This should be a slide instead of static HTML

In [10]:
os.getcwd()
babe = scm.imread("puppy.jpg")
plt.imshow(babe)
plt.show()
In [5]:
data_url = "http://kinetics.nist.gov/janaf/html/C-067.txt"
pd.read_csv(data_url, sep='\t').head()
Out[5]:
Methane (CH4) C1H4(g)
T(K) Cp S -[G-H(Tr)]/T H-H(Tr) delta-f H delta-f G log Kf
0 0. 0. INFINITE -10.024 -66.911 -66.911 INFINITE
100 33.258 149.500 216.485 -6.698 -69.644 -64.353 33.615
200 33.473 172.577 189.418 -3.368 -72.027 -58.161 15.190
250 34.216 180.113 186.829 -1.679 -73.426 -54.536 11.395

data from http://kinetics.nist.gov/janaf/html/C-067.txt

Probability Distribution

In [8]:
from scipy.stats import norm

x_norm = norm.rvs(size=500)

h = scp.histogram(x_norm, normed=True, bins=50)

# Print mean and standard deviation
print(norm.fit(x_norm))

plt.hist(x_norm, normed=True, bins=20)

x = np.linspace(-3,3,50)
plt.plot(x, norm.pdf(x), '.r-')

(-0.007462693515075159, 0.97944995438956184)
Out[8]:
[<matplotlib.lines.Line2D at 0x1078bb810>]

Using 'Traps' as numerical integration tool

In [4]:
# Compute probability that certain values 
# lie between two endpoints.

from scipy.integrate import trapz

x1 = np.linspace(-3,3,100)

p = trapz(norm.pdf(x1), x1)

# plt.plot(x, norm.pdf(x), '-b.')

plt.plot(x, norm.pdf(x, loc=0, scale=1) )

plt.plot(x, norm.pdf(x, loc=0.5, scale=2) )

plt.plot(x, norm.pdf(x, loc=0.5, scale=0.5) )
Out[4]:
[<matplotlib.lines.Line2D at 0x108facfd0>]
In [5]:
from scipy.stats import norm, ttest_ind, ttest_rel, ttest_1samp

n1 = norm(loc=0.3, scale=1.0)
n2 = norm(loc=0.0, scale=1.0)

n1_samples = n1.rvs(size=100)
n2_samples = n2.rvs(size=100)

samples = np.concatenate([n1_samples, n2_samples], axis=0)

loc, scale = norm.fit(samples)

n = norm(loc=loc, scale=scale)


x = np.linspace(-3, 3, 100)
h = plt.hist([n1_samples, n2_samples], normed=True)

plt.plot(x,n.pdf(x), 'b-')
plt.plot(x,n1.pdf(x), 'r-')
plt.plot(x,n2.pdf(x), 'k-')
Out[5]:
[<matplotlib.lines.Line2D at 0x109223750>]
In [ ]: