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add new notebooks in demo/ folder, basic cleanup
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4 files changed

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‎demo/notebook_cyclicvoltammetry.ipynb

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@@ -30,10 +30,14 @@
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"import matplotlib.pyplot as plt\n",
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"\n",
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"try:\n",
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" import gamry_parser as parser\n",
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" import gamry_parser\n",
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"except:\n",
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" !pip install -q --upgrade gamry-parser\n",
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" import gamry_parser as parser\n",
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" subprocess.run(\n",
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" [\"pip\", \"install\", \"gamry-parser\"], \n",
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" encoding=\"utf-8\", \n",
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" shell=False)\n",
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"finally:\n",
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" import gamry_parser\n",
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"\n",
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"p = parser.CyclicVoltammetry()\n",
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" \n",

‎demo/notebook_cyclicvoltammetry_peakdetect.ipynb

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

‎demo/notebook_gamry_parser.ipynb

Lines changed: 8 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -30,10 +30,14 @@
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"import matplotlib.pyplot as plt\n",
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"\n",
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"try:\n",
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" import gamry_parser as parser\n",
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" import gamry_parser\n",
3434
"except:\n",
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" !pip install -q --upgrade gamry-parser\n",
36-
" import gamry_parser as parser\n",
35+
" subprocess.run(\n",
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" [\"pip\", \"install\", \"gamry-parser\"], \n",
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" encoding=\"utf-8\", \n",
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" shell=False)\n",
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"finally:\n",
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" import gamry_parser\n",
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"\n",
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"p = parser.GamryParser()\n",
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" \n",
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"outputs": []
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}
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]
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}
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}

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