plt.plot(H1.t_mid, H1.freq)
Which is really nice and simple, unlike the messy iteration in this example.
[row['t_mid'] for row in alldata]
Queries would contain phrases like and_(H1.freq.between(4,20), H1.comment.line('%argon%')
which are quite different to text mode, but easier to compose 'on the fly'
"""
from pyfusion.data.DA_datamining import DA
from sqlalchemy import create_engine
import matplotlib.pyplot as plt
import os
if not os.path.isfile('H1_766.sqlite'): # if not there, make it
myDA = DA('H1_766.npz')
myDA.to_sqlalchemy('sqlite:///H1_766.sqlite', newfmts=dict(phases='dphi_{i:02d}'),
mytable='H1_766', chunk=5000, n_recs=1e9)
engine = create_engine('sqlite:///H1_766.sqlite')
conn = engine.connect()
result = conn.execute('select shot, t_mid, freq, a12, amp from H1_766')
alldata = result.fetchall() # all the instances, and overplot in feint colour
plt.plot([row['t_mid'] for row in alldata], [row['freq'] for row in alldata],'o',alpha=0.02)
plt.xlabel('time (s)') ; plt.ylabel('freq (kHz)')
# Ex 3a. this time, select the large rotating modes, (note - we combine two lines here,
# even though it makes it less readable)
plt.figure()
sel = conn.execute('select shot, t_mid, freq, a12, amp from H1_766 where amp> 0.05 and a12>0.7').fetchall()
# for the selected data, plot freq against t_mid in red circles('r'), whose size reflects the amplitude
plt.scatter([row['t_mid'] for row in sel], [row['freq'] for row in sel],[300*row['amp'] for row in sel],'r')
plt.show()
