i plotted a density map with histograme2d and pcolomesh using a csv file containning lat/lon.
Now i want to turn the histogram2d output into contours matplotlib.
my code is as shown below:
import csv
import numpy as np
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
lats, lons = [], []
with open('fou.csv') as f:
reader = csv.reader(f)
next(reader) # Ignore the header row.
lonMin, lonMax, dLon = -20.0, 5.0, 5
latMin, latMax, dLat = 18.0, 40.0, 5
for row in reader:
lat = float(row[2])
lon = float(row[3])
# filter lat,lons to (approximate) map view:
if lonMin <= lon <= lonMax and latMin <= lat <= latMax:
lats.append( lat )
lons.append( lon )
m = Basemap(llcrnrlon=lonMin, llcrnrlat=latMin, urcrnrlon=lonMax, urcrnrlat=latMax, projection='merc', resolution='f')
m.drawcoastlines()
m.drawcountries()
m.drawstates()
db = 1 # bin padding
lon_bins = np.linspace(min(lons)-db, max(lons)+db, 100) # 10 bins
lat_bins = np.linspace(min(lats)-db, max(lats)+db, 100) # 13 bins
density, xbins, ybins =(np.histogram2d(lats, lons, [lat_bins, lon_bins]))
plt.contourf(density.transpose(),extent=[xbins[0],xbins[-1],ybins[0],ybins[-1]],levels = [1,5,10,25,50,70,80,100])
cbar = plt.colorbar(orientation='horizontal', shrink=0.625, aspect=20, fraction=0.2,pad=0.02)
cbar.set_label('the keraunic level density',size=18)
plt.gcf().set_size_inches(15,15)
plt.show()
what am I missing ?
Related
This is my code.I mentioned here 50 intervals,when i drag the slider then only i got 6 or 7 intervals,but i want to display the all my intervals in my colorbar. So can any one please guide me.Thank you in advance.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, Button
import matplotlib.colors
ax = plt.subplot(111)
plt.subplots_adjust(left=0.25, bottom=0.25)
img_data = np.random.rand(50,50)
c_max = 2
img = ax.imshow(img_data, interpolation='nearest')
cb = plt.colorbar(img)
axcolor = 'lightgoldenrodyellow'
ax_cmax = plt.axes([0.25, 0.15, 0.65, 0.03])
s_cmax = Slider(ax_cmax, 'max', 0, 50, valfmt=c_max)
def update(val, s=None):
# _cmin = s_cmin.val
_cmax = s_cmax.val
img.set_clim(_cmax)
plt.draw()
s_cmax.on_changed(update)
plt.show()
The argument to Slider called valfmt should be a string which is used to format the slider value.
So if you wanted to display 2 decimal places to the float you would need to make c_max = "%1.2f". Note that if you want to keep the minimum value at 0 you need to set that too in img.set_clim(0, _cmax)
c_max = "%1.2f"
img = ax.imshow(img_data, interpolation='nearest')
cb = plt.colorbar(img)
axcolor = 'lightgoldenrodyellow'
ax_cmax = plt.axes([0.25, 0.15, 0.65, 0.03])
s_cmax = Slider(ax_cmax, 'max', 0, 50, valfmt=c_max)
def update(val, s=None):
# _cmin = s_cmin.val
_cmax = s_cmax.val
img.set_clim(0, _cmax)
plt.draw()
s_cmax.on_changed(update)
plt.show()
Hi I a have a data set which I project onto a sphere such that the magnitude of the data, as a function of theta and phi, is shown using a colour spectrum (which uses "ax.plot_surface", "plt.colorbar" and "facecolors"). My query is that at this stage I am limited to "cm.hot" and "cm.jet". Does anyone know of any other colour schemes which are available for this purpose. Please see my code and the figures below
Code:
from numpy import*
import math
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.cm as cm
#theta inclination angle
#phi azimuthal angle
n_theta = 100 #number of values for theta
n_phi = 100 #number of values for phi
r = 1 #radius of sphere
theta, phi = np.mgrid[0: pi:n_theta*1j,-pi:pi:n_phi*1j ]
x = r*np.sin(theta)*np.cos(phi)
y = r*np.sin(theta)*np.sin(phi)
z = r*np.cos(theta)
inp = []
f = open("data.dat","r")
for line in f:
i = float(line.split()[0])
j = float(line.split()[1])
val = float(line.split()[2])
inp.append([i, j, val])
inp = np.array(inp)
#reshape the input array to the shape of the x,y,z arrays.
c = inp[:,2].reshape((n_phi,n_theta))
#Set colours and render
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')
#use facecolors argument, provide array of same shape as z
# cm.<cmapname>() allows to get rgba color from array.
# array must be normalized between 0 and 1
surf = ax.plot_surface(
x,y,z, rstride=1, cstride=1, facecolors=cm.jet(c), alpha=0.9, linewidth=1, shade=False)
ax.set_xlim([-2.0,2.0])
ax.set_ylim([-2.0,2.0])
ax.set_zlim([-2,2])
ax.set_aspect("equal")
plt.title('Plot with cm.jet')
#Label axis.
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
#Creates array for colorbar from 0 to 1.
a = array( [1.0, 0.5, 0.0])
#Creates colorbar
m = cm.ScalarMappable(cmap=cm.jet)
m.set_array(a)
plt.colorbar(m)
plt.savefig('facecolor plots')
f.close()
plt.show()
The following is a list of colormaps provided directly by matplotlib. It's taken from the Colormap reference example.
cmaps = [('Perceptually Uniform Sequential', [
'viridis', 'plasma', 'inferno', 'magma', 'cividis']),
('Sequential', [
'Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds',
'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu', 'BuPu',
'GnBu', 'PuBu', 'YlGnBu', 'PuBuGn', 'BuGn', 'YlGn']),
('Sequential (2)', [
'binary', 'gist_yarg', 'gist_gray', 'gray', 'bone', 'pink',
'spring', 'summer', 'autumn', 'winter', 'cool', 'Wistia',
'hot', 'afmhot', 'gist_heat', 'copper']),
('Diverging', [
'PiYG', 'PRGn', 'BrBG', 'PuOr', 'RdGy', 'RdBu',
'RdYlBu', 'RdYlGn', 'Spectral', 'coolwarm', 'bwr', 'seismic']),
('Qualitative', [
'Pastel1', 'Pastel2', 'Paired', 'Accent',
'Dark2', 'Set1', 'Set2', 'Set3',
'tab10', 'tab20', 'tab20b', 'tab20c']),
('Miscellaneous', [
'flag', 'prism', 'ocean', 'gist_earth', 'terrain', 'gist_stern',
'gnuplot', 'gnuplot2', 'CMRmap', 'cubehelix', 'brg', 'hsv',
'gist_rainbow', 'rainbow', 'jet', 'nipy_spectral', 'gist_ncar'])]
To easily view them all you may e.g. use the following 3D colormap viewer (written in PyQt5).
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
from PyQt5 import QtGui, QtCore, QtWidgets
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure
import sys
class MainWindow(QtWidgets.QMainWindow):
def __init__(self):
QtWidgets.QMainWindow.__init__(self)
self.main_widget = QtWidgets.QWidget(self)
self.fig = Figure()
self.canvas = FigureCanvas(self.fig)
self.ax = self.fig.add_subplot(111, projection=Axes3D.name)
u = np.linspace(0, 2 * np.pi, 100)
v = np.linspace(0, np.pi, 100)
x = 10 * np.outer(np.cos(u), np.sin(v))
y = 10 * np.outer(np.sin(u), np.sin(v))
z = 10 * np.outer(np.ones(np.size(u)), np.cos(v))
# Plot the surface
self.surf = self.ax.plot_surface(x, y, z, cmap="YlGnBu")
self.cb = self.fig.colorbar(self.surf)
self.canvas.setSizePolicy(QtWidgets.QSizePolicy.Expanding,
QtWidgets.QSizePolicy.Expanding)
self.canvas.updateGeometry()
self.dropdown1 = QtWidgets.QComboBox()
items = []
for cats in cmaps:
items.extend(cats[1])
self.dropdown1.addItems(items)
self.dropdown1.currentIndexChanged.connect(self.update)
self.label = QtWidgets.QLabel("A plot:")
self.layout = QtWidgets.QGridLayout(self.main_widget)
self.layout.addWidget(QtWidgets.QLabel("Select Colormap"))
self.layout.addWidget(self.dropdown1)
self.layout.addWidget(self.canvas)
self.setCentralWidget(self.main_widget)
self.show()
self.update()
def update(self):
self.surf.set_cmap(self.dropdown1.currentText())
self.fig.canvas.draw_idle()
if __name__ == '__main__':
app = QtWidgets.QApplication(sys.argv)
win = MainWindow()
sys.exit(app.exec_())
from __future__ import division
import numpy as np
import math
from scipy.special import kv #calling bassel func
from scipy.special import iv #calling bassel func
from scipy.integrate import quad
from scipy.misc import derivative
import matplotlib.pylab as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
#######################################################
def B(x):
#bassel func~~~~~
#first
v0 = 0 #order of bessel func
K0 = kv(v0, r/x) #BESSEL Function
I0 = iv(v0, r/x) #BESSEL Function
#~~~second
v1 = 1 #order of bessel func
K1 = kv(v1, r/x) #BESSEL Function
I1 = iv(v1, r/x) #BESSEL Function
c = (I0*K0)-(I1*K1)
return c
#######################################################
Ms = 3e+11
ms1 = 1e11
#~~~~~~~~~~~~~~~~~~
mh = np.linspace(9.5,11.5,20)
mhalo = 10**((-0.0210331*mh**5) + (1.042316*mh**4) - (20.553*mh**3) + (201.74*mh**2) - (985.821*mh) + (1929.48))
Mh = mhalo
#~ r = 6 #in Kpc
r = np.linspace(0.01,6,20) #in Kpc
#~~~~~~~~~~~~~~~~~~
#virial radius:
Rv = 259.3*(Mh/1e12)**(1/3)
#Disk mass halo mass relation:
Md = 2.3e10*(Mh/Ms)**(3.1)/(1+(Mh/Ms)**(2.2))
#disk length to halo mass relation:
Rd = 10**(0.633+(0.379*np.log10(Md/ms1)) + (0.069*(np.log10(Md/ms1))**2))
#optical radius:
Ropt = 3.2*Rd
#Burkert-halo central density:
rho0 = 10**(-23.5153 - 0.918*(Md/ms1)**(0.308))
#burket core radius:
r0 = 10**(0.66+0.58*(np.log10(Mh/ms1)))
#burket halo density:
rho = rho0*r0**3/((r+r0)*((r**2)+(r0**2)))
#burket Halo mass:
Mhr = (1.48e31*1.6*4*rho0*r0**3)*(np.log(1+(r/r0))- np.arctan(r/r0) + 0.5*(np.log(1+(r/r0)**2)))
#Halo velocity:
Vh = 658.1*(Mhr/(ms1*r))**0.5
#disk velocity:
x = 2*Rd
Vd = 658.1*((0.5*Md/(ms1*Rd))**0.5)*(r/Rd)*B(x)**0.5
#total velocity:
vurc = ((Vd**2) + (Vh**2))**0.5
XX = r
YY = vurc
ZZ = mh
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
img = ax.plot_surface(XX, YY, ZZ)
plt.tick_params(axis='x', which='major', labelsize=15)
plt.tick_params(axis='y', which='major', labelsize=15)
plt.tick_params(axis='z', which='major', labelsize=15)
ax.set_xlabel('R/Ropt', fontsize = 15)
ax.set_ylabel('V/Vopt', fontsize = 15)
ax.set_zlabel('log(M_vir)', fontsize = 15)
plt.show()
In this code below, 3D plotting commands are not working neither giving any errors, just plotting the empty box. While, my all arrays are of same size. Could anyone hlep me out.
I have an array with the shape of (#dim1,#dim2,#channel). I want to reshape it to (#channel, #dim1,#dim2).
The plt.reshape(x, (#channel, #dim1,#dim2)) shows me a wrong image.
If you are using the Cifar10 dataset you could use the following code:
import numpy as np
import matplotlib.pyplot as plt
import cPickle
def unpickle(file):
with open(file, 'rb') as fo:
dict = cPickle.load(fo)
return dict
# Read the data
imageDict = unpickle('cifar-10-batches-py/data_batch_2')
imageArray = imageDict['data']
# Now we reshape
imageArray = np.swapaxes(imageArray.reshape(10000,32,32,3,order='F'), 1, 2)
# Get the labels
labels = ['airplane','automobile','bird','cat','deer','dog','frog','horse','ship','truck']
imageLabels = [labels[i] for i in imageDict['labels']]
# Plot some images
fig, ax = plt.subplots(4,4, figsize=(8,8))
for axIndex in [(i,j) for i in range(4) for j in range(4)]:
index = np.random.randint(0,10000)
ax[axIndex].imshow(imageArray[index], origin='upper')
ax[axIndex].set_title(imageLabels[index])
ax[axIndex].axis('off')
fig.show()
Which gives you:
I updated the code and it now provides the graph, however after giving me the graph it produces the following error messages.
Warning (from warnings module):
File "C:\Python27\lib\site-packages\matplotlib\collections.py", line 590
if self._edgecolors == str('face'):
FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
import urllib2
import time
import datetime
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import matplotlib.dates as mdates
from matplotlib.finance import candlestick_ochl
import matplotlib
import pylab
matplotlib.rcParams.update({'font.size': 9})
def rsiFunc(prices, n=14):
deltas = np.diff(prices)
seed = deltas[:n+1]
up = seed[seed>=0].sum()/n
down = -seed[seed<0].sum()/n
rs = up/down
rsi = np.zeros_like(prices)
rsi[:n] = 100. - 100./(1.+rs)
for i in range(n, len(prices)):
delta = deltas[i-1] # cause the diff is 1 shorter
if delta>0:
upval = delta
downval = 0.
else:
upval = 0.
downval = -delta
up = (up*(n-1) + upval)/n
down = (down*(n-1) + downval)/n
rs = up/down
rsi[i] = 100. - 100./(1.+rs)
return rsi
def movingaverage(values,window):
weigths = np.repeat(1.0, window)/window
smas = np.convolve(values, weigths, 'valid')
return smas # as a numpy array
def ExpMovingAverage(values, window):
weights = np.exp(np.linspace(-1., 0., window))
weights /= weights.sum()
a = np.convolve(values, weights, mode='full')[:len(values)]
a[:window] = a[window]
return a
def computeMACD(x, slow=26, fast=12):
"""
compute the MACD (Moving Average Convergence/Divergence) using a fast and slow exponential moving avg'
return value is emaslow, emafast, macd which are len(x) arrays
"""
emaslow = ExpMovingAverage(x, slow)
emafast = ExpMovingAverage(x, fast)
return emaslow, emafast, emafast - emaslow
def graphData(stock,MA1,MA2):
'''
Use this to dynamically pull a stock:
'''
try:
print 'Currently Pulling',stock
print str(datetime.datetime.fromtimestamp(int(time.time())).strftime('%Y-%m-%d %H:%M:%S'))
#Keep in mind this is close high low open data from Yahoo
urlToVisit = 'http://chartapi.finance.yahoo.com/instrument/1.0/'+stock+'/chartdata;type=quote;range=10y/csv'
stockFile =[]
try:
sourceCode = urllib2.urlopen(urlToVisit).read()
splitSource = sourceCode.split('\n')
for eachLine in splitSource:
splitLine = eachLine.split(',')
if len(splitLine)==6:
if 'values' not in eachLine:
stockFile.append(eachLine)
except Exception, e:
print str(e), 'failed to organize pulled data.'
except Exception,e:
print str(e), 'failed to pull pricing data'
try:
date, closep, highp, lowp, openp, volume = np.loadtxt(stockFile,delimiter=',', unpack=True,
converters={ 0: mdates.strpdate2num('%Y%m%d')})
x = 0
y = len(date)
newAr = []
while x < y:
appendLine = date[x],openp[x],closep[x],highp[x],lowp[x],volume[x]
newAr.append(appendLine)
x+=1
Av1 = movingaverage(closep, MA1)
Av2 = movingaverage(closep, MA2)
SP = len(date[MA2-1:])
fig = plt.figure(facecolor='#07000d')
ax1 = plt.subplot2grid((6,4), (1,0), rowspan=4, colspan=4, axisbg='#07000d')
candlestick_ochl(ax1, newAr[-SP:], width=.6, colorup='#53c156', colordown='#ff1717')#width=.6, plot_day_summary_ohlc
Label1 = str(MA1)+' SMA'
Label2 = str(MA2)+' SMA'
ax1.plot(date[-SP:],Av1[-SP:],'#e1edf9',label=Label1, linewidth=1.5)
ax1.plot(date[-SP:],Av2[-SP:],'#4ee6fd',label=Label2, linewidth=1.5)
ax1.grid(True, color='w')
ax1.xaxis.set_major_locator(mticker.MaxNLocator(10))
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax1.yaxis.label.set_color("w")
ax1.spines['bottom'].set_color("#5998ff")
ax1.spines['top'].set_color("#5998ff")
ax1.spines['left'].set_color("#5998ff")
ax1.spines['right'].set_color("#5998ff")
ax1.tick_params(axis='y', colors='w')
plt.gca().yaxis.set_major_locator(mticker.MaxNLocator(prune='upper')) #gca()
ax1.tick_params(axis='x', colors='w')
plt.ylabel('Stock price and Volume')
maLeg = plt.legend(loc=9, ncol=2, prop={'size':7},
fancybox=True, borderaxespad=0.)
maLeg.get_frame().set_alpha(0.4)
textEd = plt.gca().get_legend().get_texts()#pylab.gca() changed to plt.gca()
plt.setp(textEd[0:5], color = 'w')#changed pylab.setp to plt.setp
volumeMin = 0
ax0 = plt.subplot2grid((6,4), (0,0), sharex=ax1, rowspan=1, colspan=4, axisbg='#07000d')
rsi = rsiFunc(closep)
rsiCol = '#c1f9f7'
posCol = '#386d13'
negCol = '#8f2020'
ax0.plot(date[-SP:], rsi[-SP:], rsiCol, linewidth=1.5)
ax0.axhline(70, color=negCol)
ax0.axhline(30, color=posCol)
ax0.fill_between(date[-SP:], rsi[-SP:], 70, where=(rsi[-SP:]>=70), facecolor=negCol, edgecolor=negCol, alpha=0.5)
ax0.fill_between(date[-SP:], rsi[-SP:], 30, where=(rsi[-SP:]<=30), facecolor=posCol, edgecolor=posCol, alpha=0.5)
ax0.set_yticks([30,70])
ax0.yaxis.label.set_color("w")
ax0.spines['bottom'].set_color("#5998ff")
ax0.spines['top'].set_color("#5998ff")
ax0.spines['left'].set_color("#5998ff")
ax0.spines['right'].set_color("#5998ff")
ax0.tick_params(axis='y', colors='w')
ax0.tick_params(axis='x', colors='w')
plt.ylabel('RSI')
ax1v = ax1.twinx()
ax1v.fill_between(date[-SP:],volumeMin, volume[-SP:], facecolor='#00ffe8', alpha=.4)
ax1v.axes.yaxis.set_ticklabels([])
ax1v.grid(False)
ax1v.set_ylim(0, 3*volume.max())
ax1v.spines['bottom'].set_color("#5998ff")
ax1v.spines['top'].set_color("#5998ff")
ax1v.spines['left'].set_color("#5998ff")
ax1v.spines['right'].set_color("#5998ff")
ax1v.tick_params(axis='x', colors='w')
ax1v.tick_params(axis='y', colors='w')
ax2 = plt.subplot2grid((6,4), (5,0), sharex=ax1, rowspan=1, colspan=4, axisbg='#07000d')
# START NEW INDICATOR CODE #
# END NEW INDICATOR CODE #
plt.gca().yaxis.set_major_locator(mticker.MaxNLocator(prune='upper'))
ax2.spines['bottom'].set_color("#5998ff")
ax2.spines['top'].set_color("#5998ff")
ax2.spines['left'].set_color("#5998ff")
ax2.spines['right'].set_color("#5998ff")
ax2.tick_params(axis='x', colors='w')
ax2.tick_params(axis='y', colors='w')
ax2.yaxis.set_major_locator(mticker.MaxNLocator(nbins=5, prune='upper'))
for label in ax2.xaxis.get_ticklabels():
label.set_rotation(45)
plt.suptitle(stock.upper(),color='w')
plt.setp(ax0.get_xticklabels(), visible=False)
plt.setp(ax1.get_xticklabels(), visible=False)
'''ax1.annotate('Big news!',(date[510],Av1[510]),
xytext=(0.8, 0.9), textcoords='axes fraction',
arrowprops=dict(facecolor='white', shrink=0.05),
fontsize=14, color = 'w',
horizontalalignment='right', verticalalignment='bottom')'''
plt.subplots_adjust(left=.09, bottom=.14, right=.94, top=.95, wspace=.20, hspace=0)
plt.show()
fig.savefig('example.png',facecolor=fig.get_facecolor())
except Exception,e:
print 'main loop',str(e)
while True:
stock = raw_input('Stock to plot: ')
graphData(stock,10,50)
Please look at the thread Violin plot: warning with matplotlib 1.4.3 and pyplot fill_between warning since upgrade of numpy to 1.10.10
It seems there is a bug in matplotlib 1.4.3 (which has only started causing that error since the upgrade to numpy 1.10). This is reportedly corrected in 1.5.0 (which should be released soon). Hope this helps.