Hist Cython.ipynb

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   "source": [
    "%load_ext autoreload\n",
    "%load_ext cython\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
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   "cell_type": "code",
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      "5 5\n"
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       "<p>\n",
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       "    Click on a line that starts with a \"<code>+</code>\" to see the C code that Cython generated for it.\n",
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       "<div class=\"cython\"><pre class=\"cython line score-0\">&#xA0;<span class=\"\">001</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">002</span>: <span class=\"k\">import</span> <span class=\"nn\">cython</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">003</span>: <span class=\"k\">from</span> <span class=\"nn\">libc.stdlib</span> <span class=\"k\">cimport</span> <span class=\"n\">malloc</span></pre>\n",
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       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">005</span>: <span class=\"k\">cdef</span> <span class=\"k\">struct</span> <span class=\"nf\">Bin</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>struct __pyx_t_46_cython_magic_36bb821b6fbfb2a47a73ea90d89a35db_Bin {\n",
       "  double left;\n",
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       "};\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">006</span>:     <span class=\"n\">double</span> <span class=\"n\">left</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">007</span>:     <span class=\"n\">double</span> <span class=\"n\">right</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">008</span>:     <span class=\"nb\">long</span> <span class=\"n\">count</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">009</span>: </pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">010</span>: <span class=\"k\">cdef</span> <span class=\"k\">class</span> <span class=\"nf\">Histogram</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>struct __pyx_obj_46_cython_magic_36bb821b6fbfb2a47a73ea90d89a35db_Histogram {\n",
       "  PyObject_HEAD\n",
       "  struct __pyx_vtabstruct_46_cython_magic_36bb821b6fbfb2a47a73ea90d89a35db_Histogram *__pyx_vtab;\n",
       "  struct __pyx_t_46_cython_magic_36bb821b6fbfb2a47a73ea90d89a35db_Bin *bins;\n",
       "  int n;\n",
       "  int max_bins;\n",
       "  int n_bins;\n",
       "  double min_diff;\n",
       "  int min_idx;\n",
       "};\n",
       "/* … */\n",
       "struct __pyx_vtabstruct_46_cython_magic_36bb821b6fbfb2a47a73ea90d89a35db_Histogram {\n",
       "  struct __pyx_obj_46_cython_magic_36bb821b6fbfb2a47a73ea90d89a35db_Histogram *(*update)(struct __pyx_obj_46_cython_magic_36bb821b6fbfb2a47a73ea90d89a35db_Histogram *, double, int __pyx_skip_dispatch);\n",
       "};\n",
       "static struct __pyx_vtabstruct_46_cython_magic_36bb821b6fbfb2a47a73ea90d89a35db_Histogram *__pyx_vtabptr_46_cython_magic_36bb821b6fbfb2a47a73ea90d89a35db_Histogram;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">011</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">012</span>:     <span class=\"k\">cdef</span><span class=\"p\">:</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">013</span>:         <span class=\"n\">Bin</span> <span class=\"o\">*</span><span class=\"n\">bins</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">014</span>:         <span class=\"nb\">int</span> <span class=\"n\">n</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">015</span>:         <span class=\"nb\">int</span> <span class=\"n\">max_bins</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">016</span>:         <span class=\"nb\">int</span> <span class=\"n\">n_bins</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">017</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">018</span>:         <span class=\"n\">double</span> <span class=\"n\">min_diff</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">019</span>:         <span class=\"nb\">int</span> <span class=\"n\">min_idx</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">020</span>: </pre>\n",
       "<pre class=\"cython line score-27\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">021</span>:     <span class=\"k\">def</span> <span class=\"nf\">__cinit__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"nb\">long</span> <span class=\"n\">max_bins</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-27 '>/* Python wrapper */\n",
       "static int __pyx_pw_46_cython_magic_36bb821b6fbfb2a47a73ea90d89a35db_9Histogram_1__cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/\n",
       "static int __pyx_pw_46_cython_magic_36bb821b6fbfb2a47a73ea90d89a35db_9Histogram_1__cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {\n",
       "  long __pyx_v_max_bins;\n",
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       "    PyObject* values[1] = {0};\n",
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       "      kw_args = <span class='py_c_api'>PyDict_Size</span>(__pyx_kwds);\n",
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       "\n",
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       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">022</span>:         <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">n</span> <span class=\"o\">=</span> <span class=\"mf\">0</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_v_self-&gt;n = 0;\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">023</span>:         <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">max_bins</span> <span class=\"o\">=</span> <span class=\"n\">max_bins</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_v_self-&gt;max_bins = __pyx_v_max_bins;\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">024</span>:         <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">n_bins</span> <span class=\"o\">=</span> <span class=\"mf\">0</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_v_self-&gt;n_bins = 0;\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">025</span>:         <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">bins</span> <span class=\"o\">=</span> <span class=\"o\">&lt;</span><span class=\"n\">Bin</span><span class=\"o\">*&gt;</span><span class=\"n\">malloc</span><span class=\"p\">((</span><span class=\"n\">max_bins</span> <span class=\"o\">+</span> <span class=\"mf\">1</span><span class=\"p\">)</span> <span class=\"o\">*</span> <span class=\"n\">sizeof</span><span class=\"p\">(</span><span class=\"n\">Bin</span><span class=\"p\">))</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_v_self-&gt;bins = ((struct __pyx_t_46_cython_magic_36bb821b6fbfb2a47a73ea90d89a35db_Bin *)malloc(((__pyx_v_max_bins + 1) * (sizeof(struct __pyx_t_46_cython_magic_36bb821b6fbfb2a47a73ea90d89a35db_Bin)))));\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">026</span>:         <span class=\"k\">if</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">bins</span> <span class=\"o\">==</span> <span class=\"bp\">NULL</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_1 = ((__pyx_v_self-&gt;bins == NULL) != 0);\n",
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       "<pre class='cython code score-52 '>static PyObject *__pyx_pw_46_cython_magic_36bb821b6fbfb2a47a73ea90d89a35db_9Histogram_3update(PyObject *__pyx_v_self, PyObject *__pyx_arg_x); /*proto*/\n",
       "static struct __pyx_obj_46_cython_magic_36bb821b6fbfb2a47a73ea90d89a35db_Histogram *__pyx_f_46_cython_magic_36bb821b6fbfb2a47a73ea90d89a35db_9Histogram_update(struct __pyx_obj_46_cython_magic_36bb821b6fbfb2a47a73ea90d89a35db_Histogram *__pyx_v_self, double __pyx_v_x, int __pyx_skip_dispatch) {\n",
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       "static PyObject *__pyx_pw_46_cython_magic_36bb821b6fbfb2a47a73ea90d89a35db_9Histogram_3update(PyObject *__pyx_v_self, PyObject *__pyx_arg_x) {\n",
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       "\n",
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       "}\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">032</span>: </pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">033</span>:         <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">n</span> <span class=\"o\">+=</span> <span class=\"mf\">1</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_v_self-&gt;n = (__pyx_v_self-&gt;n + 1);\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">034</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">035</span>:         <span class=\"c\"># Insert the bin if the histogram is empty</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">036</span>:         <span class=\"k\">if</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">n_bins</span> <span class=\"o\">==</span> <span class=\"mf\">0</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_6 = ((__pyx_v_self-&gt;n_bins == 0) != 0);\n",
       "  if (__pyx_t_6) {\n",
       "/* … */\n",
       "  }\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">037</span>:             <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">bins</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">Bin</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">,</span> <span class=\"n\">x</span><span class=\"p\">,</span> <span class=\"mf\">1</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_7.left = __pyx_v_x;\n",
       "    __pyx_t_7.right = __pyx_v_x;\n",
       "    __pyx_t_7.count = 1;\n",
       "    (__pyx_v_self-&gt;bins[0]) = __pyx_t_7;\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">038</span>:             <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">n_bins</span> <span class=\"o\">+=</span> <span class=\"mf\">1</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_v_self-&gt;n_bins = (__pyx_v_self-&gt;n_bins + 1);\n",
       "</pre><pre class=\"cython line score-2\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">039</span>:             <span class=\"k\">return</span></pre>\n",
       "<pre class='cython code score-2 '>    <span class='pyx_macro_api'>__Pyx_XDECREF</span>(((PyObject *)__pyx_r));\n",
       "    __pyx_r = ((struct __pyx_obj_46_cython_magic_36bb821b6fbfb2a47a73ea90d89a35db_Histogram *)Py_None); <span class='pyx_macro_api'>__Pyx_INCREF</span>(Py_None);\n",
       "    goto __pyx_L0;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">040</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">041</span>:         <span class=\"c\"># Bisect to find where to insert the new value</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">042</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">043</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">where</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">044</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">low</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">045</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">high</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">046</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">i</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">047</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">j</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">048</span>: </pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">049</span>:         <span class=\"k\">if</span> <span class=\"n\">x</span> <span class=\"o\">&lt;</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">bins</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">left</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_6 = ((__pyx_v_x &lt; (__pyx_v_self-&gt;bins[0]).left) != 0);\n",
       "  if (__pyx_t_6) {\n",
       "/* … */\n",
       "    goto __pyx_L4;\n",
       "  }\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">050</span>:             <span class=\"n\">where</span> <span class=\"o\">=</span> <span class=\"mf\">0</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_v_where = 0;\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">051</span>:         <span class=\"k\">elif</span> <span class=\"n\">x</span> <span class=\"o\">&gt;</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">bins</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">n_bins</span> <span class=\"o\">-</span> <span class=\"mf\">1</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">right</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_6 = ((__pyx_v_x &gt; (__pyx_v_self-&gt;bins[(__pyx_v_self-&gt;n_bins - 1)]).right) != 0);\n",
       "  if (__pyx_t_6) {\n",
       "/* … */\n",
       "    goto __pyx_L4;\n",
       "  }\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">052</span>:             <span class=\"n\">where</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">n_bins</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_8 = __pyx_v_self-&gt;n_bins;\n",
       "    __pyx_v_where = __pyx_t_8;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">053</span>:         <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">054</span>: </pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">055</span>:             <span class=\"n\">low</span> <span class=\"o\">=</span> <span class=\"mf\">0</span></pre>\n",
       "<pre class='cython code score-0 '>  /*else*/ {\n",
       "    __pyx_v_low = 0;\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">056</span>:             <span class=\"n\">high</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">n_bins</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_8 = __pyx_v_self-&gt;n_bins;\n",
       "    __pyx_v_high = __pyx_t_8;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">057</span>: </pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">058</span>:             <span class=\"k\">while</span> <span class=\"n\">low</span> <span class=\"o\">&lt;</span> <span class=\"n\">high</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>    while (1) {\n",
       "      __pyx_t_6 = ((__pyx_v_low &lt; __pyx_v_high) != 0);\n",
       "      if (!__pyx_t_6) break;\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">059</span>:                 <span class=\"n\">where</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">low</span> <span class=\"o\">+</span> <span class=\"n\">high</span><span class=\"p\">)</span> <span class=\"o\">//</span> <span class=\"mf\">2</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_v_where = __Pyx_div_long((__pyx_v_low + __pyx_v_high), 2);\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">060</span>: </pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">061</span>:                 <span class=\"k\">if</span> <span class=\"n\">x</span> <span class=\"o\">&gt;</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">bins</span><span class=\"p\">[</span><span class=\"n\">where</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">right</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_6 = ((__pyx_v_x &gt; (__pyx_v_self-&gt;bins[__pyx_v_where]).right) != 0);\n",
       "      if (__pyx_t_6) {\n",
       "/* … */\n",
       "        goto __pyx_L7;\n",
       "      }\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">062</span>:                     <span class=\"n\">low</span> <span class=\"o\">=</span> <span class=\"n\">where</span> <span class=\"o\">+</span> <span class=\"mf\">1</span></pre>\n",
       "<pre class='cython code score-0 '>        __pyx_v_low = (__pyx_v_where + 1);\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">063</span>:                 <span class=\"k\">elif</span> <span class=\"n\">x</span> <span class=\"o\">&gt;=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">bins</span><span class=\"p\">[</span><span class=\"n\">where</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">left</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_6 = ((__pyx_v_x &gt;= (__pyx_v_self-&gt;bins[__pyx_v_where]).left) != 0);\n",
       "      if (__pyx_t_6) {\n",
       "/* … */\n",
       "      }\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">064</span>:                     <span class=\"k\">break</span></pre>\n",
       "<pre class='cython code score-0 '>        goto __pyx_L6_break;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">065</span>:                 <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">066</span>:                     <span class=\"n\">high</span> <span class=\"o\">=</span> <span class=\"n\">where</span></pre>\n",
       "<pre class='cython code score-0 '>      /*else*/ {\n",
       "        __pyx_v_high = __pyx_v_where;\n",
       "      }\n",
       "      __pyx_L7:;\n",
       "    }\n",
       "    __pyx_L6_break:;\n",
       "  }\n",
       "  __pyx_L4:;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">067</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">068</span>:         <span class=\"c\"># Insert the new bin</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">069</span>:         <span class=\"k\">for</span> <span class=\"n\">i</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">n_bins</span> <span class=\"o\">+</span> <span class=\"mf\">1</span><span class=\"p\">,</span> <span class=\"n\">where</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_8 = __pyx_v_where;\n",
       "  __pyx_t_9 = __pyx_t_8;\n",
       "  for (__pyx_t_10 = (__pyx_v_self-&gt;n_bins + 1); __pyx_t_10 &lt; __pyx_t_9; __pyx_t_10+=1) {\n",
       "    __pyx_v_i = __pyx_t_10;\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">070</span>:             <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">bins</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">bins</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"o\">-</span><span class=\"mf\">1</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>    (__pyx_v_self-&gt;bins[__pyx_v_i]) = (__pyx_v_self-&gt;bins[(__pyx_v_i - 1)]);\n",
       "  }\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">071</span>:         <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">bins</span><span class=\"p\">[</span><span class=\"n\">where</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">Bin</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">,</span> <span class=\"n\">x</span><span class=\"p\">,</span> <span class=\"mf\">1</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_7.left = __pyx_v_x;\n",
       "  __pyx_t_7.right = __pyx_v_x;\n",
       "  __pyx_t_7.count = 1;\n",
       "  (__pyx_v_self-&gt;bins[__pyx_v_where]) = __pyx_t_7;\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">072</span>:         <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">n_bins</span> <span class=\"o\">+=</span> <span class=\"mf\">1</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_v_self-&gt;n_bins = (__pyx_v_self-&gt;n_bins + 1);\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">073</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">074</span>:         <span class=\"c\"># Bins have to be merged if there are more than max_bins</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">075</span>:         <span class=\"k\">if</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">n_bins</span> <span class=\"o\">==</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">max_bins</span> <span class=\"o\">+</span> <span class=\"mf\">1</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_6 = ((__pyx_v_self-&gt;n_bins == (__pyx_v_self-&gt;max_bins + 1)) != 0);\n",
       "  if (__pyx_t_6) {\n",
       "/* … */\n",
       "  }\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">076</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">077</span>:             <span class=\"c\"># Find the closest pair of bins</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">078</span>:             <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">min_diff</span> <span class=\"o\">=</span> <span class=\"mf\">999</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_v_self-&gt;min_diff = 999.0;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">079</span>: </pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">080</span>:             <span class=\"k\">for</span> <span class=\"n\">i</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mf\">0</span><span class=\"p\">,</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">n_bins</span> <span class=\"o\">-</span> <span class=\"mf\">1</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_11 = (__pyx_v_self-&gt;n_bins - 1);\n",
       "    __pyx_t_12 = __pyx_t_11;\n",
       "    for (__pyx_t_8 = 0; __pyx_t_8 &lt; __pyx_t_12; __pyx_t_8+=1) {\n",
       "      __pyx_v_i = __pyx_t_8;\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">081</span>:                 <span class=\"k\">for</span> <span class=\"n\">j</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">i</span> <span class=\"o\">+</span> <span class=\"mf\">1</span><span class=\"p\">,</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">n_bins</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_9 = __pyx_v_self-&gt;n_bins;\n",
       "      __pyx_t_10 = __pyx_t_9;\n",
       "      for (__pyx_t_13 = (__pyx_v_i + 1); __pyx_t_13 &lt; __pyx_t_10; __pyx_t_13+=1) {\n",
       "        __pyx_v_j = __pyx_t_13;\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">082</span>:                     <span class=\"n\">diff</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">bins</span><span class=\"p\">[</span><span class=\"n\">j</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">right</span> <span class=\"o\">-</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">bins</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">right</span></pre>\n",
       "<pre class='cython code score-0 '>        __pyx_v_diff = ((__pyx_v_self-&gt;bins[__pyx_v_j]).right - (__pyx_v_self-&gt;bins[__pyx_v_i]).right);\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">083</span>:                     <span class=\"k\">if</span> <span class=\"n\">diff</span> <span class=\"o\">&lt;</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">min_diff</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>        __pyx_t_6 = ((__pyx_v_diff &lt; __pyx_v_self-&gt;min_diff) != 0);\n",
       "        if (__pyx_t_6) {\n",
       "/* … */\n",
       "        }\n",
       "      }\n",
       "    }\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">084</span>:                         <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">min_diff</span> <span class=\"o\">=</span> <span class=\"n\">diff</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_v_self-&gt;min_diff = __pyx_v_diff;\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">085</span>:                         <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">min_idx</span> <span class=\"o\">=</span> <span class=\"n\">i</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_v_self-&gt;min_idx = __pyx_v_i;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">086</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">087</span>:             <span class=\"c\"># Merge the bins</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">088</span>:             <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">bins</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">min_idx</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">right</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">bins</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">min_idx</span> <span class=\"o\">+</span> <span class=\"mf\">1</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">right</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_14 = (__pyx_v_self-&gt;bins[(__pyx_v_self-&gt;min_idx + 1)]).right;\n",
       "    (__pyx_v_self-&gt;bins[__pyx_v_self-&gt;min_idx]).right = __pyx_t_14;\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">089</span>:             <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">bins</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">min_idx</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">count</span> <span class=\"o\">+=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">bins</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">min_idx</span> <span class=\"o\">+</span> <span class=\"mf\">1</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">count</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_8 = __pyx_v_self-&gt;min_idx;\n",
       "    (__pyx_v_self-&gt;bins[__pyx_t_8]).count = ((__pyx_v_self-&gt;bins[__pyx_t_8]).count + (__pyx_v_self-&gt;bins[(__pyx_v_self-&gt;min_idx + 1)]).count);\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">090</span>: </pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">091</span>:             <span class=\"k\">for</span> <span class=\"n\">i</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">min_idx</span> <span class=\"o\">+</span> <span class=\"mf\">1</span><span class=\"p\">,</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">n_bins</span> <span class=\"o\">-</span> <span class=\"mf\">1</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_11 = (__pyx_v_self-&gt;n_bins - 1);\n",
       "    __pyx_t_12 = __pyx_t_11;\n",
       "    for (__pyx_t_8 = (__pyx_v_self-&gt;min_idx + 1); __pyx_t_8 &lt; __pyx_t_12; __pyx_t_8+=1) {\n",
       "      __pyx_v_i = __pyx_t_8;\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">092</span>:                 <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">bins</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">bins</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"o\">+</span><span class=\"mf\">1</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>      (__pyx_v_self-&gt;bins[__pyx_v_i]) = (__pyx_v_self-&gt;bins[(__pyx_v_i + 1)]);\n",
       "    }\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">093</span>:             <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">n_bins</span> <span class=\"o\">-=</span> <span class=\"mf\">1</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_v_self-&gt;n_bins = (__pyx_v_self-&gt;n_bins - 1);\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">094</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">095</span>:             <span class=\"c\">#self[min_idx] += self.pop(min_idx + 1)</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">096</span>: </pre>\n",
       "<pre class=\"cython line score-7\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">097</span>:     <span class=\"k\">def</span> <span class=\"nf\">__repr__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-7 '>/* Python wrapper */\n",
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       "static PyObject *__pyx_pw_46_cython_magic_36bb821b6fbfb2a47a73ea90d89a35db_9Histogram_5__repr__(PyObject *__pyx_v_self) {\n",
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       "\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">098</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">i</span></pre>\n",
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       "<pre class='cython code score-10 '>  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);\n",
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       "</pre><pre class=\"cython line score-81\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">100</span>:             <span class=\"n\">f</span><span class=\"s\">&#39;[{self.bins[i].left:.5f}, {self.bins[i].right:.5f}]: {self.bins[i].count}&#39;</span></pre>\n",
       "<pre class='cython code score-81 '>static PyObject *__pyx_pf_46_cython_magic_36bb821b6fbfb2a47a73ea90d89a35db_9Histogram_8__repr___genexpr(PyObject *__pyx_self) {\n",
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       "<pre class='cython code score-1 '>  __pyx_t_1 = ((PyObject *)((struct __pyx_vtabstruct_46_cython_magic_36bb821b6fbfb2a47a73ea90d89a35db_Histogram *)__pyx_v_46_cython_magic_36bb821b6fbfb2a47a73ea90d89a35db_hist-&gt;__pyx_vtab)-&gt;update(__pyx_v_46_cython_magic_36bb821b6fbfb2a47a73ea90d89a35db_hist, 6.0, 0));<span class='error_goto'> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 110, __pyx_L1_error)</span>\n",
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       "  <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n",
       "</pre><pre class=\"cython line score-3\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">111</span>: <span class=\"k\">print</span><span class=\"p\">(</span><span class=\"n\">hist</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-3 '>  __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyObject_CallOneArg</span>(__pyx_builtin_print, ((PyObject *)__pyx_v_46_cython_magic_36bb821b6fbfb2a47a73ea90d89a35db_hist));<span class='error_goto'> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 111, __pyx_L1_error)</span>\n",
       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
       "  <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n",
       "</pre><pre class=\"cython line score-15\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">112</span>: <span class=\"k\">print</span><span class=\"p\">(</span><span class=\"n\">hist</span><span class=\"o\">.</span><span class=\"n\">n_bins</span><span class=\"p\">,</span> <span class=\"n\">hist</span><span class=\"o\">.</span><span class=\"n\">max_bins</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-15 '>  __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyInt_From_int</span>(__pyx_v_46_cython_magic_36bb821b6fbfb2a47a73ea90d89a35db_hist-&gt;n_bins);<span class='error_goto'> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 112, __pyx_L1_error)</span>\n",
       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
       "  __pyx_t_2 = <span class='pyx_c_api'>__Pyx_PyInt_From_int</span>(__pyx_v_46_cython_magic_36bb821b6fbfb2a47a73ea90d89a35db_hist-&gt;max_bins);<span class='error_goto'> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 112, __pyx_L1_error)</span>\n",
       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n",
       "  __pyx_t_3 = <span class='py_c_api'>PyTuple_New</span>(2);<span class='error_goto'> if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 112, __pyx_L1_error)</span>\n",
       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_3);\n",
       "  <span class='refnanny'>__Pyx_GIVEREF</span>(__pyx_t_1);\n",
       "  <span class='py_macro_api'>PyTuple_SET_ITEM</span>(__pyx_t_3, 0, __pyx_t_1);\n",
       "  <span class='refnanny'>__Pyx_GIVEREF</span>(__pyx_t_2);\n",
       "  <span class='py_macro_api'>PyTuple_SET_ITEM</span>(__pyx_t_3, 1, __pyx_t_2);\n",
       "  __pyx_t_1 = 0;\n",
       "  __pyx_t_2 = 0;\n",
       "  __pyx_t_2 = <span class='pyx_c_api'>__Pyx_PyObject_Call</span>(__pyx_builtin_print, __pyx_t_3, NULL);<span class='error_goto'> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 112, __pyx_L1_error)</span>\n",
       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n",
       "  <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_3); __pyx_t_3 = 0;\n",
       "  <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n",
       "</pre></div></body></html>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%cython --annotate\n",
    "\n",
    "import cython\n",
    "from libc.stdlib cimport malloc\n",
    "\n",
    "cdef struct Bin:\n",
    "    double left\n",
    "    double right\n",
    "    long count\n",
    "    \n",
    "cdef class Histogram:\n",
    "    \n",
    "    cdef:\n",
    "        Bin *bins\n",
    "        int n\n",
    "        int max_bins\n",
    "        int n_bins\n",
    "        \n",
    "        double min_diff\n",
    "        int min_idx\n",
    "        \n",
    "    def __cinit__(self, long max_bins):\n",
    "        self.n = 0\n",
    "        self.max_bins = max_bins\n",
    "        self.n_bins = 0\n",
    "        self.bins = <Bin*>malloc((max_bins + 1) * sizeof(Bin))\n",
    "        if self.bins == NULL:\n",
    "            raise MemoryError()\n",
    "        \n",
    "    @cython.boundscheck(False) \n",
    "    @cython.wraparound(False)\n",
    "    cpdef Histogram update(self, double x):\n",
    "\n",
    "        self.n += 1\n",
    "        \n",
    "        # Insert the bin if the histogram is empty\n",
    "        if self.n_bins == 0:\n",
    "            self.bins[0] = Bin(x, x, 1)\n",
    "            self.n_bins += 1\n",
    "            return \n",
    "        \n",
    "        # Bisect to find where to insert the new value\n",
    "        \n",
    "        cdef int where\n",
    "        cdef int low \n",
    "        cdef int high\n",
    "        cdef int i\n",
    "        cdef int j\n",
    "        \n",
    "        if x < self.bins[0].left:\n",
    "            where = 0\n",
    "        elif x > self.bins[self.n_bins - 1].right:\n",
    "            where = self.n_bins\n",
    "        else:\n",
    "            \n",
    "            low = 0\n",
    "            high = self.n_bins\n",
    "            \n",
    "            while low < high:\n",
    "                where = (low + high) // 2\n",
    "\n",
    "                if x > self.bins[where].right:\n",
    "                    low = where + 1\n",
    "                elif x >= self.bins[where].left:\n",
    "                    break\n",
    "                else:\n",
    "                    high = where\n",
    "        \n",
    "        # Insert the new bin\n",
    "        for i in range(self.n_bins + 1, where):\n",
    "            self.bins[i] = self.bins[i-1]\n",
    "        self.bins[where] = Bin(x, x, 1)\n",
    "        self.n_bins += 1\n",
    "        \n",
    "        # Bins have to be merged if there are more than max_bins\n",
    "        if self.n_bins == self.max_bins + 1:\n",
    "\n",
    "            # Find the closest pair of bins\n",
    "            self.min_diff = 999\n",
    "            \n",
    "            for i in range(0, self.n_bins - 1):\n",
    "                for j in range(i + 1, self.n_bins):\n",
    "                    diff = self.bins[j].right - self.bins[i].right\n",
    "                    if diff < self.min_diff:\n",
    "                        self.min_diff = diff\n",
    "                        self.min_idx = i\n",
    "                    \n",
    "            # Merge the bins\n",
    "            self.bins[self.min_idx].right = self.bins[self.min_idx + 1].right\n",
    "            self.bins[self.min_idx].count += self.bins[self.min_idx + 1].count\n",
    "            \n",
    "            for i in range(self.min_idx + 1, self.n_bins - 1):\n",
    "                self.bins[i] = self.bins[i+1]\n",
    "            self.n_bins -= 1\n",
    "            \n",
    "            #self[min_idx] += self.pop(min_idx + 1)\n",
    "    \n",
    "    def __repr__(self):\n",
    "        cdef int i\n",
    "        return '\\n'.join(\n",
    "            f'[{self.bins[i].left:.5f}, {self.bins[i].right:.5f}]: {self.bins[i].count}'\n",
    "            for i in range(self.n_bins)\n",
    "        )\n",
    "        \n",
    "cdef Histogram hist = Histogram(5)\n",
    "hist.update(1)\n",
    "hist.update(2)\n",
    "hist.update(3)\n",
    "hist.update(3.5)\n",
    "hist.update(5)\n",
    "hist.update(6)\n",
    "print(hist)\n",
    "print(hist.n_bins, hist.max_bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "from creme import utils\n",
    "\n",
    "py_hist = utils.Histogram(100)\n",
    "\n",
    "#%timeit py_hist.update(random.random());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:00:00.066453\n"
     ]
    }
   ],
   "source": [
    "import datetime as dt\n",
    "import random\n",
    "import time\n",
    "\n",
    "c_hist = Histogram(100)\n",
    "total = 0\n",
    "\n",
    "for _ in range(10000):\n",
    "    x = random.random()\n",
    "    tic = time.perf_counter_ns()\n",
    "    c_hist.update(x)\n",
    "    total += time.perf_counter_ns() - tic\n",
    "\n",
    "print(dt.timedelta(microseconds=total / 1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe7cb7b2690>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "x1 = np.random.normal(2, 1, 1000)\n",
    "x2 = np.random.normal(6, 3, 1000)\n",
    "\n",
    "sns.distplot(x1)\n",
    "sns.distplot(x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe7c7e16210>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(np.concatenate((x1, x2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6C6Wq3jDtDEMa6LYI6i3JRSyV+X1V9dlp5+mlqp5L8ghLr0XMyovL24G3JnkzcAlwWZJ/r6q/nXIuAKrqmW55OsnnWDo1OSuvfZ0ETq74i+sB1mChr4kR+hrkbRFGlCQsncc8VlUfnHaelZLMJbm8W38R8Abgyemm+p2qel9VbaqqzSz9zH15Vso8yaVJXrK8DryR2flFSFX9CPhBkq3drh3Ad6cYaSRrvtCT/GWSk8BrgYNJvjjtTFV1Bli+LcIx4P5Zui1Ckk8CXwO2JjmZZNe0M62wHbgTuKm7vO1oN+KcBRuBh5N8m6Vf2oeqaqYuDZxhG4CvJvkW8HXgYFU9NOVMZ3sncF/3/3cb8K9TzjM0Z4pKUiPW/AhdkrTEQpekRljoktQIC12SGmGhS1IjLHRJaoSFLkmNsNAlqRH/B5yTC/EF8AwBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from creme import utils\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "h1 = utils.Histogram(50)\n",
    "\n",
    "for xi in x1:\n",
    "    h1.update(xi)\n",
    "    \n",
    "plt.bar(\n",
    "    x=[(b.left + b.right) / 2 for b in h1],\n",
    "    height=[b.count for b in h1],\n",
    "    width=[(b.right - b.left) / 2 for b in h1]\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "h2 = utils.Histogram(50)\n",
    "    \n",
    "for xi in x2:\n",
    "    h2.update(xi)\n",
    "    \n",
    "plt.bar(\n",
    "    x=[(b.left + b.right) / 2 for b in h2],\n",
    "    height=[b.count for b in h2],\n",
    "    width=[(b.right - b.left) / 2 for b in h2]\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = utils.Histogram(50)\n",
    "    \n",
    "for xi in np.concatenate((x1, x2)):\n",
    "    h.update(xi)\n",
    "    \n",
    "plt.bar(\n",
    "    x=[(b.left + b.right) / 2 for b in h],\n",
    "    height=[b.count for b in h],\n",
    "    width=[(b.right - b.left) / 2 for b in h]\n",
    ")\n",
    "\n",
    "plt.ylim(0, 200);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[-2.69596, -2.69596]: 1,\n",
       " [-2.09109, -2.09109]: 1,\n",
       " [-1.43778, -1.43778]: 1,\n",
       " [-1.22622, -0.77869]: 7,\n",
       " [-0.76283, -0.54330]: 8]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h.data[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist = h1 + h2\n",
    "\n",
    "plt.bar(\n",
    "    x=[(b.left + b.right) / 2 for b in hist],\n",
    "    height=[b.count for b in hist],\n",
    "    width=[(b.right - b.left) / 2 for b in hist]\n",
    ")\n",
    "\n",
    "plt.ylim(0, 200);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[-2.69596, -2.69596]: 1.0,\n",
       " [-2.69596, -2.09109]: 1.0,\n",
       " [-2.09109, -1.43778]: 1.0,\n",
       " [-1.22622, -1.10408]: 3.0,\n",
       " [-1.05746, -0.54330]: 12.0]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist.data[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(hist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2003.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(b.count for b in h1 + h2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(b.count for b in h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "data = np.vstack([\n",
    "    np.random.multivariate_normal([0, 0], [[1, 0], [0, 5]], 5000),\n",
    "    np.random.multivariate_normal([5, 3], [[1, 0], [0, 2]], 5000)\n",
    "])\n",
    "    \n",
    "x, y = data.T\n",
    "plt.plot(x, y, 'x')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import collections\n",
    "import textwrap\n",
    "\n",
    "\n",
    "class Slice:\n",
    "    \"\"\"A Slice is an of a MultiHistogram.\"\"\"\n",
    "\n",
    "    __slots__ = ['left', 'right', 'hist']\n",
    "\n",
    "    def __init__(self, left, right, hist):\n",
    "        self.left = left\n",
    "        self.right = right\n",
    "        self.hist = hist\n",
    "        \n",
    "    def __iadd__(self, other):\n",
    "        \"\"\"Merge with another bin.\"\"\"\n",
    "        if other.left < self.left:\n",
    "            self.left = other.left\n",
    "        if other.right > self.right:\n",
    "            self.right = other.right\n",
    "        self.hist += other.hist\n",
    "        return self\n",
    "        \n",
    "    def __repr__(self):\n",
    "        return f'[{self.left:.5f}, {self.right:.5f}]:\\n\\t{self.hist}'\n",
    "\n",
    "\n",
    "class MultiHistogram(collections.UserList):\n",
    "    \n",
    "    def __init__(self, order, max_bins=256):\n",
    "        super().__init__()\n",
    "        self.order = order\n",
    "        self.max_bins = max_bins\n",
    "        self.n = 0 \n",
    "        \n",
    "    def update(self, x):\n",
    "        \n",
    "        xi = x[self.order[0]]\n",
    "        self.n += 1\n",
    "        \n",
    "        h = utils.Histogram(max_bins=self.max_bins)\n",
    "        h.update(x[self.order[-1]])\n",
    "        s = Slice(xi, xi, h)\n",
    "\n",
    "        # Insert the bin if the histogram is empty\n",
    "        if not self:\n",
    "            self.append(s)\n",
    "            return self\n",
    "        \n",
    "        # Use bisection to find where to insert\n",
    "        lo = 0\n",
    "        hi = len(self)\n",
    "        i = (lo + hi) // 2\n",
    "        while lo < hi:\n",
    "            if self[i].right < xi:\n",
    "                lo = i + 1\n",
    "            else:\n",
    "                hi = i\n",
    "            i = (lo + hi) // 2\n",
    "\n",
    "        if i == len(self):\n",
    "            # x is past the right-most bin\n",
    "            self.append(s)\n",
    "        else:\n",
    "            # Increment the bin counter if x is part of the ith bin\n",
    "            if xi >= self[i].left:\n",
    "                self[i].hist.update(x[self.order[-1]])\n",
    "            # Insert the bin if it is between bin i-1 and bin i\n",
    "            else:\n",
    "                self.insert(i, s)\n",
    "\n",
    "        # Bins have to be merged if there are more than max_bins\n",
    "        if len(self) > self.max_bins:\n",
    "            self._shrink(1)\n",
    "\n",
    "        return self\n",
    "    \n",
    "    def _shrink(self, k):\n",
    "        \"\"\"Shrinks the histogram by merging the two closest bins.\"\"\"\n",
    "\n",
    "        indexes = range(len(self) - 1)\n",
    "\n",
    "        def distance(i):\n",
    "            return self[i + 1].right - self[i].right\n",
    "\n",
    "        if k == 1:\n",
    "            i = min(indexes, key=distance)\n",
    "            self[i] += self.pop(i + 1)  # Calls Slice.__iadd__\n",
    "            return\n",
    "\n",
    "        for i in sorted(heapq.nsmallest(n=k, iterable=indexes, key=bin_distance), reverse=True):\n",
    "            self[i] += self.pop(i + 1)  # Calls Slice.__iadd__\n",
    "    \n",
    "    def __repr__(self):\n",
    "        return '\\n'.join(str(b) for b in self)\n",
    "        \n",
    "h = MultiHistogram(order=[0, 1], max_bins=20)\n",
    "\n",
    "for i, obs in enumerate(data):\n",
    "    obs = dict(enumerate(obs))\n",
    "    h.update(obs)\n",
    "        \n",
    "len(h.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-3.374189545901298 -3.1948369944686585\n",
      "-2.9668946189155485 -2.71837951680391\n",
      "-2.652937918342111 -2.409709961360021\n",
      "-2.4080886011161233 -1.2571166779861414\n",
      "-1.2558857988674614 -0.8374604097488014\n",
      "-0.8366805227909666 -0.45606217594729553\n",
      "-0.45534303091465034 0.575660003124806\n",
      "0.5777945327592764 1.2750815020440394\n",
      "1.2758071909112476 1.8511699450853971\n",
      "1.8584027017886493 2.773675547702391\n",
      "2.79465713421882 3.2797606501785097\n",
      "3.2839813782396607 3.772147853522918\n",
      "3.7722840073600303 4.828715975431106\n",
      "4.829529004303061 5.4240837087942815\n",
      "5.4246463380539796 6.259240237409565\n",
      "6.259301291519424 7.116979919403295\n",
      "7.117576103508241 7.758515386975743\n",
      "7.86707121787045 8.115525461185149\n",
      "8.46777697457411 8.46777697457411\n",
      "9.207500654608715 9.492865916808649\n"
     ]
    }
   ],
   "source": [
    "for s in h:\n",
    "    print(s.left, s.right)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-8.5, 10)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import collections \n",
    "from matplotlib import patches\n",
    "\n",
    "fig, ax = plt.subplots(1)\n",
    "\n",
    "rectangles = []\n",
    "\n",
    "for s in h:\n",
    "    for b in s.hist:\n",
    "        xy = (s.left, b.left)\n",
    "        width = s.right - s.left\n",
    "        height = b.right - b.left\n",
    "        rect = patches.Rectangle(xy, width, height)\n",
    "        rectangles.append(rect)\n",
    "        \n",
    "pc = collections.PatchCollection(rectangles, edgecolor='k')\n",
    "ax.add_collection(pc);\n",
    "ax.set_xlim(-3.5, 9)\n",
    "ax.set_ylim(-8.5, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00000, 0.00000]: 1\n",
      "[1.00000, 1.00000]: 1\n",
      "[2.00000, 2.00000]: 1\n",
      "[3.00000, 3.00000]: 1\n"
     ]
    }
   ],
   "source": [
    "from creme import utils\n",
    "\n",
    "hist = utils.Histogram()\n",
    "for x in range(4):\n",
    "    hist = hist.update(x)\n",
    "\n",
    "print(hist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1 0.0\n",
      "0 0.25\n",
      "0.5 0.25\n",
      "1 0.5\n",
      "2.5 0.75\n",
      "3 1.0\n",
      "5 1.0\n"
     ]
    }
   ],
   "source": [
    "from creme.utils.histogram import Bin\n",
    "import math\n",
    "\n",
    "def iter_cdf(hist, X):\n",
    "\n",
    "    bins = iter(hist)\n",
    "    b = next(bins)\n",
    "    INF = Bin(math.inf, math.inf, 0)\n",
    "    \n",
    "    cdf = 0\n",
    "    \n",
    "    for x in X:\n",
    "        \n",
    "        while x >= b.right:\n",
    "            cdf += b.count\n",
    "            b = next(bins, INF)\n",
    "            \n",
    "        if x > b.left:\n",
    "            cdf += b.count * (x - b.left) / (b.right - b.left)\n",
    "            \n",
    "        yield cdf / hist.n\n",
    "            \n",
    "\n",
    "X = [-1, 0, 0.5, 1, 2.5, 3, 5]\n",
    "\n",
    "for x, cdf in zip(X, iter_cdf(hist, X)):\n",
    "    print(x, cdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " "
     ]
    },
    {
     "data": {
      "text/plain": [
       "         33 function calls in 0.000 seconds\n",
       "\n",
       "   Ordered by: internal time\n",
       "\n",
       "   ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n",
       "        1    0.000    0.000    0.000    0.000 {built-in method builtins.exec}\n",
       "        8    0.000    0.000    0.000    0.000 <ipython-input-6-11bc1491033c>:4(iter_cdf)\n",
       "        5    0.000    0.000    0.000    0.000 __init__.py:1094(__getitem__)\n",
       "        5    0.000    0.000    0.000    0.000 _collections_abc.py:879(__iter__)\n",
       "        1    0.000    0.000    0.000    0.000 <string>:1(<module>)\n",
       "        5    0.000    0.000    0.000    0.000 {built-in method builtins.next}\n",
       "        5    0.000    0.000    0.000    0.000 {built-in method builtins.isinstance}\n",
       "        1    0.000    0.000    0.000    0.000 {built-in method builtins.iter}\n",
       "        1    0.000    0.000    0.000    0.000 histogram.py:15(__init__)\n",
       "        1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%timeit list(iter_cdf(hist, X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21 µs ± 2.12 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit [hist.cdf(x) for x in X]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = iter([1, 2])\n",
    "\n",
    "next(a) if False else 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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