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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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View Code Duplication |
def data_analysis(df): |
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results_dict = {} |
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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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half_E = min(Et,Eb) + core.half_wave_potential(x,y) |
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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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if half_E>0 and 'Yes' in results_dict.values(): |
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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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@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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@app.callback(Output('files_dropdown', 'options'), |
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[Input('upload','fileNames')]) |
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def dropdown_files(fileNames): |
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mypath='./uploads/' |
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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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@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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def update_table1(value): |
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df = parse_contents(value) |
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#print(df.head()) |
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#final_dict = data_analysis(df) |
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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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df1=pd.DataFrame.from_records([final_dict]) |
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return df1.to_dict('records') |
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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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def update_figure(value): |
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df = parse_contents(value) |
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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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trace1 = go.Scatter( |
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x = df['Potential'], |
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y = df['Current'], |
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marker={ |
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'size': 15, |
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'opacity': 0.5, |
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'color' : '#F00000' |
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}) |
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trace2 = go.Scatter( |
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x = x_1, |
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y = ybase_1, |
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mode = 'lines', |
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line = dict( |
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color = ('rgb(0, 0, 256)'), |
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width = 3, |
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dash = 'dash') |
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) |
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trace3 = go.Scatter( |
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x = x_2, |
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y = ybase_2, |
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mode = 'lines', |
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line = dict( |
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color = ('rgb(0, 0, 256)'), |
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width = 3, |
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dash = 'dash') |
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) |
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trace4 = go.Scatter( |
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x = np.array(x_1[peak_i[1]]), |
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y = np.array(y_1[peak_i[1]]), |
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mode = 'markers', |
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marker={ |
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'size': 35, |
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'opacity': 0.5, |
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'color' : '#000080' |
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}) |
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trace5 = go.Scatter( |
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x = np.array(x_2[peak_i[0]]), |
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y = np.array(y_2[peak_i[0]]), |
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mode = 'markers', |
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marker={ |
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'size': 35, |
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'opacity': 0.5, |
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'color' : '#000080' |
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}) |
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data = [trace1, trace2, trace3, trace4, trace5] |
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return { |
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'data': data, |
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274
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#'layout' : {'Dash'} |
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275
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'layout': go.Layout( |
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276
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xaxis={'title': 'Voltage (V)'}, |
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yaxis={'title': 'Current (A)'}, |
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margin={'l': 40, 'b': 40, 't': 10, 'r': 10}, |
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279
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# #legend={'x': 0, 'y': 1}, |
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280
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showlegend = False, |
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281
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hovermode='closest', |
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282
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) |
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283
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} |
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284
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285
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286
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# return { |
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# 'data': [ |
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# {'x': [x1[peak_index[1]]], 'y': [x1[peak_index[1]]], 'type': 'point'}, |
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# #{'x': [1, 2, 3], 'y': [2, 4, 5], 'type': 'bar', 'name': u'Montréal'}, |
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# ], |
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# } |
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293
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294
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if __name__ == '__main__': |
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app.run_server(debug=True) |
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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.