I am trying to plot a netCDF4 file containing ocean currents from a NASA database for a project, but I keep getting errors such as "x and y coordinates are not compatible with the shape of the vector components".
I have tried changing the streamplot to a contourf (when I did it said that it needed to be a 2d array) which I tried to change but I could not get it to work.
import os
import matplotlib.pyplot as plt
from netCDF4 import Dataset as netcdf_dataset
import numpy as np
import cartopy.crs as ccrs
fname = "oscar_vel2019.nc.gz.nc"
data=netcdf_dataset(fname)
v = data.variables['v'][0, :, :, :]
vf = data.variables['vm'][0, :, :, :]
u = data.variables['u'][0, :, :, :]
uf = data.variables['um'][0, :, :, :]
lats = data.variables['latitude'][:]
lons = data.variables['longitude'][:]
ax = plt.axes(projection=ccrs.PlateCarree())
mymap=plt.streamplot(lons, lats, u, v, 60, transform=ccrs.PlateCarree())
ax.coastlines()
plt.show()
I would like it to work such that the ocean currents are visible on the plot and to show the movement of particles in the currents through an animation. I really don't have much knowledge with this which is why I am asking. Here is the link from which I got the file: https://podaac-opendap.jpl.nasa.gov/opendap/hyrax/allData/oscar/preview/L4/oscar_third_deg/oscar_vel2019.nc.gz.html
OK, I downloaded the data. The problem is that u and v are 4-dimensional, so you need to squeeze out the "depth" dimension. Cartopy also doesn't accept longitudes greater than 180, and you probably won't get away with stream plotting the whole thing. Also, density=60 will take forever...
This is ugly, but gives you the idea.
import xarray as xr
import numpy as np
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
with xr.open_dataset('/Users/jklymak/downloads/oscar_vel2019.nc.gz.nc') as ds:
print(ds)
ax = plt.axes(projection=ccrs.PlateCarree())
dec = 10
lon = ds.longitude.values[::dec]
lon[lon>180] = lon[lon>180] - 360
mymap=plt.streamplot(lon, ds.latitude.values[::dec], ds.u.values[0, 0, ::dec, ::dec], ds.v.values[0, 0, ::dec, ::dec], 6, transform=ccrs.PlateCarree())
ax.coastlines()
plt.show()
Related
So, here is my code:
import pandas as pd
import scipy.stats as st
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoMinorLocator
from fitter import Fitter, get_common_distributions
df = pd.read_csv("project3.csv")
bins = [282.33, 594.33, 906.33, 1281.33, 15030.33, 1842.33, 2154.33, 2466.33, 2778.33, 3090.33, 3402.33]
#declaring
facecolor = '#EAEAEA'
color_bars = '#3475D0'
txt_color1 = '#252525'
txt_color2 = '#004C74'
fig, ax = plt.subplots(1, figsize=(16, 6), facecolor=facecolor)
ax.set_facecolor(facecolor)
n, bins, patches = plt.hist(df.City1, color=color_bars, bins=10)
#grid
minor_locator = AutoMinorLocator(2)
plt.gca().xaxis.set_minor_locator(minor_locator)
plt.grid(which='minor', color=facecolor, lw = 0.5)
xticks = [(bins[idx+1] + value)/2 for idx, value in enumerate(bins[:-1])]
xticks_labels = [ "{:.0f}-{:.0f}".format(value, bins[idx+1]) for idx, value in enumerate(bins[:-1])]
plt.xticks(xticks, labels=xticks_labels, c=txt_color1, fontsize=13)
#beautify
ax.tick_params(axis='x', which='both',length=0)
plt.yticks([])
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
for idx, value in enumerate(n):
if value > 0:
plt.text(xticks[idx], value+5, int(value), ha='center', fontsize=16, c=txt_color1)
plt.title('Histogram of rainfall in City1\n', loc = 'right', fontsize = 20, c=txt_color1)
plt.xlabel('\nCentimeters of rainfall', c=txt_color2, fontsize=14)
plt.ylabel('Frequency of occurrence', c=txt_color2, fontsize=14)
plt.tight_layout()
#plt.savefig('City1_Raw.png', facecolor=facecolor)
plt.show()
city1 = df['City1'].values
f = Fitter(city1, distributions=get_common_distributions())
f.fit()
fig = f.plot_pdf(names=None, Nbest=4, lw=1, method='sumsquare_error')
plt.show()
print(f.get_best(method = 'sumsquare_error'))
The issue is with the plots it shows. The first histogram it generates is
Next I get another graph with best fitted distributions which is
Then an output statement
{'chi2': {'df': 10.692966790090342, 'loc': 16.690849400411103, 'scale': 118.71595997157786}}
Process finished with exit code 0
I have a couple of questions. Why is chi2, the best fitted distribution not plotted on the graph?
How do I plot these distributions on top of the histograms and not separately? The hist() function in fitter library can do that but there I don't get to control the bins and so I end up getting like 100 bins with some flat looking data.
How do I solve this issue? I need to plot the best fit curve on the histogram that looks like image1. Can I use any other module/package to get the work done in similar way? This uses least squares fit but I am OK with least likelihood or log likelihood too.
Simple way of plotting things on top of each other (using some properties of the Fitter class)
import scipy.stats as st
import matplotlib.pyplot as plt
from fitter import Fitter, get_common_distributions
from scipy import stats
numberofpoints=50000
df = stats.norm.rvs( loc=1090, scale=500, size=numberofpoints)
fig, ax = plt.subplots(1, figsize=(16, 6))
n, bins, patches = ax.hist( df, bins=30, density=True)
f = Fitter(df, distributions=get_common_distributions())
f.fit()
errorlist = sorted(
[
[f._fitted_errors[dist], dist]
for dist in get_common_distributions()
]
)[:4]
for err, dist in errorlist:
ax.plot( f.x, f.fitted_pdf[dist] )
plt.show()
Using the histogram normalization, one would need to play with scaling to generalize again.
I have a histogram and I'm trying to fit the best norm(Gaussian) function as you can see below. the problem is that the gaussian fit isn't the best fit that I expected.
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.mlab as mlab
from astropy.modeling import models, fitting
bins=np.arange(-1,8,0.3)
#Reading data
a18 = np.loadtxt('AndXII18I.srt')
arr18 = np.array(a18[:,11])
axs[0,0].hist(arr18,bins,histtype='step')
axs[0,0].set_xlim([np.min(arr18), np.max(arr18)])
x = np.linspace(-1, bins[len(bins)-2],len(bins)-1)
x1 = np.linspace(-1, 8, 1000)
# guesses for the parameters:
g_init = models.Gaussian1D(1, 0, 1.)
fit_g = fitting.LevMarLSQFitter()
axs[0,0].plot(x1,t18)
axs[0,0].plot(edges18[8],hist18[8],'o')
g18 = fit_g(g_init, x, y18[0])
a18=g18.mean
t18=g18.amplitude*np.exp(-(x1-a18)**2/(2*g18.stddev**2))
plt.show()
I am trying to create a stereographic plot using Basemap offset from the north pole, but the west-east directions are apparently reversed. Is this an error in my implementation, or a bug?
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
m = Basemap(projection='stere',
lat_0=90, lon_0=270, lat_ts=(90.+35.)/2.,
llcrnrlon=150,urcrnrlon=-60,llcrnrlat=50,urcrnrlat=50)
m.drawmeridians(np.arange(0,360,30),labels=[1,1,1,0])
m.drawparallels(np.arange(-90,90,5))
m.drawcoastlines()
m.shadedrelief()
plt.show()
Here is the result:
result from script
How might I reproduce the following map (which is offset-centred, and rotated?)
Restricted map
Using an azimuthal type of map projection always requires a set of proper parameters to get a good result. In this case, Stereographic projection centered at the north pole, its proper parameters are not what you usually use when implement with PlateCaree projection which is often used. Here is a working code that you may try.
# Stereographic projection coverage
# should be specified less than half of a hemisphere
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
m = Basemap(projection='stere', resolution='c',
lat_0=90, lon_0=270, lat_ts=(90.+35.)/2., width=15000000, height=10000000)
# (width, height) is the plot extents in meters
m.drawmeridians(np.arange(0, 360, 30), labels=[1,1,1,0])
m.drawparallels(np.arange(0, 90, 10), labels=[0,0,0,1])
m.drawcoastlines()
m.shadedrelief()
plt.show()
The resulting plot (map 1):
To get other part of the world into the plotting area is achieved by recentering the map.
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
# projection center point
lon0 = 180
lat0 = 60
m = Basemap(projection='stere', resolution='c',
lat_0=lat0, lon_0=lon0, lat_ts=lat0, width=15000000, height=10000000)
m.drawmeridians(np.arange(0, 360, 30), labels=[1,0,0,1]) # left, right, top, bottom
m.drawparallels(np.arange(0, 90, 10), labels=[0,1,1,0])
m.drawcoastlines()
m.shadedrelief()
plt.show()
The output plot (map 2):
By specifying proper values of llcrnrlon, urcrnrlon, llcrnrlat, urcrnrlat, in Basemap() one can get the map extents as required. Here is another example of plot as requested by the OP.
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure(figsize=(8,8))
m = Basemap(projection='stere', resolution='c',
lat_0=90, lon_0=-90, lat_ts=(90.+35.)/2.,
llcrnrlon=-142, urcrnrlon=78, llcrnrlat=19, urcrnrlat=45)
m.drawmeridians(np.arange(0, 360, 30), labels=[1,0,1,0]) # left, right, top, bottom
m.drawparallels(np.arange(0, 90, 10), labels=[0,1,0,1])
m.drawcoastlines()
m.shadedrelief()
plt.show()
The resulting plot (map 3):
I can generate an error-bar plot using the code below. The graph produced by the code shows vertical lines that represent the errors in y. I would like to have horizontal lines at the tips of these errors ("error bars") and am not sure how to do so.
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(1, 10, 10, dtype=int)
y = 2**x
yerr = np.sqrt(y)*10
fig, ax = plt.subplots()
ax.errorbar(x, y, yerr, solid_capstyle='projecting')
ax.grid(alpha=0.5, linestyle=':')
plt.show()
plt.close(fig)
The code generates the figure below. I've played with the solid_capstyle kwarg. Is there a specific kwarg that does what I am trying to do?
And as an example of what I'd like, the figure below:
In case it's relevant, I am using matplotlib 2.2.2
The argument you are looking for is capsize= in ax.errorbar(). The default is None so the length of the cap will default to the value of matplotlib.rcParams["errorbar.capsize"]. The number you give will be the length of the cap in points:
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(1, 10, 10, dtype=int)
y = 2**x
yerr = np.sqrt(y)*10
fig, ax = plt.subplots()
ax.errorbar(x, y, yerr, solid_capstyle='projecting', capsize=5)
ax.grid(alpha=0.5, linestyle=':')
plt.show()
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-3,3,1000)
t = np.ones(1000)
f = x**2
plt.scatter(x,f) #plot1
plt.scatter(t,f) #plot2
plt.show()
I'd like to draw pile datas up.
when you look at upper exmaple, you can see two plot.plot1 is y=x^2 and plot2 is a line of perpendicular with x-axis. datas of plot2 are showing just a line all of datas. it means, i guess there is a section of more dense near the minimum point of plot1 (0,0).
but when you look at plot2 , it just show a line because of no density.
how can i fix this code?
Try axvline if you want a line that is perpendicular to the x-axis:
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-3,3,1000)
f = x**2
plt.scatter(x,f) #plot1
plt.axvline(1) #plot2
plt.show()