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I try to make a fit of my curve. My raw data is in an xlsx file. I extract them using pandas. I want to do two different fit because there is a change in behavior from Ra = 1e6. We know that Ra is proportional to Nu**a. a = 0.25 for Ra <1e6 and if not a = 0.33.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from math import log10
from scipy.optimize import curve_fit
import lmfit
data=pd.read_excel('data.xlsx',sheet_name='Sheet2',index=False,dtype={'Ra': float})
print(data)
plt.xscale('log')
plt.yscale('log')
plt.scatter(data['Ra'].values, data['Nu_top'].values, label='Nu_top')
plt.scatter(data['Ra'].values, data['Nu_bottom'].values, label='Nu_bottom')
plt.errorbar(data['Ra'].values, data['Nu_top'].values , yerr=data['Ecart type top'].values, linestyle="None")
plt.errorbar(data['Ra'].values, data['Nu_bottom'].values , yerr=data['Ecart type bot'].values, linestyle="None")
def func(x,a):
return 10**(np.log10(x)/a)
"""maxX = max(data['Ra'].values)
minX = min(data['Ra'].values)
maxY = max(data['Nu_top'].values)
minY = min(data['Nu_top'].values)
maxXY = max(maxX, maxY)
parameterBounds = [-maxXY, maxXY]"""
from lmfit import Model
mod = Model(func)
params = mod.make_params(a=0.25)
ret = mod.fit(data['Nu_top'].head(10).values, params, x=data['Ra'].head(10).values)
print(ret.fit_report())
popt, pcov = curve_fit(func, data['Ra'].head(10).values,
data['Nu_top'].head(10).values, sigma=data['Ecart type top'].head(10).values,
absolute_sigma=True, p0=[0.25])
plt.plot(data['Ra'].head(10).values, func(data['Ra'].head(10).values, *popt),
'r-', label='fit: a=%5.3f' % tuple(popt))
popt, pcov = curve_fit(func, data['Ra'].tail(4).values, data['Nu_top'].tail(4).values,
sigma=data['Ecart type top'].tail(4).values,
absolute_sigma=True, p0=[0.33])
plt.plot(data['Ra'].tail(4).values, func(data['Ra'].tail(4).values, *popt),
'b-', label='fit: a=%5.3f' % tuple(popt))
print(pcov)
plt.grid
plt.title("Nusselt en fonction de Ra")
plt.xlabel('Ra')
plt.ylabel('Nu')
plt.legend()
plt.show()
So I use the log: logRa = a * logNu.
Ra = x axis
Nu = y axis
That's why I defined my function func in this way.
my two fit are not all correct as you can see. I have a covariance equal to [0.00010971]. So I had to do something wrong but I don't see it. I need help please.
Here the data file:
data.xlsx
I noticed that the data values for Ra are large, and after scaling them I performed an equation search - here is my result with code. I use the standard scipy genetic algorithm module differential_evolution to determine initial parameter values for curve_fit(), and that module uses the Latin Hypercube algorithm to ensure a thorough search of parameter space which requires bounds within which to search. It is much easier to give ranges for the initial parameter estimates than to find specific values. This equation works well for both nu_top and nu_bottom, note that the plots are not log scaled as it is unnecessary in this example.
import numpy, scipy, matplotlib
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
from scipy.optimize import differential_evolution
import pandas
import warnings
filename = 'data.xlsx'
data=pandas.read_excel(filename,sheet_name='Sheet2',index=False,dtype={'Ra': float})
# notice the Ra scaling by 10000.0
xData = data['Ra'].values / 10000.0
yData = data['Nu_bottom']
def func(x, a, b, c): # "Combined Power And Exponential" from zunzun.com
return a * numpy.power(x, b) * numpy.exp(c * x)
# function for genetic algorithm to minimize (sum of squared error)
def sumOfSquaredError(parameterTuple):
warnings.filterwarnings("ignore") # do not print warnings by genetic algorithm
val = func(xData, *parameterTuple)
return numpy.sum((yData - val) ** 2.0)
def generate_Initial_Parameters():
# min and max used for bounds
maxX = max(xData)
minX = min(xData)
maxY = max(yData)
minY = min(yData)
parameterBounds = []
parameterBounds.append([0.0, 10.0]) # search bounds for a
parameterBounds.append([0.0, 10.0]) # search bounds for b
parameterBounds.append([0.0, 10.0]) # search bounds for c
# "seed" the numpy random number generator for repeatable results
result = differential_evolution(sumOfSquaredError, parameterBounds, seed=3)
return result.x
# by default, differential_evolution completes by calling curve_fit() using parameter bounds
geneticParameters = generate_Initial_Parameters()
# now call curve_fit without passing bounds from the genetic algorithm,
# just in case the best fit parameters are aoutside those bounds
fittedParameters, pcov = curve_fit(func, xData, yData, geneticParameters)
print('Fitted parameters:', fittedParameters)
print()
modelPredictions = func(xData, *fittedParameters)
absError = modelPredictions - yData
SE = numpy.square(absError) # squared errors
MSE = numpy.mean(SE) # mean squared errors
RMSE = numpy.sqrt(MSE) # Root Mean Squared Error, RMSE
Rsquared = 1.0 - (numpy.var(absError) / numpy.var(yData))
print()
print('RMSE:', RMSE)
print('R-squared:', Rsquared)
print()
##########################################################
# graphics output section
def ModelAndScatterPlot(graphWidth, graphHeight):
f = plt.figure(figsize=(graphWidth/100.0, graphHeight/100.0), dpi=100)
axes = f.add_subplot(111)
# first the raw data as a scatter plot
axes.plot(xData, yData, 'D')
# create data for the fitted equation plot
xModel = numpy.linspace(min(xData), max(xData))
yModel = func(xModel, *fittedParameters)
# now the model as a line plot
axes.plot(xModel, yModel)
axes.set_xlabel('X Data') # X axis data label
axes.set_ylabel('Y Data') # Y axis data label
plt.show()
plt.close('all') # clean up after using pyplot
graphWidth = 800
graphHeight = 600
ModelAndScatterPlot(graphWidth, graphHeight)
Here I put my data x and y in log10 (). The graph is in log scale. So normally I should have two affine functions with a coefficient of 0.25 and 0.33. I change the function func in your program James and bounds for b and c but I have no good result.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from math import log10, log
from scipy.optimize import curve_fit
import lmfit
data=pd.read_excel('data.xlsx',sheet_name='Sheet2',index=False,dtype={'Ra': float})
print(data)
plt.xscale('log')
plt.yscale('log')
plt.scatter(np.log10(data['Ra'].values), np.log10(data['Nu_top'].values), label='Nu_top')
plt.scatter(np.log10(data['Ra'].values), np.log10(data['Nu_bottom'].values), label='Nu_bottom')
plt.errorbar(np.log10(data['Ra'].values), np.log10(data['Nu_top'].values) , yerr=data['Ecart type top'].values, linestyle="None")
plt.errorbar(np.log10(data['Ra'].values), np.log10(data['Nu_bottom'].values) , yerr=data['Ecart type bot'].values, linestyle="None")
def func(x,a):
return a*x
maxX = max(data['Ra'].values)
minX = min(data['Ra'].values)
maxY = max(data['Nu_top'].values)
minY = min(data['Nu_top'].values)
maxXY = max(maxX, maxY)
parameterBounds = [-maxXY, maxXY]
from lmfit import Model
mod = Model(func)
params = mod.make_params(a=0.25)
ret = mod.fit(np.log10(data['Nu_top'].head(10).values), params, x=np.log10(data['Ra'].head(10).values))
print(ret.fit_report())
popt, pcov = curve_fit(func, np.log10(data['Ra'].head(10).values), np.log10(data['Nu_top'].head(10).values), sigma=data['Ecart type top'].head(10).values, absolute_sigma=True, p0=[0.25])
plt.plot(np.log10(data['Ra'].head(10).values), func(np.log10(data['Ra'].head(10).values), *popt), 'r-', label='fit: a=%5.3f' % tuple(popt))
popt, pcov = curve_fit(func, np.log10(data['Ra'].tail(4).values), np.log10(data['Nu_top'].tail(4).values), sigma=data['Ecart type top'].tail(4).values, absolute_sigma=True, p0=[0.33])
plt.plot(np.log10(data['Ra'].tail(4).values), func(np.log10(data['Ra'].tail(4).values), *popt), 'b-', label='fit: a=%5.3f' % tuple(popt))
print(pcov)
plt.grid
plt.title("Nusselt en fonction de Ra")
plt.xlabel('log10(Ra)')
plt.ylabel('log10(Nu)')
plt.legend()
plt.show()
With polyfit I have better results.
With my code I open the file and I calculate log (Ra) and log (Nu) then plot (log (Ra), log (Nu)) in log scale.
I'm supposed to have a = 0.25 for Ra <1e6 and if not a = 0.33
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from math import log10
from numpy import polyfit
import numpy.polynomial.polynomial as poly
data=pd.read_excel('data.xlsx',sheet_name='Sheet2',index=False,dtype={'Ra': float})
print(data)
x=np.log10(data['Ra'].values)
y1=np.log10(data['Nu_top'].values)
y2=np.log10(data['Nu_bottom'].values)
x2=np.log10(data['Ra'].head(11).values)
y4=np.log10(data['Nu_top'].head(11).values)
x3=np.log10(data['Ra'].tail(4).values)
y5=np.log10(data['Nu_top'].tail(4).values)
plt.xscale('log')
plt.yscale('log')
plt.scatter(x, y1, label='Nu_top')
plt.scatter(x, y2, label='Nu_bottom')
plt.errorbar(x, y1 , yerr=data['Ecart type top'].values, linestyle="None")
plt.errorbar(x, y2 , yerr=data['Ecart type bot'].values, linestyle="None")
"""a=np.ones(10, dtype=np.float)
weights = np.insert(a,0,1E10)"""
coefs = poly.polyfit(x2, y4, 1)
print(coefs)
ffit = poly.polyval(x2, coefs)
plt.plot(x2, ffit, label='fit: b=%5.3f, a=%5.3f' % tuple(coefs))
absError = ffit - x2
SE = np.square(absError) # squared errors
MSE = np.mean(SE) # mean squared errors
RMSE = np.sqrt(MSE) # Root Mean Squared Error, RMSE
Rsquared = 1.0 - (np.var(absError) / np.var(x2))
print('RMSE:', RMSE)
print('R-squared:', Rsquared)
print()
print('Predicted value at x=0:', ffit[0])
print()
coefs = poly.polyfit(x3, y5, 1)
ffit = poly.polyval(x3, coefs)
plt.plot(x3, ffit, label='fit: b=%5.3f, a=%5.3f' % tuple(coefs))
plt.grid
plt.title("Nusselt en fonction de Ra")
plt.xlabel('log10(Ra)')
plt.ylabel('log10(Nu)')
plt.legend()
plt.show()
My problem is solved, I managed to fit my curves with more or less correct results
I have some x and y data, with which I would like to generate a 3D histogram, with a color gradient (bwr or whatever).
I have written a script which plot the interesting values, in between -2 and 2 for both x and y abscesses:
import numpy as np
import numpy.random
import matplotlib.pyplot as plt
# To generate some test data
x = np.random.randn(500)
y = np.random.randn(500)
XY = np.stack((x,y),axis=-1)
def selection(XY, limitXY=[[-2,+2],[-2,+2]]):
XY_select = []
for elt in XY:
if elt[0] > limitXY[0][0] and elt[0] < limitXY[0][1] and elt[1] > limitXY[1][0] and elt[1] < limitXY[1][1]:
XY_select.append(elt)
return np.array(XY_select)
XY_select = selection(XY, limitXY=[[-2,+2],[-2,+2]])
heatmap, xedges, yedges = np.histogram2d(XY_select[:,0], XY_select[:,1], bins = 7, range = [[-2,2],[-2,2]])
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
plt.figure("Histogram")
#plt.clf()
plt.imshow(heatmap.T, extent=extent, origin='lower')
plt.show()
And give this correct result:
Now, I would like to turn this into a 3D histogram. Unfortunatly I don't success to plot it correctly with bar3d because it takes by default the length of x and y for abscisse.
I am quite sure that there is a very easy way to plot this in 3D with imshow. Like an unknow option...
I finaly succeded in doing it. I am almost sure there is a better way to do it, but at leat it works:
import numpy as np
import numpy.random
import matplotlib.pyplot as plt
# To generate some test data
x = np.random.randn(500)
y = np.random.randn(500)
XY = np.stack((x,y),axis=-1)
def selection(XY, limitXY=[[-2,+2],[-2,+2]]):
XY_select = []
for elt in XY:
if elt[0] > limitXY[0][0] and elt[0] < limitXY[0][1] and elt[1] > limitXY[1][0] and elt[1] < limitXY[1][1]:
XY_select.append(elt)
return np.array(XY_select)
XY_select = selection(XY, limitXY=[[-2,+2],[-2,+2]])
xAmplitudes = np.array(XY_select)[:,0]#your data here
yAmplitudes = np.array(XY_select)[:,1]#your other data here
fig = plt.figure() #create a canvas, tell matplotlib it's 3d
ax = fig.add_subplot(111, projection='3d')
hist, xedges, yedges = np.histogram2d(x, y, bins=(7,7), range = [[-2,+2],[-2,+2]]) # you can change your bins, and the range on which to take data
# hist is a 7X7 matrix, with the populations for each of the subspace parts.
xpos, ypos = np.meshgrid(xedges[:-1]+xedges[1:], yedges[:-1]+yedges[1:]) -(xedges[1]-xedges[0])
xpos = xpos.flatten()*1./2
ypos = ypos.flatten()*1./2
zpos = np.zeros_like (xpos)
dx = xedges [1] - xedges [0]
dy = yedges [1] - yedges [0]
dz = hist.flatten()
cmap = cm.get_cmap('jet') # Get desired colormap - you can change this!
max_height = np.max(dz) # get range of colorbars so we can normalize
min_height = np.min(dz)
# scale each z to [0,1], and get their rgb values
rgba = [cmap((k-min_height)/max_height) for k in dz]
ax.bar3d(xpos, ypos, zpos, dx, dy, dz, color=rgba, zsort='average')
plt.title("X vs. Y Amplitudes for ____ Data")
plt.xlabel("My X data source")
plt.ylabel("My Y data source")
plt.savefig("Your_title_goes_here")
plt.show()
I use this example, but I modified it, because it introduced an offset. The result is this:
You can generate the same result using something as simple as the following:
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-2, 2, 7)
y = np.linspace(-2, 2, 7)
xx, yy = np.meshgrid(x, y)
z = xx*0+yy*0+ np.random.random(size=[7,7])
plt.imshow(z, interpolation='nearest', cmap=plt.cm.viridis, extent=[-2,2,2,2])
plt.show()
from mpl_toolkits.mplot3d import Axes3D
ax = Axes3D(plt.figure())
ax.plot_surface(xx, yy, z, cmap=plt.cm.viridis, cstride=1, rstride=1)
plt.show()
The results are given below:
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_())
I would like to add a separate colorbar to each subplot in a 2x2 plot.
fig , ( (ax1,ax2) , (ax3,ax4)) = plt.subplots(2, 2,sharex = True,sharey=True)
z1_plot = ax1.scatter(x,y,c = z1,vmin=0.0,vmax=0.4)
plt.colorbar(z1_plot,cax=ax1)
z2_plot = ax2.scatter(x,y,c = z2,vmin=0.0,vmax=40)
plt.colorbar(z1_plot,cax=ax2)
z3_plot = ax3.scatter(x,y,c = z3,vmin=0.0,vmax=894)
plt.colorbar(z1_plot,cax=ax3)
z4_plot = ax4.scatter(x,y,c = z4,vmin=0.0,vmax=234324)
plt.colorbar(z1_plot,cax=ax4)
plt.show()
I thought that this is how you do it, but the resulting plot is really messed up; it just has an all grey background and ignores the set_xlim , set_ylim commands I have (not shown here for simplicity). + it shows no color bars. Is this the right way to do it?
I also tried getting rid of the "cax = ...", but then the colorbar all goes on the bottom right plot and not to each separate plot!
This can be easily solved with the the utility make_axes_locatable. I provide a minimal example that shows how this works and should be readily adaptable:
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np
m1 = np.random.rand(3, 3)
m2 = np.arange(0, 3*3, 1).reshape((3, 3))
fig = plt.figure(figsize=(16, 12))
ax1 = fig.add_subplot(121)
im1 = ax1.imshow(m1, interpolation='None')
divider = make_axes_locatable(ax1)
cax = divider.append_axes('right', size='5%', pad=0.05)
fig.colorbar(im1, cax=cax, orientation='vertical')
ax2 = fig.add_subplot(122)
im2 = ax2.imshow(m2, interpolation='None')
divider = make_axes_locatable(ax2)
cax = divider.append_axes('right', size='5%', pad=0.05)
fig.colorbar(im2, cax=cax, orientation='vertical');
In plt.colorbar(z1_plot,cax=ax1), use ax= instead of cax=, i.e. plt.colorbar(z1_plot,ax=ax1)
Specify the ax argument to matplotlib.pyplot.colorbar(), e.g.
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots(2, 2)
for i in range(2):
for j in range(2):
data = np.array([[i, j], [i+0.5, j+0.5]])
im = ax[i, j].imshow(data)
plt.colorbar(im, ax=ax[i, j])
plt.show()
Please have a look at this matplotlib example page. There it is shown how to get the following plot with four individual colorbars for each subplot:
I hope this helps.
You can further have a look here, where you can find a lot of what you can do with matplotlib.
Try to use the func below to add colorbar:
def add_colorbar(mappable):
from mpl_toolkits.axes_grid1 import make_axes_locatable
import matplotlib.pyplot as plt
last_axes = plt.gca()
ax = mappable.axes
fig = ax.figure
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cbar = fig.colorbar(mappable, cax=cax)
plt.sca(last_axes)
return cbar
Then you codes need to be modified as:
fig , ( (ax1,ax2) , (ax3,ax4)) = plt.subplots(2, 2,sharex = True,sharey=True)
z1_plot = ax1.scatter(x,y,c = z1,vmin=0.0,vmax=0.4)
add_colorbar(z1_plot)
I have this signal :
from math import*
Fs=8000
f=500
sample=16
a=[0]*sample
for n in range(sample):
a[n]=sin(2*pi*f*n/Fs)
How can I plot a graph (this sine wave)?
and create name of xlabel as 'voltage(V)' and ylabel as 'sample(n)'
What code to do this?
I am so thanksful for help ^_^
Setting the x-axis with np.arange(0, 1, 0.001) gives an array from 0 to 1 in 0.001 increments.
x = np.arange(0, 1, 0.001) returns an array of 1000 points from 0 to 1, and y = np.sin(2*np.pi*x) you will get the sin wave from 0 to 1 sampled 1000 times
I hope this will help:
import matplotlib.pyplot as plt
import numpy as np
Fs = 8000
f = 5
sample = 8000
x = np.arange(sample)
y = np.sin(2 * np.pi * f * x / Fs)
plt.plot(x, y)
plt.xlabel('sample(n)')
plt.ylabel('voltage(V)')
plt.show()
P.S.: For comfortable work you can use The Jupyter Notebook.
import matplotlib.pyplot as plt # For ploting
import numpy as np # to work with numerical data efficiently
fs = 100 # sample rate
f = 2 # the frequency of the signal
x = np.arange(fs) # the points on the x axis for plotting
# compute the value (amplitude) of the sin wave at the for each sample
y = np.sin(2*np.pi*f * (x/fs))
#this instruction can only be used with IPython Notbook.
% matplotlib inline
# showing the exact location of the smaples
plt.stem(x,y, 'r', )
plt.plot(x,y)
import numpy as np
import matplotlib.pyplot as plt
F = 5.e2 # No. of cycles per second, F = 500 Hz
T = 2.e-3 # Time period, T = 2 ms
Fs = 50.e3 # No. of samples per second, Fs = 50 kHz
Ts = 1./Fs # Sampling interval, Ts = 20 us
N = int(T/Ts) # No. of samples for 2 ms, N = 100
t = np.linspace(0, T, N)
signal = np.sin(2*np.pi*F*t)
plt.plot(t, signal)
plt.xlabel('Time (s)')
plt.ylabel('Voltage (V)')
plt.show()
import math
import turtle
ws = turtle.Screen()
ws.bgcolor("lightblue")
fred = turtle.Turtle()
for angle in range(360):
y = math.sin(math.radians(angle))
fred.goto(angle, y * 80)
ws.exitonclick()
The window of usefulness has likely come and gone, but I was working at a similar problem. Here is my attempt at plotting sine using the turtle module.
from turtle import *
from math import *
#init turtle
T=Turtle()
#sample size
T.screen.setworldcoordinates(-1,-1,1,1)
#speed up the turtle
T.speed(-1)
#range of hundredths from -1 to 1
xcoords=map(lambda x: x/100.0,xrange(-100,101))
#setup the origin
T.pu();T.goto(-1,0);T.pd()
#move turtle
for x in xcoords:
T.goto(x,sin(xcoords.index(x)))
A simple way to plot sine wave in python using matplotlib.
import numpy as np
import matplotlib.pyplot as plt
x=np.arange(0,3*np.pi,0.1)
y=np.sin(x)
plt.plot(x,y)
plt.title("SINE WAVE")
plt.show()
import matplotlib.pyplot as plt
import numpy as np
#%matplotlib inline
x=list(range(10))
def fun(k):
return np.sin(k)
y=list(map(fun,x))
plt.plot(x,y,'-.')
#print(x)
#print(y)
plt.show()
This is another option
#!/usr/bin/env python
import numpy as np
import matplotlib
matplotlib.use('TKAgg') #use matplotlib backend TkAgg (optional)
import matplotlib.pyplot as plt
sample_rate = 200 # sampling frequency in Hz (atleast 2 times f)
t = np.linspace(0,5,sample_rate) #time axis
f = 100 #Signal frequency in Hz
sig = np.sin(2*np.pi*f*(t/sample_rate))
plt.plot(t,sig)
plt.xlabel("Time")
plt.ylabel("Amplitude")
plt.tight_layout()
plt.show()
Yet another way to plot the sine wave.
import numpy as np
import matplotlib
matplotlib.use('TKAgg') #use matplotlib backend TKAgg (optional)
import matplotlib.pyplot as plt
t = np.linspace(0.0, 5.0, 50000) # time axis
sig = np.sin(t)
plt.plot(t,sig)
from math import *
Fs = 8000
f = 500
sample = 16
a = [0] * sample
for n in range(sample):
a[n] = sin(2*pi*f*n/Fs)
creating the x coordinates
Sample = [i for i in range(sample)]
importing matplotlib for plotting
import matplotlib.pyplot as plt
adding labels and plotting
plt.xlabel('Voltage(V)')
plt.ylabel('Sample(n)')
plt.plot(Sample, a)
plt.show()