Below code is taken from http://www.acgeospatial.co.uk/sentinel-5p-and-python/
This is a very nice tutorial on how to process with Sentinel 5p netCDF data.
Now I tried to save obtained plt image as georeferenced Tiff (geoTiff). How to do this?
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
from netCDF4 import Dataset
import numpy as np
file = 'S5P_OFFL_L2__O3_____20201025T093552_20201025T111721_15720_01_020103_20201027T030032.nc'
fh = Dataset(file, mode='r')
lons = fh.groups['PRODUCT'].variables['longitude'][:][0,:,:]
lats = fh.groups['PRODUCT'].variables['latitude'][:][0,:,:]
o3 = fh.groups['PRODUCT'].variables['ozone_total_vertical_column'][0,:,:]
o3_units = fh.groups['PRODUCT'].variables['ozone_total_vertical_column_precision'].units
#o3_units = o3_units
m = Basemap(width=5000000,height=3500000,\
llcrnrlon=-15.,llcrnrlat=30.,urcrnrlon=80.,urcrnrlat=80.,\
resolution='c',projection='merc',\
#lat_ts=40,lat_0=lat_0,lon_0=lon_0)
lat_ts=40,lat_0=50,lon_0=20)
xi, yi = m(lons, lats)
# Plot Data
cs = m.pcolor(xi,yi,np.squeeze(o3*2241.15), cmap='jet')
#plt.axis('off')
plt.show()
As you see the result is presented with plt.show metod. But I would like to save it to geoTiff. Thanks!
Related
I have a sensor_msgs/PointCloud2 with [x,y,z] and how can I plot it in real-time in matplotlib like this code here. I already changed the type from Odometry to pointcloud2 but I don't know what to change in odom_callback or how to change the code in order to plot it in matplotlib. Can someone has an idea how to plot pointcloud2 in matplotlib
import matplotlib.pyplot as plt
import rospy
import tf
from sensor_msgs.msg import PointCloud2
from tf.transformations import quaternion_matrix
import numpy as np
from matplotlib.animation import FuncAnimation
class Visualiser:
def __init__(self):
self.fig, self.ax = plt.subplots()
self.ln, = plt.plot([], [], 'ro')
self.x_data, self.y_data = [] , []
def plot_init(self):
self.ax.set_xlim(0, 10000)
self.ax.set_ylim(-7, 7)
return self.ln
def getYaw(self, pose):
quaternion = (pose.orientation.x, pose.orientation.y, pose.orientation.z,
pose.orientation.w)
euler = tf.transformations.euler_from_quaternion(quaternion)
yaw = euler[2]
return yaw
def odom_callback(self, msg):
yaw_angle = self.getYaw(msg.pose.pose)
self.y_data.append(yaw_angle)
x_index = len(self.x_data)
self.x_data.append(x_index+1)
def update_plot(self, frame):
self.ln.set_data(self.x_data, self.y_data)
return self.ln
rospy.init_node('publisher_node')
vis = Visualiser()
sub = rospy.Subscriber('/scan3dd', PointCloud2, vis.odom_callback)
ani = FuncAnimation(vis.fig, vis.update_plot, init_func=vis.plot_init)
plt.show(block=True)
I am trying to show a gif file in google's colab.research. I was able to save the file in the directory with the following path name /content/BrowniamMotion.gif but I don't know how to show this GIF in my notebook to present.
The code to generate the GIF so far, in case someone can manipulate it not to save the GIF but rather to animate it directly into the google colab file was,
# Other Brownian Motion
from math import *
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
import matplotlib.animation as animation
fig = plt.figure(figsize=(8,6))
ax = plt.axes(projection='3d')
N=10
#val1 = 500
x=500*np.random.random(N)
y=500*np.random.random(N)
z=500*np.random.random(N)
def frame(w):
ax.clear()
global x,y,z
x=x+np.random.normal(loc=0.0,scale=50.0,size=10)
y=y+np.random.normal(loc=0.0,scale=50.0,size=10)
z=z+np.random.normal(loc=0.0,scale=50.0,size=10)
plt.title("Brownian Motion")
ax.set_xlabel('X(t)')
ax.set_xlim3d(-500.0,500.0)
ax.set_ylabel('Y(t)')
ax.set_ylim3d(-500.0,500.0)
ax.set_zlabel('Z(t)')
ax.set_zlim3d(-500.0,500.0)
plot=ax.scatter
3D(x, y, z, c='r')
return plot
anim = animation.FuncAnimation(fig, frame, frames=100, blit=False, repeat=True)
anim.save('BrowniamMotion.gif', writer = "pillow", fps=10 )
Sorry if this question is badly, stated. I am new to Python and using colab research.
For Colab it is easiest to use 'jshtml' to display matplotlib animation.
You need to set it up with
from matplotlib import rc
rc('animation', html='jshtml')
Then, just type your animation object. It will display itself
anim
Here's a workable colab of your code.
It has a slider where you can run back and forth at any point in time.
Using the same authors git repository seems like we have a solution to embed the plots as GIFs ( Save Matplotlib Animations as GIFs ).
#!apt install ffmpeg
#!brew install imagemagick
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import animation, rc
from IPython.display import HTML, Image # For GIF
rc('animation', html='html5')
np.random.seed(5)
# Set up formatting for the movie files
Writer = animation.writers['ffmpeg']
writer = Writer(fps=15, metadata=dict(artist='Me'), bitrate=1800)
def generateRandomLines(dt, N):
dX = np.sqrt(dt) * np.random.randn(1, N)
X = np.cumsum(dX, axis=1)
dY = np.sqrt(dt) * np.random.randn(1, N)
Y = np.cumsum(dY, axis=1)
lineData = np.vstack((X, Y))
return lineData
# Returns Line2D objects
def updateLines(num, dataLines, lines):
for u, v in zip(lines, dataLines):
u.set_data(v[0:2, :num])
return lines
N = 501 # Number of points
T = 1.0
dt = T/(N-1)
fig, ax = plt.subplots()
data = [generateRandomLines(dt, N)]
ax = plt.axes(xlim=(-2.0, 2.0), ylim=(-2.0, 2.0))
ax.set_xlabel('X(t)')
ax.set_ylabel('Y(t)')
ax.set_title('2D Discretized Brownian Paths')
## Create a list of line2D objects
lines = [ax.plot(dat[0, 0:1], dat[1, 0:1])[0] for dat in data]
## Create the animation object
anim = animation.FuncAnimation(fig, updateLines, N+1, fargs=(data, lines), interval=30, repeat=True, blit=False)
plt.tight_layout()
plt.show()
# Save as GIF
anim.save('animationBrownianMotion2d.gif', writer='pillow', fps=60)
Image(url='animationBrownianMotion2d.gif')
## Uncomment to save the animation
#anim.save('brownian2d_1path.mp4', writer=writer)
Check this link out on using the HTML to get it to work http://louistiao.me/posts/notebooks/embedding-matplotlib-animations-in-jupyter-notebooks/ .
I didn't embed a link but instead imbedded a HTML video that got it to work.
# Other Brownian Motion
from math import *
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
import matplotlib.animation as animation
from IPython.display import HTML
fig = plt.figure(figsize=(8,6))
ax = plt.axes(projection='3d')
N=10
val1 = 600
x=val1*np.random.random(N)
y=val1*np.random.random(N)
z=val1*np.random.random(N)
def frame(w):
ax.clear()
global x,y,z
x=x+np.random.normal(loc=0.0,scale=50.0,size=10)
y=y+np.random.normal(loc=0.0,scale=50.0,size=10)
z=z+np.random.normal(loc=0.0,scale=50.0,size=10)
plt.title("Brownian Motion")
ax.set_xlabel('X(t)')
ax.set_xlim3d(-val1,val1)
ax.set_ylabel('Y(t)')
ax.set_ylim3d(-val1,val1)
ax.set_zlabel('Z(t)')
ax.set_zlim3d(-val1,val1)
plot=ax.scatter3D(x, y, z, c='r')
return plot
anim = animation.FuncAnimation(fig, frame, frames=100, blit=False, repeat=True)
anim.save('BrowniamMotion.gif', writer = "pillow", fps=10 )
HTML(anim.to_html5_video())
Essentially all we did hear was add,
from IPython.display import HTML to the premable and then add the line HTML(anim.to_html5_video()). This code then produces a video and saves the gif.
I have the same file saved both as .pdf and as .svg
I'd like to insert the file in a regular matplotlib plot.
How can I do that?
import matplotlib.pyplot as plt
pdfFile = open('file.pdf')
svgFile = open('file.svg')
fig,ax = plt.subplots(1,2)
ax[0].imshow(pdfFile)
ax[1].imshoe(svgFile)
plt.show()
Alternately I've tried with
from svglib.svglib
import svg2rlg
from reportlab.graphics import renderPDF, renderPM >>> >>> drawing = svg2rlg("file.svg") >>> renderPDF.drawToFile(drawing, "file.pdf")
I have trouble with the ortho projection and pcolormesh.
It should plot a mesh of grid points. Instead, in the upper right portion of the sphere it plots strange lines instead of grid points. The mapping of the mesh looks off.
I tried the code below.
from mpl_toolkits.basemap import Basemap
import numpy as np
import matplotlib.pyplot as plt
plt.clf()
dpp =1 # degrees per pixel
lons = np.arange(-180,180+dpp,dpp)
lats = -1*np.arange(-90,90+dpp,dpp)
m = Basemap(projection='ortho', lon_0=0, lat_0=-60, resolution='l')
data = np.random.random((np.size(lats), np.size(lons)))
lons, lats = np.meshgrid(lons, lats)
x, y = m(lons, lats)
im = m.pcolormesh(x, y, data, latlon=False, cmap='RdBu')
#im = m.pcolormesh(lons, lats, data, latlon=True, cmap='RdBu')
m.colorbar(im)
plt.show()
I obtain the following plot:
The random noise should be mapped onto the entire sphere, but there is clearly an error in the upper right of the ortho map.
Does anyone else get this error with the included code?
Since basemap would require you to manually filter out unwanted data (those that are "behind the globe"), here is how to do the same with cartopy.
import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
proj = ccrs.Orthographic(central_longitude=0.0, central_latitude=-60.0)
plt.figure(figsize=(3, 3))
ax = plt.axes(projection=proj)
dpp =1
lons = np.arange(-180,180+dpp,dpp)
lats = 1*np.arange(-90,90+dpp,dpp)
data = np.random.random((np.size(lats), np.size(lons)))
lons, lats = np.meshgrid(lons, lats)
im = ax.pcolormesh(lons, lats, data, cmap='RdBu', transform=ccrs.PlateCarree())
ax.coastlines(resolution='110m')
ax.gridlines()
plt.show()
A fix to Basemap was suggested in the github basemap thread here
I want to draw a scatter trend line on matplot. How can I do that?
Python
import pandas as pd
import matplotlib.pyplot as plt
csv = pd.read_csv('/tmp/test.csv')
data = csv[['fee', 'time']]
x = data['fee']
y = data['time']
plt.scatter(x, y)
plt.show()
CSV
fee,time
100,650
90,700
80,860
70,800
60,1000
50,1200
time is integer value.
Scatter chart
I'm sorry I found the answer by myself.
How to add trendline in python matplotlib dot (scatter) graphs?
Python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
csv = pd.read_csv('/tmp/test.csv')
data = csv[['fee', 'time']]
x = data['fee']
y = data['time']
plt.scatter(x, y)
z = np.polyfit(x, y, 1)
p = np.poly1d(z)
plt.plot(x,p(x),"r--")
plt.show()
Chart
With text:
from sklearn.metrics import r2_score
plt.plot(x,y,"+", ms=10, mec="k")
z = np.polyfit(x, y, 1)
y_hat = np.poly1d(z)(x)
plt.plot(x, y_hat, "r--", lw=1)
text = f"$y={z[0]:0.3f}\;x{z[1]:+0.3f}$\n$R^2 = {r2_score(y,y_hat):0.3f}$"
plt.gca().text(0.05, 0.95, text,transform=plt.gca().transAxes,
fontsize=14, verticalalignment='top')
You also can use Seaborn lmplot:
import seaborn as sns
import pandas as pd
from io import StringIO
textfile = StringIO("""fee,time
100,650
90,700
80,860
70,800
60,1000
50,1200""")
df = pd.read_csv(textfile)
_ = sns.lmplot(x='fee', y='time', data=df, ci=None)
Output: