pulses_sql

Pulses analysis using MySQL server for data saving.
The code is focused on the comprehensive analysis of the data measured on a liquid ³He in Cornell University.

Version

The code was developed in the Anaconda environment 5.3.0,
Python build: 3.7.0

Dependencies

numpy
matplotlib
scipy
mysql.connector
inspect

Description

The code consists of two parts.

1. The createdata.py, database was created using the original .dat files for three different pressures and two quartz forks.
   Additionally, three wrapper functions for decorators were created in order to log called method, estimate processing time, and call tracking. Even all columns were updated only several will not change and those are the raw :
   index, time, Q, Inf_Freq, Tmc pulse columns are the primary and foreign keys and can be cascadely updated. The as follows

• Connect to SQL database using conf dict;
• Delete any tables if any;
• Create all necessary tables;
• Close connection.

2. The tauanalysis.py uses and updates an existing database. Here separate pulses are identified according to Q in IC tables. The pulse updates from the number greater than the number of elements in column by 1000 per each pulse. The logic is the next:

• import data with Q's
• Find indices where abs(Q) is really high
• pulse are updated by 1000 per pulse
• update pulse with negative number in the vicinity where pulse is lunches, i.e. abs(Q) is large
• non-linear regression fit of T_mc vs time dependence
• update Tmc/Tc with the fitted values
• non linear regression fit of the Tmc/Tc vs Q for HEC table
• update Tloc and Tloc/Tc for HEC
• update Tloc and Tloc/Tc for IC
• find dT_HEC/dT_IC

Usage

createdata.py

The sequence of commands as follows:

A=sql_create(conf)
A.filt_num=41
A.drop_f(forks)
for k,v in forks.items():
    A.create_table(v['tables'][0],v['tables'][1])
    A.insert_tables(v)
A.close_f()  

It will create tables and inserts raw data for further analysis. It is recommended to comment all these commands (with #) after one execution.

tauanalysis.py

First execution:

A=timetotemp(conf,sett1)
A.first_start()
data1=A.sel_onlypulse(['time','Q','Tmc','index','pulse'],A.tables[A.sett['pressure']][0])
data2=A.sel_onlypulse(['time','Q','Tmc','index'],A.tables[A.sett['pressure']][1])
A.nopulse1,A.nopulse2=A.pulse_remove(15,35,data1,data2) # for 0 bar
A.nopulse1,A.nopulse2=A.pulse_remove(10,50,data1,data2) # for 9 psi and 22 bar
del data1
del data2
f_lt,f_tl=A.temp_fit(1)
f_lt,f_tl=A.temp_fit(3) # 9psi
f_lt,f_tl=A.temp_fit(4) # for 22 bar
fit_qt=A.QtoT(10) # optimal for 0 bar and 9 psi
fit_qt=A.QtoT(16) # for 22 bar
dQ=A.QtoTic(fit_qt)
A.update_local(A.tables[A.sett['pressure']][0],fit_qt,0)
A.update_local(A.tables[A.sett['pressure']][1],fit_qt,dQ)
del A

Change to A=timetotemp(conf,sett2) and A=timetotemp(conf,sett3) for different pressures and comment corresponding lines.

Further executions

A=timetotemp(conf,sett1)
p_num=A.loop_number()
f_par=[A.pick_sep(ii,100) for ii in range(p_num)]
A.plot_dt(f_par)
A.save_dt(f_par)
dataJ=A.sel_onlypulseJoin(['time','Q','Tloc/Tc','index','pulse'],A.tables[A.sett['pressure']][0],['Q','Tloc/Tc'],A.tables[A.sett['pressure']][1])
#print(A.save_dt.__doc__)
fig1 = plt.figure(1, clear = True)
ax1 = fig1.add_subplot(211)
ax1.set_ylabel('Q')
ax1.set_xlabel('time [sec]')
ax1.set_title('Q vs time for both forks')
ax1.scatter(dataJ[0],dataJ[1],color='green', s=0.5)
ax1.scatter(dataJ[0],dataJ[5],color='red', s=0.5)
plt.grid()
ax2 = fig1.add_subplot(212)
ax2.set_ylabel('T')
ax2.set_xlabel('time [sec]')
ax2.set_title('T vs time for both forks')
ax2.scatter(dataJ[0],dataJ[2],color='green', s=0.5)
ax2.scatter(dataJ[0],dataJ[6],color='red', s=0.5)
plt.grid()
plt.show()

fig2 = plt.figure(2, clear = True)
ax1 = fig2.add_subplot(111)
ax1.set_ylabel('pulse')
ax1.set_xlabel('time [sec]')
ax1.set_title('pulse vs time for both forks')
ax1.scatter(dataJ[0],dataJ[4],color='green', s=0.5)
plt.grid()
plt.show()
A.close_f()
del A

It will calculates and save the desired values of dT_HEC/dT_IC Replace to A=timetotemp(conf,sett2) or A=timetotemp(conf,sett3) for different pressures.

Testing

test_createdata.py

Just excecute this file for a unit test of the createdata.py.

test_tauanalysis.py

In progress...

Licence

Cornell University © Dmytro Lotnyk