def test_colvariables():
result_a = col_variables('ARBIN')
assert result_a == ('Cycle_Index', 'Data_Point', 'Voltage(V)',
'Current(A)', 'Discharge_Capacity(Ah)',
'Charge_Capacity(Ah)', 'Step_Index')
result_b = col_variables('MACCOR')
assert result_b == ('Cycle_Index', 'Rec', 'Voltage(V)', 'Current(A)',
'Cap(Ah)', 'Cap(Ah)', 'Md')
try:
result_x = col_variables('not a real datatype')
except (AssertionError):
pass
else:
raise Exception('Valid datatype not being checked')
def generate_model(df_clean, filename, peak_thresh, database):
"""Wrapper for the get_model_dfs function. Takes those results
and adds them to the database with three new tables
with the suffices: '-ModPoints', 'ModParams',
and '-descriptors'."""
datatype = df_clean['datatype'].iloc[0]
(cycle_ind_col, data_point_col, volt_col, curr_col, dis_cap_col,
char_cap_col, charge_or_discharge) = col_variables(datatype)
chargeloc_dict = {}
param_df = pd.DataFrame(columns=[
'Cycle', 'Model_Parameters_charge', 'Model_Parameters_discharge'
])
if len(df_clean[cycle_ind_col].unique()) > 1:
length_list = [
len(df_clean[df_clean[cycle_ind_col] == cyc])
for cyc in df_clean[cycle_ind_col].unique() if cyc != 1
]
lenmax = max(length_list)
else:
length_list = 1
lenmax = len(df_clean)
mod_pointsdf = pd.DataFrame()
cycles_no_models = []
for cyc in df_clean[cycle_ind_col].unique():
try:
new_df_mody, model_c_vals, model_d_vals, \
peak_heights_c, peak_heights_d = get_model_dfs(
df_clean, datatype, cyc, lenmax, peak_thresh)
mod_pointsdf = mod_pointsdf.append(new_df_mody)
param_df = param_df.append(
{
'Cycle': cyc,
'Model_Parameters_charge': str(model_c_vals),
'Model_Parameters_discharge': str(model_d_vals),
'charge_peak_heights': str(peak_heights_c),
'discharge_peak_heights': str(peak_heights_d)
},
ignore_index=True)
except Exception as e:
cycles_no_models.append(cyc)
# want this outside of for loop to update the db with the complete df of
# new params
update_database_newtable(mod_pointsdf,
filename.split('.')[0] + '-ModPoints', database)
# this will replace the data table in there if it exists already
update_database_newtable(param_df,
filename.split('.')[0] + 'ModParams', database)
# the below also updates the database with the new descriptors after
# evaluating the spit out dictionary and putting those parameters
# into a nicely formatted datatable.
param_dicts_to_df(filename.split('.')[0] + 'ModParams', database)
if len(cycles_no_models) > 0:
return 'That model has been added to the database.' \
+ 'No model was generated for Cycle(s) ' + str(cycles_no_models)
return 'That model has been added to the database'
def update_slider_max(filename):
if filename is None:
filename = 'ExampleData'
database_sel = init_db
else:
filename = filename
database_sel = database
data, raw_data = pop_with_db(filename, database_sel)
datatype = data['datatype'].iloc[0]
(cycle_ind_col, data_point_col, volt_col, curr_col, dis_cap_col,
char_cap_col, charge_or_discharge) = col_variables(datatype)
return data['Cycle_Index'].max()
def peak_finder(df_run, cd, windowlength, polyorder, datatype, lenmax,
peak_thresh):
"""Determines the index of each peak in a dQdV curve
V_series = Pandas series of voltage data
dQdV_series = Pandas series of differential capacity data
cd = either 'c' for charge and 'd' for discharge.
Output:
i = list of indexes for each found peak"""
(cycle_ind_col, data_point_col, volt_col, curr_col, dis_cap_col,
char_cap_col, charge_or_discharge) = col_variables(datatype)
V_series = df_run[volt_col]
# this makes the peak finding smoothing independent of any smoothing that
# has already occured.
dQdV_series = df_run['Smoothed_dQ/dV']
sigx, sigy = cd_dataframe(V_series, dQdV_series, cd)
# the below is to make sure the window length ends up an odd number - even
# though we are basing it on the length of the df
wl = lenmax / 20
wlint = int(round(wl))
if wlint % 2 == 0:
windowlength_new = wlint + 1
else:
windowlength_new = wlint
if len(sigy) > windowlength_new and windowlength_new > polyorder:
# has to be larger than 69 so that windowlength > 3 - necessary for sav
# golay function
sigy_smooth = scipy.signal.savgol_filter(sigy, windowlength_new,
polyorder)
else:
sigy_smooth = sigy
peak_thresh_ft = float(peak_thresh)
i = peakutils.indexes(sigy_smooth,
thres=peak_thresh_ft,
min_dist=lenmax / 50)
if i is not None and len(i) > 0:
sigx_volts = list(sigx[i])
peak_heights = list(sigy[i])
else:
sigx_volts = []
peak_heights = []
return i, sigx_volts, peak_heights
def pop_with_db(filename, database):
"""Returns dataframes that can be used to populate the app graphs.
Finds the already existing file in the database and returns
the cleaned version (as a dataframe) and the raw version
(also as a dataframe)."""
cleanset_name = get_filename_pref(filename) + 'CleanSet'
rawset_name = get_filename_pref(filename) + 'Raw'
if if_file_exists_in_db(database, filename):
# then the file exists in the database and we can just read it
df_clean = get_file_from_database(cleanset_name, database)
df_raw = get_file_from_database(rawset_name, database)
datatype = df_clean['datatype'].iloc[0]
(cycle_ind_col, data_point_col, volt_col, curr_col, dis_cap_col,
char_cap_col, charge_or_discharge) = col_variables(datatype)
else:
df_clean = None
df_raw = None
peakloc_dict = {}
return df_clean, df_raw
def get_model_dfs(df_clean, datatype, cyc, lenmax, peak_thresh):
"""This is the wrapper for the model generation and fitting for the cycles.
Returns dictionaries for the charge cycle model parameters and discharge
cycle model parameters. These will each have at the least base gaussian
parameter values, with the keys: 'base_amplitude', 'base_center',
'base_fwhm', 'base_height', and 'base_sigma'. The other keys are
dependent on whether any peaks were found in the cycle.
"""
(cycle_ind_col, data_point_col, volt_col, curr_col, dis_cap_col,
char_cap_col, charge_or_discharge) = col_variables(datatype)
clean_charge, clean_discharge = sep_char_dis(
df_clean[df_clean[cycle_ind_col] == cyc], datatype)
windowlength = 9
polyorder = 3
i_charge, volts_i_ch, peak_heights_c = 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 = model_gen(V_series_c, dQdV_series_c, 'c',
i_charge, cyc, peak_thresh)
model_c = 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 = 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 = model_gen(V_series_d, dQdV_series_d, 'd',
i_discharge, cyc, peak_thresh)
model_d = 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
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
return new_df_mody, model_c_vals, model_d_vals,\
peak_heights_c, peak_heights_d
def update_figure2(filename, peak_thresh, n_clicks, show_gauss, desc_to_plot,
cd_to_plot, peaknum_to_plot):
""" This is a function to evaluate the model on a sample plot before updating the database"""
if filename is None:
filename = 'ExampleData'
database_sel = init_db
else:
database_sel = database
data, raw_data = pop_with_db(filename, database_sel)
datatype = data['datatype'].iloc[0]
(cycle_ind_col, data_point_col, volt_col, curr_col, dis_cap_col,
char_cap_col, charge_or_discharge) = col_variables(datatype)
selected_step = round(data[cycle_ind_col].max() / 2) + 1
# select a cycle in the middle of the set
dff_data = data[data[cycle_ind_col] == selected_step]
if len(data[cycle_ind_col].unique()) > 1:
lenmax = max([
len(data[data[cycle_ind_col] == cyc])
for cyc in data[cycle_ind_col].unique() if cyc != 1
])
else:
lenmax = len(data)
dff_raw = raw_data[raw_data[cycle_ind_col] == selected_step]
peak_vals_df = get_file_from_database(
filename.split('.')[0] + '-descriptors', database_sel)
fig = plotly.subplots.make_subplots(
rows=1,
cols=2,
subplot_titles=('Descriptors',
'Example Data for Model Tuning (Cycle ' +
str(int(selected_step)) + ')'),
shared_xaxes=True)
marker = {'color': ['#0074D9']}
if peak_vals_df is not None:
if n_clicks is not None:
# if the user has hit the update-model-button - remake model
new_df_mody, model_c_vals, model_d_vals, peak_heights_c, peak_heights_d = get_model_dfs(
dff_data, datatype, selected_step, lenmax, peak_thresh)
dff_mod = new_df_mody
c_sigma = model_c_vals['base_sigma']
c_center = model_c_vals['base_center']
c_amplitude = model_c_vals['base_amplitude']
c_fwhm = model_c_vals['base_fwhm']
c_height = model_c_vals['base_height']
d_sigma = model_d_vals['base_sigma']
d_center = model_d_vals['base_center']
d_amplitude = model_d_vals['base_amplitude']
d_fwhm = model_d_vals['base_fwhm']
d_height = model_d_vals['base_height']
else:
# if user hasn't pushed the button, populate with original model
# from database
modset_name = filename.split('.')[0] + '-ModPoints'
df_model = get_file_from_database(modset_name, database_sel)
dff_mod = df_model[df_model[cycle_ind_col] == selected_step]
filtpeakvals = peak_vals_df[peak_vals_df['c_cycle_number'] ==
selected_step]
filtpeakvals = filtpeakvals.reset_index(drop=True)
# grab values for the underlying gaussian in the charge:
try:
c_sigma = filtpeakvals['c_gauss_sigma'].iloc[0]
c_center = filtpeakvals['c_gauss_center'].iloc[0]
c_amplitude = filtpeakvals['c_gauss_amplitude'].iloc[0]
c_fwhm = filtpeakvals['c_gauss_fwhm'].iloc[0]
c_height = filtpeakvals['c_gauss_height'].iloc[0]
except BaseException:
# there may not be a model
pass
# grab values for the underlying discharge gaussian:
try:
d_sigma = filtpeakvals['d_gauss_sigma'].iloc[0]
d_center = filtpeakvals['d_gauss_center'].iloc[0]
d_amplitude = filtpeakvals['d_gauss_amplitude'].iloc[0]
d_fwhm = filtpeakvals['d_gauss_fwhm'].iloc[0]
d_height = filtpeakvals['d_gauss_height'].iloc[0]
except BaseException:
pass
fig.append_trace(
{
'x': dff_data[volt_col],
'y': dff_data['Smoothed_dQ/dV'],
'type': 'scatter',
'marker': marker,
'name': 'Smoothed Data'
}, 1, 2)
if len(peaknum_to_plot) > 0:
for value in peaknum_to_plot:
try:
fig.append_trace(
{
'x':
peak_vals_df['c_cycle_number'],
'y':
peak_vals_df[str(''.join(desc_to_plot)) +
str(''.join(cd_to_plot)) + value],
'type':
'scatter',
'marker':
marker,
'name':
value
}, 1, 1)
except KeyError as e:
None
fig.append_trace(
{
'x': dff_mod[volt_col],
'y': dff_mod['Model'],
'type': 'scatter',
'name': 'Model of One Cycle'
}, 1, 2)
# add if checkbox is selected to show polynomial baseline
if 'show' in show_gauss:
try:
fig.append_trace(
{
'x':
dff_mod[volt_col],
'y': ((c_amplitude /
(c_sigma * ((2 * 3.14159)**0.5))) * np.exp(
(-(dff_mod[volt_col] - c_center)**2) /
(2 * c_sigma**2))),
'type':
'scatter',
'name':
'Charge Gaussian Baseline' # plot the poly
},
1,
2)
except BaseException:
pass
# add the plot of the discharge guassian:
try:
fig.append_trace(
{
'x':
dff_mod[volt_col],
'y':
-((d_amplitude /
(d_sigma * ((2 * 3.14159)**0.5))) * np.exp(
(-(dff_mod[volt_col] - d_center)**2) /
(2 * d_sigma**2))),
'type':
'scatter',
'name':
'Discharge Gaussian Baseline' # plot the poly
},
1,
2)
except BaseException:
pass
fig['layout']['showlegend'] = True
fig['layout']['xaxis1'].update(title='Cycle Number')
fig['layout']['xaxis2'].update(title='Voltage (V)')
fig['layout']['yaxis1'].update(title='Descriptor Value')
fig['layout']['yaxis2'].update(title='dQ/dV',
range=[
dff_data['Smoothed_dQ/dV'].min(),
dff_data['Smoothed_dQ/dV'].max()
])
fig['layout']['height'] = 600
fig['layout']['margin'] = {'l': 40, 'r': 10, 't': 60, 'b': 200}
return fig
def update_figure1(selected_step, filename, showmodel):
fig = plotly.subplots.make_subplots(rows=1,
cols=2,
subplot_titles=('Raw Cycle',
'Smoothed Cycle'),
shared_xaxes=True)
marker = {'color': ['#0074D9']}
if filename is None or filename == 'options':
filename = 'ExampleData'
database_sel = init_db
else:
database_sel = database
data, raw_data = pop_with_db(filename, database_sel)
datatype = data['datatype'].iloc[0]
(cycle_ind_col, data_point_col, volt_col, curr_col, dis_cap_col,
char_cap_col, charge_or_discharge) = col_variables(datatype)
modset_name = filename.split('.')[0] + '-ModPoints'
df_model = get_file_from_database(modset_name, database_sel)
if df_model is not None:
filt_mod = df_model[df_model[cycle_ind_col] == selected_step]
if data is not None:
filtered_data = data[data[cycle_ind_col] == selected_step]
if raw_data is not None:
raw_filtered_data = raw_data[raw_data[cycle_ind_col] == selected_step]
for i in filtered_data[cycle_ind_col].unique():
if data is not None:
dff = filtered_data[filtered_data[cycle_ind_col] == i]
if raw_data is not None:
dff_raw = raw_filtered_data[raw_filtered_data[cycle_ind_col] == i]
if df_model is not None:
dff_mod = filt_mod[filt_mod[cycle_ind_col] == i]
if data is not None:
fig.append_trace(
{
'x': dff[volt_col],
'y': dff['Smoothed_dQ/dV'],
'type': 'scatter',
'marker': marker,
'name': 'Smoothed Data'
}, 1, 2)
if raw_data is not None:
fig.append_trace(
{
'x': dff_raw[volt_col],
'y': dff_raw['dQ/dV'],
'type': 'scatter',
'marker': marker,
'name': 'Raw Data'
}, 1, 1)
if df_model is not None and showmodel == 'showmodel':
fig.append_trace(
{
'x': dff_mod[volt_col],
'y': dff_mod['Model'],
'type': 'scatter',
'name': 'Model'
}, 1, 2)
fig['layout']['showlegend'] = False
fig['layout']['xaxis1'].update(title='Voltage (V)')
fig['layout']['xaxis2'].update(title='Voltage (V)')
fig['layout']['yaxis1'].update(title='dQ/dV')
fig['layout']['yaxis2'].update(title='dQ/dV')
fig['layout']['height'] = 600
fig['layout']['margin'] = {'l': 40, 'r': 10, 't': 60, 'b': 200}
return fig
