orange = [1.,0.5,0.]
gray = [0.6, 0.6, 0.6]
# Generate data
N = 100
G = 100
alpha = 3
data_type = 'wide'
# Create new data or load old data
new_data = True
if new_data:
# Simulate data and get default deft settings
data, defaults = simulate_data_1d.run(data_type,N)
pickle.dump( (data,defaults), open( "data.p", "wb" ) )
else:
data, defaults = pickle.load(open("data.p","rb"))
# Set bounding box
bbox = [defaults['box_min'], defaults['box_max']]
L = bbox[1]-bbox[0]
h = 1.*L/G
# Create a variety of histograms
num_bins = [5,20,100]
plt.figure()
# Colors to use
blue = [0.,0.,1.]
lightblue = [0.0, 0.5, 1.0]
orange = [1.,0.5,0.]
gray = [0.6, 0.6, 0.6]
# Plot histogram with density estimate on top
plt.figure(figsize=[ 11.55, 10.25])
num_rows = len(alphas)
num_cols = len(data_types)
for d, data_type in enumerate(data_types):
data, settings = simulate_data_1d.run(data_type,N)
box = [settings['box_min'], settings['box_max']]
# Histogram data
R, xs = utils.histogram_counts_1d(data, G, bbox=box, normalized=True)
h = xs[1]-xs[0]
for a, alpha in enumerate(alphas):
ax = plt.subplot(num_rows, num_cols, num_cols*a + d + 1)
# Get basis defining moments to constrain
basis = utils.legendre_basis_1d(G,alpha)
# Compute maxent distribution for histogram
start_time = time.clock()