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eval_peak_widths.ipynb
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| "metadata": {}, | |
| "source": [ | |
| "# Compare peak_widths" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "%load_ext Cython\n", | |
| "\n", | |
| "import numpy as np\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "from scipy.signal import find_peaks, peak_prominences" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Definitions" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# Import old version written with NumPy\n", | |
| "from scipy.signal import peak_widths as old" | |
| ] | |
| }, | |
| { | |
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| "execution_count": 3, | |
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| "<pre class=\"cython line score-16\" onclick='toggleDiv(this)'>+<span class=\"\">02</span>: <span class=\"k\">import</span> <span class=\"nn\">numpy</span> <span class=\"k\">as</span> <span class=\"nn\">np</span></pre>\n", | |
| "<pre class='cython code score-16 '> __pyx_t_1 = <span class='pyx_c_api'>__Pyx_Import</span>(__pyx_n_s_numpy, 0, 0);<span class='error_goto'> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 2, __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", | |
| "/* … */\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-0\"> <span class=\"\">03</span>: <span class=\"k\">cimport</span> <span class=\"nn\">numpy</span> <span class=\"k\">as</span> <span class=\"nn\">np</span></pre>\n", | |
| "<pre class=\"cython line score-0\"> <span class=\"\">04</span>: <span class=\"k\">import</span> <span class=\"nn\">cython</span></pre>\n", | |
| "<pre class=\"cython line score-0\"> <span class=\"\">05</span>: </pre>\n", | |
| "<pre class=\"cython line score-0\"> <span class=\"\">06</span>: </pre>\n", | |
| "<pre class=\"cython line score-0\"> <span class=\"\">07</span>: <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">wraparound</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n", | |
| "<pre class=\"cython line score-0\"> <span class=\"\">08</span>: <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">boundscheck</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n", | |
| "<pre class=\"cython line score-121\" onclick='toggleDiv(this)'>+<span class=\"\">09</span>: <span class=\"k\">def</span> <span class=\"nf\">inner</span><span class=\"p\">(</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">float64_t</span><span class=\"p\">[::</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">x</span> <span class=\"ow\">not</span> <span class=\"bp\">None</span><span class=\"p\">,</span></pre>\n", | |
| "<pre class='cython code score-121 '>/* Python wrapper */\n", | |
| "static PyObject *__pyx_pw_46_cython_magic_24aa845e29962e629f6f28ce60eb8ec5_1inner(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/\n", | |
| "static PyMethodDef __pyx_mdef_46_cython_magic_24aa845e29962e629f6f28ce60eb8ec5_1inner = {\"inner\", (PyCFunction)__pyx_pw_46_cython_magic_24aa845e29962e629f6f28ce60eb8ec5_1inner, METH_VARARGS|METH_KEYWORDS, 0};\n", | |
| "static PyObject *__pyx_pw_46_cython_magic_24aa845e29962e629f6f28ce60eb8ec5_1inner(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {\n", | |
| " __Pyx_memviewslice __pyx_v_x = { 0, 0, { 0 }, { 0 }, { 0 } };\n", | |
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| " __pyx_t_5numpy_float64_t __pyx_v_rel_height;\n", | |
| " __Pyx_memviewslice __pyx_v_prominences = { 0, 0, { 0 }, { 0 }, { 0 } };\n", | |
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| " __Pyx_memviewslice __pyx_v_right_bases = { 0, 0, { 0 }, { 0 }, { 0 } };\n", | |
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| " PyObject* values[6] = {0,0,0,0,0,0};\n", | |
| " if (unlikely(__pyx_kwds)) {\n", | |
| " Py_ssize_t kw_args;\n", | |
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| " switch (pos_args) {\n", | |
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| " CYTHON_FALLTHROUGH;\n", | |
| " case 5: values[4] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 4);\n", | |
| " CYTHON_FALLTHROUGH;\n", | |
| " case 4: values[3] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 3);\n", | |
| " CYTHON_FALLTHROUGH;\n", | |
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| " CYTHON_FALLTHROUGH;\n", | |
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| " CYTHON_FALLTHROUGH;\n", | |
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| " CYTHON_FALLTHROUGH;\n", | |
| " case 0: break;\n", | |
| " default: goto __pyx_L5_argtuple_error;\n", | |
| " }\n", | |
| " kw_args = <span class='py_c_api'>PyDict_Size</span>(__pyx_kwds);\n", | |
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| " CYTHON_FALLTHROUGH;\n", | |
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| " <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"inner\", 1, 6, 6, 1); <span class='error_goto'>__PYX_ERR(0, 9, __pyx_L3_error)</span>\n", | |
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| " <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"inner\", 1, 6, 6, 2); <span class='error_goto'>__PYX_ERR(0, 9, __pyx_L3_error)</span>\n", | |
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| " <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"inner\", 1, 6, 6, 3); <span class='error_goto'>__PYX_ERR(0, 9, __pyx_L3_error)</span>\n", | |
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| " <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"inner\", 1, 6, 6, 4); <span class='error_goto'>__PYX_ERR(0, 9, __pyx_L3_error)</span>\n", | |
| " }\n", | |
| " CYTHON_FALLTHROUGH;\n", | |
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| " if (likely((values[5] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_right_bases)) != 0)) kw_args--;\n", | |
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| " <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"inner\", 1, 6, 6, 5); <span class='error_goto'>__PYX_ERR(0, 9, __pyx_L3_error)</span>\n", | |
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| " }\n", | |
| " if (unlikely(kw_args > 0)) {\n", | |
| " if (unlikely(<span class='pyx_c_api'>__Pyx_ParseOptionalKeywords</span>(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, \"inner\") < 0)) <span class='error_goto'>__PYX_ERR(0, 9, __pyx_L3_error)</span>\n", | |
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| " } else if (<span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args) != 6) {\n", | |
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| " values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n", | |
| " values[1] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 1);\n", | |
| " values[2] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 2);\n", | |
| " values[3] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 3);\n", | |
| " values[4] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 4);\n", | |
| " values[5] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 5);\n", | |
| " }\n", | |
| " __pyx_v_x = __Pyx_PyObject_to_MemoryviewSlice_dc_nn___pyx_t_5numpy_float64_t(values[0]);<span class='error_goto'> if (unlikely(!__pyx_v_x.memview)) __PYX_ERR(0, 9, __pyx_L3_error)</span>\n", | |
| " __pyx_v_peaks = __Pyx_PyObject_to_MemoryviewSlice_dc_nn___pyx_t_5numpy_intp_t(values[1]);<span class='error_goto'> if (unlikely(!__pyx_v_peaks.memview)) __PYX_ERR(0, 10, __pyx_L3_error)</span>\n", | |
| " __pyx_v_rel_height = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[2]); if (unlikely((__pyx_v_rel_height == ((npy_float64)-1)) && <span class='py_c_api'>PyErr_Occurred</span>())) <span class='error_goto'>__PYX_ERR(0, 11, __pyx_L3_error)</span>\n", | |
| " __pyx_v_prominences = __Pyx_PyObject_to_MemoryviewSlice_dc_nn___pyx_t_5numpy_float64_t(values[3]);<span class='error_goto'> if (unlikely(!__pyx_v_prominences.memview)) __PYX_ERR(0, 12, __pyx_L3_error)</span>\n", | |
| " __pyx_v_left_bases = __Pyx_PyObject_to_MemoryviewSlice_dc_nn___pyx_t_5numpy_intp_t(values[4]);<span class='error_goto'> if (unlikely(!__pyx_v_left_bases.memview)) __PYX_ERR(0, 13, __pyx_L3_error)</span>\n", | |
| " __pyx_v_right_bases = __Pyx_PyObject_to_MemoryviewSlice_dc_nn___pyx_t_5numpy_intp_t(values[5]);<span class='error_goto'> if (unlikely(!__pyx_v_right_bases.memview)) __PYX_ERR(0, 14, __pyx_L3_error)</span>\n", | |
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| " goto __pyx_L4_argument_unpacking_done;\n", | |
| " __pyx_L5_argtuple_error:;\n", | |
| " <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"inner\", 1, 6, 6, <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args)); <span class='error_goto'>__PYX_ERR(0, 9, __pyx_L3_error)</span>\n", | |
| " __pyx_L3_error:;\n", | |
| " <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_24aa845e29962e629f6f28ce60eb8ec5.inner\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n", | |
| " <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n", | |
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| " __pyx_L4_argument_unpacking_done:;\n", | |
| " if (unlikely(((PyObject *)__pyx_v_x.memview) == Py_None)) {\n", | |
| " <span class='py_c_api'>PyErr_Format</span>(PyExc_TypeError, \"Argument '%.200s' must not be None\", \"x\"); <span class='error_goto'>__PYX_ERR(0, 9, __pyx_L1_error)</span>\n", | |
| " }\n", | |
| " if (unlikely(((PyObject *)__pyx_v_peaks.memview) == Py_None)) {\n", | |
| " <span class='py_c_api'>PyErr_Format</span>(PyExc_TypeError, \"Argument '%.200s' must not be None\", \"peaks\"); <span class='error_goto'>__PYX_ERR(0, 10, __pyx_L1_error)</span>\n", | |
| " }\n", | |
| " if (unlikely(((PyObject *)__pyx_v_prominences.memview) == Py_None)) {\n", | |
| " <span class='py_c_api'>PyErr_Format</span>(PyExc_TypeError, \"Argument '%.200s' must not be None\", \"prominences\"); <span class='error_goto'>__PYX_ERR(0, 12, __pyx_L1_error)</span>\n", | |
| " }\n", | |
| " if (unlikely(((PyObject *)__pyx_v_left_bases.memview) == Py_None)) {\n", | |
| " <span class='py_c_api'>PyErr_Format</span>(PyExc_TypeError, \"Argument '%.200s' must not be None\", \"left_bases\"); <span class='error_goto'>__PYX_ERR(0, 13, __pyx_L1_error)</span>\n", | |
| " }\n", | |
| " if (unlikely(((PyObject *)__pyx_v_right_bases.memview) == Py_None)) {\n", | |
| " <span class='py_c_api'>PyErr_Format</span>(PyExc_TypeError, \"Argument '%.200s' must not be None\", \"right_bases\"); <span class='error_goto'>__PYX_ERR(0, 14, __pyx_L1_error)</span>\n", | |
| " }\n", | |
| " __pyx_r = __pyx_pf_46_cython_magic_24aa845e29962e629f6f28ce60eb8ec5_inner(__pyx_self, __pyx_v_x, __pyx_v_peaks, __pyx_v_rel_height, __pyx_v_prominences, __pyx_v_left_bases, __pyx_v_right_bases);\n", | |
| "\n", | |
| " /* function exit code */\n", | |
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| "static PyObject *__pyx_pf_46_cython_magic_24aa845e29962e629f6f28ce60eb8ec5_inner(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_x, __Pyx_memviewslice __pyx_v_peaks, __pyx_t_5numpy_float64_t __pyx_v_rel_height, __Pyx_memviewslice __pyx_v_prominences, __Pyx_memviewslice __pyx_v_left_bases, __Pyx_memviewslice __pyx_v_right_bases) {\n", | |
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| " __Pyx_memviewslice __pyx_v_right_ips = { 0, 0, { 0 }, { 0 }, { 0 } };\n", | |
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| " __pyx_t_5numpy_float64_t __pyx_v_left_ip;\n", | |
| " __pyx_t_5numpy_float64_t __pyx_v_right_ip;\n", | |
| " __pyx_t_5numpy_intp_t __pyx_v_p;\n", | |
| " __pyx_t_5numpy_intp_t __pyx_v_peak;\n", | |
| " __pyx_t_5numpy_intp_t __pyx_v_i;\n", | |
| " __pyx_t_5numpy_intp_t __pyx_v_i_max;\n", | |
| " __pyx_t_5numpy_intp_t __pyx_v_i_min;\n", | |
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| " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n", | |
| " __pyx_t_2 = <span class='pyx_c_api'>__Pyx_PyObject_Call</span>(__pyx_t_5, __pyx_t_3, __pyx_t_4);<span class='error_goto'> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 24, __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_5); __pyx_t_5 = 0;\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_4); __pyx_t_4 = 0;\n", | |
| " __pyx_t_6 = __Pyx_PyObject_to_MemoryviewSlice_dc_nn___pyx_t_5numpy_float64_t(__pyx_t_2);\n", | |
| " if (unlikely(!__pyx_t_6.memview)) <span class='error_goto'>__PYX_ERR(0, 24, __pyx_L1_error)</span>\n", | |
| " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n", | |
| " __pyx_v_left_ips = __pyx_t_6;\n", | |
| " __pyx_t_6.memview = NULL;\n", | |
| " __pyx_t_6.data = NULL;\n", | |
| "</pre><pre class=\"cython line score-35\" onclick='toggleDiv(this)'>+<span class=\"\">25</span>: <span class=\"n\">right_ips</span> <span class=\"o\">=</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">empty</span><span class=\"p\">(</span><span class=\"n\">peaks</span><span class=\"o\">.</span><span class=\"n\">shape</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">],</span> <span class=\"n\">dtype</span><span class=\"o\">=</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">float64</span><span class=\"p\">)</span></pre>\n", | |
| "<pre class='cython code score-35 '> __pyx_t_2 = <span class='pyx_c_api'>__Pyx_GetModuleGlobalName</span>(__pyx_n_s_np);<span class='error_goto'> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 25, __pyx_L1_error)</span>\n", | |
| " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n", | |
| " __pyx_t_4 = <span class='pyx_c_api'>__Pyx_PyObject_GetAttrStr</span>(__pyx_t_2, __pyx_n_s_empty);<span class='error_goto'> if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 25, __pyx_L1_error)</span>\n", | |
| " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_4);\n", | |
| " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n", | |
| " __pyx_t_2 = <span class='py_c_api'>PyInt_FromSsize_t</span>((__pyx_v_peaks.shape[0]));<span class='error_goto'> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 25, __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>(1);<span class='error_goto'> if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 25, __pyx_L1_error)</span>\n", | |
| " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_3);\n", | |
| " <span class='refnanny'>__Pyx_GIVEREF</span>(__pyx_t_2);\n", | |
| " <span class='py_macro_api'>PyTuple_SET_ITEM</span>(__pyx_t_3, 0, __pyx_t_2);\n", | |
| " __pyx_t_2 = 0;\n", | |
| " __pyx_t_2 = <span class='pyx_c_api'>__Pyx_PyDict_NewPresized</span>(1);<span class='error_goto'> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 25, __pyx_L1_error)</span>\n", | |
| " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n", | |
| " __pyx_t_5 = <span class='pyx_c_api'>__Pyx_GetModuleGlobalName</span>(__pyx_n_s_np);<span class='error_goto'> if (unlikely(!__pyx_t_5)) __PYX_ERR(0, 25, __pyx_L1_error)</span>\n", | |
| " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_5);\n", | |
| " __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyObject_GetAttrStr</span>(__pyx_t_5, __pyx_n_s_float64);<span class='error_goto'> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 25, __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_5); __pyx_t_5 = 0;\n", | |
| " if (<span class='py_c_api'>PyDict_SetItem</span>(__pyx_t_2, __pyx_n_s_dtype, __pyx_t_1) < 0) <span class='error_goto'>__PYX_ERR(0, 25, __pyx_L1_error)</span>\n", | |
| " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n", | |
| " __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyObject_Call</span>(__pyx_t_4, __pyx_t_3, __pyx_t_2);<span class='error_goto'> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 25, __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_4); __pyx_t_4 = 0;\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", | |
| " __pyx_t_6 = __Pyx_PyObject_to_MemoryviewSlice_dc_nn___pyx_t_5numpy_float64_t(__pyx_t_1);\n", | |
| " if (unlikely(!__pyx_t_6.memview)) <span class='error_goto'>__PYX_ERR(0, 25, __pyx_L1_error)</span>\n", | |
| " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n", | |
| " __pyx_v_right_ips = __pyx_t_6;\n", | |
| " __pyx_t_6.memview = NULL;\n", | |
| " __pyx_t_6.data = NULL;\n", | |
| "</pre><pre class=\"cython line score-0\"> <span class=\"\">26</span>: </pre>\n", | |
| "<pre class=\"cython line score-6\" onclick='toggleDiv(this)'>+<span class=\"\">27</span>: <span class=\"k\">with</span> <span class=\"k\">nogil</span><span class=\"p\">:</span></pre>\n", | |
| "<pre class='cython code score-6 '> {\n", | |
| " #ifdef WITH_THREAD\n", | |
| " PyThreadState *_save;\n", | |
| " Py_UNBLOCK_THREADS\n", | |
| " <span class='pyx_c_api'>__Pyx_FastGIL_Remember</span>();\n", | |
| " #endif\n", | |
| " /*try:*/ {\n", | |
| "/* … */\n", | |
| " /*finally:*/ {\n", | |
| " /*normal exit:*/{\n", | |
| " #ifdef WITH_THREAD\n", | |
| " <span class='pyx_c_api'>__Pyx_FastGIL_Forget</span>();\n", | |
| " Py_BLOCK_THREADS\n", | |
| " #endif\n", | |
| " goto __pyx_L5;\n", | |
| " }\n", | |
| " __pyx_L4_error: {\n", | |
| " #ifdef WITH_THREAD\n", | |
| " <span class='pyx_c_api'>__Pyx_FastGIL_Forget</span>();\n", | |
| " Py_BLOCK_THREADS\n", | |
| " #endif\n", | |
| " goto __pyx_L1_error;\n", | |
| " }\n", | |
| " __pyx_L5:;\n", | |
| " }\n", | |
| " }\n", | |
| "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">28</span>: <span class=\"k\">for</span> <span class=\"n\">p</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">peaks</span><span class=\"o\">.</span><span class=\"n\">shape</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]):</span></pre>\n", | |
| "<pre class='cython code score-0 '> __pyx_t_7 = (__pyx_v_peaks.shape[0]);\n", | |
| " for (__pyx_t_8 = 0; __pyx_t_8 < __pyx_t_7; __pyx_t_8+=1) {\n", | |
| " __pyx_v_p = __pyx_t_8;\n", | |
| "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">29</span>: <span class=\"n\">i_min</span> <span class=\"o\">=</span> <span class=\"n\">left_bases</span><span class=\"p\">[</span><span class=\"n\">p</span><span class=\"p\">]</span></pre>\n", | |
| "<pre class='cython code score-0 '> __pyx_t_9 = __pyx_v_p;\n", | |
| " __pyx_v_i_min = (*((__pyx_t_5numpy_intp_t *) ( /* dim=0 */ ((char *) (((__pyx_t_5numpy_intp_t *) __pyx_v_left_bases.data) + __pyx_t_9)) )));\n", | |
| "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">30</span>: <span class=\"n\">i_max</span> <span class=\"o\">=</span> <span class=\"n\">right_bases</span><span class=\"p\">[</span><span class=\"n\">p</span><span class=\"p\">]</span></pre>\n", | |
| "<pre class='cython code score-0 '> __pyx_t_10 = __pyx_v_p;\n", | |
| " __pyx_v_i_max = (*((__pyx_t_5numpy_intp_t *) ( /* dim=0 */ ((char *) (((__pyx_t_5numpy_intp_t *) __pyx_v_right_bases.data) + __pyx_t_10)) )));\n", | |
| "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">31</span>: <span class=\"n\">peak</span> <span class=\"o\">=</span> <span class=\"n\">peaks</span><span class=\"p\">[</span><span class=\"n\">p</span><span class=\"p\">]</span></pre>\n", | |
| "<pre class='cython code score-0 '> __pyx_t_11 = __pyx_v_p;\n", | |
| " __pyx_v_peak = (*((__pyx_t_5numpy_intp_t *) ( /* dim=0 */ ((char *) (((__pyx_t_5numpy_intp_t *) __pyx_v_peaks.data) + __pyx_t_11)) )));\n", | |
| "</pre><pre class=\"cython line score-0\"> <span class=\"\">32</span>: <span class=\"c\"># Validate bounds and order</span></pre>\n", | |
| "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">33</span>: <span class=\"k\">if</span> <span class=\"ow\">not</span> <span class=\"mf\">0</span> <span class=\"o\"><=</span> <span class=\"n\">i_min</span> <span class=\"o\"><=</span> <span class=\"n\">peak</span> <span class=\"o\"><=</span> <span class=\"n\">i_max</span> <span class=\"o\"><</span> <span class=\"n\">x</span><span class=\"o\">.</span><span class=\"n\">shape</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]:</span></pre>\n", | |
| "<pre class='cython code score-0 '> __pyx_t_12 = (0 <= __pyx_v_i_min);\n", | |
| " if (__pyx_t_12) {\n", | |
| " __pyx_t_12 = (__pyx_v_i_min <= __pyx_v_peak);\n", | |
| " if (__pyx_t_12) {\n", | |
| " __pyx_t_12 = (__pyx_v_peak <= __pyx_v_i_max);\n", | |
| " if (__pyx_t_12) {\n", | |
| " __pyx_t_12 = (__pyx_v_i_max < (__pyx_v_x.shape[0]));\n", | |
| " }\n", | |
| " }\n", | |
| " }\n", | |
| " __pyx_t_13 = ((!(__pyx_t_12 != 0)) != 0);\n", | |
| " if (__pyx_t_13) {\n", | |
| "/* … */\n", | |
| " }\n", | |
| "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">34</span>: <span class=\"n\">raise_error</span> <span class=\"o\">=</span> <span class=\"bp\">True</span></pre>\n", | |
| "<pre class='cython code score-0 '> __pyx_v_raise_error = 1;\n", | |
| "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">35</span>: <span class=\"k\">break</span></pre>\n", | |
| "<pre class='cython code score-0 '> goto __pyx_L7_break;\n", | |
| "</pre><pre class=\"cython line score-0\"> <span class=\"\">36</span>: </pre>\n", | |
| "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">37</span>: <span class=\"n\">height</span> <span class=\"o\">=</span> <span class=\"n\">width_heights</span><span class=\"p\">[</span><span class=\"n\">p</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">x</span><span class=\"p\">[</span><span class=\"n\">peak</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"n\">prominences</span><span class=\"p\">[</span><span class=\"n\">p</span><span class=\"p\">]</span> <span class=\"o\">*</span> <span class=\"n\">rel_height</span></pre>\n", | |
| "<pre class='cython code score-0 '> __pyx_t_14 = __pyx_v_peak;\n", | |
| " __pyx_t_15 = __pyx_v_p;\n", | |
| " __pyx_t_16 = ((*((__pyx_t_5numpy_float64_t *) ( /* dim=0 */ ((char *) (((__pyx_t_5numpy_float64_t *) __pyx_v_x.data) + __pyx_t_14)) ))) - ((*((__pyx_t_5numpy_float64_t *) ( /* dim=0 */ ((char *) (((__pyx_t_5numpy_float64_t *) __pyx_v_prominences.data) + __pyx_t_15)) ))) * __pyx_v_rel_height));\n", | |
| " __pyx_v_height = __pyx_t_16;\n", | |
| " __pyx_t_17 = __pyx_v_p;\n", | |
| " *((__pyx_t_5numpy_float64_t *) ( /* dim=0 */ ((char *) (((__pyx_t_5numpy_float64_t *) __pyx_v_width_heights.data) + __pyx_t_17)) )) = __pyx_t_16;\n", | |
| "</pre><pre class=\"cython line score-0\"> <span class=\"\">38</span>: </pre>\n", | |
| "<pre class=\"cython line score-0\"> <span class=\"\">39</span>: <span class=\"c\"># Find intersection point on left side</span></pre>\n", | |
| "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">40</span>: <span class=\"n\">i</span> <span class=\"o\">=</span> <span class=\"n\">peak</span></pre>\n", | |
| "<pre class='cython code score-0 '> __pyx_v_i = __pyx_v_peak;\n", | |
| "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">41</span>: <span class=\"k\">while</span> <span class=\"n\">i_min</span> <span class=\"o\"><</span> <span class=\"n\">i</span> <span class=\"ow\">and</span> <span class=\"n\">height</span> <span class=\"o\"><</span> <span class=\"n\">x</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]:</span></pre>\n", | |
| "<pre class='cython code score-0 '> while (1) {\n", | |
| " __pyx_t_12 = ((__pyx_v_i_min < __pyx_v_i) != 0);\n", | |
| " if (__pyx_t_12) {\n", | |
| " } else {\n", | |
| " __pyx_t_13 = __pyx_t_12;\n", | |
| " goto __pyx_L11_bool_binop_done;\n", | |
| " }\n", | |
| " __pyx_t_18 = __pyx_v_i;\n", | |
| " __pyx_t_12 = ((__pyx_v_height < (*((__pyx_t_5numpy_float64_t *) ( /* dim=0 */ ((char *) (((__pyx_t_5numpy_float64_t *) __pyx_v_x.data) + __pyx_t_18)) )))) != 0);\n", | |
| " __pyx_t_13 = __pyx_t_12;\n", | |
| " __pyx_L11_bool_binop_done:;\n", | |
| " if (!__pyx_t_13) break;\n", | |
| "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">42</span>: <span class=\"n\">i</span> <span class=\"o\">-=</span> <span class=\"mf\">1</span></pre>\n", | |
| "<pre class='cython code score-0 '> __pyx_v_i = (__pyx_v_i - 1);\n", | |
| " }\n", | |
| "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">43</span>: <span class=\"n\">left_ip</span> <span class=\"o\">=</span> <span class=\"o\"><</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">float64_t</span><span class=\"o\">></span><span class=\"n\">i</span></pre>\n", | |
| "<pre class='cython code score-0 '> __pyx_v_left_ip = ((__pyx_t_5numpy_float64_t)__pyx_v_i);\n", | |
| "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">44</span>: <span class=\"k\">if</span> <span class=\"n\">x</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]</span> <span class=\"o\"><</span> <span class=\"n\">height</span><span class=\"p\">:</span></pre>\n", | |
| "<pre class='cython code score-0 '> __pyx_t_19 = __pyx_v_i;\n", | |
| " __pyx_t_13 = (((*((__pyx_t_5numpy_float64_t *) ( /* dim=0 */ ((char *) (((__pyx_t_5numpy_float64_t *) __pyx_v_x.data) + __pyx_t_19)) ))) < __pyx_v_height) != 0);\n", | |
| " if (__pyx_t_13) {\n", | |
| "/* … */\n", | |
| " }\n", | |
| "</pre><pre class=\"cython line score-0\"> <span class=\"\">45</span>: <span class=\"c\"># Interpolate if true intersection height is between samples</span></pre>\n", | |
| "<pre class=\"cython line score-9\" onclick='toggleDiv(this)'>+<span class=\"\">46</span>: <span class=\"n\">left_ip</span> <span class=\"o\">+=</span> <span class=\"p\">(</span><span class=\"n\">height</span> <span class=\"o\">-</span> <span class=\"n\">x</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">])</span> <span class=\"o\">/</span> <span class=\"p\">(</span><span class=\"n\">x</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=\"o\">-</span> <span class=\"n\">x</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">])</span></pre>\n", | |
| "<pre class='cython code score-9 '> __pyx_t_20 = __pyx_v_i;\n", | |
| " __pyx_t_16 = (__pyx_v_height - (*((__pyx_t_5numpy_float64_t *) ( /* dim=0 */ ((char *) (((__pyx_t_5numpy_float64_t *) __pyx_v_x.data) + __pyx_t_20)) ))));\n", | |
| " __pyx_t_21 = (__pyx_v_i + 1);\n", | |
| " __pyx_t_22 = __pyx_v_i;\n", | |
| " __pyx_t_23 = ((*((__pyx_t_5numpy_float64_t *) ( /* dim=0 */ ((char *) (((__pyx_t_5numpy_float64_t *) __pyx_v_x.data) + __pyx_t_21)) ))) - (*((__pyx_t_5numpy_float64_t *) ( /* dim=0 */ ((char *) (((__pyx_t_5numpy_float64_t *) __pyx_v_x.data) + __pyx_t_22)) ))));\n", | |
| " if (unlikely(__pyx_t_23 == 0)) {\n", | |
| " #ifdef WITH_THREAD\n", | |
| " PyGILState_STATE __pyx_gilstate_save = <span class='pyx_c_api'>__Pyx_PyGILState_Ensure</span>();\n", | |
| " #endif\n", | |
| " <span class='py_c_api'>PyErr_SetString</span>(PyExc_ZeroDivisionError, \"float division\");\n", | |
| " #ifdef WITH_THREAD\n", | |
| " <span class='pyx_c_api'>__Pyx_PyGILState_Release</span>(__pyx_gilstate_save);\n", | |
| " #endif\n", | |
| " <span class='error_goto'>__PYX_ERR(0, 46, __pyx_L4_error)</span>\n", | |
| " }\n", | |
| " __pyx_v_left_ip = (__pyx_v_left_ip + (__pyx_t_16 / __pyx_t_23));\n", | |
| "</pre><pre class=\"cython line score-0\"> <span class=\"\">47</span>: </pre>\n", | |
| "<pre class=\"cython line score-0\"> <span class=\"\">48</span>: <span class=\"c\"># Find intersection point on right side</span></pre>\n", | |
| "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">49</span>: <span class=\"n\">i</span> <span class=\"o\">=</span> <span class=\"n\">peak</span></pre>\n", | |
| "<pre class='cython code score-0 '> __pyx_v_i = __pyx_v_peak;\n", | |
| "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">50</span>: <span class=\"k\">while</span> <span class=\"n\">i</span> <span class=\"o\"><</span> <span class=\"n\">i_max</span> <span class=\"ow\">and</span> <span class=\"n\">height</span> <span class=\"o\"><</span> <span class=\"n\">x</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]:</span></pre>\n", | |
| "<pre class='cython code score-0 '> while (1) {\n", | |
| " __pyx_t_12 = ((__pyx_v_i < __pyx_v_i_max) != 0);\n", | |
| " if (__pyx_t_12) {\n", | |
| " } else {\n", | |
| " __pyx_t_13 = __pyx_t_12;\n", | |
| " goto __pyx_L16_bool_binop_done;\n", | |
| " }\n", | |
| " __pyx_t_24 = __pyx_v_i;\n", | |
| " __pyx_t_12 = ((__pyx_v_height < (*((__pyx_t_5numpy_float64_t *) ( /* dim=0 */ ((char *) (((__pyx_t_5numpy_float64_t *) __pyx_v_x.data) + __pyx_t_24)) )))) != 0);\n", | |
| " __pyx_t_13 = __pyx_t_12;\n", | |
| " __pyx_L16_bool_binop_done:;\n", | |
| " if (!__pyx_t_13) break;\n", | |
| "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">51</span>: <span class=\"n\">i</span> <span class=\"o\">+=</span> <span class=\"mf\">1</span></pre>\n", | |
| "<pre class='cython code score-0 '> __pyx_v_i = (__pyx_v_i + 1);\n", | |
| " }\n", | |
| "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">52</span>: <span class=\"n\">right_ip</span> <span class=\"o\">=</span> <span class=\"o\"><</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">float64_t</span><span class=\"o\">></span><span class=\"n\">i</span></pre>\n", | |
| "<pre class='cython code score-0 '> __pyx_v_right_ip = ((__pyx_t_5numpy_float64_t)__pyx_v_i);\n", | |
| "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">53</span>: <span class=\"k\">if</span> <span class=\"n\">x</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]</span> <span class=\"o\"><</span> <span class=\"n\">height</span><span class=\"p\">:</span></pre>\n", | |
| "<pre class='cython code score-0 '> __pyx_t_25 = __pyx_v_i;\n", | |
| " __pyx_t_13 = (((*((__pyx_t_5numpy_float64_t *) ( /* dim=0 */ ((char *) (((__pyx_t_5numpy_float64_t *) __pyx_v_x.data) + __pyx_t_25)) ))) < __pyx_v_height) != 0);\n", | |
| " if (__pyx_t_13) {\n", | |
| "/* … */\n", | |
| " }\n", | |
| "</pre><pre class=\"cython line score-0\"> <span class=\"\">54</span>: <span class=\"c\"># Interpolate if true intersection height is between samples</span></pre>\n", | |
| "<pre class=\"cython line score-9\" onclick='toggleDiv(this)'>+<span class=\"\">55</span>: <span class=\"n\">right_ip</span> <span class=\"o\">-=</span> <span class=\"p\">(</span><span class=\"n\">height</span> <span class=\"o\">-</span> <span class=\"n\">x</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">])</span> <span class=\"o\">/</span> <span class=\"p\">(</span><span class=\"n\">x</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=\"o\">-</span> <span class=\"n\">x</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">])</span></pre>\n", | |
| "<pre class='cython code score-9 '> __pyx_t_26 = __pyx_v_i;\n", | |
| " __pyx_t_23 = (__pyx_v_height - (*((__pyx_t_5numpy_float64_t *) ( /* dim=0 */ ((char *) (((__pyx_t_5numpy_float64_t *) __pyx_v_x.data) + __pyx_t_26)) ))));\n", | |
| " __pyx_t_27 = (__pyx_v_i - 1);\n", | |
| " __pyx_t_28 = __pyx_v_i;\n", | |
| " __pyx_t_16 = ((*((__pyx_t_5numpy_float64_t *) ( /* dim=0 */ ((char *) (((__pyx_t_5numpy_float64_t *) __pyx_v_x.data) + __pyx_t_27)) ))) - (*((__pyx_t_5numpy_float64_t *) ( /* dim=0 */ ((char *) (((__pyx_t_5numpy_float64_t *) __pyx_v_x.data) + __pyx_t_28)) ))));\n", | |
| " if (unlikely(__pyx_t_16 == 0)) {\n", | |
| " #ifdef WITH_THREAD\n", | |
| " PyGILState_STATE __pyx_gilstate_save = <span class='pyx_c_api'>__Pyx_PyGILState_Ensure</span>();\n", | |
| " #endif\n", | |
| " <span class='py_c_api'>PyErr_SetString</span>(PyExc_ZeroDivisionError, \"float division\");\n", | |
| " #ifdef WITH_THREAD\n", | |
| " <span class='pyx_c_api'>__Pyx_PyGILState_Release</span>(__pyx_gilstate_save);\n", | |
| " #endif\n", | |
| " <span class='error_goto'>__PYX_ERR(0, 55, __pyx_L4_error)</span>\n", | |
| " }\n", | |
| " __pyx_v_right_ip = (__pyx_v_right_ip - (__pyx_t_23 / __pyx_t_16));\n", | |
| "</pre><pre class=\"cython line score-0\"> <span class=\"\">56</span>: </pre>\n", | |
| "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">57</span>: <span class=\"n\">widths</span><span class=\"p\">[</span><span class=\"n\">p</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">right_ip</span> <span class=\"o\">-</span> <span class=\"n\">left_ip</span></pre>\n", | |
| "<pre class='cython code score-0 '> __pyx_t_29 = __pyx_v_p;\n", | |
| " *((__pyx_t_5numpy_float64_t *) ( /* dim=0 */ ((char *) (((__pyx_t_5numpy_float64_t *) __pyx_v_widths.data) + __pyx_t_29)) )) = (__pyx_v_right_ip - __pyx_v_left_ip);\n", | |
| "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">58</span>: <span class=\"n\">left_ips</span><span class=\"p\">[</span><span class=\"n\">p</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">left_ip</span></pre>\n", | |
| "<pre class='cython code score-0 '> __pyx_t_30 = __pyx_v_p;\n", | |
| " *((__pyx_t_5numpy_float64_t *) ( /* dim=0 */ ((char *) (((__pyx_t_5numpy_float64_t *) __pyx_v_left_ips.data) + __pyx_t_30)) )) = __pyx_v_left_ip;\n", | |
| "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">59</span>: <span class=\"n\">right_ips</span><span class=\"p\">[</span><span class=\"n\">p</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">right_ip</span></pre>\n", | |
| "<pre class='cython code score-0 '> __pyx_t_31 = __pyx_v_p;\n", | |
| " *((__pyx_t_5numpy_float64_t *) ( /* dim=0 */ ((char *) (((__pyx_t_5numpy_float64_t *) __pyx_v_right_ips.data) + __pyx_t_31)) )) = __pyx_v_right_ip;\n", | |
| " }\n", | |
| " __pyx_L7_break:;\n", | |
| " }\n", | |
| "</pre><pre class=\"cython line score-0\"> <span class=\"\">60</span>: </pre>\n", | |
| "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">61</span>: <span class=\"k\">if</span> <span class=\"n\">raise_error</span> <span class=\"ow\">is</span> <span class=\"bp\">True</span><span class=\"p\">:</span></pre>\n", | |
| "<pre class='cython code score-0 '> __pyx_t_13 = ((__pyx_v_raise_error == 1) != 0);\n", | |
| " if (__pyx_t_13) {\n", | |
| "/* … */\n", | |
| " }\n", | |
| "</pre><pre class=\"cython line score-26\" onclick='toggleDiv(this)'>+<span class=\"\">62</span>: <span class=\"k\">raise</span> <span class=\"ne\">ValueError</span><span class=\"p\">(</span><span class=\"s\">"prominence data is invalid for peak "</span> <span class=\"o\">+</span> <span class=\"nb\">str</span><span class=\"p\">(</span><span class=\"n\">peak</span><span class=\"p\">))</span></pre>\n", | |
| "<pre class='cython code score-26 '> __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyInt_From_Py_intptr_t</span>(__pyx_v_peak);<span class='error_goto'> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 62, __pyx_L1_error)</span>\n", | |
| " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", | |
| " __pyx_t_2 = <span class='py_c_api'>PyTuple_New</span>(1);<span class='error_goto'> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 62, __pyx_L1_error)</span>\n", | |
| " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n", | |
| " <span class='refnanny'>__Pyx_GIVEREF</span>(__pyx_t_1);\n", | |
| " <span class='py_macro_api'>PyTuple_SET_ITEM</span>(__pyx_t_2, 0, __pyx_t_1);\n", | |
| " __pyx_t_1 = 0;\n", | |
| " __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyObject_Call</span>(((PyObject *)(&PyUnicode_Type)), __pyx_t_2, NULL);<span class='error_goto'> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 62, __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_2); __pyx_t_2 = 0;\n", | |
| " __pyx_t_2 = <span class='pyx_c_api'>__Pyx_PyUnicode_Concat</span>(__pyx_kp_u_prominence_data_is_invalid_for_p, __pyx_t_1);<span class='error_goto'> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 62, __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_1); __pyx_t_1 = 0;\n", | |
| " __pyx_t_1 = <span class='py_c_api'>PyTuple_New</span>(1);<span class='error_goto'> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 62, __pyx_L1_error)</span>\n", | |
| " <span class='refnanny'>__Pyx_GOTREF</span>(__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_1, 0, __pyx_t_2);\n", | |
| " __pyx_t_2 = 0;\n", | |
| " __pyx_t_2 = <span class='pyx_c_api'>__Pyx_PyObject_Call</span>(__pyx_builtin_ValueError, __pyx_t_1, NULL);<span class='error_goto'> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 62, __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_1); __pyx_t_1 = 0;\n", | |
| " <span class='pyx_c_api'>__Pyx_Raise</span>(__pyx_t_2, 0, 0, 0);\n", | |
| " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n", | |
| " <span class='error_goto'>__PYX_ERR(0, 62, __pyx_L1_error)</span>\n", | |
| "</pre><pre class=\"cython line score-0\"> <span class=\"\">63</span>: </pre>\n", | |
| "<pre class=\"cython line score-22\" onclick='toggleDiv(this)'>+<span class=\"\">64</span>: <span class=\"k\">return</span> <span class=\"n\">widths</span><span class=\"o\">.</span><span class=\"n\">base</span><span class=\"p\">,</span> <span class=\"n\">width_heights</span><span class=\"o\">.</span><span class=\"n\">base</span><span class=\"p\">,</span> <span class=\"n\">left_ips</span><span class=\"o\">.</span><span class=\"n\">base</span><span class=\"p\">,</span> <span class=\"n\">right_ips</span><span class=\"o\">.</span><span class=\"n\">base</span></pre>\n", | |
| "<pre class='cython code score-22 '> <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);\n", | |
| " __pyx_t_2 = __pyx_memoryview_fromslice(__pyx_v_widths, 1, (PyObject *(*)(char *)) __pyx_memview_get_nn___pyx_t_5numpy_float64_t, (int (*)(char *, PyObject *)) __pyx_memview_set_nn___pyx_t_5numpy_float64_t, 0);;<span class='error_goto'> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 64, __pyx_L1_error)</span>\n", | |
| " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n", | |
| " __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyObject_GetAttrStr</span>(__pyx_t_2, __pyx_n_s_base);<span class='error_goto'> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 64, __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_2); __pyx_t_2 = 0;\n", | |
| " __pyx_t_2 = __pyx_memoryview_fromslice(__pyx_v_width_heights, 1, (PyObject *(*)(char *)) __pyx_memview_get_nn___pyx_t_5numpy_float64_t, (int (*)(char *, PyObject *)) __pyx_memview_set_nn___pyx_t_5numpy_float64_t, 0);;<span class='error_goto'> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 64, __pyx_L1_error)</span>\n", | |
| " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n", | |
| " __pyx_t_3 = <span class='pyx_c_api'>__Pyx_PyObject_GetAttrStr</span>(__pyx_t_2, __pyx_n_s_base);<span class='error_goto'> if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 64, __pyx_L1_error)</span>\n", | |
| " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_3);\n", | |
| " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n", | |
| " __pyx_t_2 = __pyx_memoryview_fromslice(__pyx_v_left_ips, 1, (PyObject *(*)(char *)) __pyx_memview_get_nn___pyx_t_5numpy_float64_t, (int (*)(char *, PyObject *)) __pyx_memview_set_nn___pyx_t_5numpy_float64_t, 0);;<span class='error_goto'> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 64, __pyx_L1_error)</span>\n", | |
| " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n", | |
| " __pyx_t_4 = <span class='pyx_c_api'>__Pyx_PyObject_GetAttrStr</span>(__pyx_t_2, __pyx_n_s_base);<span class='error_goto'> if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 64, __pyx_L1_error)</span>\n", | |
| " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_4);\n", | |
| " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n", | |
| " __pyx_t_2 = __pyx_memoryview_fromslice(__pyx_v_right_ips, 1, (PyObject *(*)(char *)) __pyx_memview_get_nn___pyx_t_5numpy_float64_t, (int (*)(char *, PyObject *)) __pyx_memview_set_nn___pyx_t_5numpy_float64_t, 0);;<span class='error_goto'> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 64, __pyx_L1_error)</span>\n", | |
| " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n", | |
| " __pyx_t_5 = <span class='pyx_c_api'>__Pyx_PyObject_GetAttrStr</span>(__pyx_t_2, __pyx_n_s_base);<span class='error_goto'> if (unlikely(!__pyx_t_5)) __PYX_ERR(0, 64, __pyx_L1_error)</span>\n", | |
| " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_5);\n", | |
| " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n", | |
| " __pyx_t_2 = <span class='py_c_api'>PyTuple_New</span>(4);<span class='error_goto'> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 64, __pyx_L1_error)</span>\n", | |
| " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n", | |
| " <span class='refnanny'>__Pyx_GIVEREF</span>(__pyx_t_1);\n", | |
| " <span class='py_macro_api'>PyTuple_SET_ITEM</span>(__pyx_t_2, 0, __pyx_t_1);\n", | |
| " <span class='refnanny'>__Pyx_GIVEREF</span>(__pyx_t_3);\n", | |
| " <span class='py_macro_api'>PyTuple_SET_ITEM</span>(__pyx_t_2, 1, __pyx_t_3);\n", | |
| " <span class='refnanny'>__Pyx_GIVEREF</span>(__pyx_t_4);\n", | |
| " <span class='py_macro_api'>PyTuple_SET_ITEM</span>(__pyx_t_2, 2, __pyx_t_4);\n", | |
| " <span class='refnanny'>__Pyx_GIVEREF</span>(__pyx_t_5);\n", | |
| " <span class='py_macro_api'>PyTuple_SET_ITEM</span>(__pyx_t_2, 3, __pyx_t_5);\n", | |
| " __pyx_t_1 = 0;\n", | |
| " __pyx_t_3 = 0;\n", | |
| " __pyx_t_4 = 0;\n", | |
| " __pyx_t_5 = 0;\n", | |
| " __pyx_r = __pyx_t_2;\n", | |
| " __pyx_t_2 = 0;\n", | |
| " goto __pyx_L0;\n", | |
| "</pre></div></body></html>" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ] | |
| }, | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "%%cython -a\n", | |
| "\n", | |
| "import numpy as np\n", | |
| "cimport numpy as np\n", | |
| "import cython\n", | |
| "\n", | |
| "\n", | |
| "@cython.wraparound(False)\n", | |
| "@cython.boundscheck(False)\n", | |
| "def inner(np.float64_t[::1] x not None,\n", | |
| " np.intp_t[::1] peaks not None,\n", | |
| " np.float64_t rel_height,\n", | |
| " np.float64_t[::1] prominences not None,\n", | |
| " np.intp_t[::1] left_bases not None,\n", | |
| " np.intp_t[::1] right_bases not None):\n", | |
| " cdef:\n", | |
| " np.float64_t[::1] widths, width_heights, left_ips, right_ips\n", | |
| " np.float64_t height, left_ip, right_ip\n", | |
| " np.intp_t p, peak, i, i_max, i_min\n", | |
| " bint raise_error\n", | |
| "\n", | |
| " raise_error = False\n", | |
| " widths = np.empty(peaks.shape[0], dtype=np.float64)\n", | |
| " width_heights = np.empty(peaks.shape[0], dtype=np.float64)\n", | |
| " left_ips = np.empty(peaks.shape[0], dtype=np.float64)\n", | |
| " right_ips = np.empty(peaks.shape[0], dtype=np.float64)\n", | |
| "\n", | |
| " with nogil:\n", | |
| " for p in range(peaks.shape[0]):\n", | |
| " i_min = left_bases[p]\n", | |
| " i_max = right_bases[p]\n", | |
| " peak = peaks[p]\n", | |
| " # Validate bounds and order\n", | |
| " if not 0 <= i_min <= peak <= i_max < x.shape[0]:\n", | |
| " raise_error = True\n", | |
| " break\n", | |
| "\n", | |
| " height = width_heights[p] = x[peak] - prominences[p] * rel_height\n", | |
| "\n", | |
| " # Find intersection point on left side\n", | |
| " i = peak\n", | |
| " while i_min < i and height < x[i]:\n", | |
| " i -= 1\n", | |
| " left_ip = <np.float64_t>i\n", | |
| " if x[i] < height:\n", | |
| " # Interpolate if true intersection height is between samples\n", | |
| " left_ip += (height - x[i]) / (x[i + 1] - x[i])\n", | |
| "\n", | |
| " # Find intersection point on right side\n", | |
| " i = peak\n", | |
| " while i < i_max and height < x[i]:\n", | |
| " i += 1\n", | |
| " right_ip = <np.float64_t>i\n", | |
| " if x[i] < height:\n", | |
| " # Interpolate if true intersection height is between samples\n", | |
| " right_ip -= (height - x[i]) / (x[i - 1] - x[i])\n", | |
| "\n", | |
| " widths[p] = right_ip - left_ip\n", | |
| " left_ips[p] = left_ip\n", | |
| " right_ips[p] = right_ip\n", | |
| "\n", | |
| " if raise_error is True:\n", | |
| " raise ValueError(\"prominence data is invalid for peak \" + str(peak))\n", | |
| "\n", | |
| " return widths.base, width_heights.base, left_ips.base, right_ips.base" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "def new(x, peaks, rel_height=0.5, prominence_data=None, wlen=None):\n", | |
| " # Inner function expects `x` to be C-contiguous\n", | |
| " x = np.asarray(x, order='C', dtype=np.float64)\n", | |
| " if x.ndim != 1:\n", | |
| " raise ValueError('`x` must have exactly one dimension')\n", | |
| "\n", | |
| " peaks = np.asarray(peaks)\n", | |
| " try:\n", | |
| " # Safely convert to C-contiguous array of type np.intp\n", | |
| " peaks = peaks.astype(np.intp, order='C', casting='safe',\n", | |
| " subok=False, copy=False)\n", | |
| " except TypeError:\n", | |
| " if peaks.size == 0:\n", | |
| " # Empty arrays default to np.float64 but are valid input\n", | |
| " peaks = np.array([], dtype=np.intp)\n", | |
| " else:\n", | |
| " raise TypeError(\"Cannot safely cast `peaks` to dtype('intp')\")\n", | |
| " if peaks.ndim != 1:\n", | |
| " raise ValueError('`peaks` must have exactly one dimension')\n", | |
| "\n", | |
| " if rel_height < 0.0:\n", | |
| " raise ValueError('`rel_height` must be greater or equal to 0.0')\n", | |
| "\n", | |
| " if prominence_data is None:\n", | |
| " # Calculate prominence if not supplied and use wlen if supplied.\n", | |
| " prominence_data = peak_prominences(x, peaks, wlen)\n", | |
| "\n", | |
| " return inner(x, peaks, rel_height, *prominence_data)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Compare" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Peaks: 10896\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "np.random.seed(20180322)\n", | |
| "x1 = np.random.normal(size=2 ** 15)\n", | |
| "\n", | |
| "peaks1 = find_peaks(x1)[0]\n", | |
| "print(\"Peaks:\", peaks1.size)\n", | |
| "pdata1 = peak_prominences(x1, peaks1)\n", | |
| "\n", | |
| "plt.plot(x1)\n", | |
| "plt.plot(peaks1, x1[peaks1], \"x\")\n", | |
| "plt.xlim(0, 100);" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "237 ms ± 6.89 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", | |
| "280 µs ± 1.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%timeit old(x1, peaks1, prominence_data=pdata1)\n", | |
| "%timeit new(x1, peaks1, prominence_data=pdata1)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": { | |
| "scrolled": true | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "216 ms ± 7.37 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", | |
| "427 µs ± 3.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%timeit old(x1, peaks1, rel_height=1, prominence_data=pdata1)\n", | |
| "%timeit new(x1, peaks1, rel_height=1, prominence_data=pdata1)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# Not exactly the same because of numeric errors during width calculation\n", | |
| "np.testing.assert_almost_equal(old(x1, peaks1, prominence_data=pdata1),\n", | |
| " new(x1, peaks1, prominence_data=pdata1))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Peaks: 890\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "(0, 2000)" | |
| ] | |
| }, | |
| "execution_count": 9, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "from urllib.request import urlopen\n", | |
| "\n", | |
| "# A natural ecg signal (provided by the package BioSPPy)\n", | |
| "ecg_txt = urlopen(\"https://raw.githubusercontent.com/PIA-Group/BioSPPy/e71ab177c4c8bdc7e644d962210cc40b1bdb41fb/examples/ecg.txt\")\n", | |
| "x2 = np.loadtxt(ecg_txt)\n", | |
| "\n", | |
| "peaks2 = find_peaks(x2)[0]\n", | |
| "print(\"Peaks:\", peaks2.size)\n", | |
| "pdata2 = peak_prominences(x2, peaks2) \n", | |
| "\n", | |
| "plt.plot(x2)\n", | |
| "plt.plot(peaks2, x2[peaks2], \"x\")\n", | |
| "plt.xlim(0, 2000)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "21.6 ms ± 535 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", | |
| "43.2 µs ± 177 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%timeit old(x2, peaks2, prominence_data=pdata2)\n", | |
| "%timeit new(x2, peaks2, prominence_data=pdata2)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "metadata": { | |
| "scrolled": true | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "18.1 ms ± 542 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n", | |
| "73.3 µs ± 192 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%timeit old(x2, peaks2, rel_height=1, prominence_data=pdata2)\n", | |
| "%timeit new(x2, peaks2, rel_height=1, prominence_data=pdata2)" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "language": "python", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.6.4" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
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