import sys
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
from io_helper import open_write, open_read
def usage():
print >> sys.stderr, "{} <source.npy> [output.npy]"
exit(1)
if __name__ == '__main__':
if len(sys.argv) < 2:
usage()
src = sys.argv[1]
out = None
if len(sys.argv) == 3:
out = sys.argv[2]
data = np.load(open_read(src))
if data.min() <= 0:
data += data.min() + 1
boxcox, l = stats.boxcox(data)
print >> sys.stderr, "Lambda: {}".format(l)
if out is not None:
np.save(open_write(out), boxcox)
query = sys.argv[1]
out = sys.argv[2]
client = InfluxDBClient('localhost', 8086, 'root', 'root', '')
result = client.query(query)
for rs in result:
cols = [k for k in rs[0].keys() if k != 'time']
cols_count = len(cols)
data = np.zeros((len(rs), cols_count))
r = 0
for row in rs:
c = 0
for col in cols:
data[r,c] = row[col]
c += 1
r += 1
if len(data.shape) == 2 and data.shape[1] == 1:
data = data.reshape(data.shape[0])
np.save(open_write(out), data)
# using the Box-Cox method.
# https://en.wikipedia.org/wiki/Power_transform
import sys
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
from io_helper import open_write,open_read
def usage():
print >> sys.stderr, "{} <source.npy> [output.npy]"
exit(1)
if __name__ == '__main__':
if len(sys.argv) < 2:
usage()
src = sys.argv[1]
out = None
if len(sys.argv) == 3:
out = sys.argv[2]
data = np.load(open_read(src))
if data.min() <= 0:
data += data.min() + 1
boxcox, l = stats.boxcox(data)
print >> sys.stderr, "Lambda: {}".format(l)
if out is not None:
np.save(open_write(out), boxcox)
def usage():
print >> sys.stderr, "{} <query> <output>".format(__file__)
exit(1)
if __name__ == '__main__':
if len(sys.argv) != 3:
usage()
query = sys.argv[1]
out = sys.argv[2]
client = InfluxDBClient('localhost', 8086, 'root', 'root', '')
result = client.query(query)
for rs in result:
cols = [k for k in rs[0].keys() if k != 'time']
cols_count = len(cols)
data = np.zeros((len(rs), cols_count))
r = 0
for row in rs:
c = 0
for col in cols:
data[r, c] = row[col]
c += 1
r += 1
if len(data.shape) == 2 and data.shape[1] == 1:
data = data.reshape(data.shape[0])
np.save(open_write(out), data)