def cluster_age_analysis(df, cluster_label : int, cluster_name : str, cost_function : str):
df_group = df.loc[df['label'] == cluster_label]
group_cluster = cluster(df_group, cluster_name, error_type = cost_function)
#group_cluster.photometric_cuts()
# group_cluster.output_cluster(directory = cluster_output_directory)
iso_grid = grid.large_grid(isochrone_filepath)
cluster_fitter = fitter(group_cluster, iso_grid)
age = cluster_fitter.return_best_fit(type_fit = 'chi2', plot = True, save = True)
#for g in iso_grid:
# g.plot_grid(stellar_pop = group_cluster)
return age, df_group
delivs["Delivery Volume"][i], delivs["Adj Nodes"][i])
for i in range(len(delivs))
]
#Establish set Outside, which tracks the nodes that are not assigned to a cluster
Outside = [Node[i].index for i in range(len(Node))]
#Create set that tracks the nodes that have been assigned to a cluster
Assigned_Nodes = []
#Cluster Initialization:
#Clusters take a Node object that will be the centroid, a balance target for the cluster, and a capacity.
#This line creates 3 clusters with the facilities as the central locations.
Cluster = [
cluster(Node[i], delivs["Delivery Volume"][i], 999999999)
for i in range(465, 468)
]
#This section of code assigns what are considered to be "better" initial centroids.
"""
Cluster = []
Cluster.append(cluster(Node[376], target_weight[i], 999999999))
Cluster.append(cluster(Node[49], target_weight[i], 999999999))
Cluster.append(cluster(Node[68], target_weight[i], 999999999))
"""
#Give clusters indicies for easy indentification
i = 0
for i in range(len(Cluster)):
Cluster[i].index = i
Node = [node(i, (deliv_517["Longitude"][i], deliv_517["Latitude"][i]), deliv_517["Delivery Volume"][i], deliv_517["Adj Nodes"][i]) for i in range(len(deliv_517))]
#Establish set Outside, which tracks the nodes that are not assigned to a cluster
Outside = []
i = 0
for i in range(len(Node)):
Outside.append(Node[i].index)
#Create set that tracks the nodes that have been assigned to a cluster
Assigned_Nodes = []
#Cluster Initialization
#The last three nodes are the facilities. We are using them as the initial nodes in order to build the algorithm.
#Clusters take a Node object that will be the centroid, a target weight for the cluster, and a capacity.
Cluster = [cluster(Node[i], target_weight, deliv_517["Delivery Volume"][i]) for i in range(465,468)]
#Give cluster indices in order to easily identify them
i = 0
for i in range(len(Cluster)):
Cluster[i].index = i
#Update cluster information
i = 0
for i in range(len(Cluster)):
#Assign the centroid into the cluster's Node list
Cluster[i].Nodes.append(Cluster[i].centroid)
Cluster[i].nodes_idx.append(Cluster[i].centroid.index)
#Assign the associated cluster to its centroid
Cluster[i].centroid.cluster = Cluster[i].index
from Cluster import cluster
#Create Node set
Node = [node(i, (delivs["Longitude"][i], delivs["Latitude"][i]), delivs["Delivery Volume"][i], delivs["Adj Nodes"][i]) for i in range(len(delivs))]
#Establish set Outside, which tracks the nodes that are not assigned to a cluster
Outside = [Node[i] for i in range(len(Node))]
#Create set that tracks the nodes that have been assigned to a cluster
Assigned_Nodes = []
#Cluster Initialization:
#Clusters take a Node object that will be the centroid, a balance target for the cluster, and a capacity.
#This line creates 3 clusters with the facilities as the central locations.
Cluster = [cluster(Node[i], target_card, delivs["Delivery Volume"][i], 999999999) for i in range(455,458)]
#This section of code assigns what are considered to be "better" initial centroids.
"""
Cluster = []
Cluster.append(cluster(Node[376], target_weight[i], 999999999))
Cluster.append(cluster(Node[49], target_weight[i], 999999999))
Cluster.append(cluster(Node[68], target_weight[i], 999999999))
"""
#Give clusters indicies for easy indentification
i = 0
for i in range(len(Cluster)):
Cluster[i].index = i
#Update cluster information
i = 0
def createClusters(data, title):
p1.title=title
temp_Cluster=cluster(np.asarray(data, dtype=float))
temp_Cluster.makeCluster()
Cluster_Plot_Result(temp_Cluster)
return temp_Cluster
import numpy as np
import pandas as pd
from isofit import grid, fitter
from Cluster import cluster
isochrone_filepath = '/Users/admin/Desktop/astro_research/orion/orion_populations/parsec_colibri_isochrones/output44.dat'
df = pd.read_csv(
'/Users/admin/Desktop/astro_research/orion/orion_populations/apogee_gaia_sample.csv'
)
my_grid = grid(isochrone_filepath)
lambda_orion_df = df.loc[df.label == 4]
lambda_orion = cluster(lambda_orion_df, 'Lambda Orion')
lambda_orion.photometric_cuts()
my_grid.plot_grid(lambda_orion)
#my_fitter = fitter(lambda_orion, my_grid)
#my_fitter.plot_best_fit()