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Exponential

DragExponentialManager

Bases: DragPointCollection

Collection of DragPoints representing a straight line.

Source code in itfit/fit_functions/exponential/exponential.py 
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class DragExponentialManager(DragPointCollection):
    """Collection of DragPoints representing a straight line."""

    @staticmethod 
    def function(x,a,b):
        """Exponential function.

        Parameters:
            x (float):
                independent variable.
            a (float):
                scales exponential function.
            b (float):
                scales x.

        Returns:
            (float):
                `f(x) = a*exp(b*x)`
        """
        return a*np.exp(b*x)

    @staticmethod 
    def gradient(x,a,b):
        """Exponential gradient.

        Parameters:
            x (float):
                independent variable.
            a (float):
                scales exponential function.
            b (float):
                scales x.

        Returns:
            (np.array):
                `( exp(b*x), a*x*exp(b*x) )`
        """
        dfda = np.exp(b*x)
        dfdb = a * x * np.exp(b * x) 

        return np.array([[dfda], [dfdb]])

    @staticmethod
    def get_args_length():
        """Gets number of arguments of `function`.

        Returns:
            (int): Number of arguments of `function`.
        """
        return 2

    def __init__(self,dragpoints: list[DragPoint],blit_manager: BlitManager):
        """Exponential line between 2 DragPoints. Updates with them.

        Args:
            dragpoints (list[DragPoint]): line vertices.
            blit_manager (BlitManager): used for automatic ploting.
        """ 
        super().__init__(dragpoints,blit_manager)
        # name points based on use 
        self.point_1 = self.dragpoints[0]
        self.point_2 = self.dragpoints[1]
        self.update()

    def update(self,*args,**kargs):
        """Updates line data with DragObjects positions"""
        p1_x, p2_x = self.get_xdata()
        a,b = self.get_args()

        #create x and y data of an exponential that moves across two poitns
        dx = abs(p1_x-p2_x) * 1.5 
        x = np.linspace(min(p1_x,p2_x)-dx,max(p1_x,p2_x)+dx,250)
        y = self.function(x, a, b)

        # from data coordinates to display coordinates
        xy = np.array((x,y)).T.reshape(-1,2)
        x_data, y_data = self.set_xy(xy).T 

        #set new data
        self.poly.set_xdata(x_data)
        self.poly.set_ydata(y_data)

    def get_args(self):
        """Gives exponential function parameters.

        Returns:
            (Tuple[float,float]):
                `a`, and `b` of `f(x) = a*exp(b*x)`
        """ 
        p1_x, p1_y = self.get_xy(*self.point_1.patch.get_center())
        p2_x, p2_y = self.get_xy(*self.point_2.patch.get_center())

        b = 1/(p1_x - p2_x) * np.log(p1_y/p2_y)

        a = p1_y / np.exp(b*p1_x)
        return a, b

__init__(dragpoints, blit_manager)

Exponential line between 2 DragPoints. Updates with them.

Parameters:

Name Type Description Default
dragpoints list[DragPoint]

line vertices.

 required
blit_manager BlitManager

used for automatic ploting.

 required
Source code in itfit/fit_functions/exponential/exponential.py 
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def __init__(self,dragpoints: list[DragPoint],blit_manager: BlitManager):
    """Exponential line between 2 DragPoints. Updates with them.

    Args:
        dragpoints (list[DragPoint]): line vertices.
        blit_manager (BlitManager): used for automatic ploting.
    """ 
    super().__init__(dragpoints,blit_manager)
    # name points based on use 
    self.point_1 = self.dragpoints[0]
    self.point_2 = self.dragpoints[1]
    self.update()

function(x, a, b) staticmethod

Exponential function.

Parameters:

Name Type Description Default
x float

independent variable.

 required
a float

scales exponential function.

 required
b float

scales x.

 required

Returns:

Type Description
float

f(x) = a*exp(b*x)
Source code in itfit/fit_functions/exponential/exponential.py 
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@staticmethod 
def function(x,a,b):
    """Exponential function.

    Parameters:
        x (float):
            independent variable.
        a (float):
            scales exponential function.
        b (float):
            scales x.

    Returns:
        (float):
            `f(x) = a*exp(b*x)`
    """
    return a*np.exp(b*x)

get_args()

Gives exponential function parameters.

Returns:

Type Description
Tuple[float, float]

a, and b of f(x) = a*exp(b*x)
Source code in itfit/fit_functions/exponential/exponential.py 
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def get_args(self):
    """Gives exponential function parameters.

    Returns:
        (Tuple[float,float]):
            `a`, and `b` of `f(x) = a*exp(b*x)`
    """ 
    p1_x, p1_y = self.get_xy(*self.point_1.patch.get_center())
    p2_x, p2_y = self.get_xy(*self.point_2.patch.get_center())

    b = 1/(p1_x - p2_x) * np.log(p1_y/p2_y)

    a = p1_y / np.exp(b*p1_x)
    return a, b

get_args_length() staticmethod

Gets number of arguments of function.

Returns:

Type Description
int

Number of arguments of function.
Source code in itfit/fit_functions/exponential/exponential.py 
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@staticmethod
def get_args_length():
    """Gets number of arguments of `function`.

    Returns:
        (int): Number of arguments of `function`.
    """
    return 2

gradient(x, a, b) staticmethod

Exponential gradient.

Parameters:

Name Type Description Default
x float

independent variable.

 required
a float

scales exponential function.

 required
b float

scales x.

 required

Returns:

Type Description
np.array

( exp(b*x), a*x*exp(b*x) )
Source code in itfit/fit_functions/exponential/exponential.py 
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@staticmethod 
def gradient(x,a,b):
    """Exponential gradient.

    Parameters:
        x (float):
            independent variable.
        a (float):
            scales exponential function.
        b (float):
            scales x.

    Returns:
        (np.array):
            `( exp(b*x), a*x*exp(b*x) )`
    """
    dfda = np.exp(b*x)
    dfdb = a * x * np.exp(b * x) 

    return np.array([[dfda], [dfdb]])

update(*args, **kargs)

Updates line data with DragObjects positions

Source code in itfit/fit_functions/exponential/exponential.py 
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def update(self,*args,**kargs):
    """Updates line data with DragObjects positions"""
    p1_x, p2_x = self.get_xdata()
    a,b = self.get_args()

    #create x and y data of an exponential that moves across two poitns
    dx = abs(p1_x-p2_x) * 1.5 
    x = np.linspace(min(p1_x,p2_x)-dx,max(p1_x,p2_x)+dx,250)
    y = self.function(x, a, b)

    # from data coordinates to display coordinates
    xy = np.array((x,y)).T.reshape(-1,2)
    x_data, y_data = self.set_xy(xy).T 

    #set new data
    self.poly.set_xdata(x_data)
    self.poly.set_ydata(y_data)

ExponentialFitter

Bases: GenericFitter

Exponential function fitter.

Source code in itfit/fit_functions/exponential/exponential.py 
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class ExponentialFitter(GenericFitter):
    """Exponential function fitter."""
    name = 'exponential'

    def __init__(self,app,data: DataSelection):
        """ Exponential fitter following function `f(x) = a*exp(b*x)`

        Parameters:
            app (Fitter): 
                Main application.
            data (DataSelection): 
                Data to fit.
        """

        super().__init__(app,data)

        ## Create DragPoints and DragLines needed

        self.drag_points = [DragPoint(*self.ax.transAxes.transform((0.4,0.2)), None),
                            DragPoint(*self.ax.transAxes.transform((0.3,0.5)), None)]
        self.drag_points_managers = [DragPointManager(p,self.app.blit_manager) for p in self.drag_points]
        self.fitter_drag_collection = DragExponentialManager(self.drag_points, self.app.blit_manager)
        self.function = self.fitter_drag_collection.function
        self.gradient = self.fitter_drag_collection.gradient

        ## Connect Exponential to Points change events

        self.drag_points_cids = [] #Connection ids for change events
        for dp in self.drag_points_managers:
            self.drag_points_cids.append(
                dp.connect(self.fitter_drag_collection.update)
            )

        ## Add created DragPoints and DragLines to BlitManager's artists
        self.app.blit_manager.artists.append(self.fitter_drag_collection)
        for dpm in self.drag_points_managers:
            self.app.blit_manager.artists.append(dpm)
        self.fig.canvas.draw_idle()

__init__(app, data)

Exponential fitter following function f(x) = a*exp(b*x)

Parameters:

Name Type Description Default
app Fitter

Main application.

 required
data DataSelection

Data to fit.

 required
Source code in itfit/fit_functions/exponential/exponential.py 
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def __init__(self,app,data: DataSelection):
    """ Exponential fitter following function `f(x) = a*exp(b*x)`

    Parameters:
        app (Fitter): 
            Main application.
        data (DataSelection): 
            Data to fit.
    """

    super().__init__(app,data)

    ## Create DragPoints and DragLines needed

    self.drag_points = [DragPoint(*self.ax.transAxes.transform((0.4,0.2)), None),
                        DragPoint(*self.ax.transAxes.transform((0.3,0.5)), None)]
    self.drag_points_managers = [DragPointManager(p,self.app.blit_manager) for p in self.drag_points]
    self.fitter_drag_collection = DragExponentialManager(self.drag_points, self.app.blit_manager)
    self.function = self.fitter_drag_collection.function
    self.gradient = self.fitter_drag_collection.gradient

    ## Connect Exponential to Points change events

    self.drag_points_cids = [] #Connection ids for change events
    for dp in self.drag_points_managers:
        self.drag_points_cids.append(
            dp.connect(self.fitter_drag_collection.update)
        )

    ## Add created DragPoints and DragLines to BlitManager's artists
    self.app.blit_manager.artists.append(self.fitter_drag_collection)
    for dpm in self.drag_points_managers:
        self.app.blit_manager.artists.append(dpm)
    self.fig.canvas.draw_idle()

ExponentialTool

Bases: GenericFitterTool

Toggles Exponential Tool.

Source code in itfit/fit_functions/exponential/exponential.py 
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class ExponentialTool(GenericFitterTool):
    """ Toggles Exponential Tool."""

    # default_keymap = '' 
    description = 'Exponentiate me please'

    def enable(self,*args):
        """Triggered when ExponentialTool is enabled.
        Uses BLitManager for faster rendering of DragObjects.
        """ 
        super().enable()
        self.fitter = ExponentialFitter(self.app, self.data)

    def disable(self,*args):
        """ Triggered when ExponentialTool is disabled
        Removes DragObjects and disables BLitManager.
        """ 
        super().disable()

disable(*args)

Triggered when ExponentialTool is disabled Removes DragObjects and disables BLitManager.

Source code in itfit/fit_functions/exponential/exponential.py 
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def disable(self,*args):
    """ Triggered when ExponentialTool is disabled
    Removes DragObjects and disables BLitManager.
    """ 
    super().disable()

enable(*args)

Triggered when ExponentialTool is enabled. Uses BLitManager for faster rendering of DragObjects.

Source code in itfit/fit_functions/exponential/exponential.py 
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def enable(self,*args):
    """Triggered when ExponentialTool is enabled.
    Uses BLitManager for faster rendering of DragObjects.
    """ 
    super().enable()
    self.fitter = ExponentialFitter(self.app, self.data)