def change_cycle_def(
search_word
): # Rewrites cycle column according to first instance of discharge or charge
import add_to_rawdata
database = user_setup.database
all_files = support.find_files(search_word,
database) # Finds and returns files as list
support.print_files_nr(all_files) # prints files with nr
response = support.input_cool(
'yellow',
"Which file do you want to change the cycle definition? Write nr: "
) # Response from user
file = all_files[int(response)]
df_change = access_data.access_cell_data(file.stem)
df_change = add_to_rawdata.add_cycle_incr(df_change)
response2 = support.input_cool(
'yellow',
"Overwrite existing file? (no/any): ") # Response from user
if response2.lower() == 'no':
new_name = support.input_cool('yellow', 'Write new cell name: ')
df_change.to_pickle(database.as_posix() + "/" + new_name)
support.print_cool('green', new_name + ' saved to database.')
else:
df_change.to_pickle(database.as_posix() + "/" + file.stem)
support.print_cool('green', 'Updated pickle saved to database.')
return
def check_char_mass (found_mass):
use_mass = None # value to be returned
if found_mass:
support.print_cool('blue', 'Found this/these characteristic mass (mg): ', found_mass)
response = support.input_cool('yellow', '\nUse this? (enter/no): \n(If multiple masses, will use first) ')
if response == 'no':
use_mass = support.input_cool('yellow', 'Please write desired mass (mg): ')
elif type(found_mass)==list:
use_mass = found_mass[0]
else:
use_mass = found_mass
else:
use_mass = support.input_cool('yellow', 'No characteristic mass found. Please write desired mass (mg): ')
return use_mass
def lanhe(data_url, cell_key, database):
# Imports raw data:
df = id.import_lanhe(data_url)
# Clean up raw data:
df = fix.lanhe(df)
# Tries to calculate characteristic mass and verifies with user / input from user:
try:
last_cap_incr = df['cap_incr'].iloc[-1]
last_cap_incr_spec = df['cap_incr_spec'].iloc[-1]
char_mass = last_cap_incr / last_cap_incr_spec * 1000
char_mass = fix_rawdata.check_char_mass(char_mass)
except:
char_mass = support.input_cool(
'yellow', 'No characteristic mass found. Please input mass: ')
#Add additional variables to pandas
# First, need incremental cycle, discharge_incr and charge_incr (have cap_incr) to use AddSpecificCapacity functions:
df = add.incremental_cycle_charge_discharge(df)
# Then, can add specific incremental capacity (not really necessary, is in fact exported from Lanhe) and cyclebased:
df = add.specific_capacity_incremental(df, char_mass)
df = add.specific_capacity_cycle(df, char_mass)
df = add.diffcap(df)
df = add.cell_info(df, char_mass, cell_key)
df.to_pickle((database + "/" + cell_key)) # Store data as a Pickle
return df
def auto_import(search_word, **kwargs):
try:
if kwargs['testing'] == True:
raw_data = Path(r'..\PyBat2-master\testing')
database = Path(r'..\PyBat2-master\testing\database')
else:
raw_data = user_setup.raw_data
except:
raw_data = user_setup.raw_data
database = user_setup.database
all_files = support.search_file(
search_word, raw_data
) # Search for cells with a specific name in the raw data folder.
response = support.input_cool(
'yellow',
'Do you want to convert these files? yes/no: \n(.mpr and .mgr files will be skipped) '
)
if response.lower() == "yes":
for line in all_files: #Loop through all input files.
file_name = line.stem #Saves the file name
file_path = line.as_posix() #Saves the file path
if line.suffix == '.mpr' or line.suffix == '.mgr':
continue
support.print_cool(
'blue', '-' * 80 + '\nConverting: ' + file_name + line.suffix)
response = support.input_cool(
'yellow',
'Do you want to change cell key (what the cell will be saved as)? (yes/any button): '
)
if response == 'yes':
file_name = support.input_cool('yellow', 'Write cell key: ')
else:
None
identifier = identify_file(line.suffix, file_path)
if identifier == 'biologic':
import_cell.biologic(file_path, file_name, database.as_posix())
support.print_cool(
'green',
'Converted from Biologic: ' + file_name + line.suffix)
elif identifier == 'vmp3':
import_cell.vmp3(file_path, file_name, database.as_posix())
support.print_cool(
'green', 'Converted from VMP3: ' + file_name + line.suffix)
elif identifier == 'impedance':
import_cell.vmp3(file_path, file_name, database.as_posix())
support.print_cool(
'green', 'Converted from VMP3: ' + file_name + line.suffix)
elif identifier == 'lanhe':
import_cell.lanhe(file_path, file_name, database.as_posix())
support.print_cool(
'green',
'Converted from Lanhe: ' + file_name + line.suffix)
elif identifier == 'maccor':
import_cell.maccor(file_path, file_name, database.as_posix())
support.print_cool(
'green',
'Converted from Maccor: ' + file_name + line.suffix)
else:
support.print_cool('red', 'File not recognized: ' + file_path)
elif response.lower() == 'no':
support.print_cool('green', 'No files converted')
else:
support.print_cool('red', 'Input invalid')
return
def batch_plot(search_word, **kwargs):
import plot_support
plots_folder = user_setup.plots
database = user_setup.database
colors_qual = user_setup.colors_qual
# Identifying cells to plot
cell_names = [] # Initiates list for cells that will be plotted.
cell_paths = [] # Initiates list for paths to cells to be plotted.
finished = False # Determines if user is finished with input
while finished == False:
files = support.find_files(search_word,
database) # Finds and returns files as list
if len(files) == 1:
support.print_cool('green', "Found only one file, using this.")
cell_names.append(
files[0].stem
) # Saves the cell name (.stem returns last path-element)
cell_paths.append(str(files[0])) # Saves the full path to the cell
finished = True
continue
support.print_files_nr(files) # prints files with nr
response = support.input_cool(
'yellow',
'Which of these cells do you want to plot? Write corresponding numbers, separated with "+" (Ex: 0+2+3): '
)
c_response = response.split(
'+') # Splits string by plus sign and stores new strings in list
for i in range(0, len(c_response)): # Loop through all cells to plot.
cell_names.append(
(files[int(c_response[i])]).stem
) # Saves the cell name (.stem returns last path-element)
cell_paths.append(str(files[int(
c_response[i])])) # Saves the full path to the cell
response2 = support.input_cool('yellow',
'Search for more cells? (yes/any): ')
if response2 == 'yes':
search_word = support.input_cool('yellow',
'Write new search word: ')
else:
finished = True
for nr in range(0, len(cell_names)):
list = access_data.columns(cell_paths[nr], [
'cycle_nr', 'discharge_spec', 'charge_spec', 'cap_incr_spec',
'potential', 'cycle'
])
df = access_data.access_cell_as_string(cell_names[nr])
cycles, discharge, charge, capacity_incr, potential, cycle_incr = list[
0][0], list[1][0], list[2][0], list[3][0], list[4][0], list[5][0]
CE = [float(ai) / bi * 100 for ai, bi in zip(discharge, charge)
] # Obtaining Coulombic efficiencies
fontsize = 10
fig, axs = plt.subplots(3, 2, figsize=(7, 7))
""" Capacity vs cycle nr """
axs[0, 0].scatter(cycles, discharge, color=colors_qual[0])
axs[0, 0].scatter(cycles, charge, marker='D', color=colors_qual[1])
axs[0, 0].set_xlabel('Cycle number', size=fontsize)
axs[0, 0].set_ylabel('Capacity (mAh/g)', size=fontsize)
axs[0,
0].tick_params(axis='both', which='major', labelsize=fontsize
) # Setting ticks size equally enlarged to fontsize
axs[0, 0].tick_params(direction='in') # Ticks pointing inwards.
axs[0, 0].legend(['Discharge', 'Charge'], prop={'size': fontsize})
#plt.savefig((str('C:\\Users\hennika\OneDrive - NTNU\PhD\Results\Cycling\Plots\MP1H') + '\\' + 'MP1H_D1_16_100-250mA_capacity-cycle-nr' + '.png'),format='png', dpi=1000) # > 300 DPI is recommended by NTNU in master theses.
""" Coulombic efficiency """
axs[0, 1].scatter(cycles, CE, color=colors_qual[0])
axs[0, 1].set_xlabel('Cycle number', size=fontsize)
axs[0, 1].set_ylabel('Coulombic efficiency (%)', size=fontsize)
axs[0,
1].tick_params(axis='both', which='major', labelsize=fontsize
) # Setting ticks size equally enlarged to fontsize
axs[0, 1].tick_params(direction='in') # Ticks pointing inwards.
axs[0, 1].legend(['C.E.'], prop={'size': fontsize})
# plt.savefig((str('C:\\Users\hennika\OneDrive - NTNU\PhD\Results\Cycling\Plots\MP1H') + '\\' + 'MP1H_D1_16_100-250mA_capacity-cycle-nr' + '.png'),format='png', dpi=1000) # > 300 DPI is recommended by NTNU in master theses.
""" Voltage profiles (all) """
color_list = plot_support.get_colors(cycle_incr, color_scheme='Blues')
last_cycle = df['cycle'].as_matrix().astype(
int
)[-1] # Converts cycle column to int, and get last element (last cycle nr).
cycles = range(0, last_cycle,
1) # Makes variable with cycles to plot (all)
for i in range(0, last_cycle, 1): # Iterates through all cycles
df_cycle_x = df[df['cycle'].astype(float) ==
cycles[i]] # Make new data frame for given cycle
axs[1, 0].scatter(df_cycle_x['cap_incr_spec'].astype(float),
df_cycle_x['potential'].astype(float),
s=1,
c=color_list[i])
axs[1, 0].set_xlabel('Capacity (mAh/g)', size=fontsize)
axs[1, 0].set_ylabel('Voltage', size=fontsize)
axs[1,
0].tick_params(axis='both', which='major', labelsize=fontsize
) # Setting ticks size equally enlarged to fontsize
axs[1, 0].tick_params(direction='in') # Ticks pointing inwards.
axs[1, 0].legend(['All cycles'], prop={'size': fontsize})
#axs[1,0].legend(['Voltage profile'], prop={'size': fontsize})
# plt.savefig((str('C:\\Users\hennika\OneDrive - NTNU\PhD\Results\Cycling\Plots\MP1H') + '\\' + 'MP1H_D1_16_100-250mA_capacity-cycle-nr' + '.png'),format='png', dpi=1000) # > 300 DPI is recommended by NTNU in master theses.
""" Voltage profiles (selected) """
cycles = [1, 2, 10, 50, 100] # Makes variable with cycles to plot
color_list = plot_support.get_colors(cycle_incr,
cycles=cycles,
color_scheme='Qualitative')
for i in range(0, len(cycles), 1): # Iterates through all cycles
df_cycle_x = df[df['cycle'].astype(float) ==
cycles[i]] # Make new data frame for given cycle
axs[1, 1].scatter(df_cycle_x['cap_incr_spec'].astype(float),
df_cycle_x['potential'].astype(float),
s=1,
c=color_list[i],
label='%s' % cycles[i])
axs[1, 1].set_xlabel('Capacity (mAh/g)', size=fontsize)
axs[1, 1].set_ylabel('Voltage', size=fontsize)
axs[1,
1].tick_params(axis='both', which='major', labelsize=fontsize
) # Setting ticks size equally enlarged to fontsize
axs[1, 1].tick_params(direction='in') # Ticks pointing inwards.
axs[1, 1].legend(prop={'size': fontsize})
# plt.savefig((str('C:\\Users\hennika\OneDrive - NTNU\PhD\Results\Cycling\Plots\MP1H') + '\\' + 'MP1H_D1_16_100-250mA_capacity-cycle-nr' + '.png'),format='png', dpi=1000) # > 300 DPI is recommended by NTNU in master theses.
""" Differential capacity (selected cycles) """
cycles = [1, 2, 10, 50, 100] # Makes variable with cycles to plot
color_list = plot_support.get_colors(cycle_incr,
cycles=cycles,
color_scheme='Qualitative')
for i in range(0, len(cycles), 1): # Iterates through all cycles
df_cycle_x = df[df['cycle'].astype(float) ==
cycles[i]] # Make new data frame for given cycle
axs[2, 0].scatter(df_cycle_x['potential_diff_cap'].astype(float),
df_cycle_x['diff_cap'].astype(float),
s=1,
c=color_list[i],
label='%s' % cycles[i])
axs[2, 0].set_xlabel('Potential (V)', size=fontsize)
axs[2, 0].set_ylabel('Diff. capacity (mAh/g/V)', size=fontsize)
axs[2,
0].tick_params(axis='both', which='major', labelsize=fontsize
) # Setting ticks size equally enlarged to fontsize
axs[2, 0].tick_params(direction='in') # Ticks pointing inwards.
axs[2, 0].legend(prop={'size': fontsize})
# plt.savefig((str('C:\\Users\hennika\OneDrive - NTNU\PhD\Results\Cycling\Plots\MP1H') + '\\' + 'MP1H_D1_16_100-250mA_capacity-cycle-nr' + '.png'),format='png', dpi=1000) # > 300 DPI is recommended by NTNU in master theses.
""" Add more here """
axs[2, 1].text(0.35, 0.5, 'Awesome!')
fig.tight_layout() # Makes sure everything is within figure
""" Saving figures """
from pathlib import Path
Path(str(plots_folder) + '\\' + cell_names[nr]).mkdir(
parents=True, exist_ok=True
) # Make new folder for cell if it doesn't exist already
resolution = 500
extent = axs[0, 0].get_window_extent().transformed(
fig.dpi_scale_trans.inverted()) # Collecting subplot to save
fig.savefig(str(plots_folder) + '\\' + cell_names[nr] + '\\' +
'capacity_vs_cycles.png',
dpi=resolution,
bbox_inches=extent.expanded(1.45, 1.5)) # Saving subplot
extent = axs[1, 1].get_window_extent().transformed(
fig.dpi_scale_trans.inverted()) # Collecting subplot to save
fig.savefig(str(plots_folder) + '\\' + cell_names[nr] + '\\' +
'Voltage_profiles_cycle_1-2-10-50-100.png',
dpi=resolution,
bbox_inches=extent.expanded(1.4, 1.42)) # Saving subplot
plt.show()
return
def auto_plot(search_word, **kwargs):
import plot_support
try:
if kwargs['testing'] == True:
database = Path(r'..\PyBat2-master\testing\database')
plots_folder = Path(r'..\PyBat2-master\testing\plots')
else:
database = Path(r'..\PyBat2-master\testing')
plots_folder = user_setup.plots
except:
plots_folder = user_setup.plots
database = user_setup.database
# Identifying cells to plot
cell_names = [] # Initiates list for cells that will be plotted.
cell_paths = [] # Initiates list for paths to cells to be plotted.
finished = False # Determines if user is finished with input
while finished == False:
files = support.find_files(search_word,
database) # Finds and returns files as list
support.print_files_nr(files) # prints files with nr
response = support.input_cool(
'yellow',
'Which of these cells do you want to plot? Write corresponding numbers, separated with "+" (Ex: 0+2+3): '
)
c_response = response.split(
'+') # Splits string by plus sign and stores new strings in list
for i in range(0, len(c_response)): # Loop through all cells to plot.
cell_names.append(
(files[int(c_response[i])]).stem
) # Saves the cell name (.stem returns last path-element)
cell_paths.append(str(files[int(
c_response[i])])) # Saves the full path to the cell
response2 = support.input_cool('yellow',
'Search for more cells? (yes/any): ')
if response2 == 'yes':
search_word = support.input_cool('yellow',
'Write new search word: ')
else:
finished = True
try: # Checks legend and use cellnames if not found.
legend_use = kwargs['legend']
except:
legend_use = cell_names
x1, y1, xlabel, ylabel, xlim, ylim, xticks, yticks, type, markersize, legend_list, legend_loc, legend_color_list, custom_code, custom_code_first, save_path_png, save_path_tiff = plot_support.set_plot_specs(
autolegend=legend_use, **kwargs) # Sets specifications for plot
pickle_name, df, cycles, color, color_list, legend_color_list, marker, markerfill = plot_support.set_pickle_specs(
legend_color_list, pickle1=cell_paths[0],
**kwargs) # Sets specifications for first pickle
plot_support.AddPickleToPlot(
df, cycles, x1, y1, color_list, type, marker, markerfill, markersize,
custom_code_first) # Adds this pickle with specifications to plot
for nr in range(2, len(cell_names) + 1):
# try:
next_pickle_response = cell_paths[
nr -
1] # Looks up index in files given by the next cell in the response
next_pickle_response_nr = 'pickle' + str(nr)
next_pickle_name, next_x, next_y, next_cycles, next_color, next_color_scheme, next_marker, next_markerfill = plot_support.set_next_pickle(
nr, override=next_pickle_response, **kwargs)
pickle_name, df, cycles, color, color_list, legend_color_list, marker, markerfill = plot_support.set_pickle_specs(
legend_color_list,
pickle1=next_pickle_name,
cycles1=next_cycles,
x1=next_x,
y1=next_y,
color1=next_color,
color_scheme1=next_color_scheme,
marker1=next_marker,
markerfill1=next_markerfill)
plot_support.AddPickleToPlot(df, cycles, next_x, next_y, color_list,
type, marker, markerfill, markersize)
# except:
# continue # Script moves to next iteration, checking for yet another pickle. (should not be needed here)
plot_support.plot_plot(
x1, y1, xlabel, ylabel, xlim, ylim, xticks, yticks, legend_list,
legend_color_list, legend_loc, custom_code, save_path_png,
save_path_tiff) # Add labels and legend, and shows plot
return