Table basics#
OOMMF saves the scalar data of micromagnetic simulations in .odt files, whereas mumax\(^{3}\) saves it in .txt files. ubermagtable is a convenience tool which provides functions for reading, manipulating, and visualising data from those files.
Reading data from OOMMF and mumax3 table files is done using ubermagtable.Table.fromfile class method. It takes filename as an input argument.
[1]:
import os
import ubermagtable as ut
# Sample OOMMF .odt file
dirname = os.path.join("..", "ubermagtable", "tests", "test_sample")
odtfile = os.path.join(dirname, "oommf-new-file2.odt")
table = ut.Table.fromfile(odtfile)
Table data is now loaded into pandas.DataFrame and it can be accessed via data attribute.
[2]:
table.data
[2]:
| E | E_calc_count | max_dmdt | dE/dt | delta_E | average_u | E_exchange | max_spin_ang_exchange | stage_max_spin_ang_exchange | run_max_spin_ang_exchange | E_dmi | E_uniaxialanisotropy | iteration | stage_iteration | stage | mx | my | mz | last_time_step | t | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -3.138757e-19 | 61.0 | 5105.786662 | 1.131548e-09 | 1.234439e-22 | 400.0 | 7.819720e-19 | 24.981919 | 24.981919 | 24.981919 | -1.791404e-18 | 6.955566e-19 | 8.0 | 8.0 | 0.0 | -0.004356 | 0.001711 | 0.779309 | 1.146802e-13 | 1.000000e-12 |
| 1 | -3.123365e-19 | 92.0 | 5393.820357 | 1.881778e-09 | 2.714936e-22 | 400.0 | 7.859070e-19 | 25.999114 | 25.999114 | 25.999114 | -1.795166e-18 | 6.969222e-19 | 14.0 | 5.0 | 1.0 | -0.007399 | 0.002527 | 0.779212 | 1.481817e-13 | 2.000000e-12 |
| 2 | -3.101629e-19 | 129.0 | 5478.105937 | 2.410525e-09 | 1.525909e-22 | 400.0 | 7.908188e-19 | 26.839540 | 26.839540 | 26.839540 | -1.800156e-18 | 6.991741e-19 | 21.0 | 6.0 | 2.0 | -0.010058 | 0.002596 | 0.779103 | 6.371456e-14 | 3.000000e-12 |
| 3 | -3.075651e-19 | 172.0 | 5775.596117 | 2.750961e-09 | 2.026333e-22 | 400.0 | 7.963184e-19 | 27.523018 | 27.523018 | 27.523018 | -1.806035e-18 | 7.021517e-19 | 28.0 | 6.0 | 3.0 | -0.012243 | 0.002078 | 0.779037 | 7.397143e-14 | 4.000000e-12 |
| 4 | -3.046980e-19 | 209.0 | 6099.408455 | 2.938665e-09 | 1.417659e-22 | 400.0 | 8.020796e-19 | 27.968494 | 27.968494 | 27.968494 | -1.812650e-18 | 7.058726e-19 | 35.0 | 6.0 | 4.0 | -0.013915 | 0.001139 | 0.779058 | 4.838847e-14 | 5.000000e-12 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 195 | -4.666219e-20 | 7786.0 | 6419.713717 | 2.798949e-10 | 1.373560e-23 | 400.0 | 1.222876e-18 | 29.279242 | 29.396487 | 33.740702 | -1.937412e-18 | 6.678731e-19 | 1372.0 | 6.0 | 195.0 | -0.007893 | -0.008017 | 0.899229 | 5.094469e-14 | 1.960000e-10 |
| 196 | -4.634379e-20 | 7829.0 | 6444.188876 | 2.763562e-10 | 1.564748e-23 | 400.0 | 1.222846e-18 | 29.358338 | 29.358338 | 33.740702 | -1.936677e-18 | 6.674871e-19 | 1379.0 | 6.0 | 196.0 | -0.007885 | -0.008007 | 0.899298 | 6.044352e-14 | 1.970000e-10 |
| 197 | -4.601751e-20 | 7866.0 | 6397.295744 | 2.905618e-10 | 1.926693e-23 | 400.0 | 1.222790e-18 | 29.418283 | 29.418283 | 33.740702 | -1.935846e-18 | 6.670387e-19 | 1386.0 | 6.0 | 197.0 | -0.007876 | -0.007998 | 0.899383 | 7.101084e-14 | 1.980000e-10 |
| 198 | -4.568193e-20 | 7909.0 | 6374.587944 | 3.124776e-10 | 1.795553e-23 | 400.0 | 1.222710e-18 | 29.397007 | 29.419861 | 33.740702 | -1.934920e-18 | 6.665281e-19 | 1393.0 | 6.0 | 198.0 | -0.007866 | -0.007989 | 0.899482 | 5.961504e-14 | 1.990000e-10 |
| 199 | -4.533615e-20 | 7946.0 | 6479.674566 | 3.377564e-10 | 2.490729e-23 | 400.0 | 1.222607e-18 | 29.301311 | 29.397007 | 33.740702 | -1.933899e-18 | 6.659556e-19 | 1400.0 | 6.0 | 199.0 | -0.007854 | -0.007981 | 0.899595 | 7.547542e-14 | 2.000000e-10 |
200 rows × 20 columns
Apart from the data, units for individual columns are stored in units attribute.
[3]:
table.units
[3]:
{'E': 'J',
'E_calc_count': '',
'max_dmdt': 'deg/ns',
'dE/dt': 'J/s',
'delta_E': 'J',
'average_u': 'm/s',
'E_exchange': 'J',
'max_spin_ang_exchange': 'deg',
'stage_max_spin_ang_exchange': 'deg',
'run_max_spin_ang_exchange': 'deg',
'E_dmi': 'J',
'E_uniaxialanisotropy': 'J',
'iteration': '',
'stage_iteration': '',
'stage': '',
'mx': '',
'my': '',
'mz': '',
'last_time_step': 's',
't': 's'}
This returns a dictionary, whose keys are column names and its values are the units.
By default, column names from OOMMF .odt and mumax3 .txt files are shortened. This can be changed by passing rename=False to the Table.fromfile class method:
[4]:
table_not_renamed = ut.Table.fromfile(odtfile, rename=False)
The column names are now:
[5]:
table_not_renamed.data
[5]:
| SpinTEvolve:evolver:Total energy | SpinTEvolve:evolver:Energy calc count | SpinTEvolve:evolver:Max dm/dt | SpinTEvolve:evolver:dE/dt | SpinTEvolve:evolver:Delta E | SpinTEvolve:evolver:average u | UniformExchange:exchange:Energy | UniformExchange:exchange:Max Spin Ang | UniformExchange:exchange:Stage Max Spin Ang | UniformExchange:exchange:Run Max Spin Ang | DMI_Cnv:dmi:Energy | UniaxialAnisotropy:uniaxialanisotropy:Energy | TimeDriver::Iteration | TimeDriver::Stage iteration | TimeDriver::Stage | TimeDriver::mx | TimeDriver::my | TimeDriver::mz | TimeDriver::Last time step | TimeDriver::Simulation time | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -3.138757e-19 | 61.0 | 5105.786662 | 1.131548e-09 | 1.234439e-22 | 400.0 | 7.819720e-19 | 24.981919 | 24.981919 | 24.981919 | -1.791404e-18 | 6.955566e-19 | 8.0 | 8.0 | 0.0 | -0.004356 | 0.001711 | 0.779309 | 1.146802e-13 | 1.000000e-12 |
| 1 | -3.123365e-19 | 92.0 | 5393.820357 | 1.881778e-09 | 2.714936e-22 | 400.0 | 7.859070e-19 | 25.999114 | 25.999114 | 25.999114 | -1.795166e-18 | 6.969222e-19 | 14.0 | 5.0 | 1.0 | -0.007399 | 0.002527 | 0.779212 | 1.481817e-13 | 2.000000e-12 |
| 2 | -3.101629e-19 | 129.0 | 5478.105937 | 2.410525e-09 | 1.525909e-22 | 400.0 | 7.908188e-19 | 26.839540 | 26.839540 | 26.839540 | -1.800156e-18 | 6.991741e-19 | 21.0 | 6.0 | 2.0 | -0.010058 | 0.002596 | 0.779103 | 6.371456e-14 | 3.000000e-12 |
| 3 | -3.075651e-19 | 172.0 | 5775.596117 | 2.750961e-09 | 2.026333e-22 | 400.0 | 7.963184e-19 | 27.523018 | 27.523018 | 27.523018 | -1.806035e-18 | 7.021517e-19 | 28.0 | 6.0 | 3.0 | -0.012243 | 0.002078 | 0.779037 | 7.397143e-14 | 4.000000e-12 |
| 4 | -3.046980e-19 | 209.0 | 6099.408455 | 2.938665e-09 | 1.417659e-22 | 400.0 | 8.020796e-19 | 27.968494 | 27.968494 | 27.968494 | -1.812650e-18 | 7.058726e-19 | 35.0 | 6.0 | 4.0 | -0.013915 | 0.001139 | 0.779058 | 4.838847e-14 | 5.000000e-12 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 195 | -4.666219e-20 | 7786.0 | 6419.713717 | 2.798949e-10 | 1.373560e-23 | 400.0 | 1.222876e-18 | 29.279242 | 29.396487 | 33.740702 | -1.937412e-18 | 6.678731e-19 | 1372.0 | 6.0 | 195.0 | -0.007893 | -0.008017 | 0.899229 | 5.094469e-14 | 1.960000e-10 |
| 196 | -4.634379e-20 | 7829.0 | 6444.188876 | 2.763562e-10 | 1.564748e-23 | 400.0 | 1.222846e-18 | 29.358338 | 29.358338 | 33.740702 | -1.936677e-18 | 6.674871e-19 | 1379.0 | 6.0 | 196.0 | -0.007885 | -0.008007 | 0.899298 | 6.044352e-14 | 1.970000e-10 |
| 197 | -4.601751e-20 | 7866.0 | 6397.295744 | 2.905618e-10 | 1.926693e-23 | 400.0 | 1.222790e-18 | 29.418283 | 29.418283 | 33.740702 | -1.935846e-18 | 6.670387e-19 | 1386.0 | 6.0 | 197.0 | -0.007876 | -0.007998 | 0.899383 | 7.101084e-14 | 1.980000e-10 |
| 198 | -4.568193e-20 | 7909.0 | 6374.587944 | 3.124776e-10 | 1.795553e-23 | 400.0 | 1.222710e-18 | 29.397007 | 29.419861 | 33.740702 | -1.934920e-18 | 6.665281e-19 | 1393.0 | 6.0 | 198.0 | -0.007866 | -0.007989 | 0.899482 | 5.961504e-14 | 1.990000e-10 |
| 199 | -4.533615e-20 | 7946.0 | 6479.674566 | 3.377564e-10 | 2.490729e-23 | 400.0 | 1.222607e-18 | 29.301311 | 29.397007 | 33.740702 | -1.933899e-18 | 6.659556e-19 | 1400.0 | 6.0 | 199.0 | -0.007854 | -0.007981 | 0.899595 | 7.547542e-14 | 2.000000e-10 |
200 rows × 20 columns
We can specify the name of an independent variable in the table.
[6]:
table.x = "t"
If we already knew what the independent variable was going to be when we loaded the file, we could have passed it to fromfile method.
[7]:
table = ut.Table.fromfile(odtfile, x="t")
If table data was obtained by using time driver, table data store different values which are all a function of time. Column names of all time-dependent data can be obtained using y:
[8]:
table.y
[8]:
['E',
'E_calc_count',
'max_dmdt',
'dE/dt',
'delta_E',
'average_u',
'E_exchange',
'max_spin_ang_exchange',
'stage_max_spin_ang_exchange',
'run_max_spin_ang_exchange',
'E_dmi',
'E_uniaxialanisotropy',
'iteration',
'stage_iteration',
'stage',
'mx',
'my',
'mz',
'last_time_step']
Similarly, column name storing independent variable:
[9]:
table.x
[9]:
't'
The maximum value of x:
[10]:
table.xmax
[10]:
2e-10
Finally, the representation string of the table is:
[11]:
table
[11]:
E E_calc_count max_dmdt dE/dt delta_E \
0 -3.138757e-19 61.0 5105.786662 1.131548e-09 1.234439e-22
1 -3.123365e-19 92.0 5393.820357 1.881778e-09 2.714936e-22
2 -3.101629e-19 129.0 5478.105937 2.410525e-09 1.525909e-22
3 -3.075651e-19 172.0 5775.596117 2.750961e-09 2.026333e-22
4 -3.046980e-19 209.0 6099.408455 2.938665e-09 1.417659e-22
.. ... ... ... ... ...
195 -4.666219e-20 7786.0 6419.713717 2.798949e-10 1.373560e-23
196 -4.634379e-20 7829.0 6444.188876 2.763562e-10 1.564748e-23
197 -4.601751e-20 7866.0 6397.295744 2.905618e-10 1.926693e-23
198 -4.568193e-20 7909.0 6374.587944 3.124776e-10 1.795553e-23
199 -4.533615e-20 7946.0 6479.674566 3.377564e-10 2.490729e-23
average_u E_exchange max_spin_ang_exchange \
0 400.0 7.819720e-19 24.981919
1 400.0 7.859070e-19 25.999114
2 400.0 7.908188e-19 26.839540
3 400.0 7.963184e-19 27.523018
4 400.0 8.020796e-19 27.968494
.. ... ... ...
195 400.0 1.222876e-18 29.279242
196 400.0 1.222846e-18 29.358338
197 400.0 1.222790e-18 29.418283
198 400.0 1.222710e-18 29.397007
199 400.0 1.222607e-18 29.301311
stage_max_spin_ang_exchange run_max_spin_ang_exchange E_dmi \
0 24.981919 24.981919 -1.791404e-18
1 25.999114 25.999114 -1.795166e-18
2 26.839540 26.839540 -1.800156e-18
3 27.523018 27.523018 -1.806035e-18
4 27.968494 27.968494 -1.812650e-18
.. ... ... ...
195 29.396487 33.740702 -1.937412e-18
196 29.358338 33.740702 -1.936677e-18
197 29.418283 33.740702 -1.935846e-18
198 29.419861 33.740702 -1.934920e-18
199 29.397007 33.740702 -1.933899e-18
E_uniaxialanisotropy iteration stage_iteration stage mx \
0 6.955566e-19 8.0 8.0 0.0 -0.004356
1 6.969222e-19 14.0 5.0 1.0 -0.007399
2 6.991741e-19 21.0 6.0 2.0 -0.010058
3 7.021517e-19 28.0 6.0 3.0 -0.012243
4 7.058726e-19 35.0 6.0 4.0 -0.013915
.. ... ... ... ... ...
195 6.678731e-19 1372.0 6.0 195.0 -0.007893
196 6.674871e-19 1379.0 6.0 196.0 -0.007885
197 6.670387e-19 1386.0 6.0 197.0 -0.007876
198 6.665281e-19 1393.0 6.0 198.0 -0.007866
199 6.659556e-19 1400.0 6.0 199.0 -0.007854
my mz last_time_step t
0 0.001711 0.779309 1.146802e-13 1.000000e-12
1 0.002527 0.779212 1.481817e-13 2.000000e-12
2 0.002596 0.779103 6.371456e-14 3.000000e-12
3 0.002078 0.779037 7.397143e-14 4.000000e-12
4 0.001139 0.779058 4.838847e-14 5.000000e-12
.. ... ... ... ...
195 -0.008017 0.899229 5.094469e-14 1.960000e-10
196 -0.008007 0.899298 6.044352e-14 1.970000e-10
197 -0.007998 0.899383 7.101084e-14 1.980000e-10
198 -0.007989 0.899482 5.961504e-14 1.990000e-10
199 -0.007981 0.899595 7.547542e-14 2.000000e-10
[200 rows x 20 columns]
