I'm using OpenCV 3.0.0 with Python 2.7 and trying something that ought to be simple.
I want to stack images vertically.
This simple example:
import cv2
import numpy as np
comb = np.vstack((row_0, row_1))
cv2.imwrite('foo.png', comb)
consistently produces a foo.png that is drastically narrower (in the browser) than row_0 and row_1.
Details:
row_0.shape
(1074, 785, 3)
row_1.shape
(1187, 785, 3)
comb.shape
(2261, 785, 3)
If I look at row_0.png in the browser, it is WAY wider than foo.png.
Question
How can I alter my code so row_0.png is the same width as foo.png in the browser?
np.vstack does two things - make sure the inputs are at least 2d (here they are 3d), and joins them on axis=0 (rows). In other words
np.concatenate((row0, row1), axis=0)
That's what I see happening - two dimensions are the same, the first is the sum of the 2 inputs:
(1074, 785, 3)
+
(1187, 785, 3)
=
(2261, 785, 3)
If the comb looks narrower, it is probably because of scaling. The ratio of 2nd dim to 1st has gotten smaller; that's to be expected if you join 2 arrays in this way. And given the dimensions, that's the only possible way.
Viewed as arrays, comb has more rows, same number of columns. But if 2261 is the image display width, then relative height will be less.
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
Like this:
Calculating coordinates looks not so good, maybe there is a better way?
This code works fine (), but it's complicated always calculate where to place index for each letter.
image = Image.new('I', (300, 100), "white").convert('RGBA')
font = ImageFont.truetype(font=r"C:\Windows\Fonts\Arial.ttf", size=39)
draw = ImageDraw.Draw(image, 'RGBA')
draw.text((10, 10), "P", fill="black", font=font, align="center")
font = ImageFont.truetype(font=r"C:\Windows\Fonts\Arial.ttf", size=20)
draw.text((25, 35), "2", fill="black", font=font, align="center")
image.save(output_folder + 'test.png')
One possibility for you might be to use ImageMagick which understands Pango Markup Language - which looks kind of like HTML.
So, at the command-line you could run this:
convert -background white pango:'<span size="49152">Formula: <b>2P<sub><small><small>2</small></small></sub>O<sub><small><small>5</small></small></sub></b></span>' formula.png
which produces this PNG file:
Change to -background none to write on a piece of transparent canvas if you want to preserve whatever is underneath the text in your original image.
You can also put all the markup in a separate text file, called say "pango.txt" like this:
<span size="49152">Formula: <b>2P<sub><small><small>2</small></small></sub>O<sub><small><small>5</small></small></sub></b></span>
and pass that into ImageMagick like this:
convert pango:#pango.txt result.png
You could shell out and do this using:
subprocess.call()
Then you can easily load the resultant image and composite/paste it in where you want it - that would take about 3 lines of Python that you could put in a function.
Here is a further example of an image generated with Pango by Anthony Thyssen so you can see some of the possibilities:
There is loads of further information on Pango by Anthony here.
Note that there are also Python bindings for ImageMagick but I am not very familiar with them, but that may be cleaner than shelling out.
Keywords: Pango, PIL, Pillow, Python, markup, subscript, superscript, formula, chemical formulae, ImageMagick, image, image processing, SGML, HTML.
You can also do this sort of thing using Mathtext in Matplotlib:
#!/usr/bin/env python3
import matplotlib.pyplot as plt
plt.axes([0.025, 0.025, 0.95, 0.95])
# Some formula with superscripts, subscripts, square roots, fractions and integrals
eq = r"$ 2P_2 O_5 + H^{2j}$"
size = 50
x,y = 0.5, 0.5
alpha = 1
params = {'mathtext.default': 'regular' }
plt.rcParams.update(params)
plt.text(x, y, eq, ha='center', va='center', color="#11557c", alpha=alpha,
transform=plt.gca().transAxes, fontsize=size, clip_on=True)
# Suppress ticks
plt.xticks(())
plt.yticks(())
# Save on transparent background
plt.savefig('result.png', transparent=True)
You can also save the output in a memory buffer (without going to disk) and then use that in your PIL-based image processing.
Note that I have explicitly named and assigned all the parameters (x, y, size and alpha) so you can play with them and that makes the code look longer and more complicated than it actually is.
Keywords: Python, PIL, Pillow, maths, mathematical symbols, formula with superscripts, subscripts, square roots, fractions and integrals.
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.
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:
I need to draw a bar graph for the values:
male=('2', '1', '2', '6', '6', '1') # list may increase
time=('Tue_Aug_13_04:37:40_2013', 'Mon_Jul__1_02:33:11_2013','Tue_Aug_13_04:37:40_2013', 'Thu_Jul__4_01:53:32_2013', 'Mon_Jul__1_10:05:55_2013','Mon_Jul__1_04:15:25_2013')# list may increase
female=(16, 11, 16, 12, 12, 11) # list may increase
Male in green colour, female in red colour as the image attached below:
The code which I tried:
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse, Polygon
fig = plt.figure()
ax1 = fig.add_subplot(131)
ax1.bar(male, color='red', edgecolor='black')
ax1.bar(bottom=range(female), color='blue', edgecolor='black')
ax1.set_xticks(time)
plt.show()
What modifications do I need to make in order to draw the bar graph as shown in the image attached for my values?
1.) I strongly suggest that you familiarize yourself with the python syntax:
What's the difference between lists enclosed by square brackets and parentheses?
What's the difference between '2' and 2?
2.) Make use of the matplotlib documentation to figure out the correct syntaxt for the plot commands you are using.
3.) In this particular case: To get you going, change your data to:
male=[2, 1, 2, 6, 6, 1] # list may increase
time=['Tue_Aug_13_04:37:40_2013', 'Mon_Jul__1_02:33:11_2013','Tue_Aug_13_04:37:40_2013', 'Thu_Jul__4_01:53:32_2013', 'Mon_Jul__1_10:05:55_2013','Mon_Jul__1_04:15:25_2013']# list may increase
female=[16, 11, 16, 12, 12, 11] # list may increase
Please examine carefully what has changed.
4.) The bar command you try to call has not enough input arguments. With the changed data from above, try this:
ax1.bar(range(len(time)),male,width=0.5, color='red', edgecolor='black')
ax1.bar(range(len(time)),female,width=0.5,bottom=male,color='blue', edgecolor='black')
What has changed?
you need the following inputs: left, height, width=0.8
you had only one of those
due to the fact that your dates are given as strings, you need a generic counter for the x-axis, hence the range(len(time)) to provide as many tics as there are entries in time.
now, you specify the height according to the values in male and female - none of which should be strings!
define a width
in your case, you want the bars to be stacked - therefore, specify the first set of values as bottom for the second
4.) Because time is made up of strings, you cannot use it for the ticks. Instead, try:
ax1.set_xticklabels(time,rotation=90)
Here, you use the strings from time as tick-labels. The rotation=90 is a nice feature so that the long strings do not overlap.
5.) If the labels are cut off by the plot window, try this:
plt.tight_layout()
plt.show()
This should get you back on track.
Good key words for a web-search inlcude:
matplotlib stacked bar
matplotlib tick labels rotation
matplotlib ticks date