def imp_one_cycle(testdf, cd, cyc_loop, battery, datatype, windowlength,
polyorder, lenmax):
"""imports and fits a single charge discharge cycle of a battery
file_val = directory containing current cycle
cd = either 'c' for charge or 'd' for discharge
cyc_loop = cycle number
battery = battery name
output: a dictionary of descriptors for a single battery"""
# make sure this is an Excel spreadsheet by checking the file extension
# assert file_val.split('.')[-1] == ('xlsx' or 'xls')
# reads excel file into pandas
# testdf = pd.read_excel(file_val)
# extracts charge and discharge from the dataset
(cycle_ind_col, data_point_col, volt_col, curr_col, dis_cap_col,
char_cap_col, charge_or_discharge) = ccf.col_variables(datatype)
charge, discharge = ccf.sep_char_dis(testdf, datatype)
# determines if the charge, discharge indicator was inputted correctly
# assigns daframe for fitting accordingly
if cd == 'c':
df_run = charge
elif cd == 'd':
df_run = discharge
else:
raise TypeError(
"Cycle type must be either 'c' for charge or 'd' for discharge."
)
print('Generating descriptors for cycle number: ' + str(cyc_loop) + cd)
# determines if a cycle should be passed into the descriptor
# fitting function
if (len(charge[volt_col].index) >= 10) and (len(
discharge[volt_col].index) >= 10):
# generates a dictionary of descriptors
c = fitters.descriptor_func(df_run, cd, cyc_loop, battery,
windowlength, polyorder, datatype,
lenmax)
# df_run[volt_col], df_run['Smoothed_dQ/dV']
#c is the dictionary of descriptors here
# eliminates cycle number and notifies user of cycle removal
else:
notice = 'Cycle ' + str(cyc_loop) + ' in battery ' + battery + \
' had fewer than 10 datapoints and was removed from the dataset.'
print(notice)
c = 'throw'
# print('here is the c parameter in the imp_one_cycle: ')
# print(c)
return c
def imp_one_cycle(file_val, cd, cyc_loop, battery):
"""imports and fits a single charge discharge cycle of a battery
file_val = directory containing current cycle
cd = either 'c' for charge or 'd' for discharge
cyc_loop = cycle number
battery = battery name
output: a dictionary of descriptors for a single battery"""
# make sure this is an Excel spreadsheet by checking the file extension
assert file_val.split('.')[-1] == ('xlsx' or 'xls')
# reads excel file into pandas
testdf = pd.read_excel(file_val)
# extracts charge and discharge from the dataset
charge, discharge = ccf.sep_char_dis(testdf)
# determines if the charge, discharge indicator was inputted correctly
# assigns daframe for fitting accordingly
if cd == 'c':
df_run = charge
elif cd == 'd':
df_run = discharge
else:
raise TypeError(
"Cycle type must be either 'c' for charge or 'd' for discharge.")
# determines if a cycle should be passed into the descriptor
# fitting function
if (len(charge['Voltage(V)'].index) >= 10) and (len(discharge['Voltage(V)'].index) >= 10):
# generates a dictionary of descriptors
c = fitters.descriptor_func(
df_run['Voltage(V)'], df_run['Smoothed_dQ/dV'], cd, cyc_loop, battery)
# eliminates cycle number and notifies user of cycle removal
else:
notice = 'Cycle ' + str(cyc_loop) + ' in battery ' + battery + \
' had fewer than 10 datapoints and was removed from the dataset.'
print(notice)
c = 'throw'
return c
def update_figure2(selected_step, contents, filename, date,
selected_row_indices):
data = parse_contents(contents, filename, date)
(charge, discharge) = ccf.sep_char_dis(data)
filtered_data = discharge[discharge['Cycle_Index'] == selected_step]
for i in filtered_data['Cycle_Index'].unique():
dff = filtered_data[filtered_data['Cycle_Index'] == i]
fig = plotly.tools.make_subplots(
rows=2,
cols=1,
subplot_titles=('Smoothed dQ/dV Discharge Cycle',
'Cleaned dQ/dV Discharge Cycle'),
shared_xaxes=True)
marker = {'color': ['#0074D9'] * len(dff)}
for i in (selected_row_indices or []):
marker['color'][i] = '#FF851B'
fig.append_trace(
{
'x': dff['Voltage(V)'],
'y': dff['Smoothed_dQ/dV'],
'type': 'scatter',
'marker': marker,
'name': 'Smoothed Data'
}, 1, 1)
fig.append_trace(
{
'x': dff['Voltage(V)'],
'y': dff['dQ/dV'],
'type': 'scatter',
'marker': marker,
'name': 'Raw Data'
}, 2, 1)
fig['layout']['showlegend'] = False
fig['layout']['height'] = 800
fig['layout']['margin'] = {'l': 40, 'r': 10, 't': 60, 'b': 200}
fig['layout']['yaxis2']
return fig
def update_slider_value(contents, filename, date):
data = parse_contents(contents, filename, date)
charge, discharge = ccf.sep_char_dis(data)
return charge['Cycle_Index'].max()
def update_table2(contents, filename, date):
data = parse_contents(contents, filename, date)
charge, discharge = ccf.sep_char_dis(data)
return discharge.to_dict('records')
import dash_table_experiments as dt
import pandas as pd
import io
import json
import plotly
import base64
##########################################
#Load Data
##########################################
#eventually add everything in folder and create a dropdown that loads that data into data
#for now just use some data we have
data = pd.read_excel('data/Clean_Whole_Sets/CS2_33_12_16_10CleanSet.xlsx')
charge, discharge = ccf.sep_char_dis(data)
#df_dqdv = ccf.calc_dv_dqdv('data/CS2_33/CS2_33_10_04_10.xlsx')
#df_dqdv = ccf.calc_dv_dqdv(data)
#charge, discharge = ccf.sep_char_dis(data)
##########################################
#App Layout
##########################################
app = dash.Dash() #initialize dash
Introduction = dcc.Markdown('''
# ChaChi
## Interface for visualizing battery cycle data
#'''), #Add some Markdown
def get_model_dfs_for_jupyter(df_clean, datatype, cyc, lenmax, peak_thresh):
# this function is analagous to the get_model_dfs function in the app.py file
(cycle_ind_col, data_point_col, volt_col, curr_col, dis_cap_col,
char_cap_col, charge_or_discharge) = ccf.col_variables(datatype)
clean_charge, clean_discharge = ccf.sep_char_dis(
df_clean[df_clean[cycle_ind_col] == cyc], datatype)
windowlength = 75
polyorder = 3
# speed this up by moving the initial peak finder out of this, and just have those two things passed to it
i_charge, volts_i_ch, peak_heights_c = descriptors.fitters.peak_finder(
clean_charge, 'c', windowlength, polyorder, datatype, lenmax,
peak_thresh)
V_series_c = clean_charge[volt_col]
dQdV_series_c = clean_charge['Smoothed_dQ/dV']
par_c, mod_c, indices_c = descriptors.fitters.model_gen(
V_series_c, dQdV_series_c, 'c', i_charge, cyc, peak_thresh)
model_c = descriptors.fitters.model_eval(V_series_c, dQdV_series_c, 'c',
par_c, mod_c)
if model_c is not None:
mod_y_c = mod_c.eval(params=model_c.params, x=V_series_c)
myseries_c = pd.Series(mod_y_c)
myseries_c = myseries_c.rename('Model')
model_c_vals = model_c.values
new_df_mody_c = pd.concat([
myseries_c, V_series_c, dQdV_series_c, clean_charge[cycle_ind_col]
],
axis=1)
else:
mod_y_c = None
new_df_mody_c = None
model_c_vals = None
# now the discharge:
i_discharge, volts_i_dc, peak_heights_d = descriptors.fitters.peak_finder(
clean_discharge, 'd', windowlength, polyorder, datatype, lenmax,
peak_thresh)
V_series_d = clean_discharge[volt_col]
dQdV_series_d = clean_discharge['Smoothed_dQ/dV']
par_d, mod_d, indices_d = descriptors.fitters.model_gen(
V_series_d, dQdV_series_d, 'd', i_discharge, cyc, peak_thresh)
model_d = descriptors.fitters.model_eval(V_series_d, dQdV_series_d, 'd',
par_d, mod_d)
if model_d is not None:
mod_y_d = mod_d.eval(params=model_d.params, x=V_series_d)
myseries_d = pd.Series(mod_y_d)
myseries_d = myseries_d.rename('Model')
new_df_mody_d = pd.concat([
-myseries_d, V_series_d, dQdV_series_d,
clean_discharge[cycle_ind_col]
],
axis=1)
model_d_vals = model_d.values
else:
mod_y_d = None
new_df_mody_d = None
model_d_vals = None
# save the model parameters in the database with the data
if new_df_mody_c is not None or new_df_mody_d is not None:
new_df_mody = pd.concat([new_df_mody_c, new_df_mody_d], axis=0)
else:
new_df_mody = None
# combine the charge and discharge
# update model_c_vals and model_d_vals with peak heights
return new_df_mody, model_c_vals, model_d_vals, peak_heights_c, peak_heights_d