data analysis

derivative.py

# derivative
def dydx(x,y):
    dy = np.zeros(y.shape,np.float) #we know it will be this size
    dy[0:-1] = np.diff(y)/np.diff(x)
    dy[-1] = (y[-1] - y[-2])/(x[-1] - x[-2])
    return dy

legendoutside.py

legend(loc='center left', bbox_to_anchor=(1, 0.5))

plot_props.py

#nice default plot props
%matplotlib inline
matplotlib.rcParams['font.size'] = 21
matplotlib.rcParams['figure.figsize'] = (12.0, 8.0)
matplotlib.rcParams['legend.fontsize'] = 21
matplotlib.rcParams['figure.figsize'] = [6, 6]
matplotlib.rcParams['xtick.major.pad']='3'
matplotlib.rcParams['ytick.major.pad']='3'
matplotlib.rcParams['xtick.major.width'] = 2
matplotlib.rcParams['ytick.major.width'] = 2
matplotlib.rcParams['axes.linewidth'] = 2
matplotlib.rcParams['mathtext.fontset'] = 'stixsans'

shiftedcolormap.py

def shiftedColorMap(cmap, start=0, midpoint=0.5, stop=1.0, name='shiftedcmap'):
    '''
    Function to offset the "center" of a colormap. Useful for
    data with a negative min and positive max and you want the
    middle of the colormap's dynamic range to be at zero

    Input
    -----
      cmap : The matplotlib colormap to be altered
      start : Offset from lowest point in the colormap's range.
          Defaults to 0.0 (no lower ofset). Should be between
          0.0 and `midpoint`.
      midpoint : The new center of the colormap. Defaults to 
          0.5 (no shift). Should be between 0.0 and 1.0. In
          general, this should be  1 - vmax/(vmax + abs(vmin))
          For example if your data range from -15.0 to +5.0 and
          you want the center of the colormap at 0.0, `midpoint`
          should be set to  1 - 5/(5 + 15)) or 0.75
      stop : Offset from highets point in the colormap's range.
          Defaults to 1.0 (no upper ofset). Should be between
          `midpoint` and 1.0.
    '''
    cdict = {
        'red': [],
        'green': [],
        'blue': [],
        'alpha': []
    }

    # regular index to compute the colors
    reg_index = np.linspace(start, stop, 257)

    # shifted index to match the data
    shift_index = np.hstack([
        np.linspace(0.0, midpoint, 128, endpoint=False), 
        np.linspace(midpoint, 1.0, 129, endpoint=True)
    ])

    for ri, si in zip(reg_index, shift_index):
        r, g, b, a = cmap(ri)

        cdict['red'].append((si, r, r))
        cdict['green'].append((si, g, g))
        cdict['blue'].append((si, b, b))
        cdict['alpha'].append((si, a, a))

    newcmap = matplotlib.colors.LinearSegmentedColormap(name, cdict)
    plt.register_cmap(cmap=newcmap)

    return newcmap