test that hypothesis can be mesh plane

PET/protokoll/protokoll.tex

@@ -622,7 +622,8 @@ in~\ref{fig:tom1} dargestellt und wird im Folgenden nachvollzogen.
      \draw[black] (4.5,3) circle (.6cm) node {3};
      \draw (3,3) circle (.6cm) node {Leer};
      \node[draw] at (1.5,2) {Wachs};
-   \end{tikzpicture}}
+   \end{tikzpicture}
+ }
  \caption[Quellenkonstellation]{Quellenkonstellation}
  \label{fig:sourccof}
 \end{wrapfigure}

tem/auswertung/gold_diffr.ipynb

@@ -98,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 138,
    "metadata": {
     "autoscroll": false,
     "collapsed": false,
@@ -110,19 +110,16 @@
    },
    "outputs": [
     {
-     "name": "stdout",
+     "ename": "TypeError",
-     "output_type": "stream",
+     "evalue": "object of type 'function' has no len()",
-     "text": [
+     "output_type": "error",
-      "(array([ 4,  4,  9, 11, 13, 23, 28, 31, 40]), array([[0.4365859 , 0.00314502, 0.03385928],\n",
+     "traceback": [
-      "       [0.38654813, 0.00246542, 0.01311343],\n",
+      "\u001b[0;31m\u001b[0m",
-      "       [0.39925472, 0.00175345, 0.0193526 ],\n",
+      "\u001b[0;31mTypeError\u001b[0mTraceback (most recent call last)",
-      "       [0.39935277, 0.00158683, 0.00165195],\n",
+      "\u001b[0;32m<ipython-input-138-7fa66147a009>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhypothesis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mevaluate_hypothesis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcandidates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md_candidates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msigma_candidates\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaximum\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m80\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhypothesis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "       [0.4097683 , 0.00153681, 0.02061948],\n",
+      "\u001b[0;32m~/Documents/Projects/UNI/Prakt/FP/tem/auswertung/utility.py\u001b[0m in \u001b[0;36mevaluate_hypothesis\u001b[0;34m(analyzed, maximum, gold)\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0mdiffs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaximum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0manalyzed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    228\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 229\u001b[0;31m     \u001b[0msquared_ds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaximum\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfind_miller_indices\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    230\u001b[0m     \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msquared_ds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m     \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0manalyzed\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "       [0.40763549, 0.00114339, 0.02000247],\n",
+      "\u001b[0;32m~/Documents/Projects/UNI/Prakt/FP/tem/auswertung/utility.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0mdiffs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaximum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0manalyzed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    228\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 229\u001b[0;31m     \u001b[0msquared_ds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaximum\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfind_miller_indices\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    230\u001b[0m     \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msquared_ds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m     \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0manalyzed\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "       [0.40854715, 0.00104092, 0.0108266 ],\n",
+      "\u001b[0;31mTypeError\u001b[0m: object of type 'function' has no len()"
-      "       [0.4080742 , 0.00098699, 0.00785981],\n",
-      "       [0.40573231, 0.00085894, 0.01187421]]), array([0.0287859 , 0.02125187, 0.00854528, 0.00844723, 0.0019683 ,\n",
-      "       0.00016451, 0.00074715, 0.0002742 , 0.00206769]))\n"
      ]
     }
    ],
@@ -222,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 137,
    "metadata": {
     "autoscroll": false,
     "collapsed": false,
@@ -232,8 +229,26 @@
      "slide_type": "-"
     }
    },
-   "outputs": [],
+   "outputs": [
-   "source": []
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5 & \\mqty{2 & 0 & 0} \\\\\n",
+      "10 & \\mqty{2 & 2 & 1}, \\mqty{3 & 0 & 0} \\\\\n",
+      "12 & \\mqty{3 & 1 & 1} \\\\\n",
+      "14 & \\mqty{3 & 2 & 0} \\\\\n",
+      "24  \\\\\n",
+      "29  \\\\\n",
+      "32  \\\\\n",
+      "41 & \\mqty{6 & 2 & 0} \\\\\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(generate_miller_table(hypothesis[0]))"
+   ]
   }
  ],
  "metadata": {

tem/auswertung/hrtem.ipynb

@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 35,
    "metadata": {
     "autoscroll": false,
     "collapsed": false,
@@ -111,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 39,
    "metadata": {
     "autoscroll": false,
     "collapsed": false,
@@ -214,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 116,
    "metadata": {
     "autoscroll": false,
     "collapsed": false,
@@ -224,8 +224,20 @@
      "slide_type": "-"
     }
    },
-   "outputs": [],
+   "outputs": [
-   "source": []
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4 & \\mqty{1 & 1 & 1} \\\\\n",
+      "5 & \\mqty{2 & 0 & 0} \\\\\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(generate_miller_table(hypothesis[0]))"
+   ]
   }
  ],
  "metadata": {

tem/auswertung/out/figlist.txt

@@ -22,4 +22,76 @@
   \caption{}
   \label{fig:gold_hires-profile_10}
 \end{figure}
+    
+\begin{figure}[H]\centering
+  \input{../auswertung/figs/gold_hires/profile_1.pgf}
+  \caption{}
+  \label{fig:gold_hires-profile_1}
+\end{figure}
+    
+\begin{figure}[H]\centering
+  \input{../auswertung/figs/gold_hires/profile_4.pgf}
+  \caption{}
+  \label{fig:gold_hires-profile_4}
+\end{figure}
+    
+\begin{figure}[H]\centering
+  \input{../auswertung/figs/gold_hires/profile_6.pgf}
+  \caption{}
+  \label{fig:gold_hires-profile_6}
+\end{figure}
+    
+\begin{figure}[H]\centering
+  \input{../auswertung/figs/gold_hires/profile_10.pgf}
+  \caption{}
+  \label{fig:gold_hires-profile_10}
+\end{figure}
+    
+\begin{figure}[H]\centering
+  \input{../auswertung/figs/gold_hires/profile_1.pgf}
+  \caption{}
+  \label{fig:gold_hires-profile_1}
+\end{figure}
+    
+\begin{figure}[H]\centering
+  \input{../auswertung/figs/gold_hires/profile_4.pgf}
+  \caption{}
+  \label{fig:gold_hires-profile_4}
+\end{figure}
+    
+\begin{figure}[H]\centering
+  \input{../auswertung/figs/gold_hires/profile_6.pgf}
+  \caption{}
+  \label{fig:gold_hires-profile_6}
+\end{figure}
+    
+\begin{figure}[H]\centering
+  \input{../auswertung/figs/gold_hires/profile_10.pgf}
+  \caption{}
+  \label{fig:gold_hires-profile_10}
+\end{figure}
+    
+\begin{figure}[H]\centering
+  \input{../auswertung/figs/gold_hires/profile_1.pgf}
+  \caption{}
+  \label{fig:gold_hires-profile_1}
+\end{figure}
+    
+\begin{figure}[H]\centering
+  \input{../auswertung/figs/gold_hires/profile_4.pgf}
+  \caption{}
+  \label{fig:gold_hires-profile_4}
+\end{figure}
+    
+\begin{figure}[H]\centering
+  \input{../auswertung/figs/gold_hires/profile_6.pgf}
+  \caption{}
+  \label{fig:gold_hires-profile_6}
+\end{figure}
+    
+\begin{figure}[H]\centering
+  \input{../auswertung/figs/gold_hires/profile_10.pgf}
+  \caption{}
+  \label{fig:gold_hires-profile_10}
+\end{figure}
     

tem/auswertung/utility.py

@@ -201,11 +201,40 @@ def analyze_profile(profile, limits=(0, -1), save=None, **peak_args):
 
     return l, dl, sigma
 
+def find_miller_indices(squares):
+    squares = np.asarray(squares)
+    if squares.size > 1:
+        return np.array([find_miller_indices(x) for x in squares])
+    square = squares
+    return np.array([(a, b, c) for (a, b, c) \
+                in np.ndindex((square+1, square+1, square+1)) \
+       if a**2 + b**2 + c**2 == square and a >= b >= c])
+
+def can_be_sum_of_squares(square):
+    for a, b, c in np.ndindex((square+1, square+1, square+1)):
+        if a**2 + b**2 + c**2 == square:
+            return True
+
+    return False
+
+def generate_miller_table(squares):
+    squares = np.unique(squares)
+    inds = find_miller_indices(squares)
+    out = ''
+    for i, ind_list in zip(squares, inds):
+        out += f'{i + 1} & '
+        for ind in ind_list:
+            out += r'\mqty{' + ' & '.join(ind.astype(str)) + '}, '
+        out = out[:-2]
+
+        out += r' \\' + '\n'
+    return out
 
 def evaluate_hypothesis(analyzed, maximum=10, gold=.4078):
     diffs = np.empty((maximum, analyzed.shape[0]))
 
-    squared_ds = np.arange(1, maximum + 1, 1)
+    squared_ds = np.array([x for x in np.arange(1, maximum + 1, 1) \
+                           if can_be_sum_of_squares(x)])
     ds = np.sqrt(squared_ds)
     a = analyzed[:,0][:, None] * ds[None, :]
     diff = np.abs(a - gold)