Two sections of my code are giving me trouble, I am trying to get the basemap created in this first section here:
#Basemap
epsg = 6060; width = 2000.e3; height = 2000.e3 #epsg 3413. 6062
m=Basemap(epsg=epsg,resolution='l',width=width,height=height) #lat_ts=(90.+35.)/2.
m.drawcoastlines(color='white')
m.drawmapboundary(fill_color='#99ffff')
m.fillcontinents(color='#cc9966',lake_color='#99ffff')
m.drawparallels(np.arange(10,70,20),labels=[1,1,0,0])
m.drawmeridians(np.arange(-100,0,20),labels=[0,0,0,1])
plt.title('ICESAT2 Tracks in Greenland')
plt.figure(figsize=(20,10))
Then my next section is meant to plot the data its getting from a file, and plot these tracks on top of the Basemap. Instead, it creates a new plot entirely. I have tried rewording the secondary plt.scatter to match Basemap, such as m.scatter, m.plt, etc. But it only returns with “RuntimeError: Can not put single artist in more than one figure” when I do so.
Any ideas on how to get this next section of code onto the basemap? Here is the next section, focus on the end to see where it is plotting.
icesat2_data[track] = dict() # creates a sub-dictionary, track
icesat2_data[track][year+month+day] = dict() # and one layer more for the date under the whole icesat2_data dictionary
icesat2_data[track][year+month+day] = dict.fromkeys(lasers)
for laser in lasers: # for loop, access all the gt1l, 2l, 3l
if laser in f:
lat = f[laser]["land_ice_segments"]["latitude"][:] # data for a particular laser's latitude.
lon = f[laser]["land_ice_segments"]["longitude"][:] #data for a lasers longitude
height = f[laser]["land_ice_segments"]["h_li"][:] # data for a lasers height
quality = f[laser]["land_ice_segments"]["atl06_quality_summary"][:].astype('int')
# Quality filter
idx1 = quality == 0 # data dictionary to see what quality summary is
#print('idx1', idx1)
# Spatial filter
idx2 = np.logical_and( np.logical_and(lat>=lat_min, lat<=lat_max), np.logical_and(lon>=lon_min, lon<=lon_max) )
idx = np.where( np.logical_and(idx1, idx2) ) # combines index 1 and 2 from data quality filter. make sure not empty. if empty all data failed test (low quality or outside box)
icesat2_data[track][year+month+day][laser] = dict.fromkeys(['lat','lon','height']) #store data, creates empty dictionary of lists lat, lon, hi, those strings are the keys to the dict.
icesat2_data[track][year+month+day][laser]['lat'] = lat[idx] # grabbing only latitudes using that index of points with good data quality and within bounding box
icesat2_data[track][year+month+day][laser]['lon'] = lon[idx]
icesat2_data[track][year+month+day][laser]['height'] = height[idx]
if lat[idx].any() == True and lon[idx].any() == True:
x, y = transformer.transform(icesat2_data[track][year+month+day][laser]['lon'], \
icesat2_data[track][year+month+day][laser]['lat'])
plt.scatter(x, y, marker='o', color='#000000')
Currently, they output separately, like this:
Not sure if you're still working on this, but here's a quick example I put together that you might be able to work with (obviously I don't have the data you're working with). A couple things that might not be self-explanatory - I used m() to transform the coordinates to map coordinates. This is Basemap's built-in transformation method so you don't have to use PyProj. Also, setting a zorder in the scatter function ensures that your points are plotted above the countries layer and don't get hidden underneath.
#Basemap
epsg = 6060; width = 2000.e3; height = 2000.e3 #epsg 3413. 6062
plt.figure(figsize=(20,10))
m=Basemap(epsg=epsg,resolution='l',width=width,height=height) #lat_ts=(90.+35.)/2.
m.drawcoastlines(color='white')
m.drawmapboundary(fill_color='#99ffff')
m.fillcontinents(color='#cc9966',lake_color='#99ffff')
m.drawparallels(np.arange(10,70,20),labels=[1,1,0,0])
m.drawmeridians(np.arange(-100,0,20),labels=[0,0,0,1])
plt.title('ICESAT2 Tracks in Greenland')
for coord in [[68,-39],[70,-39]]:
lat = coord[0]
lon = coord[1]
x, y = m(lon,lat)
m.scatter(x,y,color='red',s=100,zorder=10)
plt.show()
I think you might need:
plt.figure(figsize(20,10))
before creating the basemap, not after. As it stands it's creating a map and then creating a new figure after that which is why you're getting two figures.
Then your plotting line should be m.scatter() as you mentioned you tried before.
Related
I am not sure on how to plot a dotted line from a shapefile in Python. It appears that readshapefile() does not have any linestyle for me to set. Below I have a working code where I take a shapefile and plot it, but it only plots a solid line. Any ideas to set me in the right direction? Thanks!
The shapefile can be found here: http://www.natice.noaa.gov/products/daily_products.html, where the Start Date is Feb 15th, end date is Feb 17th, and the Date Types is Ice Edge. It should be the first link.
#!/awips2/python/bin/python
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
map = Basemap(llcrnrlon=-84.37,llcrnrlat=42.11,urcrnrlon=-20.93,urcrnrlat=66.48,
resolution='i', projection='tmerc', lat_0 = 55., lon_0 = -50.)
map.drawmapboundary(fill_color='aqua')
map.fillcontinents(color='#ddaa66',lake_color='aqua')
map.drawcoastlines(zorder = 3)
map.readshapefile('nic_autoc2018046n_pl_a', 'IceEdge', zorder = 2, color = 'blue')
plt.show()
From the Basemap documentation:
A tuple (num_shapes, type, min, max) containing shape file info is
returned. num_shapes is the number of shapes, type is the type code
(one of the SHPT* constants defined in the shapelib module, see
http://shapelib.maptools.org/shp_api.html) and min and max are
4-element lists with the minimum and maximum values of the vertices.
If drawbounds=True a matplotlib.patches.LineCollection object is
appended to the tuple.
drawbounds is True by default, so all you have to do is collect the return value of readshapefile and alter the linestyle of the returned LineCollection object, which can be done with LineCollection.set_linestyle(). So in principle you can change the linestyle of your plotted shape file with something like this:
result = m.readshapefile('shapefiles/nic_autoc2018046n_pl_a', 'IceEdge', zorder = 10, color = 'blue')#, drawbounds = False)
col = result[-1]
col.set_linestyle('dotted')
plt.show()
However, your shapefile contains 5429 separate line segments of different length and somehow matplotlib does not seem to be able to deal with this large amount of non-continuous lines. At least on my machine the plotting did not finish within one hour, so I interrupted the process. I played a bit with your file and it seems like many of the lines are broken into segments unnecessarily (I'm guessing this is because the ice sheet outlines are somehow determined on tiles and then pieced together afterwards, but only the providers will really know). Maybe it would help to piece together adjacent pieces, but I'm not sure.
I was also wondering whether the result would even look that great with a dotted line, because there are so many sharp bends. Below I show a picture where I only plot the 100 longest line segments (leaving out drawcoastlines and with thicker lines) using this code:
import numpy as np
result = m.readshapefile('shapefiles/nic_autoc2018046n_pl_a', 'IceEdge', zorder = 10, color = 'blue')#, drawbounds = False)
col = result[-1]
segments = col.get_segments()
seglens = [len(seg) for seg in col.get_segments()]
segments = np.array(segments)
seglens = np.array(seglens)
idx = np.argsort(seglens)
seglens = seglens[idx]
segments = segments[idx]
col.remove()
new_col = LineCollection(segments[-100:],linewidths = 2, linestyles='dotted', colors='b')
ax.add_collection(new_col)
plt.show()
And the result looks like this:
Suppose I had a list, or pandas series, or latitude longitude pairs. With Folium, I can plot markers for a single pair of coordinates using
coords = [46.8354, -121.7325]
map_4 = folium.Map(location=[46.8527, -121.7649], tiles='Stamen Terrain',
zoom_start=13)
folium.Marker(location=coords).add_to(map_4)
But when I try to pass a list of list, nothing is plotted. I could loop through a list of lists and plot the markers, but I am wondering if I can just pass an argument and have several markers plotted.
You can do in this way:
map = folium.Map(location = [lat, lng], zoom_start = 4, tiles = "Mapbox bright")
feature_group = folium.FeatureGroup("Locations")
for lat, lng, name in zip(lat_lst, lng_lst, name_lst):
feature_group.add_child(folium.Marker(location=[lat,lon],popup=name))
map.add_child(feature_group)
You can also create an html file from it to see whether markers are been added or not
map.save(outfile = "test.html")
Now open the test.html file in browser and check the markers
I create a function to add an individual points and then use DataFrame.apply() to run every row through the function.
Here is are some examples in a notebook.
Is there a way to extract the data from an array, which corresponds to a line of a contourplot in python? I.e. I have the following code:
n = 100
x, y = np.mgrid[0:1:n*1j, 0:1:n*1j]
plt.contour(x,y,values)
where values is a 2d array with data (I stored the data in a file but it seems not to be possible to upload it here). The picture below shows the corresponding contourplot. My question is, if it is possible to get exactly the data from values, which corresponds e.g. to the left contourline in the plot?
Worth noting here, since this post was the top hit when I had the same question, that this can be done with scikit-image much more simply than with matplotlib. I'd encourage you to check out skimage.measure.find_contours. A snippet of their example:
from skimage import measure
x, y = np.ogrid[-np.pi:np.pi:100j, -np.pi:np.pi:100j]
r = np.sin(np.exp((np.sin(x)**3 + np.cos(y)**2)))
contours = measure.find_contours(r, 0.8)
which can then be plotted/manipulated as you need. I like this more because you don't have to get into the deep weeds of matplotlib.
plt.contour returns a QuadContourSet. From that, we can access the individual lines using:
cs.collections[0].get_paths()
This returns all the individual paths. To access the actual x, y locations, we need to look at the vertices attribute of each path. The first contour drawn should be accessible using:
X, Y = cs.collections[0].get_paths()[0].vertices.T
See the example below to see how to access any of the given lines. In the example I only access the first one:
import matplotlib.pyplot as plt
import numpy as np
n = 100
x, y = np.mgrid[0:1:n*1j, 0:1:n*1j]
values = x**0.5 * y**0.5
fig1, ax1 = plt.subplots(1)
cs = plt.contour(x, y, values)
lines = []
for line in cs.collections[0].get_paths():
lines.append(line.vertices)
fig1.savefig('contours1.png')
fig2, ax2 = plt.subplots(1)
ax2.plot(lines[0][:, 0], lines[0][:, 1])
fig2.savefig('contours2.png')
contours1.png:
contours2.png:
plt.contour returns a QuadContourSet which holds the data you're after.
See Get coordinates from the contour in matplotlib? (which this question is probably a duplicate of...)
I am plotting weather model precipitation data on a map. The map is a contour fill. Over that data I would like to plot text at specific grid points that tells the value of the precipitation at the point in time, however I am struggling to do this. I have a list of lat/lon points but cannot figure out how to map it properly to the data I have. The data itself comes with it's own set of lat/lon points that uniquely map to the data. Here's example code that I have:
grib = 'gfs.t00z.pgrb2.0p25.f084'
grbs = pygrib.open(grib)
grb = grbs.select(name='Total Precipitation',typeOfLevel='surface')[0]
precip = grb.values *.039370
lat,lon = grb.latlons()
x, y = m(lon,lat)
with open('mydata') as f:
for line in f:
myline = line.replace("\n", "")
myline = myline.split(",")
uniquepoints.append(myline) # contains specific lat, lon points
intervals = [0.0,0.01,0.1,0.25,0.5,0.75,1.00,1.25,1.50,1.75,2.0,2.50,3.00]
m=Basemap(projection='lcc',lon_0 = -95.,llcrnrlon=-125.,
urcrnrlon=-55.,llcrnrlat=20.,urcrnrlat=50., lat_1=25.,lat_2=46., resolution='l',area_thresh=10000,ax=ax)
obsobj = m.contourf(x,y,precip,intervals,cmap=plt.cm.jet)
I know you are suppose to use plt.text, but I can't configure it correctly so that the unique points map correctly to the precip data.
It can be easy to make simple mistakes with placing points on Basemap as you have to convert between lat,long,height and the windows coordinate system in x,y. However, if your data is plotting at the correct points on the map, you can use those positions to plot your labels at those exact points with some specified offset.
A pythonic example of how to do this:
lats = [x['LLHPosition'][0] for x in unit_data]
lons = [x['LLHPosition'][1] for x in unit_data]
x,y = m(lons,lats)
label_text = [x['UnitName'] for x in unit_data]
x_offsets = [2000] * len(unit_data)
y_offsets = [2000] * len(unit_data)
for label, xpt, ypt, x_offset, y_offset in zip(label_text, x, y, x_offsets, y_offsets):
plt.text(xpt+x_offset, ypt+y_offset, label)
I am currently working with BUFR files with wind data. When I read this file on python I get 4 large vectors, latitude vector, longitude vector, wind_direction vector, and wind_speed vector.
Both wind vectors are masked python arrays because there is non-valid data. This happens because the data comes from a non-geostationary satellite. In fact I successfully generated the following image from this BUFR file to show you the general shape that the data takes.
In this image I have plotted a color field to represent the wind speed, while the arrows obviously represent the wind direction.
Please notice the two bands of actual data. Unfortunately the way I am plotting the data, generates a third band (where the color field is smooth), in-between the actual data bands. This is an artefact of the function pcolormesh. If I could superimpose two `pcolormesh plots, each one representing one of the bands, this problem would disappear.
Unfortunately, I do not know how I could separate the data "regions". I have thought about clustering techniques but do not know how to cluster along latlon data using ANOTHER array (the wind data) as the clustering rule.
This is my current code:
#!/usr/bin/python
import bufr
import numpy as np
import sys
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
from matplotlib import mlab
WIND_DIR_INDEX = 97
WIND_SPEED_INDEX = 96
bfrfile = sys.argv[1]
print bfrfile
bfr = bufr.BUFRFile(bfrfile)
lon = []
lat = []
wind_d = []
wind_s = []
for record in bfr:
for entry in record:
if entry.index == WIND_DIR_INDEX:
wind_d.append(entry.data)
if entry.index == WIND_SPEED_INDEX:
wind_s.append(entry.data)
if entry.name.find("LONGITUDE") == 0:
lon.append(entry.data)
if entry.name.find("LATITUDE") == 0:
lat.append(entry.data)
lons = np.concatenate(lon)
lats = np.concatenate(lat)
winds_d = np.concatenate(wind_d)
winds_s = np.concatenate(wind_s)
winds_d = np.ma.masked_greater(winds_d,1.0e+6)
winds_s = np.ma.masked_greater(winds_s,1.0e+6)
windu = np.cos((winds_d-180)*(np.pi/180))
windv = np.sin((winds_d-180)*(np.pi/180))
# Data interpolation for pcolormesh (needs gridded data)
xi = np.linspace(lons.min(),lons.max(),lons.size/10)
yi = np.linspace(lats.min(),lats.max(),lats.size/10)
Z = mlab.griddata(lons,lats,winds_s,xi,yi)
X,Y = np.meshgrid(xi,yi)
mydpi = 96
fig = plt.figure(frameon=True)
fig.set_size_inches(1600/mydpi,1200/mydpi)
ax = plt.Axes(fig,[0,0,1,1])
#ax.set_axis_off()
fig.add_axes(ax)
plt.hold(True);
plt.quiver(lons[::5],lats[::5],windu[::5],windv[::5],linewidths=0)
for method in (ax.set_xticks,ax.set_xticklabels,ax.set_yticks,ax.set_yticklabels):
method([])
fig.savefig('/home/cendas/bin/python/bufr_ascat.png',bbox_inches=0,dpi=5*mydpi)
mydpi = 96
fig = plt.figure(frameon=True)
fig.set_size_inches(1600/mydpi,1200/mydpi)
ax = plt.Axes(fig,[0,0,1,1])
#ax.set_axis_off()
fig.add_axes(ax)
plt.hold(True);
try:
plt.pcolormesh(X,Y,Z,alpha=None)
plt.clim(0,10)
except ValueError:
pass
print "Warning: Empty data array."
for method in (ax.set_xticks,ax.set_xticklabels,ax.set_yticks,ax.set_yticklabels):
method([])
fig.savefig('/home/cendas/bin/python/bufr_ascat_color.png',bbox_inches=0,dpi=5*mydpi)
I then usually follow this python code with the following terminal commands to combine the images:
convert bufr_ascat.png -transparent white bufr_ascat.png
convert bufr_ascat_color.png -transparent white bufr_ascat_color.png
composite bufr_ascat.png bufr_ascat_color.png bufrascat.png
Don't abuse clustering for this.
What you need is a simple selection / filtering; not a structure discovery process.
Choose the mean of the masked data. All non-masked data left of that mean is the left part, all non-masked data on the right is the other?
Clustering is the wrong tool for this task.