def make_cadence_map(csv_input, csv_output, index_of_interest, lol):
#Variable Setup.
headers = gd2.get_headers()
#print headers
start_measure_index = headers.index("start_measure")
#print start_measure_index
id_index = headers.index("id")
header = headers[index_of_interest]
headers += ["{}_before".format(header)]
headers += ["{}_after".format(header)]
new_dicts = gd2.get_data_list_of_dicts()
data = lol
data_by_composition = {row[id_index]:[] for row in data}
new_data = []
#Sort (ascending) the individual entries for each composition by the first element.
for row in data:
composition_id = row[id_index]
data_by_composition[composition_id].append(row)
#try:
data_by_composition[composition_id].sort(key=lambda x: int(x[start_measure_index]))
#data_by_composition[composition_id].sort(key = start_measure_index)
#except ValueError:
#print row
#For each entry, find the next one consecutively.
for row in data:
comp_entries = data_by_composition[row[id_index]]
i = comp_entries.index(row)
element_before = comp_entries[i-1][index_of_interest] if i>0 else "None"
element_after = comp_entries[i+1][index_of_interest] if i<len(comp_entries)-1 else "None"
new_data.append(row + [element_before, element_after])
print "File written successfully! Added 'before' and 'after' for {}".format(header)
gd2.write_data(csv_output, new_headers, new_data)
import get_data as gd
import get_data2 as gd2
import get_data3 as gd3
list_of_dicts = gd.get_data_list_of_dicts()
full = gd2.get_data_list_of_dicts()
full_headers = gd2.get_headers()
headers = gd.get_headers()
headers_income = gd3.get_headers()
codes = {}
full_clean = []
final_headers = []
for h in headers:
h = h.split(" - ")
code = h[0]
try:
name = h[1]
codes[code] = name
except:
print h
for h2 in headers_income:
h2 = h2.split(" - ")
code = h2[0]
try:
if not "Error" in h2[1]:
name = h2[1]
codes[code] = name
except:
import get_data as gd
import get_data2 as gd2
import get_data3 as gd3
list_of_dicts = gd.get_data_list_of_dicts()
full = gd2.get_data_list_of_dicts()
full_headers = gd2.get_headers()
headers = gd.get_headers()
headers_income = gd3.get_headers()
codes = {}
full_clean = []
final_headers = []
for h in headers:
h = h.split(" - ")
code = h[0]
try:
name = h[1]
codes[code] = name
except:
print h
for h2 in headers_income:
h2 = h2.split(" - ")
code = h2[0]
try:
if not "Error" in h2[1]:
name = h2[1]
codes[code] = name
except:
#from sklearn.neighbors import KNeighborsClassifier from sklearn.neighbors import NearestNeighbors from sklearn.neighbors.kd_tree import KDTree #from sklearn.neighbors import DistanceMetric import numpy as np import get_data2 as gd headers = gd.get_headers() dicts = gd.get_data_list_of_dicts() rows_lol = [] for i in range(len(gd.get_data_slice(headers[0], dicts))): rows_lol.append([]) for i in range(len(headers)): if i ==1 or i==4: column = gd.get_data_slice_numbers(headers[i], dicts) else: column = gd.get_data_slice_numbers(headers[i], dicts) for j in range(len(gd.get_data_slice(headers[0], dicts))): rows_lol[j].append(column[j]) X = np.array(rows_lol) #nbrs = NearestNeighbors(n_neighbors=5, algorithm ='kd_tree', metric ='jaccard').fit(X) kdt = KDTree(X, leaf_size=30, metric='euclidean') kdt.query(X, k=3, return_distance=False)
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import NearestNeighbors
#from sklearn.neighbors.kd_tree import KDTree
#from sklearn.neighbors import DistanceMetric
import numpy as np
import get_data2 as gd
import json
headers = gd.get_headers()
dicts = gd.get_data_list_of_dicts()
rows_lol = []
for i in range(len(gd.get_data_slice(headers[0], dicts))):
rows_lol.append([])
print len(rows_lol)
for i in range(len(headers)):
column = gd.get_data_slice(headers[i], dicts)
for j in range(len(gd.get_data_slice(headers[0], dicts))):
rows_lol[j].append(column[j])
print rows_lol[0]
#actually get similarities
def compare_rows(row1, row2):
counter = 0
for i in range(len(row1)):
