def example1():
import sys
import scipy as sp
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import pyfits
#Path to fits file to be imported
data1 = "/Users/destry/Documents/Github/truffles_examples/GALFA_HI_RA+DEC_092.00+10.35_W.fits"
#Read out basic info
pyfits.info(data1)
#Load file header into keys
header = pyfits.getheader(data1)
header.keys()
#Load actual data
data_cube = pyfits.getdata(data1, 0)
print 'Type: ', type(data_cube)
print 'Shape:', data_cube.shape
#I'm just going to look at a random slice
slice1 = data_cube[45, :, :]
#Show the slice
plt.imshow(slice1)
plt.winter()
plt.show()
def scatterd(a):
clrs = a.nlab().flatten()
sz = a.data.shape
if (sz[1] > 1):
plt.scatter(a.data[:, 0], a.data[:, 1], c=clrs)
ylab = a.featlab[1]
else:
plt.scatter(a.data[:, 0], numpy.zeros((sz[0], 1)), c=clrs)
ylab = ''
plt.title(a.name)
plt.xlabel('Feature ' + str(a.featlab[0]))
plt.ylabel('Feature ' + str(ylab))
plt.winter()
def scatter3d(a):
clrs = a.nlab().flatten()
sz = a.data.shape
if (sz[1]>2):
ax = plt.axes(projection='3d')
ax.scatter3D(a.data[:,0],a.data[:,1],a.data[:,2],c=clrs)
ylab = a.featlab[1]
zlab = a.featlab[2]
else:
raise ValueError('Please supply at least 3D data.')
plt.title(a.name)
ax.set_xlabel('Feature '+str(a.featlab[0]))
ax.set_ylabel('Feature '+str(ylab))
ax.set_zlabel('Feature '+str(zlab))
plt.winter()
def main():
#Get the raster from the disk
rast_data, x_cellsize, y_cellsize = get_array("./data/elevation.tif")
slope = generic_filter(rast_data, calc_slope, size=3, extra_arguments=(x_cellsize, y_cellsize))
plt.imshow(ma.masked_equal(slope, -9999), cmap=plt.winter(), origin="lower")
plt.show()
def Cp_plot():
"""範囲指定"""
plt.xlim(-3.0, 3.0)
plt.ylim(-3.0, 3.0)
"""翼を描く(翼はξ、ηの配列のr=0の部分)"""
plt.plot(xi[:, 0], eta[:, 0])
"""等圧線を描く"""
plt.contour(xi[:, :],
eta[:, :],
Cp0[:, :],
locator=plt.MultipleLocator(0.1))
plt.winter()
plt.suptitle = 'Kutta'
plt.colorbar()
plt.show()
def scatterr(a):
sz = a.data.shape
if (sz[1]==1):
plt.scatter(a.data[:,0],a.targets)
plt.title(a.name)
plt.xlabel('Feature '+str(a.featlab[0]))
plt.ylabel('Target')
plt.winter()
elif (sz[1]==2):
ax = plt.axes(projection='3d')
ax.scatter3D(a.data[:,0],a.data[:,1],a.targets)
ylab = a.featlab[1]
plt.title(a.name)
ax.set_xlabel('Feature '+str(a.featlab[0]))
ax.set_ylabel('Feature '+str(ylab))
ax.set_zlabel('Targets')
else:
raise ValueError('Please supply at least 2D data.')
def Cp_plot():
"""範囲指定"""
plt.xlim(-3.0, 3.0)
plt.ylim(-3.0, 3.0)
"""翼を描く(翼はξ、ηの配列のr=0の部分)"""
plt.plot(xi[:, 0], eta[:, 0])
"""等圧線を描く"""
plt.contour(xi[:, :],
eta[:, :],
Cp0[:, :],
locator=plt.MultipleLocator(0.1))
plt.winter()
plt.title('Isobar')
plt.xlabel("ξ")
plt.ylabel("η")
cbar = plt.colorbar()
cbar.set_label("Cp")
plt.show()
def plot4DGraph(clusters):
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
iter = 0
for cluster in clusters:
u_val = [obj[0] for obj in clusters[cluster]]
v_val = [obj[1] for obj in clusters[cluster]]
w_val = [obj[2] for obj in clusters[cluster]]
x_val = [obj[3] for obj in clusters[cluster]]
if iter == 0:
img1 = ax.scatter(u_val,
v_val,
w_val,
s=75,
c=x_val,
cmap=plt.winter(),
label='cluster1')
cbar = fig.colorbar(img1, shrink=0.5, aspect=10)
elif iter == 1:
img2 = ax.scatter(u_val,
v_val,
w_val,
s=75,
c=x_val,
cmap=plt.spring(),
label='cluster2')
cbar = fig.colorbar(img2, shrink=0.5, aspect=10)
else:
img3 = ax.scatter(u_val,
v_val,
w_val,
s=75,
c=x_val,
cmap=plt.gray(),
label='cluster3')
cbar = fig.colorbar(img3, shrink=0.5, aspect=10)
iter += 1
cbar.ax.get_yaxis().labelpad = 15
cbar.ax.set_ylabel('petal width in cm')
cbar.ax.get_xaxis().labelpad = 15
cbar.ax.set_xlabel('cluster' + str(iter))
ax.set_xlabel('sepal length in cm', rotation=150)
ax.set_ylabel('sepal width in cm')
ax.set_zlabel(r'petal length in cm', rotation=60)
plt.title("4D representation of clustering solution")
plt.show()
def main():
#Get the raster from the disk
rast_data, x_cellsize, y_cellsize = get_array("./data/elevation.tif")
slope = generic_filter(rast_data,
calc_slope,
size=3,
extra_arguments=(x_cellsize, y_cellsize))
plt.imshow(ma.masked_equal(slope, -9999),
cmap=plt.winter(),
origin="lower")
plt.show()
def play_plot(days=365):
plays = link.get_plays(days)
by_game = {}
for p in plays:
if p['name'] in by_game:
by_game[p['name']].extend([std(p['date'])] * p['plays'])
else:
by_game[p['name']] = [std(p['date'])]
fig, ax = plt.subplots()
plt.title(
f"Game Plays, Past {'Year' if -5 <= days - 365 <= 5 else f'{days} Day'+('s' if days != 1 else '')} [Size ∝ Plays]"
)
plt.winter()
plt.xticks(rotation=45)
plt.yticks(weight='bold')
plt.xlabel("Date")
colors = ['red', 'orange', '#FADA5e', 'green', 'blue', 'indigo', 'violet']
for i, [game, dates] in enumerate(by_game.items()):
date_count = Counter(dates)
for date, count in sorted(list(date_count.items()),
key=lambda c: c[1])[::-1]:
ax.plot_date(
date,
game,
ms=5 + count,
alpha=1 if count == 1 else max(1 / (0.8 * count), 0.5),
color=colors[i % len(colors)])
for i, ytick in enumerate(ax.get_yticklabels()):
ytick.set_color(colors[i % len(colors)])
ax.set_xlim(right=datetime.today())
gcf = plt.gcf()
gcf.autofmt_xdate()
plt.show()
def scatterr(a):
plt.scatter(a.data[:, 0], a.targets)
plt.title(a.name)
plt.xlabel('Feature ' + str(a.featlab[0]))
plt.ylabel('Target')
plt.winter()
def labelPlot(xName, yName, labelName='Part #', **kwargs):
plt.ioff()
cmap = plt.winter()
x = np.array(oap[xName])
y = np.array(oap[yName])
c = np.array(oap.get(kwargs.get('cName', None), np.zeros(np.shape(x))))
try:
d = c + 1
except:
d = list(set(c))
d.sort()
c = np.array(map(lambda a: d.index(a), c)) + 1
names = oap.get(labelName, 'Part #')
xscale = kwargs.get('xscale', 'linear')
yscale = kwargs.get('yscale', 'linear')
cscale = kwargs.get('cscale', 'linear')
xlim = kwargs.get('xlim', (0, np.Inf) if xscale == 'log' else
(-np.Inf, np.Inf))
ylim = kwargs.get('ylim', (0, np.Inf) if yscale == 'log' else
(-np.Inf, np.Inf))
clim = kwargs.get('clim', (0, np.Inf) if cscale == 'log' else
(-np.Inf, np.Inf))
mask = np.array(kwargs.get('mask', np.array(
[False] * np.shape(x)[0]))) | filterMask(
kwargs['filter']) | (x <= xlim[0]) | (x > xlim[1]) | (
y <= ylim[0]) | (y > ylim[1]) | (c <= clim[0]) | (c > clim[1])
xm = np.ma.masked_array(x, mask)
ym = np.ma.masked_array(y, mask)
cm = np.ma.masked_array(c, mask)
sm = np.ma.masked_array(kwargs.get('s', 17 * np.ones(np.shape(x))), mask)
namesm = np.ma.masked_array(names, mask)
xp = np.log10(xm) if xscale == "log" else xm
yp = np.log10(ym) if yscale == "log" else ym
cp = np.log10(cm) if cscale == "log" else cm
fig, ax = plt.subplots(figsize=(8, 4.5), dpi=128)
sc = ax.scatter(xp, yp, c=cp, s=sm, cmap=cmap, alpha=0.7)
sc.set_clim(np.min(cp), np.max(cp))
cb = plt.colorbar(sc)
if xscale == "log":
xMinorTicks, xMinorTickLabels = logTicks(np.min(xp), np.max(xp))
ax.set_xticks(xMinorTicks)
ax.set_xticklabels(xMinorTickLabels)
if yscale == "log":
yMinorTicks, yMinorTickLabels = logTicks(np.min(yp), np.max(yp))
ax.set_yticks(yMinorTicks)
ax.set_yticklabels(yMinorTickLabels)
if cscale == "log":
cMinorTicks, cMinorTickLabels = logTicks(np.min(cp), np.max(cp))
cb.set_ticks(cMinorTicks)
cb.set_ticklabels(cMinorTickLabels)
sc.set_clim(np.min(cMinorTicks), np.max(cMinorTicks))
ax.grid(which="both")
tooltipLabels = [
'<span>{label}</span>'.format(label=l)
for l in namesm.compressed().ravel().tolist()
]
css = "span {background-color: white; font-weight: bold;}"
tooltip = mpld3.plugins.PointHTMLTooltip(sc, tooltipLabels, css=css)
mpld3.plugins.connect(fig, tooltip)
ret = mpld3.fig_to_html(fig, figid="figure")
plt.close(fig)
return ret
"""
An example of processing as an n-dimensional array.
"""
import numpy as np
from scipy import ndimage
import matplotlib.pyplot as plt
from glob import glob
lsat = None
try:
import arcpy
lsat = np.dstack(arcpy.RasterToNumPyArray(in_raster)
for in_raster in glob("./data/lsat7_2002_*.tif"))
except:
lsat = np.dstack(np.mean(plt.imread(in_raster, format="TIFF"), axis=2)
for in_raster in glob("./data/lsat7_2002_*.tif"))
print lsat.shape
median = ndimage.median_filter(lsat, size=9)
print median[:, :, 5].shape
plt.subplot(211)
plt.imshow(lsat[:, :, 5], cmap=plt.winter(), origin="lower")
plt.title("Original")
plt.subplot(212)
plt.imshow(median[:, :, 5], cmap=plt.winter(), origin="lower")
plt.title("Filtered")
plt.show()
"""
An example of processing as an n-dimensional array.
"""
import numpy as np
from scipy import ndimage
import matplotlib.pyplot as plt
from glob import glob
lsat = None
try:
import arcpy
lsat = np.dstack(arcpy.RasterToNumPyArray(in_raster) for in_raster in glob("./data/lsat7_2002_*.tif"))
except:
lsat = np.dstack(
np.mean(plt.imread(in_raster, format="TIFF"), axis=2) for in_raster in glob("./data/lsat7_2002_*.tif")
)
print lsat.shape
median = ndimage.median_filter(lsat, size=9)
print median[:, :, 5].shape
plt.subplot(211)
plt.imshow(lsat[:, :, 5], cmap=plt.winter(), origin="lower")
plt.title("Original")
plt.subplot(212)
plt.imshow(median[:, :, 5], cmap=plt.winter(), origin="lower")
plt.title("Filtered")
plt.show()
standard_deviation = standard_deviation_t
else:
break
if len(x) > len(x1):
f, ax = plt.subplots()
ax.set_title("KL-Divergence (%s)" % len(d1))
ax.set_ylabel('density')
ax.set_xlabel('q_value')
points = ax.scatter(x1, y1, c=d1, s=10, cmap=cmap)
f.colorbar(points)
plt.ylim((y_min, y_max))
plt.show()
print("")
cmap = plt.winter()
avg_0 = np.average(d)
d_l = list()
d_u = list()
x_l = list()
x_u = list()
y_l = list()
y_u = list()
for index in range(len(d)):
if d[index] < avg_0:
x_l.append(x[index])
y_l.append(y[index])
d_l.append(d[index])
elif d[index] > avg_0:
x_u.append(x[index])
plons.append (sum ([float(hotel["lng"]) for hotel in hotels]) / len (hotels))
else:
plons.append ((sum ([float(hotel["lng"]) for hotel in hotels]) / len (hotels))-0.05)
# Special case because of the shape of this department
# Calculating mean of latitudes
plats.append (sum ([float(hotel["lat"]) for hotel in hotels]) / len (hotels))
# Calculating total capacity
pcap.append (sum ([int(hotel[HIST_KEY]) for hotel in hotels]))
# Calculating mean of rankings (in stars)
pstars.append (sum ([int(hotel["classement"].split()[0]) for hotel in hotels]) / len (hotels))
# Calculating size and color of points (values between 0 and 1)
plt.winter()
psizes = [s / 85 for s in pcap]
# Displaying map background
bmap = basemap (projection = "merc", llcrnrlon = BMAP_LONMIN, llcrnrlat = BMAP_LATMIN, urcrnrlon = BMAP_LONMAX, urcrnrlat = BMAP_LATMAX)
# Displaying shape boundaries
bmap.readshapefile ("map_bounds/geoflar-departements", "map_bounds", drawbounds = True)
# Displaying points
plons, plats = bmap (plons, plats)
bmap.scatter (plons, plats, s = psizes, c= pstars, alpha = BMAP_ALPHA, marker = BMAP_MARKER)
# Displaying keys
leg_etoilefaible= mpatches.Rectangle((1,2), height = 1, width = 2, facecolor = (89/255,89/255,1))
leg_etoileeleve = mpatches.Rectangle((1,3), height = 1, width = 2, facecolor=(89/255,1,172/255))
plt.legend([leg_etoilefaible,leg_etoileeleve],["Nombre moyen d'étoiles faible","Nombre moyen d'étoiles élevé"] , loc =4)
df = pd.read_csv('ML_Data_Insight_121016.csv', header=1)
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
fig = plt.figure()
ax2 = fig.add_subplot(111, projection='3d')
x1 = df.ix[0:, 'x1']
x2 = df.ix[0:, 'x2']
x3 = df.ix[0:, 'x3']
y = df.ix[0:, 'y']
if sys.argv[1:] == ['winter']:
p = ax2.scatter(x1, x2, x3, c=y, cmap=plt.winter())
elif sys.argv[1:] == ['cool']:
p = ax2.scatter(x1, x2, x3, c=y, cmap=plt.cool())
elif sys.argv[1:] == ['viridis']:
p = ax2.scatter(x1, x2, x3, c=y, cmap=plt.viridis())
elif sys.argv[1:] == ['plasma']:
p = ax2.scatter(x1, x2, x3, c=y, cmap=plt.plasma())
elif sys.argv[1:] == ['inferno']:
p = ax2.scatter(x1, x2, x3, c=y, cmap=plt.inferno())
elif sys.argv[1:] == ['jet']:
p = ax2.scatter(x1, x2, x3, c=y, cmap=plt.jet())
elif sys.argv[1:] == ['gist_ncar']:
p = ax2.scatter(x1, x2, x3, c=y, cmap=plt.gist_ncar())
elif sys.argv[1:] == ['rainbow']:
p = ax2.scatter(x1, x2, x3, c=y, cmap=plt.nipy_spectral())
else:
