I have a file abc.dat having data set with column "ec", "ev", "eig", "ep". I have to plot E vs x graph. Here the values for x, E and other variables related to this are given below.
import numpy as np
import matplotlib.pyplot as plt
data = np.loadtxt('/Users/abc.dat')
ec = data[0:,0]
ev = data[0:,1]
eig = data[0:,2]
ep = data[0:,3]
a=(eig-(ec-ev))
A= 5
B=1/eig**2
for x in range(1,500):
g=(1/(0.5* np.sqrt(2*3.14)))* np.exp((-(x-eig)**2)/(2*(0.5)**2))
ep_1=(ep*g)**2
P=sum(ep_1)
for i in a:
if i==0:
D=1
else:
D=0
e=A*B*P*D
plt.plot(x,e)
Your code has the plt.plot() command buried in loops, which is probably not what you are trying to do. You will need x to be an array the same size as e, and from your question it might have fewer points than you expect since it has shape (409,).
Try this:
import numpy as np
import matplotlib.pyplot as plt
data = np.loadtxt('/Users/abc.dat')
ec = data[0:,0]
ev = data[0:,1]
eig = data[0:,2]
ep = data[0:,3]
a=(eig-(ec-ev))
A= 5
B=1/eig**2
x = np.arange(len(eig))
g=(1/(0.5* np.sqrt(2*3.14)))* np.exp((-(x-eig)**2)/(2*(0.5)**2))
P=((g*np.broadcast_to(ep, (len(g), len(ep))).T)**2).sum(axis=0)
e=np.where(a==0, A*B*P, 0)
plt.plot(x,e)
I am not sure from the question if I have the logic for P calculating as you want it to.
Related
what i am trying is masking a value from nc file with numpy array, according to specific location but it gives me 1d array and i can not use this array for plotting here my code.
from netCDF4 import Dataset
import matplotlib.pyplot as plt
import numpy as np
import numpy.ma as ma
file = './sample_data/NSS.AMBX.NK.D08214.S0740.E0931.B5312324.WI.nc'
data = Dataset(file,mode='r')
fcdBT89gHz = np.asarray(data.groups['Data_Fields']['fcdr_brightness_temperature_1'][:])
fcdBT150gHz = np.asarray(data.groups['Data_Fields']['fcdr_brightness_temperature_2'][:])
fcdBT183_1gHz = np.asarray(data.groups['Data_Fields']['fcdr_brightness_temperature_3'][:])
fcdBT183_3gHz = np.asarray(data.groups['Data_Fields']['fcdr_brightness_temperature_4'][:])
fcdBT183_7gHz = np.asarray(data.groups['Data_Fields']['fcdr_brightness_temperature_5'][:])
lats = data.groups['Geolocation_Time_Fields']['latitude'] #Enlem degerleri
lons = data.groups['Geolocation_Time_Fields']['longitude'] #Boylam degerleri
latlar = np.asarray(lats[:]) # Lati
lonlar = np.asarray(lons[:]) # Long
lo = ma.masked_outside(lonlar,105,110)
la = ma.masked_outside(latlar,30,35)
merged_coord=~ma.mask_or(la.mask,lo.mask)
h = plt.plot(fcdBT150gHz[merged_coord])
The output is like that but i need latitudes in x axis like this plot
If you need shape of variables:
lo.shape = (2495, 90)
la.shape = (2495, 90)
fcdBT150gHz[merged_coord].shape = (701,)
Maybe i did not use true way for masking. If data is needed here.
I have to write a function to_red that should zero out the green and blue color components and return the result. I wrote the below code for an Image(.png) to zero out green and blue color and return red and it worked. However, as mentioned in title, the input parameter has to be a 3-d array and return a 3-d array. How should my below code be changed for that.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.pyplot import imshow
def to_red()
src = plt.imread("C:\src\painting.png")
red_channel = src[:,:,0]
red_img = np.zeros(src.shape)
red_img[:,:,0] = red_channel
plt.imshow(red_img)
plt.show()
You can write your function like this:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.pyplot import imshow
def to_red(src):
# Check if the input dimension is 3
if not src.ndim == 3:
# Raise exception or do something
print ("Dimension mismatch")
return 0
red_channel = src[:,:,0]
red_img = np.zeros(src.shape)
red_img[:,:,0] = red_channel
return red_img
And then you can call it like this
source_image = plt.imread("C:\src\painting.png")
red_image = to_red(source_image)
plt.imshow(red_image)
plt.show()
I also added a line to check if the input is actually 3 dimensional.
You can use numpy's powerful indexing capabilities
def to_red(src):
ret = a.copy()
ret[:,:,1:] = 0
return ret
I want to plot any part or the data
here is the code
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from matplotlib import style
... ...
xs = []
avg = []
for line in lines:
if len(line) > 1:
x, y1 = line.split(',')
xs.append(float(x))
avg.append(float(y1))
ax1.plot(xs, avg, label='avg')
I added some of the code so you can see the type of the variables
I tried :
ax1.plot(xs[avg>0], avg[avg>0], label='avg')
and didnt work
im matlab i would do some thing like :
Indxs=find (ys>0)
Plot(xs(indxs),ys(indxs))
The syntax is correct. The problem is that xs and avg are no numpy arrays. So you first need to convert those lists to numpy arrays, then the slicing will work as expected.
xs = np.array(xs)
avg = np.array(avg)
ax1.plot(xs[avg>0], avg[avg>0], label='avg')
What you doesen't work since your index (avg > 0) in python is a boolean. When you are used to Matlab then you should definitely try numpy Boolean indexing.
you can do:
import numpy as np
xs = numpy.asarray(x)
ys = numpy.asarray(y)
ys_filtered = ys[x > 0]
I'm trying to calculate the discrete derivative using gaussian_filter from scipy.ndimage and so the output is presenting some strange behavior with boundary conditions. The code is below:
from scipy import ndimage
import numpy as np
import matplotlib.pyplot as plt
y = np.linspace(0.2*np.pi,0.7*np.pi,100)
U = np.sin(y)
sg = 1
Uy = ndimage.gaussian_filter1d(U, sigma=sg,order=1,mode='constant',cval=0)
Uy2 = ndimage.gaussian_filter1d(U, sigma=sg,order=1,mode='nearest')
Uy3 = ndimage.gaussian_filter1d(U, sigma=sg,order=1,mode='reflect')
Uy4 = ndimage.gaussian_filter1d(U, sigma=sg,order=1,mode='mirror')
Uy5 = ndimage.gaussian_filter1d(U, sigma=sg,order=1,mode='wrap')
fig,(a1,a2) = plt.subplots(1,2)
a1.plot(U , y,label='data')
a2.plot(Uy, y,label='constant')
a2.plot(Uy2,y,label='nearest')
a2.plot(Uy3,y,label='reflect')
a2.plot(Uy4,y,label='mirror')
a2.plot(Uy5,y,label='wrap')
a1.legend(loc='best')
a2.legend(loc='best')
What happened? Constant mode should result cval on boudary? Is that the expected result?
I need to use the "savefig" in Python to save the plot of each iteration of a while loop, and I want that the name i give to the figure contains a literal part and a numerical part. This one comes out from an array or is the number associated to the index of iteration. I make a simple example:
# index.py
from numpy import *
from pylab import *
from matplotlib import *
from matplotlib.pyplot import *
import os
x=arange(0.12,60,0.12).reshape(100,5)
y=sin(x)
i=0
while i<99
figure()
a=x[:,i]
b=y[:,i]
c=a[0]
plot(x,y,label='%s%d'%('x=',c))
savefig(#???#) #I want the name is: x='a[0]'.png
#where 'a[0]' is the value of a[0]
thanks a lot.
Well, it should be simply this:
savefig(str(a[0]))
This is a toy example. Works for me.
import pylab as pl
import numpy as np
# some data
x = np.arange(10)
pl.figure()
pl.plot(x)
pl.savefig('x=' + str(10) + '.png')
I had the same demand recently and figured out the solution. I modify the given code and correct several explicit errors.
from pylab import *
import matplotlib.pyplot as plt
x = arange(0.12, 60, 0.12).reshape(100, 5)
y = sin(x)
i = 0
while i < 99:
figure()
a = x[i, :] # change each row instead of column
b = y[i, :]
i += 1 # make sure to exit the while loop
flag = 'x=%s' % str(a[0]) # use the first element of list a as the name
plot(a, b, label=flag)
plt.savefig("%s.png" % flag)
Hope it helps.
Since python 3.6 you can use f-strings to format strings dynamically:
import matplotlib.pyplot as plt
for i in range(99):
plt.figure()
a = x[:, i]
b = y[:, i]
c = a[0]
plt.plot(a, b, label=f'x={c}')
plt.savefig(f'x={c}.png')