def main():
print "reading data"
df = pd.read_csv("../data/CCS/subsetShots_5pct.csv")
print "preprocessing data"
df = normalizeRows(df, useWidths=False)
nrow = df.shape[0]
rowsum = df.iloc[:,3:].sum(axis=0)
cumulative = rowsum.cumsum()
wavelengths = map(extractWavelength, df.columns[3:])
k = 50 # desired number of features
alphas = [nrow * (2*j - 1) / (2.0 * k) for j in range(1, k+1)]
new_alphas = []
new_xs = []
alpha_index = 0
w_index = 0
print "computing density-dependent centers"
while True:
if alpha_index >= len(alphas) or w_index >= len(wavelengths):
break
if cumulative[w_index] >= alphas[alpha_index]:
if len(new_xs) == 0 or wavelengths[w_index] != new_xs[-1]:
new_xs.append(wavelengths[w_index])
new_alphas.append(alphas[alpha_index])
alpha_index += 1
else:
w_index += 1
print new_xs
print "creating plot"
fig = plt.figure()
ax = plt.gca()
for a in new_alphas:
ax.axhline(y=a / float(nrow), linestyle='-', color='#cccccc')
for x in new_xs:
ax.axvline(x=x, linestyle='-', color='#cccccc')
#ax.twinx().plot(wavelengths, rowsum / float(nrow), color=(0.5,0.7,0.5,0.3))
ax.plot(wavelengths, cumulative / float(nrow))
ax.set_xlabel("wavelength")
ax.set_ylabel("cumulative spectral intensity $\\hat F_D$")
plt.savefig("wavelengths_density_dependent.pdf")
plt.close(fig)
print "reducing data"
mapping = nearestColumnMapping(new_xs, df.columns[3:])
newData = df.apply(reduceDimRow, axis = 1, centers = mapping)
print "saving reduced data"
newData.to_csv("../data/CCS/subsetShots_5pct_reduced_by_density.csv", index=None)
def main():
print "reading data"
df = pd.read_csv("../data/CCS/subsetShots_5pct.csv")
print "preprocessing data"
df = normalizeRows(df, useWidths=False)
k = 50 # desired number of features
wavelengths = map(extractWavelength, df.columns[3:])
new_xs = np.linspace(min(wavelengths), max(wavelengths), k)
print new_xs
print "reducing data"
mapping = nearestColumnMapping(new_xs, df.columns[3:])
newData = df.apply(reduceDimRow, axis = 1, centers = mapping)
print "saving reduced data"
newData.to_csv("../data/CCS/subsetShots_5pct_reduced_uniform.csv", index=None)
