def __call__(self, x):
return PchipInterpolator.__call__(self, x, 1)
def __call__(self, x):
return PchipInterpolator.__call__(self, x, 1)
def read_excel_data_to_spline(read_excel_file, write_dir, discrete_points, spline_selector):
cutoff = [10,100]
# read_excel_file part
df = pd.read_excel(read_excel_file, sheet_name='raw', header=[0, 1], index_col=0)
# take only strain columns and make into a new df
strain = df.xs('Strain (%)', level='Data', axis=1)
strain = strain.values.T.tolist() # .T to ensure that each sub list is along the column rather than rows of the df
# strain store is a list of 1d ndarray, with each inner list being one set of strain data for one experiment
strain_store = []
for single_exp in strain:
strain_single = [x for x in single_exp if not np.isnan(x)]
strain_store.append(np.array(strain_single))
# Similar to the above process, but repeat for relative resistance instead
r = df.xs('R', level='Data', axis=1)
r = r.values.T.tolist()
r_store = []
for single_exp in r:
r_single = [x for x in single_exp if not np.isnan(x)]
r_store.append(r_single)
# Calculation of metrics for each experiment
summary_store = []
cutoff_store = []
cutoff_store2 = [] # second method to do cutoff
plot_store = []
gf20_store = []
for strain, r in zip(strain_store, r_store):
eval_x = np.linspace(0, strain[-1], num=discrete_points) # To evaluate spline at
eval_x_plot = np.linspace(0, strain[-1], num=100) # For plotting
if spline_selector == 1:
spline = PchipInterpolator(strain, r)
linear_spline = interp1d(strain, r)
# Getting GF for 20 points
gf = np.minimum(np.concatenate(
(
(linear_spline.__call__(eval_x[:-1]+0.01)-linear_spline.__call__(eval_x[:-1]))*100/0.01,
[(linear_spline.__call__(eval_x[-1])-linear_spline.__call__(eval_x[-1]-0.01))*100/0.01]
)
), 1e6)
# Store the processed labels. Labels for one example is 1d ndarray of
# [End_point of strain curve, r1, r2, r3, ... , r_end]
y_discrete = spline.__call__(eval_x)
gradient_store = (y_discrete[1:] - y_discrete[:-1])/(eval_x[1]-eval_x[0]) * 100
# If indexError occurs ==> np.where found nothing ==> there is no points with the required gradient
# So just put the end point as the answer.
try:
cutoff_one = eval_x[np.where(gradient_store>=cutoff[0])[0][0]]
except IndexError:
cutoff_one = eval_x[-1]
try:
cutoff_two = eval_x[np.where(gradient_store >=cutoff[1])[0][0]]
except IndexError:
cutoff_two = eval_x[-1]
cutoff_store.append([cutoff_one, cutoff_two, strain[-1]])
summary_store.append(np.concatenate([[strain[-1]], y_discrete]))
plot_store.append([eval_x_plot, spline.__call__(eval_x_plot)])
gf20_store.append(gf)
elif spline_selector == 2:
# NOT IN USE
# csaps implementation
fitted_curve = csaps.UnivariateCubicSmoothingSpline(strain, r, smooth=0.7)
summary_store.append(np.concatenate([[strain[-1]], fitted_curve(eval_x)]))
plot_store.append([eval_x_plot, fitted_curve(eval_x_plot)])
elif spline_selector == 3:
# NOT IN USE
# Scripy implementation
spline = CubicSpline(strain, r)
# Store the processed labels. Labels for one example is 1d ndarray of
# [End_point of strain curve, r1, r2, r3, ... , r_end]
summary_store.append(np.concatenate([[strain[-1]], spline.__call__(eval_x)]))
plot_store.append([eval_x_plot, spline.__call__(eval_x_plot)])
# Second cutoff method
r = np.array(r)
strain = np.array(strain)
gf_store = (r[1:] - r[:-1])/(strain[1:] - strain[:-1]) * 100
if gf_store[-1]<-1:
cutoff_one = -1
cutoff_two = -1
else:
try:
cut_idx = np.where(gf_store>=cutoff[0])[0][0]
if strain[cut_idx] > 0:
cutoff_one = strain[cut_idx]
else:
cutoff_one = strain[cut_idx+1]
except IndexError:
cutoff_one = strain[-1]
try:
cut_idx = np.where(gf_store>=cutoff[1])[0][0]
if strain[cut_idx] > 0:
cutoff_two = strain[cut_idx]
else:
cutoff_two = strain[cut_idx+1]
except IndexError:
cutoff_two = strain[-1]
cutoff_store2.append([cutoff_one, cutoff_two, strain[-1]])
# Print to write_excel_file
excel_name = write_dir + '/results.xlsx'
wb = openpyxl.Workbook()
wb.create_sheet('points')
ws = wb['points']
header = np.array(range(np.shape(strain_store)[0])) + 1 # Index to label Exp 1, 2, 3, ...
columns = list(range(0,1+discrete_points))
columns[0] = 'END'
header = list(header)
df_write = pd.DataFrame(summary_store, index=header, columns=columns)
rows = dataframe_to_rows(df_write)
for r_idx, row in enumerate(rows, 1):
for c_idx, value in enumerate(row, 1):
ws.cell(row=r_idx + 1, column=c_idx, value=value)
wb.create_sheet('gf20')
ws = wb['gf20']
header = np.array(range(np.shape(strain_store)[0])) + 1 # Index to label Exp 1, 2, 3, ...
columns = list(range(1,1+discrete_points))
header = list(header)
df_write = pd.DataFrame(gf20_store, index=header, columns=columns)
rows = dataframe_to_rows(df_write)
for r_idx, row in enumerate(rows, 1):
for c_idx, value in enumerate(row, 1):
ws.cell(row=r_idx + 1, column=c_idx, value=value)
wb.create_sheet('cutoff')
ws = wb['cutoff']
header = np.array(range(np.shape(strain_store)[0])) + 1 # Index to label Exp 1, 2, 3, ...
columns = ['cut={}'.format(x) for x in cutoff] + ['END']
header = list(header)
df_write = pd.DataFrame(cutoff_store, index=header, columns=columns)
rows = dataframe_to_rows(df_write)
for r_idx, row in enumerate(rows, 1):
for c_idx, value in enumerate(row, 1):
ws.cell(row=r_idx + 1, column=c_idx, value=value)
wb.save(excel_name)
wb.close()
wb.create_sheet('cutoff2')
ws = wb['cutoff2']
header = np.array(range(np.shape(strain_store)[0])) + 1 # Index to label Exp 1, 2, 3, ...
columns = ['cut={}'.format(x) for x in cutoff] + ['END']
header = list(header)
df_write = pd.DataFrame(cutoff_store2, index=header, columns=columns)
rows = dataframe_to_rows(df_write)
for r_idx, row in enumerate(rows, 1):
for c_idx, value in enumerate(row, 1):
ws.cell(row=r_idx + 1, column=c_idx, value=value)
wb.save(excel_name)
wb.close()
# Plotting
for idx, [strain, r, plot, summary] in enumerate(zip(strain_store, r_store, plot_store, summary_store)):
plt.scatter(strain, r, c='r', marker='x', label='Expt. Points')
plt.plot(plot[0], plot[1], label='Spline Fit')
eval_x = np.linspace(0, summary[0], num=discrete_points)
plt.scatter(eval_x, summary[1:], marker='+', label='Discrete Points')
plt.legend(loc='upper left')
plt.title('Expt. ' + str(idx + 1))
plt.savefig(write_dir + '/plots' + '/Expt ' + str(idx + 1) + ' _spline' + '.png', bbox_inches='tight')
plt.close()
