diff --git a/flatmassdist.csv b/flatmassdist.csv
index e62c519..898a9e7 100644
--- a/flatmassdist.csv
+++ b/flatmassdist.csv
@@ -1,1000 +1,2000 @@
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+3.315210408749213467e-01,1.928115097807544000e+15,1.689791266000000000e+09
+3.165048880681918531e-02,9.350005261094086250e+14,1.689791267000000000e+09
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+3.909255496792654605e-02,1.829955955867080000e+15,1.689791269000000000e+09
+9.006672049469222063e-01,1.293158304649588250e+15,1.689791270000000000e+09
+4.839178324577567691e-01,8.566035768355203750e+14,1.689791271000000000e+09
+9.238509572426044369e-02,1.746914257824570750e+15,1.689791272000000000e+09
+9.402849993520935312e-01,6.328527618705550000e+14,1.689791273000000000e+09
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+1.759400107266831381e-01,7.304518564572990000e+14,1.689791275000000000e+09
+9.424516596562271253e-01,6.712140405484080000e+14,1.689791276000000000e+09
+1.056362185161369593e+00,1.738004401340389750e+15,1.689791277000000000e+09
+9.010843034923831096e-01,1.355789385179966000e+15,1.689791278000000000e+09
+8.349432807556064207e-01,1.312526316237017250e+15,1.689791279000000000e+09
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+1.018254760347037768e+00,8.273632372651496250e+14,1.689791299000000000e+09
diff --git a/make_sz_cluster.py b/make_sz_cluster.py
index 7c817c9..cda5c56 100644
--- a/make_sz_cluster.py
+++ b/make_sz_cluster.py
@@ -1,7 +1,9 @@
 import numpy as np
 import simsz.utils as utils
+import scipy
 
 from scipy.interpolate import interp1d
+from colossus.cosmology import cosmology
 from colossus.halo import mass_adv
 
 from astropy.constants import G, sigma_T, m_e, c, h, k_B
@@ -11,8 +13,8 @@ class GenerateCluster():
 
     def __init__(self):
         self.data =[]
-        
-        
+
+
     def P200_Battaglia2012(self,cosmo,redshift_z,M200,R200):
         '''
         Calculates the P200 pressure profile of a cluster, as defined in Battaglia 2012
@@ -25,15 +27,15 @@ def P200_Battaglia2012(self,cosmo,redshift_z,M200,R200):
 
         Returns P200, the thermal pressure profile of the shell defined by R200
         '''
-        
+
         P200 = G * M200*u.Msun * 200. * cosmo.critical_density(redshift_z) * (cosmo.Ob0/cosmo.Om0) / (2. * R200*u.Mpc) #From Battaglia 2012
         P200=P200.to(u.keV/u.cm**3.) #Unit conversion to keV/cm^3
-        
+
         return(P200)
-    
+
     def param_Battaglia2012(self,A0,alpha_m,alpha_z,M200,redshift_z):
         '''
-        Calculates independent params as using the formula from Battaglia 2012, Equation 11 
+        Calculates independent params as using the formula from Battaglia 2012, Equation 11
         in order for use in the pressure profile defined in Equation 10
 
         Parameters:
@@ -42,14 +44,14 @@ def param_Battaglia2012(self,A0,alpha_m,alpha_z,M200,redshift_z):
         alpha_z, power law index for the redshift dependent part
         M200, the mass of the cluster at 200 times the critical density of the universe
         redshift_z, the redshift of the cluster
-        
+
         Returns the parameter A given the formula from Eq 11
         '''
-        
+
         A = A0 * (M200/1e14)**alpha_m * (1.+redshift_z)**alpha_z
 
         return(A)
-    
+
     def Pth_Battaglia2012(self,radius,R200,gamma,alpha,beta,xc,P0):
         '''
         Calculates Pth using the battaglia fit profile, Battaglia 2012, Equation 10
@@ -68,16 +70,16 @@ def Pth_Battaglia2012(self,radius,R200,gamma,alpha,beta,xc,P0):
         '''
 
         x=radius/R200
-        
+
         Pth = P0 * (x/xc)**gamma * (1+(x/xc)**alpha)**(-beta)
-        
+
         return(Pth)
-    
-    def epp_to_y(self, profile, radii, P200, R200, **kwargs): 
+
+    def epp_to_y(self, profile, radii, P200, R200, **kwargs):
         '''
         Converts from an electron pressure profile to a compton-y profile,
         integrates over line of sight from -1 to 1 Mpc relative to center.
-        Parameters: 
+        Parameters:
         profile, Method to get thermal pressure profile in Kev/cm^3, accepts radius, R200 and **kwargs
         radii, the array of radii corresponding to the profile in Mpc
         P200, as defined in battaglia2012, needed for normalization of Battaglia profile
@@ -94,7 +96,7 @@ def epp_to_y(self, profile, radii, P200, R200, **kwargs):
         thermal_to_electron_pressure = 1/1.932 # from Battaglia 2012, assumes fully ionized medium
         for radius in radii:
             #Multiply profile by P200 specifically for Battaglia 2012 profile, since it returns Pth/P200 instead of Pth
-            th_pressure = profile(np.sqrt(l_mpc**2 + radius.value**2), R200=R200, **kwargs) * P200.value #pressure as a function of l, multiplication by P20 needed ad 
+            th_pressure = profile(np.sqrt(l_mpc**2 + radius.value**2), R200=R200, **kwargs) * P200.value #pressure as a function of l, multiplication by P20 needed ad
             th_pressure = th_pressure * keVcm_to_Jm.value # Use multiplication by a precaluated factor for efficiency
             pressure = th_pressure* thermal_to_electron_pressure
             integral = np.trapz(pressure, l) * 2 #integrate over pressure in J/m^3 to get J/m^2, multiply by factor of 2 to get from -R200 to R200 (assuming spherical symmetry)
@@ -102,12 +104,12 @@ def epp_to_y(self, profile, radii, P200, R200, **kwargs):
             i += 1
         y_pro = pressure_integrated * sigma_T.value/ (m_e.value * c.value**2)
         return y_pro
-    
+
     def make_y_submap(self, profile, redshift_z, cosmo, width, pix_size, *args, **kwargs):
         '''
         Converts from an electron pressure profile to a compton-y profile,
         integrates over line of sight from -1 to 1 Mpc relative to center.
-        Parameters: 
+        Parameters:
         profile, Method to get thermal pressure profile in Kev/cm^3, accepts radius, and **kwargs
         redshift_z, the redshift of the cluster
         cosmo, background cosmology for density calculation
@@ -123,11 +125,11 @@ def make_y_submap(self, profile, redshift_z, cosmo, width, pix_size, *args, **kw
 
         R = []
         y_map = np.empty((X.size, Y.size))
-        
+
         for i in X:
             for j in Y:
                 R.append(np.sqrt(i**2 + j**2))
-    
+
         R = np.array(R)
         cy = self.epp_to_y(profile, R, **kwargs) #evaluate compton-y for each neccesary radius
 
@@ -136,7 +138,7 @@ def make_y_submap(self, profile, redshift_z, cosmo, width, pix_size, *args, **kw
                 y_map[i][j] = cy[np.where(R == np.sqrt(X[i]**2 + Y[j]**2))[0]][0] # assign the correct compton-y to the radius
     
         return y_map
-  
+
     def f_sz(self, freq, T_CMB, *args, **kwargs):
         '''
         Input: Observation frequency f in GHz, Temperature of cmb T_CMB
@@ -155,9 +157,21 @@ def f_sz(self, freq, T_CMB, *args, **kwargs):
 ####Functions needed in this file:
 # 3) Convolve submap with beam
 
-    
+    def convolve_map_with_gaussian_beam(self, pix_size, beam_size_fwhp, map):
+        '''
+        Input: pixel size, beam size in arcmin, image
+        Return: convolved map
+
+        Note - pixel size and beam_size need to be in the same units
+        '''
+        gaussian = utils.gaussian_kernal(pix_size, beam_size_fwhp)
+        convolved_map = scipy.signal.fftconvolve(map, gaussian, mode = 'same')
+        
+        return(convolved_map)
+
+
 # 5) Generate noise map
-    
+
     def generate_cluster(self, radius, profile, f, noise_level, beam_size, z, nums, p = None): #SOME OF THE ABOVE WILL BE TAKEN FROM THIS OUTDATED FUNCTION
         """
         combine all elements to generate a cluster object
@@ -178,12 +192,12 @@ def generate_cluster(self, radius, profile, f, noise_level, beam_size, z, nums,
         if 2 in nums:
             pa = p + '2' + '.png'
             plot_img(y_img, z, path = pa)
-        
+
         if 3 in nums:
             pa = p + '3' + '.png'
             gaussian = gaussian_kernal(0.5, beam_size)
             plot_img(gaussian, z, opt = 2, path = pa)
-        
+
         if 4 in nums:
             pa = p + '4' + '.png'
             plot_img(y_con, z, path = pa)
@@ -204,11 +218,11 @@ def generate_cluster(self, radius, profile, f, noise_level, beam_size, z, nums,
             plot_img(y_noise, z, opt = 3, path = pa) #vizualization starts working from z = 0.115
 
         #return tSZ_signal(z, y_con), tSZ_signal(z, y_noise)
-        return y_img, y_con, cmb_img, noise, cmb_noise, y_noise, SZsignal, aperture   
-   
-   
+        return y_img, y_con, cmb_img, noise, cmb_noise, y_noise, SZsignal, aperture
 
-#FUNCTIONS BELOW HERE HAVE NOT BEEN TESTED OR USED RECENTLY; MIGHT BE USEFUL FOR THE ABOVE TO-DO LIST     
+
+
+#FUNCTIONS BELOW HERE HAVE NOT BEEN TESTED OR USED RECENTLY; MIGHT BE USEFUL FOR THE ABOVE TO-DO LIST
 
 
     def make_proj_image_new(radius, profile,range=18,pixel_scale=0.5,extrapolate=False):
@@ -240,53 +254,55 @@ def tSZ_signal(z, Map):
         disk_mean = Map[r < rin].mean()
         ring_mean = Map[(r >= rin) & (r < rout)].mean()
         tSZ = disk_mean - ring_mean
-        
+
         return tSZ, rin
 
 
     def battaglia_profile(r, Mvir, z, cosmo): #THIS IS OLD; WILL LIKELY DELETE SOON
         '''
-        Using Battaglia et al (2012). Eq. 10. 
+        Using Battaglia et al (2012). Eq. 10.
         Input: Virial Mass in solar mass and Radius in Mpc
         Return: Pressure profile in keV/cm^3 at radius r
         '''
-        
+
         a = cosmo.scale_factor(z)
         rho_critical = cosmo.critical_density(0.5).to(u.M_sun/u.Mpc**3) #In M_sun/Mpc^3
-        
-        
+
+
 
         M200, R200, c200 = mass_adv.changeMassDefinitionCModel(Mvir/cosmo.h, z, 'vir', '200c', c_model = 'ishiyama21')
         #M200, R200, c200 = mass_adv.changeMassDefinitionCModel(Mvir/cosmo_h, z, 'vir', '200c', c_model = 'ishiyama21')
         #M200, R200, c200 = mass_adv.changeMassDefinitionCModel(M500/cosmo_h, z, '500c', '200c', c_model = 'ishiyama21')
         #cvir = concentration.concentration(Mvir, 'vir', z, model = 'ishiyama21')      #Ishiyama et al. (2021)
         #Option to customize concentration, currently default, using Bullock et al. (2001)
- 
+
         M200 *= cosmo.h
         R200 = R200 / 1000 * cosmo.h * (1.+z)
         x = r / R200
-        
+
         G2 = G * 1e-6 / float(Mpc_to_m) * m_sun
         gamma = -0.3
-        
+
         P200 =  G2 * M200 * 200. * rho_critical * (omega_b / omega_m) / 2. / (R200 / (1. + z))    # Msun km^2 / s^2 / Mpc^3
 
         P0 = 18.1 * ((M200 / 1e14)**0.154 * (1. + z)**-0.758)
         xc = 0.497 * ((M200 / 1e14)**-0.00865 * (1. + z)**0.731)
-        beta = 4.35 * ((M200 / 1e14)**0.0393 * (1. + z)**0.415) 
+        beta = 4.35 * ((M200 / 1e14)**0.0393 * (1. + z)**0.415)
         pth = P200 * P0 * (x / xc)**gamma * (1. + (x/xc))**(-1. * beta)      # Msun km^2 / s^2 / Mpc^3
 
         pth *= (m_sun * 1e6 * j_to_kev  / ((Mpc_to_m*100)**3))       # keV/cm^3
         p_e = pth * 0.518       # Vikram et al (2016)
 
-        return p_e, M200, R200, c200 
+        return p_e, M200, R200, c200
 
-    
-        
-   
+    def m200_to_r200(self,cosmo,sigma8,ns,M200,z):
 
-   
- 
-        
-    
-        
\ No newline at end of file
+        params = {'flat': True, 'H0': cosmo.H0.value, 'Om0': cosmo.Om0, 'Ob0': cosmo.Ob0, 'sigma8':sigma8, 'ns': ns}
+        cosmology.addCosmology('myCosmo', **params)
+        cosmo_colossus= cosmology.setCosmology('myCosmo')
+
+        M200, R200, c200 = mass_adv.changeMassDefinitionCModel(M200/cosmo.h, z, '200c', '200c', c_model = 'ishiyama21')
+        M200 *= cosmo.h #From M_solar/h to M_solar
+        R200 = R200*cosmo.h/1000 #From kpc/h to Mpc
+        R200 = R200/cosmo.scale_factor(z) #From Mpc proper to Mpc comoving
+        return M200, R200, c200
diff --git a/run_sims.ipynb b/run_sims.ipynb
index 33251d3..bbbe470 100644
--- a/run_sims.ipynb
+++ b/run_sims.ipynb
@@ -13,6 +13,7 @@
     "import make_sz_cluster\n",
     "import simsz.read_yaml as read_yaml\n",
     "import simsz.load_vars as load_vars\n",
+    "from colossus.halo import mass_adv\n",
     "\n",
     "import time\n",
     "import numpy as np\n",
@@ -89,30 +90,36 @@
      "data": {
       "image/png": 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\n",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
      "data": {
       "image/png": 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\n",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
      "data": {
       "image/png": 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\n",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -166,26 +173,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
     "z=0.48\n",
-    "Mvir=2.21e14 #solar masses"
+    "M200=194038855760143.47 #solar masses"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
-    "(M200,R200,c200)=halo.vir_to_200_colossus(cosmo,sigma8,ns,Mvir,z) #Convert virial mass to M200 and get R200, concentration from Colossus"
+    "(M200,R200,c200)=szcluster.m200_to_r200(cosmo,sigma8,ns,M200,z) #Use M200 to get R200 and concentration from Colossus"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -194,7 +201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -205,7 +212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -214,7 +221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -223,17 +230,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -249,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 30,
    "metadata": {
     "scrolled": true
    },
@@ -260,16 +269,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "2.0780684899743718e-05"
+       "2.0780684899743705e-05"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -280,7 +289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 32,
    "metadata": {
     "scrolled": true
    },
@@ -289,7 +298,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-110.632216673186 uK\n"
+      "-110.63221667318591 uK\n"
      ]
     }
    ],
@@ -302,17 +311,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -327,24 +338,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "max:  2.1512691818266286e-05\n"
+      "max:  2.1512691818266272e-05\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -364,19 +377,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "max:  114.52927533159712 uK\n"
+      "max:  5.436578006902421e-06\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -385,6 +398,103 @@
      "output_type": "display_data"
     }
    ],
+   "source": [
+    "beam_size_fwhp = 1.25\n",
+    "conv_map = szcluster.convolve_map_with_gaussian_beam(pix_size, beam_size_fwhp, y_map)\n",
+    "im = plt.imshow(conv_map, norm=LogNorm())\n",
+    "im = plt.imshow(conv_map, norm=LogNorm())\n",
+    "cbar = plt.colorbar(im)\n",
+    "im.set_extent([-width,width,-width,width])\n",
+    "plt.title('Mvir = 2.21e14, z=0.48, Beam-Convolved Compton y map')\n",
+    "plt.ylabel('arcmin')\n",
+    "plt.xlabel('arcmin')\n",
+    "cbar.set_label('Compton y', rotation=270)\n",
+    "print('max: ', conv_map.max())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "max:  114.52927533159703 uK\n",
+      "Error in callback <function _draw_all_if_interactive at 0x000002227DE916C0> (for post_execute):\n"
+     ]
+    },
+    {
+     "ename": "UnitConversionError",
+     "evalue": "Can only apply 'greater' function to dimensionless quantities when other argument is not a quantity (unless the latter is all zero/infinity/nan)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mUnitConversionError\u001b[0m                       Traceback (most recent call last)",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\matplotlib\\pyplot.py:120\u001b[0m, in \u001b[0;36m_draw_all_if_interactive\u001b[1;34m()\u001b[0m\n\u001b[0;32m    118\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_draw_all_if_interactive\u001b[39m():\n\u001b[0;32m    119\u001b[0m     \u001b[39mif\u001b[39;00m matplotlib\u001b[39m.\u001b[39mis_interactive():\n\u001b[1;32m--> 120\u001b[0m         draw_all()\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\matplotlib\\_pylab_helpers.py:132\u001b[0m, in \u001b[0;36mGcf.draw_all\u001b[1;34m(cls, force)\u001b[0m\n\u001b[0;32m    130\u001b[0m \u001b[39mfor\u001b[39;00m manager \u001b[39min\u001b[39;00m \u001b[39mcls\u001b[39m\u001b[39m.\u001b[39mget_all_fig_managers():\n\u001b[0;32m    131\u001b[0m     \u001b[39mif\u001b[39;00m force \u001b[39mor\u001b[39;00m manager\u001b[39m.\u001b[39mcanvas\u001b[39m.\u001b[39mfigure\u001b[39m.\u001b[39mstale:\n\u001b[1;32m--> 132\u001b[0m         manager\u001b[39m.\u001b[39mcanvas\u001b[39m.\u001b[39mdraw_idle()\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\matplotlib\\backend_bases.py:2082\u001b[0m, in \u001b[0;36mFigureCanvasBase.draw_idle\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2080\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_is_idle_drawing:\n\u001b[0;32m   2081\u001b[0m     \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_idle_draw_cntx():\n\u001b[1;32m-> 2082\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdraw(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\matplotlib\\backends\\backend_agg.py:400\u001b[0m, in \u001b[0;36mFigureCanvasAgg.draw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    396\u001b[0m \u001b[39m# Acquire a lock on the shared font cache.\u001b[39;00m\n\u001b[0;32m    397\u001b[0m \u001b[39mwith\u001b[39;00m RendererAgg\u001b[39m.\u001b[39mlock, \\\n\u001b[0;32m    398\u001b[0m      (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtoolbar\u001b[39m.\u001b[39m_wait_cursor_for_draw_cm() \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtoolbar\n\u001b[0;32m    399\u001b[0m       \u001b[39melse\u001b[39;00m nullcontext()):\n\u001b[1;32m--> 400\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfigure\u001b[39m.\u001b[39mdraw(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrenderer)\n\u001b[0;32m    401\u001b[0m     \u001b[39m# A GUI class may be need to update a window using this draw, so\u001b[39;00m\n\u001b[0;32m    402\u001b[0m     \u001b[39m# don't forget to call the superclass.\u001b[39;00m\n\u001b[0;32m    403\u001b[0m     \u001b[39msuper\u001b[39m()\u001b[39m.\u001b[39mdraw()\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\matplotlib\\artist.py:95\u001b[0m, in \u001b[0;36m_finalize_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     93\u001b[0m \u001b[39m@wraps\u001b[39m(draw)\n\u001b[0;32m     94\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdraw_wrapper\u001b[39m(artist, renderer, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m---> 95\u001b[0m     result \u001b[39m=\u001b[39m draw(artist, renderer, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m     96\u001b[0m     \u001b[39mif\u001b[39;00m renderer\u001b[39m.\u001b[39m_rasterizing:\n\u001b[0;32m     97\u001b[0m         renderer\u001b[39m.\u001b[39mstop_rasterizing()\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer)\u001b[0m\n\u001b[0;32m     69\u001b[0m     \u001b[39mif\u001b[39;00m artist\u001b[39m.\u001b[39mget_agg_filter() \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m     70\u001b[0m         renderer\u001b[39m.\u001b[39mstart_filter()\n\u001b[1;32m---> 72\u001b[0m     \u001b[39mreturn\u001b[39;00m draw(artist, renderer)\n\u001b[0;32m     73\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m     74\u001b[0m     \u001b[39mif\u001b[39;00m artist\u001b[39m.\u001b[39mget_agg_filter() \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\matplotlib\\figure.py:3140\u001b[0m, in \u001b[0;36mFigure.draw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   3137\u001b[0m         \u001b[39m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[0;32m   3139\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpatch\u001b[39m.\u001b[39mdraw(renderer)\n\u001b[1;32m-> 3140\u001b[0m mimage\u001b[39m.\u001b[39m_draw_list_compositing_images(\n\u001b[0;32m   3141\u001b[0m     renderer, \u001b[39mself\u001b[39m, artists, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msuppressComposite)\n\u001b[0;32m   3143\u001b[0m \u001b[39mfor\u001b[39;00m sfig \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msubfigs:\n\u001b[0;32m   3144\u001b[0m     sfig\u001b[39m.\u001b[39mdraw(renderer)\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\matplotlib\\image.py:131\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    129\u001b[0m \u001b[39mif\u001b[39;00m not_composite \u001b[39mor\u001b[39;00m \u001b[39mnot\u001b[39;00m has_images:\n\u001b[0;32m    130\u001b[0m     \u001b[39mfor\u001b[39;00m a \u001b[39min\u001b[39;00m artists:\n\u001b[1;32m--> 131\u001b[0m         a\u001b[39m.\u001b[39mdraw(renderer)\n\u001b[0;32m    132\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    133\u001b[0m     \u001b[39m# Composite any adjacent images together\u001b[39;00m\n\u001b[0;32m    134\u001b[0m     image_group \u001b[39m=\u001b[39m []\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer)\u001b[0m\n\u001b[0;32m     69\u001b[0m     \u001b[39mif\u001b[39;00m artist\u001b[39m.\u001b[39mget_agg_filter() \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m     70\u001b[0m         renderer\u001b[39m.\u001b[39mstart_filter()\n\u001b[1;32m---> 72\u001b[0m     \u001b[39mreturn\u001b[39;00m draw(artist, renderer)\n\u001b[0;32m     73\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m     74\u001b[0m     \u001b[39mif\u001b[39;00m artist\u001b[39m.\u001b[39mget_agg_filter() \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\matplotlib\\axes\\_base.py:3064\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   3061\u001b[0m \u001b[39mif\u001b[39;00m artists_rasterized:\n\u001b[0;32m   3062\u001b[0m     _draw_rasterized(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfigure, artists_rasterized, renderer)\n\u001b[1;32m-> 3064\u001b[0m mimage\u001b[39m.\u001b[39m_draw_list_compositing_images(\n\u001b[0;32m   3065\u001b[0m     renderer, \u001b[39mself\u001b[39m, artists, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfigure\u001b[39m.\u001b[39msuppressComposite)\n\u001b[0;32m   3067\u001b[0m renderer\u001b[39m.\u001b[39mclose_group(\u001b[39m'\u001b[39m\u001b[39maxes\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m   3068\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstale \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\matplotlib\\image.py:131\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    129\u001b[0m \u001b[39mif\u001b[39;00m not_composite \u001b[39mor\u001b[39;00m \u001b[39mnot\u001b[39;00m has_images:\n\u001b[0;32m    130\u001b[0m     \u001b[39mfor\u001b[39;00m a \u001b[39min\u001b[39;00m artists:\n\u001b[1;32m--> 131\u001b[0m         a\u001b[39m.\u001b[39mdraw(renderer)\n\u001b[0;32m    132\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    133\u001b[0m     \u001b[39m# Composite any adjacent images together\u001b[39;00m\n\u001b[0;32m    134\u001b[0m     image_group \u001b[39m=\u001b[39m []\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer)\u001b[0m\n\u001b[0;32m     69\u001b[0m     \u001b[39mif\u001b[39;00m artist\u001b[39m.\u001b[39mget_agg_filter() \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m     70\u001b[0m         renderer\u001b[39m.\u001b[39mstart_filter()\n\u001b[1;32m---> 72\u001b[0m     \u001b[39mreturn\u001b[39;00m draw(artist, renderer)\n\u001b[0;32m     73\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m     74\u001b[0m     \u001b[39mif\u001b[39;00m artist\u001b[39m.\u001b[39mget_agg_filter() \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\matplotlib\\image.py:641\u001b[0m, in \u001b[0;36m_ImageBase.draw\u001b[1;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m    639\u001b[0m         renderer\u001b[39m.\u001b[39mdraw_image(gc, l, b, im, trans)\n\u001b[0;32m    640\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 641\u001b[0m     im, l, b, trans \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmake_image(\n\u001b[0;32m    642\u001b[0m         renderer, renderer\u001b[39m.\u001b[39mget_image_magnification())\n\u001b[0;32m    643\u001b[0m     \u001b[39mif\u001b[39;00m im \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m    644\u001b[0m         renderer\u001b[39m.\u001b[39mdraw_image(gc, l, b, im)\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\matplotlib\\image.py:949\u001b[0m, in \u001b[0;36mAxesImage.make_image\u001b[1;34m(self, renderer, magnification, unsampled)\u001b[0m\n\u001b[0;32m    946\u001b[0m transformed_bbox \u001b[39m=\u001b[39m TransformedBbox(bbox, trans)\n\u001b[0;32m    947\u001b[0m clip \u001b[39m=\u001b[39m ((\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_clip_box() \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxes\u001b[39m.\u001b[39mbbox) \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_clip_on()\n\u001b[0;32m    948\u001b[0m         \u001b[39melse\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfigure\u001b[39m.\u001b[39mbbox)\n\u001b[1;32m--> 949\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_make_image(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_A, bbox, transformed_bbox, clip,\n\u001b[0;32m    950\u001b[0m                         magnification, unsampled\u001b[39m=\u001b[39munsampled)\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\matplotlib\\image.py:456\u001b[0m, in \u001b[0;36m_ImageBase._make_image\u001b[1;34m(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border)\u001b[0m\n\u001b[0;32m    454\u001b[0m fact \u001b[39m=\u001b[39m \u001b[39m1e7\u001b[39m \u001b[39mif\u001b[39;00m scaled_dtype \u001b[39m==\u001b[39m np\u001b[39m.\u001b[39mfloat64 \u001b[39melse\u001b[39;00m \u001b[39m1e4\u001b[39m\n\u001b[0;32m    455\u001b[0m newmin \u001b[39m=\u001b[39m vmid \u001b[39m-\u001b[39m dv \u001b[39m*\u001b[39m fact\n\u001b[1;32m--> 456\u001b[0m \u001b[39mif\u001b[39;00m newmin \u001b[39m<\u001b[39m a_min:\n\u001b[0;32m    457\u001b[0m     newmin \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m    458\u001b[0m \u001b[39melse\u001b[39;00m:\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\numpy\\ma\\core.py:4191\u001b[0m, in \u001b[0;36mMaskedArray.__gt__\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m   4190\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__gt__\u001b[39m(\u001b[39mself\u001b[39m, other):\n\u001b[1;32m-> 4191\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_comparison(other, operator\u001b[39m.\u001b[39mgt)\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\numpy\\ma\\core.py:4123\u001b[0m, in \u001b[0;36mMaskedArray._comparison\u001b[1;34m(self, other, compare)\u001b[0m\n\u001b[0;32m   4119\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m   4120\u001b[0m     \u001b[39m# For regular arrays, just use the data as they come.\u001b[39;00m\n\u001b[0;32m   4121\u001b[0m     sdata \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdata\n\u001b[1;32m-> 4123\u001b[0m check \u001b[39m=\u001b[39m compare(sdata, odata)\n\u001b[0;32m   4125\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(check, (np\u001b[39m.\u001b[39mbool_, \u001b[39mbool\u001b[39m)):\n\u001b[0;32m   4126\u001b[0m     \u001b[39mreturn\u001b[39;00m masked \u001b[39mif\u001b[39;00m mask \u001b[39melse\u001b[39;00m check\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\astropy\\units\\quantity.py:591\u001b[0m, in \u001b[0;36mQuantity.__array_ufunc__\u001b[1;34m(self, function, method, *inputs, **kwargs)\u001b[0m\n\u001b[0;32m    569\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Wrap numpy ufuncs, taking care of units.\u001b[39;00m\n\u001b[0;32m    570\u001b[0m \n\u001b[0;32m    571\u001b[0m \u001b[39mParameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    585\u001b[0m \u001b[39m    Results of the ufunc, with the unit set properly.\u001b[39;00m\n\u001b[0;32m    586\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    587\u001b[0m \u001b[39m# Determine required conversion functions -- to bring the unit of the\u001b[39;00m\n\u001b[0;32m    588\u001b[0m \u001b[39m# input to that expected (e.g., radian for np.sin), or to get\u001b[39;00m\n\u001b[0;32m    589\u001b[0m \u001b[39m# consistent units between two inputs (e.g., in np.add) --\u001b[39;00m\n\u001b[0;32m    590\u001b[0m \u001b[39m# and the unit of the result (or tuple of units for nout > 1).\u001b[39;00m\n\u001b[1;32m--> 591\u001b[0m converters, unit \u001b[39m=\u001b[39m converters_and_unit(function, method, \u001b[39m*\u001b[39minputs)\n\u001b[0;32m    593\u001b[0m out \u001b[39m=\u001b[39m kwargs\u001b[39m.\u001b[39mget(\u001b[39m'\u001b[39m\u001b[39mout\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mNone\u001b[39;00m)\n\u001b[0;32m    594\u001b[0m \u001b[39m# Avoid loop back by turning any Quantity output into array views.\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\astropy\\units\\quantity_helper\\converters.py:192\u001b[0m, in \u001b[0;36mconverters_and_unit\u001b[1;34m(function, method, *args)\u001b[0m\n\u001b[0;32m    190\u001b[0m             converters[i] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m    191\u001b[0m         \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 192\u001b[0m             \u001b[39mraise\u001b[39;00m UnitConversionError(\n\u001b[0;32m    193\u001b[0m                 \u001b[39m\"\u001b[39m\u001b[39mCan only apply \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m function to \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    194\u001b[0m                 \u001b[39m\"\u001b[39m\u001b[39mdimensionless quantities when other \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    195\u001b[0m                 \u001b[39m\"\u001b[39m\u001b[39margument is not a quantity (unless the \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    196\u001b[0m                 \u001b[39m\"\u001b[39m\u001b[39mlatter is all zero/infinity/nan)\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    197\u001b[0m                 \u001b[39m.\u001b[39mformat(function\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m))\n\u001b[0;32m    198\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[0;32m    199\u001b[0m     \u001b[39m# _can_have_arbitrary_unit failed: arg could not be compared\u001b[39;00m\n\u001b[0;32m    200\u001b[0m     \u001b[39m# with zero or checked to be finite. Then, ufunc will fail too.\u001b[39;00m\n\u001b[0;32m    201\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mUnsupported operand type(s) for ufunc \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m: \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    202\u001b[0m                     \u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(function\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m,\n\u001b[0;32m    203\u001b[0m                                    \u001b[39m'\u001b[39m\u001b[39m,\u001b[39m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mjoin([arg\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m\n\u001b[0;32m    204\u001b[0m                                              \u001b[39mfor\u001b[39;00m arg \u001b[39min\u001b[39;00m args])))\n",
+      "\u001b[1;31mUnitConversionError\u001b[0m: Can only apply 'greater' function to dimensionless quantities when other argument is not a quantity (unless the latter is all zero/infinity/nan)"
+     ]
+    },
+    {
+     "ename": "UnitConversionError",
+     "evalue": "Can only apply 'greater' function to dimensionless quantities when other argument is not a quantity (unless the latter is all zero/infinity/nan)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mUnitConversionError\u001b[0m                       Traceback (most recent call last)",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\IPython\\core\\formatters.py:340\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    338\u001b[0m     \u001b[39mpass\u001b[39;00m\n\u001b[0;32m    339\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 340\u001b[0m     \u001b[39mreturn\u001b[39;00m printer(obj)\n\u001b[0;32m    341\u001b[0m \u001b[39m# Finally look for special method names\u001b[39;00m\n\u001b[0;32m    342\u001b[0m method \u001b[39m=\u001b[39m get_real_method(obj, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprint_method)\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152\u001b[0m, in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[0;32m    149\u001b[0m     \u001b[39mfrom\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mbackend_bases\u001b[39;00m \u001b[39mimport\u001b[39;00m FigureCanvasBase\n\u001b[0;32m    150\u001b[0m     FigureCanvasBase(fig)\n\u001b[1;32m--> 152\u001b[0m fig\u001b[39m.\u001b[39mcanvas\u001b[39m.\u001b[39mprint_figure(bytes_io, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkw)\n\u001b[0;32m    153\u001b[0m data \u001b[39m=\u001b[39m bytes_io\u001b[39m.\u001b[39mgetvalue()\n\u001b[0;32m    154\u001b[0m \u001b[39mif\u001b[39;00m fmt \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39msvg\u001b[39m\u001b[39m'\u001b[39m:\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\matplotlib\\backend_bases.py:2342\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[0;32m   2336\u001b[0m     renderer \u001b[39m=\u001b[39m _get_renderer(\n\u001b[0;32m   2337\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfigure,\n\u001b[0;32m   2338\u001b[0m         functools\u001b[39m.\u001b[39mpartial(\n\u001b[0;32m   2339\u001b[0m             print_method, orientation\u001b[39m=\u001b[39morientation)\n\u001b[0;32m   2340\u001b[0m     )\n\u001b[0;32m   2341\u001b[0m     \u001b[39mwith\u001b[39;00m \u001b[39mgetattr\u001b[39m(renderer, \u001b[39m\"\u001b[39m\u001b[39m_draw_disabled\u001b[39m\u001b[39m\"\u001b[39m, nullcontext)():\n\u001b[1;32m-> 2342\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfigure\u001b[39m.\u001b[39mdraw(renderer)\n\u001b[0;32m   2344\u001b[0m \u001b[39mif\u001b[39;00m bbox_inches:\n\u001b[0;32m   2345\u001b[0m     \u001b[39mif\u001b[39;00m bbox_inches \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mtight\u001b[39m\u001b[39m\"\u001b[39m:\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\matplotlib\\artist.py:95\u001b[0m, in \u001b[0;36m_finalize_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     93\u001b[0m \u001b[39m@wraps\u001b[39m(draw)\n\u001b[0;32m     94\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdraw_wrapper\u001b[39m(artist, renderer, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m---> 95\u001b[0m     result \u001b[39m=\u001b[39m draw(artist, renderer, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m     96\u001b[0m     \u001b[39mif\u001b[39;00m renderer\u001b[39m.\u001b[39m_rasterizing:\n\u001b[0;32m     97\u001b[0m         renderer\u001b[39m.\u001b[39mstop_rasterizing()\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer)\u001b[0m\n\u001b[0;32m     69\u001b[0m     \u001b[39mif\u001b[39;00m artist\u001b[39m.\u001b[39mget_agg_filter() \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m     70\u001b[0m         renderer\u001b[39m.\u001b[39mstart_filter()\n\u001b[1;32m---> 72\u001b[0m     \u001b[39mreturn\u001b[39;00m draw(artist, renderer)\n\u001b[0;32m     73\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m     74\u001b[0m     \u001b[39mif\u001b[39;00m artist\u001b[39m.\u001b[39mget_agg_filter() \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\matplotlib\\figure.py:3140\u001b[0m, in \u001b[0;36mFigure.draw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   3137\u001b[0m         \u001b[39m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[0;32m   3139\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpatch\u001b[39m.\u001b[39mdraw(renderer)\n\u001b[1;32m-> 3140\u001b[0m mimage\u001b[39m.\u001b[39m_draw_list_compositing_images(\n\u001b[0;32m   3141\u001b[0m     renderer, \u001b[39mself\u001b[39m, artists, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msuppressComposite)\n\u001b[0;32m   3143\u001b[0m \u001b[39mfor\u001b[39;00m sfig \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msubfigs:\n\u001b[0;32m   3144\u001b[0m     sfig\u001b[39m.\u001b[39mdraw(renderer)\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\matplotlib\\image.py:131\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    129\u001b[0m \u001b[39mif\u001b[39;00m not_composite \u001b[39mor\u001b[39;00m \u001b[39mnot\u001b[39;00m has_images:\n\u001b[0;32m    130\u001b[0m     \u001b[39mfor\u001b[39;00m a \u001b[39min\u001b[39;00m artists:\n\u001b[1;32m--> 131\u001b[0m         a\u001b[39m.\u001b[39mdraw(renderer)\n\u001b[0;32m    132\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    133\u001b[0m     \u001b[39m# Composite any adjacent images together\u001b[39;00m\n\u001b[0;32m    134\u001b[0m     image_group \u001b[39m=\u001b[39m []\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer)\u001b[0m\n\u001b[0;32m     69\u001b[0m     \u001b[39mif\u001b[39;00m artist\u001b[39m.\u001b[39mget_agg_filter() \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m     70\u001b[0m         renderer\u001b[39m.\u001b[39mstart_filter()\n\u001b[1;32m---> 72\u001b[0m     \u001b[39mreturn\u001b[39;00m draw(artist, renderer)\n\u001b[0;32m     73\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m     74\u001b[0m     \u001b[39mif\u001b[39;00m artist\u001b[39m.\u001b[39mget_agg_filter() \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\matplotlib\\axes\\_base.py:3064\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   3061\u001b[0m \u001b[39mif\u001b[39;00m artists_rasterized:\n\u001b[0;32m   3062\u001b[0m     _draw_rasterized(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfigure, artists_rasterized, renderer)\n\u001b[1;32m-> 3064\u001b[0m mimage\u001b[39m.\u001b[39m_draw_list_compositing_images(\n\u001b[0;32m   3065\u001b[0m     renderer, \u001b[39mself\u001b[39m, artists, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfigure\u001b[39m.\u001b[39msuppressComposite)\n\u001b[0;32m   3067\u001b[0m renderer\u001b[39m.\u001b[39mclose_group(\u001b[39m'\u001b[39m\u001b[39maxes\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m   3068\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstale \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\matplotlib\\image.py:131\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    129\u001b[0m \u001b[39mif\u001b[39;00m not_composite \u001b[39mor\u001b[39;00m \u001b[39mnot\u001b[39;00m has_images:\n\u001b[0;32m    130\u001b[0m     \u001b[39mfor\u001b[39;00m a \u001b[39min\u001b[39;00m artists:\n\u001b[1;32m--> 131\u001b[0m         a\u001b[39m.\u001b[39mdraw(renderer)\n\u001b[0;32m    132\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    133\u001b[0m     \u001b[39m# Composite any adjacent images together\u001b[39;00m\n\u001b[0;32m    134\u001b[0m     image_group \u001b[39m=\u001b[39m []\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer)\u001b[0m\n\u001b[0;32m     69\u001b[0m     \u001b[39mif\u001b[39;00m artist\u001b[39m.\u001b[39mget_agg_filter() \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m     70\u001b[0m         renderer\u001b[39m.\u001b[39mstart_filter()\n\u001b[1;32m---> 72\u001b[0m     \u001b[39mreturn\u001b[39;00m draw(artist, renderer)\n\u001b[0;32m     73\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m     74\u001b[0m     \u001b[39mif\u001b[39;00m artist\u001b[39m.\u001b[39mget_agg_filter() \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\matplotlib\\image.py:641\u001b[0m, in \u001b[0;36m_ImageBase.draw\u001b[1;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m    639\u001b[0m         renderer\u001b[39m.\u001b[39mdraw_image(gc, l, b, im, trans)\n\u001b[0;32m    640\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 641\u001b[0m     im, l, b, trans \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmake_image(\n\u001b[0;32m    642\u001b[0m         renderer, renderer\u001b[39m.\u001b[39mget_image_magnification())\n\u001b[0;32m    643\u001b[0m     \u001b[39mif\u001b[39;00m im \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m    644\u001b[0m         renderer\u001b[39m.\u001b[39mdraw_image(gc, l, b, im)\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\matplotlib\\image.py:949\u001b[0m, in \u001b[0;36mAxesImage.make_image\u001b[1;34m(self, renderer, magnification, unsampled)\u001b[0m\n\u001b[0;32m    946\u001b[0m transformed_bbox \u001b[39m=\u001b[39m TransformedBbox(bbox, trans)\n\u001b[0;32m    947\u001b[0m clip \u001b[39m=\u001b[39m ((\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_clip_box() \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxes\u001b[39m.\u001b[39mbbox) \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_clip_on()\n\u001b[0;32m    948\u001b[0m         \u001b[39melse\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfigure\u001b[39m.\u001b[39mbbox)\n\u001b[1;32m--> 949\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_make_image(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_A, bbox, transformed_bbox, clip,\n\u001b[0;32m    950\u001b[0m                         magnification, unsampled\u001b[39m=\u001b[39munsampled)\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\matplotlib\\image.py:456\u001b[0m, in \u001b[0;36m_ImageBase._make_image\u001b[1;34m(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border)\u001b[0m\n\u001b[0;32m    454\u001b[0m fact \u001b[39m=\u001b[39m \u001b[39m1e7\u001b[39m \u001b[39mif\u001b[39;00m scaled_dtype \u001b[39m==\u001b[39m np\u001b[39m.\u001b[39mfloat64 \u001b[39melse\u001b[39;00m \u001b[39m1e4\u001b[39m\n\u001b[0;32m    455\u001b[0m newmin \u001b[39m=\u001b[39m vmid \u001b[39m-\u001b[39m dv \u001b[39m*\u001b[39m fact\n\u001b[1;32m--> 456\u001b[0m \u001b[39mif\u001b[39;00m newmin \u001b[39m<\u001b[39m a_min:\n\u001b[0;32m    457\u001b[0m     newmin \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m    458\u001b[0m \u001b[39melse\u001b[39;00m:\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\numpy\\ma\\core.py:4191\u001b[0m, in \u001b[0;36mMaskedArray.__gt__\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m   4190\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__gt__\u001b[39m(\u001b[39mself\u001b[39m, other):\n\u001b[1;32m-> 4191\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_comparison(other, operator\u001b[39m.\u001b[39mgt)\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\numpy\\ma\\core.py:4123\u001b[0m, in \u001b[0;36mMaskedArray._comparison\u001b[1;34m(self, other, compare)\u001b[0m\n\u001b[0;32m   4119\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m   4120\u001b[0m     \u001b[39m# For regular arrays, just use the data as they come.\u001b[39;00m\n\u001b[0;32m   4121\u001b[0m     sdata \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdata\n\u001b[1;32m-> 4123\u001b[0m check \u001b[39m=\u001b[39m compare(sdata, odata)\n\u001b[0;32m   4125\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(check, (np\u001b[39m.\u001b[39mbool_, \u001b[39mbool\u001b[39m)):\n\u001b[0;32m   4126\u001b[0m     \u001b[39mreturn\u001b[39;00m masked \u001b[39mif\u001b[39;00m mask \u001b[39melse\u001b[39;00m check\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\astropy\\units\\quantity.py:591\u001b[0m, in \u001b[0;36mQuantity.__array_ufunc__\u001b[1;34m(self, function, method, *inputs, **kwargs)\u001b[0m\n\u001b[0;32m    569\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Wrap numpy ufuncs, taking care of units.\u001b[39;00m\n\u001b[0;32m    570\u001b[0m \n\u001b[0;32m    571\u001b[0m \u001b[39mParameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    585\u001b[0m \u001b[39m    Results of the ufunc, with the unit set properly.\u001b[39;00m\n\u001b[0;32m    586\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    587\u001b[0m \u001b[39m# Determine required conversion functions -- to bring the unit of the\u001b[39;00m\n\u001b[0;32m    588\u001b[0m \u001b[39m# input to that expected (e.g., radian for np.sin), or to get\u001b[39;00m\n\u001b[0;32m    589\u001b[0m \u001b[39m# consistent units between two inputs (e.g., in np.add) --\u001b[39;00m\n\u001b[0;32m    590\u001b[0m \u001b[39m# and the unit of the result (or tuple of units for nout > 1).\u001b[39;00m\n\u001b[1;32m--> 591\u001b[0m converters, unit \u001b[39m=\u001b[39m converters_and_unit(function, method, \u001b[39m*\u001b[39minputs)\n\u001b[0;32m    593\u001b[0m out \u001b[39m=\u001b[39m kwargs\u001b[39m.\u001b[39mget(\u001b[39m'\u001b[39m\u001b[39mout\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mNone\u001b[39;00m)\n\u001b[0;32m    594\u001b[0m \u001b[39m# Avoid loop back by turning any Quantity output into array views.\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\julia\\anaconda3\\Lib\\site-packages\\astropy\\units\\quantity_helper\\converters.py:192\u001b[0m, in \u001b[0;36mconverters_and_unit\u001b[1;34m(function, method, *args)\u001b[0m\n\u001b[0;32m    190\u001b[0m             converters[i] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m    191\u001b[0m         \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 192\u001b[0m             \u001b[39mraise\u001b[39;00m UnitConversionError(\n\u001b[0;32m    193\u001b[0m                 \u001b[39m\"\u001b[39m\u001b[39mCan only apply \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m function to \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    194\u001b[0m                 \u001b[39m\"\u001b[39m\u001b[39mdimensionless quantities when other \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    195\u001b[0m                 \u001b[39m\"\u001b[39m\u001b[39margument is not a quantity (unless the \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    196\u001b[0m                 \u001b[39m\"\u001b[39m\u001b[39mlatter is all zero/infinity/nan)\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    197\u001b[0m                 \u001b[39m.\u001b[39mformat(function\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m))\n\u001b[0;32m    198\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[0;32m    199\u001b[0m     \u001b[39m# _can_have_arbitrary_unit failed: arg could not be compared\u001b[39;00m\n\u001b[0;32m    200\u001b[0m     \u001b[39m# with zero or checked to be finite. Then, ufunc will fail too.\u001b[39;00m\n\u001b[0;32m    201\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mUnsupported operand type(s) for ufunc \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m: \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    202\u001b[0m                     \u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(function\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m,\n\u001b[0;32m    203\u001b[0m                                    \u001b[39m'\u001b[39m\u001b[39m,\u001b[39m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mjoin([arg\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m\n\u001b[0;32m    204\u001b[0m                                              \u001b[39mfor\u001b[39;00m arg \u001b[39min\u001b[39;00m args])))\n",
+      "\u001b[1;31mUnitConversionError\u001b[0m: Can only apply 'greater' function to dimensionless quantities when other argument is not a quantity (unless the latter is all zero/infinity/nan)"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "dT_map = (y_map * cosmo.Tcmb0 * -fSZ).to(u.uK)\n",
     "im = plt.imshow(dT_map, norm=LogNorm())\n",
@@ -446,7 +556,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.3"
   }
  },
  "nbformat": 4,