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Either a number, " }, { "__type": "InlineCode", "__tag": 4051, "value": "None" }, { "__type": "Text", "__tag": 4046, "value": ", an array matching " }, { "__type": "ParamRef", "__tag": 4071, "name": "x" }, { "__type": "Text", "__tag": 4046, "value": " or a 2-element sequence of the former. The first element is always interpreted as the minimal and the second, if supplied as the maximal required plateau size." } ] }, { "__type": "Admonition", "__tag": 4056, "kind": "versionadded", "base_type": "neutral", "children": [ { "__type": "AdmonitionTitle", "__tag": 4055, "children": [ { "__type": "Text", "__tag": 4046, "value": "versionadded 1.2.0" } ] } ] } ] } ] } ], "title": [], "level": 0, "target": null }, "Extended Summary": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "This function takes a 1-D array and finds all local maxima by simple comparison of neighboring values. Optionally, a subset of these peaks can be selected by specifying conditions for a peak's properties." } ] } ], "title": [], "level": 0, "target": null }, "Other Parameters": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null } }, "_ordered_sections": [ "Summary", "Extended Summary", "Parameters", "Attributes", "Methods", "Returns", "Yields", "Receives", "Other Parameters", "Raises", "Warns", "Warnings", "Notes" ], "item_file": "/scipy/signal/_peak_finding.py", "item_line": 729, "item_type": "function", "aliases": [ "scipy.signal.find_peaks" ], "example_section_data": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Text", "__tag": 4046, "value": "To demonstrate this function's usage we use a signal `x` supplied with\nSciPy (see `scipy.datasets.electrocardiogram`). Let's find all peaks (local\nmaxima) in `x` whose amplitude lies above 0.\n\n" }, { "__type": "Code", "__tag": 4050, "value": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.datasets import electrocardiogram\nfrom scipy.signal import find_peaks\n", "execution_status": "success" }, { "__type": "Code", "__tag": 4050, "value": "x = electrocardiogram()[2000:4000]\npeaks, _ = find_peaks(x, height=0)\nplt.plot(x)\nplt.plot(peaks, x[peaks], \"x\")\nplt.plot(np.zeros_like(x), \"--\", color=\"gray\")\n", "execution_status": "unexpected_exception" }, { "__type": "Code", "__tag": 4050, "value": "plt.show()\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\nWe can select peaks below 0 with ``height=(None, 0)`` or use arrays matching\n`x` in size to reflect a changing condition for different parts of the\nsignal.\n\n" }, { "__type": "Code", "__tag": 4050, "value": "border = np.sin(np.linspace(0, 3 * np.pi, x.size))\npeaks, _ = find_peaks(x, height=(-border, border))\nplt.plot(x)\nplt.plot(-border, \"--\", color=\"gray\")\nplt.plot(border, \":\", color=\"gray\")\nplt.plot(peaks, x[peaks], \"x\")\n", "execution_status": "unexpected_exception" }, { "__type": "Code", "__tag": 4050, "value": "plt.show()\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\nAnother useful condition for periodic signals can be given with the\n`distance` argument. In this case, we can easily select the positions of\nQRS complexes within the electrocardiogram (ECG) by demanding a distance of\nat least 150 samples.\n\n" }, { "__type": "Code", "__tag": 4050, "value": "peaks, _ = find_peaks(x, distance=150)\nnp.diff(peaks)\nplt.plot(x)\nplt.plot(peaks, x[peaks], \"x\")\n", "execution_status": "unexpected_exception" }, { "__type": "Code", "__tag": 4050, "value": "plt.show()\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\nEspecially for noisy signals peaks can be easily grouped by their\nprominence (see `peak_prominences`). E.g., we can select all peaks except\nfor the mentioned QRS complexes by limiting the allowed prominence to 0.6.\n\n" }, { "__type": "Code", "__tag": 4050, "value": "peaks, properties = find_peaks(x, prominence=(None, 0.6))\nproperties[\"prominences\"].max()\nplt.plot(x)\nplt.plot(peaks, x[peaks], \"x\")\n", "execution_status": "unexpected_exception" }, { "__type": "Code", "__tag": 4050, "value": "plt.show()\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\nAnd, finally, let's examine a different section of the ECG which contains\nbeat forms of different shape. To select only the atypical heart beats, we\ncombine two conditions: a minimal prominence of 1 and width of at least 20\nsamples.\n\n" }, { "__type": "Code", "__tag": 4050, "value": "x = electrocardiogram()[17000:18000]\npeaks, properties = find_peaks(x, prominence=1, width=20)\nproperties[\"prominences\"], properties[\"widths\"]\nplt.plot(x)\nplt.plot(peaks, x[peaks], \"x\")\nplt.vlines(x=peaks, ymin=x[peaks] - properties[\"prominences\"],\n ymax = x[peaks], color = \"C1\")\nplt.hlines(y=properties[\"width_heights\"], xmin=properties[\"left_ips\"],\n xmax=properties[\"right_ips\"], color = \"C1\")\n", "execution_status": "unexpected_exception" }, { "__type": "Code", "__tag": 4050, "value": "plt.show()\n", "execution_status": "success" } ], "title": [], "level": 0, "target": null }, "see_also": [ { "__type": "SeeAlsoItem", "__tag": 4028, "name": { "__type": "CrossRef", "__tag": 4002, "value": "find_peaks_cwt", "reference": { "__type": "LocalRef", "__tag": 4022, "kind": "module", "path": "scipy.signal._peak_finding:find_peaks_cwt" }, "kind": "module" }, "descriptions": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Find peaks using the wavelet transformation." } ] } ], "type": "func" }, { "__type": "SeeAlsoItem", "__tag": 4028, "name": { "__type": "CrossRef", "__tag": 4002, "value": "peak_prominences", "reference": { "__type": "LocalRef", "__tag": 4022, "kind": "module", "path": "scipy.signal._peak_finding:peak_prominences" }, "kind": "module" }, "descriptions": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Directly calculate the prominence of peaks." } ] } ], "type": "func" }, { "__type": "SeeAlsoItem", "__tag": 4028, "name": { "__type": "CrossRef", "__tag": 4002, "value": "peak_widths", "reference": { "__type": "LocalRef", "__tag": 4022, "kind": "module", "path": "scipy.signal._peak_finding:peak_widths" }, "kind": "module" }, "descriptions": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Directly calculate the width of peaks." } ] } ], "type": "func" } ], "signature": { "__type": "SignatureNode", "__tag": 4029, "kind": "function", "parameters": [ { "__type": "SigParam", "__tag": 4030, "name": "x", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": { "__type": "Empty", "__tag": 4031 } }, { "__type": "SigParam", "__tag": 4030, "name": "height", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "None" }, { "__type": "SigParam", "__tag": 4030, "name": "threshold", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "None" }, { "__type": "SigParam", "__tag": 4030, "name": "distance", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "None" }, { "__type": "SigParam", "__tag": 4030, "name": "prominence", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "None" }, { "__type": "SigParam", "__tag": 4030, "name": "width", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "None" }, { "__type": "SigParam", "__tag": 4030, "name": "wlen", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "None" }, { "__type": "SigParam", "__tag": 4030, "name": "rel_height", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "0.5" }, { "__type": "SigParam", "__tag": 4030, "name": "plateau_size", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "None" } ], "return_annotation": { "__type": "Empty", "__tag": 4031 }, "target_name": "find_peaks" }, "references": null, "qa": "scipy.signal._peak_finding:find_peaks", "arbitrary": [], "local_refs": [ "distance", "height", "peaks", "plateau_size", "prominence", "properties", "rel_height", "threshold", "width", "wlen", "x" ] }