def plot_tafel(data=None,
use_all=False,
fig=None,
ax=None,
labels=None,
line=False,
scatter=True,
errs=False,
current_column=3,
potential_column=2,
err_column=3,
xunits='',
yunits='V',
plot_slope=True,
imin=None,
imax=None,
export_name=None,
export_type='png',
fig_kw={},
**plot_kw):
if data is None:
return None
if fig is None and ax is None:
fig, ax = plt.subplots(**fig_kw)
for d in data:
if (not use_all) and d.get_expt_type() != 'lsv':
continue
this_data = d.get_processed_data()
this_data.columns = utils.check_labels(this_data)
this_label = d.get_label()
# x = np.array(df['log(i)'])
# y = np.array(df['eta'])
# print('here')
x = datums.find_col(this_data, 'tafelcurrent', current_column)
y = datums.find_col(this_data, 'overpotential', potential_column)
plotter(ax, x, y, None, this_label, line, scatter, errs, None,
**plot_kw)
if plot_slope:
if imin is None:
imin = min(x)
if imax is None:
imax = max(x)
ax.axvline(x=imin, c='red', lw=0.5)
ax.axvline(x=imax, c='red', lw=0.5)
a, b, r2, itrim, vtrim = datums.tafel_slope(x, y, imin, imax)
d.set_tafel_slope(a)
d.set_exchg_curr(b)
d.set_tafel_rsq(r2)
vfit = datums.tafel_eqn(itrim, b, a)
ax.scatter(itrim, vfit, s=1, c='orange', zorder=200)
if len(data) > 1:
ax.legend(loc='best', edgecolor='k')
ax.set_xlabel('log(current)')
ax.set_ylabel(build_axlabel('Overpotential', yunits))
if export_name:
fig_saver(export_name, export_type)
return fig, ax
def check_errs(errs, df, err_name, err_col):
"""
Check if data set contains error data and return valid error data
Auxilliary function to check if the given data set contains data that can be used to draw error bars. Returns the error values if possible, otherwise returns an array of zeros.
Parameters
___________
errs: bool
Whether data set should be checked for error data. If False, an array of zeros is returned.
df: DataFrame
DataFrame that should contain error data
err_name: str
String of the form '[datatype]_std' (ex: 'current_std'). Used to parse column labels of df
err_col: int or str
Index or label of the column containing data to draw error bars. Used only if automatic column identification fails. Ignored if errs=False
Returns
________
err: numpy array
Array of values which can be used to draw error bars. If valid error values could not be found, an array of zeros of the same length as df is returned
"""
count = df.shape[0]
if errs:
try:
err = datums.find_col(df, err_name, err_col)
except:
err = np.zeros(count)
_log.warning('Unable to use the specified error values')
else:
err = np.zeros(count)
return err
def plot_lsv(data=None,
use_all=False,
fig=None,
ax=None,
labels=None,
line=False,
scatter=True,
errs=False,
current_column=1,
potential_column=2,
err_column=3,
xunits='V',
yunits=r'$mA/cm^2$',
export_name=None,
export_type='png',
fig_kw={},
**plot_kw):
"""
Plot linear sweep voltammetry data
Parameters
___________
data: Datum object(default=None)
Datum object containing LSV data
labels: array-like (default=None)
List of labels to be used in the legend. If unspecified, keys of data are used as labels.
line: bool (default=True)
Whether to draw a line connecting the individual data points
scatter: bool (default=False)
Whether to draw a marker at each data point
errs: bool (default=False)
Whether to include an error bar at each data point
current_column: int or str (default=1)
Index or label of the column containing current data. Used only if automatic column identification fails
potential_column: int or str (default=0)
Index or label of the column containing potential data. Used only if automatic column identification fails
err_column: int or str (default=3)
Index or label of the column containing data to draw error bars. Used only if automatic column identification fails. Ignored if errs=False
xunits: str (default='V')
Units of the x-axis
yunits: str (default=r'$mA/cm^2$')
Units of the y-axis
export_name: str, path object, or file-like (default=None)
If specified, the figure will be saved as an image. Can either be a complete file path to save the image in a specific directory or a file name to save the image in the current directory
export_type: str (default='png')
File type to save the image as. Only used if export_name is specified and export_name does not include the file type
fig_kw: dict
Dict with keywords passed to the plt.subplots function to create the figure
err_kw: dict
Dict with keywords passed to the plt.errorbars function used to draw errorbars
**plot_kw:
all remaining keyword arguments are passed to the plt.plot or plt.scatter function used to draw the graphs
Returns
________
fig: Figure
Figure object containing all plot elements
ax: Axes
Axes object containing the plotted data
"""
if data is None:
return None
if fig is None and ax is None:
fig, ax = plt.subplots(**fig_kw)
for d in data:
if (not use_all) and d.get_expt_type() != 'lsv':
continue
this_data = d.get_processed_data()
this_data.columns = utils.check_labels(this_data)
this_label = d.get_label()
x = datums.find_col(this_data, 'overpotential', potential_column)
y = datums.find_col(this_data, 'current', current_column)
lines, caps, bars = plotter(ax, x, y, None, this_label, line, scatter,
errs, None, **plot_kw)
d.set_line(lines)
d.set_errcaps(caps)
d.set_errbars(bars)
if len(data) > 1:
ax.legend(loc='best', edgecolor='k')
ax.set_xlabel(build_axlabel('Overpotential', xunits))
ax.set_ylabel(build_axlabel('Current Density', yunits))
# ymin = min(y) - 0.01 * min(y)
# ymax = max(y) + 0.01 * max(y)
# ax.set_ylim((ymin, ymax))
if export_name:
fig_saver(export_name, export_type)
return fig, ax
def plot_cp_raw(data=None,
use_all=False,
fig=None,
ax=None,
labels=None,
line=False,
scatter=True,
errs=False,
current_column=2,
potential_column=1,
time_column=0,
err_column=(4, 5),
xunits='s',
yunits=('V', 'mA'),
export_name=None,
export_type='png',
fig_kw={},
err_kw={},
**plot_kw):
"""
Plot raw chronopotentiometry data
Note: it is strongly reccomended to use this function with data from only a single test and the default values of line, scatter, and errs to avoid overplotting.
Parameters
___________
data: Datum object(default=None)
Datum object containing CP data
labels: array-like (default=None)
List of labels to be used in the legend. If unspecified, keys of data are used as labels.
line: bool (default=True)
Whether to draw a line connecting the individual data points
scatter: bool (default=False)
Whether to draw a marker at each data point
errs: bool (default=False)
Whether to include an error bar at each data point
current_column: int or str (default=2)
Index or label of the column containing current data. Used only if automatic column identification fails
potential_column: int or str (default=1)
Index or label of the column containing potential data. Used only if automatic column identification fails
time_column: int or str (default=0)
Index or label of the column containing time data. Used only if automatic column identification fails
err_column: tuple or list of int or str (default=(4,5))
Indices or labels of the columns containing data to draw error bars. Used only if automatic column identification fails. Ignored if errs=False
xunits: str (default=r'$mA/cm^2$')
Units of the x-axis
yunits: tuple or list of str (default=('ma','V'))
Units of the y-axis
export_name: str, path object, or file-like (default=None)
If specified, the figure will be saved as an image. Can either be a complete file path to save the image in a specific directory or a file name to save the image in the current directory
export_type: str (default='png')
File type to save the image as. Only used if export_name is specified and export_name does not include the file type
fig_kw: dict
Dict with keywords passed to the plt.subplots function to create the figure
err_kw: dict
Dict with keywords passed to the plt.errorbars function used to draw errorbars
**plot_kw:
all remaining keyword arguments are passed to the plt.plot or plt.scatter function used to draw the graphs
Returns
________
fig: Figure
Figure object containing all plot elements
ax: Tuple
Tuple of axes objects containing the plotted data
"""
if data is None:
return None
if fig is None and ax is None:
fig, ax = plt.subplots(**fig_kw)
ax2 = ax.twinx()
color1 = 'tab:red'
color2 = 'tab:blue'
for d in data:
if (not use_all) and d.get_expt_type() != 'cp':
continue
this_data = d.get_raw_data()
this_data.columns = utils.check_labels(this_data)
this_label = d.get_label()
x = datums.find_col(this_data, 'time', time_column)
y1 = datums.find_col(this_data, 'potential', potential_column)
y2 = datums.find_col(this_data, 'current', current_column)
yerr1 = check_errs(errs, this_data, 'potential_err', err_column[0])
yerr2 = check_errs(errs, this_data, 'current_err', err_column[1])
plotter(ax,
x,
y1,
yerr1,
this_label,
line,
scatter,
errs,
err_kw,
c=color1,
**plot_kw)
plotter(ax2,
x,
y2,
yerr2,
this_label,
line,
scatter,
errs,
err_kw,
c=color2,
**plot_kw)
if len(data) > 1:
ax.legend(loc='best', edgecolor='k')
# color = 'tab:red'
ax.set_xlabel(build_axlabel('Time', xunits))
ax.set_ylabel(build_axlabel('Potential', yunits[0]))
ax2.set_ylabel(build_axlabel('Current', yunits[1]))
ax.tick_params(axis='y', labelcolor=color1)
ax2.tick_params(axis='y', labelcolor=color2)
if export_name:
fig_saver(export_name, export_type)
return fig, (ax, ax2)