matplotlib-checkpoint



In [ ]:
%matplotlib inline
from pylab import plot,ylim,xlabel,ylabel,show
from numpy import linspace,sin,cos
x = linspace(0,10,100)
y1 = sin(x)
y2 = cos(x)
plot(x,y1,"k-")
plot(x,y2,"k--")
ylim(-1.1,1.1)
xlabel("x axis")
ylabel("y = sin x or y = cos x")

In [ ]:
%matplotlib inline
from matplotlib.pyplot import imshow
from PIL import Image, ImageDraw

w, h = 1200,1200

# create a new image with a white background

img = Image.new('RGB',(w,h),(0,0,0))
draw = ImageDraw.Draw(img)

# draw axis

draw.line((0,h/2,w,h/2),fill=(255,255,255))
draw.line((w/2,0,w/2,h),fill=(255,255,255))

imshow(img)

In [ ]:
from PIL import Image, ImageDraw
im = Image.new('RGBA', (400, 400), (0, 0, 0, 0)) 
draw = ImageDraw.Draw(im) 
draw.line((100,200, 150,300), fill=(255,0,0))
imshow(im)

In [ ]:
import pylab #Imports matplotlib and a host of other useful modules
cir1 = pylab.Circle((0,0), radius=0.75,  fc='y') #Creates a patch that looks like a circle (fc= face color)
cir2 = pylab.Circle((.5,.5), radius=0.25, alpha =.2, fc='b') #Repeat (alpha=.2 means make it very translucent)
ax = pylab.axes(aspect=1) #Creates empty axes (aspect=1 means scale things so that circles look like circles)
ax.add_patch(cir1) #Grab the current axes, add the patch to it
ax.add_patch(cir2) #Repeat
pylab.show()

In [ ]:
# Import matplotlib (plotting) and numpy (numerical arrays).
# This enables their use in the Notebook.
%matplotlib inline
import matplotlib.pyplot as plt 
import numpy as np

# Create an array of 30 values for x equally spaced from 0 to 5. 
x = np.linspace(0, 5, 30)
y = x**2

# Plot y versus x
fig, ax = plt.subplots(nrows=1, ncols=1)
ax.plot(x, y, color='red')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_title('A simple graph of $y=x^2$');

In [ ]:
# Import matplotlib (plotting) and numpy (numerical arrays).
# This enables their use in the Notebook.
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np

# Import IPython's interact function which is used below to
# build the interactive widgets
from IPython.html.widgets import interact

def plot_sine(frequency=4.0, grid_points=12, plot_original=True):
    """
    Plot discrete samples of a sine wave on the interval ``[0, 1]``.
    """
    x = np.linspace(0, 1, grid_points + 2)
    y = np.sin(2 * frequency * np.pi * x)

    xf = np.linspace(0, 1, 1000)
    yf = np.sin(2 * frequency * np.pi * xf)

    fig, ax = plt.subplots(figsize=(8, 6))
    ax.set_xlabel('x')
    ax.set_ylabel('signal')
    ax.set_title('Aliasing in discretely sampled periodic signal')

    if plot_original:
        ax.plot(xf, yf, color='red', linestyle='solid', linewidth=2)

    ax.plot(x,  y,  marker='o', linewidth=2)

# The interact function automatically builds a user interface for exploring the
# plot_sine function.
interact(plot_sine, frequency=(1.0, 22.0, 0.5), grid_points=(10, 16, 1), plot_original=True);

In [ ]:
# Import matplotlib (plotting), skimage (image processing) and interact (user interfaces)
# This enables their use in the Notebook.
%matplotlib inline
from matplotlib import pyplot as plt

from skimage import data
from skimage.feature import blob_doh
from skimage.color import rgb2gray

from IPython.html.widgets import interact, fixed

# Extract the first 500px square of the Hubble Deep Field.
image = data.hubble_deep_field()[0:500, 0:500]
image_gray = rgb2gray(image)

def plot_blobs(max_sigma=30, threshold=0.1, gray=False):
    """
    Plot the image and the blobs that have been found.
    """
    blobs = blob_doh(image_gray, max_sigma=max_sigma, threshold=threshold)
    
    fig, ax = plt.subplots(figsize=(8,8))
    ax.set_title('Galaxies in the Hubble Deep Field')
    
    if gray:
        ax.imshow(image_gray, interpolation='nearest', cmap='gray_r')
        circle_color = 'red'
    else:
        ax.imshow(image, interpolation='nearest')
        circle_color = 'yellow'
    for blob in blobs:
        y, x, r = blob
        c = plt.Circle((x, y), r, color=circle_color, linewidth=2, fill=False)
        ax.add_patch(c)

# Use interact to explore the galaxy detection algorithm.
interact(plot_blobs, max_sigma=(10, 40, 2), threshold=(0.005, 0.02, 0.001));