Iris Yoon irishryoon

## build_graph.py
def continue_building_graph(G, queue, visited, ID_artists, max_count, sp):
    """Builds a weighted graph G using BFS.

    Given a (possibly empty) graph G and a (possibly empty) queue,
    (1) create nodes for new artists,
    (2) create edges for new collaborations,
    (3) update the 'albums' attribute of an edge to keep track of all albums accounted for.
    In the very last step, find the weight of each edge as the number of albums
    in the 'albums' attribute.
    Furthermore, update the dictionary of artist ID and names.

## covid19.ipynb

      
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                irishryoon
                / covid19.ipynb
            
            
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              March 27, 2020 01:23
            
              
                COVID19.ipynb
              
          
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## fit_SIR_model.py
from symfit import Parameter, variables, Fit, D, ODEModel
import numpy as np

# define number of susceptible population
n_susceptible = 195000

# Some data
data_I = np.array([1, 2, 11, 23, 36, 75, 104, 137, 166, 209, 313, 400, 496, 693, 914, 1635, 2391, 5213, 8054, 11293, 15157, 19938, 24336, 29010])
data_R = np.array([0, 0, 0, 0, 0, 1, 2, 5, 7, 11, 15, 21, 29, 39, 53, 71, 104, 152, 256, 417, 643, 946, 1345, 1831])
data_S = [n_susceptible - x - data_R[idx] for idx, x in enumerate(data_I)]
	def continue_building_graph(G, queue, visited, ID_artists, max_count, sp):
	"""Builds a weighted graph G using BFS.

	Given a (possibly empty) graph G and a (possibly empty) queue,
	(1) create nodes for new artists,
	(2) create edges for new collaborations,
	(3) update the 'albums' attribute of an edge to keep track of all albums accounted for.
	In the very last step, find the weight of each edge as the number of albums
	in the 'albums' attribute.
	Furthermore, update the dictionary of artist ID and names.
	from symfit import Parameter, variables, Fit, D, ODEModel
	import numpy as np

	# define number of susceptible population
	n_susceptible = 195000

	# Some data
	data_I = np.array([1, 2, 11, 23, 36, 75, 104, 137, 166, 209, 313, 400, 496, 693, 914, 1635, 2391, 5213, 8054, 11293, 15157, 19938, 24336, 29010])
	data_R = np.array([0, 0, 0, 0, 0, 1, 2, 5, 7, 11, 15, 21, 29, 39, 53, 71, 104, 152, 256, 417, 643, 946, 1345, 1831])
	data_S = [n_susceptible - x - data_R[idx] for idx, x in enumerate(data_I)]