hypecycle 0.5.2

hypecycle is the fundamental package needed for creating, visualizing, and annotating Gartner Hype Cycle with Python

Package Name: hypecycle

hypecycle is a fundamental package for creating Gartner Hype Cycle with Python. Purpose Statement:

• to generate data that are Gartner Hype Cycle distributed

• to visualize Gartner Hype Cycle curves

• to add annotations to Gartner Hype Cycle curves

1.Source Code

1.1Dependencies

import numpy as np

import matplotlib.pyplot as plt

1.2Create

def create(*x,crest1=50,crest2=25,stp_crest1=0.8,stp_crest2=0.2,midPoint=15,var=10):

    '''
    to create a Story HypeCycle
        x: Input data
        crest1,crest2: Controlling the height of the first wave crest and the second wave crest respectively.
        stp_crest1,stp_crest2: Representing steepness.
        midPoint: The location of the mean value mainly controls the location of the first wave crest.
        var: Displaying moves or penalties for midpoint
        Recommended parameters:crest1=50,crest2=25,stp_crest1=0.8,stp_crest2=0.2,midPoint=15,var=10
        x = np.linspace(0, 50, 5000)
    '''
    x = x
    # If user doesn't submit submit x, it will create a default DataSet.
    if x==():
        x = np.linspace(0, 70, 5000)
    else:
        x=x[0]

    lf_11 = crest1/(1+ np.exp( 1 * stp_crest1 * (x-midPoint-var)))
    lf_12 = crest1/(1+ np.exp( 1 * stp_crest1 * (x-midPoint)))
    lf_13 = crest2/(1+ np.exp( 1 * stp_crest2 * (x-midPoint-var)))
    hype= lf_11- lf_12- lf_13
    return hype

1.3Visualize

def visualize(*x, color="red", linewidth=3, linestyle="-"):

    """
     to visualize the HypeCycle Data into a HypeCycle Curve
    """
    x = x
    # If user doesn't submit submit x, it will create a default DataSet.
    if x==():
        x = np.linspace(0, 70, 5000)
    else:
        x=x[0]

    plt.plot(x, create(x), linewidth = linewidth, c = color,linestyle = linestyle)

1.4Annotate

def annotate(*x, x_value = 20, text = "None",color = "red",fontproperties='FZShuTi', fontsize = 13, alpha = 0.8,rotation=3):

    """
     to annaotate the HypeCycle Curve
    """

    x = x
    # If user doesn't submit submit x, it will create a default DataSet.
    if x==():
        x = np.linspace(0, 70, 5000)
    else:
        x=x[0]

    plt.plot(x, create(x), linewidth=3)

    x_val = x_value
    t = text
    c = color
    fp= fontproperties
    fs = fontsize
    a = alpha

    plt.rcParams["figure.figsize"] = [12, 6]
    plt.scatter(x_val, create(x_val), color="blue")
    plt.text(x_val, create(x_val), t ,color=c,fontproperties= fp, fontsize=fs, alpha=a,rotation=rotation)

2.Installing

pip install hyperycle

3.Usage

from hypecycle import HypeCycle

4.Examples

4.1Importing hypecycle

from hypecycle import HypeCycle as hc

4.2Testing Data

import numpy as np
x = np.linspace(0, 50, 5000)
x

array([0.00000000e+00, 1.00020004e-02, 2.00040008e-02, ...,
       4.99799960e+01, 4.99899980e+01, 5.00000000e+01])

4.3API-Create

# Default
y = hc.create( )
y

array([-2.48323716e+01, -2.48319021e+01, -2.48314312e+01, ...,
       -3.10218943e-03, -3.09351479e-03, -3.08486440e-03])

# With parameters
y = hc.create(x)
y

array([-24.83237162, -24.83203636, -24.83170041, ...,  -0.16798742,
        -0.16765397,  -0.16732117])

4.4API-visualize

# Default diagram
hc.visualize()

# Custom chart
x = np.linspace(0, 70, 5000)
hc.visualize(x,"blue",8)

4.5API-anotate

hc.annotate(x,x_value = 0,text = "the trigger",rotation=20)
hc.annotate(x,x_value = 15,text = "the growth",color="blue")
hc.annotate(x,x_value = 20,text = "the peak",color="red")
hc.annotate(x,x_value = 27,text = "the trough",color="green")
hc.annotate(x,x_value = 35,text = "the slope",color="blue",rotation=20)
hc.annotate(x,x_value = 45,text = "the Pleateau",color="red",rotation=15)

5.Authors

Creator ：Chaolemen Borjigin, Sun Zhizhong, Zhang Chen

Contact： [e-mail]

License：BSD 3

6.Citation

If hypecycle contributes to a project that leads to a scientific publication, please acknowledge this fact by citing the project.