def find_col(data, col_type, label=None):
"""
Finds column containing the desired measurement
Parameters
___________
data: DataFrame
DataFrame containing the full data set
col_type: one of {'current', 'potential', 'time', 'current_err', 'potential_err', 'overpotential', 'tafelcurrent', 'realcurr', 'imagcurr'}
Type of data being searched for
label: str or int (default=None)
Label or index of the desired column if the column label cannot be automatically parsed
Returns
________
col: numpy array
Array of the desired measurement values
"""
default_label = col_default_labels[col_type]
default_id = col_default_ids[col_type]
newdf = data.copy()
newdf.columns = utils.check_labels(newdf)
if default_label in newdf.columns:
col = newdf[default_label]
elif label:
if utils.check_str(label):
col = newdf[label]
else:
col = newdf.iloc[:, label]
else:
col = newdf.iloc[:, default_id]
col = np.asarray(col)
return col
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 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)