Metadata-Version: 2.1
Name: npsolve
Version: 0.0.1
Summary: Easier object-oriented calculations for numerical solvers.
Home-page: https://github.com/pythoro/npsolve.git
Author: Reuben Rusk
Author-email: pythoro@mindquip.com
License: UNKNOWN
Download-URL: https://github.com/pythoro/npsolve/archive/v0.0.1.zip
Description: # npsolve
        
        Many numerical solvers (like those in scipy) provide candidate solutions as a numpy array. They often also require a numpy array as a return value (e.g. an array of derivatives) during the solution. These requirements can make it difficult to use an object oriented approach to performing the calculations. 
        
        The *npsolve* package is a small, simple package built on *numpy* and *fastwire* to make it easy to use object-oriented code for the calculation step for numerical solvers.
        
        
        ## Basic usage tutorial
        
        
        First, setup some classes that you want to do calculations with. We do this by using the *add_var* method to setup variables and their initial values.
        
        ```python
        
        import numpy as np
        import npsolve
        
        class Component1(npsolve.Partial):
            def __init__(self):
                super().__init__()
                self.add_var('position', init=0.1)
                self.add_var('velocity', init=0.3)
            
        class Component2(npsolve.Partial):
            def __init__(self):
                super().__init__()
                self.add_var('force', init=-0.1)
        
        ```
        
        Next override the *set_vectors* method to store views you might want. In this case, we'll just save the variables as attributes. Note that these are actually views, that are automatically updated by the solver. We'll do it differently with Component2.
        
        ```python
        
        
        class Component1(npsolve.Partial):
            def __init__(self):
                super().__init__()
                self.add_var('position', init=0.1)
                self.add_var('velocity', init=0.3)
            
            def set_vectors(self, state_dct, ret_dct):
                ''' Set some state views for use during calculations '''
                self.position = state_dct['position']
                self.velocity = state_dct['velocity']
                self.force = state_dct['force']
            
        
        class Component2(npsolve.Partial):
            def __init__(self):
                super().__init__()
                self.add_var('force', init=-0.1)
        
        ```
        
        Note that variables are made available to all Partial instances automatically.
        
        Then, we'll tell them how to do the calculations. The *step* method is called automatically and expects a dictionary of return values (e.g. derivatives). A dictionary of the current state values is provided (again), but we're going to use the views we set in the *set_vectors* method.
        
        ```python
        
        class Component1(npsolve.Partial):
            def __init__(self):
                super().__init__()
                self.add_var('position', init=0.1)
                self.add_var('velocity', init=0.3)
            
            def set_vectors(self, state_dct, ret_dct):
                ''' Set some state views for use during calculations '''
                self.position = state_dct['position']
                self.velocity = state_dct['velocity']
                self.force = state_dct['force']
            
            def step(self, state_dct, *args):
                ''' Called by the solver at each time step 
                Calculate acceleration based on the 
                '''
                acceleration = 1.0 * self.force
                derivatives = {'position': self.velocity,
                               'velocity': acceleration}
                return derivatives
        		
        
        class Component2(npsolve.Partial):
            def __init__(self):
                super().__init__()
                self.add_var('force', init=-0.1)
        
            def calculate(self, state_dct, t):
                ''' Some arbitrary calculations based on current time t
                and the position at that time calculated in Component1.
                This returns a derivative for variable 'c'
                '''
                dc = 1.0 * np.cos(2*t) * state_dct['position']
                derivatives = {'force': dc}
                return derivatives
            
            def step(self, state_dct, t, *args):
                ''' Called by the solver at each time step '''
                return self.calculate(state_dct, t)
                
        		
        ```
        
        Now, we'll set up the solver. By default, Solvers have a *step* method that's ready to use, and after initialisation, the initial values set by the Partial classes are captured in the *npsolve_initial_values* attribute. By default, the Solver's *step* method returns a vector of all the return values, the same size as the Solver's npsolve_initial_values array.
        
        
        ```python
        
        from scipy.integrate import odeint
        
        class Solver(npsolve.Solver):
            def solve(self):
                self.t_vec = np.linspace(0, 5, 1001)
                result = odeint(self.step, self.npsolve_initial_values, self.t_vec)
                return result
        ```
        
        
        To run, we just have to instantiate the Solver before the Partials that use it, then call the *npsolve_init* method. It doesn't matter where in the code we create the Solver and Partial instances - they'll link up automatically through *fastwire*.
        
        
        ```python
        
        def run():
            s = Solver()
            c1 = Component1()
            c2 = Component2()
            s.npsolve_init()
            # Now we can run!
            res = s.solve()
            return res, s
        
        ```
        
        Let's set up a plot to see the results. Use the *npsolve_slices* attribute of the Solver to get the right columns.
        
        ```python
        
        def plot(res, s):
            slices = s.npsolve_slices
            
            plt.plot(s.t_vec, res[:,slices['position']], label='position')
            plt.plot(s.t_vec, res[:,slices['velocity']], label='velocity')
            plt.plot(s.t_vec, res[:,slices['force']], label='force')
            plt.legend()
        
        ```
        
        Run it and see what happens!
        
        ```python
        
        res, s = run()
        plot(res, s)
        
        ```
        
        
        
        
        
        
        
        
Keywords: NUMERICAL SOLVER,NUMPY,SCIPY,ODE,INTEGRATION
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Natural Language :: English
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
