini :</span></dt><dd>0.2</dd><dt><span>mod_depth_4_fin :</span></dt><dd>0</dd><dt><span>mod_depth_4_ini :</span></dt><dd>0.15</dd><dt><span>mod_depth_ini :</span></dt><dd>1</dd><dt><span>mod_depth_on_final :</span></dt><dd>0.35</dd><dt><span>mod_depth_on_initial :</span></dt><dd>0</dd><dt><span>mot_3d_amp :</span></dt><dd>0.62</dd><dt><span>mot_3d_camera_exposure_time :</span></dt><dd>0.002</dd><dt><span>mot_3d_camera_trigger_duration :</span></dt><dd>0.00025</dd><dt><span>mot_3d_freq :</span></dt><dd>101.8</dd><dt><span>mot_load_duration :</span></dt><dd>2</dd><dt><span>odt_axis_camera_trigger_duration :</span></dt><dd>0.002</dd><dt><span>odt_hold_time_1 :</span></dt><dd>0.01</dd><dt><span>odt_hold_time_2 :</span></dt><dd>0.1</dd><dt><span>odt_hold_time_3 :</span></dt><dd>0.1</dd><dt><span>odt_hold_time_4 :</span></dt><dd>1</dd><dt><span>pow_arm_1 :</span></dt><dd>7</dd><dt><span>pow_arm_2 :</span></dt><dd>0</dd><dt><span>pow_fin_arm_2 :</span></dt><dd>5</dd><dt><span>pow_ini_arm_2 :</span></dt><dd>0</dd><dt><span>pulse_delay :</span></dt><dd>8e-05</dd><dt><span>push_amp :</span></dt><dd>0.16</dd><dt><span>push_freq :</span></dt><dd>102.25</dd><dt><span>ramp_duration :</span></dt><dd>1</dd><dt><span>save_results :</span></dt><dd>False</dd><dt><span>stern_gerlach_duration :</span></dt><dd>0.001</dd><dt><span>truncation_value :</span></dt><dd>1.0</dd><dt><span>wait_after_2dmot_off :</span></dt><dd>0</dd><dt><span>wait_time_between_images :</span></dt><dd>0.22</dd><dt><span>x_offset :</span></dt><dd>0</dd><dt><span>x_offset_img :</span></dt><dd>0</dd><dt><span>y_offset :</span></dt><dd>0</dd><dt><span>y_offset_img :</span></dt><dd>0</dd><dt><span>z_offset :</span></dt><dd>compZ_current_sg</dd><dt><span>z_offset_img :</span></dt><dd>compZ_current_sg</dd><dt><span>compZ_current_sg :</span></dt><dd>[0.175 0.176 0.177 0.178 0.179 0.18  0.181 0.182 0.183 0.184 0.185 0.186\n",
-       " 0.187 0.188 0.189 0.19  0.191 0.192 0.193 0.194 0.195 0.196 0.197 0.198\n",
-       " 0.199 0.2   0.201 0.202 0.203 0.204 0.205 0.206 0.207 0.208 0.209 0.21\n",
-       " 0.211 0.212 0.213 0.214 0.215 0.216 0.217 0.218 0.219 0.22  0.221 0.222\n",
-       " 0.223 0.224 0.225 0.226 0.227 0.228 0.229 0.23  0.231 0.232 0.233 0.234\n",
-       " 0.235 0.236 0.237 0.238 0.239 0.24  0.241 0.242 0.243 0.244 0.245 0.246\n",
-       " 0.247 0.248 0.249 0.25  0.251 0.252 0.253 0.254 0.255 0.256 0.257 0.258\n",
-       " 0.259 0.26  0.261 0.262 0.263 0.264 0.265 0.266 0.267 0.268 0.269 0.27\n",
-       " 0.271 0.272 0.273 0.274 0.275 0.276 0.277 0.278 0.279 0.28  0.281 0.282\n",
-       " 0.283 0.284 0.285 0.286 0.287 0.288 0.289 0.29  0.291 0.292 0.293 0.294\n",
-       " 0.295 0.296 0.297 0.298 0.299 0.3   0.301 0.302 0.303 0.304 0.305 0.306\n",
-       " 0.307 0.308 0.309 0.31  0.311 0.312 0.313 0.314 0.315 0.316 0.317 0.318\n",
-       " 0.319 0.32  0.321 0.322 0.323 0.324 0.325 0.326 0.327 0.328 0.329 0.33\n",
-       " 0.331 0.332 0.333 0.334 0.335 0.336 0.337 0.338 0.339 0.34  0.341 0.342\n",
-       " 0.343 0.344 0.345 0.346 0.347 0.348 0.349 0.35  0.351 0.352 0.353 0.354\n",
-       " 0.355 0.356 0.357 0.358 0.359 0.36  0.361 0.362 0.363 0.364 0.365 0.366\n",
-       " 0.367 0.368 0.369 0.37  0.371 0.372 0.373 0.374 0.375 0.376 0.377 0.378\n",
-       " 0.379 0.38  0.381 0.382 0.383 0.384 0.385 0.386 0.387 0.388 0.389 0.39\n",
-       " 0.391 0.392 0.393 0.394 0.395 0.396 0.397 0.398 0.399 0.4   0.401 0.402\n",
-       " 0.403 0.404 0.405 0.406 0.407 0.408 0.409 0.41  0.411 0.412 0.413 0.414\n",
-       " 0.415 0.416 0.417 0.418 0.419 0.42  0.421 0.422 0.423 0.424 0.425 0.426\n",
-       " 0.427 0.428 0.429 0.43  0.431 0.432 0.433 0.434 0.435 0.436 0.437 0.438\n",
-       " 0.439 0.44  0.441 0.442 0.443 0.444 0.445 0.446 0.447 0.448 0.449 0.45\n",
-       " 0.451 0.452 0.453 0.454 0.455 0.456 0.457 0.458 0.459 0.46  0.461 0.462\n",
-       " 0.463 0.464 0.465 0.466 0.467 0.468 0.469 0.47  0.471 0.472 0.473 0.474\n",
-       " 0.175 0.176 0.177 0.178 0.179 0.18  0.181 0.182 0.183 0.184 0.185 0.186\n",
-       " 0.187 0.188 0.189 0.19  0.191 0.192 0.193 0.194 0.195 0.196 0.197 0.198\n",
-       " 0.199 0.2   0.201 0.202 0.203 0.204 0.205 0.206 0.207 0.208 0.209 0.21\n",
-       " 0.211 0.212 0.213 0.214 0.215 0.216 0.217 0.218 0.219 0.22  0.221 0.222\n",
-       " 0.223 0.224 0.225 0.226 0.227 0.228 0.229 0.23  0.231 0.232 0.233 0.234\n",
-       " 0.235 0.236 0.237 0.238 0.239 0.24  0.241 0.242 0.243 0.244 0.245 0.246\n",
-       " 0.247 0.248 0.249 0.25  0.251 0.252 0.253 0.254 0.255 0.256 0.257 0.258\n",
-       " 0.259 0.26  0.261 0.262 0.263 0.264 0.265 0.266 0.267 0.268 0.269 0.27\n",
-       " 0.271 0.272 0.273 0.274 0.275 0.276 0.277 0.278 0.279 0.28  0.281 0.282\n",
-       " 0.283 0.284 0.285 0.286 0.287 0.288 0.289 0.29  0.291 0.292 0.293 0.294\n",
-       " 0.295 0.296 0.297 0.298 0.299 0.3   0.301 0.302 0.303 0.304 0.305 0.306\n",
-       " 0.307 0.308 0.309 0.31  0.311 0.312 0.313 0.314 0.315 0.316 0.317 0.318\n",
-       " 0.319 0.32  0.321 0.322 0.323 0.324 0.325 0.326 0.327 0.328 0.329 0.33\n",
-       " 0.331 0.332 0.333 0.334 0.335 0.336 0.337 0.338 0.339 0.34  0.341 0.342\n",
-       " 0.343 0.344 0.345 0.346 0.347 0.348 0.349 0.35  0.351 0.352 0.353 0.354\n",
-       " 0.355 0.356 0.357 0.358 0.359 0.36  0.361 0.362 0.363 0.364 0.365 0.366\n",
-       " 0.367 0.368 0.369 0.37  0.371 0.372 0.373 0.374 0.375 0.376 0.377 0.378\n",
-       " 0.379 0.38  0.381 0.382 0.383 0.384 0.385 0.386 0.387 0.388 0.389 0.39\n",
-       " 0.391 0.392 0.393 0.394 0.395 0.396 0.397 0.398 0.399 0.4   0.401 0.402\n",
-       " 0.403 0.404 0.405 0.406 0.407 0.408 0.409 0.41  0.411 0.412 0.413 0.414\n",
-       " 0.415 0.416 0.417 0.418 0.419 0.42  0.421 0.422 0.423 0.424 0.425 0.426\n",
-       " 0.427 0.428 0.429 0.43  0.431 0.432 0.433 0.434 0.435 0.436 0.437 0.438\n",
-       " 0.439 0.44  0.441 0.442 0.443 0.444 0.445 0.446 0.447 0.448 0.449 0.45\n",
-       " 0.451 0.452 0.453 0.454 0.455 0.456 0.457 0.458 0.459 0.46  0.461 0.462\n",
-       " 0.463 0.464 0.465 0.466 0.467 0.468 0.469 0.47  0.471 0.472 0.473 0.474]</dd><dt><span>runs :</span></dt><dd>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
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-       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
-       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
-       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
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-       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
-       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
-       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
-       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
-       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
-       " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
-       " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
-       " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
-       " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
-       " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
-       " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
-       " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
-       " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
-       " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
-       " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
-       " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
-       " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]</dd><dt><span>scanAxis :</span></dt><dd>[&#x27;compZ_current_sg&#x27;, &#x27;runs&#x27;]</dd><dt><span>scanAxisLength :</span></dt><dd>[600. 600.]</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:           (compZ_current_sg: 300, runs: 2, x: 1200, y: 1920)\n",
-       "Coordinates:\n",
-       "  * compZ_current_sg  (compZ_current_sg) float64 0.175 0.176 ... 0.473 0.474\n",
-       "  * runs              (runs) float64 0.0 1.0\n",
-       "Dimensions without coordinates: x, y\n",
-       "Data variables:\n",
-       "    atoms             (compZ_current_sg, runs, x, y) uint16 dask.array<chunksize=(14, 2, 1200, 1920), meta=np.ndarray>\n",
-       "    background        (compZ_current_sg, runs, x, y) uint16 dask.array<chunksize=(14, 2, 1200, 1920), meta=np.ndarray>\n",
-       "    dark              (compZ_current_sg, runs, x, y) uint16 dask.array<chunksize=(14, 2, 1200, 1920), meta=np.ndarray>\n",
-       "    shotNum           (compZ_current_sg, runs) <U3 dask.array<chunksize=(300, 2), meta=np.ndarray>\n",
-       "    OD                (compZ_current_sg, runs, x, y) float64 dask.array<chunksize=(14, 2, 1200, 1920), meta=np.ndarray>\n",
-       "Attributes: (12/100)\n",
-       "    TOF_free:                          0.02\n",
-       "    abs_img_freq:                      110.866\n",
-       "    absorption_imaging_flag:           True\n",
-       "    backup_data:                       True\n",
-       "    blink_off_time:                    nan\n",
-       "    blink_on_time:                     nan\n",
-       "    ...                                ...\n",
-       "    z_offset:                          compZ_current_sg\n",
-       "    z_offset_img:                      compZ_current_sg\n",
-       "    compZ_current_sg:                  [0.175 0.176 0.177 0.178 0.179 0.18  0...\n",
-       "    runs:                              [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0...\n",
-       "    scanAxis:                          ['compZ_current_sg', 'runs']\n",
-       "    scanAxisLength:                    [600. 600.]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "dataset = imageAnalyser.get_absorption_images(dataset)\n",
     "\n",
@@ -1591,11 +165,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "imageAnalyser.center = (880, 960)\n",
+    "imageAnalyser.center = (529, 962)\n",
     "imageAnalyser.span = (100,100)\n",
     "imageAnalyser.fraction = (0.1, 0.1)\n",
     "\n",
@@ -1612,34 +186,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.plot.facetgrid.FacetGrid at 0x29de9b6b160>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 504x432 with 5 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "dataset_crop.OD.isel(**{scanAxis[0]:np.arange(2), 'runs':range(dataset_crop.OD['runs'].size)}).plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[0], row=scanAxis[1])"
+    "dataset_crop.OD.isel(runs=[0]).plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[0], row=scanAxis[1])\n",
+    "# dataset_crop.OD.plot()"
    ]
   },
   {
@@ -1652,7 +204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1661,7 +213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1678,18 +230,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "fitAnalyser = FitAnalyser(\"Gaussian-2D\", fitDim=2)\n",
+    "fitAnalyser = FitAnalyser(\"Two Gaussian-2D\", fitDim=2)\n",
     "\n",
     "params = fitAnalyser.guess(dataset_crop.OD, dask=\"parallelized\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1698,43 +250,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "fitCurve = fitAnalyser.eval(fitResult, x=np.arange(100), y=np.arange(100))"
+    "fitCurve = fitAnalyser.eval(fitResult, x=np.arange(100), y=np.arange(100)).load()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.plot.facetgrid.FacetGrid at 0x29df5f34a00>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 504x432 with 5 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "fitCurve.isel(**{scanAxis[0]:np.arange(2), 'runs':range(dataset_crop.OD['runs'].size)}).plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[0], row=scanAxis[1])"
+    "fitCurve.isel(**{scanAxis[0]:np.arange(30), 'runs':range(dataset_crop.OD['runs'].size)}).plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[0], row=scanAxis[1])"
    ]
   },
   {
@@ -1747,7 +276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1756,68 +285,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'Line2D' object has no property 'fig'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[1;32md:\\Jianshun Gao\\Simulations\\analyseScripts\\test.ipynb Cell 27\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/Jianshun%20Gao/Simulations/analyseScripts/test.ipynb#X36sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m fig \u001b[39m=\u001b[39m plt\u001b[39m.\u001b[39mfigure()\n\u001b[1;32m----> <a href='vscode-notebook-cell:/d%3A/Jianshun%20Gao/Simulations/analyseScripts/test.ipynb#X36sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m Ncount\u001b[39m.\u001b[39;49mplot(fig\u001b[39m=\u001b[39;49mfig)\n",
-      "File \u001b[1;32mD:\\Program Files\\Python\\Python38\\Lib\\site-packages\\xarray\\plot\\accessor.py:46\u001b[0m, in \u001b[0;36mDataArrayPlotAccessor.__call__\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m     44\u001b[0m \u001b[39m@functools\u001b[39m\u001b[39m.\u001b[39mwraps(dataarray_plot\u001b[39m.\u001b[39mplot, assigned\u001b[39m=\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39m__doc__\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m__annotations__\u001b[39m\u001b[39m\"\u001b[39m))\n\u001b[0;32m     45\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Any:\n\u001b[1;32m---> 46\u001b[0m     \u001b[39mreturn\u001b[39;00m dataarray_plot\u001b[39m.\u001b[39;49mplot(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_da, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
-      "File \u001b[1;32mD:\\Program Files\\Python\\Python38\\Lib\\site-packages\\xarray\\plot\\dataarray_plot.py:312\u001b[0m, in \u001b[0;36mplot\u001b[1;34m(darray, row, col, col_wrap, ax, hue, subplot_kws, **kwargs)\u001b[0m\n\u001b[0;32m    308\u001b[0m     plotfunc \u001b[39m=\u001b[39m hist\n\u001b[0;32m    310\u001b[0m kwargs[\u001b[39m\"\u001b[39m\u001b[39max\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m ax\n\u001b[1;32m--> 312\u001b[0m \u001b[39mreturn\u001b[39;00m plotfunc(darray, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
-      "File \u001b[1;32mD:\\Program Files\\Python\\Python38\\Lib\\site-packages\\xarray\\plot\\dataarray_plot.py:517\u001b[0m, in \u001b[0;36mline\u001b[1;34m(darray, row, col, figsize, aspect, size, ax, hue, x, y, xincrease, yincrease, xscale, yscale, xticks, yticks, xlim, ylim, add_legend, _labels, *args, **kwargs)\u001b[0m\n\u001b[0;32m    513\u001b[0m ylabel \u001b[39m=\u001b[39m label_from_attrs(yplt, extra\u001b[39m=\u001b[39my_suffix)\n\u001b[0;32m    515\u001b[0m _ensure_plottable(xplt_val, yplt_val)\n\u001b[1;32m--> 517\u001b[0m primitive \u001b[39m=\u001b[39m ax\u001b[39m.\u001b[39;49mplot(xplt_val, yplt_val, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[0;32m    519\u001b[0m \u001b[39mif\u001b[39;00m _labels:\n\u001b[0;32m    520\u001b[0m     \u001b[39mif\u001b[39;00m xlabel \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
-      "File \u001b[1;32mD:\\Program Files\\Python\\Python38\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:1632\u001b[0m, in \u001b[0;36mAxes.plot\u001b[1;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1390\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m   1391\u001b[0m \u001b[39mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[0;32m   1392\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1629\u001b[0m \u001b[39m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[0;32m   1630\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m   1631\u001b[0m kwargs \u001b[39m=\u001b[39m cbook\u001b[39m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[39m.\u001b[39mLine2D)\n\u001b[1;32m-> 1632\u001b[0m lines \u001b[39m=\u001b[39m [\u001b[39m*\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_lines(\u001b[39m*\u001b[39margs, data\u001b[39m=\u001b[39mdata, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)]\n\u001b[0;32m   1633\u001b[0m \u001b[39mfor\u001b[39;00m line \u001b[39min\u001b[39;00m lines:\n\u001b[0;32m   1634\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39madd_line(line)\n",
-      "File \u001b[1;32mD:\\Program Files\\Python\\Python38\\Lib\\site-packages\\matplotlib\\axes\\_base.py:312\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[1;34m(self, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m    310\u001b[0m     this \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m args[\u001b[39m0\u001b[39m],\n\u001b[0;32m    311\u001b[0m     args \u001b[39m=\u001b[39m args[\u001b[39m1\u001b[39m:]\n\u001b[1;32m--> 312\u001b[0m \u001b[39myield from\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_plot_args(this, kwargs)\n",
-      "File \u001b[1;32mD:\\Program Files\\Python\\Python38\\Lib\\site-packages\\matplotlib\\axes\\_base.py:538\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[1;34m(self, tup, kwargs, return_kwargs)\u001b[0m\n\u001b[0;32m    536\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mlist\u001b[39m(result)\n\u001b[0;32m    537\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 538\u001b[0m     \u001b[39mreturn\u001b[39;00m [l[\u001b[39m0\u001b[39m] \u001b[39mfor\u001b[39;00m l \u001b[39min\u001b[39;00m result]\n",
-      "File \u001b[1;32mD:\\Program Files\\Python\\Python38\\Lib\\site-packages\\matplotlib\\axes\\_base.py:538\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    536\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mlist\u001b[39m(result)\n\u001b[0;32m    537\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 538\u001b[0m     \u001b[39mreturn\u001b[39;00m [l[\u001b[39m0\u001b[39m] \u001b[39mfor\u001b[39;00m l \u001b[39min\u001b[39;00m result]\n",
-      "File \u001b[1;32mD:\\Program Files\\Python\\Python38\\Lib\\site-packages\\matplotlib\\axes\\_base.py:531\u001b[0m, in \u001b[0;36m<genexpr>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    528\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    529\u001b[0m     labels \u001b[39m=\u001b[39m [label] \u001b[39m*\u001b[39m n_datasets\n\u001b[1;32m--> 531\u001b[0m result \u001b[39m=\u001b[39m (make_artist(x[:, j \u001b[39m%\u001b[39;49m ncx], y[:, j \u001b[39m%\u001b[39;49m ncy], kw,\n\u001b[0;32m    532\u001b[0m                       {\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs, \u001b[39m'\u001b[39;49m\u001b[39mlabel\u001b[39;49m\u001b[39m'\u001b[39;49m: label})\n\u001b[0;32m    533\u001b[0m           \u001b[39mfor\u001b[39;00m j, label \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(labels))\n\u001b[0;32m    535\u001b[0m \u001b[39mif\u001b[39;00m return_kwargs:\n\u001b[0;32m    536\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mlist\u001b[39m(result)\n",
-      "File \u001b[1;32mD:\\Program Files\\Python\\Python38\\Lib\\site-packages\\matplotlib\\axes\\_base.py:351\u001b[0m, in \u001b[0;36m_process_plot_var_args._makeline\u001b[1;34m(self, x, y, kw, kwargs)\u001b[0m\n\u001b[0;32m    349\u001b[0m default_dict \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_getdefaults(\u001b[39mset\u001b[39m(), kw)\n\u001b[0;32m    350\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_setdefaults(default_dict, kw)\n\u001b[1;32m--> 351\u001b[0m seg \u001b[39m=\u001b[39m mlines\u001b[39m.\u001b[39;49mLine2D(x, y, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkw)\n\u001b[0;32m    352\u001b[0m \u001b[39mreturn\u001b[39;00m seg, kw\n",
-      "File \u001b[1;32mD:\\Program Files\\Python\\Python38\\Lib\\site-packages\\matplotlib\\lines.py:393\u001b[0m, in \u001b[0;36mLine2D.__init__\u001b[1;34m(self, xdata, ydata, linewidth, linestyle, color, marker, markersize, markeredgewidth, markeredgecolor, markerfacecolor, markerfacecoloralt, fillstyle, antialiased, dash_capstyle, solid_capstyle, dash_joinstyle, solid_joinstyle, pickradius, drawstyle, markevery, **kwargs)\u001b[0m\n\u001b[0;32m    389\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mset_markeredgewidth(markeredgewidth)\n\u001b[0;32m    391\u001b[0m \u001b[39m# update kwargs before updating data to give the caller a\u001b[39;00m\n\u001b[0;32m    392\u001b[0m \u001b[39m# chance to init axes (and hence unit support)\u001b[39;00m\n\u001b[1;32m--> 393\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mupdate(kwargs)\n\u001b[0;32m    394\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpickradius \u001b[39m=\u001b[39m pickradius\n\u001b[0;32m    395\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mind_offset \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n",
-      "File \u001b[1;32mD:\\Program Files\\Python\\Python38\\Lib\\site-packages\\matplotlib\\artist.py:1064\u001b[0m, in \u001b[0;36mArtist.update\u001b[1;34m(self, props)\u001b[0m\n\u001b[0;32m   1062\u001b[0m             func \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mset_\u001b[39m\u001b[39m{\u001b[39;00mk\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m)\n\u001b[0;32m   1063\u001b[0m             \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m callable(func):\n\u001b[1;32m-> 1064\u001b[0m                 \u001b[39mraise\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mtype\u001b[39m(\u001b[39mself\u001b[39m)\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m\u001b[39m!r}\u001b[39;00m\u001b[39m object \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m   1065\u001b[0m                                      \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mhas no property \u001b[39m\u001b[39m{\u001b[39;00mk\u001b[39m!r}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[0;32m   1066\u001b[0m             ret\u001b[39m.\u001b[39mappend(func(v))\n\u001b[0;32m   1067\u001b[0m \u001b[39mif\u001b[39;00m ret:\n",
-      "\u001b[1;31mAttributeError\u001b[0m: 'Line2D' object has no property 'fig'"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# fig = plt.figure()\n",
-    "# Ncount.plot(fig=fig)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'Line2D' object has no attribute 'ylim'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[1;32md:\\Jianshun Gao\\Simulations\\analyseScripts\\test.ipynb Cell 28\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/d%3A/Jianshun%20Gao/Simulations/analyseScripts/test.ipynb#X40sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m ax[\u001b[39m0\u001b[39;49m]\u001b[39m.\u001b[39;49mylim([\u001b[39m0\u001b[39m, \u001b[39m2000\u001b[39m])\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/Jianshun%20Gao/Simulations/analyseScripts/test.ipynb#X40sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m plt\u001b[39m.\u001b[39mshow()\n",
-      "\u001b[1;31mAttributeError\u001b[0m: 'Line2D' object has no attribute 'ylim'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "fig"
+    "fig = plt.figure()\n",
+    "Ncount.plot(fig=fig)"
    ]
   },
   {
@@ -1844,7 +317,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.9.13"
   },
   "orig_nbformat": 4,
   "vscode": {