class Grapher:
def __init__(self):
self.x_data = None
self.y_data = None
self.x_col_name = None
self.y_col_name = None
self.fit_index_start = None
self.fit_index_end = None
self.popt = None
self.sheet = None
self.subtracted_peak_intensity = None
self.subtracted_amount = None
self.params = None
self.y_data_subtract = None
def load_sheet_from_excel(self, filename, sheet_name):
self.sheet = Sheet()
self.sheet.load_excel_sheet(filename, sheet_name)
def set_params(self, params):
self.params = params
def original_func(self, x_index):
return self.y_data[x_index]
def set_fit_index_start(self):
points = 50
self.fit_index_start = 25 + int(
points * 0.75) + FindExtrema.get_index_of_first_drop(
self.y_data, number_of_points=points, percent_drop=0.35)
print('Fit Index Start set to: ' + str(self.fit_index_start))
def set_fit_index_end(self):
# TODO implementation of minima after fit_index_start
self.fit_index_end = self.params.fit_index_end
print('Fit Index End set to: ' + str(self.fit_index_end))
def get_data_in_fit_range(self, data):
return pd.concat(
[data[self.fit_index_start:self.fit_index_end], data[1300:2000]],
ignore_index=True)
def get_x_data_for_fit(self):
return self.get_data_in_fit_range(self.x_data)
def get_y_data_for_fit(self):
return self.get_data_in_fit_range(self.y_data)
def setup_for_fit(self):
self.x_data = self.sheet.get_measurements(self.x_col_name)
self.y_data = self.sheet.get_measurements(self.y_col_name)
self.set_fit_index_start()
self.set_fit_index_end()
def calc_exp_model(self, x_col_name, y_col_name):
self.x_col_name = x_col_name
self.y_col_name = y_col_name
self.setup_for_fit()
self.popt, pcov = curve_fit(self.params.fit_function,
self.get_x_data_for_fit(),
self.get_y_data_for_fit(),
p0=self.params.fit_p0)
print('popt: ' + str(self.popt))
def print_data(self):
print('X data: ' + str(self.x_data) + '\nY data:\n' + str(self.y_data))
def save_graph(self, graph_name):
plt.title('Frequency Distribution')
plt.xlabel('Wavelength (nm)')
plt.xlim(xmin=500, xmax=1000)
plt.ylabel('Intensity of ' + self.y_col_name)
plt.ylim(ymin=0, ymax=65000)
# draw gray vertical line where the peak is expected
plt.axvline(x=self.x_data[self.params.x_index_of_peak],
color='#aaaaaa',
linewidth=1)
plt.plot(self.x_data, self.y_data)
if self.popt is not None:
x_data_fit = self.x_data[525:]
y_data_fit = self.params.fit_function(self.x_data[525:],
*self.popt)
plt.plot(x_data_fit, y_data_fit, '--')
self.y_data_subtract = np.subtract(
self.original_func(np.arange(2048)[525:]),
self.params.fit_function(x_data_fit, *self.popt))
plt.plot(x_data_fit, self.y_data_subtract, '-')
# evaluate at approximate peak
self.subtracted_peak_intensity = self.y_data_subtract[
self.params.x_index_of_peak]
self.subtracted_amount = self.original_func(
self.params.x_index_of_peak) - self.subtracted_peak_intensity
print(
str(self.original_func(self.params.x_index_of_peak)) + ' -> ' +
str(self.subtracted_peak_intensity))
plt.savefig('../output/plots/' + str(graph_name) + '.png')
#plt.show()
plt.gcf().clear()
def get_subtracted_peak_intensity(self):
return self.subtracted_peak_intensity
