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I have a for loop trying to plot multiple graphs, but they are plotted over each other, what am I doing wrong?

I have this code, which is supposed to return multiple graphs, for some reason I cannot figure out (never had this problem). I get multiple graphs, but as it iterates through the loop the graph after the first contains all previous ones.
for x in np.arange(0,3):
t1= table.iloc[x,2]
t2= table.iloc[44,2]
dts = np.arange(-0.02,0.02,0.0001)
cc = np.zeros(len(dts))
for i,dt in enumerate(dts):
n1,v=np.histogram(t1,899*100)
n2,v=np.histogram(t2-dt,bins=v)
cc[i] = np.corrcoef(n1,n2)[0,1]
plt.plot(dts,cc)
#plt.title(str(table[table['ClusterNames']==test1.iloc[x,0]].iloc[0,0])+'_'+str(table[table['ClusterNames']==test1.iloc[x,1]].iloc[0,0]))
filename='step'+str(x).zfill(6)+'.png'
plt.savefig(filename, form='png', dpi = 96, transparent = True)
What am I doing wrong?
Thanks!

There are many ways to do this. To clear the plot after saves, you could add plt.clf() to the end of your loop. To plot everything on one page, you can use plt.subplot2grid OR plt.subplots.

If you want each figure in a different window, you should add plt.figure() before plt.plot()

Adding 'tick marks' to histogram python (JES)

Hi guys sorry if bad question but I have this histogram (pic attached) I need to somehow in my code include 'tick marks' up the horizontal axis as well as numbers 0,2,4,,6,8,10, 10 being the maximum in my example.
I have no idea how to add these tick marks I cannot use imports, dicts, anything like that. The closest idea that I have is some loop including
i * ([(max-min)/5])
where max and min are the beginning and end of the horizontal axis.
Have been staring at this for over a week and this is the final piece and I am drawing a blank so any help would be very appreciated!

To customise the appearance of the ticks, you can use the Axes.tick_params() method.
ex:
fig, ax = plt.subplots()
ax.plot(datasetname['Bronze'],women_degrees['Craft'],label='datasetname')
ax.tick_params(bottom="on", top="off", left="on", right="off")
plt.show()

Matplotlib: Change color of individual grid lines

I've only been using Python for about a month now, so I'm sorry if there's some simple solution to this that I overlooked.
Basically I have a figure with 4 subplots, the 2 on the left show longitudinal plots and the ones on the right show scatter plots at certain points of the longitudinal plots. You can click through the scatter plots at different points of the longitudinal plot with buttons, and the tick label of the longitudinal plot you're currently at will be highlighted in blue.
Coloring a certain tick label already works with this:
xlabels = []
labelcolors = []
for i, item in enumerate(mr.segmentlst):
if re.search('SFX|MC|MQ|MS|MKC', item):
xlabels.append(mr.segmentlst[i])
else:
xlabels.append('')
for i, item in enumerate(mr.segmentlst):
if re.search('SFX', item):
labelcolors.append('black')
else:
labelcolors.append('gray')
labelcolors[self.ind]='blue'
[t.set_color(i) for (i,t) in zip(labelcolors, ax1.xaxis.get_ticklabels())]
[t.set_color(i) for (i,t) in zip(labelcolors, ax2.xaxis.get_ticklabels())]
It only shows certain tick labels and changes their colors accordingly (I don't know if there is another solution for this, it's the only one I could find). Don't mind the mr.segmentlist, I've currently hardcoded the plot to use an attribute from another method so I can easily keep testing it in Spyder.
I'd like to also change the grid line color of the currently highlighted tick label (only xgridlines are visible) in the longitudinal plots, is there some kind of similar way of doing this? I've searched the internet for a solution for about 2 hours now and didn't really find anything helpful.
I thought something like ax1.get_xgridlines() might be used, but I have no idea how I could transform it into a useful list.
Thanks,
Tamara

get_xgridlines() returns a list of Line2D objects, so if you can locate which line you want to modify, you can modify any of their properties
x = np.random.random_sample((10,))
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(x,x)
ax.grid()
a = ax.get_xgridlines()
b = a[2]
b.set_color('red')
b.set_linewidth(3)

since the above solution only works with major gridlines
(since get_gridlines() is currently hardcoded to use only the major ones),
here's how you can also access the minor gridlines by adapting
the get_gridlines() function (from here):
from matplotlib import cbook
def get_gridlines(ax, which):
'''
Parameters:
ax : ax.xaxis or ax.yaxis instance
which : 'major' or 'minor'
Returns:
The grid lines as a list of Line2D instance
'''
if which == 'major':
ticks = ax.get_major_ticks()
if which == 'minor':
ticks = ax.get_minor_ticks()
return cbook.silent_list('Line2D gridline',
[tick.gridline for tick in ticks])

Matplotlib: Hiding specific y-tick labels not working when ticks on the right side of plot

I'm creating a subplot figure with 2 columns and a number of rows. I'm using the following code to move my tick labels and axis label to the right side for the right column (but still keeping the tick marks on both sides):
fig, ax = plt.subplots(4, 2, sharex=False, sharey=False)
fig.subplots_adjust(wspace=0, hspace=0)
for a in ax[:,1]:
a.yaxis.tick_right()
a.yaxis.set_ticks_position('both')
a.yaxis.set_label_position('right')
Then, because the subplots are close together (which is what I want, I don't want any padding in between the plots), the top and bottom y-tick labels overlap between plots. I have attempted to fix this using the method described here (this selects only those ticks that are inside the view interval - check the link for more info):
import matplotlib.transforms as mtransforms
def get_major_ticks_within_view_interval(axis):
interval = axis.get_view_interval()
ticks_in_view_interval = []
for tick, loc in zip(axis.get_major_ticks(), axis.get_major_locator()()):
if mtransforms.interval_contains(interval, loc):
ticks_in_view_interval.append(tick)
return ticks_in_view_interval
for i,a in enumerate(ax.ravel()):
nplots = len(ax.ravel())
yticks = get_major_ticks_within_view_interval(a.yaxis)
if i != 0 and i != 1:
yticks[-1].label.set_visible(False)
if i != nplots-2 and i != nplots-1:
yticks[0].label.set_visible(False)
This seems to work fine for the left column, but in the right column the overlapping ticks are still visible. Does anyone know why this happens, and how to fix it? I just can't seem to figure it out.

I have finally found the solution, so I figured I'd put it here as well in case someone ever has the same problem (or if I forget what I did, haha). I found out when I happened upon the following page: http://matplotlib.org/1.3.1/users/artists.html
What I didn't realize is that the labels on the left and the right of the y-axis can be modified independently of each other. When using yticks[0].label.set_visible(False), the label refers only to the left side labels, so the right side labels stay unchanged. To fix it, I replaced
yticks[0].label.set_visible(False)
by
yticks[0].label1.set_visible(False)
yticks[0].label2.set_visible(False)
(and the same for yticks[-1]). Now it works like a charm!

Generally I've found that problems with overlap in matplotlib can be solved by using
plt.tight_layout()
have you tried that?

're-sort' / adapt ticks of matshow matrix plot

I tried hard, but I'm stuck with matplotlib here. Please overlook, that the mpl docs are a bit confusing to me . My question concerns the following:
I draw a symmetrical n*n matrix D with matshow function. That works.
I want to do the same thing, just with different order of (the n) items in D
D = [:,neworder]
D = [neworder,:]
Now, how do I make the ticks reproduce this neworder, preferably using additionally MaxNLocator?
As far as I understand...
set_xticklabels assigns labels to the ticks by order, independently of where the ticks are set?!
set_xticks (mpl docs: 'Set the x ticks with list of ticks') here I'm really not sure what it does. Can somebody explain it precisely? I don't know, whether these functions are helpful in my case at all. Maybe even things are different between using a common xy plot and matshow.
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.gca()
D = np.arange(100).reshape(10,10)
neworder = np.arange(10)
np.random.shuffle(neworder)
D = D[:,neworder]
D = D[neworder, :]
# modify ticks somehow...
ax.matshow(D)
plt.show()
Referring to Paul's answer, think I tried smth like this. Using the neworder to define positions and using it for the labels, I added plt.xticks(neworder, neworder) as tick-modifier. For example with neworder = [9 8 4 7 2 6 3 0 1 5] I get is this
The order of the labels is correct, but the ticks are not. The labels should be independently show the correct element independently of where the ticks are set. So where is the mistake?

I think what you want to do is set the labels on the new plot to show the rearranged order of the values. Is that right? If so, you want to keep the tick locations the same, but change the labels:
plt.xticks(np.arange(0,10), neworder)
plt.yticks(np.arange(0,10), neworder)
Edit: Note that these commands must be issued after matshow. This seems to be a quirk of matshow (plot does not show this behaviour, for example). Perhaps it's related to this line from the plt.matshow documentation:
Because of how :func:matshow tries to set the figure aspect ratio to be the
one of the array, if you provide the number of an already
existing figure, strange things may happen.
Perhaps the safest way to go is to issue plt.matshow(D) without first creating a figure, then use plt.xticks and plt.yticks to make adjustments.
Your question also asks about the set_ticks and related axis methods. The same thing can be accomplished using those tools, again after issuing matshow:
ax = plt.gca()
ax.xaxis.set_ticks(np.arange(0,10)) # turn on all tick locations
ax.xaxis.set_ticklabels(neworder) # use neworder for labels
Edit2: The next part of your question is related to setting a max number of ticks. 20 would require a new example. For our example I'll set the max no. of ticks at 2:
ax = plt.gca()
ax.xaxis.set_major_locator(plt.MaxNLocator(nbins=3)) # one less tick than 'bin'
tl = ax.xaxis.get_ticklocs() # get current tick locations
tl[1:-1] = [neworder[idx] for idx in tl[1:-1]] # find what the labels should be at those locs
ax.xaxis.set_ticklabels(tl) # set the labels
plt.draw()

You are on the right track. The plt.xticks command is what you need.
You can specify the xtick locations and the label at each position with the following command.
labelPositions = arange(len(D))
newLabels = ['z','y','x','w','v','u','t','s','q','r']
plt.xticks(labelPositions,newLabels)
You could also specify an arbitrary order for labelPositions, as they will be assigned based on the values in the vector.
labelPositions = [0,9,1,8,2,7,3,6,4,5]
newLabels = ['z','y','x','w','v','u','t','s','q','r']
plt.xticks(labelPositions,newLabels)