def main(filename, seed, fntSize=18):
fgsz = (6, 6)
results = {}
ns = range(2, 13) # number of states
plt.rcParams.update({'font.size': fntSize})
for n in ns:
with open(f"{filename}_{n}_{seed}.json") as f:
results[n] = json.load(f)
ylabels = ['log-likelihood', 'K']
scales = [1, 1]
for i, idx in enumerate(['likelihood', 'K-S']):
fig = plt.figure(figsize=fgsz)
ax1 = fig.add_subplot(111)
fig.subplots_adjust(left=0.2)
training = [results[n][idx + f'_100'][0] for n in ns]
test = [results[n][idx + f'_100'][1] for n in ns]
ax1.plot(ns, training, '-.')
ax1.plot(ns, test, ':')
ax1.legend(['Training', 'Test'])
ax1.set_xlabel('n')
ax1.xaxis.set_major_locator(MaxNLocator(integer=True))
ax1.set_ylabel(ylabels[i])
fmmts = matplotlib.ticker.ScalarFormatter()
fmmts.set_powerlimits((-4, 3))
ax1.yaxis.set_major_formatter(fmmts)
auxFs.clean_axes(ax1)
if i == 1:
ax1.set_ylim(0, 0.07)
fig.savefig(f"{filename}_{idx}.pdf")
def main(fileName, fntSize=16):
numLags = 60
ds = np.loadtxt(f"{fileName}.txt").transpose()
trDs, valDs = auxFs.divide_data(ds)
data = valDs.flatten(order="C")
lengthdata = data.shape[0]
plt.rcParams.update({'font.size': fntSize})
cols = ['C0', 'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9']
lnstyle = ['-', ':', '-.', '--']
fig1 = plt.figure(figsize=(8, 8))
fig2 = plt.figure(figsize=(8, 8))
ax1 = fig1.add_subplot(111, position=[0.175, 0.1, 0.65, 0.65])
ax2 = fig2.add_subplot(111, position=[0.175, 0.1, 0.65, 0.65])
xKde = np.linspace(0.0, 1.5, num=150)
ax1.plot(xKde, kde_fit_estimate(data, xKde), 'k')
acfTst = acf(data, nlags=numLags)
maeDict = {}
ax2.plot(acf(trDs.flatten(order="c"), nlags=numLags),
color='k',
linestyle='-')
ax2.plot(acfTst, color='k', linestyle=':')
scales = [20, 30, 40, 50, 60, 80, 100]
filenames = [f"{fileName}_3_{i}_fixedMeans.txt" for i in scales]
for i, v in enumerate(filenames):
X = np.loadtxt(v)
ax1.plot(xKde,
kde_fit_estimate(X.flatten(), xKde),
linestyle=lnstyle[i % 3],
color=cols[i % 10])
acfModel = auxFs.ACF(X.flatten(), numLags)
ax2.plot(acfModel, linestyle=lnstyle[i % 3], color=cols[i % 10])
maeDict[str(scales[i] / 100)] = mean_abs_error(acfTst, acfModel)
legendTxt = ["Test data"
] + [r"$\phi$" + f" = {i/100:0.2f}" for i in scales]
ax1.legend(legendTxt,
loc='lower center',
bbox_to_anchor=(0.5, 1.01),
ncol=4)
ax1.set_xlim(0, 1.75)
ax1.set_ylim(0, 4.0)
ax1.set_ylabel("PDF")
ax1.set_xlabel("CSI")
auxFs.clean_axes(ax1)
ax2.legend(["Training data"] + legendTxt,
loc='lower center',
bbox_to_anchor=(0.5, 1.01),
ncol=3)
ax2.set_xlim(0, 60)
ax2.set_ylabel("ACF")
ax2.set_xlabel("lags (minute)")
auxFs.clean_axes(ax2)
fig1.savefig(f"{fileName}_scaled_histograms.pdf")
fig2.savefig(f"{fileName}_scaled_acf.pdf")
with open(f"{fileName}_acf_mae_scaled.json", 'w') as f:
json.dump(maeDict, f, indent=4)
def main(fileName, ymax, fntSize=16, numLags=60, fgSz=(6, 6)):
ds = np.loadtxt(f"{fileName}.txt").transpose()
trDs, valDs = auxFs.divide_data(ds)
lengthTr = auxFs.calculate_length_sequence(trDs)
lengthVal = auxFs.calculate_length_sequence(valDs)
trDs = trDs.reshape(-1, 1)
valDs = valDs.reshape(-1, 1)
dataSets = [trDs, valDs]
lengths = [lengthTr, lengthVal]
sample = np.loadtxt(f"{fileName}_3_100_fixedMeans.txt")
fig = plt.figure(figsize=fgSz)
plt.rcParams.update({'font.size': fntSize})
ax = fig.add_subplot(111)
acfTrnData = acf(trDs, nlags=numLags)
acfTstData = acf(valDs, nlags=numLags)
acfSample = auxFs.ACF(sample, numLags)
ax.plot(acfTrnData, color='k', linestyle='-')
ax.plot(acfTstData, color='k', linestyle=':')
ax.plot(acfSample)
ax.set_xlabel("lags (minute)")
ax.set_ylabel("ACF")
ax.set_xlim(0, 60)
auxFs.clean_axes(ax)
ax.legend(["Training data", "Test data", "model sample"])
fig.savefig(f"{fileName}_acf_fixedMeans.pdf")
print(mean_abs_error(acfTstData, acfSample))
histFigure = plt.figure(figsize=(6, 6))
ax2 = histFigure.add_subplot(111)
ax2.hist(valDs, 100, density=True)
def do_on_axes(ax):
ax.set_xlabel("CSI")
ax.set_ylabel("PDF")
auxFs.clean_axes(ax)
ax.set_xlim(0, 1.75)
ax.set_ylim(top=ymax)
#ax.set_xticklabels([])
#ax.set_yticklabels([])
do_on_axes(ax2)
histFigure.savefig(f"{fileName}_histogram_fixedMeans_test.pdf")
hist2 = plt.figure(figsize=(6, 6))
ax2 = hist2.add_subplot(111)
ax2.hist(sample, 100, density=True)
do_on_axes(ax2)
hist2.savefig(f"{fileName}_histogram_fixedMeans_sample.pdf")
def main(fileName, ymax=4.0, fntSize=14):
nbins = 100
seed = 100
ds = np.loadtxt(f"{fileName}.txt").transpose()
trDs, valDs = auxFs.divide_data(ds)
data = valDs.flatten(order="C")
lengthdata = data.shape[0]
plt.rcParams.update({'font.size': fntSize})
# Plot histogram
fig = plt.figure(figsize=(6,8.5))
ax = plt.subplot(2,4,1)
ax.hist(data, nbins, density=True)
ax.set_ylabel("PDF")
#ax.set_xlabel("CSI")
ax.set_title("Test data")
ax.set_xlim(0,1.75)
ax.set_ylim(0,ymax)
ax.set_yticklabels([])
ax.set_xticklabels([])
auxFs.clean_axes(ax)
scales = [20, 30, 40, 50, 60, 80, 100]
filenames = [f"{fileName}_3_{i}_fixedMeans.txt" for i in scales]
for i, v in enumerate(filenames):
X = np.loadtxt(v)
#fig = plt.figure(figsize=(6,6))
ax = plt.subplot(2,4, i+2)
ax.hist(X, nbins, density=True)
if i in [3]:
ax.set_ylabel("PDF")
if i in range(3,8):
ax.set_xlabel("CSI")
ax.set_title(r"$\phi$ = "+ f"{scales[i]/100:.2f}")
#ax.set_xlabel("CSI")
auxFs.clean_axes(ax)
ax.set_xlim(0,1.75)
ax.set_ylim(0,ymax)
ax.set_yticklabels([])
ax.set_xticklabels([])
fig.savefig(f"{fileName}_scaled_process_histogram.pdf")
def main(fntSize=18):
maeDict1 = read_json(f"Ahawaii_acf_mae.json")
maeDict2 = read_json(f"A_acf_mae.json")
labels = ['Hawaii', 'Norrköping']
ns = range(2,13)
fgsz = (6,6)
seed = 100
plt.rcParams.update({'font.size': fntSize})
trainTimeHawaii, trainTimeNorr = {}, {}
for n in ns:
trainTimeHawaii[n] = read_json(f"Ahawaii_{n}_{seed}.json")
trainTimeNorr[n] = read_json(f"A_{n}_{seed}.json")
# Plot the acf error as function of the state
fig1 = plt.figure(figsize=fgsz)
ax1 = fig1.add_subplot(111)
fig1.subplots_adjust(left=0.2)
ax1.plot(ns, maeDict1.values())
ax1.plot(ns, maeDict2.values())
ax1.set_xlabel('n')
ax1.xaxis.set_major_locator(MaxNLocator(integer=True))
ax1.set_ylabel('ACF MAE')
ax1.legend(labels)
auxFs.clean_axes(ax1)
ax1.set_ylim(0)
fig1.savefig(f"acf_mae.pdf")
# Plot the training time
fig2 = plt.figure(figsize=fgsz)
ax2 = fig2.add_subplot(111)
fig2.subplots_adjust(left=0.2)
timeH = [trainTimeHawaii[n][f'train_time_100'] for n in ns]
timeN = [trainTimeNorr[n][f'train_time_100'] for n in ns]
ax2.plot(ns, timeH)
ax2.plot(ns, timeN)
ax2.set_xlabel('n')
ax2.xaxis.set_major_locator(MaxNLocator(integer=True))
ax2.legend(labels)
ax2.set_ylabel('Training time (second)')
auxFs.clean_axes(ax2)
ax2.set_ylim(0)
fig2.savefig(f"train_time.pdf")
def main(fileName, ymax=3.5, fntSize=14):
nbins = 100
seed = 100
ds = np.loadtxt(f"{fileName}.txt").transpose()
trDs, valDs = auxFs.divide_data(ds)
data = valDs.flatten(order="C")
lengthdata = data.shape[0]
plt.rcParams.update({'font.size': fntSize})
# Plot histogram
fig = plt.figure(figsize=(6, 8.5))
ax = plt.subplot(4, 3, 1)
ax.hist(data, nbins, density=True)
ax.set_ylabel("PDF")
#ax.set_xlabel("CSI")
ax.set_title("Test data")
ax.set_xlim(0, 1.75)
ax.set_ylim(0, ymax)
ax.set_yticklabels([])
ax.set_xticklabels([])
auxFs.clean_axes(ax)
for i in range(2, 13):
fn = f"{fileName}_{i}_100.txt"
X = np.loadtxt(fn)
#fig = plt.figure(figsize=(6,6))
ax = plt.subplot(4, 3, i)
ax.hist(X, nbins, density=True)
if i in [4, 7, 10]:
ax.set_ylabel("PDF")
if i in [10, 11, 12]:
ax.set_xlabel("CSI")
ax.set_title(f"n = {i}")
#ax.set_xlabel("CSI")
auxFs.clean_axes(ax)
ax.set_xlim(0, 1.75)
ax.set_ylim(0, ymax)
ax.set_yticklabels([])
ax.set_xticklabels([])
fig.savefig(f"{fileName}_test_histogram.pdf")
def main(fntSize=18):
plt.rcParams.update({'font.size': fntSize})
numLags = 60
length = 9000
mu = [0.433, 0.982, 1.025]
variance = [0.0312, 0.00032, 0.016]
samples = [
np.random.normal(mu[i], np.sqrt(variance[i]), int(length / 3))
for i in range(3)
]
series1 = np.concatenate(samples)
#series1.reshape(3,-1).flatten(order='F') # flatten row wise
fig = plt.figure(figsize=(10.5, 10.5 * 9 / 16))
ax1 = fig.add_subplot(111)
ax1.plot(acf(series1, nlags=numLags))
np.random.shuffle(series1)
ax1.plot(acf(series1, nlags=numLags))
ax1.set_xlim(0.0)
ax1.set_ylabel("ACF")
ax1.set_xlabel("lags")
auxFs.clean_axes(ax1)
ax1.legend(["Process 1", "Process 2"])
fig.savefig(f"ACF_twoProcesses.pdf")
def do_on_axes(ax):
ax.set_xlabel("CSI")
ax.set_ylabel("PDF")
auxFs.clean_axes(ax)
ax.set_xlim(0, 1.75)
ax.set_ylim(top=ymax)