Interactive Plot and Viewarr
Purpose
The Python script interactive_plot.py allows you to quickly create a Graphical User Interface to a figure plotting 1-D function, given a set of parameters. The parameters are then represented as sliders below the figure, and you can then see how the function changes as a function of the parameters.
The function can be anything, even the outcome of a complicated model. As long as you can package your model into a Python function, with a 1-D coordinate x as input, as well as one or more parameters (say, a, b and c), and one or more values as output.
The purpose of interactive_plot.py is to make it easier to investigate how the results of simple (= quick-to-calculate) models are dependent on the parameters.
As an add-on to interactive_plot.py this package also contain the script viewarr.py, which allows you to very quickly plot 1-D cuts through an N-dimensional numpy array, scanning the other dimensions with sliders. It can be helpful to get a better insight into the data in a complex high-dimensional array.
Examples of use of interactive_plot.py
Example 1 (a simple function with one parameter):
from interactive_plot import *
def func(x,param): return param[0]*np.sin(param[1]*x)
x = np.linspace(0,2*np.pi,100)
params = [np.linspace(0.1,1.,30),np.linspace(1.,3.,30)] # Choices of parameter values
interactive_plot(x, func, params, ymax=1., ymin=-1., parnames=['A = ','omega = '])
Example 1-a (As above, but now with a plotting button instead of automatic replot; useful for heavier models):
from interactive_plot import *
def func(x,param): return param[0]*np.sin(param[1]*x)
x = np.linspace(0,2*np.pi,100)
params = [np.linspace(0.1,1.,30),np.linspace(1.,3.,30)] # Choices of parameter values
interactive_plot(x, func, params, ymax=1., ymin=-1., parnames=['A = ','omega = '],plotbutton=True)
EXAMPLE 1-b (Plotting the content of a pre-calculated 2-D array):
from interactive_plot import *
x = np.linspace(0,2*np.pi,100)
y_array = np.zeros((30,100))
omega = np.linspace(1,3.,30)
for i in range(30): y_array[i,:] = np.sin(omega[i]*x)
def func(x,param): return y_array[param[0],:]
params = [np.arange(30)] # Choices of parameter values
interactive_plot(x, func, params)
EXAMPLE 2 (Model fitting to data):
import numpy as np
import matplotlib.pyplot as plt
from interactive_plot import *
def func(x,param): return param[0]*np.sin(param[1]*x)
x = np.linspace(0,2*np.pi,100)
data = 0.5*np.sin(2.*x)*(1.0+0.6*np.random.normal(size=len(x)))
fig = plt.figure(1)
ax = plt.axes(xlim=(x.min(),x.max()),ylim=(-1.2,1.2))
axd, = ax.plot(x,data,'o',label='data')
plt.xlabel('x [cm]')
plt.ylabel('f [erg/s]')
params = [np.linspace(0.1,1.,30),np.linspace(1.,3.,30)] # Choices of parameter values
parstart = [0.6,2.0] # Initial guesses for parameters
interactive_plot(x, func, params, parnames=['A = ','omega = '], fig=fig, ax=ax, label='model',parstart=parstart)
ax.legend()
plt.show()
EXAMPLE 2-a (Model overplotting over an image):
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from interactive_plot import *
def func(x,param): return param[0]*np.sin(param[1]*x)
x = np.linspace(0,2*np.pi,100)
image = np.random.normal(size=(70,70)) # Make some image
fig = plt.figure(1)
extent = [x.min(),x.max(),-1.2,1.2]
axd = plt.imshow(image,extent=extent,cmap=cm.hot)
ax = plt.gca()
plt.axis(extent)
plt.xlabel('x [cm]')
plt.ylabel('f [erg/s]')
params = [np.linspace(0.1,1.,30),np.linspace(1.,3.,30)] # Choices of parameter values
parstart = [0.6,2.0] # Initial guesses for parameters
interactive_plot(x, func, params, parnames=['A = ','omega = '], fig=fig, ax=ax, label='model',parstart=parstart)
ax.legend()
plt.show()
EXAMPLE 3 (Fitting two models simultaneously to data):
import numpy as np
import matplotlib.pyplot as plt
from interactive_plot import *
def func(x,param): return np.vstack((param[0]*np.sin(param[1]*x),param[0]*np.cos(param[1]*x)))
x = np.linspace(0,2*np.pi,100)
data = 0.5*np.sin(2.*x)*(1.0+0.6*np.random.normal(size=len(x)))
fig = plt.figure(1)
ax = plt.axes(xlim=(x.min(),x.max()),ylim=(-1.2,1.2))
axd, = ax.plot(x,data,'o',label='data')
axm0, = ax.plot(x,data,'--',label='sin')
axm1, = ax.plot(x,data,':',label='cos')
axmodel= [axm0,axm1]
plt.xlabel('x [cm]')
plt.ylabel('f [erg/s]')
params = [np.linspace(0.1,1.,30),np.linspace(1.,3.,30)]
interactive_plot(x, func, params, parnames=['A = ','omega = '], fig=fig, ax=ax, axmodel=axmodel)
ax.legend()
plt.show()
EXAMPLE 3-a (Fitting two models in two separate plots simultaneously):
import numpy as np
import matplotlib.pyplot as plt
from interactive_plot import *
def func(x,param): return np.vstack((param[0]*np.sin(param[1]*x),param[0]*np.cos(param[1]*x)))
x = np.linspace(0,2*np.pi,100)
data = 0.5*np.sin(2.*x)*(1.0+0.6*np.random.normal(size=len(x)))
extent = [x.min(),x.max(),-1.2,1.2]
fig, axes = plt.subplots(ncols=2)
axes[0].axis(extent)
axes[1].axis(extent)
axd0, = axes[0].plot(x,data,'o',label='data')
axm0, = axes[0].plot(x,data,'--',label='sin')
axd1, = axes[1].plot(x,data,'o',label='data')
axm1, = axes[1].plot(x,data,':',label='cos')
axmodel= [axm0,axm1]
params = [np.linspace(0.1,1.,30),np.linspace(1.,3.,30)]
interactive_plot(x, func, params, parnames=['A = ','omega = '], fig=fig, ax=0, axmodel=axmodel)
plt.show()
EXAMPLE 4: (passing additional fixed parameters to function):
from interactive_plot import *
def func(x,param,fixedpar={}): return param[0]*np.sin(param[1]*x)+fixedpar['offset']
x = np.linspace(0,2*np.pi,100)
params = [np.linspace(0.1,1.,30),np.linspace(1.,3.,30)] # Choices of parameter values
interactive_plot(x, func, params, ymax=1., ymin=-1., parnames=['A = ','omega = '],fixedpar={'offset':0.6})
Examples of use of viewarr.py
EXAMPLE 1:
from viewarr import *
data=np.arange(64).reshape((4,4,4)) # Dummy dataset
viewarr(data)
EXAMPLE 2:
from viewarr import *
data=np.arange(64).reshape((4,4,4)) # Dummy dataset
viewarr(data,index=1)
EXAMPLE 3:
from viewarr import *
data=np.arange(64).reshape((4,4,4)) # Dummy dataset
viewarr(data,index=1,idxnames=['ix','iy','iz'])
EXAMPLE 4:
from viewarr import *
data=np.arange(64).reshape((4,4,4)) # Dummy dataset
viewarr(data,index=1,idxnames=['x','y','z'],idxvals=[['a','b','c','d'],[-3,-1,1,3],[1.0,2.0,3.0,4.0]])
EXAMPLE 5:
from viewarr import *
data1=np.arange(64).reshape((4,4,4)) # Dummy dataset
data2=64-data1
viewarr([data1,data2],index=1,idxnames=['x','y','z'],idxvals=[['a','b','c','d'],[-3,-1,1,3],[1.0,2.0,3.0,4.0]],ylabel=['Bla','adfsd'])
Package dependencies
numpy, matplotlib
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