Trying to fit a sin function using curve_fit to some points here. When plotting both the fit and the points, I get something I have no idea how to explain, so I better post some images.
Using the following code line yields:
plt.plot(phase_min, sinusoidal_function(phase_min, *popt), '.', lw=3)
Using the line style '-', I get:
How can I just have a damn line connecting each adjacent dot, not all the dots in between?
Thanks!
When you plot a line in matplotlib, it automatically connects the points in the same order as they are provided. See the example below:
import matplotlib.pyplot as plt
plt.plot([1,3,2], [1,2,3])
Your problem is that your phase_min is not sorted and matplotlib is trying to connect your data points in order. Actually since you already got the fitted function, you don't need to use the original data to plot the function. You can just define the data points you want to plot the line as below. In this way you can have as many data points as you want to make the plot, so the line will be more smooth than if you make the plots using your original data points.
x=np.arange(0, 1, 0.001)
y=sinusoidal_function(x, *popt)
plt.plot(x,y)
You could use np.argsort:
order = np.argsort(x)
xsorted = x[order]
ysorted = y[order]
where x andy are the coordinates of your orange dots.
Related
I am new to python and I am trying to plot x and y (both have a large number of data) but when I use a plt.plot there is not plot visible on the output.
The code I have been using is
for i in range(len(a)):
plt.plot(a[i],b[i])
plt.figure()
plt.show()
when I tried a scatter plot
for i in range(len(a)):
plt.scatter(a[i],b[i])
plt.figure()
plt.show()
I am not able to understand the reason for missing the line plot and even when I try seaborn it showing me an error ValueError: If using all scalar values, you must pass an index
import numpy as np
import matplotlib.pyplot as plt
a = np.linspace(0,5,100)
b = np.linspace(0,10,100)
plt.plot(a,b)
plt.show()
I think this answers your question. I have taken sample values of a and b. The matplotlib line plots are not required to run in loops
A line is created between two points. If you are plotting single values, a line can't be constructed.
Well, you might say "but I am plotting many points," which already contains part of the answer (points). Actually, matplotlib.plot() plots line-objects. So every time, you call plot, it creates a new one (no matter if you are calling it on the same or on a new axis). The reason why you don't get lines is that only single points are plotted. The reason why you're not even seeing the these points is that plot() does not indicate the points with markers per default. If you add marker='o' to plot(), you will end up with the same figure as with scatter.
A scatter-plot on the other hand is an unordered collection of points. There characteristic is that there are no lines between these points because they are usually not a sequence. Nonetheless, because there are no lines between them, you can plot them all at once. Per default, they have all the same color but you can even specify a color vector so that you can encode a third information in it.
import matplotlib.pyplot as plt
import numpy as np
# create random data
a = np.random.rand(10)
b = np.random.rand(10)
# open figure + axes
fig,axs = plt.subplots(1,2)
# standard scatter-plot
axs[0].scatter(a,b)
axs[0].set_title("scatter plot")
# standard line-plot
axs[1].plot(a,b)
axs[1].set_title("line plot")
I was trying to plot a vertical line with markers on it using ax.axvline but the markers only show up on the bottom and top of the figure. I have played around with the markevery kwarg but it does not seem to have any effect when I change it even though it works for a normal line plot. Does anyone know if this is because no discrete values are specified along the axis or am I just doing something wrong?
I realize that I can plot a vertical line on my own and specify the markers, but I figure given the purpose of axvline I should use it.
Here is an example code of what I am talking about:
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(-10,10)
y = x**2-15.
fig = plt.figure(figsize=(4,4))
ax = plt.subplot(111)
ax.plot(y,x) #Test curve
ax.plot(2+np.zeros(len(x)),x,marker='X',markevery=1) #another way to plot what I want.
ax.axvline(0,c='r',marker='X',markevery=1) #markerevery doesn't seem to work
plt.show()
As mentioned by ImportanceofBeingErnest, the markereverykwarg does not apply for axvline or axhline because there are technically only 2 points used to draw the line at the boundaries.
I have a Data Frame df with two columns 'Egy' and 'fx' that I plot in this way:
plot_1 = df_data.plot(x="Egy", y="fx", color="red", ax=ax1, linewidth=0.85)
plot_1.set_xscale('log')
plt.show()
But then I want to smooth this curve using spline like this:
from scipy.interpolate import spline
import numpy as np
x_new = np.linspace(df_data['Egy'].min(), df_data['Egy'].max(),500)
f = spline(df_data['Egy'], df_data['fx'],x_new)
plot_1 = ax1.plot(x_new, f, color="black", linewidth=0.85)
plot_1.set_xscale('log')
plt.show()
And the plot I get is this (forget about the scatter blue points).
There are a lot of "peaks" in the smooth curve, mainly at lower x. How Can I smooth this curve properly?
When I consider the "busybear" suggestion of use np.logspace instead of np.linspace I get the following picture, which is not very satisfactory either.
You have your x values linearly scaled with np.linspace but your plot is log scaled. You could try np.geomspace for your x values or plot without the log scale.
Using spline will only work well for functions that are already smooth. What you need is to regularize the data and then interpolate afterwards. This will help to smooth out the bumps. Regularization is an advanced topic, and it would not be appropriate to discuss it in detail here.
Update: for regularization using machine learning, you might look into the scikit library for Python.
I am having trouble with two things on a plot I am generating within a for loop, my code loads some data in, fits it to a function using curve_fit and then plots measured data and the fit on the same plot for 5 different sets of measured y value (the measured data is represent by empty circle markers and fit by a solid line as the same color as the marker)
Firstly I am struggling to reduce the linewidth of the fit (solid line) however much I reduce the float value of linewidth, I can increase the size just not decrease it by the value displayed in the output below. Secondly I would like the legend to display only circle markers not circles with lines through - I cannot seem to get this to work, any ideas?
Here is my code and attached is the output plot and data file on google drive share link (for some reason it's cutting off long lines of text on this post):
import scipy
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
#define vogel-fulcher-tamman (VFT) function
def vft(x,sigma_0,temp_vf,D):
return np.log(sigma_0)-((D*temp_vf)/(x-temp_vf))
#load and sort data
data=np.genfromtxt('data file',skip_header=3)
temp=data[:,0]
inverse_temp=data[:,1]
dc_conduct=np.log10(data[:,2:11])
only_adam=dc_conduct[:,4:9]
colors = ['b','g','r','c','m']
labels = ['50mg 2-adam','300mg 2-adam','100 mg 2-adam','150 mg 2-adam','250mg
2-adam']
for i in range(0,len(only_adam)):
#fit VTF function
y=only_adam[:,i]
popt, pcov = curve_fit(vft,temp,y)
#plotting
plt.plot(inverse_temp,y,color=colors[i],marker='o',markerfacecolor='none',
label=labels[i])
plt.plot(inverse_temp,vft(temp, *popt),linewidth=0.00001,linestyle='-
',color=colors[i])
plt.ylabel("Ionic Conductivity [Scm**2/mol]")
plt.xlabel("1000 / [T(K)]")
plt.axis('tight')
plt.legend(loc='lower left')
You are looping over the rows of only_adam, but index the columns of that array with the loop variable i. This does not make sense and leads to the error shown.
The plot that shows the data points has lines in it. Those are the lines shown. You cannot make them smaller by decreasing the other plot's linewidth. Instead you need to set the linestyle of that plot off, e.g. plot(..., ls="")
I'm plotting some 2D fields using matplotlib and the fields have to be seen with equal aspect ratio. But when I set the aspect ratio I find that there are unnecessary blank spaces. Please consider the following example:
from matplotlib import pyplot as plt
import numpy as np
x=np.arange(100)
y=np.arange(100)
Y, X = np.meshgrid(y,x)
Z = X + Y
plt.contourf(X, Y, Z)
#plt.axes().set_aspect('equal', 'datalim')
plt.tight_layout()
plt.colorbar()
plt.grid()
plt.show()
If I run that command I get this figure:
However, let's say I uncomment the line that sets the equal ratio . So let's say I include this:
plt.axes().set_aspect('equal', 'datalim')
I get the following output:
Which is a very poor use of space. I can't make the actual plot take better advantage of the figure space no matter how hard I try (I don't have that much knowledge of pyplot).
I there a way to expand the actual data part of the equal-ratio plot so that I have less white space?
Thank you.
The issue you're having is caused by "datalim", which asks the axes to apply the usual limits you would expect from a normal line or scatter plot, e.g. the use of 5% margin on each side of the shown data.
I do not see any reason to use "datalim" here. So you may just leave it out,
plt.axes().set_aspect('equal')
and get a plot with equal aspect and no white space around.