I have a lot of GOES data that I'd like to plot with coastlines. I've added matplotlib axes with the proper project of each data and plotted the data. I tried to add coastlines with cartopy but they do not appear.
import metpy
import xarray as xr
import matplotlib.pyplot as plt
def get_projection(ds, variable):
dat = ds.metpy.parse_cf(variable)
return dat.metpy.cartopy_crs
aod = xr.open_dataset('GOES/AODC/2021/001/00/OR_ABI-L2-AODC-M6_G16_s20210010001176_e20210010003549_c20210010006090.nc')
albedo = xr.open_dataset('GOES/LSAC/2021/230/21/OR_ABI-L2-LSAC-M6_G16_s20212302131172_e20212302133545_c20212302135044.nc')
rainfall = xr.open_dataset('GOES/RRQPEF/2021/001/00/OR_ABI-L2-RRQPEF-M6_G16_s20210010000209_e20210010009517_c20210010010020.nc')
precipitable_water = xr.open_dataset('GOES/TPWC/2021/001/00/OR_ABI-L2-TPWC-M6_G16_s20210010001176_e20210010003549_c20210010005512.nc')
aod_proj = get_projection(aod, 'AOD')
albedo_proj = get_projection(albedo, 'LSA')
rainfall_proj = get_projection(rainfall, 'RRQPE')
pwv_proj = get_projection(precipitable_water, 'TPW')
figsize = (11, 8.5)
fig = plt.figure(figsize=figsize)
ax1 = fig.add_subplot(2, 2, 1, projection=aod_proj)
ax2 = fig.add_subplot(2, 2, 2, projection=albedo_proj)
ax3 = fig.add_subplot(2, 2, 3, projection=rainfall_proj)
ax4 = fig.add_subplot(2, 2, 4, projection=pwv_proj)
aod['AOD'].plot(vmin=0, vmax=1, ax=ax1, transform=aod_proj)
albedo['LSA'].plot(ax=ax2, transform=albedo_proj)
rainfall['RRQPE'].plot(ax=ax3, transform=rainfall_proj)
precipitable_water['TPW'].plot(ax=ax4, transform=pwv_proj)
for ax in [ax1, ax2, ax3, ax4]:
ax.coastlines(resolution='50m', color='red', linewidth=1)
fig.tight_layout()
What I get is an image with no coastlines. I'm assuming that this is a projection issue but I don't know where the problem would be.
Related
In order to create figures with some same graphs, I would like to define a function per group of graph. These should be called depending on the subfigure provided in order to have these graphs at the right location. Consequently, I would liek to split this code below into separate functions as a code like the one provided after this one.
fig = plt.figure(constrained_layout=True, figsize=(10, 8))
# create top/bottom subfigs
(subfig_t, subfig_b) = fig.subfigures(2, 1, hspace=0.05, height_ratios=[1, 3])
# put ax0 in top subfig
ax0 = subfig_t.subplots()
ax0.set_title('ax0')
subfig_t.supxlabel('xlabel0')
# create left/right subfigs nested in bottom subfig
(subfig_bl, subfig_br) = subfig_b.subfigures(1, 2, wspace=0.1, width_ratios=[3, 1])
# put ax1-ax3 in gridspec of bottom-left subfig
gs = subfig_bl.add_gridspec(nrows=1, ncols=9)
ax1 = subfig_bl.add_subplot(gs[0, :1])
ax2 = subfig_bl.add_subplot(gs[0, 1:6], sharey=ax1)
ax3 = subfig_bl.add_subplot(gs[0, 6:], sharey=ax1)
ax1.set_title('ax1')
ax2.set_title('ax2')
ax3.set_title('ax3')
ax2.get_yaxis().set_visible(False)
ax3.get_yaxis().set_visible(False)
subfig_bl.supxlabel('xlabel1-3')
# put ax4 in bottom-right subfig
ax4 = subfig_br.subplots()
ax4.set_title('ax4')
subfig_br.supxlabel('xlabel4')
Below is the code-like I would like to have, to avoid to write the same code multiple times.
fig = plt.figure(constrained_layout=True, figsize=(10, 8))
# create top/bottom subfigs
(subfig_t, subfig_b) = fig.subfigures(2, 1, hspace=0.05, height_ratios=[1, 3])
(subfig_bl, subfig_br) = subfig_b.subfigures(1, 2, wspace=0.1, width_ratios=[3, 1])
def func1(subfig_t):
# put ax0 in top subfig
ax0 = subfig_t.subplots()
ax0.set_title('ax0')
subfig_t.supxlabel('xlabel0')
return subfig_t
def func2(subfig_bl):
# put ax1-ax3 in gridspec of bottom-left subfig
gs = subfig_bl.add_gridspec(nrows=1, ncols=9)
ax1 = subfig_bl.add_subplot(gs[0, :1])
ax2 = subfig_bl.add_subplot(gs[0, 1:6], sharey=ax1)
ax3 = subfig_bl.add_subplot(gs[0, 6:], sharey=ax1)
ax1.set_title('ax1')
ax2.set_title('ax2')
ax3.set_title('ax3')
ax2.get_yaxis().set_visible(False)
ax3.get_yaxis().set_visible(False)
subfig_bl.supxlabel('xlabel1-3')
return subfig_bl
def func3(subfig_br):
# put ax4 in bottom-right subfig
ax4 = subfig_br.subplots()
ax4.set_title('ax4')
subfig_br.supxlabel('xlabel4')
return subfig_bl
def func_save(fig, OutputPath):
fig.savefig(OutputPath, dpi=300, format='png', bbox_inches='tight')
subfig_t = func1(subfig_t)
subfig_bl = func2(subfig_bl)
subfig_br = func3(subfig_br)
func_save(fig, OutputPath)
The functions are not defined as functions, few of the syntax changes and the code is good to run. Python syntax is quite different from other programming languages. It is very simple to learn, and even complex to understand the unknown.
The below code will run perfectly, hope you find it useful.
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure(constrained_layout=True, figsize=(10, 8))
# create top/bottom subfigs
(subfig_t, subfig_b) = fig.subfigures(2, 1, hspace=0.05, height_ratios=[1, 3])
(subfig_bl, subfig_br) = subfig_b.subfigures(1, 2, wspace=0.1, width_ratios=[3, 1])
def func1(subfig_t):
# put ax0 in top subfig
ax0 = subfig_t.subplots()
ax0.set_title('ax0')
subfig_t.supxlabel('xlabel0')
return subfig_t
def func2(subfig_bl):
# put ax1-ax3 in gridspec of bottom-left subfig
gs = subfig_bl.add_gridspec(nrows=1, ncols=9)
ax1 = subfig_bl.add_subplot(gs[0, :1])
ax2 = subfig_bl.add_subplot(gs[0, 1:6], sharey=ax1)
ax3 = subfig_bl.add_subplot(gs[0, 6:], sharey=ax1)
ax1.set_title('ax1')
ax2.set_title('ax2')
ax3.set_title('ax3')
ax2.get_yaxis().set_visible(False)
ax3.get_yaxis().set_visible(False)
subfig_bl.supxlabel('xlabel1-3')
return subfig_bl
def func3(subfig_br):
# put ax4 in bottom-right subfig
ax4 = subfig_br.subplots()
ax4.set_title('ax4')
subfig_br.supxlabel('xlabel4')
return subfig_bl
def func_save(fig, OutputPath):
fig.savefig(OutputPath, dpi=300, format='png', bbox_inches='tight')
# Enter the path for output here
OutputPath = "output.png"
subfig_t = func1(subfig_t)
subfig_bl = func2(subfig_bl)
subfig_br = func3(subfig_br)
func_save(fig, OutputPath)
Happy coding :)
The first image is the figure I'm trying to reproduce, and the second image is the data I have. Does anyone have a clean way to do this with pandas or matplotlib?
Just transpose the DataFrame and use df.plot with the stacked flag set to true:
import pandas as pd
from matplotlib import pyplot as plt
df = pd.DataFrame({'squad': [0.6616, 0.1245, 0.0950],
'quac': [0.83, 0.065, 0.0176],
'quoref': [0.504, 0.340364, 0.1067]})
# Transpose
plot_df = df.T
# plot
ax = plot_df.plot(kind='bar', stacked=True, rot='horizontal')
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)
ax.set_ylabel("% of Questions")
plt.tight_layout()
plt.show()
You can try this:
data = {'squad':[0.661669, 0.127516, 0.095005],
'quac':[0.930514, 0.065951, 0.017680],
'quoref': [0.504963, 0.340364, 0.106700]}
df = pd.DataFrame(data)
bars_1 = df.iloc[0]
bars_2 = df.iloc[1]
bars_3 = df.iloc[2]
# Heights of bars_1 + bars_2
bars_1_to_2 = np.add(bars_1, bars_2).tolist()
# The position of the bars on the x-axis
r = [0, 1, 2]
plt.figure(figsize = (7, 7))
plt.bar(r, bars_1, color = 'lightgrey', edgecolor = 'white')
plt.bar(r, bars_2, bottom = bars_1, color = 'darkgrey', edgecolor = 'white')
plt.bar(r, bars_3, bottom = bars_1_to_2, color = 'dimgrey', edgecolor = 'white')
plt.yticks(np.arange(0, 1.1, 0.1))
plt.xticks(ticks = r, labels = df.columns)
plt.ylabel('% of Questions')
plt.show()
Suppose something comes up in my plot that mandates that I change the height ratio between two subplots that I've generated within my plot. I've tried changing GridSpec's height ratio to no avail.
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
fig = plt.figure()
gs = GridSpec(2, 1, height_ratios=[2, 1])
ax1 = fig.add_subplot(gs[0])
ax1 = fig.axes[0]
ax2 = fig.add_subplot(gs[1])
ax2 = fig.axes[1]
ax1.plot([0, 1], [0, 1])
ax2.plot([0, 1], [1, 0])
gs.height_ratios = [2, 5]
The last line has no effect on the plot ratio.
In my actual code, it is not feasible without major reworking to set the height_ratios to 2:5 ahead of time.
How do I get this to update like I want?
The axes of relevant subplots can be manipulated and adjusted to get new height ratios.
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
fig = plt.figure()
gs = GridSpec(2, 1, height_ratios=[2, 1]) #nrows, ncols
ax1 = fig.add_subplot(gs[0])
ax1 = fig.axes[0]
ax2 = fig.add_subplot(gs[1])
ax2 = fig.axes[1]
ax1.plot([0, 1], [0, 1])
ax2.plot([0, 1], [1, 0])
# new height ratio: 2:5 is required for the 2 subplots
rw, rh = 2, 5
# get dimensions of the 2 axes
box1 = ax1.get_position()
box2 = ax2.get_position()
# current dimensions
w1,h1 = box1.x1-box1.x0, box1.y1-box1.y0
w2,h2 = box2.x1-box2.x0, box2.y1-box2.y0
top1 = box1.y0+h1
#top2 = box2.y0+h2
full_h = h1+h2 #total height
# compute new heights for each axes
new_h1 = full_h*rw/(rw + rh)
new_h2 = full_h*rh/(rw + rh)
#btm1,btm2 = box1.y0, box2.y0
new_bottom1 = top1-new_h1
# finally, set new location/dimensions of the axes
ax1.set_position([box1.x0, new_bottom1, w1, new_h1])
ax2.set_position([box2.x0, box2.y0, w2, new_h2])
plt.show()
The output for ratio: (2, 5):
The output for (2, 10):
f = plt.figure(figsize=(12,10))
ax1 = f.add_subplot(411)
ax2 = f.add_subplot(422)
ax3 = f.add_subplot(423)
ax4 = f.add_subplot(424)
ax5 = f.add_subplot(425)
ax6 = f.add_subplot(426)
ax7 = f.add_subplot(427)
ax8 = f.add_subplot(428)
I want to increase space between two rows: ax1 and ax2-ax3. Other spaces should remain the same. Using "f.subplots_adjust(hspace = 0.2, wspace= 0.25)" adjusts the spacing for all subplots. What can I do to increase hspace for the top-most subplot only?
import matplotlib.pyplot as plt
fig, axs = plt.subplot_mosaic([['top', 'top'],['left1', 'right1'], ['left2', 'right2']],
constrained_layout=True)
axs['top'].set_xlabel('Xlabel\n\n')
plt.show()
This will make all the y-axes the same size. If that is not important to you, then #r-beginners answer is helpful. Note that you need-not use subplot mosaic, though it is a useful new feature.
If you are not worried about the axes sizes matching, then a slightly better way than proposed above is to use the new subfigure functionality:
import matplotlib.pyplot as plt
fig = plt.figure(constrained_layout=True)
subfigs = fig.subfigures(2, 1, height_ratios=[1, 2], hspace=0.15)
# top
axtop = subfigs[0].subplots()
# 2x2 grid
axs = subfigs[1].subplots(2, 2)
plt.show()
Based on the gridspec sample in the official reference, I customized it using this example answer.The point is to use gridspec for the separate graphs you want to configure.
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
def format_axes(fig):
for i, ax in enumerate(fig.axes):
ax.text(0.5, 0.5, "ax%d" % (i+1), va="center", ha="center")
ax.tick_params(labelbottom=False, labelleft=False)
fig = plt.figure()
gs_top = GridSpec(3, 3, top=0.95)
gs_base = GridSpec(3, 3)
ax1 = fig.add_subplot(gs_top[0, :])
# identical to ax1 = plt.subplot(gs.new_subplotspec((0, 0), colspan=3))
ax2 = fig.add_subplot(gs_base[1, :-1])
ax3 = fig.add_subplot(gs_base[1:, -1])
ax4 = fig.add_subplot(gs_base[-1, 0])
ax5 = fig.add_subplot(gs_base[-1, -2])
# fig.suptitle("GridSpec")
format_axes(fig)
plt.show()
Here is my script to plot data from a Geogtiff file using basemap. The data is categorical and there are 13 categories within this domain. The problem is that some categories get bunched up into one colour and thus some resolution is lost.
Unfortunately, I do not know how to fix this. I read that plt.cm.get_cmp is better for discrete datasets but I have not gotten it to work unfortunately.
gtif = 'some_dir'
ds = gdal.Open(gtif)
data = ds.ReadAsArray()
gt = ds.GetGeoTransform()
proj = ds.GetProjection()
xres = gt[1]
yres = gt[5]
xmin = gt[0] + xres
xmax = gt[0] + (xres * ds.RasterXSize) - xres
ymin = gt[3] + (yres * ds.RasterYSize) + yres
ymax = gt[3] - yres
xy_source = np.mgrid[xmin:xmax+xres:xres, ymax+yres:ymin:yres]
ds = None
fig2 = plt.figure(figsize=[12, 11])
ax2 = fig2.add_subplot(111)
ax2.set_title("Land use plot")
bm2 = Basemap(ax=ax2,projection='cyl',llcrnrlat=ymin,urcrnrlat=ymax,llcrnrlon=xmin,urcrnrlon=xmax,resolution='l')
bm2.drawcoastlines(linewidth=0.2)
bm2.drawcountries(linewidth=0.2)
data_new=np.copy(data)
data_new[data_new==255] = 0
nbins = np.unique(data_new).size
cb =plt.cm.get_cmap('jet', nbins+1)
img2 =bm2.imshow(np.flipud(data_new), cmap=cb)
ax2.set_xlim(3, 6)
ax2.set_ylim(50,53)
plt.show()
labels = [str(i) for i in np.unique(data_new)]
cb2=bm2.colorbar(img2, "right", size="5%", pad='3%', label='NOAH Land Use Category')
cb2.set_ticklabels(labels)
cb2.set_ticks(np.unique(data_new))
Here are the categories that are found within the domain (numbered classes):
np.unique(data_new)
array([ 0, 1, 4, 5, 7, 10, 11, 12, 13, 14, 15, 16, 17], dtype=uint8)
Thanks so much for any help here. I have also attached the output image that shows the mismatch. (not working)
First, this colormap problem is independent of the use of basemap. The following is therefore applicable to any matplotlib plot.
The problem here is that creating a colormap from n values distributes those values equally over the colormap range. Some values from the image therefore fall into the same colorrange within the colormap.
To prevent this, one can generate a colormap with the initial number of categories as shown below.
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.colors
# generate some data
data = np.array( [ 0, 1, 4, 5, 7, 10]*8 )
np.random.shuffle(data)
data = data.reshape((8,6))
# generate colormap and norm
unique = np.unique(data)
vals = np.arange(int(unique.max()+1))/float(unique.max())
cols = plt.cm.jet(vals)
cmap = matplotlib.colors.ListedColormap(cols, int(unique.max())+1)
norm=matplotlib.colors.Normalize(vmin=-0.5, vmax=unique.max()+0.5)
fig, ax = plt.subplots(figsize=(5,5))
im = ax.imshow(data, cmap=cmap, norm=norm)
for i in range(data.shape[0]):
for j in range(data.shape[1]):
ax.text(j,i,data[i,j], color="w", ha="center", va="center")
cb = fig.colorbar(im, ax=ax, norm=norm)
cb.set_ticks(unique)
plt.show()
This can be extended to exclude the colors not present in the image as follows:
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.colors
# generate some data
data = np.array( [ 0, 1, 4, 5, 7, 10]*8 )
np.random.shuffle(data)
data = data.reshape((8,6))
unique, newdata = np.unique(data, return_inverse=1)
newdata = newdata.reshape(data.shape)
# generate colormap and norm
new_unique = np.unique(newdata)
vals = np.arange(int(new_unique.max()+1))/float(new_unique.max())
cols = plt.cm.jet(vals)
cmap = matplotlib.colors.ListedColormap(cols, int(new_unique.max())+1)
norm=matplotlib.colors.Normalize(vmin=-0.5, vmax=new_unique.max()+0.5)
fig, ax = plt.subplots(figsize=(5,5))
im = ax.imshow(newdata, cmap=cmap, norm=norm)
for i in range(newdata.shape[0]):
for j in range(newdata.shape[1]):
ax.text(j,i,data[i,j], color="w", ha="center", va="center")
cb = fig.colorbar(im, ax=ax, norm=norm)
cb.ax.set_yticklabels(unique)
plt.show()