def get_statistics(X, y):
"""
================
INPUT: array of X data (temperature), array of Y data (heat flow), int
OUTPUT: float, float, float, float, tuple
================
Returns four key release characteristics:
1. Enthalpy
2. Peak heat flow
3. Temperature at peak
4. Rough standard deviation of peak
5. Onset and end points
"""
peak_X, peak_y = pdetect.get_peak_X_y(X, y)
enthalpy = get_enthalpy(X, y)
peak_heat_flow = np.min(peak_y) # find the minimum y
peak_temp = peak_X[np.argmin(peak_y)] # find the x for which y is minimized
heat_flow_std = np.std(peak_y)
onset, end = pdetect.get_points_coordinates(X, y)
return enthalpy, peak_heat_flow, peak_temp, heat_flow_std, (onset, end)
def fit_line(X, y):
"""
================
INPUT: array of X data (temperature), array of Y data (heat flow)
OUTPUT: array of X data (temperature), array of Y data (best-fit-line heat
flow)
================
Returns two arrays, of the x's and y's of the best-fit-line.
"""
peak_X, peak_y = pdetect.get_peak_X_y(X, y)
onset_point, end_point = pdetect.get_points(X, y)
(x_0, y_0), (x_1, y_1) = pdetect.get_points_coordinates(X, y)
line_X, line_y = construct_line(x_0,
y_0,
x_1,
y_1,
peak_X
)
return line_X, line_y
def get_enthalpy(X, y):
"""
================
INPUT: array of X data (temperature), array of Y data (heat flow)
OUTPUT: float
================
Returns the enthalpy of the reaction.
In order, this function:
1. Gets the interval of the peak
2. Fits a line between its end points
3. Takes the difference between the line and the peak
4. Finds the integral of the difference
"""
peak_X, peak_y = pdetect.get_peak_X_y(X, y)
onset_point, end_point = pdetect.get_points(X, y)
line_X, line_y = fit_line(X, y)
differences = line_y - peak_y
return simps(differences, x = peak_X)