def grafico(self, other, \
interp='n', \
cifre=('"%.0s*10^{%S}"','"%.0s*10^{%S}"'), \
terminal='x11', \
etichetteassi=('x', 'y'), \
output='grafico'):
glt = Gnuplot.Gnuplot(debug=1) #apre gnuplot
glt('set tics nomirror; unset key; set format x %s; set format y %s' %
cifre)
glt('set terminal %s' % terminal)
glt('set output "%s"' % output)
glt.xlabel(etichetteassi[0])
glt.ylabel(etichetteassi[1])
nome = 'temp'
colonne = (1, 2)
scrivivalori((self.valori, other.valori), nome)
if interp != 'n':
parametri = self.interpolazionelineare(other)
f = Gnuplot.Func('%.14f*x%+.14f' % (parametri[2], parametri[0]))
glt.plot(Gnuplot.File(nome, using=colonne), f)
if terminal == 'x11': chiudigrafico()
return
glt.plot(Gnuplot.File(nome, using=colonne))
glt('set output')
if terminal == 'x11': chiudigrafico()
def main():
"""Exercise the Gnuplot module."""
print (
'This program exercises many of the features of Gnuplot.py. The\n'
'commands that are actually sent to gnuplot are printed for your\n'
'enjoyment.'
)
wait('Popping up a blank gnuplot window on your screen.')
g = Gnuplot.Gnuplot(debug=1)
g.clear()
# Make a temporary file:
filename1 = tempfile.mktemp()
f = open(filename1, 'w')
try:
for x in Numeric.arange(100)/5. - 10.:
f.write('%s %s %s\n' % (x, math.cos(x), math.sin(x)))
f.close()
print '############### test Func ###################################'
wait('Plot a gnuplot-generated function')
g.plot(Gnuplot.Func('sin(x)'))
wait('Set title and axis labels and try replot()')
g.title('Title')
g.xlabel('x')
g.ylabel('y')
g.replot()
wait('Style linespoints')
g.plot(Gnuplot.Func('sin(x)', with='linespoints'))
def plot_line(m, b, xrange=None, yrange=None):
"""plot a line with slope m and bias b, currently using Gnuplot"""
if(G):
line = Gnuplot.Func ('%f + (%f) * x' % (b, m))
if xrange: G.set_range('xrange', xrange)
if yrange: G.set_range('yrange', yrange)
G.plot(line)
wait_for_input()
def demo():
g = Gnuplot.Gnuplot(debug=1)
g.title('A simple example') # (optional)
g('set data style linespoints') # give gnuplot an arbitrary command
# Plot a list of (x, y) (tuples or a numpy array would also be OK):
g.plot([[0, 1.1], [1, 5.8], [2, 3.3], [3, 4.2]])
raw_input('Please press return to continue...\n')
g.reset()
x = arange(10, dtype='float_')
y1 = x**2
d = Gnuplot.Data(x, y1, title='Calculated by Python', with_='points 3 3')
g.title('Data can be computed by Python or gnuplot')
g.xlabel('x')
g.ylabel('x squared')
# Plot a function alongside the Data PlotItem defined above:
g.plot(Gnuplot.Func('x**2', title='calculated by gnuplot'), d)
raw_input('Please press return to continue...\n')
# Save what we just plotted as a color postscript file.
g.ylabel('x^2')
g.hardcopy('gp_test.ps', enhanced=1, color=1)
print('\n**** Saved plot to postscript file "gp_test.ps" ******\n')
raw_input('Please press return to continue...\n')
g.reset()
# Demonstrate a 3-d plot:
x = arange(35) / 2.0
y = arange(30) / 10.0 - 1.5
# Make a 2-d array containing a function of x and y. First create
# xm and ym which contain the x and y values in a matrix form
xm = x[:, newaxis]
ym = y[newaxis, :]
m = (sin(xm) + 0.1 * xm) - ym**2
g('set parametric')
g('set data style lines')
g('set hidden')
g('set contour base')
g.title('An example of a surface plot')
g.xlabel('x')
g.ylabel('y')
g.splot(Gnuplot.GridData(m, x, y, binary=0))
raw_input('Please press return to continue...\n')
# plot another function, but letting GridFunc tabulate its values
def f(x, y):
return 1.0 / (1 + 0.01 * x**2 + 0.5 * y**2)
g.splot(Gnuplot.funcutils.compute_GridData(x, y, f, binary=0))
raw_input('Please press return to continue...\n')
def grafico(nomefile,
colonne=(1, 2),
etichetteassi=('x', 'y'),
cifredecimali=('.g', '.g')):
decimali = ('"%' + cifredecimali[0] + '"', '"%' + cifredecimali[1] + '"')
(x, y) = legginumeri(nomefile, colonne)
parametri = interpolazionelineare(x, y)
f = Gnuplot.Func('%.14f*x%+.14f' % (parametri[2], parametri[0]))
glt = Gnuplot.Gnuplot(debug=1) #apre gnuplot
glt('set tics nomirror;unset key;set format x %s;set format y %s' %
decimali)
glt.xlabel(etichetteassi[0])
glt.ylabel(etichetteassi[1])
glt.plot(Gnuplot.File(nomefile, using=colonne), f)
def Plot(self):
g = Gnuplot.Gnuplot()
g.clear()
plots = []
color = 1
g.xlabel('time (seconds)')
for plot in self.plots:
fn = plot.WriteToTempFile()
g.ylabel(plot.title)
plots.append(Gnuplot.File(fn, with_='lines linecolor %d' % color, title='%s (%.2f - %.2f)' % (plot.title, plot.min, plot.max)))
if plot.IsMidLineVisible():
plots.append(Gnuplot.Func('%.4f' % plot.GetMidLine(), with_='lines linecolor %d' % color, title=''))
color += 1
g.plot(*plots)
wait()
def graficoPST(nomefile,
colonne=(1, 2),
etichetteassi=('x', 'y'),
cifredecimali=('.g', '.g')):
glt = Gnuplot.Gnuplot(debug=1) #apre gnuplot
glt('set terminal pstricks; set output %s' % ('"' + nomefile + '.tex"'))
decimali = ('"%' + cifredecimali[0] + '"', '"%' + cifredecimali[1] + '"')
(x, y) = legginumeri(nomefile, colonne)
parametri = interpolazionelineare(x, y)
f = Gnuplot.Func('%.14f*x%+.14f' % (parametri[2], parametri[0]))
#glt = Gnuplot.Gnuplot(debug=1) #apre gnuplot
glt('set tics nomirror;unset key;set format x %s;set format y %s' %
decimali)
glt('set xlabel %s; set ylabel %s' % etichetteassi)
glt.plot(Gnuplot.File(nomefile, every=10, using=colonne), f)
glt('set output')
def Fit(data, cols=(0, 1)):
import scipy.linalg
a, b = cols
matrix = []
imatrix = []
for d in data:
matrix.append([1.0, d[a]]) # for y = a + bx
imatrix.append([1.0, d[b]]) # for x = a + by
coeffs = scipy.linalg.lstsq(matrix, map(lambda x: x[b], data))[0]
icoeffs = scipy.linalg.lstsq(imatrix, map(lambda x: x[a], data))[0]
f = "%f + %f*x" % (coeffs[0], coeffs[1])
r2 = coeffs[1] * icoeffs[1]
return Gnuplot.Func(f, title="%s (r^2=%f)" % (f, r2))
def dipolePlot(dipole, freq):
data = dipole[freq]
cosTheta = []
del_int = []
for data2 in data:
cosTheta.append(data2[0])
del_int.append(data2[1])
def f(
a, x
): # let's not forget to define the function we want to fit our data to
return a * x
soln, extra = curve_fit(f, cosTheta,
del_int) #fitting fittting fitting....
func = str(soln[0])
g = Gnuplot.Gnuplot(debug=1)
d = Gnuplot.Data(cosTheta, del_int)
g.xlabel('cosTheta')
g.ylabel('deltaI/I')
g.plot(Gnuplot.Func(func + '*x'), d)
def main():
"""Exercise the Gnuplot module."""
print(
'This program exercises many of the features of Gnuplot.py. The\n'
'commands that are actually sent to gnuplot are printed for your\n'
'enjoyment.')
wait('Popping up a blank gnuplot window on your screen.')
g = Gnuplot.Gnuplot(debug=1)
g.clear()
# Make two temporary files:
if hasattr(tempfile, 'mkstemp'):
(
fd,
filename1,
) = tempfile.mkstemp(text=1)
f = os.fdopen(fd, 'w')
(
fd,
filename2,
) = tempfile.mkstemp(text=1)
else:
filename1 = tempfile.mktemp()
f = open(filename1, 'w')
filename2 = tempfile.mktemp()
try:
for x in numpy.arange(100.) / 5. - 10.:
f.write('%s %s %s\n' % (x, math.cos(x), math.sin(x)))
f.close()
print '############### test Func ###################################'
wait('Plot a gnuplot-generated function')
g.plot(Gnuplot.Func('sin(x)'))
wait('Set title and axis labels and try replot()')
g.title('Title')
g.xlabel('x')
g.ylabel('y')
g.replot()
wait('Style linespoints')
g.plot(Gnuplot.Func('sin(x)', with_='linespoints'))
wait('title=None')
g.plot(Gnuplot.Func('sin(x)', title=None))
wait('title="Sine of x"')
g.plot(Gnuplot.Func('sin(x)', title='Sine of x'))
wait('axes=x2y2')
g.plot(Gnuplot.Func('sin(x)', axes='x2y2', title='Sine of x'))
print 'Change Func attributes after construction:'
f = Gnuplot.Func('sin(x)')
wait('Original')
g.plot(f)
wait('Style linespoints')
f.set_option(with_='linespoints')
g.plot(f)
wait('title=None')
f.set_option(title=None)
g.plot(f)
wait('title="Sine of x"')
f.set_option(title='Sine of x')
g.plot(f)
wait('axes=x2y2')
f.set_option(axes='x2y2')
g.plot(f)
print '############### test File ###################################'
wait('Generate a File from a filename')
g.plot(Gnuplot.File(filename1))
wait('Style lines')
g.plot(Gnuplot.File(filename1, with_='lines'))
wait('using=1, using=(1,)')
g.plot(Gnuplot.File(filename1, using=1, with_='lines'),
Gnuplot.File(filename1, using=(1, ), with_='points'))
wait('using=(1,2), using="1:3"')
g.plot(Gnuplot.File(filename1, using=(1, 2)),
Gnuplot.File(filename1, using='1:3'))
wait('every=5, every=(5,)')
g.plot(Gnuplot.File(filename1, every=5, with_='lines'),
Gnuplot.File(filename1, every=(5, ), with_='points'))
wait('every=(10,None,0), every="10::5"')
g.plot(Gnuplot.File(filename1, with_='lines'),
Gnuplot.File(filename1, every=(10, None, 0)),
Gnuplot.File(filename1, every='10::5'))
wait('title=None')
g.plot(Gnuplot.File(filename1, title=None))
wait('title="title"')
g.plot(Gnuplot.File(filename1, title='title'))
print 'Change File attributes after construction:'
f = Gnuplot.File(filename1)
wait('Original')
g.plot(f)
wait('Style linespoints')
f.set_option(with_='linespoints')
g.plot(f)
wait('using=(1,3)')
f.set_option(using=(1, 3))
g.plot(f)
wait('title=None')
f.set_option(title=None)
g.plot(f)
print '############### test Data ###################################'
x = numpy.arange(100) / 5. - 10.
y1 = numpy.cos(x)
y2 = numpy.sin(x)
d = numpy.transpose((x, y1, y2))
wait('Plot Data against its index')
g.plot(Gnuplot.Data(y2, inline=0))
wait('Plot Data, specified column-by-column')
g.plot(Gnuplot.Data(x, y2, inline=0))
wait('Same thing, saved to a file')
Gnuplot.Data(x, y2, inline=0, filename=filename1)
g.plot(Gnuplot.File(filename1))
wait('Same thing, inline data')
g.plot(Gnuplot.Data(x, y2, inline=1))
wait('Plot Data, specified by an array')
g.plot(Gnuplot.Data(d, inline=0))
wait('Same thing, saved to a file')
Gnuplot.Data(d, inline=0, filename=filename1)
g.plot(Gnuplot.File(filename1))
wait('Same thing, inline data')
g.plot(Gnuplot.Data(d, inline=1))
wait('with_="lp 4 4"')
g.plot(Gnuplot.Data(d, with_='lp 4 4'))
wait('cols=0')
g.plot(Gnuplot.Data(d, cols=0))
wait('cols=(0,1), cols=(0,2)')
g.plot(Gnuplot.Data(d, cols=(0, 1), inline=0),
Gnuplot.Data(d, cols=(0, 2), inline=0))
wait('Same thing, saved to files')
Gnuplot.Data(d, cols=(0, 1), inline=0, filename=filename1)
Gnuplot.Data(d, cols=(0, 2), inline=0, filename=filename2)
g.plot(Gnuplot.File(filename1), Gnuplot.File(filename2))
wait('Same thing, inline data')
g.plot(Gnuplot.Data(d, cols=(0, 1), inline=1),
Gnuplot.Data(d, cols=(0, 2), inline=1))
wait('Change title and replot()')
g.title('New title')
g.replot()
wait('title=None')
g.plot(Gnuplot.Data(d, title=None))
wait('title="Cosine of x"')
g.plot(Gnuplot.Data(d, title='Cosine of x'))
print '############### test compute_Data ###########################'
x = numpy.arange(100) / 5. - 10.
wait('Plot Data, computed by Gnuplot.py')
g.plot(
Gnuplot.funcutils.compute_Data(x, lambda x: math.cos(x), inline=0))
wait('Same thing, saved to a file')
Gnuplot.funcutils.compute_Data(x,
lambda x: math.cos(x),
inline=0,
filename=filename1)
g.plot(Gnuplot.File(filename1))
wait('Same thing, inline data')
g.plot(Gnuplot.funcutils.compute_Data(x, math.cos, inline=1))
wait('with_="lp 4 4"')
g.plot(Gnuplot.funcutils.compute_Data(x, math.cos, with_='lp 4 4'))
print '############### test hardcopy ###############################'
print '******** Generating postscript file "gp_test.ps" ********'
wait()
g.plot(
Gnuplot.Func('cos(0.5*x*x)',
with_='linespoints 2 2',
title='cos(0.5*x^2)'))
g.hardcopy('gp_test.ps')
wait('Testing hardcopy options: mode="eps"')
g.hardcopy('gp_test.ps', mode='eps')
wait('Testing hardcopy options: mode="landscape"')
g.hardcopy('gp_test.ps', mode='landscape')
wait('Testing hardcopy options: mode="portrait"')
g.hardcopy('gp_test.ps', mode='portrait')
wait('Testing hardcopy options: eps=1')
g.hardcopy('gp_test.ps', eps=1)
wait('Testing hardcopy options: mode="default"')
g.hardcopy('gp_test.ps', mode='default')
wait('Testing hardcopy options: enhanced=1')
g.hardcopy('gp_test.ps', enhanced=1)
wait('Testing hardcopy options: enhanced=0')
g.hardcopy('gp_test.ps', enhanced=0)
wait('Testing hardcopy options: color=1')
g.hardcopy('gp_test.ps', color=1)
# For some reason,
# g.hardcopy('gp_test.ps', color=0, solid=1)
# doesn't work here (it doesn't activate the solid option), even
# though the command sent to gnuplot looks correct. I'll
# tentatively conclude that it is a gnuplot bug. ###
wait('Testing hardcopy options: color=0')
g.hardcopy('gp_test.ps', color=0)
wait('Testing hardcopy options: solid=1')
g.hardcopy('gp_test.ps', solid=1)
wait('Testing hardcopy options: duplexing="duplex"')
g.hardcopy('gp_test.ps', solid=0, duplexing='duplex')
wait('Testing hardcopy options: duplexing="defaultplex"')
g.hardcopy('gp_test.ps', duplexing='defaultplex')
wait('Testing hardcopy options: fontname="Times-Italic"')
g.hardcopy('gp_test.ps', fontname='Times-Italic')
wait('Testing hardcopy options: fontsize=20')
g.hardcopy('gp_test.ps', fontsize=20)
print '******** Generating svg file "gp_test.svg" ********'
wait()
g.plot(
Gnuplot.Func('cos(0.5*x*x)',
with_='linespoints 2 2',
title='cos(0.5*x^2)'))
g.hardcopy('gp_test.svg', terminal='svg')
wait('Testing hardcopy svg options: enhanced')
g.hardcopy('gp_test.ps', terminal='svg', enhanced='1')
print '############### test shortcuts ##############################'
wait('plot Func and Data using shortcuts')
g.plot('sin(x)', d)
print '############### test splot ##################################'
wait('a 3-d curve')
g.splot(Gnuplot.Data(d, with_='linesp', inline=0))
wait('Same thing, saved to a file')
Gnuplot.Data(d, inline=0, filename=filename1)
g.splot(Gnuplot.File(filename1, with_='linesp'))
wait('Same thing, inline data')
g.splot(Gnuplot.Data(d, with_='linesp', inline=1))
print '############### test GridData and compute_GridData ##########'
# set up x and y values at which the function will be tabulated:
x = numpy.arange(35) / 2.0
y = numpy.arange(30) / 10.0 - 1.5
# Make a 2-d array containing a function of x and y. First create
# xm and ym which contain the x and y values in a matrix form that
# can be `broadcast' into a matrix of the appropriate shape:
xm = x[:, numpy.newaxis]
ym = y[numpy.newaxis, :]
m = (numpy.sin(xm) + 0.1 * xm) - ym**2
wait('a function of two variables from a GridData file')
g('set parametric')
g('set data style lines')
g('set hidden')
g('set contour base')
g.xlabel('x')
g.ylabel('y')
g.splot(Gnuplot.GridData(m, x, y, binary=0, inline=0))
wait('Same thing, saved to a file')
Gnuplot.GridData(m, x, y, binary=0, inline=0, filename=filename1)
g.splot(Gnuplot.File(filename1, binary=0))
wait('Same thing, inline data')
g.splot(Gnuplot.GridData(m, x, y, binary=0, inline=1))
wait('The same thing using binary mode')
g.splot(Gnuplot.GridData(m, x, y, binary=1))
wait('Same thing, using binary mode and an intermediate file')
Gnuplot.GridData(m, x, y, binary=1, filename=filename1)
g.splot(Gnuplot.File(filename1, binary=1))
wait('The same thing using compute_GridData to tabulate function')
g.splot(
Gnuplot.funcutils.compute_GridData(
x,
y,
lambda x, y: math.sin(x) + 0.1 * x - y**2,
))
wait('Same thing, with an intermediate file')
Gnuplot.funcutils.compute_GridData(
x,
y,
lambda x, y: math.sin(x) + 0.1 * x - y**2,
filename=filename1)
g.splot(Gnuplot.File(filename1, binary=1))
wait('Use compute_GridData in ufunc and binary mode')
g.splot(
Gnuplot.funcutils.compute_GridData(
x,
y,
lambda x, y: numpy.sin(x) + 0.1 * x - y**2,
ufunc=1,
binary=1,
))
wait('Same thing, with an intermediate file')
Gnuplot.funcutils.compute_GridData(
x,
y,
lambda x, y: numpy.sin(x) + 0.1 * x - y**2,
ufunc=1,
binary=1,
filename=filename1)
g.splot(Gnuplot.File(filename1, binary=1))
wait('And now rotate it a bit')
for view in range(35, 70, 5):
g('set view 60, %d' % view)
g.replot()
time.sleep(1.0)
wait(prompt='Press return to end the test.\n')
finally:
os.unlink(filename1)
os.unlink(filename2)
def ratioPlotter():
# For a particular benchmark, plots the ratio of time per rep
# of to a base file against the data size.
# The base file is (usually) the file containing data for
# the 1 OpenMP thread per MPI process benchmark run.
global gplotter
#Find the output file name
print("Enter file name for hardcopy of plot...")
outFileName = sys.stdin.readline()
#setup file name
outFormat = ".eps"
outFormat = outFormat.rstrip('\n')
outFileName = outFileName.rstrip('\n') + outFormat
#gplotter = Gnuplot.Gnuplot()
#Clear the gunplot object
gplotter.clear()
#Setup axis labels in plot
gplotter('set border')
gplotter.xlabel('Message Size (bytes)')
gplotter.ylabel('Ratio')
#Use a logscale along x axis..
gplotter('set logscale x')
# ..and format of numbers along x axis is 10^
gplotter('set format x "10^{%L}"')
#Uncomment next two lines for log scale along y axis
#gplotter('set logscale y')
#gplotter('set format y "10^{%L}"')
#Setup legend
gplotter('set key spacing 1.5') #gap between legend entries
gplotter('set key height 2') #gap b/w top/bottom legend & frame
gplotter('set key width 2') #gap b/w left/right legend & frame
gplotter('set key box') #turn on frame around key
#Line below sets position of the key in the plot - change this for different position
gplotter('set key top left')
#Start reading in names of data from base file
#1) Read the name of base file.
print "Enter name of the file to be used as the divisor (output for 1 OpenMP thread per MPI Process).."
str = sys.stdin.readline()
baseDataFileName = str.rstrip('\n') #remove newline from end
#2) Open the base file
baseFileHandle = open(baseDataFileName, 'r')
#3) Initialise the base file message size and time lists...
baseFileMsgSize = []
baseFileTime = []
#4) Read data from baseFile
str = baseFileHandle.readline()
while (str != ''): # while not EOF
str = str.lstrip() # Remove whitespace from start of line
if (not str.startswith('#')): # Check if line is a comment
file1str = str.split() # Split line at whitespaces
baseFileMsgSize.append(file1str[0]) # Add to message size list ..
baseFileTime.append(file1str[1]) # ...and to time per rep list
str = baseFileHandle.readline() # Read next line
#Close base file after reading
baseFileHandle.close()
#Reading data from other files
while (True):
#Read file name
print "Enter the name of file to get the ratio of, 0 to exit"
ratioFileName = sys.stdin.readline()
#Remove \n from fileName
ratioFileName = ratioFileName.rstrip('\n')
if (ratioFileName == '0'): #if user eneteed a '0'
#exit out of loop
break
else:
#Open the file
ratioFileHandle = open(ratioFileName, 'r')
#Initialise the ratio file message size and time lists...
ratioListMsgSize = []
ratioListTime = []
#Need counter so that only get ratio for same message sizes...
#..initialise msgSizePosCounter to 0.
msgSizePosCounter = 0
#Start reading the file
line = ratioFileHandle.readline()
while (line != ''): #while not EOF
line = line.lstrip() #remove whitespaces from start of line
if (not line.startswith('#')): #if not a comment
msgSizeReadComplete = False
lineSplit = line.split() #split the line at whitespace
msgSize = lineSplit[
0] #first element of line split is message size
#Now need to find if the same message size is in baseFileMsgSize list...
while (msgSizeReadComplete == False):
# ...if we've read more message sizes than length of baseFileMsgSize list
if (msgSizePosCounter > len(baseFileMsgSize) - 1):
# don't use that message size
msgSizeReadComplete = True
# ...if message size is equal baseFileMsgSize[msgSizePosCounter]
elif (int(msgSize) == int(
baseFileMsgSize[msgSizePosCounter])):
#keep mesaage size and find ratio
ratioListMsgSize.append(int(msgSize))
ratioListTime.append(
float(lineSplit[1]) /
float(baseFileTime[msgSizePosCounter]))
msgSizeReadComplete = True # finsihed with this message size: go to next one.
msgSizePosCounter = msgSizePosCounter + 1
# ..if message size if greater than baseFileMsgSize[msgSizePosCounter]
elif (int(msgSize) > int(
baseFileMsgSize[msgSizePosCounter])):
#move to next element in baseFileMsgSize list
msgSizePosCounter = msgSizePosCounter + 1
msgSizeReadComplete = False
# ...if message size if less than baseFileMsgSize[msgSizePosCounter]
elif (int(msgSize) < int(
baseFileMsgSize[msgSizePosCounter])):
#this message file doesn't exist in baseFileMsgSize list: read to next line
msgSizeReadComplete = True
#Read the next line from the file and loop again
line = ratioFileHandle.readline()
#When finished reading file, close it.
ratioFileHandle.close()
#Now plot the ratios...
#First need to convert the lists to numpy arrays
ratioArrayMsgSize = numpy.array(ratioListMsgSize)
ratioArrayTime = numpy.array(ratioListTime)
#Find title for the plot
splitName = ratioFileName.split('_') #Split at '_'
procs = splitName[1].rstrip(
'MPIProcs') #Find number of MPI Procs (2nd element)
threads = splitName[2].rstrip(
'Threads.dat') #and number of threads (3rd element)
t = threads + ' Threads, ' + procs + ' Processes' #create title
#Plot the data
gplotter.replot(
Gnuplot.Data(ratioArrayMsgSize,
ratioArrayTime,
with_='linespoints',
title=t))
print "\"", t, "\"", "plotted"
gplotter('set xrange[1:100000000] writeback')
#Plot line along y=1 to represent 1 OpenMP thread per MPI process
gplotter.replot(Gnuplot.Func('1', with_='lines lt -1', title=None))
#Write hardcopy
print 'Hardcopy written to:', outFileName
gplotter.hardcopy(outFileName, eps=1, color=1, enhanced=1)
gplotter.close()
#change yellow lines to brown
command = "sed -i 's/^LT5/LT7/g' " + outFileName
os.system(command)
import Gnuplot, math
import time
def f(x):
return (11*x - 6)*(2*x - 3)
gp = Gnuplot.Gnuplot(debug=0)
x = arange(10, dtype='float_')
y = x**2
# save plots for displaying later
plot1 = Gnuplot.Data(x, y, title='squares', with_='points 3 3')
plot2 = Gnuplot.Func('x**2', title='calculated by gnuplot')
# Plot
gp.title('The function x**2')
gp.xlabel('x')
gp.ylabel('x**2')
min = -1000
max = +1000
# keep replotting graph to animate
for i in range(min, max):
x = i
y = f(x)
plot1 = Gnuplot.Data([min,x,max], [0,y,f(max)], with_='points 1 3')
gp.plot(plot1)
g.xlabel(legend[0])
for x in range(1, len(legend)):
g.replot(
Gnuplot.Data(data,
cols=(0, x),
xwith=param_with,
title=legend[x]))
if param_fit:
g.replot(Fit(data, cols=(0, x)))
if param_function:
for f in param_function:
g.replot(Gnuplot.Func(f))
g.refresh()
if param_hardcopy:
g.hardcopy(param_hardcopy, terminal=param_terminal)
## g.replot( Gnuplot.File( fn1,
## using="1:2",
## with = param_with,
## title=legend[x]) )
# print d.filename
## temps += PlotFit( g, data, cols=(0, x) )
# print d.filename
#!/usr/bin/env python
import Gnuplot
gp = Gnuplot.Gnuplot()
gp('set title "sin(x) and cos(x)"')
gp('set terminal png size 2560,1600')
gp('set xlabel "x-axis: from -2*pi to +2*pi"')
gp('set ylabel "y-axis: from -1 to +1"')
gp('set terminal png')
gp('set output "sincos.png"')
gp('set xrange [-2*pi:2*pi]')
gp('set xtics ("0" 0,"-270" -(3/2)*pi, "-180" -pi, "-90" -pi/2, "90" pi/2, "180" pi, "270" (3/2)*pi)'
)
gp('set ytics ("0" 0, "0.5" 0.5, "1" 1, "-0.5" -0.5, "-1" -1)')
gp('set grid')
gf = Gnuplot.Func('sin(x), cos(x)')
gp.plot(gf)
f.close()
print '############### test Func ###################################'
wait('Plot a gnuplot-generated function')
g.plot(Gnuplot.Func('sin(x)'))
wait('Set title and axis labels and try replot()')
g.title('Title')
g.xlabel('x')
g.ylabel('y')
g.replot()
wait('Style linespoints')
g.plot(Gnuplot.Func('sin(x)', with='linespoints'))
wait('title=None')
g.plot(Gnuplot.Func('sin(x)', title=None))
wait('title="Sine of x"')
g.plot(Gnuplot.Func('sin(x)', title='Sine of x'))
wait('axes=x2y2')
g.plot(Gnuplot.Func('sin(x)', axes='x2y2', title='Sine of x'))
print 'Change Func attributes after construction:'
f = Gnuplot.Func('sin(x)')
wait('Original')
g.plot(f)
wait('Style linespoints')
f.set_option(with='linespoints')
g.plot(f)
wait('title=None')
f.set_option(title=None)
g.plot(f)
# Plot one dataset from an array and one via a gnuplot function;
# also demonstrate the use of item-specific options:
x = arange(10, typecode=Float)
y1 = x**2
# Notice how this plotitem is created here but used later? This
# is convenient if the same dataset has to be plotted multiple
# times. It is also more efficient because the data need only be
# written to a temporary file once.
d = Gnuplot.Data(x, y1,
title='calculated by python',
with='points 3 3')
g.title('Data can be computed by python or gnuplot')
g.xlabel('x')
g.ylabel('x squared')
# Plot a function alongside the Data PlotItem defined above:
g.plot(Gnuplot.Func('x**2', title='calculated by gnuplot'), d)
raw_input('Please press return to continue...\n')
# Save what we just plotted as a color postscript file.
# With the enhanced postscript option, it is possible to show `x
# squared' with a superscript (plus much, much more; see `help set
# term postscript' in the gnuplot docs). If your gnuplot doesn't
# support enhanced mode, set `enhanced=0' below.
g.ylabel('x^2') # take advantage of enhanced postscript mode
g.hardcopy('gp_test.ps', enhanced=1, color=1)
print ('\n******** Saved plot to postscript file "gp_test.ps" ********\n')
raw_input('Please press return to continue...\n')
g.reset()
# Demonstrate a 3-d plot:
G = Gnuplot.Gnuplot(debug=0)
except: G = None
def wait_for_input():
raw_input("press enter to exit graph")
def plot_bananas(pts1, pts2, filename, m=None, b=0, title='', \
x_label='', y_label='', x_range=(1,100), y_range=(1,100), output=None):
"""plot a line and a set of points
TODO: extend to arbitrary amt of sets of points **pts"""
if(G):
G.reset()
p1 = Gnuplot.Data(pts1, title="apples", with="points 1 2")
p2 = Gnuplot.Data(pts2, title="bananas", with="points 3")
if m: line = Gnuplot.Func ('%f + (%f) * x' % (b, m))
G.title("apples and bananas")
G.xlabel("size")
G.ylabel("yellowness")
G.set_range('xrange', (0,100))
G.set_range('yrange', (0,100))
if m: G.plot(line, p1, p2)
else: G.plot(p1, p2)
G.hardcopy(filename, terminal='png')
wait_for_input()
def plot_line(m, b, xrange=None, yrange=None):
"""plot a line with slope m and bias b, currently using Gnuplot"""
if(G):
line = Gnuplot.Func ('%f + (%f) * x' % (b, m))
if xrange: G.set_range('xrange', xrange)
############################################################
#
# plotting styles
#
############################################################
from numpy import *
import Gnuplot
import math
gp = Gnuplot.Gnuplot(debug=1)
x = arange(10, dtype='float_')
y = x**2
# save plots for displaying later
plot1 = Gnuplot.Func('x**2/100.', title='x**2', with_='boxes')
plot2 = Gnuplot.Func('cos(x)', title='cos(x)', with_='lines')
plot3 = Gnuplot.Func('norm(x)', title='norm(x)', with_='errorbars')
# Plot
gp.title('Various functions and styles')
gp.xlabel('x')
gp.ylabel('y')
gp.plot(plot1, plot2, plot3)
1
#!/usr/bin/env python
import Gnuplot
gp = Gnuplot.Gnuplot()
gp('set title "x^2 and 1/x^2"')
gp('set xlabel "x-axis: from -10 to +10"')
gp('set ylabel "y-axis: from 0 to 100"')
gp('set terminal png')
gp('set terminal png size 1280,800')
gp('set output "xx.png"')
gp('set xrange [-10:10]')
gf = Gnuplot.Func('x*x, 1/(x*x) with filledcurves fs')
gp.plot(gf)
def rotation():
"""Demonstrate the Gnuplot package."""
# Create arrays of points
xCorrection = [0, 1, 2, 3, 4, 5]
yCorrection = [1.1, 3, 7.5, 8, 1, 2]
# A straightforward use of gnuplot. The `debug=1' switch is used
# in these examples so that the commands that are sent to gnuplot
# are also output on stderr.
g = Gnuplot.Gnuplot(debug=1)
g('set multiplot')
g('set data style points') # give gnuplot an arbitrary command
# Plot a list of (x, y) pairs (tuples or a numpy array would
# also be OK):
for i in range(0, 5):
g.plot([xCorrection[i], yCorrection[i]])
g.title('Rotation') # (optional)
raw_input('Please press return to continue...\n')
g.reset()
# Plot one dataset from an array and one via a gnuplot function;
# also demonstrate the use of item-specific options:
x = arange(10, dtype='float_')
y1 = x**2
# Notice how this plotitem is created here but used later? This
# is convenient if the same dataset has to be plotted multiple
# times. It is also more efficient because the data need only be
# written to a temporary file once.
d = Gnuplot.Data(x, y1, title='calculated by python', with_='points 3 3')
g.title('Data can be computed by python or gnuplot')
g.xlabel('x')
g.ylabel('x squared')
# Plot a function alongside the Data PlotItem defined above:
g.plot(Gnuplot.Func('x**2', title='calculated by gnuplot'), d)
raw_input('Please press return to continue...\n')
# Save what we just plotted as a color postscript file.
# With the enhanced postscript option, it is possible to show `x
# squared' with a superscript (plus much, much more; see `help set
# term postscript' in the gnuplot docs). If your gnuplot doesn't
# support enhanced mode, set `enhanced=0' below.
g.ylabel('x^2') # take advantage of enhanced postscript mode
g.hardcopy('gp_test.ps', enhanced=1, color=1)
print('\n******** Saved plot to postscript file "gp_test.ps" ********\n')
raw_input('Please press return to continue...\n')
g.reset()
# Demonstrate a 3-d plot:
# set up x and y values at which the function will be tabulated:
x = arange(35) / 2.0
y = arange(30) / 10.0 - 1.5
# Make a 2-d array containing a function of x and y. First create
# xm and ym which contain the x and y values in a matrix form that
# can be `broadcast' into a matrix of the appropriate shape:
xm = x[:, newaxis]
ym = y[newaxis, :]
m = (sin(xm) + 0.1 * xm) - ym**2
g('set parametric')
g('set data style lines')
g('set hidden')
g('set contour base')
g.title('An example of a surface plot')
g.xlabel('x')
g.ylabel('y')
# The `binary=1' option would cause communication with gnuplot to
# be in binary format, which is considerably faster and uses less
# disk space. (This only works with the splot command due to
# limitations of gnuplot.) `binary=1' is the default, but here we
# disable binary because older versions of gnuplot don't allow
# binary data. Change this to `binary=1' (or omit the binary
# option) to get the advantage of binary format.
g.splot(Gnuplot.GridData(m, x, y, binary=0))
raw_input('Please press return to continue...\n')
# plot another function, but letting GridFunc tabulate its values
# automatically. f could also be a lambda or a global function:
def f(x, y):
return 1.0 / (1 + 0.01 * x**2 + 0.5 * y**2)
g.splot(Gnuplot.funcutils.compute_GridData(x, y, f, binary=0))
raw_input('Please press return to continue...\n')
else:
print "error"
class0 = np.array(class0)
class1 = np.array(class1)
func = "(-1)*" \
+ "("+ str(A) +")" \
+ "/" \
+ "("+str(B)+")" \
+ "*x+" \
+ "(-1)*" \
+ "("+str(C)+")" \
+ "/" + "("+str(B)+")"
print func
#if __name__ == "__main__":
g = Gnuplot.Gnuplot(debug=1)
g.title('SVM') # (optional)
g('set data style linespoints')
d0 = Gnuplot.Data(class0, title = "Class 0")
d1 = Gnuplot.Data(class1, title = "Class 1")
d2 = Gnuplot.Func(func, title = "Separating Plane")
g.plot(d0, d1, d2)
raw_input()
