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Merge pull request #43 from bcliang/new-demo-notebooks
Add Examples: Peak Finding, Impedance
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,253 @@ | ||
| { | ||
| "nbformat": 4, | ||
| "nbformat_minor": 0, | ||
| "metadata": { | ||
| "colab": { | ||
| "name": "Gamry-Parser CyclicVoltammetry Peak Detection Example", | ||
| "provenance": [], | ||
| "collapsed_sections": [] | ||
| }, | ||
| "kernelspec": { | ||
| "name": "python3", | ||
| "display_name": "Python 3" | ||
| } | ||
| }, | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "metadata": { | ||
| "id": "YyeVlSYkhahF", | ||
| "cellView": "form" | ||
| }, | ||
| "source": [ | ||
| "#@title Imports, initial setup (Ctrl+F9 to run all)\n", | ||
| "import os\n", | ||
| "import re\n", | ||
| "import pandas as pd\n", | ||
| "import matplotlib.pyplot as plt\n", | ||
| "from scipy.signal import find_peaks\n", | ||
| "import copy\n", | ||
| "\n", | ||
| "try:\n", | ||
| " import gamry_parser\n", | ||
| "except:\n", | ||
| " subprocess.run(\n", | ||
| " [\"pip\", \"install\", \"gamry-parser\"], \n", | ||
| " encoding=\"utf-8\", \n", | ||
| " shell=False)\n", | ||
| "finally:\n", | ||
| " import gamry_parser\n", | ||
| "\n", | ||
| "gp = gamry_parser.CyclicVoltammetry()\n", | ||
| "\n", | ||
| "print('Done.')" | ||
| ], | ||
| "execution_count": null, | ||
| "outputs": [] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "metadata": { | ||
| "id": "ZGoqracvk9q2", | ||
| "cellView": "form" | ||
| }, | ||
| "source": [ | ||
| "\"\"\"\n", | ||
| "### SCRIPT CONFIGURATION SETTINGS ###\n", | ||
| "\"\"\"\n", | ||
| "#@markdown **Experimental Setup**\n", | ||
| "\n", | ||
| "#@markdown Where should the notebook search for DTA files? Examples (using google colab):\n", | ||
| "#@markdown - Mounted google drive folder: `/content/drive/`\n", | ||
| "#@markdown - If uploading files manually, : `/content/`).\n", | ||
| "\n", | ||
| "data_path = \"/content/\" #@param {type:\"string\"}\n", | ||
| "\n", | ||
| "#@markdown Filter which files we want to analyze\n", | ||
| "file_pattern = \"Search-For-Text\" #@param {type:\"string\"}\n", | ||
| "\n", | ||
| "#@markdown Extract trace labels from file name (e.g. `[17:].lower()` => drop the first 17 characters from the filename and convert to lowercase). The trace labels are used for category labeling (and plot legends)\n", | ||
| "file_label_xform = \"[51:]\" #@param {type:\"string\"}\n", | ||
| "\n", | ||
| "# create a \"results\" dataframe to contain the values we care about\n", | ||
| "data_df = pandas.DataFrame()\n", | ||
| "settings_df = pandas.DataFrame()\n", | ||
| "peaks_df = pandas.DataFrame()\n", | ||
| "\n", | ||
| "# identify files to process\n", | ||
| "files = [f for f in os.listdir(data_path) if \n", | ||
| " os.path.splitext(f)[1].lower() == \".dta\" and\n", | ||
| " len(re.findall(file_pattern.upper(), f.upper())) > 0\n", | ||
| " ]\n" | ||
| ], | ||
| "execution_count": null, | ||
| "outputs": [] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "metadata": { | ||
| "cellView": "form", | ||
| "id": "8MFNF2Qz6lef" | ||
| }, | ||
| "source": [ | ||
| "#@markdown **Process Data and Detect Peaks**\n", | ||
| "\n", | ||
| "#@markdown Which CV curves (cycle number) should be sampled? (`0` would select the first CV curve from each file)\n", | ||
| "curves_to_sample = \"0\" #@param {type:\"string\"}\n", | ||
| "curves_to_sample = [int(item.strip()) for item in curves_to_sample.split(\",\")]\n", | ||
| "\n", | ||
| "#@markdown Peak Detection: specify the peak detection parameters\n", | ||
| "peak_width_mV = 75 #@param {type:\"integer\"}\n", | ||
| "peak_height_nA = 25 #@param {type:\"integer\"}\n", | ||
| "peak_thresh_max_mV = 800 #@param {type:\"integer\"}\n", | ||
| "peak_thresh_min_mV = -100 #@param {type:\"integer\"}\n", | ||
| "\n", | ||
| "# this method finds the row that has an index value closest to the desired time elapsed\n", | ||
| "def duration_lookup(df, elapsed):\n", | ||
| " return df.index.get_loc(elapsed, method='nearest')\n", | ||
| "\n", | ||
| "# iterate through each DTA file\n", | ||
| "for index, file in enumerate(files):\n", | ||
| " print(\"Checking File {}\".format(file))\n", | ||
| "\n", | ||
| " label, ext = os.path.splitext(file)\n", | ||
| " my_label = \"-\".join(eval(\"label{}\".format(file_label_xform)).strip().split())\n", | ||
| "\n", | ||
| " # load the dta file using gamry parser\n", | ||
| " gp.load(filename=os.path.join(data_path, file))\n", | ||
| "\n", | ||
| " is_cv = gp.get_header().get(\"TAG\") == \"CV\"\n", | ||
| " if not is_cv:\n", | ||
| " # if the DTA file is a different experiment type, skip it and move to the next file.\n", | ||
| " print(\"File `{}` is not a CV experiment. Skipping\".format(file))\n", | ||
| " del files[index] # remove invalid file from list\n", | ||
| " continue\n", | ||
| " \n", | ||
| " # for each CV file, let's extract the relevant information\n", | ||
| " cv = gamry_parser.CyclicVoltammetry(filename=os.path.join(data_path, file))\n", | ||
| " cv.load()\n", | ||
| " for curve_num in curves_to_sample:\n", | ||
| " print(\"\\tProcessing Curve #{}\".format(curve_num))\n", | ||
| " v1, v2 = cv.get_v_range()\n", | ||
| " settings = pandas.DataFrame({\n", | ||
| " \"label\": my_label,\n", | ||
| " \"curves\": cv.get_curve_count(),\n", | ||
| " \"v1_mV\": v1*1000,\n", | ||
| " \"v2_mV\": v2*1000,\n", | ||
| " \"rate_mV\": cv.get_scan_rate(),\n", | ||
| " }, index=[0])\n", | ||
| " settings_df = settings_df.append(settings)\n", | ||
| "\n", | ||
| " data = copy.deepcopy(cv.get_curve_data(curve=curve_num))\n", | ||
| " data.Im = data.Im*1e9\n", | ||
| " data.Vf = data.Vf*1e3\n", | ||
| " data[\"label\"] = my_label #\"{:03d}-{}\".format(index, curve_num)\n", | ||
| "\n", | ||
| " data_df = data_df.append(data)\n", | ||
| "\n", | ||
| " # find peaks in the data\n", | ||
| " dV = cv.get_scan_rate() # in mV\n", | ||
| " peak_width = int(peak_width_mV/dV)\n", | ||
| " peaks_pos, props_pos = find_peaks(\n", | ||
| " data.Im, \n", | ||
| " width=peak_width, \n", | ||
| " distance=2*peak_width, \n", | ||
| " height=peak_height_nA\n", | ||
| " )\n", | ||
| " peaks_neg, props_neg = find_peaks(\n", | ||
| " -data.Im, \n", | ||
| " width=peak_width, \n", | ||
| " distance=2*peak_width, \n", | ||
| " height=peak_height_nA\n", | ||
| " )\n", | ||
| " peaks = list(peaks_pos) + list(peaks_neg)\n", | ||
| " # remove peaks that are out of min/max range\n", | ||
| " peaks = [peak \n", | ||
| " for peak in peaks \n", | ||
| " if data.Vf.iloc[peak] >= peak_thresh_min_mV and data.Vf.iloc[peak] <= peak_thresh_max_mV]\n", | ||
| "\n", | ||
| " # add detected peaks to aggregated peak dataframe\n", | ||
| " peaks = data.iloc[peaks].sort_values(by=\"Vf\")\n", | ||
| " peaks[\"index\"] = peaks.index\n", | ||
| " peaks.reset_index(level=0, inplace=True)\n", | ||
| " peaks_df = peaks_df.append(peaks)\n", | ||
| " peaks_df = peaks_df[[\"label\", \"index\", \"Vf\", \"Im\"]]\n", | ||
| " # print(\"\\tdetected peaks (mV)\", [int(peak) for peak in data.iloc[peaks].Vf.sort_values().tolist()])\n", | ||
| "\n", | ||
| "print(\"\\nFile Metadata\")\n", | ||
| "print(settings_df.to_string(index=False))\n", | ||
| "\n", | ||
| "print(\"\\nPeaks Detected\")\n", | ||
| "print(peaks_df.to_string(index=False))" | ||
| ], | ||
| "execution_count": null, | ||
| "outputs": [] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "metadata": { | ||
| "id": "Ulne80RrpBrW", | ||
| "cellView": "form" | ||
| }, | ||
| "source": [ | ||
| "#@markdown **I-V plot**: Overlay the loaded CyclicVoltammetry Curves\n", | ||
| "\n", | ||
| "from plotly.subplots import make_subplots\n", | ||
| "import plotly.graph_objects as go\n", | ||
| "from plotly.colors import DEFAULT_PLOTLY_COLORS\n", | ||
| "\n", | ||
| "fig = make_subplots(rows=1, cols=1, shared_xaxes=True, vertical_spacing=0.02)\n", | ||
| "\n", | ||
| "for (index, exp_id) in enumerate(data_df.label.unique()):\n", | ||
| " data = data_df.loc[data_df.label == exp_id]\n", | ||
| " newTrace = go.Scatter(\n", | ||
| " x=data.Vf,\n", | ||
| " y=data.Im,\n", | ||
| " mode='lines',\n", | ||
| " name=exp_id,\n", | ||
| " legendgroup=files[index],\n", | ||
| " line=dict(color=DEFAULT_PLOTLY_COLORS[index]),\n", | ||
| " )\n", | ||
| " fig.add_trace(newTrace, row=1, col=1)\n", | ||
| " peak = peaks_df.loc[peaks_df.label == exp_id]\n", | ||
| " newTrace = go.Scatter(\n", | ||
| " x=peak.Vf, y=peak.Im, \n", | ||
| " mode=\"markers\", \n", | ||
| " showlegend=False, \n", | ||
| " marker=dict(size=12,\n", | ||
| " color=DEFAULT_PLOTLY_COLORS[index],\n", | ||
| " )\n", | ||
| " )\n", | ||
| " fig.add_trace(newTrace, row=1, col=1)\n", | ||
| "\n", | ||
| "layout = {\n", | ||
| " 'title': {'text': 'Cyclic Voltammetry Overlay',\n", | ||
| " 'yanchor': 'top',\n", | ||
| " 'y': 0.95,\n", | ||
| " 'x': 0.5 },\n", | ||
| " 'xaxis': {\n", | ||
| " 'anchor': 'x',\n", | ||
| " 'title': 'voltage, mV'\n", | ||
| " },\n", | ||
| " 'yaxis': {\n", | ||
| " 'title': 'current, nA',\n", | ||
| " 'type': 'linear'\n", | ||
| " ''\n", | ||
| " },\n", | ||
| " 'width': 1200,\n", | ||
| " 'height': 500,\n", | ||
| " 'margin': dict(l=30, r=20, t=60, b=20),\n", | ||
| "}\n", | ||
| "fig.update_layout(layout)\n", | ||
| "\n", | ||
| "config={\n", | ||
| " 'displaylogo': False,\n", | ||
| " 'modeBarButtonsToRemove': ['select2d', 'lasso2d', 'hoverClosestCartesian', 'toggleSpikelines','hoverCompareCartesian']\n", | ||
| "}\n", | ||
| "fig.show(config=config)" | ||
| ], | ||
| "execution_count": null, | ||
| "outputs": [] | ||
| } | ||
| ] | ||
| } |
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