def iso_combo_plot(bet_results, mask_results, save_file=True):
"""Creates an image displaying the relative pressure range with minimum
error and the BET isotherm on the same plot. The point where n/nm = 1 is
is the point where the BET monolayer loading occurs.
Parameters
----------
bet_results : named tuple
The bet_results.iso_df element is used to
create a plot of isotherm data.
mask_results : named tuple
The mask_results.mask element is used to mask the BET results so that
only valid results are displayed.
save_file : boolean
When save_file = True a png of the figure is created in the
working directory.
Returns
-------
"""
mask = mask_results.mask
if mask.all():
logging.warning(
"No valid relative pressure ranges. BET isotherm combo plot not created."
)
return
df = bet_results.iso_df
nm = np.ma.array(bet_results.nm, mask=mask)
c = np.ma.array(bet_results.c, mask=mask)
err = np.ma.array(bet_results.err, mask=mask)
err_max, err_max_idx, err_min, err_min_idx = util.max_min(err)
c_min_err = c[err_min_idx[0], err_min_idx[1]]
nnm_min = nm[err_min_idx[0], err_min_idx[1]]
ppo = np.arange(0, 0.9001, 0.001)
synth_min = 1 / (1 - ppo) - 1 / (1 + (c_min_err - 1) * ppo)
expnnm_min = df.n / nnm_min
err_min_i = int(err_min_idx[0] + 1)
err_min_j = int(err_min_idx[1])
expnnm_min_used = expnnm_min[err_min_j:err_min_i]
ppo_expnnm_min_used = df.relp[err_min_j:err_min_i]
f, ax1 = plt.subplots(1, 1, figsize=(10, 10))
ax1.set_title("BET Isotherm and Experimental data")
ax1.set_ylim(0, synth_min[-2] + 1)
ax1.set_xlim(0, 1)
ax1.set_ylabel("n/nm")
ax1.set_xlabel("P/Po")
ax1.grid(b=True, which="major", color="gray", linestyle="-")
ax1.plot(
ppo,
synth_min,
linestyle="-",
linewidth=1,
c="black",
label="Theoretical isotherm",
marker="",
)
ax1.plot(
ppo_expnnm_min_used,
expnnm_min_used,
c="gray",
label="Experimental isotherm - used data",
marker="o",
linewidth=0,
)
ax1.plot(
df.relp,
expnnm_min,
c="grey",
fillstyle="none",
label="Experimental isotherm",
marker="o",
linewidth=0,
)
ax1.plot([0, 1], [1, 1], c="grey", linestyle="--", linewidth=1, marker="")
ax1.legend(loc="upper left", framealpha=1)
if save_file is True:
f.savefig("isothermcomp_%s.png" % (bet_results.info), bbox_inches="tight")
logging.info(
"Experimental and theoretical isotherm plot saved as:\
isothermcomp_%s.png"
% (bet_results.info)
)
return
def ssa_heatmap(bet_results, mask_results, save_file=True, gradient="Greens"):
"""Creates a heatmap of specific surface areas.
Shading corresponds to specific surface area, normalized for the minimum
and maximum specific sa values.
Parameters
----------
bet_results : namedtuple
The bet_results.ssa element is used to create a heatmap of specific
surface area answers.
mask_results : namedtuple
The mask_results.mask element is used to mask the
specific surface area heatmap so that only valid results are
displayed.
save_file : boolean
When save_file = True a png of the figure is created in the
working directory.
gradient : string
Color gradient for heatmap, must be a vaild color gradient name
in the seaborn package.
Returns
-------
"""
mask = mask_results.mask
if mask.all():
logging.warning(
"No valid relative pressure ranges. Specific surface area"
" heatmap not created."
)
return
df = bet_results.iso_df
# creating a masked array of ssa values
ssa = np.ma.array(bet_results.ssa, mask=mask)
# finding max and min sa to normalize heatmap colours
ssamax, ssa_max_idx, ssamin, ssa_min_idx = util.max_min(ssa)
hm_labels = round(df.relp * 100, 1)
fig, (ax) = plt.subplots(1, 1, figsize=(13, 13))
sns.heatmap(
ssa,
vmin=ssamin,
vmax=ssamax,
square=True,
cmap=gradient,
mask=(ssa == 0),
xticklabels=hm_labels,
yticklabels=hm_labels,
linewidths=1,
linecolor="w",
cbar_kws={"shrink": 0.78, "aspect": len(df.relp)},
)
ax.invert_yaxis()
ax.set_title(
"Specific Surface Area m^2/g"
)
plt.xticks(rotation=45, horizontalalignment="right")
plt.xlabel("Start Relative Pressure")
plt.yticks(rotation=45, horizontalalignment="right")
plt.ylabel("End Relative Pressure")
if save_file is True:
fig.savefig("ssa_heatmap_%s.png" % (bet_results.info), bbox_inches="tight")
logging.info(
"Specific surface area heatmap saved as: ssa_heatmap_%s.png"
% (bet_results.info)
)
return
def bet_combo_plot(bet_results, mask_results, save_file=True):
"""Creates a BET plots for the minimum and maxium error data sets.
Only datapoints in the minimum and maximum error data sets are plotted.
Equation for best fit line and corresponding R value are annotated on plot.
Parameters
----------
bet_results : namedtuple
Namedtuple where the bet_results.iso_df element is used to
create a plot of isotherm BET values.
mask_results : namedtuple
The mask_results.mask element is used to mask the BET results so that
only valid results are displayed.
save_file : boolean
When save_file = True a png of the figure is created in the
working directory.
Returns
-------
"""
mask = mask_results.mask
if mask.all():
logging.warning(
"No valid relative pressure ranges. BET combo plot not created."
)
return
df = bet_results.iso_df
err = np.ma.array(bet_results.err, mask=mask)
err_max, err_max_idx, err_min, err_min_idx = util.max_min(err)
min_start = int(err_min_idx[1])
min_stop = int(err_min_idx[0])
max_start = int(err_max_idx[1])
max_stop = int(err_max_idx[0])
slope, intercept, r_val, p_value, std_err = sp.stats.linregress(
df.relp[min_start : min_stop + 1], df.bet[min_start : min_stop + 1]
)
min_liney = np.zeros(2)
min_liney[0] = slope * (df.relp[min_start] - 0.01) + intercept
min_liney[1] = slope * (df.relp[min_stop] + 0.01) + intercept
min_linex = np.zeros(2)
min_linex[0] = df.relp[min_start] - 0.01
min_linex[1] = df.relp[min_stop] + 0.01
(
slope_max,
intercept_max,
r_value_max,
p_value_max,
std_err_max,
) = sp.stats.linregress(
df.relp[max_start : max_stop + 1], df.bet[max_start : max_stop + 1]
)
max_liney = np.zeros(2)
max_liney[0] = slope_max * (df.relp[max_start] - 0.01) + intercept_max
max_liney[1] = slope_max * (df.relp[max_stop] + 0.01) + intercept_max
max_linex = np.zeros(2)
max_linex[0] = df.relp[max_start] - 0.01
max_linex[1] = df.relp[max_stop] + 0.01
figure, ax1 = plt.subplots(1, figsize=(10, 10))
ax1.set_title("BET Plot")
ax1.set_xlim(0, max(min_linex[1], max_linex[1]) * 1.1)
ax1.set_ylabel("1/[n(P/Po-1)]")
ax1.set_ylim(0, max(min_liney[1] * 1.1, max_liney[1] * 1.1))
ax1.set_xlabel("P/Po")
ax1.grid(b=True, which="major", color="gray", linestyle="-")
ax1.plot(
df.relp[min_start : min_stop + 1],
df.bet[min_start : min_stop + 1],
label="min error (exp. data)",
c="grey",
marker="o",
linewidth=0,
fillstyle="none",
)
ax1.plot(min_linex, min_liney, color="black", label="min error (linear regression)")
ax1.plot(
df.relp[max_start : max_stop + 1],
df.bet[max_start : max_stop + 1],
label="Max Error Experimental Data",
c="grey",
marker="x",
linewidth=0,
)
ax1.plot(
max_linex,
max_liney,
color="black",
linestyle="--",
label="max error (linear regression)",
)
ax1.legend(loc="upper left", framealpha=1)
ax1.annotate(
"min error (linear regression): \nm = %.3f \nb = %.3f \nR = \
%.3f \n\nmax error (linear regression): \nm = %.3f \nb = %.3f \
\nR = %.3f"
% (slope, intercept, r_val, slope_max, intercept_max, r_value_max),
bbox=dict(boxstyle="round", fc="white", ec="gray", alpha=1),
textcoords="axes fraction",
xytext=(0.695, 0.017),
xy=(df.relp[min_stop], df.bet[min_start]),
size=11,
)
if save_file is True:
figure.savefig("betplot_%s.png" % (bet_results.info), bbox_inches="tight")
logging.info("BET plot saved as: betplot_%s.png" % (bet_results.info))
return
def err_heatmap(bet_results, mask_results, save_file=True, gradient="Greys"):
"""Creates a heatmap of error values.
Shading corresponds to average error between experimental data and the
the theoretical BET isotherm, normalized so that, with default shading,
0 is displayed as white and the maximum error value is black.
Parameters
----------
bet_results : namedtuple
The bet_results.err element is used to create a heatmap of error
values.
mask_results : namedtuple
The mask_results.mask element is used to mask the error heatmap so that
only valid results are displayed.
save_file : boolean
When save_file = True a png of the figure is created in the
working directory.
gradient : string
Color gradient for heatmap, must be a vaild color gradient name
in the seaborn package, default is grey.
Returns
-------
"""
mask = mask_results.mask
if mask.all():
logging.warning(
"No valid relative pressure ranges. Error heat map not created."
)
return
df = bet_results.iso_df
# creating a masked array of error values
err = np.ma.array(bet_results.err, mask=mask)
errormax, error_max_idx, errormin, error_min_idx = util.max_min(err)
hm_labels = round(df.relp * 100, 1)
fig, (ax) = plt.subplots(1, 1, figsize=(13, 13))
sns.heatmap(
err,
vmin=0,
vmax=errormax,
square=True,
cmap=gradient,
mask=(err == 0),
xticklabels=hm_labels,
yticklabels=hm_labels,
linewidths=1,
linecolor="w",
cbar_kws={"shrink": 0.78, "aspect": len(df.relp)},
)
ax.invert_yaxis()
ax.set_title(
"Average Error per Point Between Experimental and" " Theoretical Isotherms"
)
plt.xticks(rotation=45, horizontalalignment="right")
plt.xlabel("Start Relative Pressure")
plt.yticks(rotation=45, horizontalalignment="right")
plt.ylabel("End Relative Pressure")
if save_file is True:
fig.savefig("error_heatmap_%s.png" % (bet_results.info), bbox_inches="tight")
logging.info("Error heatmap saved as: error_heatmap_%s.png" % (bet_results.info))
return
def ascii_tables(bet_results, mask_results):
"""Creates and prints ASCII formatted tables of BET results.
Parameters
----------
bet_results : namedtuple
Contains elements that result from BET analysis. Relevant fields are:
- ``bet_results.iso_df`` (DataFrame) : experimental isotherm data.
- ``bet_results.ssa`` (ndarray) : specific surface areas for all
relp ranges.
- ``bet_results.c`` (ndarray) : BET constants for all relp ranges.
- ``bet_results.err`` (ndarray) : error values for all relp ranges.
mask_results : namedtuple
Contains the results of applying the Rouquerol criteria to BET
results. Relevant fields are:
- ``mask_results.mask`` (MaskedArray) : object where invalid BET
results are masked.
Returns
-------
table : prettytable
Summary of BET results, highlighting the high, low, and
average values of specific surface area. ASCII formatted table.
table2 : prettytable
Summary of BET results, highlighting the high, low, and
average values of the BET constant. ASCII formatted table.
ssa_std : float
Atandard deviation of valid specific surface area values.
c_std : float
Standard deviation of valid BET constant values.
"""
mask = mask_results.mask
if mask.all():
msg = "No valid relative pressure ranges. ASCII tables not created."
logging.warning(msg)
return
df = bet_results.iso_df
ssa = np.ma.array(bet_results.ssa, mask=mask)
c = np.ma.array(bet_results.c, mask=mask)
err = np.ma.array(bet_results.err, mask=mask)
ssamax, ssa_max_idx, ssamin, ssa_min_idx = util.max_min(ssa)
cmax, c_max_idx, cmin, c_min_idx = util.max_min(c)
ssamean = np.ma.mean(ssa)
ssamedian = np.ma.median(ssa)
cmean = np.ma.mean(c)
cmedian = np.ma.median(c)
ssa_std = np.nan_to_num(ssa)[ssa != 0].std()
c_std = np.nan_to_num(c)[c != 0].std()
err_max, err_max_idx, err_min, err_min_idx = util.max_min(err)
cmax_err = float(c[err_max_idx[0], err_max_idx[1]])
cmin_err = float(c[err_min_idx[0], err_min_idx[1]])
# these are just variables to print in tables
ssa_min = round(ssamin, 3)
ssa_min_c = round(float(c[ssa_min_idx[0], ssa_min_idx[1]]), 3)
ssa_min_start_ppo = round(float(df.relp[ssa_min_idx[1]]), 3)
ssa_min_end_ppo = round(float(df.relp[ssa_min_idx[0]]), 3)
ssa_max = round(ssamax, 3)
ssa_max_c = round(float(c[ssa_max_idx[0], ssa_max_idx[1]]), 3)
ssa_max_start_ppo = round(float(df.relp[ssa_max_idx[1]]), 3)
ssa_max_end_ppo = round(float(df.relp[ssa_max_idx[0]]), 3)
ssa_mean = round(ssamean, 3)
ssa_median = round(ssamedian, 3)
c_min = round(cmin, 3)
c_min_sa = round(float(ssa[c_min_idx[0], c_min_idx[1]]), 3)
c_min_start_ppo = round(float(df.relp[c_min_idx[1]]), 3)
c_min_end_ppo = round(float(df.relp[c_min_idx[0]]), 3)
c_min_err = round(float(err[c_min_idx[0], c_min_idx[1]]), 3)
c_max = round(cmax, 3)
c_max_sa = round(float(ssa[c_max_idx[0], c_max_idx[1]]), 3)
c_max_start_ppo = round(float(df.relp[c_max_idx[1]]), 3)
c_max_end_ppo = round(float(df.relp[c_max_idx[0]]), 3)
c_max_err = round(float(err[c_max_idx[0], c_max_idx[1]]), 3)
c_mean = round(cmean, 3)
c_median = round(cmedian, 3)
cmin_err = round(cmin_err, 3)
c_min_err_sa = round(float(ssa[err_min_idx[0], err_min_idx[1]]), 3)
c_min_err_start_ppo = round(float(df.relp[err_min_idx[1]]), 3)
c_min_err_end_ppo = round(float(df.relp[err_min_idx[0]]), 3)
err_min = round(err_min, 3)
cmax_err = round(cmax_err, 3)
c_max_err_sa = round(float(ssa[err_max_idx[0], err_max_idx[1]]), 3)
c_max_err_start_ppo = round(float(df.relp[err_max_idx[1]]), 3)
c_max_err_end_ppo = round(float(df.relp[err_max_idx[0]]), 3)
err_max = round(err_max, 3)
table = PrettyTable()
table.field_names = ["", "Spec SA m2/g", "C", "Start P/Po", "End P/Po"]
table.add_row([
"Min Spec SA", ssa_min, ssa_min_c, ssa_min_start_ppo, ssa_min_end_ppo
])
table.add_row([
"Max Spec SA", ssa_max, ssa_max_c, ssa_max_start_ppo, ssa_max_end_ppo
])
table.add_row(["Mean Spec SA", ssa_mean, "n/a", "n/a", "n/a"])
table.add_row(["Median Spec SA", ssa_median, "n/a", "n/a", "n/a"])
logging.info(table)
logging.info("Standard deviation of specific surface area = %.3f" %
(ssa_std))
table2 = PrettyTable()
table2.field_names = [
"",
"C, BET Constant",
"Spec SA",
"Start P/Po",
"End P/Po",
"Error",
]
table2.add_row(
["Min C", c_min, c_min_sa, c_min_start_ppo, c_min_end_ppo, c_min_err])
table2.add_row(
["Max C", c_max, c_max_sa, c_max_start_ppo, c_max_end_ppo, c_max_err])
table2.add_row(["Mean C", c_mean, "n/a", "n/a", "n/a", "n/a"])
table2.add_row(["Median C", c_median, "n/a", "n/a", "n/a", "n/a"])
table2.add_row([
"Min Error C",
cmin_err,
c_min_err_sa,
c_min_err_start_ppo,
c_min_err_end_ppo,
err_min,
])
table2.add_row([
"Max Error C",
cmax_err,
c_max_err_sa,
c_max_err_start_ppo,
c_max_err_end_ppo,
err_max,
])
logging.info(table2)
logging.info("Standard deviation of BET constant (C) = %.5f" % (c_std))
return table, table2, ssa_std, c_std
def dataframe_tables(bet_results, mask_results):
"""Creates and populates pandas dataframes summarizing BET results.
Parameters
----------
bet_results : namedtuple
Contains elements that result from BET analysis. Relevant fields are:
- ``bet_results.iso_df`` (DataFrame) : experimental isotherm data.
- ``bet_results.ssa`` (ndarray) : specific surface areas for all
relp ranges.
- ``bet_results.c`` (ndarray) : BET constants for all relp ranges.
- ``bet_results.err`` (ndarray) : error values for all relp ranges.
mask_results : namedtuple
Contains the results of applying the Rouquerol criteria to BET
results. Relevant fields are:
- ``mask_results.mask`` (MaskedArray) : object where invalid BET
results are masked.
Returns
-------
ssa_table : DataFrame
Summary of BET results, highlighting the high, low, and
average values of specific surface area.
c_table : DataFrame
Summary of BET results, highlighting the high, low, and
average values of the BET constant.
ssa_std : float
Atandard deviation of valid specific surface area values.
c_std : float
Standard deviation of valid BET constant values.
"""
mask = mask_results.mask
if mask.all():
logging.warning(
"No valid relative pressure ranges. Tables not created.")
ssa_dict = {
" ":
["Min Spec SA", "Max Spec SA", "Mean Spec SA", "Median Spec SA"],
"Spec SA m2/g": ["n/a", "n/a", "n/a", "n/a"],
"C": ["n/a", "n/a", "n/a", "n/a"],
"Start P/Po": ["n/a", "n/a", "n/a", "n/a"],
"End P/Po": ["n/a", "n/a", "n/a", "n/a"],
}
ssa_table = pd.DataFrame(data=ssa_dict)
c_dict = {
" ": [
"Min C", "Max C", "Mean C", "Median C", "Min Error C",
"Max Error C"
],
"C": ["n/a", "n/a", "n/a", "n/a", "n/a", "n/a"],
"Spec SA": ["n/a", "n/a", "n/a", "n/a", "n/a", "n/a"],
"Start P/Po": ["n/a", "n/a", "n/a", "n/a", "n/a", "n/a"],
"End P/Po": ["n/a", "n/a", "n/a", "n/a", "n/a", "n/a"],
"Error": ["n/a", "n/a", "n/a", "n/a", "n/a", "n/a"],
}
msg = "No valid relative pressure ranges. Standard deviations not calculated."
logging.warning(msg)
c_table = pd.DataFrame(data=c_dict)
ssa_sdev = 0
c_sdev = 0
return ssa_table, c_table, ssa_sdev, c_sdev
df = bet_results.iso_df
ssa = np.ma.array(bet_results.ssa, mask=mask)
c = np.ma.array(bet_results.c, mask=mask)
err = np.ma.array(bet_results.err, mask=mask)
c = np.nan_to_num(c)
ssamax, ssa_max_idx, ssamin, ssa_min_idx = util.max_min(ssa)
cmax, c_max_idx, cmin, c_min_idx = util.max_min(c)
ssamean = np.ma.mean(ssa)
ssamedian = np.ma.median(ssa)
cmean = np.ma.mean(c)
cmedian = np.ma.median(c)
ssa_std = np.nan_to_num(ssa)[ssa != 0].std()
c_std = np.nan_to_num(c)[c != 0].std()
err_max, err_max_idx, err_min, err_min_idx = util.max_min(err)
cmax_err = float(c[err_max_idx[0], err_max_idx[1]])
cmin_err = float(c[err_min_idx[0], err_min_idx[1]])
# these are just variables to print in tables
ssa_min = round(ssamin, 3)
ssa_min_c = round(float(c[ssa_min_idx[0], ssa_min_idx[1]]), 3)
ssa_min_start_ppo = round(float(df.relp[ssa_min_idx[1]]), 3)
ssa_min_end_ppo = round(float(df.relp[ssa_min_idx[0]]), 3)
ssa_max = round(ssamax, 3)
ssa_max_c = round(float(c[ssa_max_idx[0], ssa_max_idx[1]]), 3)
ssa_max_start_ppo = round(float(df.relp[ssa_max_idx[1]]), 3)
ssa_max_end_ppo = round(float(df.relp[ssa_max_idx[0]]), 3)
ssa_mean = round(ssamean, 3)
ssa_median = round(ssamedian, 3)
c_min = round(cmin, 3)
c_min_sa = round(float(ssa[c_min_idx[0], c_min_idx[1]]), 3)
c_min_start_ppo = round(float(df.relp[c_min_idx[1]]), 3)
c_min_end_ppo = round(float(df.relp[c_min_idx[0]]), 3)
c_min_err = round(float(err[c_min_idx[0], c_min_idx[1]]), 3)
c_max = round(cmax, 3)
c_max_sa = round(float(ssa[c_max_idx[0], c_max_idx[1]]), 3)
c_max_start_ppo = round(float(df.relp[c_max_idx[1]]), 3)
c_max_end_ppo = round(float(df.relp[c_max_idx[0]]), 3)
c_max_err = round(float(err[c_max_idx[0], c_max_idx[1]]), 3)
c_mean = round(cmean, 3)
c_median = round(cmedian, 3)
cmin_err = round(cmin_err, 3)
c_min_err_sa = round(float(ssa[err_min_idx[0], err_min_idx[1]]), 3)
c_min_err_start_ppo = round(float(df.relp[err_min_idx[1]]), 3)
c_min_err_end_ppo = round(float(df.relp[err_min_idx[0]]), 3)
err_min = round(err_min, 3)
cmax_err = round(cmax_err, 3)
c_max_err_sa = round(float(ssa[err_max_idx[0], err_max_idx[1]]), 3)
c_max_err_start_ppo = round(float(df.relp[err_max_idx[1]]), 3)
c_max_err_end_ppo = round(float(df.relp[err_max_idx[0]]), 3)
err_max = round(err_max, 3)
ssa_dict = {
" ": ["Min Spec SA", "Max Spec SA", "Mean Spec SA", "Median Spec SA"],
"Spec SA m2/g": [ssa_min, ssa_max, ssa_mean, ssa_median],
"C": [ssa_min_c, ssa_max_c, "n/a", "n/a"],
"Start P/Po": [ssa_min_start_ppo, ssa_max_start_ppo, "n/a", "n/a"],
"End P/Po": [ssa_min_end_ppo, ssa_max_end_ppo, "n/a", "n/a"],
}
ssa_table = pd.DataFrame(data=ssa_dict)
c_dict = {
" ":
["Min C", "Max C", "Mean C", "Median C", "Min Error C", "Max Error C"],
"C": [c_min, c_max, c_mean, c_median, cmin_err, cmax_err],
"Spec SA":
[c_min_sa, c_max_sa, "n/a", "n/a", c_min_err_sa, c_max_err_sa],
"Start P/Po": [
c_min_start_ppo,
c_max_start_ppo,
"n/a",
"n/a",
c_min_err_start_ppo,
c_max_err_start_ppo,
],
"End P/Po": [
c_min_end_ppo,
c_max_end_ppo,
"n/a",
"n/a",
c_min_err_end_ppo,
c_max_err_end_ppo,
],
"Error": [c_min_err, c_max_err, "n/a", "n/a", err_min, err_max],
}
c_table = pd.DataFrame(data=c_dict)
return ssa_table, c_table, ssa_std, c_std
def ssa_answer(bet_results, mask_results, criterion="error"):
"""
Logs a single specific surface area answer from the valid relative
pressure range with the lowest error, most number of points, maximum
specific surface area, or minimum specific surface area.
Parameters
----------
bet_results : named tuple
``bet_results.ssa`` contains the array of specific surface values.
rouq_mask : named tuple
``rouq_mask.mask`` contains the mask used to remove invaid specific
surface area values from consideration.
criterion : str
Used to specify the criterion for a final specific surface area answer,
either 'error', 'points', 'max', or 'min. Defaults to 'error'.
Returns
-------
ssa_ans : float
Specific surface answer corresponding to user defined criteria.
"""
mask = mask_results.mask
if mask.all():
msg = "No valid relative pressure ranges. Specific surface area not calculated."
raise ValueError(msg)
ssa = np.ma.array(bet_results.ssa, mask=mask)
if criterion == "points":
pts = np.ma.array(bet_results.num_pts, mask=mask)
max_pts = np.max(pts)
ssa_ans_array = np.ma.masked_where(pts < max_pts, ssa)
try:
ssa_ans = float(ssa_ans_array.compressed())
except ValueError:
raise Exception(
"Error, so single specific surface area answer. Multiple" +
"relative pressure ranges with the maximum number of points.")
return 0
logging.info(
"The specific surface area value, based on %s is %.2f m2/g." %
(criterion, ssa_ans))
return ssa_ans
if criterion == "error":
err = np.ma.array(bet_results.err, mask=mask)
errormax, error_max_idx, errormin, error_min_idx = util.max_min(err)
ssa_ans = ssa[int(error_min_idx[0]), int(error_min_idx[1])]
logging.info(
"The specific surface area value, based on %s is %.2f m2/g." %
(criterion, ssa_ans))
return ssa_ans
if criterion == "max":
ssa_ans = np.max(ssa)
logging.info(
"The specific surface area value, based on %s is %.2f m2/g." %
(criterion, ssa_ans))
return ssa_ans
if criterion == "min":
ssa_ans = np.min(ssa)
logging.info(
"The specific surface area value, based on %s is %.2f m2/g." %
(criterion, ssa_ans))
return ssa_ans
else:
raise ValueError(
"Invalid criterion, must be points, error, min, or max.")
