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import dash_resumable_upload
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import dash
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import dash_html_components as html
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from dash.dependencies import Input, Output
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import base64
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from os import listdir,system,path,remove
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import dash_table_experiments as dt
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import dash_core_components as dcc
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from os.path import isfile, join
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import shutil
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import time
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import core
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import io
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import plotly.graph_objs as go
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import pandas as pd
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import numpy as np
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#try:
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# system("rm -r uploads")
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#except:
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# pass
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directory = './uploads'
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if path.exists(directory):
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system("rm -r uploads")
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# remove(directory)
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else:
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pass
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app = dash.Dash('')
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#external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css', 'https://codepen.io/rmarren1/pen/eMQKBW.css']
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#app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
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colors = {
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'background': '#ECF0F1',
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'text': '#800000'
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}
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image_filename = 'Logo.png' # replace with your own image
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encoded_image = base64.b64encode(open(image_filename, 'rb').read()).decode('ascii')
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dash_resumable_upload.decorate_server(app.server, "uploads")
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app.scripts.config.serve_locally = True # Uploaded to npm, this can work online now too.
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app.css.append_css({
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"external_url": "https://codepen.io/rmarren1/pen/eMQKBW.css"
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})
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app.layout = html.Div(style={'backgroundColor': colors['background']}, children=[
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html.H1(
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children='VoltCycle',
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style={
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'textAlign': 'center',
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'color': colors['text']
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}
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),
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html.Div([
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html.Img(draggable=True, style={
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'height': '20%',
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'width': '20%'
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}, src='data:image/png;base64,{}'.format(encoded_image))
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], style={'textAlign': 'center'}),
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html.H2(children='A Tool for Accelerating the Analysis of Cyclic Voltammetry Data', style={
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'textAlign': 'center',
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'color': colors['text']
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}),
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html.Br(),
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html.Div([
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html.Link(rel='stylesheet', href='https://codepen.io/rmarren1/pen/eMQKBW.css'),
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dash_resumable_upload.Upload(
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id='upload',
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maxFiles=1,
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maxFileSize=1024*1024*1000, # 100 MB
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service="/upload_resumable",
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textLabel="Upload Files",
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startButton=False)
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]),
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html.Div(id='output_uploaded_file'),
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html.Br(),
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html.H2(
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children='Select File to Analyze',
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style={
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'textAlign': 'center',
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'color': colors['text']
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}
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),
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html.Div([
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dcc.Dropdown(id='files_dropdown')
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],style={'width': '70%', 'height': '40', 'display': 'inline-block', 'textAlign': 'center'}
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),
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html.Div([
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html.Br(),
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dcc.Graph(id='CV_graph'),
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],style={
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'columnCount': 1,
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'width':'70%',
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'height': '80%',
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}
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),
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html.Div([
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html.Br(),
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html.H2(
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children='Redox Properties',
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style={
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'color': colors['text']
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}
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),
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dt.DataTable(
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rows=[{}],
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row_selectable=True,
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filterable=True,
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selected_row_indices=[],
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id='datatable_initial'
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),
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html.Div(id='selected-indexes'),
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],
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style={
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'width': '98%',
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#'height': '60px',
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#'lineHeight': '60px',
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'margin': '10px'
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},
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)
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])
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def parse_contents(value):
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if path.exists(directory):
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lines1 = base64.b64encode(open("uploads/%s" % (value), 'rb').read())
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lines2 = base64.b64decode(lines1).decode('utf-8').split('\n')
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dict_1, n_cycle = core.read_file_dash(lines2)
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#print(n_cycle)
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df = core.data_frame(dict_1, 1)
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return df
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def data_analysis(df):
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results_dict = {}
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152
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# df = main.data_frame(dict_1,1)
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x = df['Potential']
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y = df['Current']
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# Peaks are here [list]
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peak_index = core.peak_detection_fxn(y)
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# Split x,y to get baselines
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x1,x2 = core.split(x)
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y1,y2 = core.split(y)
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y_base1 = core.linear_background(x1,y1)
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y_base2 = core.linear_background(x2,y2)
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# Calculations based on baseline and peak
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values = core.peak_values(x,y)
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Et = values[0]
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Eb = values[2]
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dE = core.del_potential(x,y)
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167
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half_E = min(Et,Eb) + core.half_wave_potential(x,y)
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168
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ia = core.peak_heights(x,y)[0]
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ic = core.peak_heights(x,y)[1]
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ratio_i = core.peak_ratio(x,y)
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results_dict['Peak Current Ratio'] = ratio_i
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results_dict['Ipc (A)'] = ic
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results_dict['Ipa (A)'] = ia
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results_dict['Epc (V)'] = Eb
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results_dict['Epa (V)'] = Et
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results_dict['∆E (V)'] = dE
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results_dict['Redox Potential (V)'] = half_E
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if dE>0.3:
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results_dict['Reversible'] = 'No'
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else:
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results_dict['Reversible'] = 'Yes'
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183
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if half_E>0 and 'Yes' in results_dict.values():
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184
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results_dict['Type'] = 'Catholyte'
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elif 'Yes' in results_dict.values():
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results_dict['Type'] = 'Anolyte'
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return results_dict, x1, x2, y1, y2, y_base1, y_base2, peak_index
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#return results_dict
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190
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191
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@app.callback(Output('output_uploaded_file', 'children'),
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[Input('upload', 'fileNames')])
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def display_files(fileNames):
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if fileNames is not None:
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return html.Ul([html.Li(html.A(x), style={'textAlign': 'center'}) for x in fileNames])
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return html.Ul(html.Li("No Files Uploaded Yet!"), style={'textAlign': 'center'})
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198
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199
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@app.callback(Output('files_dropdown', 'options'),
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[Input('upload','fileNames')])
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201
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def dropdown_files(fileNames):
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202
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mypath='./uploads/'
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203
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onlyfiles = [f for f in listdir(mypath) if isfile(join(mypath, f))]
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return [{'label': i, 'value': i} for i in onlyfiles]
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206
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207
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@app.callback( #update charge datatable
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Output('datatable_initial', 'rows'),
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[Input('files_dropdown', 'value')])
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210
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def update_table1(value):
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211
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212
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df = parse_contents(value)
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213
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#print(df.head())
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#final_dict = data_analysis(df)
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215
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final_dict, x_1, x_2, y_1, y_2, ybase_1, ybase_2, peak_i = data_analysis(df)
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216
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df1=pd.DataFrame.from_records([final_dict])
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217
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return df1.to_dict('records')
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219
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220
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@app.callback(
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Output('CV_graph', 'figure'),
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[Input('files_dropdown', 'value')])
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223
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def update_figure(value):
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224
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df = parse_contents(value)
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225
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final_dict, x_1, x_2, y_1, y_2, ybase_1, ybase_2, peak_i = data_analysis(df)
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226
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227
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trace1 = go.Scatter(
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228
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x = df['Potential'],
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229
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y = df['Current'],
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230
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marker={
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231
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'size': 15,
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232
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'opacity': 0.5,
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233
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'color' : '#F00000'
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234
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})
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235
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trace2 = go.Scatter(
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236
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x = x_1,
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237
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y = ybase_1,
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238
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mode = 'lines',
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239
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line = dict(
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240
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color = ('rgb(0, 0, 256)'),
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241
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width = 3,
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242
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dash = 'dash')
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243
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)
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244
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trace3 = go.Scatter(
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245
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x = x_2,
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246
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y = ybase_2,
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247
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mode = 'lines',
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248
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line = dict(
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249
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color = ('rgb(0, 0, 256)'),
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250
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width = 3,
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251
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dash = 'dash')
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252
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)
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253
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trace4 = go.Scatter(
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254
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x = np.array(x_1[peak_i[1]]),
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255
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y = np.array(y_1[peak_i[1]]),
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256
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mode = 'markers',
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257
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marker={
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258
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'size': 35,
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259
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'opacity': 0.5,
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260
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'color' : '#000080'
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261
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})
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262
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trace5 = go.Scatter(
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263
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x = np.array(x_2[peak_i[0]]),
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264
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y = np.array(y_2[peak_i[0]]),
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265
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mode = 'markers',
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|
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266
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marker={
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|
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267
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'size': 35,
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268
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'opacity': 0.5,
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269
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'color' : '#000080'
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270
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})
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271
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data = [trace1, trace2, trace3, trace4, trace5]
|
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272
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|
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|
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|
|
|
|
|
273
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return {
|
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274
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'data': data,
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275
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#'layout' : {'Dash'}
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276
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'layout': go.Layout(
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277
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xaxis={'title': 'Voltage (V)'},
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278
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yaxis={'title': 'Current (A)'},
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279
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margin={'l': 40, 'b': 40, 't': 10, 'r': 10},
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280
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# #legend={'x': 0, 'y': 1},
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|
|
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281
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showlegend = False,
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|
|
|
|
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282
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hovermode='closest',
|
|
283
|
|
|
)
|
|
284
|
|
|
}
|
|
285
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|
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286
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287
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288
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# return {
|
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289
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# 'data': [
|
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290
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|
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# {'x': [x1[peak_index[1]]], 'y': [x1[peak_index[1]]], 'type': 'point'},
|
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291
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# #{'x': [1, 2, 3], 'y': [2, 4, 5], 'type': 'bar', 'name': u'Montréal'},
|
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292
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# ],
|
|
293
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|
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# }
|
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294
|
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|
295
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|
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296
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if __name__ == '__main__':
|
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297
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app.run_server(debug=True)
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298
|
|
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|
sabiharustam /
voltcycle
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The coding style of this project requires that you add a docstring to this code element. Below, you find an example for methods:
If you would like to know more about docstrings, we recommend to read PEP-257: Docstring Conventions.