def as7262_outliers(data, scatter_correction=None):
data_columns = data[as7262_wavelengths]
print(data_columns)
# data_columns.T.plot()
# plt.plot(data_columns.T)
plt.show()
if scatter_correction == "SNV":
data_columns = processing.snv(data_columns)
elif scatter_correction == "MSC":
data_columns, _ = processing.msc(data_columns)
# svm = OneClassSVM().fit_predict(snv_data)
# print(svm)
robust_cov = MinCovDet().fit(data_columns)
mahal_dist = robust_cov.mahalanobis(data_columns)
# mahal_dist = MahalanobisDist(np.array(data_columns), verbose=True)
print(mahal_dist)
zscore(data_columns)
print('+++++')
mean = np.mean(mahal_dist)
std = 3*np.std(mahal_dist)
print(mean, std)
print(mean - std, mean + std)
zscore_mahal = (mahal_dist - mean) / np.std(mahal_dist)
# print(zscore_mahal)
# print(zscore_mahal.max(), zscore_mahal.argmax(), data_columns.loc[zscore_mahal.argmax()])
print('pppp')
print(data_columns)
print(zscore_mahal.argmax())
outliers = data_columns.loc[zscore_mahal > 3].index
outliers = data_columns.iloc[zscore_mahal.argmax()].name
# print(data_columns.loc[zscore_mahal > 3].index)
rows = data_columns.loc[outliers]
# print(data_columns.loc[zscore_mahal.argmax()].name)
print(data_columns.shape)
print(rows)
# print((mahal_dist-mahal_dist.mean()).std())
# print(mahal_dist.std())
# print(mahal_dist.mean() + 3*mahal_dist.std())
# mahal_dist2 = MahalanobisDist(np.array(data_columns), verbose=True)
n, bins, _ = plt.hist(zscore_mahal, bins=40)
plt.show()
# x_hist = np.linspace(min(mahal_dist), max(mahal_dist), 100)
#
# popt, pcov = curve_fit(gauss_function, bins[:len(n)], n, maxfev=100000, p0=[300, 0, 20])
# new_fit = gauss_function(x_hist, *popt)
# plt.plot(x_hist, new_fit, 'r--')
# color = data_columns.shape[0] * ["#000000"]
# color[data_columns.loc[zscore_mahal.argmax()].name] = "#FF0000"
plt.plot(data_columns.T, c="black")
plt.plot(rows.T, c="red")
plt.plot(data_columns.mean(), c="blue", lw=4)
# snv_data.T.plot(color=color)
plt.show()
# # wavelength=data_columns[l],
# # invert_y=False)
# if invert_y:
# y = 1 / y
# fit_values, _ = curve_fit(model, x, y, maxfev=10 ** 6)
# y_fit = model(x, *fit_values)
# if invert_y:
# y = 1 / y
# y_fit = 1 / y_fit
# r2 = r2_score(y, y_fit)
# mae = mean_absolute_error(y, y_fit)
# print(r2, mae)
# except:
# pass
x_msc, _ = processing.msc(x_data)
x_inv_msc = 1 / x_msc.copy()
data_sets = [
x_data.copy(), 1 / x_data,
processing.snv(x_data), 1 / processing.snv(x_data), x_msc, x_inv_msc,
StandardScaler().fit_transform(x_data),
StandardScaler().fit_transform(processing.snv(x_data)),
StandardScaler().fit_transform(x_msc),
RobustScaler().fit_transform(x_data),
RobustScaler().fit_transform(processing.snv(x_data))
]
data_set_names = [
"raw", "inverse", "SNV", "Invert SNV", "MSC", "inverse msc",
"standard scalar", "Standard Scalar SNV", "Standard Scalar MSC",
"Robust Scalar", "Robust Scalar SNV", "Robust Scalar MSC"
]
except:
pass
with pd.ExcelWriter(filename, mode='a') as writer:
results_df.to_excel(writer, sheet_name=sheet_name)
ys = {
"Normal Y": _y,
"Inverse Y": 1 / _y,
"Log Y": np.log(_y),
"Inverse Log Y": np.log(1 / _y),
"Exp Y": np.exp(-_y),
}
ys = {"Normal Y": _y}
mcs_x, _ = processing.msc(x_data)
print(mcs_x)
Xs = {"Normal X": x_data, "SNV": processing.snv(x_data)}
# Xs = {"MSC": mcs_x}
with pd.ExcelWriter(filename, mode='w') as writer:
results_df.to_excel(writer)
for y_name, y_inner in ys.items():
for x_name, x_inner in Xs.items():
new_sheet = x_name + ' ' + y_name
run_scan(x_inner, y_inner, new_sheet)
print(regressors)
print(training_scores)
print(test_scores)
fig, axes = plt.subplots(nrows=3,
ncols=1,
figsize=(7, 9),
constrained_layout=True)
fig.suptitle("AS7263 measurements of Cananga")
print(spectrum_data.shape)
print(data.columns)
# axes[0].plot(wavelengths, spectrum_data.T)
axes[0].plot(spectrum_data.T)
# axes[0].set_ylim([0, 200])
axes[0].set_title('Raw Data')
axes[0].set_ylabel("Raw Sensor Counts")
axes[0].annotate("A", xy=(.04, 0.80), xycoords='axes fraction', size=24)
[axes[0].lines[i].set_color(color) for i, color in enumerate(colors)]
snv_data = processing.snv(spectrum_data)
axes[1].plot(wavelengths, snv_data.T, color=colors)
axes[1].set_title('Standard Normal Variate Data')
axes[1].annotate("B", xy=(.04, 0.80), xycoords='axes fraction', size=24)
[axes[1].lines[i].set_color(color) for i, color in enumerate(colors)]
msc_data, _ = processing.msc(spectrum_data)
axes[2].plot(wavelengths, msc_data.T, color=colors)
axes[2].set_title("Multiplicative Scatter Correction Data")
axes[2].set_xlabel("Wavelength (nm)")
axes[2].annotate("C", xy=(.04, 0.80), xycoords='axes fraction', size=24)
[axes[2].lines[i].set_color(color) for i, color in enumerate(colors)]
plt.show()
BACKGROUND = [
687.65, 9453.7, 23218.35, 9845.05, 15496.7, 18118.55, 7023.8, 7834.1,
28505.9, 4040.9, 5182.3, 1282.55, 2098.85, 1176.1, 994.45, 496.45, 377.55,
389.75
]
x, y = get_data.get_data("mango",
"as7265x",
int_time=150,
position=2,
led="b'White'",
led_current="25 mA")
y = y['Avg Total Chlorophyll (µg/cm2)']
x_reflect = x / BACKGROUND
x_snv = processing.snv(x_reflect)
x_msc, _ = processing.msc(x_reflect)
x_robust = RobustScaler().fit_transform(x_msc)
plt.style.use('dark_background')
pls = PLS(n_components=6)
pls.fit(x, y)
x_fit = pls.predict(x)
pls.fit(x_msc, y)
svr = SVR()
svr.fit(x_msc, y)
print(svr.score(x, y))
ridge = RidgeCV()
ridge.fit(x_msc, y)
print(pls.score(x, y))
if __name__ == "__main__":
for sensor in sensors:
for leaf in leafs:
x, y = get_data.get_data(leaf,
sensor,
int_time=150,
position=2,
led_current="25 mA")
print(y.columns)
y_column = 'Total Chlorophyll (µg/cm2)'
# y_column = 'Total Chlorophyll (µg/mg)'
if y_column not in y.columns:
y_column = 'Avg Total Chlorophyll (µg/cm2)'
# y_column = 'Avg Total Chlorophyll (µg/mg)'
y = y[y_column]
print(x)
x_msc, _ = processing.msc(x)
x_snv = processing.snv(x)
xs = [("Normal", x), ("MSC", x_msc),
("SNV", x_snv), ("Inv", 1 / x), ("Log", np.log(x)),
("Inv Log", np.log(1 / x))]
name = f"{sensor}_{leaf}"
for processor_name, new_x in xs:
print(processor_name)
print(new_x)
run_scan(new_x, y, name, processor_name)
