I am trying to get two plots on one figure using matplotlib's subplots() command. I want the two plots to share an x-axis and have one legend for the whole plot. The code I have right now is:
observline = mlines.Line2D([], [], color=(1,0.502,0),\
markersize=15, label='Observed',linewidth=2)
wrfline=mlines.Line2D([], [], color='black',\
markersize=15, label='WRF',linewidth=2)
fig,axes=plt.subplots(2,1,sharex='col',figsize=(18,10))
df08.plot(ax=axes[0],linewidth=2, color=(1,0.502,0))\
.legend(handles=[observline,wrfline],loc='lower center', bbox_to_anchor=(0.9315, 0.9598),prop={'size':16})
axes[0].set_title('WRF Model Comparison Near %.2f,%.2f' %(lat,lon),fontsize=24)
axes[0].set_ylim(0,360)
axes[0].set_yticks(np.arange(0,361,60))
df18.plot(ax=axes[1],linewidth=2, color='black').legend_.remove()
plt.subplots_adjust(hspace=0)
axes[1].set_ylim(0,360)
axes[1].set_yticks(np.arange(0,361,60))
plt.ylabel('Wind Direction [Degrees]',fontsize=18,color='black')
axes[1].yaxis.set_label_coords(-0.05, 1)
plt.xlabel('Time',fontsize=18,color='black')
#plt.savefig(df8graphfile, dpi = 72)
plt.show()
and it produces four figures, each with two subplots. The top is always empty. The bottom is filled for three of them with my 2nd dataframe. The indices for each dataframe is a datetimeindex in the format YYYY-mm-DD HH:MM:SS. The data is values from 0-360 nearly randomly across the whole time series, which is for two months.
Here is an example of each figure produced:
Related
I am trying to create a corner plot for an upcoming paper, but I'm running into difficulty. I am creating an N x N array of subplots (currently, N = 6) and then deleting a bit over half of them. The issue is that the figure doesn't seem to resize itself after I delete the extraneous subplots, so when I later add a legend using a dummy subplot, it exists in the area where a full row and column of deleted subplots were, thus enlarging the figure. I've been working on this for several hours now and haven't found a solution. Here is the MWE:
import matplotlib.pyplot as plt
%matplotlib notebook
n_char = 8
# Set up the main figure.
fig, ax = plt.subplots(n_char, n_char, figsize=(n_char, n_char))
# Get rid of the axis labels unless it's on the left-most column or bottom-most row.
for i in range(0, n_char):
# For each row, loop over each column.
for j in range(0, n_char):
# If the plot isn't in the bottom-most row, get rid of the x-axis tick labels.
if i != n_char - 1:
ax[i, j].set_xticklabels([])
# If the plot isn't in the left-most column, get rid of the y-axis tick labels.
if j != 0:
ax[i, j].set_yticklabels([])
# Remove the plots that are repetitive or boring (plotting against the same characteristic).
for i in range(0, n_char):
# For each row, loop over each column.
for j in range(0, n_char):
# Delete the offending axes.
if j >= i:
ax[i, j].remove()
# Set the spacing between the plots to a much smaller value.
fig.subplots_adjust(hspace=0.00, wspace=0.00)
# Create a big plot for the legend. Have the frame hidden.
fig.add_subplot(111, frameon=False, xticks=[], yticks=[], xticklabels=[], yticklabels=[])
# Create some dummy data to serve as the source of the legend.
plt.scatter([10], [10], color="k", s=5, zorder=2, label="Targets")
# Set the x-axis limits such that the dummy data point is invisible.
fig.gca().set_xlim(-1, 1)
# Add the legend to the plot. Have it located in the upper right.
plt.legend(scatterpoints=1, loc="upper right", fontsize=5)
# Save the final plot.
fig.savefig("./../Code Output/Other Plots/Corner_Plot_Test.png", bbox_inches="tight", dpi=500)
I have looked at many different questions here on Stack Overflow. The two most promising candidates was this one, but I found the solution wasn't quite workable due to the large number of plots (and, to be frank, I didn't fully understand the solution). I thought that the first answer in this one might also work, as I thought it was a sizing issue (i.e. the figure wasn't resizing, so creating a new subplot was creating one the size of the original figure), but all it did was resize the entire figure, so that didn't work either.
To help, I will also include an image. I took the output of the code above and edited it to show what I want:
I should add that if I don't add a subplot, the output is as I expected (i.e. it's the proper size), so the issue comes in when adding the subplot, i.e. the line fig.add_subplot(111, frameon=False, xticks=[], yticks=[], xticklabels=[], yticklabels=[]).
The use of GridSpec may help.
GridSpec is used to specify array of axes to plot. You can set widths for columns and heights for rows as ratios in the option. The unneeded row should have very small height ratio, while unneeded column very small width ratio.
Here is the runnable code and output plot:-
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
#import numpy as np
fig = plt.figure(figsize=(8, 8))
nn = 6
# will create gridspec of 6 rows, 6 columns
# 1st row will occupy v small heights
# last column will occupy v small widths
sm = 0.01 # the v small width/height
wh = (1.-sm)/(nn-1.) # useful width/height
gs = gridspec.GridSpec(nn, nn, width_ratios=[*[wh]*(nn-1), sm], \
height_ratios= [sm, *[wh]*(nn-1)])
cols, rows = nn, nn
ax = [[0 for i in range(cols)] for j in range(rows)]
for ea in range(nn):
for eb in range(nn):
ax[ea][eb] = fig.add_subplot(gs[ea, eb])
ax[ea][eb].set_xticklabels([])
ax[ea][eb].set_yticklabels([])
if eb>=ea:
ax[ea][eb].remove()
# plot data on some axes
# note that axes on the first row (index=0) are gone
ax[2][0].plot([2,5,3,7])
ax[4][2].plot([2,3,7])
# make legend in upper-right axes (GridSpec's first row, last column)
# first index: 0
# second index: nn-1
rx, cx = 0, nn-1
ax[rx][cx] = fig.add_subplot(gs[rx,cx])
hdl = ax[rx][cx].scatter([10], [10], color="k", s=5, zorder=2, label="Targets")
ax[rx][cx].set_axis_off()
#ax[rx][cx].set_visible(True) # already True
ax[rx][cx].set_xticklabels([])
ax[rx][cx].set_yticklabels([])
# plot legend
plt.legend(bbox_to_anchor=(1.0, 1.0), loc='upper right', borderaxespad=0.)
fig.subplots_adjust(hspace=0.00, wspace=0.00)
plt.show
I have created a map of precipitation levels in a region based on precipitation data from NetCDF files. I would like to add a custom scale such that if precipitation is less than 800mm it would be one colour, 800-1000mm another, etc. Similar to the map found here: http://www.metmalawi.com/climate/climate.php
At the moment I am using a gradient scale but it isn't showing the detail I need. This is the code for the plot at the moment (where 'Average' is my data that I have already formatted).
#load color palette
colourA = mpl_cm.get_cmap('BuPu')
#plot map with physical features
ax = plt.axes(projection=cartopy.crs.PlateCarree())
ax.add_feature(cartopy.feature.COASTLINE)
ax.add_feature(cartopy.feature.BORDERS)
ax.add_feature(cartopy.feature.LAKES, alpha=0.5)
ax.add_feature(cartopy.feature.RIVERS)
#set map boundary
ax.set_extent([32.5, 36., -9, -17])
#set axis tick marks
ax.set_xticks([33, 34, 35])
ax.set_yticks([-10, -12, -14, -16])
lon_formatter = LongitudeFormatter(zero_direction_label=True)
lat_formatter = LatitudeFormatter()
ax.xaxis.set_major_formatter(lon_formatter)
ax.yaxis.set_major_formatter(lat_formatter)
#plot data and set colour range
plot = iplt.contourf(Average, cmap=colourA, levels=np.arange(0,15500,500), extend='both')
#add colour bar index and a label
plt.colorbar(plot, label='mm per year')
#give map a title
plt.title('Pr 1990-2008 - Average_ERAINT ', fontsize=10)
#save the image of the graph and include full legend
plt.savefig('ERAINT_Average_Pr_MAP_Annual', bbox_inches='tight')
plt.show()
Anyone know how I can do this?
Thank you!
This is a matplotlib question disguised as an Iris question as the issue has appeared via Iris plotting routines, but to answer this we need only a couple of matplotlib commands. As such, I'm basing this answer on this matplotlib gallery example. These are levels (containing values for the upper bound of each contour) and colors (specifying the colours to shade each contour). It's best if there are the same number of levels and colours.
To demonstrate this, I put the following example together. Given that there's no sample data provided, I made my own trigonometric data. The levels are based on the trigonometric data values, so do not reflect the levels required in the question, but could be changed to the original levels. The colours used are the hex values of the levels specified by image in the link in the question.
The code:
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(-25, 25)
y = np.arange(-20, 20)
x2d, y2d = np.meshgrid(x, y)
vals = (3 * np.cos(x2d)) + (2 * np.sin(y2d))
colours = ['#bf8046', '#df9f24', '#e0de30', '#c1de2d', '#1ebf82',
'#23de27', '#1dbe20', '#11807f', '#24607f', '#22427e']
levels = range(-5, 6)
plt.contourf(vals, levels=levels, colors=colours)
plt.colorbar()
plt.show()
The produced image:
Colours could also be selected from a colormap (one way of doing this is shown in this StackOverflow answer). There are also other ways, including in the matplotlib gallery example linked above. Given, though, that the sample map linked in the question had specific colours I chose to use those colours directly.
Since I didn't find anywhere else this topic I will ask it here. I am getting data from CSV file, I have written datetime format in one of columns. I get that column with pandas module and then I need to count occurrences in specific time slots and plot that with matplotlib. Bellow you can see example of column.
Time and Date
0 2015-08-21 10:51:06.398000
1 2015-08-21 10:51:00.017000
2 2015-08-21 10:52:06.402000
3 2015-08-21 10:54:06.407000
...
I know how I can split time like so:
pd.date_range("10:50", "12:30", freq="1min").time
But how can I assign occurrences of my read values from CSV and then plot it? Any advice or direction would help.
It's hard to tell what you want as you haven't posted desired output but if I understand you correctly you want to count the number of rows in time intervals of certain length. You can do this by combining resample and len. To use resample, first set the index to 'Time and Date:
df.set_index('Date and Time', drop=False)
Note that drop=False is only necessary if the data frame has no other columns.
Then to get the number of rows in each 1-minute interval do
counts = df.resample('1min', len).astype(int)
If there are multiple dates and you want to sum the counts for each time interval over dates do
counts.groupby(lambda ts: ts.time()).sum()
Begin Edit
After initial post I continued playing with my code. In my subplots I am making four plots of the same data set, with each subplot having a different time range. However, if I give each subplot the SAME time range then the minor ticks do not disappear. This may be why Deditos could not reproduce my issue.
That being said, if I manually create each subplot (with each having a different x-axis range), set the minor tick locations, THEN set each subplot's xrange I do not see the minor ticks disappear until I set ax3's (i.e. the last subplot) range.
It seems the issue is in having different x-axis ranges. Bizarre, I would think that by setting each axis' properties individually they would not all be tied together.
End Edit
I am creating one figure that has four sub-plots, all of which are time series. I have the xaxis major ticks spaced every four hours, and want minor ticks every hour. When I set the minor ticks for the first subplot (called ax1) the minor ticks appear, as they should. However, when I set the minor ticks in ax2 they show up in ax2, but the minor ticks in ax1 disappear. This repeats for ax3, and ax4. So, in the end I have only minor xaxis ticks in the fourth subplot. I had the same problem with the yaxis, but resolved this issue using yaxis.set_minor_locator(MultipleLocator(5)) for each axis (see below). However, MultipleLocator does not seem to work for time series data. Does anyone know how I can keep my minor xaxis ticks?
from pylab import *
from matplotlib.ticker import AutoMinorLocator, MultipleLocator
minor = AutoMinorLocator()
# Start plotting
fig = figure( figsize=(22,11) )
ax1 = fig.add_subplot(221) # 8-August 2011
ax2 = fig.add_subplot(222) # 9-August 2011
ax3 = fig.add_subplot(223) # 23-August 2011
ax4 = fig.add_subplot(224) # 24-August 2011
# This is repeated for ax2, ax3, and ax4, yielding a 2x2 grid of subplots.
# Plot 8-August 2011 data
ax1.plot(tpan.index,tpan.no2,'.-',markersize=10)
ax1.errorbar(tacam.index,tacam.no2,yerr=0.15,fmt='r.',markersize=12)
# Format plots
suptitle('Pandora/ACAM NO$_2$ Comparison', fontsize=22)
# Define xtick locations/string labels
xtickloc = [dt.datetime.combine(dates[0],dt.time())+dt.timedelta(hours=h) for h in range(0,25,4)]
xticklab = [dt.datetime.strftime(h,'%H:%M') for h in xtickloc]
ax1.set_xlabel('Hour of Day (UTC, EST+5)',fontsize=14)
ax1.set_ylabel('NO$_2$ Column Density (molec*cm$^{-2}$ E16)',fontsize=14)
ax1.xaxis.set_ticks(xtickloc)
ax1.yaxis.set_ticks(linspace(0,1.5,7))
ax1.xaxis.set_minor_locator(minor)
ax1.yaxis.set_minor_locator(MultipleLocator(5))
ax1.set_xticklabels(xticklab,fontsize=12,fontweight='bold')
ax1.set_yticklabels(linspace(0,1.5,7),fontsize=12,fontweight='bold')
ax1.axis( (dates[0],dates[0]+dt.timedelta(days=1),-0.05,1.5),fontsize=6,fontweight='bold')
ax1.tick_params(which='both',width=2,top='on')
ax1.tick_params(which='major',length=7)
ax1.tick_params(which='minor',length=4)
ax1.grid(linestyle='-',which='major',linewidth=1)
ax1.set_title('08-August 2011',fontsize=16)
ax1.legend( ('Pandora VCD','ACAM dSCD'),loc=2,ncol=2)
I was facing the same problem. I think what you need to do is:
ax1.xaxis.set_minor_locator(AutoMinorLocator())
instead of
ax1.xaxis.set_minor_locator(minor)
You are passing the same object to each of your axes. This object's contents are modified when you plot on ax4 based on the range on that subplot. Hope it helps.
I have a dataset like below in dictionary format,
data={'a': [10, 11,12,5,4,3,1], 'b': [7, 18,5,11,9,2,0]}
How we can make a scatter plot in python using rpy2? where x axis is the months and y axis are the mutiples of 5? we need to plot the graph with the above values where a and b are the data points
Months should be based on the length of each key i.e for the above data we have 7 months since we have 7 data points
This is a pretty involved data structure, and it's not completely clear what you're looking to do in terms of plotting. Here are a few hints, but it'd be easiest to help you if you would post the code you've tried but hasn't worked.
The R plot function takes two vectors corresponding to the x-axis values (months, here), and y-axis values (frequencies?). You'll want to go through your graph_data dictionary and calculate the y-axis values you want to plot for each month, and then make a corresponding vector for x containing the month numbers. For example:
x = [1,2,3,4]
y = [0.7, 0.9, 0.2, 0.4]
To do the plotting from rpy2, you'll need to convert the lists to vectors like so:
from rpy2 import robjects
x_vector = robjects.IntVector(x)
y_vector = robjects.FloatVector(y)
Then do the plotting:
robjects.r.plot(x_vector, y_vector, xlab="month", ylab="freq", main="")