I want to plot some data from a csv file using dataframe.
My code currently displays 266=14*19 separate subplots. Currently, it is coded to display all 266 subplots on one screen and each is small and difficult to read.
I tried to use a 1x1 plot
#fig, cx=plt.subplots(1,1, sharex=False, sharey=False, figsize=(18,12))
Main code:
fig, cx=plt.subplots(14,19, sharex=False, sharey=False, figsize=(18,12))
#fig, cx=plt.subplots(1,1, sharex=False, sharey=False, figsize=(18,12))
plt.subplots_adjust(hspace=0.5)
cx = cx.ravel()
for i in range(0,len(Bond)):
cx[i].plot(VelLog[Bond[i]], color='b')
cx[i].set_xlabel('Time (ms)')
cx[i].set_ylabel('Velocity (m/s)')
cx[i].set_ylim(-250,150)
cx[i].set_title(Bond[i])
plt.savefig('Velocity.png', dpi=120)
plt.show()
##################################
Error message when I un-comment Line 1
cx[i].plot(VelLog[Bond[i]], color='b')
IndexError: index 209 is out of bounds for axis 0 with size 209
How can I only display a few subplots on a screen at a time to increase readability?
Like 5x5 + 5x5 + 5x5 + 5x5 + 5x5,+ 5x5 + 5x5 + 5x5 + 5x5 + 5x5 + 4x4 = 266
11 different screens.
Is there a way to add a chart filter as an alternative?
Here is my updated code with your suggestions. It now creates 11 figures. I was able to plot all 266 graphs but each graph looks the same
Nrows, Ncols = 14, 19
Nplots = Nrows*Ncols
nrows, ncols = 5, 5
naxes = nrows*ncols
nfigures = Nplots//naxes
count = 0
figures = []
for fignum in range(nfigures+1):
fig, axes = plt.subplots(nrows, ncols, sharex=False, sharey=False, figsize=(18,12))
plt.subplots_adjust(hspace=0.5)
#axes = axes.ravel()
figures.append(fig)
axes = axes.flat
for ax in axes:
#print(count)
if count<Nplots:
for i in range(0,len(Bond)):
cvs_row, cvs_col = divmod(count, Ncols)
cvs_row, cvs_col = cvs_row+1, cvs_col+1
ax.plot(VelLog[Bond[i]], color='b', label='%d,%d'%(cvs_row, cvs_col))
ax.set_xlabel('Time (ms)')
ax.set_ylabel('Velocity (m/s)')
ax.set_ylim(-250,150)
ax.set_title(Bond[i])
count = count+1
plt.savefig('Velocity.png', dpi=120)
plt.show()
Here is the result for one figure
enter image description here
Here I plot the same function 14*19 times, but you can get an idea
We have two problems, keeping track of what we are plotting and
delaying the actual plotting until we are finished with all of them.
The first issue is solved here using a global counter and the divmod
builtin, the second storing all the figures in a list and calling
plt.show() only at the very end of the plotting phase.
To show what I mean with "keeping track" I have added a label and a
legend to each individual subplot.
To keep things reasonable I don't check if the last figure is empty
and also I don't check is some subplots of the last figures are empty,
but remember that you can remove some subplots from a figure if you
want a clean last figure.
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, np.pi, 31)
y = np.sin(x)
Nrows, Ncols = 14, 19
Nplots = Nrows*Ncols
nrows, ncols = 5, 5
naxes = nrows*ncols
nfigures = Nplots//naxes
count = 0
figures = []
for fignum in range(nfigures+1):
fig, axes = plt.subplots(nrows, ncols)
figures.append(fig)
axes = axes.flat
for ax in axes:
print(count)
if count<Nplots:
cvs_row, cvs_col = divmod(count, Ncols)
cvs_row, cvs_col = cvs_row+1, cvs_col+1
ax.plot(x, y, label='%d,%d'%(cvs_row, cvs_col))
ax.legend()
count = count+1
plt.show()
Here I show just the last figure, to show what I mean with "empty
subplots"...
Related
I'm trying to draw with matplotlib two average vertical line for every overlapping histograms using a loop. I have managed to draw the first one, but I don't know how to draw the second one. I'm using two variables from a dataset to draw the histograms. One variable (feat) is categorical (0 - 1), and the other one (objective) is numerical. The code is the following:
for chas in df[feat].unique():
plt.hist(df.loc[df[feat] == chas, objective], bins = 15, alpha = 0.5, density = True, label = chas)
plt.axvline(df[objective].mean(), linestyle = 'dashed', linewidth = 2)
plt.title(objective)
plt.legend(loc = 'upper right')
I also have to add to the legend the mean and standard deviation values for each histogram.
How can I do it? Thank you in advance.
I recommend you using axes to plot your figure. Pls see code below and the artist tutorial here.
import numpy as np
import matplotlib.pyplot as plt
# Fixing random state for reproducibility
np.random.seed(19680801)
mu1, sigma1 = 100, 8
mu2, sigma2 = 150, 15
x1 = mu1 + sigma1 * np.random.randn(10000)
x2 = mu2 + sigma2 * np.random.randn(10000)
fig, ax = plt.subplots(1, 1, figsize=(7.2, 7.2))
# the histogram of the data
lbs = ['a', 'b']
colors = ['r', 'g']
for i, x in enumerate([x1, x2]):
n, bins, patches = ax.hist(x, 50, density=True, facecolor=colors[i], alpha=0.75, label=lbs[i])
ax.axvline(bins.mean())
ax.legend()
Background:
I have a list_of_x_and_y_list that contains x and y values which looks like:
[[(44800, 14888), (132000, 12500), (40554, 12900)], [(None, 193788), (101653, 78880), (3866, 160000)]]
I have another data_name_list ["data_a","data_b"] so that
"data_a" = [(44800, 14888), (132000, 12500), (40554, 12900)]
"data_b" = [(None, 193788), (101653, 78880), (3866, 160000)]
The len of list_of_x_and_y_list / or len of data_name_list is > 20.
Question:
How can I create a scatter plot for each item (being the same colour) in the data_name_list?
What I have tried:
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax = plt.axes(facecolor='#FFFFFF')
prop_cycle = plt.rcParams['axes.prop_cycle']
colors = prop_cycle.by_key()['color']
print(list_of_x_and_y_list)
for x_and_y_list, data_name, color in zip(list_of_x_and_y_list, data_name_list, colors):
for x_and_y in x_and_y_list,:
print(x_and_y)
x, y = x_and_y
ax.scatter(x, y, label=data_name, color=color) # "label=data_name" creates
# a huge list as a legend!
# :(
plt.title('Matplot scatter plot')
plt.legend(loc=2)
file_name = "3kstc.png"
fig.savefig(file_name, dpi=fig.dpi)
print("Generated: {}".format(file_name))
The Problem:
The legend appears to be a very long list, which I don't know how to rectify:
Relevant Research:
Matplotlib scatterplot
Scatter Plot
Scatter plot in Python using matplotlib
The reason you get a long repeated list as a legend is because you are providing each point as a separate series, as matplotlib does not automatically group your data based on the labels.
A quick fix is to iterate over the list and zip together the x-values and the y-values of each series as two tuples, so that the x tuple contains all the x-values and the y tuple the y-values.
Then you can feed these tuples to the plt.plot method together with the labels.
I felt that the names list_of_x_and_y_list were uneccessary long and complicated, so in my code I've used shorter names.
import matplotlib.pyplot as plt
data_series = [[(44800, 14888), (132000, 12500), (40554, 12900)],
[(None, 193788), (101653, 78880), (3866, 160000)]]
data_names = ["data_a","data_b"]
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax = plt.axes(facecolor='#FFFFFF')
prop_cycle = plt.rcParams['axes.prop_cycle']
colors = prop_cycle.by_key()['color']
for data, data_name, color in zip(data_series, data_names, colors):
x,y = zip(*data)
ax.scatter(x, y, label=data_name, color=color)
plt.title('Matplot scatter plot')
plt.legend(loc=1)
To only get one entry per data_name, you should add data_name only once as a label. The rest of the calls should go with label=None.
The simplest you can achieve this using the current code, is to set data_name to None at the end of the loop:
from matplotlib import pyplot as plt
from random import randint
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.set_facecolor('#FFFFFF')
# create some random data, suppose the sublists have different lengths
list_of_x_and_y_list = [[(randint(1000, 4000), randint(2000, 5000)) for col in range(randint(2, 10))]
for row in range(10)]
data_name_list = list('abcdefghij')
colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
for x_and_y_list, data_name, color in zip(list_of_x_and_y_list, data_name_list, colors):
for x_and_y in x_and_y_list :
x, y = x_and_y
ax.scatter(x, y, label=data_name, color=color)
data_name = None
plt.legend(loc=2)
plt.show()
Some things can be simplified, making the code 'more pythonic', for example:
for x_and_y in x_and_y_list :
x, y = x_and_y
can be written as:
for x, y in x_and_y_list:
Another issue, is that with a lot of data calling scatter for every point could be rather slow. All the x and y belonging to the same list can be plotted together. For example using list comprehension:
for x_and_y_list, data_name, color in zip(list_of_x_and_y_list, data_name_list, colors):
xs = [x for x, y in x_and_y_list]
ys = [y for x, y in x_and_y_list]
ax.scatter(xs, ys, label=data_name, color=color)
scatter could even get a list of colors per point, but plotting all the points in one go, wouldn't allow for labels per data_name.
Very often, numpy is used to store numerical data. This has some advantages, such as vectorization for quick calculations. With numpy the code would look like:
import numpy as np
for x_and_y_list, data_name, color in zip(list_of_x_and_y_list, data_name_list, colors):
xys = np.array(x_and_y_list)
ax.scatter(xys[:,0], xys[:,1], label=data_name, color=color)
Before I start I want to say that I've tried follow this and this post on the same problem however they are doing it with imshow heatmaps unlike 2d histogram like I'm doing.
Here is my code(the actual data has been replaced by randomly generated data but the gist is the same):
import matplotlib.pyplot as plt
import numpy as np
def subplots_hist_2d(x_data, y_data, x_labels, y_labels, titles):
fig, a = plt.subplots(2, 2)
a = a.ravel()
for idx, ax in enumerate(a):
image = ax.hist2d(x_data[idx], y_data[idx], bins=50, range=[[-2, 2],[-2, 2]])
ax.set_title(titles[idx], fontsize=12)
ax.set_xlabel(x_labels[idx])
ax.set_ylabel(y_labels[idx])
ax.set_aspect("equal")
cb = fig.colorbar(image[idx])
cb.set_label("Intensity", rotation=270)
# pad = how big overall pic is
# w_pad = how separate they're left to right
# h_pad = how separate they're top to bottom
plt.tight_layout(pad=-1, w_pad=-10, h_pad=0.5)
x1, y1 = np.random.uniform(-2, 2, 10000), np.random.uniform(-2, 2, 10000)
x2, y2 = np.random.uniform(-2, 2, 10000), np.random.uniform(-2, 2, 10000)
x3, y3 = np.random.uniform(-2, 2, 10000), np.random.uniform(-2, 2, 10000)
x4, y4 = np.random.uniform(-2, 2, 10000), np.random.uniform(-2, 2, 10000)
x_data = [x1, x2, x3, x4]
y_data = [y1, y2, y3, y4]
x_labels = ["x1", "x2", "x3", "x4"]
y_labels = ["y1", "y2", "y3", "y4"]
titles = ["1", "2", "3", "4"]
subplots_hist_2d(x_data, y_data, x_labels, y_labels, titles)
And this is what it's generating:
So now my problem is that I could not for the life of me make the colorbar apply for all 4 of the histograms. Also for some reason the bottom right histogram seems to behave weirdly compared with the others. In the links that I've posted their methods don't seem to use a = a.ravel() and I'm only using it here because it's the only way that allows me to plot my 4 histograms as subplots. Help?
EDIT:
Thomas Kuhn your new method actually solved all of my problem until I put my labels down and tried to use plt.tight_layout() to sort out the overlaps. It seems that if I put down the specific parameters in plt.tight_layout(pad=i, w_pad=0, h_pad=0) then the colorbar starts to misbehave. I'll now explain my problem.
I have made some changes to your new method so that it suits what I want, like this
def test_hist_2d(x_data, y_data, x_labels, y_labels, titles):
nrows, ncols = 2, 2
fig, axes = plt.subplots(nrows, ncols, sharex=True, sharey=True)
##produce the actual data and compute the histograms
mappables=[]
for (i, j), ax in np.ndenumerate(axes):
H, xedges, yedges = np.histogram2d(x_data[i][j], y_data[i][j], bins=50, range=[[-2, 2],[-2, 2]])
ax.set_title(titles[i][j], fontsize=12)
ax.set_xlabel(x_labels[i][j])
ax.set_ylabel(y_labels[i][j])
ax.set_aspect("equal")
mappables.append(H)
##the min and max values of all histograms
vmin = np.min(mappables)
vmax = np.max(mappables)
##second loop for visualisation
for ax, H in zip(axes.ravel(), mappables):
im = ax.imshow(H,vmin=vmin, vmax=vmax, extent=[-2,2,-2,2])
##colorbar using solution from linked question
fig.colorbar(im,ax=axes.ravel())
plt.show()
# plt.tight_layout
# plt.tight_layout(pad=i, w_pad=0, h_pad=0)
Now if I try to generate my data, in this case:
phi, cos_theta = get_angles(runs)
detector_x1, detector_y1, smeared_x1, smeared_y1 = detection_vectorised(1.5, cos_theta, phi)
detector_x2, detector_y2, smeared_x2, smeared_y2 = detection_vectorised(1, cos_theta, phi)
detector_x3, detector_y3, smeared_x3, smeared_y3 = detection_vectorised(0.5, cos_theta, phi)
detector_x4, detector_y4, smeared_x4, smeared_y4 = detection_vectorised(0, cos_theta, phi)
Here detector_x, detector_y, smeared_x, smeared_y are all lists of data point
So now I put them into 2x2 lists so that they can be unpacked suitably by my plotting function, as such:
data_x = [[detector_x1, detector_x2], [detector_x3, detector_x4]]
data_y = [[detector_y1, detector_y2], [detector_y3, detector_y4]]
x_labels = [["x positions(m)", "x positions(m)"], ["x positions(m)", "x positions(m)"]]
y_labels = [["y positions(m)", "y positions(m)"], ["y positions(m)", "y positions(m)"]]
titles = [["0.5m from detector", "1.0m from detector"], ["1.5m from detector", "2.0m from detector"]]
I now run my code with
test_hist_2d(data_x, data_y, x_labels, y_labels, titles)
with just plt.show() turned on, it gives this:
which is great because data and visual wise, it is exactly what I want i.e. the colormap corresponds to all 4 histograms. However, since the labels are overlapping with the titles, I thought I would just run the same thing but this time with plt.tight_layout(pad=a, w_pad=b, h_pad=c) hoping that I would be able to adjust the overlapping labels problem. However this time it doesn't matter how I change the numbers a, b and c, I always get my colorbar lying on the second column of graphs, like this:
Now changing a only makes the overall subplots bigger or smaller, and the best I could do was to adjust it with plt.tight_layout(pad=-10, w_pad=-15, h_pad=0), which looks like this
So it seems that whatever your new method is doing, it made the whole plot lost its adjustability. Your solution, as wonderful as it is at solving one problem, in return, created another. So what would be the best thing to do here?
Edit 2:
Using fig, axes = plt.subplots(nrows, ncols, sharex=True, sharey=True, constrained_layout=True) along with plt.show() gives
As you can see there's still a vertical gap between the columns of subplots for which not even using plt.subplots_adjust() can get rid of.
Edit:
As has been noted in the comments, the biggest problem here is actually to make the colorbar for many histograms meaningful, as ax.hist2d will always scale the histogram data it receives from numpy. It may therefore be best to first calculated the 2d histogram data using numpy and then use again imshow to visualise it. This way, also the solutions of the linked question can be applied. To make the problem with the normalisation more visible, I put some effort into producing some qualitatively different 2d histograms using scipy.stats.multivariate_normal, which shows how the height of the histogram can change quite dramatically even though the number of samples is the same in each figure.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import gridspec as gs
from scipy.stats import multivariate_normal
##opening figure and axes
nrows=3
ncols=3
fig, axes = plt.subplots(nrows,ncols)
##generate some random data for the distributions
means = np.random.rand(nrows,ncols,2)
sigmas = np.random.rand(nrows,ncols,2)
thetas = np.random.rand(nrows,ncols)*np.pi*2
##produce the actual data and compute the histograms
mappables=[]
for mean,sigma,theta in zip( means.reshape(-1,2), sigmas.reshape(-1,2), thetas.reshape(-1)):
##the data (only cosmetics):
c, s = np.cos(theta), np.sin(theta)
rot = np.array(((c,-s), (s, c)))
cov = rot#np.diag(sigma)#rot.T
rv = multivariate_normal(mean,cov)
data = rv.rvs(size = 10000)
##the 2d histogram from numpy
H,xedges,yedges = np.histogram2d(data[:,0], data[:,1], bins=50, range=[[-2, 2],[-2, 2]])
mappables.append(H)
##the min and max values of all histograms
vmin = np.min(mappables)
vmax = np.max(mappables)
##second loop for visualisation
for ax,H in zip(axes.ravel(),mappables):
im = ax.imshow(H,vmin=vmin, vmax=vmax, extent=[-2,2,-2,2])
##colorbar using solution from linked question
fig.colorbar(im,ax=axes.ravel())
plt.show()
This code produces a figure like this:
Old Answer:
One way to solve your problem is to generate the space for your colorbar explicitly. You can use a GridSpec instance to define how wide your colorbar should be. Below your subplots_hist_2d() function with a few modifications. Note that your use of tight_layout() shifted the colorbar into a funny place, hence the replacement. If you want the plots closer to each other, I'd rather recommend to play with the aspect ratio of the figure.
def subplots_hist_2d(x_data, y_data, x_labels, y_labels, titles):
## fig, a = plt.subplots(2, 2)
fig = plt.figure()
g = gs.GridSpec(nrows=2, ncols=3, width_ratios=[1,1,0.05])
a = [fig.add_subplot(g[n,m]) for n in range(2) for m in range(2)]
cax = fig.add_subplot(g[:,2])
## a = a.ravel()
for idx, ax in enumerate(a):
image = ax.hist2d(x_data[idx], y_data[idx], bins=50, range=[[-2, 2],[-2, 2]])
ax.set_title(titles[idx], fontsize=12)
ax.set_xlabel(x_labels[idx])
ax.set_ylabel(y_labels[idx])
ax.set_aspect("equal")
## cb = fig.colorbar(image[-1],ax=a)
cb = fig.colorbar(image[-1], cax=cax)
cb.set_label("Intensity", rotation=270)
# pad = how big overall pic is
# w_pad = how separate they're left to right
# h_pad = how separate they're top to bottom
## plt.tight_layout(pad=-1, w_pad=-10, h_pad=0.5)
fig.tight_layout()
Using this modified function, I get the following output:
I am trying to make a 2x2 subplot, with each of the inner subplots consisting of two x axes and two y axes; the first xy correspond to a linear scale and the second xy correspond to a logarithmic scale. Before assuming this question has been asked before, the matplotlib docs and examples show how to do multiple scales for either x or y but not both. This post on stackoverflow is the closest thing to my question, and I have attempted to use this idea to implement what I want. My attempt is below.
Firstly, we initialize data, ticks, and ticklabels. The idea is that the alternate scaling will have the same tick positions with altered ticklabels to reflect the alternate scaling.
import numpy as np
import matplotlib.pyplot as plt
# xy data (global)
X = np.linspace(5, 13, 9, dtype=int)
Y = np.linspace(7, 12, 9)
# xy ticks for linear scale (global)
dtick = dict(X=X, Y=np.linspace(7, 12, 6, dtype=int))
# xy ticklabels for linear and logarithmic scales (global)
init_xt = 2**dtick['X']
dticklabel = dict(X1=dtick['X'], Y1=dtick['Y']) # linear scale
dticklabel['X2'] = ['{}'.format(init_xt[idx]) if idx % 2 == 0 else '' for idx in range(len(init_xt))] # log_2 scale
dticklabel['Y2'] = 2**dticklabel['Y1'] # log_2 scale
Borrowing from the linked SO post, I will plot the same thing in each of the 4 subplots. Since similar methods are used for both scalings in each subplot, the method is thrown into a for-loop. But we need the row number, column number, and plot number for each.
# 2x2 subplot
# fig.add_subplot(row, col, pnum); corresponding iterables = (irows, icols, iplts)
irows = (1, 1, 2, 2)
icols = (1, 2, 1, 2)
iplts = (1, 2, 1, 2)
ncolors = ('red', 'blue', 'green', 'black')
Putting all of this together, the function to output the plot is below:
def initialize_figure(irows, icols, iplts, ncolors, figsize=None):
""" """
fig = plt.figure(figsize=figsize)
for row, col, pnum, color in zip(irows, icols, iplts, ncolors):
ax1 = fig.add_subplot(row, col, pnum) # linear scale
ax2 = fig.add_subplot(row, col, pnum, frame_on=False) # logarithmic scale ticklabels
ax1.plot(X, Y, '-', color=color)
# ticks in same positions
for ax in (ax1, ax2):
ax.set_xticks(dtick['X'])
ax.set_yticks(dtick['Y'])
# remove xaxis xtick_labels and labels from top row
if row == 1:
ax1.set_xticklabels([])
ax2.set_xticklabels(dticklabel['X2'])
ax1.set_xlabel('')
ax2.set_xlabel('X2', color='gray')
# initialize xaxis xtick_labels and labels for bottom row
else:
ax1.set_xticklabels(dticklabel['X1'])
ax2.set_xticklabels([])
ax1.set_xlabel('X1', color='black')
ax2.set_xlabel('')
# linear scale on left
if col == 1:
ax1.set_yticklabels(dticklabel['Y1'])
ax1.set_ylabel('Y1', color='black')
ax2.set_yticklabels([])
ax2.set_ylabel('')
# logarithmic scale on right
else:
ax1.set_yticklabels([])
ax1.set_ylabel('')
ax2.set_yticklabels(dticklabel['Y2'])
ax2.set_ylabel('Y2', color='black')
ax1.tick_params(axis='x', colors='black')
ax1.tick_params(axis='y', colors='black')
ax2.tick_params(axis='x', colors='gray')
ax2.tick_params(axis='y', colors='gray')
ax1.xaxis.tick_bottom()
ax1.yaxis.tick_left()
ax1.xaxis.set_label_position('top')
ax1.yaxis.set_label_position('right')
ax2.xaxis.tick_top()
ax2.yaxis.tick_right()
ax2.xaxis.set_label_position('top')
ax2.yaxis.set_label_position('right')
for ax in (ax1, ax2):
ax.set_xlim([4, 14])
ax.set_ylim([6, 13])
fig.tight_layout()
plt.show()
plt.close(fig)
Calling initialize_figure(irows, icols, iplts, ncolors) produces the figure below.
I am applying the same xlim and ylim so I do not understand why the subplots are all different sizes. Also, the axis labels and axis ticklabels are not in the specified positions (since fig.add_subplot(...) indexing starts from 1 instead of 0.
What is my mistake and how can I achieve the desired result?
(In case it isn't clear, I am trying to put the xticklabels and xlabels for the linear scale on the bottom row, the xticklabels and xlabels for the logarithmic scale on the top row, the 'yticklabelsandylabelsfor the linear scale on the left side of the left column, and the 'yticklabels and ylabels for the logarithmic scale on the right side of the right column. The color='black' kwarg corresponds to the linear scale and the color='gray' kwarg corresponds to the logarithmic scale.)
The irows and icols lists inn the code do not serve any purpose. To create 4 subplots in a 2x2 grid you would loop over the range(1,5),
for pnum in range(1,5):
ax1 = fig.add_subplot(2, 2, pnum)
This might not be the only problem in the code, but as long as the subplots aren't created correctly it's not worth looking further down.
I have reviewed the response to this question: How would I iterate over a list of files and plot them as subplots on a single figure?
But am none the wiser on how to achieve my goal. I would like to plot multiple data sets, with differing x axes, onto a single figure in Python. I have included a snippet of my code below, which performs an FFT on a dataset, then calculates 3 Butterworth filter outputs. Ideally I would like to have all plotted on a single figure, which I have attempted to achieve in the code below.
The for loop calculates the 3 Butterworth filter outputs, the code above - the FFT and the code directly below attempts to append the FFT curve and sqrt(0.5) line to the previously generated plots for display.
Any Direction or advice would be appreciated.
"""Performs a Fast Fourier Transform on the data specified at the base of the code"""
def FFT(col):
x = io2.loc[1:,'Time']
y = io2.loc[1:,col]
# Number of samplepoints
#N = 600
N = pd.Series.count(x)
N2 = int(N/2)
# sample spacing
#T = 1.0 / 800.0
T = 1/(io2.loc[2,'Time'] - io2.loc[1,'Time'])
#x = np.linspace(0.0, N*T, N)
#y = np.sin(50.0 * 2.0*np.pi*x) + 0.5*np.sin(80.0 * 2.0*np.pi*x)
yf = scipy.fftpack.fft(y)
xf = np.linspace(0.0, 1.0/(2.0*T), N2)
fig=plt.figure()
plt.clf()
i=1
for order in [3, 6, 9]:
ax=fig.add_subplot(111, label="order = %d" % order)
b, a = butter_lowpass(cutoff, fs, order=order)
w, h = freqz(b, a, worN=2000)
ax.plot((fs * 0.5 / np.pi) * w, abs(h))
i=i+1
ax4=fig.add_subplot(111, label='sqrt(0.5)', frame_on=False)
ax5=fig.add_subplot(111, label="FFT of "+col, frame_on=False)
ax4.plot([0, 0.5 * fs], [np.sqrt(0.5), np.sqrt(0.5)], '--')
ax5.plot(xf, 2.0/N * np.abs(yf[:N2]))
plt.xlabel('Frequency (Hz)')
plt.ylabel('Gain')
plt.grid(True)
plt.legend(loc='best')
#fig, ax = plt.subplots()
#ax.plot(xf, 2.0/N * np.abs(yf[:N2]), label="FFT of "+col)
plt.axis([0,5000,0,0.1])
#plt.xlabel('Frequency (Hz)')
#plt.ylabel('Amplitude (mm)')
#plt.legend(loc=0)
plt.show()
return
Kind Regards,
Here you can find a minimal example of how to plot multiple lines with different x and y datasets. You are recreating the plot every time you type add_subplot(111). Instead, you should call plot multiple times. I have added an example for a single plot with multiple lines, as well as an example for one subplot per line.
import numpy as np
import matplotlib.pyplot as plt
x1 = np.arange(0, 10, 1)
x2 = np.arange(3, 12, 0.1)
x3 = np.arange(2, 8, 0.01)
y1 = np.sin(x1)
y2 = np.cos(x2**0.8)
y3 = np.sin(4.*x3)**3
data = []
data.append((x1, y1, 'label1'))
data.append((x2, y2, 'label2'))
data.append((x3, y3, 'label3'))
# All lines in one plot.
plt.figure()
for n in data:
plt.plot(n[0], n[1], label=n[2])
plt.legend(loc=0, frameon=False)
# One subplot per data set.
cols = 2
rows = len(data)//2 + len(data)%2
plt.figure()
gs = plt.GridSpec(rows, cols)
for n in range(len(data)):
i = n%2
j = n//2
plt.subplot(gs[j,i])
plt.plot(data[n][0], data[n][1])
plt.title(data[n][2])
plt.tight_layout()
plt.show()