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! wget http://www.hobieco.com/linked_images/H18-Magnum.jpg
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from IPython.display import Image
Image(filename='H18-Magnum.jpg')
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from matplotlib.pyplot import imshow
import numpy as np
from PIL import Image
%matplotlib inline
pil_im = Image.open('H18-Magnum.jpg', 'r')
imshow(np.asarray(pil_im))
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%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
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from ipywidgets import interactive
from IPython.display import Audio, display
import numpy as np
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def beat_freq(f1=220.0, f2=224.0):
max_time = 3
rate = 8000
times = np.linspace(0,max_time,rate*max_time)
signal = np.sin(2*np.pi*f1*times) + np.sin(2*np.pi*f2*times)
print(f1, f2, abs(f1-f2))
display(Audio(data=signal, rate=rate))
return signal
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v = interactive(beat_freq, f1=(200.0,300.0), f2=(200.0,300.0))
display(v)
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v.kwargs
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f1, f2 = v.children
f1.value = 255
f2.value = 260
plt.plot(v.result[0:6000])
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'''
Make a colorbar as a separate figure.
'''
from matplotlib import pyplot
import matplotlib as mpl
# Make a figure and axes with dimensions as desired.
fig = pyplot.figure(figsize=(8,3))
ax1 = fig.add_axes([0.05, 0.80, 0.9, 0.15])
ax2 = fig.add_axes([0.05, 0.475, 0.9, 0.15])
ax3 = fig.add_axes([0.05, 0.15, 0.9, 0.15])
# Set the colormap and norm to correspond to the data for which
# the colorbar will be used.
cmap = mpl.cm.cool
norm = mpl.colors.Normalize(vmin=5, vmax=10)
# ColorbarBase derives from ScalarMappable and puts a colorbar
# in a specified axes, so it has everything needed for a
# standalone colorbar. There are many more kwargs, but the
# following gives a basic continuous colorbar with ticks
# and labels.
cb1 = mpl.colorbar.ColorbarBase(ax1, cmap=cmap,
norm=norm,
orientation='horizontal')
cb1.set_label('Some Units')
# The second example illustrates the use of a ListedColormap, a
# BoundaryNorm, and extended ends to show the "over" and "under"
# value colors.
cmap = mpl.colors.ListedColormap(['r', 'g', 'b', 'c'])
cmap.set_over('0.25')
cmap.set_under('0.75')
# If a ListedColormap is used, the length of the bounds array must be
# one greater than the length of the color list. The bounds must be
# monotonically increasing.
bounds = [1, 2, 4, 7, 8]
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
cb2 = mpl.colorbar.ColorbarBase(ax2, cmap=cmap,
norm=norm,
# to use 'extend', you must
# specify two extra boundaries:
boundaries=[0]+bounds+[13],
extend='both',
ticks=bounds, # optional
spacing='proportional',
orientation='horizontal')
cb2.set_label('Discrete intervals, some other units')
# The third example illustrates the use of custom length colorbar
# extensions, used on a colorbar with discrete intervals.
cmap = mpl.colors.ListedColormap([[0., .4, 1.], [0., .8, 1.],
[1., .8, 0.], [1., .4, 0.]])
cmap.set_over((1., 0., 0.))
cmap.set_under((0., 0., 1.))
bounds = [-1., -.5, 0., .5, 1.]
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
cb3 = mpl.colorbar.ColorbarBase(ax3, cmap=cmap,
norm=norm,
boundaries=[-10]+bounds+[10],
extend='both',
# Make the length of each extension
# the same as the length of the
# interior colors:
extendfrac='auto',
ticks=bounds,
spacing='uniform',
orientation='horizontal')
cb3.set_label('Custom extension lengths, some other units')
pyplot.show()
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"""
Show how to make date plots in matplotlib using date tick locators and
formatters. See major_minor_demo1.py for more information on
controlling major and minor ticks
All matplotlib date plotting is done by converting date instances into
days since the 0001-01-01 UTC. The conversion, tick locating and
formatting is done behind the scenes so this is most transparent to
you. The dates module provides several converter functions date2num
and num2date
"""
import datetime
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.cbook as cbook
%matplotlib inline
years = mdates.YearLocator() # every year
months = mdates.MonthLocator() # every month
yearsFmt = mdates.DateFormatter('%Y')
# load a numpy record array from yahoo csv data with fields date,
# open, close, volume, adj_close from the mpl-data/example directory.
# The record array stores python datetime.date as an object array in
# the date column
datafile = cbook.get_sample_data('goog.npy')
r = np.load(datafile).view(np.recarray)
fig, ax = plt.subplots()
ax.plot(r.date, r.adj_close)
# format the ticks
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(yearsFmt)
ax.xaxis.set_minor_locator(months)
datemin = datetime.date(r.date.min().year, 1, 1)
datemax = datetime.date(r.date.max().year+1, 1, 1)
ax.set_xlim(datemin, datemax)
# format the coords message box
def price(x): return '$%1.2f'%x
ax.format_xdata = mdates.DateFormatter('%Y-%m-%d')
ax.format_ydata = price
ax.grid(True)
# rotates and right aligns the x labels, and moves the bottom of the
# axes up to make room for them
fig.autofmt_xdate()
plt.show()
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'''
Demo to show use of the engineering Formatter.
'''
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
from matplotlib.ticker import EngFormatter
fig, ax = plt.subplots()
ax.set_xscale('log')
formatter = EngFormatter(unit='Hz', places=1)
ax.xaxis.set_major_formatter(formatter)
xs = np.logspace(1, 9, 100)
ys = (0.8 + 0.4 * np.random.uniform(size=100)) * np.log10(xs)**2
ax.plot(xs, ys)
plt.show()
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"""
Show how to modify the coordinate formatter to report the image "z"
value of the nearest pixel given x and y
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
X = 10*np.random.rand(5,3)
fig, ax = plt.subplots()
ax.imshow(X, cmap=cm.jet, interpolation='nearest')
numrows, numcols = X.shape
def format_coord(x, y):
col = int(x+0.5)
row = int(y+0.5)
if col>=0 and col<numcols and row>=0 and row<numrows:
z = X[row,col]
return 'x=%1.4f, y=%1.4f, z=%1.4f'%(x, y, z)
else:
return 'x=%1.4f, y=%1.4f'%(x, y)
ax.format_coord = format_coord
plt.show()
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"""
Thanks to Tony Yu <tsyu80@gmail.com> for the logo design
"""
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.cm as cm
mpl.rcParams['xtick.labelsize'] = 10
mpl.rcParams['ytick.labelsize'] = 12
mpl.rcParams['axes.edgecolor'] = 'gray'
axalpha = 0.05
#figcolor = '#EFEFEF'
figcolor = 'white'
dpi = 80
fig = plt.figure(figsize=(6, 1.1),dpi=dpi)
fig.figurePatch.set_edgecolor(figcolor)
fig.figurePatch.set_facecolor(figcolor)
def add_math_background():
ax = fig.add_axes([0., 0., 1., 1.])
text = []
text.append((r"$W^{3\beta}_{\delta_1 \rho_1 \sigma_2} = U^{3\beta}_{\delta_1 \rho_1} + \frac{1}{8 \pi 2} \int^{\alpha_2}_{\alpha_2} d \alpha^\prime_2 \left[\frac{ U^{2\beta}_{\delta_1 \rho_1} - \alpha^\prime_2U^{1\beta}_{\rho_1 \sigma_2} }{U^{0\beta}_{\rho_1 \sigma_2}}\right]$", (0.7, 0.2), 20))
text.append((r"$\frac{d\rho}{d t} + \rho \vec{v}\cdot\nabla\vec{v} = -\nabla p + \mu\nabla^2 \vec{v} + \rho \vec{g}$",
(0.35, 0.9), 20))
text.append((r"$\int_{-\infty}^\infty e^{-x^2}dx=\sqrt{\pi}$",
(0.15, 0.3), 25))
#text.append((r"$E = mc^2 = \sqrt{{m_0}^2c^4 + p^2c^2}$",
# (0.7, 0.42), 30))
text.append((r"$F_G = G\frac{m_1m_2}{r^2}$",
(0.85, 0.7), 30))
for eq, (x, y), size in text:
ax.text(x, y, eq, ha='center', va='center', color="#11557c", alpha=0.25,
transform=ax.transAxes, fontsize=size)
ax.set_axis_off()
return ax
def add_matplotlib_text(ax):
ax.text(0.95, 0.5, 'matplotlib', color='#11557c', fontsize=65,
ha='right', va='center', alpha=1.0, transform=ax.transAxes)
def add_polar_bar():
ax = fig.add_axes([0.025, 0.075, 0.2, 0.85], polar=True)
ax.axesPatch.set_alpha(axalpha)
ax.set_axisbelow(True)
N = 7
arc = 2. * np.pi
theta = np.arange(0.0, arc, arc/N)
radii = 10 * np.array([0.2, 0.6, 0.8, 0.7, 0.4, 0.5, 0.8])
width = np.pi / 4 * np.array([0.4, 0.4, 0.6, 0.8, 0.2, 0.5, 0.3])
bars = ax.bar(theta, radii, width=width, bottom=0.0)
for r, bar in zip(radii, bars):
bar.set_facecolor(cm.jet(r/10.))
bar.set_alpha(0.6)
for label in ax.get_xticklabels() + ax.get_yticklabels():
label.set_visible(False)
for line in ax.get_ygridlines() + ax.get_xgridlines():
line.set_lw(0.8)
line.set_alpha(0.9)
line.set_ls('-')
line.set_color('0.5')
ax.set_yticks(np.arange(1, 9, 2))
ax.set_rmax(9)
if __name__ == '__main__':
main_axes = add_math_background()
add_polar_bar()
add_matplotlib_text(main_axes)
plt.show()
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import matplotlib.path as mpath
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
Path = mpath.Path
fig, ax = plt.subplots()
pp1 = mpatches.PathPatch(
Path([(0, 0), (1, 0), (1, 1), (0, 0)],
[Path.MOVETO, Path.CURVE3, Path.CURVE3, Path.CLOSEPOLY]),
fc="none", transform=ax.transData)
ax.add_patch(pp1)
ax.plot([0.75], [0.25], "ro")
ax.set_title('The red point should be on the path')
plt.show()
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"""
Illustrate some helper functions for shading regions where a logical
mask is True
See :meth:`matplotlib.collections.BrokenBarHCollection.span_where`
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.collections as collections
t = np.arange(0.0, 2, 0.01)
s1 = np.sin(2*np.pi*t)
s2 = 1.2*np.sin(4*np.pi*t)
fig, ax = plt.subplots()
ax.set_title('using span_where')
ax.plot(t, s1, color='black')
ax.axhline(0, color='black', lw=2)
collection = collections.BrokenBarHCollection.span_where(
t, ymin=0, ymax=1, where=s1>0, facecolor='green', alpha=0.5)
ax.add_collection(collection)
collection = collections.BrokenBarHCollection.span_where(
t, ymin=-1, ymax=0, where=s1<0, facecolor='red', alpha=0.5)
ax.add_collection(collection)
plt.show()
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"""
Use a Text as a watermark
"""
import numpy as np
#import matplotlib
#matplotlib.use('Agg')
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot(np.random.rand(20), '-o', ms=20, lw=2, alpha=0.7, mfc='orange')
ax.grid()
# position bottom right
fig.text(0.95, 0.05, 'Property of MPL',
fontsize=50, color='gray',
ha='right', va='bottom', alpha=0.5)
plt.show()
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# a bar plot with errorbars
import numpy as np
import matplotlib.pyplot as plt
N = 5
menMeans = (20, 35, 30, 35, 27)
menStd = (2, 3, 4, 1, 2)
ind = np.arange(N) # the x locations for the groups
width = 0.35 # the width of the bars
fig, ax = plt.subplots()
rects1 = ax.bar(ind, menMeans, width, color='r', yerr=menStd)
womenMeans = (25, 32, 34, 20, 25)
womenStd = (3, 5, 2, 3, 3)
rects2 = ax.bar(ind+width, womenMeans, width, color='y', yerr=womenStd)
# add some text for labels, title and axes ticks
ax.set_ylabel('Scores')
ax.set_title('Scores by group and gender')
ax.set_xticks(ind+width)
ax.set_xticklabels( ('G1', 'G2', 'G3', 'G4', 'G5') )
ax.legend( (rects1[0], rects2[0]), ('Men', 'Women') )
def autolabel(rects):
# attach some text labels
for rect in rects:
height = rect.get_height()
ax.text(rect.get_x()+rect.get_width()/2., 1.05*height, '%d'%int(height),
ha='center', va='bottom')
autolabel(rects1)
autolabel(rects2)
plt.show()
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