From 4ef07d3df492d52f82950cb3c21a2a75842c605f Mon Sep 17 00:00:00 2001 From: Sumit112192 Date: Mon, 11 Mar 2024 00:17:02 +0530 Subject: [PATCH 1/6] Added necessary files --- .../VelocityPacketTrackerFirstObjective.md | 2798 +++++++++++++ FirstObjectiveMarkdown/output_10_1.png | Bin 0 -> 80089 bytes FirstObjectiveMarkdown/output_24_1.png | Bin 0 -> 19009 bytes FirstObjectiveMarkdown/output_32_1.png | Bin 0 -> 14925 bytes VelocityPacketTrackerFirstObjective.ipynb | 3493 +++++++++++++++++ 5 files changed, 6291 insertions(+) create mode 100644 FirstObjectiveMarkdown/VelocityPacketTrackerFirstObjective.md create mode 100644 FirstObjectiveMarkdown/output_10_1.png create mode 100644 FirstObjectiveMarkdown/output_24_1.png create mode 100644 FirstObjectiveMarkdown/output_32_1.png create mode 100644 VelocityPacketTrackerFirstObjective.ipynb diff --git a/FirstObjectiveMarkdown/VelocityPacketTrackerFirstObjective.md b/FirstObjectiveMarkdown/VelocityPacketTrackerFirstObjective.md new file mode 100644 index 00000000000..c5110c7728d --- /dev/null +++ b/FirstObjectiveMarkdown/VelocityPacketTrackerFirstObjective.md @@ -0,0 +1,2798 @@ +# Velocity Packet Tracker Visulization + +## In the below cell, we import necessaries libraries and download the required dataset + + +```python +from tardis import run_tardis +from tardis.io.atom_data.util import download_atom_data + +download_atom_data('kurucz_cd23_chianti_H_He') +``` + + + Iterations: 0/? [00:00 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
09.93e+03 K1.01e+04 K0.40.507
59.85e+03 K1.02e+04 K0.2110.197
109.78e+03 K1.01e+04 K0.1430.117
159.71e+03 K9.87e+03 K0.1050.0869
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 9933.952 K + Expected t_inner for next iteration = 10703.212 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 2 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.071e+43 erg / s + Luminosity absorbed = 3.576e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.01e+04 K1.08e+04 K0.5070.525
51.02e+04 K1.1e+04 K0.1970.203
101.01e+04 K1.08e+04 K0.1170.125
159.87e+03 K1.05e+04 K0.08690.0933
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10703.212 K + Expected t_inner for next iteration = 10673.712 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 3 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.074e+43 erg / s + Luminosity absorbed = 3.391e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.08e+04 K1.1e+04 K0.5250.483
51.1e+04 K1.12e+04 K0.2030.189
101.08e+04 K1.1e+04 K0.1250.118
151.05e+04 K1.06e+04 K0.09330.0895
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10673.712 K + Expected t_inner for next iteration = 10635.953 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 4 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.058e+43 erg / s + Luminosity absorbed = 3.352e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4830.469
51.12e+04 K1.12e+04 K0.1890.182
101.1e+04 K1.1e+04 K0.1180.113
151.06e+04 K1.07e+04 K0.08950.0861
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10635.953 K + Expected t_inner for next iteration = 10638.407 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 5 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.055e+43 erg / s + Luminosity absorbed = 3.399e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + Iteration converged 1/4 consecutive times. (base.py:261) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4690.479
51.12e+04 K1.13e+04 K0.1820.178
101.1e+04 K1.1e+04 K0.1130.113
151.07e+04 K1.07e+04 K0.08610.0839
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10638.407 K + Expected t_inner for next iteration = 10650.202 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 6 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.061e+43 erg / s + Luminosity absorbed = 3.398e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + Iteration converged 2/4 consecutive times. (base.py:261) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4790.47
51.13e+04 K1.12e+04 K0.1780.185
101.1e+04 K1.11e+04 K0.1130.112
151.07e+04 K1.07e+04 K0.08390.0856
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10650.202 K + Expected t_inner for next iteration = 10645.955 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 7 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.061e+43 erg / s + Luminosity absorbed = 3.382e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + Iteration converged 3/4 consecutive times. (base.py:261) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.470.47
51.12e+04 K1.13e+04 K0.1850.178
101.11e+04 K1.11e+04 K0.1120.112
151.07e+04 K1.07e+04 K0.08560.086
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10645.955 K + Expected t_inner for next iteration = 10642.050 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 8 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.062e+43 erg / s + Luminosity absorbed = 3.350e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + Iteration converged 4/4 consecutive times. (base.py:261) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.11e+04 K0.470.472
51.13e+04 K1.14e+04 K0.1780.175
101.11e+04 K1.11e+04 K0.1120.111
151.07e+04 K1.07e+04 K0.0860.084
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10642.050 K + Expected t_inner for next iteration = 10636.106 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 9 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.052e+43 erg / s + Luminosity absorbed = 3.411e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + Iteration converged 5/4 consecutive times. (base.py:261) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4720.469
51.14e+04 K1.15e+04 K0.1750.17
101.11e+04 K1.11e+04 K0.1110.109
151.07e+04 K1.08e+04 K0.0840.0822
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10636.106 K + Expected t_inner for next iteration = 10654.313 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 10 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.070e+43 erg / s + Luminosity absorbed = 3.335e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.1e+04 K0.4690.475
51.15e+04 K1.14e+04 K0.170.177
101.11e+04 K1.11e+04 K0.1090.112
151.08e+04 K1.06e+04 K0.08220.0878
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10654.313 K + Expected t_inner for next iteration = 10628.190 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 11 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.053e+43 erg / s + Luminosity absorbed = 3.363e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + Iteration converged 1/4 consecutive times. (base.py:261) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4750.472
51.14e+04 K1.12e+04 K0.1770.184
101.11e+04 K1.1e+04 K0.1120.114
151.06e+04 K1.06e+04 K0.08780.0859
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10628.190 K + Expected t_inner for next iteration = 10644.054 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 12 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.056e+43 erg / s + Luminosity absorbed = 3.420e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.11e+04 K0.4720.467
51.12e+04 K1.13e+04 K0.1840.176
101.1e+04 K1.11e+04 K0.1140.11
151.06e+04 K1.08e+04 K0.08590.0821
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10644.054 K + Expected t_inner for next iteration = 10653.543 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 13 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.062e+43 erg / s + Luminosity absorbed = 3.406e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + Iteration converged 1/4 consecutive times. (base.py:261) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4670.466
51.13e+04 K1.13e+04 K0.1760.18
101.11e+04 K1.11e+04 K0.110.111
151.08e+04 K1.08e+04 K0.08210.0841
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10653.543 K + Expected t_inner for next iteration = 10647.277 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 14 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.063e+43 erg / s + Luminosity absorbed = 3.369e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + Iteration converged 2/4 consecutive times. (base.py:261) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4660.469
51.13e+04 K1.13e+04 K0.180.182
101.11e+04 K1.1e+04 K0.1110.113
151.08e+04 K1.07e+04 K0.08410.0854
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10647.277 K + Expected t_inner for next iteration = 10638.875 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 15 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.053e+43 erg / s + Luminosity absorbed = 3.417e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + Iteration converged 3/4 consecutive times. (base.py:261) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.1e+04 K0.4690.484
51.13e+04 K1.13e+04 K0.1820.181
101.1e+04 K1.1e+04 K0.1130.113
151.07e+04 K1.07e+04 K0.08540.0858
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10638.875 K + Expected t_inner for next iteration = 10655.125 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 16 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.059e+43 erg / s + Luminosity absorbed = 3.445e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + Iteration converged 4/4 consecutive times. (base.py:261) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4840.472
51.13e+04 K1.13e+04 K0.1810.177
101.1e+04 K1.1e+04 K0.1130.113
151.07e+04 K1.06e+04 K0.08580.0858
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10655.125 K + Expected t_inner for next iteration = 10655.561 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 17 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.067e+43 erg / s + Luminosity absorbed = 3.372e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.11e+04 K0.4720.468
51.13e+04 K1.14e+04 K0.1770.175
101.1e+04 K1.11e+04 K0.1130.11
151.06e+04 K1.08e+04 K0.08580.0816
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10655.561 K + Expected t_inner for next iteration = 10636.536 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 18 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.057e+43 erg / s + Luminosity absorbed = 3.365e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + Iteration converged 1/4 consecutive times. (base.py:261) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4680.464
51.14e+04 K1.13e+04 K0.1750.177
101.11e+04 K1.1e+04 K0.110.113
151.08e+04 K1.07e+04 K0.08160.0848
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10636.536 K + Expected t_inner for next iteration = 10641.692 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 19 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.056e+43 erg / s + Luminosity absorbed = 3.405e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + Iteration converged 2/4 consecutive times. (base.py:261) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4640.466
51.13e+04 K1.13e+04 K0.1770.177
101.1e+04 K1.11e+04 K0.1130.111
151.07e+04 K1.07e+04 K0.08480.0853
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10641.692 K + Expected t_inner for next iteration = 10650.463 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Simulation finished in 19 iterations + Simulation took 54.57 s + (base.py:469) + [tardis.simulation.base][INFO ] + + Starting iteration 20 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.061e+43 erg / s + Luminosity absorbed = 3.401e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + + +## Import the SDECPlotter class for plotting spectral element Decomposition Plot and obtain the necessary data from the simulation we just ran above. + + +```python +from tardis.visualization import SDECPlotter +plotter = SDECPlotter.from_simulation(sim) +``` + +## Let's now plot the SDEC Plot ( Second part of our First Objective ) + + +```python +plotter.generate_plot_mpl() +``` + + + + + + + + + + +![png](output_10_1.png) + + + +## Let's now work on the abundance vs velocity plot +For the next few cells, we'll modify the abundace dataframe so as to plot it easily + +### Get the abundance data from the simulation_state + + +```python +abundance = sim.simulation_state.abundance +abundance +``` + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
012345678910111213141516171819
atomic_number
80.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.19
120.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.03
140.520.520.520.520.520.520.520.520.520.520.520.520.520.520.520.520.520.520.520.52
160.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.19
180.040.040.040.040.040.040.040.040.040.040.040.040.040.040.040.040.040.040.040.04
200.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.03
+
+ + + +### Transpose the abundance dataframe so to get atomic number as columns heads + + +```python +abundance = abundance.transpose() +abundance +``` + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
atomic_number81214161820
00.190.030.520.190.040.03
10.190.030.520.190.040.03
20.190.030.520.190.040.03
30.190.030.520.190.040.03
40.190.030.520.190.040.03
50.190.030.520.190.040.03
60.190.030.520.190.040.03
70.190.030.520.190.040.03
80.190.030.520.190.040.03
90.190.030.520.190.040.03
100.190.030.520.190.040.03
110.190.030.520.190.040.03
120.190.030.520.190.040.03
130.190.030.520.190.040.03
140.190.030.520.190.040.03
150.190.030.520.190.040.03
160.190.030.520.190.040.03
170.190.030.520.190.040.03
180.190.030.520.190.040.03
190.190.030.520.190.040.03
+
+ + + +### Get the velocity at the middle of each shell + + +```python +v_middle = sim.simulation_state.v_middle +v_middle +``` + + + + +$[1.1225 \times 10^{9},~1.1675 \times 10^{9},~1.2125 \times 10^{9},~1.2575 \times 10^{9},~1.3025 \times 10^{9},~1.3475 \times 10^{9},~1.3925 \times 10^{9},~1.4375 \times 10^{9},~1.4825 \times 10^{9},~1.5275 \times 10^{9},~1.5725 \times 10^{9},~1.6175 \times 10^{9},~1.6625 \times 10^{9},~1.7075 \times 10^{9},~1.7525 \times 10^{9},~1.7975 \times 10^{9},~1.8425 \times 10^{9},~1.8875 \times 10^{9},~1.9325 \times 10^{9},~1.9775 \times 10^{9}] \; \mathrm{\frac{cm}{s}}$ + + + +### Renaming the abundance columns( atomic number to atomic symbol ) + + +```python +from tardis.util.base import atomic_number2element_symbol +columns = {head: atomic_number2element_symbol(head) for head in abundance.columns} +abundance.rename(columns = columns, inplace = True) +abundance +``` + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
atomic_numberOMgSiSArCa
00.190.030.520.190.040.03
10.190.030.520.190.040.03
20.190.030.520.190.040.03
30.190.030.520.190.040.03
40.190.030.520.190.040.03
50.190.030.520.190.040.03
60.190.030.520.190.040.03
70.190.030.520.190.040.03
80.190.030.520.190.040.03
90.190.030.520.190.040.03
100.190.030.520.190.040.03
110.190.030.520.190.040.03
120.190.030.520.190.040.03
130.190.030.520.190.040.03
140.190.030.520.190.040.03
150.190.030.520.190.040.03
160.190.030.520.190.040.03
170.190.030.520.190.040.03
180.190.030.520.190.040.03
190.190.030.520.190.040.03
+
+ + + +### Add a new column of ```v_middle``` in units of km/s + + +```python +import astropy.units as u +abundance['v_middle'] = [u.Quantity(vel, u.km/u.s).value for vel in v_middle] +abundance +``` + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
atomic_numberOMgSiSArCav_middle
00.190.030.520.190.040.0311225.0
10.190.030.520.190.040.0311675.0
20.190.030.520.190.040.0312125.0
30.190.030.520.190.040.0312575.0
40.190.030.520.190.040.0313025.0
50.190.030.520.190.040.0313475.0
60.190.030.520.190.040.0313925.0
70.190.030.520.190.040.0314375.0
80.190.030.520.190.040.0314825.0
90.190.030.520.190.040.0315275.0
100.190.030.520.190.040.0315725.0
110.190.030.520.190.040.0316175.0
120.190.030.520.190.040.0316625.0
130.190.030.520.190.040.0317075.0
140.190.030.520.190.040.0317525.0
150.190.030.520.190.040.0317975.0
160.190.030.520.190.040.0318425.0
170.190.030.520.190.040.0318875.0
180.190.030.520.190.040.0319325.0
190.190.030.520.190.040.0319775.0
+
+ + + + +```python +abundance.columns.name = 'atomic symbol' +``` + +## Plot of Abundance vs velocity + + +```python +abundance.plot(x = 'v_middle', xlabel = "$v_{middle}$ in km/s", ylabel = "Fractional Abundance", title = "Abundace vs velocity").legend(loc = 'upper right') +``` + + + + + + + + + + +![png](output_24_1.png) + + + +### Things to note in above graph +1. Since data overlap, we can only see four out of six line plots +2. Fractional abundance is uniform throughout the ejecta. + +## Our final task was to plot the total number of interactions that escape the simulation from the different elements +### I have worked it out for virtual packets, similar thing could be applied for the real packets as well + +I found out that most of the necessary data was alreadly pre computed during plotting of SDEC and available in the plotter object. + + +Get the line interaction data + + +```python +line_interaction_df = plotter.data["virtual"].packets_df_line_interaction +line_interaction_df +``` + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
nuslambdasenergieslast_interaction_typelast_line_interaction_out_idlast_line_interaction_in_idlast_line_interaction_in_nulast_line_interaction_atomlast_line_interaction_species
102.610913e+151148.2283610.000000e+002355153431.736483e+15141402
112.623633e+151142.6616430.000000e+002355153431.736483e+15141402
122.635277e+151137.6127830.000000e+002355153431.736483e+15141402
132.652415e+151130.2620870.000000e+002355153431.736483e+15141402
142.666043e+151124.4845570.000000e+002355153431.736483e+15141402
..............................
26527351.086349e+152759.6323376.346043e-072769776971.109094e+15121201
26527361.092227e+152744.7820746.591144e-072769776971.109094e+15121201
26527371.099582e+152726.4225686.837456e-072769776971.109094e+15121201
26527381.109661e+152701.6577697.107076e-072769776971.109094e+15121201
26527391.115506e+152687.5019447.237699e-072769776971.109094e+15121201
+

1071430 rows × 9 columns

+
+ + + +### Some slight pre processing ( refinement, counting ) + + +```python +#Adding a new column to get the count of interactions +line_interaction_df['count'] = 1 + +#Since only count is required, let's use only count and atomic_number columns +line_interaction_count_df = line_interaction_df[['last_line_interaction_atom', 'count']] + +#Group by the last_line_interaction_atom +line_interaction_count_df = line_interaction_count_df.groupby(['last_line_interaction_atom']).count()[['count']] +line_interaction_count_df['atomic_number'] = line_interaction_count_df.index +line_interaction_count_df.reset_index(drop = True, inplace = True) + +#Add a new column with the correspong atomic symbols +line_interaction_count_df['symbol'] = line_interaction_count_df['atomic_number'].apply(atomic_number2element_symbol) + +#Rearranging the columns +line_interaction_count_df = line_interaction_count_df[['atomic_number', 'symbol', 'count']] + +#Sorting according to the count value +line_interaction_count_df = line_interaction_count_df.sort_values('count', ascending = False) +line_interaction_count_df +``` + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
atomic_numbersymbolcount
214Si665620
316S219410
112Mg75800
08O39400
520Ca37650
418Ar33550
+
+ + + +## Finally, the required plot. + + +```python +line_interaction_count_df.plot.bar(x = 'symbol' , y = 'count', rot = 0) +``` + + + + + + + + + + +![png](output_32_1.png) + + + +## Thanks for giving your time. Please suggest any impovements or any mistakes I made. + + +```python + +``` diff --git a/FirstObjectiveMarkdown/output_10_1.png b/FirstObjectiveMarkdown/output_10_1.png new file mode 100644 index 0000000000000000000000000000000000000000..d9c447be6af2432d8893984fdad004571d9115ee GIT binary patch literal 80089 zcma%jcOaGj+dmYR-1mE2*K57ct49jb=gv}}#lpfm_wd0zWh^Y5cr2__4rfloPjY-1 zvfzJ$PEt>t9^0BYx#~L@W6A3~*;&~-S(zJLb}@EvG`F?k;Sk{9V!v$W zUoYUWbui`REhx!`ixAj7&~U`UBGE_w!_E*-H^;)p!g_d5Ld7k1dDPX7tgpX*ebuTJ zop$NM>J5WndYCb z$K2dJzBMS%S7lBrGvO6~9fj^3iLT(e_UXOjYDI8X7OUi$E6+*ck0z$%svbJzZ^%FG zt>4!_{;%IK`KSN;#YAKI7 z&vnE|D=S|(M}7Ng&*z6u3tdU|o1wIV@2rPQ*AG_faJq7Jgl#GpnaAAc>0DzbzXla~ zAGq*Y4cKMcNdFsd4mK(aPk(y%ne)fH(I; zZ6Atl$H!_qEJ_UcVnl~D*eObFMt}2OQ~!6JHdX==&U*NWy}20M-JN0EirJ4^85%{* zDbnF{@FSC*;Hat&)_MyUnRGOo z6rE>a@U5_w{m*R@L{Gb8yP!v2Wo7-$)fqOd>a$?FdbQDTxG|9Mp}f2?20bcra=2N2 zfUc@uYb3;~hB=BA_58?ZJ(QTmI{PEiv@20UE?$JHM%Q-`{a;Vd1b6M%yLd0JzsRtK zI1lR<7gyJ`;|Eq%tPH(6G2)exVjbr@l?n``(i9UE 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Shell No.t_radnext_t_radwnext_w
09.93e+03 K1.01e+04 K0.40.507
59.85e+03 K1.02e+04 K0.2110.197
109.78e+03 K1.01e+04 K0.1430.117
159.71e+03 K9.87e+03 K0.1050.0869
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Shell No.t_radnext_t_radwnext_w
01.01e+04 K1.08e+04 K0.5070.525
51.02e+04 K1.1e+04 K0.1970.203
101.01e+04 K1.08e+04 K0.1170.125
159.87e+03 K1.05e+04 K0.08690.0933
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Shell No.t_radnext_t_radwnext_w
01.08e+04 K1.1e+04 K0.5250.483
51.1e+04 K1.12e+04 K0.2030.189
101.08e+04 K1.1e+04 K0.1250.118
151.05e+04 K1.06e+04 K0.09330.0895
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10673.712 K\n", + "\tExpected t_inner for next iteration = 10635.953 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 4 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.058e+43 erg / s\n", + "\tLuminosity absorbed = 3.352e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4830.469
51.12e+04 K1.12e+04 K0.1890.182
101.1e+04 K1.1e+04 K0.1180.113
151.06e+04 K1.07e+04 K0.08950.0861
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10635.953 K\n", + "\tExpected t_inner for next iteration = 10638.407 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 5 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.055e+43 erg / s\n", + "\tLuminosity absorbed = 3.399e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\tIteration converged 1/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4690.479
51.12e+04 K1.13e+04 K0.1820.178
101.1e+04 K1.1e+04 K0.1130.113
151.07e+04 K1.07e+04 K0.08610.0839
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10638.407 K\n", + "\tExpected t_inner for next iteration = 10650.202 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 6 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.061e+43 erg / s\n", + "\tLuminosity absorbed = 3.398e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\tIteration converged 2/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4790.47
51.13e+04 K1.12e+04 K0.1780.185
101.1e+04 K1.11e+04 K0.1130.112
151.07e+04 K1.07e+04 K0.08390.0856
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10650.202 K\n", + "\tExpected t_inner for next iteration = 10645.955 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 7 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.061e+43 erg / s\n", + "\tLuminosity absorbed = 3.382e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\tIteration converged 3/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.470.47
51.12e+04 K1.13e+04 K0.1850.178
101.11e+04 K1.11e+04 K0.1120.112
151.07e+04 K1.07e+04 K0.08560.086
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10645.955 K\n", + "\tExpected t_inner for next iteration = 10642.050 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 8 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.062e+43 erg / s\n", + "\tLuminosity absorbed = 3.350e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\tIteration converged 4/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.11e+04 K0.470.472
51.13e+04 K1.14e+04 K0.1780.175
101.11e+04 K1.11e+04 K0.1120.111
151.07e+04 K1.07e+04 K0.0860.084
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10642.050 K\n", + "\tExpected t_inner for next iteration = 10636.106 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 9 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.052e+43 erg / s\n", + "\tLuminosity absorbed = 3.411e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\tIteration converged 5/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4720.469
51.14e+04 K1.15e+04 K0.1750.17
101.11e+04 K1.11e+04 K0.1110.109
151.07e+04 K1.08e+04 K0.0840.0822
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10636.106 K\n", + "\tExpected t_inner for next iteration = 10654.313 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 10 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.070e+43 erg / s\n", + "\tLuminosity absorbed = 3.335e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.1e+04 K0.4690.475
51.15e+04 K1.14e+04 K0.170.177
101.11e+04 K1.11e+04 K0.1090.112
151.08e+04 K1.06e+04 K0.08220.0878
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10654.313 K\n", + "\tExpected t_inner for next iteration = 10628.190 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 11 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.053e+43 erg / s\n", + "\tLuminosity absorbed = 3.363e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\tIteration converged 1/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4750.472
51.14e+04 K1.12e+04 K0.1770.184
101.11e+04 K1.1e+04 K0.1120.114
151.06e+04 K1.06e+04 K0.08780.0859
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10628.190 K\n", + "\tExpected t_inner for next iteration = 10644.054 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 12 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.056e+43 erg / s\n", + "\tLuminosity absorbed = 3.420e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.11e+04 K0.4720.467
51.12e+04 K1.13e+04 K0.1840.176
101.1e+04 K1.11e+04 K0.1140.11
151.06e+04 K1.08e+04 K0.08590.0821
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10644.054 K\n", + "\tExpected t_inner for next iteration = 10653.543 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 13 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.062e+43 erg / s\n", + "\tLuminosity absorbed = 3.406e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\tIteration converged 1/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4670.466
51.13e+04 K1.13e+04 K0.1760.18
101.11e+04 K1.11e+04 K0.110.111
151.08e+04 K1.08e+04 K0.08210.0841
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10653.543 K\n", + "\tExpected t_inner for next iteration = 10647.277 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 14 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.063e+43 erg / s\n", + "\tLuminosity absorbed = 3.369e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\tIteration converged 2/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4660.469
51.13e+04 K1.13e+04 K0.180.182
101.11e+04 K1.1e+04 K0.1110.113
151.08e+04 K1.07e+04 K0.08410.0854
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10647.277 K\n", + "\tExpected t_inner for next iteration = 10638.875 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 15 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.053e+43 erg / s\n", + "\tLuminosity absorbed = 3.417e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\tIteration converged 3/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.1e+04 K0.4690.484
51.13e+04 K1.13e+04 K0.1820.181
101.1e+04 K1.1e+04 K0.1130.113
151.07e+04 K1.07e+04 K0.08540.0858
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10638.875 K\n", + "\tExpected t_inner for next iteration = 10655.125 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 16 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.059e+43 erg / s\n", + "\tLuminosity absorbed = 3.445e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\tIteration converged 4/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4840.472
51.13e+04 K1.13e+04 K0.1810.177
101.1e+04 K1.1e+04 K0.1130.113
151.07e+04 K1.06e+04 K0.08580.0858
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10655.125 K\n", + "\tExpected t_inner for next iteration = 10655.561 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 17 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.067e+43 erg / s\n", + "\tLuminosity absorbed = 3.372e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.11e+04 K0.4720.468
51.13e+04 K1.14e+04 K0.1770.175
101.1e+04 K1.11e+04 K0.1130.11
151.06e+04 K1.08e+04 K0.08580.0816
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10655.561 K\n", + "\tExpected t_inner for next iteration = 10636.536 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 18 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.057e+43 erg / s\n", + "\tLuminosity absorbed = 3.365e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\tIteration converged 1/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4680.464
51.14e+04 K1.13e+04 K0.1750.177
101.11e+04 K1.1e+04 K0.110.113
151.08e+04 K1.07e+04 K0.08160.0848
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10636.536 K\n", + "\tExpected t_inner for next iteration = 10641.692 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 19 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.056e+43 erg / s\n", + "\tLuminosity absorbed = 3.405e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\tIteration converged 2/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4640.466
51.13e+04 K1.13e+04 K0.1770.177
101.1e+04 K1.11e+04 K0.1130.111
151.07e+04 K1.07e+04 K0.08480.0853
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10641.692 K\n", + "\tExpected t_inner for next iteration = 10650.463 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tSimulation finished in 19 iterations \n", + "\tSimulation took 54.57 s\n", + " (\u001b[1mbase.py\u001b[0m:469)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 20 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.061e+43 erg / s\n", + "\tLuminosity absorbed = 3.401e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n" + ] + } + ], + "source": [ + "sim = run_tardis(\"tardis_example.yml\", virtual_packet_logging = True)" + ] + }, + { + "cell_type": "markdown", + "id": "062f5e2c-1a31-47ab-b4a7-2cbf49b9d168", + "metadata": {}, + "source": [ + "## Import the SDECPlotter class for plotting spectral element Decomposition Plot and obtain the necessary data from the simulation we just ran above." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "fcc3da4e-b70d-4960-bdca-4c47f8e1c763", + "metadata": {}, + "outputs": [], + "source": [ + "from tardis.visualization import SDECPlotter\n", + "plotter = SDECPlotter.from_simulation(sim)" + ] + }, + { + "cell_type": "markdown", + "id": "84a175b0-c284-4ba5-99c7-c409d480efe1", + "metadata": {}, + "source": [ + "## Let's now plot the SDEC Plot ( Second part of our First Objective )" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5d968c4c-8354-4907-9eb6-09ecbe233143", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ff8/IkSOZPXs2PXv2dC+tsmbNGq+MYWmqXr06H374Ibt376ZDhw60bNmSZcuWAfDggw+ydu1a0tLSeOKJJ+jSpQvPPfccO3fuLHbGcP78+XTs2JEXXniBu+66i+3bt/PLL78QEhLi0a5SpUps3bqV3r178/bbb9O9e3eeeeYZkpOTCQ8PB8Df359Zs2aRkJDAbbfdRsuWLX0OXXaZOnUqb7/9Nj/99BO9e/dm7Nix3HbbbWzcuNHnnE4hROlSVFWVcYt5pKSkEBISQnJyMsHBwVe6OyKP116DCRO09/JdK4QQ146srCyOHj1K9erVMZvNHvtiY2N58sknsdlsV6h3GqPRyLRp04iKirqi/RDiWlPYz79LWf393NXvlwDfd1Z6soC34ap+RjLUVpQZeQrlCSGEuE5ERUUxbdo0UlJSrmg/goODJegUQoiLIIGnKDN+//1K90AIIcSVEBUVJUGfEEKUcTLHU5QZf/6Z+37v3ivXDyGEEEIIIUTJSOApyoxq1XLfn4t3XrmOCCGEEEIIIUpEAk9RZvTsmfveLtWFhBBCCCGEKDMk8BRlxvnzue9lvqcQQgghhBBlhwSeosxISsp9b3fIWp5CCCGEEEKUFRJ4ijKjVq3c99k2GWorhBBCCCFEWSGBpygz8g61/WCi/sp1RAghhBBCCFEiso6nKDO+/vpK90AIIcSVkJKSQlZW1hXtg9lsJjg4+Ir2QQghyjIJPEWZUa0anDlzpXshhBDickpJSeHLL7/E4XBc0X7o9XoGDhwowacQQlwgGWoryoywsCvdAyGEEJdbVlbWFQ86ARwOxwVnXefMmYOiKAV+rVu3zt02JiaGQYMGlU6nffjss8+YM2fOJTv/xbDZbHz++ee0bNmS8PBw/P39qVatGrfffjtLliy5ZNct6JmcPn2acePGsWvXrkt27YL8999/DBs2jJo1a2I2mwkLC6NDhw7MmzcP9SKWlBs0aBAxMTGl11EhSkAynqLMWLHC87OqgiLFbYUQQpQRs2fPpm7dul7b69evf9n68Nlnn1GuXLlLGtxeqIceeojFixczfPhwxo8fj8lk4siRI6xcuZKff/6ZO++885Jct6Bncvr0acaPH09MTAxNmjS5JNf25Y8//qB3794EBgby/PPP06hRI5KTk1m0aBEPPvggy5YtY/78+eh0kj8SZYsEnqLMkqBTCCFEWdKgQQNatGhxpbtRbDabDUVRMBgu/a+LR48eZeHChbz22muMHz/evb1z584MGTIEp9N5yftwuWRmZmI2m1F8/CKTlJTEXXfdRUhICFu2bKF8+fLufbfffjuNGjXipZdeokmTJrz00kuXs9viGnX27FneeustfvzxR06dOkVUVBRNmjRh+PDhdO7cuVSvJX8qEWXWRYw0EUIIIcqMlJQURo8eTfXq1fHz86NSpUoMHz6c9PR0j3ZOp5MpU6bQpEkTLBYLoaGhtGnThqVLlwLaMN59+/axfv169zBf17DLdevWoSgKX3/9NaNGjaJSpUqYTCYOHToEwKxZs2jcuDFms5nw8HDuvPNODhw44HH9QYMGERgYyKFDh+jZsyeBgYFUqVKFUaNGYbVaC73HhIQEACpWrOhzf/7sXlJSEqNGjaJGjRqYTCaioqLo2bMnf//9t7vN+PHjad26NeHh4QQHB9OsWTNmzpzpMVS1oGeybt06WrZsCcAjjzzi3jdu3Dj3sdu3b6dv376Eh4djNptp2rQpixYt8uina5j1qlWrePTRR4mMjMTf37/A5zFjxgxiY2N5++23PYJOlxdeeIG6devyzjvvYLPZgNx/u2+++YaxY8cSHR1NcHAwXbp04eDBgwU9ckAL7OvWres1fFdVVW644QZ69epV6PGibDt27BjNmzdnzZo1TJ48mb/++ouVK1fSsWNHnn766VK/nmQ8RZm1YweUoT8cCyGEuM45HA7sdrvHNkVR0OsLXiIsIyOD9u3bc/LkSV5++WUaNWrEvn37eO211/jrr7/49ddf3ZmzQYMGMXfuXAYPHswbb7yBn58fO3fu5NixYwAsWbKEfv36ERISwmeffQaAyWTyuN6YMWNo27Yt06ZNQ6fTERUVxcSJE3n55Ze5//77mThxIgkJCYwbN462bduybds2auVZaNtms9G3b18GDx7MqFGj+O2335gwYQIhISG89tprBd5nvXr1CA0NZfz48eh0Om677bYC5yKmpqZy8803c+zYMV588UVat25NWloav/32G2fOnHEPZz527BhPPPEEVatWBWDz5s0888wznDp1yt2Xgp5JzZo1mT17No888givvPKKOwCrXLkyAGvXrqV79+60bt2aadOmERISwoIFC+jfvz8ZGRlew3YfffRRevXqxddff016ejpGo9Hnvf3yyy/o9Xr69Onjc7+iKPTt25fJkyezY8cO2rRp49738ssvc9NNNzFjxgxSUlJ48cUX6dOnDwcOHCjwe+y5557j9ttvZ/Xq1XTp0sW9fcWKFRw+fJiPP/7Y53Hi2jB06FAURWHr1q0EBAS4t9944408+uijALz//vvMnj2bI0eOEB4eTp8+fZg8eTKBgYElvp4EnqLMyrapgIy3FUIIUTbkDRJc9Hq9VzCa18cff8yePXvYsmWLe5hu586dqVSpEv369WPlypX06NGD33//na+//pqxY8fy5ptvuo/v3r27+33Tpk2xWCwEBwf77AtAzZo1+fbbb92fk5KSmDBhAj179mT+/Pnu7R06dKBWrVqMGzeOefPmubdnZ2czfvx47rnnHndft2/fzvz58wsNPAMCApg3bx4DBw7kiSeeACAiIoJOnTrx0EMPeQRiH374Ifv27eOXX37xCJbuuusuj3POnj3b/d7pdNKhQwdUVeWjjz7i1VdfRVGUQp9JgwYN3M8k/76hQ4dy4403smbNGvdQ5G7duhEfH8/LL7/Mww8/7JGl7dy5M59//nmB9+9y4sQJIiMjPYKA/KpXr+5um7df9evXZ+7cue7Per2ee++9l23bthX47927d29q1KjBJ5984vEsP/nkE2rWrEmPHj2K7LO4uqSkpHh8NplMXn9gAkhMTGTlypW89dZbPr/fQkNDAW20wccff0xMTAxHjx5l6NChvPDCC+4/1JSEDLUVZZaMtBVCCFGWfPXVV2zbts3ja8uWLYUes3z5cho0aECTJk2w2+3ur27dunlUxF2RU4HvYofH3X333R6fN23aRGZmplcGr0qVKnTq1InVq1d7bFcUxStb16hRI44fP17ktXv27MmJEydYsmQJo0eP5sYbb+T777+nb9++DBs2zN1uxYoV1K5d2yNQ8mXNmjV06dKFkJAQ9Ho9RqOR1157jYSEBGJjY4vsT0EOHTrE33//zQMPPADg8e/Ss2dPzpw54zXENf9zvRiuYbH554j27dvX43OjRo0ACn32Op2OYcOGsXz5ck6cOAHA4cOHWblypTsbJsqWKlWqEBIS4v6aOHGiz3aHDh1CVVWfBc/yGj58OB07dqR69ep06tSJCRMmeA0pL65rOvCcOHEiiqIwfPjwK90VUQoefNDz89atV6YfQgghxIWoV68eLVq08Phq3rx5ocecO3eOPXv2YDQaPb6CgoJQVZX4+HgA4uLi0Ov1VKhQ4aL6mH+OZWFzL6Ojo937Xfz9/TGbzR7bTCZTsZeisVgs3HHHHbzzzjusX7+eQ4cOUb9+fT799FP27dsHaPfqGvJakK1bt3LbbbcB8MUXX/DHH3+wbds2xo4dC2gFfi7UuXPnABg9erTXv8vQoUMB3P8uLgXNXc2vatWqxMXFec3fzcs1dLpKlSoe2yMiIjw+u7JcRd3ro48+isViYdq0aQB8+umnWCwW91BLUbb8999/JCcnu7/GjBnjs11Bf8DIb+3atXTt2pVKlSoRFBTEww8/TEJCQqHfowW5Zofabtu2jenTp7v/2iPKvjyjRwBQruk/mwghhBBQrlw5LBYLs2bNKnA/QGRkJA6Hg7NnzxY7yPEl/y+hrmDmzJkzXm1Pnz7tvv6lUrVqVR5//HGGDx/Ovn37uPHGG4mMjOTkyZOFHrdgwQKMRiPLly/3CIS///77i+6T657HjBnjNbzXpU6dOh6fi5s57Nq1K6tWrWLZsmXcd999XvtVVWXp0qWEh4cX+UeL4goJCWHgwIHMmDGD0aNHM3v2bAYMGOAeainKluDgYIKDg4tsV6tWLRRF4cCBA9xxxx0+2xw/fpyePXvy5JNPMmHCBMLDw9mwYQODBw92F7cqiWvyV/e0tDQeeOABvvjiC8LCwq50d8QlUrlK0W2EEEKIsqx3794cPnyYiIgIr2xpixYt3AV4XHPxpk6dWuj5TCZTibJ9bdu2xWKxeMwdBDh58iRr1qwpteUWUlNTSUtL87nPVT03Ojoa0O71n3/+Yc2aNQWez7UMTN6iOpmZmXz99ddebQt6JgVlDOvUqUOtWrXYvXu3z3+TFi1aEBQUVMQd+/bYY48RFRXFmDFjfA4Hnjx5Mn///TcvvPBCgQWKLsSzzz5LfHw8/fr1IykpyWNos7g2hYeH061bNz799FOf2cukpCS2b9+O3W7nvffeo02bNtSuXZvTp09f8DWvyYzn008/Ta9evejSpYvHBHtfrFarR0nr/BNyxdUropwUFxJCCFF27N2712choZo1axIZGenzmOHDh/Pdd99x6623MmLECBo1aoTT6eTEiROsWrWKUaNG0bp1a2655RYeeugh3nzzTc6dO0fv3r0xmUz8+eef+Pv788wzzwDQsGFDFixYwMKFC6lRowZms5mGDRsW2OfQ0FBeffVVd8Gc+++/n4SEBMaPH4/ZbOb1118vlWdz8OBBunXrxn333Uf79u2pWLEi58+f58cff2T69Ol06NCBdu3auZ/JwoULuf3223nppZdo1aoVmZmZrF+/nt69e9OxY0d69erF+++/z4ABA3j88cdJSEjg3Xff9VlkpaBnUrNmTSwWC/PmzaNevXoEBgYSHR1NdHQ0n3/+OT169KBbt24MGjSISpUqkZiYyIEDB9i5c6dHgaaSCA0NZfHixfTu3ZvmzZvz/PPP07hxY1JSUli4cCHz5s2jf//+PP/88xf1vPOrXbs23bt3Z8WKFdx88800bty4VM8vrk6fffYZ7dq1o1WrVrzxxhs0atQIu93OL7/8wtSpU/nmm2+w2+1MmTKFPn368Mcff7iHZF+Iay7wXLBgATt37mTbtm3Faj9x4kSPhYpF2ZGYeKV7IIQQ4lIzm83o9XocDscV7Yder/eau1hSjzzyiM/tX3zxBY899pjPfQEBAfz++++8/fbbTJ8+naNHj2KxWKhatSpdunTxWHJkzpw57rUq58yZg8VioX79+rz88svuNuPHj+fMmTMMGTKE1NRUqlWr5p4zWJAxY8YQFRXFxx9/zMKFC7FYLHTo0IH//e9/HkupXIwbbriBkSNHsmbNGn744Qfi4uIwGo3UqlWLN998k5EjR7qrxAYFBbFhwwbGjRvH9OnTGT9+PGFhYbRs2ZLHH38cgE6dOjFr1iwmTZpEnz59qFSpEkOGDCEqKorBgwd7XLugZ+Lv78+sWbMYP348t912Gzabjddff51x48bRsWNHtm7dyltvvcXw4cM5f/48ERER1K9fn3vvvfeinsVNN93Enj17mDRpEh999BEnT57EYrHQuHFj5s6dy4ABAy5J0Z/+/fuzYsUKyXZeR6pXr87OnTt56623GDVqFGfOnCEyMpLmzZszdepUmjRpwvvvv8+kSZMYM2YMt956KxMnTuThhx++oOspav4VY8uw//77jxYtWrBq1Sr3X2o6dOhAkyZN+PDDD30e4yvjWaVKFZKTk4s1PlpcPvn/G/vHdiftml+To8WFEOK6kpWVxdGjR6levbrP4C4lJaXYxWkuFbPZLL8XiGva3XffzebNmzl27FipDuMtSlE//6D9NyAkJKTM/X7u6vdLwMX92apoWcDbcFU/o2sq47ljxw5iY2M9Jls7HA5+++03PvnkE6xWq9cCugWtbSOuLtu3e2+z+F/+fgghhLj8ilssQwhRMlarlZ07d7J161aWLFnC+++/f1mDTnF9uaYCz86dO/PXX395bHvkkUeoW7cuL774olfQKcqOPElpt/VroWm9y98XIYQQQohrwZkzZ2jXrh3BwcE88cQT7rnAQlwK11TgGRQURIMGDTy2BQQEEBER4bVdlH0jntYxfOiV7oUQQgghRNkUExPDNTTrTlzlZIKcEEIIIYQQQohL6prKePqybt26K90FIYQQQgghhLiuScZTlAkyUloIIYQQQoiySwJPUSaEhFzpHgghhBBCCCEulASeokw4fvxK90AIIYQQQghxoSTwFGXCqVNXugdCCCGEEEKICyWBpxBCCCGEEEKIS0oCT1HmGCrarnQXhBBCiGK58847sVgsJCUlFdjmgQcewGg0cu7cOebMmYOiKBw7dqxY5//ss8+YM2dOqfS1KCXp25YtW7jzzjupWrUqJpOJ8uXL07ZtW0aNGnXpO1oMl/O5CSE0EniKMscer7/SXRBCCCGKZfDgwWRlZTF//nyf+5OTk1myZAm9e/emfPny9OrVi02bNlGxYsVinf9qDKB+/PFH2rVrR0pKCpMnT2bVqlV89NFH3HTTTSxcuPBKdw+4Op+bENe6a34dT3FtKF8+zweb/L1ECCFE2dCjRw+io6OZNWsWQ4cO9dr/zTffkJmZyeDBgwGIjIwkMjKyyPNmZGTg7+9f6v0tDZMnT6Z69er8/PPPGAy5v2red999TJ48+Qr27MLYbDYURfG4FyFEyclv8KJMqFkz9331XX8X2E5V4cYb4Zdf1cvQKyGEEKJwer2egQMHsmPHDv766y+v/bNnz6ZixYr06NED8D2ctUOHDjRo0IDffvuNdu3a4e/vz6OPPkpMTAz79u1j/fr1KIqCoijExMQUeB6AdevWoSgK69atc2/75ZdfuP3226lcuTJms5kbbriBJ554gvj4+Au654SEBMqVK+czUNPpPH/1jImJoXfv3ixZsoRGjRphNpupUaMGH3/8sdexKSkpjB49murVq+Pn50elSpUYPnw46enpHu2cTidTpkyhSZMmWCwWQkNDadOmDUuXLnVfs6Dn5no+X3/9NaNGjaJSpUqYTCYOHTrEuHHjUBTFq1++nrXrvpYvX07Tpk2xWCzUq1eP5cuXu4+pV68eAQEBtGrViu3bt5foGQtRFkngKcqEjIzc9+bGme73ar748rvvYP9++GSKBJ5CCCGuDo8++iiKojBr1iyP7fv372fr1q0MHDgQvb7waSRnzpzhwQcfZMCAAfz0008MHTqUJUuWUKNGDZo2bcqmTZvYtGkTS5YsKXH/Dh8+TNu2bZk6dSqrVq3itddeY8uWLdx8883YbCWvq9C2bVu2bNnCs88+y5YtW4o8x65duxg+fDgjRoxgyZIltGvXjueee453333X3SYjI4P27dvz5Zdf8uyzz7JixQpefPFF5syZQ9++fVHz/EIwaNAgnnvuOVq2bMnChQtZsGABffv2dQeGxXluY8aM4cSJE0ybNo1ly5YRFRVV4uewe/duxowZw4svvsjixYsJCQnhrrvu4vXXX2fGjBn873//Y968eSQnJ9O7d28yMzOLPqkQZZiMGRBlwu7dvrerKuT942NwsPa6fr33XySFEEKUTWfOaF95hYVB9eqQlaX9wTG/Zs2014MHIV9CjJgYCA+HuDj47z/PfUFBUKsWOBye/++pWFH7uhA33HADt956K3PnzmXy5MkYjUYAdyD66KOPFnmOxMREvv32Wzp16uSx3WKxEBwcTJs2bS6sc8CTTz7pfq+qKu3ataNDhw5Uq1aNFStW0Ldv3xKd7+233+bvv/9mypQpTJkyBaPRSMuWLenTpw/Dhg0jMDDQo/3p06f5888/ady4MaANT46NjWXChAkMHToUf39/Pv74Y/bs2cOWLVto0aIFAJ07d6ZSpUr069ePlStX0qNHD37//Xe+/vprxo4dy5tvvum+Rvfu3d3vXRnIwp5bzZo1+fbbb0t03/klJCSwefNmKlWqBEB0dDRNmjThiy++4NChQ+6h0oqicMcdd/Drr7/Sp0+fi7qmEFczyXiKMi1/xjMlRXtNTpbAUwghrhWffw7Nm3t+vfqqtu/kSe99zZvnHjtokPe+n37S9i1a5L1v2DBtX3q65/bPP7+4exg8eDDx8fHu4Z52u525c+dyyy23UKtWrSKPDwsL8wo6S0tsbCxPPvkkVapUwWAwYDQaqVatGgAHDhwo8fkiIiL4/fff2bZtG2+//Ta33347//zzD2PGjKFhw4ZeQ3hvvPFGd9DpMmDAAFJSUti5cycAy5cvp0GDBjRp0gS73e7+6tatm8fQ4RUrVgDw9NNPl7jfed19990XdTxAkyZN3EEnQL169QBt6HTe+bmu7cePH7/oawpxNZOMpygTnE7f20+c0P7i7XLPPZenP0IIIS6fJ56A/Em3sDDttXJl2LGj4GPnzPGd8QS4915o29ZzX1CQ9hoQ4HneC812uvTr149nnnmG2bNnc/fdd/PTTz9x7tw5Jk2aVKzji1vltqScTie33XYbp0+f5tVXX6Vhw4YEBATgdDpp06bNRQ3/bNGihTs7abPZePHFF/nggw+YPHmyR5GhChUqeB3r2paQkADAuXPnOHTokDtbnJ8rmI2Li0Ov1/s8Z0mUxvMODw/3+Ozn51fo9qysrIu+phBXMwk8RZm2ZYtn4OlSrpwTSegLIcS1obBhrmZz7rBaX+rUKXhfZKT25YteX/h5S8pisXD//ffzxRdfcObMGWbNmkVQUBD3FPMvpr6K2hTGbDYDYLVaPbbnzzbu3buX3bt3M2fOHAYOHOjefujQoRJdryhGo5HXX3+dDz74gL1793rsO3v2rFd717aIiAgAypUrh8Vi8Zon61KuXDlAqwrscDg4e/bsRQWPvp533mdqMpnc2y+0CJMQ1xv5zVyUCRZL8dq5fkno2s1+6TojhBBCXIDBgwfjcDh45513+Omnn7jvvvsuekkUk8nkMyvpqtK6Z88ej+2uob4urgArbyAF8PlFjC0+k39Cbg7XsN3o6GiP7fv27WN3vmIO8+fPJygoiGY5/2Pv3bs3hw8fJiIiwp1Jzfvlul9XdeCpU6cW2seCnlthCnqmy5YtK9F5hLheScZTlAlNm2qvisFzUmfeeTwAf/6pva74Ub61hRBCXF1atGhBo0aN+PDDD1FV1b1258Vo2LAhCxYsYOHChdSoUQOz2UzDhg1p2bIlderUYfTo0djtdsLCwliyZAkbNmzwOL5u3brUrFmTl156CVVVCQ8PZ9myZfzyyy8X3Kdu3bpRuXJl+vTpQ926dXE6nezatYv33nuPwMBAnnvuOY/20dHR9O3bl3HjxlGxYkXmzp3LL7/8wqRJk9yB+fDhw/nuu++49dZbGTFiBI0aNcLpdHLixAlWrVrFqFGjaN26NbfccgsPPfQQb775JufOnaN3796YTCb+/PNP/P39eeaZZwp9boXp2bMn4eHhDB48mDfeeAODwcCcOXP4L3+FKiGET/LbuSgTXCNedEEOj+2hoZ7tXMWGkpIkmS+EEOLqM3jwYJ577jnq169P69atL/p848eP58yZMwwZMoTU1FSqVavGsWPH0Ov1LFu2jGHDhvHkk09iMpm47777+OSTT+jVq5f7eKPRyLJly3juued44oknMBgMdOnShV9//ZWqVateUJ9eeeUVfvjhBz744APOnDmD1WqlYsWKdOnShTFjxriL6bg0adKERx55hNdff51///2X6Oho3n//fUaMGOFuExAQwO+//87bb7/N9OnTOXr0KBaLhapVq9KlSxd3NhK0NTKbNWvGzJkzmTNnDhaLhfr16/Pyyy8X+dwKExwczMqVKxk+fDgPPvggoaGhPPbYY/To0YPHHnvsgp6VENcTRVXz1wW9vqWkpBASEkJycjLBrrU5xBV39CjUqAFhdyVR4bujHFC0FGhQkEpKSu48jLxTMuQ7WwghyoasrCyOHj1K9erV3fPoxPUhJiaGBg0asHz58ivdFXGFFOfnv6z+fu7q90vApf4vWxbwNlzVz0jSQqJMSE3VXo0x2dprDWvOdlk2RQghhBBCiKudBJ6iTJg4UXv1q6UFnIYKNgCqVpe0phBCCCGEEFc7meMpygRX0T+nn7agZ8BtqWRuDOTE0YIznklJ3nNAhRBCCHH1KGpepRDi2iEZT1EmuNZhU0K14kL6MEchrTX5CvcJIYQQQgghrhAJPEWZEB6uvRoraUNsnanat25MbWeBxyxfXvA+IYQQQgghxOUjQ21FmRARob26igv51csCICUJmD4dpk6FtDT2YeQYMfxFQ8JS24OtKxiNV6bTQgghhBBCCEACT1FGuJdJ0atU//soe9IaA5AYq9NK3u7aBUB9oD4H6MkKmD8ZVpeHJUugbdsr0m8hhBBCCCGEDLUVZYTTCUayGf3GB/zQ4F7ufvuH3J133AHLl8Mff9CZX3mCacxhILFEQloa5FuoWgghhBBCCHF5ScZTlAl+58+xmn7cMkWrGNTMvjN3Z82a2hewBlhDZ6bzBAZs2H77K7e0rarC8OFwzz1w882Xtf9CCCGEEEJczyTjKa5+p07R4fVbuYUNpAYH8szidxn98OQiD4uI1kGzZrkbFiyAjz+GW2+FMWPAbr+EnRZCCCGEEEK4SOAprm5xcdChA4Gn/iEprCr3bvua1Xd2wq+utcBDysdo1WyrVM8XWPbsCYMGaZnPt9/WPicmXsLOCyGEEDBnzhwURfH4ioyMpEOHDixfvtyrvaIojBs37pL1R1EUhg0bVmibdevWoSgK//d//3fJ+uEybtw4FKXgdblLqkOHDh7P2mKx0LhxYz788EOcTqdHuwYNGpTadTMyMhg3bhzr1q0rtXNerOL8WwtxuUjgKa5eNhvceSccOsQJfQyNz//G8drVtH2FrJSScEr7n9f2P0yeO0JCYPZsWLQI/P3hl1+gVSv4++9LdANCCCFErtmzZ7Np0yY2btzI9OnT0ev19OnTh2XLll3prl1zatSowaZNm9i0aRMLFy6kUqVKjBgxgjFjxlyya2ZkZDB+/PirKvAU4moigae4ehmN0L8/RERwm2MFJ6iGM137lg3snVzgYc6ilu+85x7YtAliYuDwYbjlFtixo/T6LYQQQvjQoEED2rRpQ9u2bbnzzjtZvnw5JpOJb7755kp37ZpjsVho06YNbdq0oW/fvvzwww/UqFGDTz75BJvNdqW7d02x2WzYZfqSKAYJPMXV7Zln4NAhglrU9dis81cLPMTpyB2us3p1AY0aNYKtW6FFC0hKgrNnS6GzQgghRPGZzWb8/PwwFrHedFxcHEOHDqV+/foEBgYSFRVFp06d+P33373aWq1W3njjDerVq4fZbCYiIoKOHTuycePGAs+vqiovv/wyRqORL774wmNfVlYWI0eOpEKFClgsFtq3b8+ff/7pdY6lS5fStm1b/P39CQoKomvXrmzatMmr3Y8//kiTJk0wmUxUr16dd99916tN586dqVu3Lqrq+f96VVW54YYb6NWrV4H3UhCj0Ujz5s3JyMggLi7OY9+2bdu45ZZb8Pf3p0aNGrz99tseQ3IBTpw4wYMPPkhUVBQmk4l69erx3nvvudsdO3aMyMhIAMaPH+8e5jto0CD3OTZs2EDnzp0JCgrC39+fdu3a8eOPP3pcJyMjg9GjR1O9enXMZjPh4eG0aNHC448TgwYNIjAwkH379tG5c2cCAgKIjIxk2LBhZGRk+Lz/r7/+mnr16uHv70/jxo19DvH+999/GTBggMc9fvrppx5tXEOwv/76a0aNGkWlSpUwmUwcOnQIgF9//ZXOnTsTHByMv78/N910E6sL/GVMXG8k8BRXn8REsOaZwxkamrsMp67ggNOXr78upH1kpBaZ/vgjXMD/xIQQQlwm6ekFf2VlFb9tZuaFty0FDocDu92OzWbj5MmTDB8+nPT0dAYMGFDocYk59Qhef/11fvzxR2bPnk2NGjXo0KGDx7BOu91Ojx49mDBhAr1792bJkiXMmTOHdu3aceLECZ/ntlqtDBgwgE8++YRly5YxZMgQj/0vv/wyR44cYcaMGcyYMYPTp0/ToUMHjhw54m4zf/58br/9doKDg/nmm2+YOXMm58+fp0OHDmzYsMHdbvXq1dx+++0EBQWxYMEC3nnnHRYtWsTs2bM9rvncc89x8OBBr4BlxYoVHD58mKeffrrQ51WQw4cPYzAYCAsLc287e/YsDzzwAA8++CBLly6lR48ejBkzhrlz57rbxMXF0a5dO1atWsWECRNYunQpXbp0YfTo0e75kxUrVmTlypUADB482D3M99VXXwVg/fr1dOrUieTkZGbOnMk333xDUFAQffr0YeHChe5rjRw5kqlTp/Lss8+ycuVKvv76a+655x4SEhI87sVms9GzZ086d+7M999/z7Bhw/j888/p37+/133/+OOPfPLJJ7zxxht89913hIeHc+edd3r8G+7fv5+WLVuyd+9e3nvvPZYvX06vXr149tlnGT9+vNc5x4wZw4kTJ5g2bRrLli0jKiqKuXPncttttxEcHMyXX37JokWLCA8Pp1u3bhJ8Co0qPCQnJ6uAmpycfKW7cv3q109V69RR1c2b3Zvq1VNVUNU6WX+q9dSdaj11pxpwW7Jq9Hd4HKpVDsr9euFFZ8muffy4qm7aVBp3IYQQopgyMzPV/fv3q5mZmb4b5P+Pe96vnj092/r7F9y2fXvPtuXKFdy2RYtSu7/Zs2ergNeXyWRSP/vsMx+3i/r6668XeD673a7abDa1c+fO6p133une/tVXX6mA+sUXXxTaH0B9+umn1YSEBPXmm29WK1WqpO7atcujzdq1a1VAbdasmep05v6/9NixY6rRaFQfe+wxVVVV1eFwqNHR0WrDhg1VhyP3/8mpqalqVFSU2q5dO/e21q1bq9HR0R7/zikpKWp4eLia91dSh8Oh1qhRQ7399ts9+tSjRw+1Zs2aHv3xpX379uqNN96o2mw21WazqadPn1ZfeuklFVDvuecej3aAumXLFo/j69evr3br1s392XVs/nZPPfWUqiiKevDgQVVVVTUuLq7Af7s2bdqoUVFRampqqnub3W5XGzRooFauXNl9Tw0aNFDvuOOOQu9v4MCBKqB+9NFHHtvfeustFVA3bNjg3gao5cuXV1NSUtzbzp49q+p0OnXixInubd26dVMrV67s9fvvsGHDVLPZrCYmJqqqmvt9ceutt3q0S09PV8PDw9U+ffp4bHc4HGrjxo3VVq1aFXg/Rf78q2X393N3vx9FVZ+8tF/Jj3LVPyPJeIqry4oV8H//B4cOgdns3vzvv9pr3qJ3frWshNcqfE7BgpJMmzl+XJvv2a2bzPkUQghR6r766iu2bdvGtm3bWLFiBQMHDuTpp5/mk08+KfLYadOm0axZM8xmMwaDAaPRyOrVqzlw4IC7zYoVKzCbzTz66KNFnu/o0aO0bduWlJQUNm/eTOPGjX22GzBggEfF2WrVqtGuXTvWrl0LwMGDBzl9+jQPPfQQOl3ur5WBgYHcfffdbN68mYyMDNLT09m2bRt33XUX5jz/f3dl/fLS6XQMGzaM5cuXuzO1hw8fZuXKlQwdOrRYFXD37duH0WjEaDQSHR3Ne++9xwMPPOA1lLhChQq0atXKY1ujRo04fvy4+/OaNWuoX7++V7tBgwahqipr1qwptC/p6els2bKFfv36ERgY6N6u1+t56KGHOHnyJAcPHgSgVatWrFixgpdeeol169aRWUjm/YEHHvD47Mqcu/5tXDp27EhQUJD7c/ny5YmKinLfY1ZWFqtXr+bOO+/E398fu93u/urZsydZWVls3rzZ45x33323x+eNGzeSmJjIwIEDPY53Op10796dbdu2kZ6eXuhzEtc+w5XugBBu2dnanE6A556DPP8TdM9ZN+QOnQ3snUzjxjogwuM0pgAVa7r2P6UTJ0pQnj0yEqpXh/Xr4bbb4PffoX79C7kTIYQQpSktreB9er3n59jYgtvq8v29/dix4rctBfXq1aNFixbuz927d+f48eO88MILPPjgg4SGhvo87v3332fUqFE8+eSTTJgwgXLlyqHX63n11Vc9As+4uDiio6M9AsCCbN26lfj4eN566y0qV65cYLsKFSr43LZ7924A9xDQihUrerWLjo7G6XRy/vx5VFXF6XQWeL78Hn30UV577TWmTZvG//73Pz799FMsFkuxgmqAmjVrsmDBAhRFwWw2U716dfz9/b3aRUREeG0zmUweAV9CQgIxMTE+78+1vzCu+y/oGeU9x8cff0zlypVZuHAhkyZNwmw2061bN9555x1q1arlPs5gMHj13fUc8/enqHtMSEjAbrczZcoUpkyZ4vMe4uPjPT7nv5dz584B0K9fP5/HgzZkPCAgoMD94tongae4ovbt01ZNadIEmD5dqzJboQLkW78sMlJb0lPJ8//SwO6pNMMPV+C5c6e23RV0uthsWoHcIvn7w7Jl0LUrbNkCPXporz7+hyiEEOIyKskvq5eq7SXSqFEjfv75Z/755x+vjJrL3Llz6dChA1OnTvXYnpqa6vE5MjKSDRs24HQ6iww++/fvT4UKFRg7dixOp5NXXnnFZ7uzPorvnT171h3MuF7PnDnj1e706dPodDrCwsJQVRVFUQo8X34hISEMHDiQGTNmMHr0aGbPns2AAQMKDM7zM5vNHkH+xYiIiCjw/gDKlStX6PFhYWHodLpinSMgIIDx48czfvx4zp07585+9unTh7/zLP9mt9tJSEjwCCpdz9FXoFlU/1zZ14Lmz1avXt3jc/6ss6v/U6ZMoU2bNj7PUb58+RL1S1x7ZKituKJatICmTdH+mj1hgrbx9dchz5AQ0IJO0CbeuFj3mTm2PnetzubNfV9j+HMlKEgUFKQVG6pdG06cgN69tSITQgghxCWwa9cuAHdFVF8URcFk8lybes+ePV5VY3v06EFWVhZz5swp1rVfeeUVPvzwQ1577bUC17f85ptvPKrLHj9+nI0bN9KhQwcA6tSpQ6VKlZg/f75Hu/T0dL777jt3pduAgABatWrF4sWLycpTECo1NbXAdUyfffZZ4uPj6devH0lJSe5CPpdb586d2b9/Pztdf+HO8dVXX6EoCh07dgRw/xvlHx4bEBBA69atWbx4scc+p9PJ3LlzqVy5MrVr1/a6bvny5Rk0aBD3338/Bw8e9KpYO2/ePI/P8+fPB3D/2xSXv78/HTt25M8//6RRo0a0aNHC66uoYPamm24iNDSU/fv3+zy+RYsW+Pn5lahf4tojGU9xRfXqBelpKnz4oTY8qmZNGDzYq1316nD0qOe2819E8NMvobCv8Gv85l1tvnAREfDTT9CmjTbX8/77YckS7+FcQgghRAns3bvXvd5hQkICixcv5pdffuHOO+/0yijl1bt3byZMmMDrr79O+/btOXjwIG+88QbVq1f3WD/x/vvvZ/bs2Tz55JMcPHiQjh074nQ62bJlC/Xq1eO+++7zOvdzzz1HYGAgjz/+OGlpaXz88cce2azY2FjuvPNOhgwZQnJyMq+//jpms9kdqOp0OiZPnswDDzxA7969eeKJJ7BarbzzzjskJSXx9ttvu881YcIEunfvTteuXRk1ahQOh4NJkyYREBDgrtybV+3atenevTsrVqzg5ptvLnAe6qU2YsQIvvrqK3r16sUbb7xBtWrV+PHHH/nss8946qmn3EFjUFAQ1apV44cffqBz586Eh4dTrlw5YmJimDhxIl27dqVjx46MHj0aPz8/PvvsM/bu3cs333zjfuatW7emd+/eNGrUiLCwMA4cOMDXX3/tDuBd/Pz8eO+990hLS6Nly5Zs3LiRN998kx49enDzzTeX+B4/+ugjbr75Zm655RaeeuopYmJiSE1N5dChQyxbtqzIeayBgYFMmTKFgQMHkpiYSL9+/YiKiiIuLo7du3cTFxfnlbEX1x8JPMUVlZiYM3XHNWn9jTd8jot1BZ55R3YoCqDmbmjaFHwsLcbevSWY5+lSsyYsXQodO8KRI5CQAFFRJT+PEEIIkeORRx5xvw8JCaF69eq8//77DB06tNDjxo4dS0ZGBjNnzmTy5MnUr1+fadOmsWTJEo/lVAwGAz/99BMTJ07km2++4cMPPyQoKIjGjRvTvXv3As8/ePBgAgICeOihh0hPT2fGjBnuff/73//Ytm0bjzzyCCkpKbRq1YoFCxZQs2ZNd5sBAwYQEBDAxIkT6d+/P3q9njZt2rB27VratWvnbte1a1e+//57XnnlFfdQ36FDh5KZmelzyQ7QhgSvWLHiimU7QctGb9y4kTFjxjBmzBhSUlKoUaMGkydPZuTIkR5tZ86cyfPPP0/fvn2xWq0MHDiQOXPm0L59e9asWcPrr7/OoEGDcDqdNG7cmKVLl9K7d2/38Z06dWLp0qV88MEHZGRkUKlSJR5++GHGjh3rcR2j0cjy5ct59tlnefPNN7FYLAwZMoR33nnngu6xfv367Ny5kwkTJvDKK68QGxtLaGgotWrVomfPnsU6x4MPPkjVqlWZPHkyTzzxBKmpqURFRdGkSROP9UzF9UtR846LEKSkpBASEkJycjLBwcFXujvXPFcgqTpV2LhRyzL6yCx26gRr10I9NTeyPDeyEsqKMOIPGD3O5Ut8vJbIzGvKFHjzTZUzZ5SCa0isXQvNmkFISEluSwghRAlkZWVx9OhRqlev7lHxVAhXZdxjx45hLFbBhmvfoEGD+L//+z/SCiu6VYYU5+e/rP5+7u73oxB8iUcap2RDyCyu6mckczzFVeHnVfFw000FDmfNKZ7nSadqK6HlcBVSK1c/26tpzhQaD88+C7GxCvbCVmTp2NEz6JT5nkIIIcQlZbVa2bRpEx999BFLlizh+eefl6BTiGuABJ7iinrypk2Ecp7u3QsuqgC+s5mGKDsh1XOjRletgojWTgjzbHv+fMHnLlbOX1XhvfegTh04daoYBwghhBDiQpw5c4Z27drx2muv8cQTT/CMa6k1IUSZJoGnuKKG7x3GCarSmV8LbTd4METf4PDYFvFCLPeuyF2vbfly7fX8cQNU8jz+nnsKPrfTWYyOZmXBl19qQeddd+VGuUIIIYQoVTExMaiqSnJyMlOnTkUvxf08zJkz55oZZiuuLxJ4iitn+3bqJO/Ej2wc9QNwOBwFNnU6PdfwdHHgna7MOquDfMHk/fcVHF0WK/C0WOD77yEsDLZuhaFDi5kqFUIIIYQQQkjgKa6cnLLaK4N60nfIFmw2W4FNu3eHPiM8s4xx4yvwZbOKXm11FhXy/XH0pxUFVx4q9h9Sa9SAhQtBp4PZs+Gzz4p5oBBCCCGEENc3CTzFlXH+POo33wAwOXU0e/+qQVaWyrJlcPaMdyaxc2fo+qRn4GndbSH2z9wFtd0FvBTVa45ncnLBgWeJCih27QqTJ2vvR4yAbdtKcLAQQojCSKF9Ia4/8nN//ZDAU1wZc+eiZGaym0ZspB2zZvVlxw4dfftCxWiF7HyFaffuhb/We6YmU5eEenyuV097VXCW6Ds7b1XblBT45ZciDhg5Eu6+G2w2uPdeSE0t/sWEEEJ4MRqNKIpCulQOF+K6k5GRASCVi68DhivdAXF92j3ySxoDM3gM0LKR33yTG1jabOCXZ72j6dPh+3WBBO7J3aaYnKjW3AjzySdhyxbQhzmg4FG7AOzbl/s+KQnKldPed+oEO3YUMX1TUWDmTO0kTz0FgYGFX0wIIUSh9Ho9ISEhxMXFYbVaCQ4OxmAwoBS2QLMQokxTVZWMjAxiY2MJDQ2VIlLXAQk8xeV34gQN7TuxYeAb7ndvtlpzm9jtdvJ+e373HZw+baBentPoQhw4YnMDz0GDoEJjJwPe08HxwrvQoEHu+xdf1OLItDQt6AQt8Cz0952QENizB+Svc0IIUSoqVKiAxWIhNjaWlJSUK90dIcRlEhoaSoUKFa50N8RlIIGnuPyqVqUqJ2jDZhIo596clJSN61ty9uy1DB/e1b3v9Gnv0xirZuOINeJ0avV+9uyB9asgKdUIJagyPmuWFnh2zb1c4UGnuwN5gs6UFDhzRlvnUwghRIkpikJoaCghISE4HI6cP0AKIa5lRqNRMp3XEQk8xRVxisp8R798W/8DtMDt1VdvYvjwws8RdEcyWdsD3EHi4sUw/TMFtY3iVdW2ODZvLvkxABw8CL17a+uy7NypZUOFEEJcEEVRMBgMGAzyK4oQQlxLpLiQuLwKmTy5fHlutjAtzb/IU6X/GgTkZic9hsd6r7JSoEqVnNxzj+e28PDiH09UlDYp9cgRGDJE1vcUQgghhBAiHwk8xeX19NPQvTs3seGiT5WxTgs8XUUQPeI9H0Nlz53zfZ5Tp3T83/95bjt/HubP1+aWFiksTFvf02CAb7+FadOKcZAQQgghhBDXDwk8xaXhK+tnt8OiRfDzz5iweu+/QK6lV4oqCFTSKv0PPAAvv1zM7GXr1jBpkvZ+xAjYtatkFxNCCCGEEOIaJoGnKHW9esEdd3pvd6xZDwkJqBERrKc9YWGFVy08depUsa7nqj9RpQrULKS2T97lWYqrXLmi27iNGAF9+mjleWV9TyGEEEIIIdwk8BSl7qef4IcfvFOPByZo41mPNr4DBwbOnw8u9Dzx8fGF7re00VKYrsDz8cfhxUkFt8/KKvR0Pm3cWII15BQF5szRIuB//4UxY0p+QSGEEEIIIa5BEniKy8PhIHLDYgCONM9fzfbC+N2YCWjrb4I21NbpLJVTe1GLWzAoPFyb79mrF7z22qXpjBBCCCGEEGWMBJ7i8tiwgfLEkkgY0//tVKxDsl2TNwtgiNRSnStXap/HjoWheWPaWyHilkz3xxo1StRjtx07oHJl+OefYh7Qti0sX65VuxVCCCGEEEJI4CkunczMPB9yysP+wO18+33xJlsqhVUKAswtMwCoXNkG5NQzyntICGRn5i7oqcv5bi/paifffw+nTyvs21ey49wWL5b5nkIIIYQQ4romgae4ZDyGvbZsyS904f8onWG2ilNFQbvAsWPbgZyqtvnaZZ7KXYD89Gnt9e+/S3atN9/UXr/8Mp6lP5RwLO8rr8Ddd8NTT8n6nkIIIYQQ4rolgacodbNmaa9r16i5BX0eeojb+IWf6FXs8xQ6r1JVcZzUMqdZVu3bePNmOHfaM/Q0Nso9R4aWICUhodhd8PDDD+W4/Y4S/sh07w56Pcybl/tghBBCCCGEuM5I4ClK3bFj2mufvgp//XXh5/njjz8K3KdzqpAzBTQ5SQtA16/3bmcLyg1EXRlYm+3C+wQQGxtb/MY335ybMh02jIt6IEIIIYQQQpRREniKUvfGG7nv//4b+OGH3HGupUTvVFGStPfpGQV/G9uzFYjQsp6uBGq3btprvcUXNmkzJaXw9Ue9vPCClvnMytLW93SV4RVCCCGEEOI6IYGnuKSeezgR7roLKlWiAmdK7bw6hxPbATMAsbH+BTdUQckpLusKPCMjtdesmAu79rZt20p2gE4HX30F0dFaJP700xd2YSGEEEIIIcooCTxFqcpfvLU7K7Uxrg0bcpaKpXYdo82ByWIFQKcvrOCPgloRDGFOsq1a5Nm0ac6egMKr5hZk9Wot0HU6yZ3DWpTISPjmm9wgdMeOC7q2EEIIIYQQZZEEnqJUffWV5+feLAfAelvvUr2OOdNGZP3zAESUyyi8sQL28zpOntImdzZrBlFVnJhqW6n8zrESX3vNmuY8+KBWM8hiKcGBt94K778PP/4IzZuX+LpCCCGEEEKUVRJ4ilI1dWruex0OuvEzQImq2QJUqZk7LPeMjxG6piw74RHaXMt69RMBbSjtqFk+KuGmapnNxd/bAXA4QMn5zje3Tnc3m/7XC9w/64si+xYSmMK8ecW6DW/PPQc9e17gwUIIIYQQQpRNEniKUrUvT72epvxJBIkkE8zob1uV6Dz/Hc4dluv0MZLWmO1Al6EFmU4lb+VaH4FnTi2fw4f07vPp9Fo7483aWNlqDc5xLjiK4JsyeX7nO4X2bddf9d3vDYaLWJvz+HF4++0LP14IIYQQQogywnClOyCuXV35BYC1dKTNzQaOzL+w8xw+7L3NaHMQGp2If/kMEpO15VSqV4fQWoA5f2snoMMVk44YAXUeTWMygKLj0bXLqBCQyn5LNSzOZAzn7cXum91+YfNESU6GFi0gPh4qVIBBgy7sPEIIIYQQQpQBkvEUl8xtrALgF7qiv4g/cRiNnp8Vp4rB5sAvw0HGOX8MfqGAtn7orl+8v6X1Ri3iDAnRXqOioHKt3DSqX2072eV1nDUFQZYRJUNfZJ969kwtsk2hQkJg+HDt/dChnqliIYQQQgghrjESeIpLpj8LuZ/5/MDtbNl84efJH3ga7A4MDidGuwOAoNDwQo/X+WtBZpVqWrC4YAHMGuvn3p9pNhJrCiDdaMbuMOKwmors008/Bbnf//hj7lItJTJmDHTtCpmZ2vqe6elFHyOEEEIIIUQZJIGnKFWuJB5AHFEs4H5OUZnz57NLdJ6Q0NyMoj4nAWmqqp1Db3disDkx2nOKBamFB34Gi9bO7lwPwKZN8McPuYFnmp8f6SY/7EYj2Q4/VGvJfix694YFCy4g8tTpYO5cqFgR9u+X9T2FEEIIIcQ1SwJPUarq1PG93c+UVKLzdO6amyJ1BZWmppkA6B1ODHYH5mxXQKniqi9kyhlOq5AbCBoMWsYzM0sLNp1OIM9o2nSDH1l+fth0RlSbAbJ13DVuZYn6mxhf2FqihYiKyl3f88svYc6cCzuPEEIIIYQQJbRx40b0ej3du3e/5NeSwFOUqqee0l5n8igvMZFwEgBwOIqeN5lXWmqA+329etqratWiS4PdicHmIMgvg26v/8b5zHU4HNoqJdU6a2399Db38bqcsrjLF7fP6QsoebqTaTBgNyrYdQaMWU50Nqhd6z9ad/iz2P2tUMFRovvz0L49vPGG9n7GDN9lfIUQQgghhChls2bN4plnnmHDhg2cOHGiwHaqqmK3F78Apy8SeIpSV5HTPMps3mKse9vZM+VKdI5VK9u531sssHkzVP76OAAGmwNzlh2zYqN6k9P4BaThcMBPP4E1QzvGoMv9wVDPawHr+cQQQIvrlDzf+TadDrtOj+IEP6sdk9VBQJqNwABrsfvbu/dFBJ6gzfecMgV+/VXLfgohhBBCCHEJpaens2jRIp566il69+7NnDwj79atW4eiKPz888+0aNECk8nE77//flHXk99wRanryFoAdtKMRCIu+nxHjmiJQFfG08/qwJxpw5GpY+fC+sSfCXUnCR1ObZitQclTtTbE5nG+bt2g3SOZ7s/ZOgNpikELaK12/Kx2LBnZ7Nleu9h9zMy6yMBTp4Nhw8DstRaMEEIIIYQQxZKSkuLxZbUWnEhZuHAhderUoU6dOjz44IPMnj0bNV/hlBdeeIGJEydy4MABGjVqdFF9k8BTlLoOrANgDZ3c2ypXPVfi8xgM2hosZ89qgacjSRsfa7BrgafTqmPrgkbsWF8vz+hUFVDR63IDQf+oLI/z7tkDkbVyix1loyNTMWDKsmGy2jHaHeiBAYNWABBe7nyRfR32jIm4uBLfom8OB7z2GkybVkonFEIIIYQQ14MqVaoQEhLi/po4cWKBbWfOnMmDDz4IQPfu3UlLS2P16tUebd544w26du1KzZo1iYi4uITSRayuKIRvrsBzPe3d206fLN5Q24iIJEzmbJLOB9GunTbc1vWHF1WnvdE5VfysdnQ5Ozb/3ChP4KmgKCr6PBlPk8Gzou64cWC0hHNDxn8A2NCBAiFZmRizHejsOcuvVDnHW9M/QrHZePnp0YX2e948I8eOqWzYoBTrPgv13XcwYYK2jkzTptC69cWfUwghhBBCXPP+++8/goOD3Z9NJt/LBB48eJCtW7eyePFiQEv49O/fn1mzZtGlSxd3uxYtWpRa3yTwFKUqmlPU4hAOdGzgZvd2p7N4xYUGDFjB0aOVWb26JfqcdVTcQWVOft5V1VZRcoM8gwEeeQTWpYBiU/HT29ArDpyqDotBG2JgsmQDWmVbW2beZL92HnOGDaPVjk7N2eTUYcQJip6AgAzS0/0L7fsff5RC0Alwzz2waJEWgPbrBzt2aNVvhRBCCCGEKERwcLBH4FmQmTNnYrfbqVSpknubqqoYjUbOn88d7RcQEODr8AsiQ21FqWqPtlbmTpqRQkiJj58y5X5UVeGN8VPdY8zzB55+Vjs6pzakFiCqWgJmM8yaBebyWsYzyC8Dg86Boqj4+1mJLJ9I/Zv+LfTaloxsLFk299Isiqqg2EGxK0UGnQA63QWs5emLosDs2draNCdPwn33wUVWERNCCCGEEALAbrfz1Vdf8d5777Fr1y731+7du6lWrRrz5s27JNeVwFOUmvPnIYpY0ghgHR1KfPztd2hB648/3ozql7u9ShV44QXQhdlRnCqGbAeKqmLQO6nb6ghdBm/C4YD9+8Fu1YoLBZvSMemzUVAxG7KpVDmOgIiMQq9vybJhysotRKQ4VbDr0DugefMDRfb/nl7xJb7nAgUFweLFEBAAa9fCK6+U3rmFEEIIIcR1a/ny5Zw/f57BgwfToEEDj69+/foxc+bMS3JdCTxFqUlLg48YThjneZPiB0rNmv1NTMxp2rfPXTfzi2l3ud/XqAGTJoG+nFYwyJJhQ1HBaHQQHJqOqlNISoIbb4S0/5woikqYKRU/vQ1FUbEYrAy4fxV1bzleaD+MWXYMjtyspQLoVO31wQd/Jqb6qUKPf+HxwjOqJVa/vpbGBe0BLFlSuucXQgghhBDXnZkzZ9KlSxdCQrxHJ959993s2rWLnTt3lvp1ZY6nKDV+OVlKO8ZiD7ONjErE6GdDp3OioAWhiYnBHDsSDWjjy8+fh7/+AmcrBb2fislqQ1FVVBW2rmrIzvV1+ehx7XyqqqJTVIJM6Zj0NnfGE1XFmbe4reI9LNZgd3ptU5y57VRn4XM4U3WX4Mfp3nu1RUynToXMzKLbCyGEEEIIUYhly5YVuK9Zs2bu6W4jR44s1etKxlOUmqy04s1D7Hp0NZFVEql/42GGDf+G7t0307//KgAeemglTZv+7RHkbdsG7duDI9aAoqr42RwoTnA4tOJDdquRvEsOGRQHAcYs/PQ2LQj1S+eTKffyx7fNAS2IHXL2hEefFIcTv2zv/ucNNQcO+on27bdTpcpZ2ty6y6vtV99GF3nvzz+vTeH8/XcVtbhTQidNgp07YcCAYh4ghBBCCCHE1UUCT1FqQscO5W/q0I9vC21nstpRHQqBIRkEBtgJDUknMirFvV+nA1VVcOZUFXIXF1IAFYzZDkBFyZO1zNvGbLDib8jCqLOjVxxYDFYURUXNWdqzYUPYPCHUo0/+GTZMWYUHzmGhadx+x++MGDGffves5s13P/TYfzrOz/eBebz7rvZ6661K8QNPoxHq1cv9nJCQ54aFEEIIIYS4+kngKUpN4I711OEfsjAX2s6SkY3RaEenVz2GsrooOhVVVXA4tEgxw1UTSKcVDtLbHCgq6PVa8NWg+z+oqhafoagEGLMwGbIx6u3odU5MBm2up9OhIzlZO9Vfn3gOBTZm27Gkea736ZuCoujQORX8AzwDVUVXsmAwIaFEzTXbt0PjxvDGGxdwsBBCCCGEEFeGBJ6idJw7h/7QPzhRPNbv9CUwxcqZ45EcOljFvS3vkNYbap7knv6/uj8PH669OtN0oGrreCo56cJyUecxmO2Eh8Op0yqmShBgzMRPZyPAmIVecWLU2dHpnDhVXYHTJPUOJ4Fp1iJv09VPVVVwOnSYzVY6d/8NgBXLKhR6bP4kZVpakZfztncvnDoF48dr63wKIYQQQghRBkjgKUrHH38AsJcGJBFWaFNLhpZZjI/13a58+fO0bLXf/fm//3Le5CRH81aefXLEd7R+YA8vvQRRkQrZ5xWC/DIw6W0EGDPRKU70igNFgcLykTqniiW96MDTRVEVFKfCxIlT6f/YT8U6ZtMmz8/W4l8u16BB8Nxz2vuHH4Y9ey7gJEIIIYQQQlxe11TgOXHiRFq2bElQUBBRUVHccccdHDx48Ep3q0z79lvQ64vRMCfw/IObimxqzrLR74FfefLpb/FVJzY2NoyNmxvicHgOw9VH2lFULUh0Te8M8M/CYHLyb85KJumnnRh0Dvz0Nm2Op86BXufkoYd+onmf/eSM3vWiONUSlXhWwB3J2u25D6iweZt++aaAZhdnZK8v774LXbpoY5Bvvx3iS3H9UCGEEEIIIS6BayrwXL9+PU8//TSbN2/ml19+wW63c9ttt5Genn6lu1ZmJSbiLqlcqA0btJcihtmCVhyodev93FDL97qYx45VZPHCzjhzKts+9xwEBKnowxzaHE9Hbu5y2Xc3s3fFDe7soSNbwaizY9Q50CtODIr2GhKSgdk/N8VY/s4Uj2sabCUv1qOoWv9s9tw5rUohK67o8v20XVDGE8BggIULoWZNOHYM7rkHbLYLPJkQQgghhBCX3jW1jufKlSs9Ps+ePZuoqCh27NjBrbfeeoV6VfYVFkwBWuYtZ5HZ4mQ8DXYtgFQUxWeK0FWt1jUncvVqSE9VUHSACn5WhztTeuxwJcIDEimfcxoVNSfT6USvc2Ay2NApTn79tQVn1TASeuZcw+wZaFrSS55+dPUhNTOIux/6ibN7glHVmwt8Xi1aeH6uWtnOBf8IhofDDz9Amzawbh288w68/PKFnUsIIYQQQohL7JrKeOaXnFPCNDw8vMA2VquVlJQUjy+Ra/Fi3JnHAqWlwaBBnKjRgeNUK/KcBptTGypbQCZVp/NcRmXv3nzH23PHyyqKiupU3G1VVUGnc6KgYtA5CDJmoCgqR45UIu5ouHt4a+wPwR7n9L+AwNPFnO4g0Gzlj903k5hYvDVSapl+IzSwgEpHxXXjjTBvHvTqBc88c3HnEkIIIYQQ4hK6ZgNPVVUZOXIkN998Mw0aNCiw3cSJEwkJCXF/ValSpcC216NiTR+MioIvvmDZyLXgc9amJ4PdCWruPM38XBnD48f/896nqujyDLXV1ufUMXJkzoYg0CtOFEXFpLcRbErHoDi0LKpTJStLaxbaJVU7Pmc5l4BiVLQtiKIoGHIqH2VnFy/wTLJH88vSC1lPJZ++fWHZMggKuvhzCSGEEEIIcYlcs4HnsGHD2LNnD998802h7caMGUNycrL767//vIOd61mtWsVvm5iY+14XaC+wncHuKDDoBAgKSqd6jROcOXMa8Cxu5Cou5L6OouJ0KtxyC0yfpaKGqOgUFQVtPc8gvwwCjFnoctYGXblSO9YYbc85n4ricHpkUUtKcSruYPnIkZM+25w+nfs+mq3EOW5gzaoLz7J6diDn4qoK770HO3aUznmFEEIIIYQoJddk4PnMM8+wdOlS1q5dS+XKlQttazKZCA4O9vgSubp0KaKB0wnbtoHNxrZtuZsVo0qleud8HqLLqVZbUG60Vq2TPDLk//Dz04LD6Og85803PPfWW//khnYnWL4cHn9UIcumR6c4URQI9MsgyC8dRdEyo4qqkpKiHa/ac/uiU0FvL3lxIXefUDDqtfPGxSX5bLN1a+77szQFwJpZygWBPv0URo+GPn3yrEEjhBBCCCHElXdNBZ6qqjJs2DAWL17MmjVrqF69+pXuUpmXM02W48chPc1HcLZ/P7RqBZUrs21L7n5nup5zhyO8mjceuQ+ds/Agz+lUUJ1G9xTQvDGUouKRLW3e/CCVW5xh7FhXh3O/pQONGQSbtIrGzZodpEHTI/j7a9c2RGqRp8HuwJht14b/XgRXX7/+spzP/YsW5b53YgQgu7QDz4ceggYN4MwZbd6nzFcWQgghhBBXiWsq8Hz66aeZO3cu8+fPJygoiLNnz3L27FkyMy+yiMt17FTOiicxMTD9Cx8NcpZRoWFDzsbmfju1PLcJe7Z3xVZHsk5bh7OQa+7bV4PXxz5Derq2TMljj0HdVnmGxuZp+++/lTn7TyTHj+dssIGSM98yyC8Tf4M2d7NJk39p1PRfKlbU9vnVzUJxqhizHfhZLz7wjIzUIvRDh/x87neNhm0W8IN7m81e9HzYEgkJgR9/hAoV4K+/oH9/sBc85FkIIYQQQojL5ZoKPKdOnUpycjIdOnSgYsWK7q+FCxde6a6VWXmTxjt9TR384w/t9SbPZVQUp+9JnIn7wgqd3wm5y6moOetkZmWBNVN7n7ewEMCqVa3ZuzzPRFRT7lud4sBPr2UVT52K5PR/5dzVbxWLE51TJTghC4PNidF24XM88/Y5LcX3mrGuwNPhUKjEFgC+W1/3oq7pU9WqWrEhiwVWroTHHy+werAQQgghhBCXyzUVeKqq6vNr0KBBV7prZdaePbnvfa5PWUDgqSsg1vGz2Iqse5s/8Jw7F47+pVUYMticXm1Vp0Lnzjkb8kzR1Sm4A881a5qzbmVz6tbVjteF29HbnZhsNozZDozWi8sMJiSEAPDf6Yo+9/vlJEID1DMMoQ0t+ZSK4ckXdc28Dh/O86FFC1iwAHQ6mD1b1vcUQgghhBBX3DUVeIrSt3lz7vvE8/l2nj4NR49qAU6bNh679A7fQ1ertyq66E3+wDOv/JlJnU5FdSg8/zw0a62CPm+ArLoDT53OiaoqlC+f0y8/rY9+VgeWDBumzIsLPE0mrUKt0eB73maFCtprtKpVYNrG0/x7KuqirumydCnccANs355nY9++MH26VhK4Zs1SuY4QQgghhBAXSgJPUai802N//DHft8umTdprw4aQrxqwzqky8MllXuer2/5okdcsLPDUZ3sGnidPRnJkU1V27oSj/4KiUzHrtXmdCipGnRZQxsaG89/RiixerO1L+TUQg82BX6Yd/1QrxotYTgWgSpVY7hm0gnGj3va5/5ln4KMR66lm//miruPLgQPaq1ch28GDtZ2PPVbq1xRCCCGEEKIkJPAUhTp2zHvbXXfBhx+SG3i2beuxXx+kDV+98cajNG9+wGOf0V70fMMbbjjJq69NJzhYmy/57bfw2ExtWKoxwzOjmJ7uD2g1dc4nKuBU8dPnFCJSVAw6LaA8cUJLOR49qgWefvWyMNoc+CdnY8nIRlcK8yDbtd5NdB3vjG5srLYkzLTFNxLk9Fzn01YKhW0DArRXg97HPeRdiDU+PndotBBCCCGEEJeRd9lRIfJw5EsEqiosWaJ9Dd9+P0REQOvWHm0UPfhl29GpKjt21PPYV9RSKgBGo4OQkHR0Oi3jOWAAhFQOIPJRMKRk+zzGFU8pDhVDTpZTAQyK5/WqVNECz6C7EjHEOQhLSSfdaSqVwHPf7hsINMTwwEOe2996S3s9cNx7qZXMTDAaL+66jz0GnW5KYM+BEOLiDERG+mh05gx06KANj167VpsHKoQQQgghxGUiGU9RIgsW5PnQvDmMGQOdOnm0sScZ8bM6UJwq4XkK6IQ2P4/eUXSAd+ZMBF/O6U1KipbNtNkg/qj2NxJDvuPr1z/i8Vmn5A6vVRTQ5QSetWtr663YXUuYGHSYrA5M2TYsmTaUUgg8l/xfZz785D6sVs/trVoVfExpZDwzM6FyTDb3P2Cgf/8C7iM8HKpUgbQ06NZNW25FCCGEEEKIy0QCT1EiAwYU3cZcOwNTTpXYl8Z8RWRUIgC2VGOxArz0dDN//VULq9U7FWjwUVwoslYC3bppnxWDilGX28ZVaKhNm72AFngqioqiA0O2Hb1DxT81G6UYAXFREuLDAO9gMitLe326+Ye5/UZ7Pq5lUAuTmgpJSQXvDw+HkHCtmu7xYwXch8mkpalbt4bEROjaFf75p+iLCyGEEEIIUQok8BQX5KN7VsGiRdoQznzsiUaM2XYUFQwGB2HhKQCk/xOIroD1PfPyVVwovKYWzfnlKwK0d29N4v6NYP58mPmDik6vYtB7V6h1nbNuXX86363tN2Y70TmcmDOtRa4tWhwPDvwRALNZCxYVBdas0eafAhyMv8Hd9rnofgAsWezgm2+0eaAFqVYNwsJ878ufXU1NVTmfv/qwS1AQrFgBTZrAuXPQubNWlVgIIYQQQohLTAJPcUFq/vIJ9O8PM2Z47TNEZ3sse5KZYXa/Vy4g8GzSBGp009KGBQWu4eFQoZJWydageFeorV79NL3HrqVXr2wmfauV6jXmZDz9bA50RS0uWgzRFeK08xohTnvLxj+cNGyYc19qbr+y/LQM5an/bAwYAA884Pu+VBXOnwej0fd+k8nzc1y8njmzCqnQGxYGq1ZBvXpw8qQWfJ48WXB7IYQQQgghSoEEnuKC1EvShq7mrWjrWlGlwpiTHoFnQGCW+31xhtrqcr4rnU4tGnQ4QKfXtukL+I797juY/p5nJdu8QkIyqNr4NFarQuxZ7SR+WQ502U4MF7mUisuJ4xXd710Fg5o1znYvcarmCZpvbXSQ/tzBtIlaxjE11fu5LF4M9epp22224kfGX32lUuhjjoyEX3/V1vfU6bwrSAkhhBBCCFHKJPAUJRbFOWpwFFVRoGVL9/aUlJw3JtDbc6vJqnkKyxZnSGtYWAo9e/1OUJCWmXQ4AJ12YP7As3PnbQSXT2PXLti4Jifj6SPwjI0NY9uChkyZEsD9DbWiRX4JDgw2J/pSyHYC+Pvnjnt1DYE9n2Bj9+6cjfacRVEVBVOUyjplAje00ar+2rKdvPyy5/nuvhsOHsztnN17BDHHj3tv27XHUHTtoOhoWL0afvtNG8srhBBCCCHEJSSBp/AyZEhuUR5f2rAZAGfduhAS4rVfZ3FisDtR0JY0qVQ5LndfMZZTCQlJp2OnbQQGaoHawIFQtWOWz7aKomX3VBWtYJDOgd5H4HnuXBjbFjciMVHB5K8FsZEpKRhKMduXkhLgfr98ufa68mcdY8Zo76MTtZLAfoGBZIbaOKc2dLff+aeBiRMLP/9nn3k/O1eAO3LsRo/thuIslFStmhaAuixd6nPOrhBCCCGEEBdLAk/hwW73OW3TgyvwdOTJduZlPWrCmJ0b0Dns2rdZSMfzxSoulJlp4q99N5CZqU1gfOEFqHF7BuA9R1RRVFSngiue9dPb0ftIqxoMWoOsLMX9Xe9n9d22NIwYob02b54bMMdkLwPAPywESy3vir1KEX0JD/Jew/TAAe21ah3P1GdGetEBvocffoC77tLW+jx1qmTHCiGEEEIIUQQJPIWHwpbtcHEFnvYWLXzuz9wV4LHsSVJSEACqXfEYgluQxMRgvprVl/h4LZu6fTskHs+Z5Om1jucxmt2+X8t45qzbqfNRXMho1KriZmQo6HTaUGC/DEehmd2SatvWe3zril9yCyvp0PoeXr0ijnLegaeqKuzZk1sFN7/qFVM9PttscMcd2ntHOSdP2H5y70tJLmEmt2FDqFRJW2KlQwcpOCSEEEIIIUqVBJ7Cg9lc+H49dlqyDSg44+nM1HsUF2rVeh8Agc1TMdqKDjzzV7Xt1w92TQ/U9lk9j4+OjiO0QiqdOkHve3P6qHhfw89PCzzT0xVQtMDT4vDOIF4MXZ6fJtd6p6t+CfBqV7FBFAnmSG6Nnum1r3Fj6N3b9/n/O+4ZTGblGX2c3U5hg6E8RoM2EfTP7SUMPGvUgPXrISYGDh2C9u3hxImSnUMIIYQQQogCSOApPBRVdNaBnvrs514W4qhd22eb8i+e8hhqazRo7/X+jmJVtXUFnq7plw4HqPqc46yeAdVnn93N0rc6sXChyoG9KgoqOh9DVkNC0ohp8R9jx8YzdW8yxmwHBko4HLUEli0reJ+5lj+nTTHoTPpCz3HrrZ6f//nH874yc2oVLXn1/9gZWAEnOmx2bXLn6DFF/AXBl5gYLfisUQOOHIFbbtEyoEIIIYQQQlwkCTyFh/T0oloo/EdVvuVezsXF+WxhrGL1yHhWqXKOns+vp/wDZ4s1x1On0wJCB1rG0+nMDTz1Ds9g8cSJCgDMnKmw8VcdKFpl2/xCQjJo98AeVL2Vw7v0mNOyS3WYbX6pqQXvs4XDOXMFjIGFP4vffvP8/OWiYI/ProynnzmJIzrPzGqvzilckKpVteCzdm0t43nzzXD69IWdSwghhBBCiBwSeAoPJlMpnEPn8JjLaTbZqNv2GP5hWcWqamswOIiKikefkyl1OMDpCjxzgsodO3awZ88eKlWK9ThWQfUZUDocCulxFrp1rsmotiGYbD7WJsmRmJhYZB8LEhjuO3JvYlzofn86LRHVauC2Lt8VeB5XNjOvIyc9g8uRI7XXXmMfI0vxLGObmHAR1XorV4bff4dmzeDee6FixaKPEUIIIYQQohASeAoP+sJHfzKVJ3mJiYSTUGAbg13FkC8zqc8pCuRj+qWXiIgUXnjha8pHaxnVc+dAH6Adb8gJKpOSkkhLSyM+PtTjWG0JF+9MYnq6hR/+1ym3XSFDfv/888+iO1mArk9u8Lm9m+0J9/tEQzDhyVkYK5ipw/c+28+Ynhs47nuyk882ef9I4Mz5UX7gt/UAbNoVVpJue4uK0jKfH3+cu7ZOMYZJCyGEEEII4YsEnsKDveBEIOEk8CSfM5GXAc/M4NGjue0MNofXkFq93YHOWXjAl5dOB6gK585pn9Wc43R5gsqsrCyCgrRlVvxz1ubUKU4UH9GtTud5XV0B3fj9998BOHmBVV3DKyX73G4hd3tyYHks6U6MoSb6Wl7iyRbverV/dnjuXwCiI877PKevVU/iWhXxl4OSCAzMrZiUna2V0P3669I7vxBCCCGEuG5I4Ck8OAoZodmaLQAcpDaJRDBmTBX3vvN5YiOT1e6VHdM7VHROtViBZ2JiMC+99DRH/o0mKCjn+ApaRKzLM4xWVVVGjFhA9zfXsX59Bs+848Skz8bX1E2vYLSAfmRna5VuDx48WGQ/faloiS+yTVpwOczZDgjxp0FTf8pV8DGuNg8lLIhFT73MxPt/8Ni+Xktu8vjcX9zbTvpZco9T4KWXStD5wsyZA0uXwsMPw//+J9lPIYQQQghRIhJ4Cg+xsQXvc63fuYm2AKxfX9m97803c9vpHU7yF5bVO5zFmt+pUcnO9sPp0LmXd/nn01DtTbbnOTIzTWz4uCXlyjkpX1WraOtrjqd3xrPowMmeJ/17/rzvrGN+fs7cizdppqWBb1Xe8mhjMSkoTgNZAcFUaFCeqJq2wvtROYTTlvJM++Umn/szm+ReU1U8f6STzxeSwi6Jxx6DUaO092PHwuOPawuJCiGEEEIIUQwSeAoPDz5Y8L62bAJgM23c21wZ0v/+y22nOFWvrKPOqWKwewekvrjX8URxB8K289q3qpJvOZXt2+uSFhtA9epBfDFORZ9TEffvv//2vH5O4HnXoF95LekEOrt3R5z5AuPk5NzhsUlJSUV3HDzu7+Zm21n56UA6qa+4t5mjy2NRnagOPamWEIw3GGnT9Qg/HH+Jbnet83lOaw2V4e8/x/H4cj73nwrzXivUZdp0A3/9VayuF06ng3ffhU8+0d7PmKEtOJpygdVzhRBCCCHEdcVQdBNxPXEt0QEQn2fUqA6He6ht3sAzORnCw7XVN7Zvz2nrY8kUxal6rO1ZkNOnT7Nz5zngMVSnQs7IV2LeOYsKqFlOjwDRbM52vz9+wMCNbbQMn5Iv7ennZ+Plj2ewZfWN/LnAgqFN0X3Zu3cvERERJCYmUqVKlSLbg+cc1kdemE3c+kiP/SEx4WQ4dNgdCol+UZgrmdHFmDlTvjqvzpjKTW238NrzL7rbL3lsOFnRuWuzqCpeGd3YID+Pz1UfOsOJr3Mr0TZqVIojY59+Wlty5b77YNUqba3PH3/UKuEKIYQQQggvuz+oTWBwKdbh8CEtxQGzru711yXjKTzcfnvu+4yM3Pf1OEAwqaQRwF4auLe7as907pzbVvEReOqcKn7Zvod9Hj9+HIBjx45x4MCB3Iynqq3hCaAzaW90NqfHEFibzfOH2KA4cvrl+a2tKKB3wonDdfhvkwmD6h145g9W7XY7586dw2azkZU3Ii9E3qDbGG3EWM7zbztBNUNRsvXoHSrpfgHYI2ykRYexw1AZv1ALvZ/41qN9z1Y7sPvl9tVXN+wWz3v16+a9pIuiwN69xbqFovXpo00wLV9eqyqVUHCFYyGEEEIIIUACT5HPpEm+t9fgCJmY2UZLHHkS5Tt3aq+u6rPge/6kzqmitzt9Fv7Jv26mn18Ko0fPpXrN0+6hvGpOfKl3OD0K/6Smeg4zNei0A1LyDQF1OmH2h334d2846FSfVW0Lm8d5+vRpduzYUeB+F73DyXN/fsLe44+R6l8ZZ7Dnj1hgVQtW1YLOoWJ3GjhXtzznA8pxSh9EplIONSA3aPzpzrvJqhyEqndw95BFAKSl5Z5r61Z4+pW95FvCE0N/38Nfl39vLbL/xdaiBWzerBUcaty49M4rhBBCCCGuSRJ4igKtXJn7fhl9CSGZB5jn0cY1t/NlbYUVgpsk+x5qq2pzPH3JP7dSp3NQqVI8ZnO2O+Op5kSKBqcTW56iNiEhaR7HuuZ45p+TqShw8lj53M8++vjvv//67J9LUlKSx7xPX3QOlX/W1uKt1+8lWwlANXpGhYYKVtLUABRAVQ0k+IdynBDSHAqJRKLoFJ6e8BEAgSF60qo5sZtsPD3xA1ZOeYWQkNxzxcZC7Tu9g2XF4Cu8hzGvmihG7Fx8MTHQoUPu5/Xr4cUXCy+NLIQQQgghrksSeIoC5YyAdbPhxxmiPbYNG+YZwNnT9Oh9BJgFzfGM9VFG1243sWhRJ06djCI6Gn7+GfwbamNM9ajuNT0BmjTxHMuu87GGJ2iBp3tJFZ3v9UTVYkyE3OlK8RZA53SyYmQ3vplzG1n4Y9fnzr80WCw4Klix2k1anxxwWl+BnakWsm1Odp6xg6rQ55ElAATVMGENspJtziI0Io1WdXdhVHLntN5+O/y23vPfw+XBp37zuf3bBaVU5Ta/lBS45x6YPFkrOlTMKsBCCCGEEOL6IIGnKFD16kW3CQ/3/GyMtPnMeOpUFZPVe/mNI0eOeG1TVQObNjUiISEEvR5OnADCtIDJmG9etpKnjGzt1tnoCwg8ARSdSqWaqVS/La1Y64n64nQ6C8165h3Ce44o0gy5KUpzaDA2iz96m5aRNNicbPWvyzH/SqhOhdO6KFSnH1GV4vnn1K1UbZWIzWwHvZOkuPqM/eIxDu/XMrwOh/Zl9ff9I3zfhM00quKdwW3fMtFH61IQHAxTpoDFoqXKW7eG/fsvzbWEEEIIIUSZI4GnKFBMjPbajZX8RQNeZ5xXm7r1PD+n7AjxWUQof8bz1KlThVxZCx5Vp8KiRTBkCKRsswBgyBd46vVOAiK0eZH/bPHzmfE8ePAgR44cQVFUTh0O4tga/2It61KQojKjbZ7cQfnKZ4knCLvJ6N4eUiWKJHN5/LK0ANxgc3LSFEG8JQzFDrH6cthtoSiYUf2cJNfLxG5RUfDjTEZzpi66g337tGu7RhtbTdpQXmO+204MCODu7r979e3Y0dIqb+tD//7wxx9QpQr8+y+0bAlz51666wkhhBBCiDJDAk9RoLNntdeb2UAD9hHDsSKPUbN1GGw+htqqKgaHtt3hcHDq1ClsNhuZmZnebXOiQqcTzpzRtmWd0gK4/LMXg4IyueO1te7PvjKedrudjIwMoitp68P8+XmYz3U8S0uz3n/z9vqxZGJE9cud46kPMhEfEI05MyfwtDtxKAZUdJjSHcQaI0jKroJCMA6jjYyIFBxmBajCFkt9ACa9rwXgrmVmVD/tPhSrTVs/Nee20kwmHhuyhM3vPu7Rt6EvlSc1lUunaVNtXZ3OnbWyyA89BE88AfZLNMRXCCGEEEKUCRJ4igLt3q1FMW3ZBMAm2nq1aeijoKmvOZ46VSu8A7Bx40ZSU1P57bffcDpzl0fJzommcpdTUfj7b+14e4JeC6zwzjhanNpxVVpmuava5uV0OlFVlf+OVwAgpEGKuy95FWeOJ+QWLlq3bp3H0i4upkwbSeYwEvAnw2RBZ9SCZr2fHqu/BT+rdowh51XncBIWl4Hi0HPYWQunGozdlI1Tb8epNxJrrc6JOC2ru3WXOeeeoG5dMAfnzH11ONHbHRizbBitdrIUP2wRmdRu7p1ZDgws1m1euKgobWLu669rk2szM0F/adeuEkIIIYQQVzcJPEWB/ANAj51WbAVgI+282pw5k+TxObBNCnqnj3mWqoo+J+OZP8BLS0vjzz//5L+cErmKYqdDhx1ERp53Z13tSVrgiariyFM1NTPTxLxXewJg8Nf5HGobGxvrcc1KQ05g8FHoqLiOHDnChg0bcDgc7oq8qXnSiOaMbFKUEI4RTLJ/CKbgIO2+jHpUmx+GnPVCzRla5tOY7aBcbBrGbDtHlfLExmVg98vCYbCTkamyJTYYzmjXufMWreJTaCgcOAA3tNMCS71DW64mMC4Dc2o259OzyYhIJaO8j2HPvoveAlqNINczvyh6PYwbB2vWwNSpuRfNzi70MCGEEEIIcW2SwPM699tvULOm7xUwzpyBBuwliDRSCGI/9b3aLF9m9PjsyNL5rmqr+s6EuuRdy1Onc3D77b8TXSmWRx7RtgXdmYTO4URRtUA1r8w0LQuYlmZFV8DkTVfgaTTbibw3FmOW9w1brcVb51JVVXdb13nP5VnI1ORUsflHcIwAkgP9MVi0KrY6o0KWI8jdzmy1EZCShTnTRlhiOsFJmZyyW8iwBYBOQTU4ycxQOeaoStihDOq0+Bu94hlIOsnNmgavOElIbDqBCRmcSFPICkklMzSLyS9+Qo96G9zHzPNcEcdD584w9sVMFi3S5pFu317w2q7F0qEDBOSstaqqcOed8NhjnguSCiGEEEKIa54Ente5HTvgyBHfU/D278sdZruZNjjxHi6ZkuQ5bjNzV6DPqrYARlvxsoyqCkeOVCQ9LYCOHWHBSjuGylq13Pzrbyp5MpyBtZPw03tXzoXctUJtWQYcBh1+Tjvp6emsX78e0OadOi5g/ck///yTf//9lzOuyaiA0amSpvPHigGrvxG9nxacO3UKVoe/u11QchaWDBuBKVkEpFoJOZ/JMUtlzlgr4cgOyOmXgYykACJsVgxBdtKt2o/sv/9qWc8j20MBsCRlEbPmOEFn0wg6lUZWlhmn2pjsQLh72Hd8N3ac+7oPPuh5D++8oyUk58zRAs1VqxXuv1/l1VfstGsHL71U4sfi2+bNsGIFzJypzQXdurWUTiyEEEIIIa52Enhe5yIitFedj++E6ErQjo2A7/mdvoTeGe8744lWTKe4pkzpz/x5vYiMhI7dVHQmLehU7fkDz9zPKYcD8NPlRtDp6enu93nnYsauKofe6XTPL/3jjz/4448/it23vNLT0zlx4oQ7sAVQbA7SFW39TrtOjyUiGIBsvRnVmqfYkE7BaLMTlJSJyWon8mwqaqaRw86aOJ2GnH4bMZxVCbLZ0ZscJKVp5124EJKTwWrQhrCGnUqlUQ0F89qzhJxKwZ7txyF7Y7KDypEanYq1ool1W/t59d9uhxde0N67sssnT5lxOhUmTTa4q+eWirZttaG3VarAoUPQrh28+abvdLsQQgghhLimSOB5nXMlnXwFGC1awEkq8w+1fM7v9CV9d6B7Lmd+roBUKWySIbnTAc+fD+Htt6G8YsSZpkNRVXRK/ra5gWfq4cACM55553g6FD06u0pQkDbsNSsrC9tFRlh5s6V6m4MUVRtem42e4GpadJ9psODM9hyabMh2EJKYic6pEp6YTlByFolKONk2rW92uwHzySwsTgc3NtlLp/q/AbBsmXa8NafOb0hcOnq9Qv3yTkKSsrBnmzhBFClUB10giXXSMZbzXn/05ZeLd3//ei8JemE6dIDdu7WlVxwOePVVaN8eDh8upQsIIYQQQoirkQSe1zlX1VinE7KyPPfZbPAyE6nDP6yiW7HOZztiLjCz6QpIfVWC9SUlOZAxY3L6l65DUb0zswZD7rWyE/zQ56lqGxcX5/O86TsKDo4vVN7A1mBzYM1JtlrRE1xTGzarBPqhWj2HK4eeSyc0Ph3FqWJJsxIWn44128z5dC1w/Te9HuFJWRgNCi077ee+G6cCuX8wSI3X2vkfTwHAz08hJNuKkqVwhmD+ySpPUpKT9PBk9BHnvfq9alXx7q927eK1K5awMPjmG/jqKwgK0tb+vPdebYy1EEIIIYS4JkngeZ1zZRd1Ou/A85WxuelFXZCDkHviizxfhQ+OF5zxzNnu9FX1tiiqZ39dlHzFhBRU9/kPHz7M2rXaGp95A8OQ286jOFX+LbU0nsa1HIxOUXBmaENiMx0KdTobsYSFkWrJxJjhOaw0Ii6NyPhUdKqK3qlS4WQSzgwjqbZgHLZQ9ugbEOrUAvW4sxH8frylx/GWnlo13ajs3MJIFrsD/9RsziqB7DuvJyktDLs5C7/gDB4d8znTn5gOwLlzWvIxv9k/P1s6D6QwiqKt8bl7N3TpAp98Uni5XSGEEEIIUaZJ4Hmd+/VX7VWv9044RRKLDi1QqpOyh6pzix4OaWqYhaGAIkL5CwMVxt8/k3Ydtrk/q05tnqgu31jbvLGKPtCBooAuT1o0b5D76quzaLv0D/Rds9GpKikpKcXuT3H4+fm532foLACcT8vC1iIWU0gg/jf4Y0n3XE6k/KlkLFY7ilNFpyiEJGViSney31GfbFsoSUoIJoP23HZtasxTM9/1+HfKCNDmgvrnGcFrNkJAVjYndEEkKBGcyazsflDD/vcRD7dZAKrK+PG+76N68y0X+yiKr3p1+OUXbf6ny0cfweTJviteCSGEEEKIMkkCTwFoQWf+hNO33EMSofTgJ62NUvS3i9Oh8xhqu3PnTvd7XQmGUt5zzxpq33DQ/VnRg1LU8YoKqB5Lm7ioqkp4eAp+za2oirYmaP71REuT6xmkKQFYw1T8gv1xBusxZ3nOJQ2yZqMoCrqchx+UmkWV44n8p1ThZFIQSpzTHWxn6i04Vb07HvMzObAqesxWBzpy70WnU/CzOTiPGVuygX+zb0B16gAdSQkhDFn0Int3ZVFQ4jk4IpVnJr4PwI1NtLVVRzzve9hyqTt+HF58Uftq21YrsyuEEEIIIco8CTyFW97A04CNlmwjiDSOUEPbr0JQRHoBR2sCb0t1D6nNXym2JBnPJk3+pXxUbgCpWIoenquioAAJCQkFtrHrtQyhTr20gaefVcv6puqDAT/8gi2YHE7M2Z5ZPF2+aN+ggn+mDbJ0nLJHU2Fv7vBmV8VeqxUMBrhj4GFAwT/Nhj5fJlif7SA4NgN7nIn/UqqQkRaIopbD6dDz9YpuTP9c5fPPtbbRkfG8+dkEAH7a/CgAj7w0A4B9u6oAkJEee/EPpTiqVoWpUyEkRAs6W7WCYcMgKenyXF8IIYQQQlwSEnhe5zp31l5/+83zd/vG7MafTBIJ4x9yKsuocEODE0We07WOZ1a+SaNKCeK8P/+sxelTFbj33pxzmtWij7eoGHQOnwGla5viVHJeVfz9/b3alRbXmqXasGMDhkATBqMOQ+GHAeBntYNVx+nMipS35w7N9VO0bGl6ujYKNaqmVqXWmO09JNWclk34ofOExGURfN5GQnp54uIMhISnATDl89x7/31nP/oO+Zk9mU2Ibpa7tua01Y+wfPe9KIqTzz+7kWPHSvgQLoSiaOu6/P03DBigpeI//RTq1oV586QAkRBCCCFEGSWB53Vu9WrtNTYWfvwxd3ve9TurvXca0L5Zzh4rV8QZlRJlNguycmVbDv5dm0WLtM/ODF2RQYextQ2Dzl5oJtM1XFfnBIOhOGHghTHYHRitdgx2Jw6i8AvyQ1c+zL1/t6+qPjl0die6RDhqroF/nqG5/gYrFSqeJTNT+2w3a0G03uodeJqsdipsOUP4uTTKHTlPdraJs+ejsWaaPNo1u/EsyZXOYTNUxm624TTmLHmj+tGq43aiG+0lpu5RAP7cmTt3V1W1zKurL4U5dAhGjFRLlrSsUEELNFevhjp1tEpIgwfDf/+V4CRCCCGEEOJqIYGnACA7Gxo1yv3clk2AFng6DblZwjqNjhd5Ll0BgWdJapYqiopTzT3CcV7vs1ruqVOn3O/tRl2BGU/3eZ1qzlcJOnMBdCoEpWShdziJpxamCDPWsNygz2w2F3ysohB+JJ3MTH+Mutx7iQk7zrQf3iQ8XPuclVNYyJSY4XUOY6adatkZBGdlE5ltJTM5iIOJDbBb/Xho1Gx3u/h4PSgKpxx10anaHxV0BJOYGEZyUigAY6eN085psrJwIaxZAx98AGYz+PtrCcmCvPQS9OgBH36gcNzHt05KCsyYoQWydjv07Qtr1+YJpDt10irfvvUWjBunDcV1SUsr+MJCCCGEEOKqIoGnALR1PB15itHmzXjGzdQiHb3DSdfeW4iKKngOJXgWAVIuaomM3GMVo4rO5h0tpqSkYDZrS4k4Mg3oFSepqale7fR6fU7fcuaaZio4HL6r716ovOuTKk6VsIR09HYHJ9RKqIFmslSLe39R2dYgHASey/J4fmEGK45sJ9Ys7Tlk+ms/vgFx3mlHY7adIDOYHQ70OoWEzEgyjgSRlh5G/2Hz3O0mjdaixn9Op5GQpPUvPrsltgw/UpPDsVn9qNfsAD8e70z7TkmMGgXDhqn8/Xfus/tiZr51eHK8/DJMmqRlPAGaNNFG0rpqP/21V2XCBBgyBH7+GfbsgWXLYOlP+f5dTCbtZC+9lLtt0yaoUgXee0/7q4kQQgghhLiqSeApAG0dz9PaiFqqcIJqnMCOnq20InOPFpAY7E70DpWhQ/+PUeO/LvhcpTDUVqdT0QH9+2ufjZVt6By+526275JTOdeuQ6c4sVqtXu1cFKeKoqoYnfYLW0+0EHv27Mm9jqpiSc8mMMXKErUOUc1CSSa02OfS252EnfEMoK1ZAdx982fUq699dpTX+m/a7F34x2TQgn6DXgtcD6fXolyCjSNJtdi/vYG7ne7mAyhqEEn2cI6kV0FVFQ6dsROfGk3a+XDsVj9wGKhY9QxnT5+nUyc4cEDhiy/07nOkpfte9mTiRN/39v1SO4oCjRoqvPuutq1HD2jeXHu/bFkx/l1mzNAmJY8eDfXrw/ffy/xPIYQQQoirmASeAoDU1NxRjNn48SpvMI0nSSPI3UZvd6JzOAkOziAyMrnAc5WkiFBBqlY9S1hYMgsX5tnoI/AMDAzE4p8TaCoKesV3tVp3cSFVK1JkoPTXiExPz634qzhVTFl2jNkOjupC0N+oYFVzh9oWVVFXb3cQFuc5lNRk0jJ7CQnaj21WzhIqtQK87yV/plkXp8PicHDiZG32bGoCQNXyZ4lpfoBMWwxpaSHs09XHYbNwJLU2/2e4i1PnaqA69Nitfpw4VJXaN9zI1z7+3vDZ/GVe2wpLJjuVgr93AFLTvYcOe5k+HWbO1OaCHj4Md94JN98M69cXfawQQgghhLjsJPAUAGzYoLqXUzlHBd7kVZ7hE482eodTG0arKGD3nZXSCgtd/FDb++77lVatdtCgATS8UxtKqvMxx7Nq1ar8sz8nYnaCUsDkzYwMLZjRObXgU6eqZBanMk4J5M2g6pyqVmAop7qtvrKKw6llCePj44sMPA12J8GqZ/RmMnmuAZqVE4CaizFhtcLJFBRFIfxIFvc9M5db2m/jpw0DMRrtZBGGkmLEbvUjK8tIWlIoUYfTMcQasGX7EZtYifSUgALPXb6Gd6GkV1/Nfd+q0w7WOUZx37ClAAwdElFoX2NPFr4fAL0eHn0U/vlHG4ZrscDGjdChAzz2WNHHCyGEEEKIy0oCTwHA//2fwvbtvvcF3ZUEaEuEuGrd6AsInBTVs3CPr/mWxWGz6XGqeoKCwBiqBWC+htoCtGm7V3vjV3ABI1dQ6Mp4Kqo2P7Q05Q08DXYHBrsTU6YWLNr9FeIUrXjPv//+W4yMp9OrmJKiwMQfX2HDtB8AiGx8Dr9Mm0cBooJYdNq5zA6V8tEJzPh2BNnVTwJwPAEUK+hTFdIyQgk84aD2gTM4svw4GxvD+aOV0Pl4sHcMWsHsjQ/y5WctefVVSEzM3de2be775yZ9TLoulGpjfAfItVse4rXD44jsFufelne+bKGCgrTCQ4cOwdCh2gKnN91UvGOFEEIIIcRlc1HrSezcufOCjqtfv36hVT3F5WcwqPj5KYSTQAfW8Tu3EEcUAIF9XetFOkBVUQBdnsAp71RJnVP12Jednc3q1avp7FowtJheeOEZKlU6w6lTUK+i9vcRX8u07N+/n1q1jQQ+lElakgXwPdTWxTXHszSWfMkvb+BpSrOhU1X8k7XCO2kOM7o07cfN4XBw+vRpateuXeC59Dol5zl6RnxLZ/TBWDEb2/kDDFQcWDKyURSFtLQ0AgMDCzyfK/OsAMlJYRhDEkCvPYMkmz9Kpp6wk2nsD2xIcIoVvU7BbjWQdTqU1DORRNX4B4AGLfcxedEoHA4nlWqeZNq4p5k+/m4A4uMdTJ2q58QJrTqt+1lYAvmKdvwdHUylzvGcWu25JE9CQiiLq/Tittc3sL9pdf58uwkzZuzmySebF/K084mO1srrjh6tFR1ymTkTfv1Vy4o2bFj88wkhhBBCiFJ1UYFnixYtLmgo5bZt22jWrNnFXFqUMrtdYdEi6MxqFtGfP2lCM/4EwNJGm7tosDvc8zfzzuNs1y73vc7hBFUlqUSLNvp26lRFAA4s9qcevrOZVqsVRVGxZmrfykV9Oyqqis41ZPgSCkizYs6yYQ3U5nVadSEY7Frm1ul0Fquirq+frUO7azB6SSQ39j1G2i1+mJO1jGpSUlKhgWdecQnRhEed1a6hhnA4ozb+aSpWNZjjunAaZCaBAnqHij3LDMkmEuKrsVOt73WupPhQ9/v0rHSysoI5fNizzZG0VuxBq4xc4btDnArVAs/eL//EUVNlEn6N5MY9Z6hz4CxnYssD8NRTzfl/9s47zpKqzN/POafSzZ2nJzPDDDMMUXKQJCLBhKBizrjmXV31p6yC4prXNewuJlTMYkDMKCBKEiTHgZlhcuycbq6q8/vj3Hu7e7p7pmemB0TOw+dy7606VXWq+nbP/db7vt/3/geqfO2r7pTnoTXcfDOcdtqYhYsWjb6OYxMNXbcOfvITOP98+I//gGOOmdZ1slgsFovFYrHMHPskPAH+4z/+gwMPPHBaY6Mo4uKLL97XQ1r2EzfdBP+LMWe5mVMby6sbPPxlZZwp6jrvvHP0tYpMKmupNHmLjb3hgOfWajyZKBa11gg0YbUWFZ1kzFhEbFJ296nLyzRIj5RRhZAw6QHQUxA4tXrPaaeRTkL32nYAXnL+Ak4Y2UxqYGiP95mv9ecE0LqV9RzAsSPrCD3FYNiEFANALTpaaEYJoMufbFcsO/KxxuvvX5XlN9fCN79p3n/9z2/gnWd/kzuXj/beDEoVjn7z3Vz4rvvY0lSFkuTAizZx7J/WoSJNhxxNzf7611z+9T0rOfjggycct7cXTjkFVq6EF78YFi82uvLQQ2n0OUVKuOYa+NSn4Oc/N863114L55wDH/mITcm1WCwWi8VieRLZZ+H5ghe8gOOOO25aY6Mo4i3W+OMfmlO5GYC/MhpGkikjOOvCaVfIeOajia2HVSgwecRT11J/hdZMTEydZH4Vkwo8mYidSfxSFT+KKNXqNB+Ri0kPlRtz3ldKJUmf9JnTZUyTwjBkx44dzJo1a7fbVoeSo6/jFKmRMm45RGhN0+bxrWiqgykUEVHZpe4rNZbz3/wLPnHxJxrvBwbgO98xr48+7S5uLb+Td+k3sfTR7VQ9xfz1/Tz2uXZuzi4h3BaxcHs/biVE1ep3j12xktQHh/nW514KwBNPPDGp8Pzv/zaiE+BXpuSVL34RTjlFc8UVggMPNH5DHHkk/PSnZvCnPw0/+hFcd515fPjDRpRaLBaLxWKxWPY7+2Qu9Mtf/pJly5ZNe7xSil/+8pcsWbJkXw5r2U+00sNhGKOeWzilsdxbZqKXKowbwk4Ar3nd7zn1LX8ft4+6AdC+RPV25p4v5mrHnCjYWltbEULjTJYDPAlqQCPj/R/xdCsRQghETazflTkYZ6f5b9myZZ+OURUOTbWepVEU8fDDD7OyrsZ2wcrqCrQ2F6A3nIdfjvBKIanhMgtW944b65fM/EtDGaLKxLRXIeBH9144btkb3wiXfO4GpNQMVNJUQ8G8Df0cdu8WMkMl4tBhg5MjNVShY9sQ8zb0N7aVAhYv2jH6Xiom43Wvm/zc7r1Xc8YZcNV3dvocHHwwfO978PjjcPHF4Lpw3nmj63t7TU8hi8VisVgsFst+YZ+E54tf/GJyudweb5PNZvflsJb9xLO5FYBHOZge2hvLRe1T4lbHp9r+4HvncfOV46PdKo4R2ji3TsZYA549RVUnbtvV1YUUGlmz0t2dnhRV9nt9JxhzIDA1ryLW9DhpZKTHXRffnzx9dTIeeughAE543z0ArPu0ce+RteuplBFo02kR09JVJA4VIPjTwDySwyVjZiQE2Xxl3Nj69azmE0TVyestlz9rJa9+71WN9y97GZx4zp+RzKcrbiXXV2L21kHadwyTGinjVSJAkBso0do9QnKnYyZKVc4/36R8//GPCyc95pw5k59bPi/p6YH+7vzkAw480PQA3bx5fKrtJz5hTIk+8AHYuHHybS0Wi8VisVgse41tp2JpMFmaLdBQH2oahjiqVgc6lcCsVCqTLp8Oojzx+HEcI4VG1CKdcjc9LY2r7V5PYbfcdttt496rMCaRr5ja0lhTrY724qyLxelQNyNacfIqNn33hWRn1c43rD1L86vc398/+Q7GkB0qE8cuggO5PXEsix/vaqybSrhLIdDR1EJ5sLdp3PueXk1eL+HhPpf56/tIDZdxopjUSJmO7UOoMGL25gFUrCccUwCZTL52PhOvUakEJ5wAzz6/wItG/krLv5oI6fse/GNjzOMbdnODo6NjNG9Ya7jtNhgchP/6L2NQdP758Mc/jrdstlgsFovFYrHsNTMmPOM45nvf+95M7c7yFHAGNwFw5/zjxy1XzUb0TGUuVKflmCG8SrTLqONY4bWnyEkinnXxJmtq0pG7TvF1ifZrxHNnUyW/FJLrL+CVQ+RIdVx959jXmzZt4r777pt0n93do/0t03GFjQtn033UAYARswAbNmxojLnlllt2OUcB6MhjXXgsQTHCneblKBcSU6679Fsf5dbhYzj7Fb8DYNbie+kNm7iHZzFvQ3/jM+FWIpY9sp3n/u5RZm2buo/qihXrzFyT5QnrnngCHn0Uznj3raxOZWn/zDYO/d1Kftc+Wt/685/59PSY15/+NDz44C5OTAjjkPWb38AZZxix+atfGROipUvha1/bxcYWi8VisVgslukwY8KzWq3yxje+caZ2Z3kKeBG/5o18m991nTfperUb4Tnv+V34pV0Ly30x1plMMNb3p0RNHMtdR2U9Ee6XHp5jGdfPM4pJ5iukhstkiuVx5/9Ere9IoVBg1apV9PX1TZqiXCwWGxFPJ4ron5+kd5lxy5XRxAjzdOpry+UM9w6kSA9O33043z91uxbHiUimC7z7M1/ksm9dRueCTdwdLSHIxzT3FRrjBNDUX2TBur5d3qAIgiptHf3ccMN4s6Q4Hs2Qve4A05ZFBprovBKyU/PKx74PQGHEp73dZNRecgk897maP/4R1q6d4oBSwgteAH/+s1G173kP5HJmg/Xrx0/ARkEtFovFYrFY9pg9crW9/PLLp1y3L5Esyz8Gm5nPVbwRyiCbQuIB8/GIiwKZ0LuNeK79YScLXvnELscM74OBy2QpsnVB5ikjtnYnPB2ihgHS/qKe9lpn4Zoeyr6DkmJc/86hoSHuvfde8vnResSNGzeydOnSCfus90VV1ZhyVnN/YHpqiknOZTp1tA+PHM0D2YW0b5866rgzYWliqq2OFWLMNZ+zcCsvftPPkGT4hXs4Bxe24VYmCuHpeDvlWkeIdsrM/vWvTUYswNAc0bhzlhouEylBjz+H+f+7lk3vWgzA+/4tBBy6uwXnnAOzZkWcfpogmZKcc3aVl73cmdgv9eCD4ctfNqHSq682UdA6f/gDvPOdxt3o9a83NaMWi8VisVgslt2yRxHP//zP/+SRRx5h06ZNEx776tBp+cciOHyMSU1N28idBE1b2/h6wswxQ40I3FRs3rx5j+fS+pwRwNRn7pzKWjfTqdd2Oruo8SyXy0hiVLj/zYXGkhoq4ZeN+NpZlPb390+oe935d6mvr6/x2qnGbHc7uBrTYmQyw6XpcH94IJv8ucxbv/ua0MY8+mvRRy0IKy4j/U0M9zSj40n+jOgczb15soNF5F5e7ndc/As+8e2PE4654VF3sz3pf1YjAwgKFXL9Bdq6hjn8ns3M29jPCcc93Bj/s1+Mv7e2Y4fi6p9KvvMduOgVLieeOMJOZbmjJJPGoveAA0aX/eQnsGGDMSNassQ0E73yShiavoC3WCwWi8VieSayRxHPww47jNe97nU8//nPn7CuVCpx1VVXzdS8LE8qmu/zWu7lKL7JxYyQYefOJTKKcSvjo4k9Pc3j3kdZ0ag5nIqRkZE9nl1YNhEpJSZuX09drZsLiV04B8VxjCyDM4lJ0f4kPVjCrbVVmY6T7eOPP87cuXMBI0J7e0dbnLjDIbe5h9CNqbd09jLtszvfRvu2EVoGd++CW2dwvbGSLeWTVEs+xYEcXUNzmBOuo2XO1nFjI52mua9AZmj6qbw7IwW89rQr+d5ZEX/6k/EAamqC9vmD8PZeZOjS1FfglBtX0zU7w9KVO9g6v5mFQ738KlPmxDPv5a/XngjARW+4hpUPL6WtqZ+u7g4efmA5AHfemeHZz4bjT3iQ//vfDh5+2OWEE1qZskvUN75h2rB897tw/fVw663m8Z73wItfbJqYBsFen7PFYrFYLBbLPyt7JDwvvvjicamCY3Fdl8suu2xGJmV5clnOY7yGH/JSfs5XeTst79lO7vwB1j1neW2E6X25uxrPyNm98NwbYuqOtUz5+VONSOeuhacumXTbJ5NkqdoQnjt27NjNaKY0IFq/fj0d8xexwW1pLFPVqNFqZU/I9IXkegf27OdVNH8u+vtm4cQRhWKGwqpOikEv7CQ8R+JO/FJI+/a9T63u788AcP31xkBKCLjvPvhy5W5+rlqYtXWQ5Q9vo7m/QFN/AQHM2dSP1JBqKZIUZVLNefL9KZYftY1jjtxI2ZUkKzHf+Z7kofsOahzrzjsO55hjRo/9ne/A8cfDsmWm/LNBIgGvfKV5bNkCP/iBEaErV5ra0LGi85574LDDwPP2+hpYLBaLxWKx/LOwR8LzbW9725TrlFJWeD5NOYvrAbiFUyiRIMiPULor2VivAo2oarzqbgRbVaB2k2q7NzRfbOxJxS6EpyMikk5pl7WDWmtkaGpFp9rPTBBF0bhWKUoKmjcONeawJ4xNTQ7DEE/HDKhUY5msxvTU7VvH8OCDD3L44YdTKBTo7+9vRFDrJMrhbo2gdkaHilI+xZYNS+lo2sK2jQdSGspRGJrYy/eWwQOMqVJ+79vneJ7Z9rwLVgIHE0XQ1DzEY7XGspnBEovX1D4btW3qab0f/NAP8OUwzzp/FbGvyWyroBA4FfP5fO0r/8hN83YwNJLitpuOGnfcY09+gDe+8QiUijntNM2110oyGUEYglKjXViYOxf+3/+DD37QiMx68SnAyIhJw/V9eMlL4OUvh+c8x4pQi8VisVgsz1hsH09LQ3hez1kARP0O2/7fgsZ6ERtjIa+0a7fUptf2zFjEM5cbrZnznmuiZlLoKR1bpYxxZcjOPjFjieMYYnM++1N4TsYBq0xLlOk4zgLcfvvt3H777eOMh6rVKo4jcGs1qtmePMl8ZVIzoe7ubu644w7uvvvuRpT1/vvvb6xPDZdJD0ydBjs2vbeOQLBp7XIqW3OsWnskxc0tiIJDfmvbhLEbKjmWrtw+rXOdilSqzGvf+nvO+/AdaG18fL72w0d4VGQBaOorTGl4FZRCiiRp7yvQsbWA2umWhOtGnPWce3jpi25uLDvz7Ntpah5i87rZAESR5M9/VmSzAiHAdeGd74ypVncythUCjjkGzjxzdNmqVSYveGDAhE/PPRfa2uCii+BHP4Jp9Fu1WCwWi8Vi2Z90dXXxL//yLyxYsADf9+ns7OTss8/mb3/723453j4LTxvlfHrjUeZ0/gKMCk//0PGCRGiNiuJGuuhUJE7Kz5jwPPPMexqvZbJWv8nUjq0CjZS7jrYKIZBaI0ONtx8jTxs3bpywLPAF5XJ52hHPYrHYME6q03DwrdWoZgZLeO7USjufz1OtVunv7+fGG28cJyZnbxsku4v6zvvvv5+urq4JyzeuXkF5MAWbUsiikXNhxaVaHn89ddknPbL30c46ax+Zy+VveAE7dmg2bIDVqZi49mdr1o5dp/EmCvW638mvUX3pf/7nV/nPT/0f5z3/Ti699FvMndfNyWfczQUXXTdhmx/+KI/nwcteFrLLewhHHQWbNsFf/gJvfzt0dsLwMPz0p/DqVxsxWmc/9pW1WCwWi8VimYoLL7yQBx54gO9+97usWrWKX//615x++unjjC1nkn0Wnp///OdnYh6Wp4jT+CsZRthGJw9wBACJE/Pjxkht6jt3V+OpEVOOufXWW3nwwQenPa+5c0dFT1wcFQ5TCU8pNJJdzy+TyeCKEFndv1/0JxNsAI6zR5ntE2gIzxGTItvUX9jV8F3iVuNxqdN33XUXK1euBGi47NYjpXfccUdjnNiYQBZc3FA3hFupmKY8Mpr+CzAUZce9759GhO++++6bsCyOJV2PtHPtteZcVy025kyJfIXOzQO73N90WraAiaymElVkJBAIXvmKP/HSC//MySc9zMsvuo6TTh29CZLLGXOra65xcF0YHg65rqZPo2h8y0+UgtNOgyuuMPWgd9wBH/4wrFhhjIjqfOc7cPjhJmX3xhuhXJ7mzC0Wi8VisVj2joGBAW699VY++9nPcsYZZ7Bw4UKOO+44PvzhD09qJDsT7LPw3NOaNcs/DqUSvIhfA/AbXoiufRzCLe64cUKDVwknRDPlmP6Nx133IFJrxBSfh3K5THd397TntmbN/IkL9a4jnrtytK3jiXC3/Uj3lbFzHJtaO7buc1/2m+o1NwbUXkSXx7ZqkUI0fn+HhobYunUra9as4d577wWMgL7xxhvHpftOdgbVoQRRLeIp6QS9hJFovBCt73Mqtm7dSl9fH3/961/HLX/2s+8H4N//PUEyGbN9hZnv0Xeu36vznw5SCKh6yNjhxBMeY9nSjbzstb/jhFPvYdPG2ePGvuXN6zj3XPje93Zw0UWwaBHMX7Cdd7xjp5sCUhq3ok99Ch55ZHz/z1//Gh56CD7/eXjuc6GlBV7wAvif/zEpu/ZvrMVisVgslmkyNDQ07lGe4oZ2Op0mnU5z7bXXTjlmptln4TlVGpvlH5/HHoOAEmU8fs2LGsvjwviPhYg1QbGK3OkL8KGHrm28lkeXTX/MGfqOfP/9o46j9dCVrMZTC0+hUbtJte3v70cS7feI59g57tx3dF+oi1hfG8G/c1/V6fD444+Pey+EGJcavGHDhnFCcyxT3WSSWhBVXdAef91yJD8uvpTsJG1UbrnllkmNkHp6elhfCxXuXAM7Z04vS5+zlkJB4vhVSp4DWjNn08CuTnNGOezQ9Zx01GqWHLCNE599HyeeZiKgiWSRQW3Mnz760Zhf/MKM37ypk69+NUkiEXPjjdMwcPrWt0zd5+tfb1JyCwX43e9Mi5aDDx5vWjSDnyeLxWKxWCz/fMyfP59cLtd4fPrTn550nOM4XHXVVXz3u9+lqamJk08+mUsuuWSPMhT3FGsu9AzmWc+Ci7mSNnr4E89rLE+clKftrNHopIxjEoVqI+J59913A3DIIaPCcyR2kBETxOne4nmjX9ijXpOiKqpTCy1XhiTUru/WDAwMICJQu9jPTDBWoE3XTGg61PdVdw5WlT0/j8nE43SyFiYTjGMp5VMM9Wd4pHIwt6oFLFg3WhswNGSMoiqVSkNg1nnkkUd44IEHxtWz7hwdXfrS9Sgn4oN/+SUAC9f2kh2Yfv/RfUXU7nwc/aw1vOrVf+T5Z/2d8y+4kf/34e+y5fH5tLb1c/JZd/Kpr4yWHbS191EqSS75yEq+//0il1yyi8Bla6tpz3LVVbB1K9x/P3z2s8YF99RTjUlRnTPOMGL07W+Hq6+G7ftm4GSxWCwWi+Wfi02bNjE4ONh4fPjDH55y7IUXXsjWrVv59a9/zdlnn81f/vIXjjrqKK666qr9Mrd9KzqzPO247jpjsDk2o3GEzIRxPde3N1671ZhkvoKoCc/BWgSmo2OgMSZs0STz0Yy1Uzn//B/w5S9/wBx/rhGhohJNKZJcFe722FEU4YxIZLR/I577yzG3Hj2t19H6+eqUEeA9YTrzHRoaorW1dcr1mzcvpdK3DK/gk5+TIj0m4nnXXXc1Xg8ODnLHHXdMGVUFE5lev349BxxwAABzlvXwksIN/NA1UfC5G/sbbVOebMJSQCpV5rRTHgQEb37Tb9FoI069kA9++Ft86xsX4jrmmq55Yj6ve10CgMMPv5vnP/9oEgmB48DQEPz3f8Py5aZfaG8vPPe5Ao44wjw++MHx9rmFgmnbUq2adIWvfc0sX74cTj/d/GK/6EVYLBaLxWJ55pLNZslms7sfWCMIAs466yzOOussLr30Ut7ylrdw2WWX8YY3vGHG52ZrPJ9h1NMBTzsNOtgx+aCdfqRONSIojk8ZrFQq41xkhWOMhdzyzIiuVGqAefPvJTN7EK+pts9QT/l5UyJG7CbPN45jqILST16NZ9PYaNUM7dcrmesRFCpIuee/wv39/eOu49i6z6kYHh7eZVr94Jp5lJ7oYMHqAdq6R0jUPi+TmQrtSnTWeeKJJxqv3WrEI24H9Zzr+Rv+EVqRiDGvaq+LCWZ3DPMfH/kOJ5zwMG97x0/5j4+Mutf++PYtZLOCjg6NEJDLwcc/boKdhUoXF14Ys2iRWd4wvR37800mYccO+NWv4L3vNSkLQoyK0B/+cHSs1vClL8Ftt9n0XIvFYrFYLNNmxYoV0/qutjfsc8Tz5JNPnol5WJ4krrzSPB/AOtaxmLs4hpO5jSqj7TDc+eNFpluNCErVcS6ht9xyCyeffO64cTLWeJWZSS3VWrPi4D9ROeYgBlhMhAatpxQ/vqpOy1xIRKDYvz086wJx+/btdHZ2zvj+vaJxnQ2qe3etC4VCQxCPjIw0XGx3RbW661pFrxITVyVSx5x6/eMNA6fVq1fv1RzH0tY1Mu59asSkVEdRhFIKrTXbt29nzZo1nHLKKePG6l18ZvZm3HQQCE499QHzJpa8730/RPmDbN64EID+/onH+cqfbiG55CTW32vMi1Z13U1397PwPEUuN2Zgc7OJatYjm/39cMstJoXhhBNGx61ebcQpmAakRx0FJ54Ixx5reo4uWTJe1FosFovFYnlG0dvby8te9jLe9KY3cfjhh5PJZLj77rv53Oc+x4vHuu/PIPssPG+44YZpjVu3bh2LFi3a18NZZohX8BMABsmNE50AzqzxgsYrhyQKRpyMrcULArNMKCMyhNYkC5UZqWvUWiMlVNEEWlIWEY4A13UnHe+p6m4jnlprEqqM1ME+z2933HXXXYyMjIwTnvX2JPuKFxrh7MTxXhWAx3HcEFnpdHpa20wnHbee/pobLKG1plAoMDy8616b06GpL4/QGi0EHduHGqJ2aGiINWvWMDIy0hD7N998M8uXL6ejowOYvvnZ/jRJmz+/Vi8dmrrXFSespDiSpPVZXTTNHeCQznUoX/PWF13L42d3kJmfZ6Qj5GWvXMhfb2znYx/TXHqp2UWxKOjpgc99zqTnvvvdOwnROtUqvOQlcPvtJkp6553mUefSS024FSCfN7WlBx5oxajFYrFYLM8Q0uk0xx9/PF/84hd54oknqFarzJ8/n4svvphLLrlkvxxzv9Z4VqtVfvnLX/LNb36Tm266aUaNViz7xqv4EQA/5pUT1oU945tm+OWQRMFEvB555JEJ43VsvrTLWJPIV2ak7rClpQUpI8JSSCoUDHogSxGFwuS9K5XY/TG11jg6Qob7Pz28bqgz9obLTHz+w3C0llVGeq/sr/cmCjsyMjLp8ltvvZVnP/vZE5YLIdiwYcMeH2cs999/P0ceeSRSQ7oQMpxyOeqO0X329/c3rnOdarXKww8/zHOe85xxy0ulEn/7298QQjBr1iza2tpob29nd8xkJHT27F4uuPAGlhz/OLPdKgNVSZIITwsoATk4gU1QhniD5u+pDUA7H/uY4Cc/XYfreuzYNpfW1oiVKxWOE/HWt4b4vj/xYIccAtdcY1Ju16+Hv/3NPO65B+67D448cnTsX/8Kz38+ZLNw9NEmOnrYYaa36MEHQ7D/b9RYLBaLxWJ5cvF9n09/+tNTut7uD/aL8Fy5ciVXXnkl3//+9+nt7SWbzfL2t799fxzKshccykMcxsNUcLmGCyasr67xyLTlGe4xvRj9UkhQMsJzcGxrhxpzXmJaSshYE5SqM9ILyHVdPC+mmg8JKoAHDFVpaWmZEDnUWqNkhBR6t2mjsqoRT06rIgDWrl2L1prFixePa1uyt1QqFWTNlVfGU9e87oqBgYGG6Lr55pv3ei633HLLLq/32Oj43jA2Wtq6bZDhJW209ZkbD3Ecs27dukm301qzefNm5s2b19jPXXfd1bhWW7duZevWrQRBQBga4XbC2DTVMcxkJFQIOOXZD9fmIWhyY2Dy/UspuOCkO2mSBW7+43E89qi5eTFn7iDnv+FrdF73bG666WSuu+6PhOHZPO95ikzNI+z974czzzReQwjB//1+EWvXLuIL//MqMyAMx5sWbd1qxOXQENx0k3nUUQp+/nM4/3zzvqfHREgXLDAnZLFYLBaLxTJNZkx4FotFrr76aq688kr+9re/NSIFl112GR/4wAdIJBIzdSjLXlLPlnwlPwbgD5zLAM0TB2pondvfEJ5BsYpTnTzV8n0//S4PnNPBVjpxqjEqjNm6des+z3X9+vW47mz6Hh9iYUFDGnQhmrTYeWRkBEeY+e2qFlFrDSGIPvGkRt/XrVs3pUjaU0qlEk6tD6lbCvdKeD7yyCOcfvrpwO5rN6fiL3/5SyP9Np/Pk0qlJozZ1zTbSqXS2HdLT56Ni1oIho1Rzu76o65evZr+/n4GBgYIw8mvU30fYRhy4403IoRAKUVLSwtz585leHiYhQsX7tGcpxMhna6Y9f0q5552L+ecei+33nkoW7uaeN6rbyA76NE2qwuAT3x2Fvf8TXHAAREHHaT4r/+CL3zBPO6805R0vutdZn833wzf/z686EUOy5eb17kc8Ja3mB6ijz5qIqL33w8PPQQPPgh9fbB48eikfvhD+Ld/M9HRQw81EdHly0efDzjAiFWLxWKxWCyWndhj4XnXXXfx+te/nu3bt/PJT36S4447jiuvvJIf//jHDA0N0dLSwrve9S4uuOACzjjjDE4//XQrOv9B0BoUIa/l+wD8iFdNOs5fXuLxB5Y33ruVydukbNiwAXd2lqhWF6aiGBlrcrkcmzdv3qe5GmEo6Fo5gBqMkG0awnhCaiWY6JcUMULsOgIYhiGujAjC6j5H455K3G0mZOsVq3slPOuCcU9F56ZNm5g/fz4PPfTQuJrPTZs2sWzZMmBUVIVhOCNtZYrFIqlUitRIiZaefKOOdO3atbvcLo5jurq69uhYWmvCMKSrq6ux7fr161m8eDHz58+f1j72R62oEHDKCQ+bN8MBSDj+mE00ZX5Kf38znef/kntvP5o//WkBb3r5Ot46//18Y9MvOP54+Mbbv82P/m2YV33pX7n7bnjPqx5n9eplrF4Nd99ZIJFOcuKJIFx3tI1LHa1NNHTWrNFlvb3GrGhoyNSP3n77+MnedZcxLwKjfJ94wojSgw6CSW5OWCwWi8Vieeawx8LzDW94A2984xs5/PDDOffccxFCIKXkzDPP5E1vehPnn38+nudNmpJpeWoRAs7hOuazmR5auZbzJx3nZMcLBhXFTFZCGUURfgR5N2nGhTFCGzfXfUVrjVJQGqiit5VxFjrofDRp/agRnhrBroVnFEW4MsQZimfEbfWpwPd9gmIRr1gl0DFqL6NLt91227TcbMeyatUqVq1aNWF5oVCYILj2dN9TUa0acT174wDz1vU1lvf09MzI/ndHGIaN83Zdl1NPPXW/uRXvCUrFrFixFTDZBWecuorVfyvhbniC+S89gFff+788cPsKtv/2E+jBPl55ckT54XUczS1cz/14qsCrLsrTNWB+d3/5S+jsBN83XVuWLcP8wZg7d/yBL78cPvIRePxxePhh87xypWnpsmpVbcMa3/seXHHF6PvOTmNgtGSJeX7Xu4xLr8VisVgslmcEeyw8N2zYwHHHHceKFSsay84++2wuvfRSjjvuuBmdnGVm0Rr+xPN4KT+jiQEqTGJKgqkdHIsTRohJBF2xWCQbRhQdYz5Sj4pO1rtxz+eqcRyIK5rqjhKyHBD1lKfsW+nI3UfXwjBESY1TjGYkGvdUsHnzZpYtW0bTDiP2JosAT4fdparuCYODg1Sr1XGOwzPV37c+z6ah0fkODg4+JT+/arXKjTfeCBgL8oGBARKJBAsWLKBQKDAyMsKSJUvwPG83e5p5lPBZfpJPeNIKHAKOOSvkUPdzOM96FSLTxnFSUFw2jNP8HC5dejE9A7O54k+X055Yz0i1lbt/9Wc+/d0XEGtzI+M5z4n5+MeHSLmrWbflWI44wmhFADzPmA8ddtj4ScTxeFfcAw+EZz/biNKeHti+3Txuu82sr+cAA1xyCfzxj+OF6cKF5jF/vjU4slgsFovln4A9Fp6vfvWr+fd//3fmzJnD0qVLWbJkCddddx1/+MMfOPjgg3nLW97Ca17zminbXlieOoSAKh6/4KW7HreT8HSrkwvPoaEhOtFE0nyMVBQjYEZcbevtVNBQ2lrCKTjIfDRp/djIyAgZr4AS8ZTCFEZTTLNOYcaE0ZNNPZLYsXngqZ3IGOI45uabb2bu3LmEYcjs2bNZs2bNjOy7v79/XBsmrfWUDrtPJvWofqlUGnejZdu2bTQ3N+M4DgcddBDBFIJpJt1yx+IwejzvtOcgxagIThx2JFEckZ2tSVYkLxr5CScedw+JDp/w8VW849Wb2Pyow7aRpax7ZB4ffMd21m07iO090DmryKMrfZqbd9FuZeffvfe9zzyAyo5+3I1PIJ5YY9Jvt24dH+28/364917zmIyBARoNTa+5BjZtMqJ0wQLz3NJizY4sFovFYvkHZ4+F59e+9jWuvvpqBgYG+Pa3v01bWxubN2/mW9/6FldddRXve9/7+NCHPsRzn/tchBD7tT+eZc+QxMDEL46pM4fJ35hpvN+5nlNGk4u0SqXC2NaZk9WB7i1aa1zXfHYGHh9ElgIUmsokka4wDHGDkMCpTGpyU6cuiAO1d4Y6/wjUTZHSfTMXsZwptmzZAsxcv1KYmLIrhJiRiPr+pD6/7u5upJR4nsdBBx1EsVhkwYIFwMRa0FWrVpHP51mxYsWk7VHqN0r25O/pWNFZR0lz40Yl4LSXbAIxC60c1JFtHEjIgUeHwEPAQ0SR4OtfW8DixbdwzwPHcsQRBe65J025bIKYz33u+H2vWgVXX22yaz/3OXjta+EjH9Ekk4LPfa6Z17zmGL70pWP43Ofgyz+HixS85z0mwMmXvwxvexusqQnTtWthwwbzcJxR0Qnw7W/D7343/uDJpBGg8+bBb39rorJghGylAnPmmFTfpyAabbFYLBaLxbDHwlMIwSte8Ypxy+bNm8dll13GpZdeyp/+9Ce++c1v8pvf/AatNa997Wu5+OKLedOb3sTs2bNnbOKWyYljCCON64hGAOD//T947TndHPKWE/gA/8IXeS8hoxHpsaITjICcN28HmzcbUxEZa4SemJ6ptUZW9ZjtZi6KGMcxyaQ5gb7VQ7QXWxHoSft4aq1JeUVSbpGBgYFd7hNAyXhGorJPBfWobVPPRHfff0Ymcx9+OtWPx3FMqVTiwQcfBKC9vZ1EIsG9997LkUceSblcZt26dWzbtg0wfVGFEGSzWXK5HEuXLgX2j2mRdHadlaKU5h3v/BVaa048ZSO33H0QP75rHdUHzuT9l7RywQXG2HbxYigVqrztHaP7++HDWxnY2MUXv7eMwqYE85cN839XpHnhC0e4fd0aurqexf/8D1xxRcw114ywdGnEt285j2uvFfzrv5a5+L+T+D6UikW6nyjywG9Nq1EhgOc8B51IUH1iI+7WDYgdO6BQgJUr0Zs3I8aKy49+FH7/+9H37e2mbnXOHPP46leNsAUjdoWAjg5rhGSxWCwWy35gRvt4CiE4++yzOfvss+np6eE73/kO3/72t/noRz/K5ZdfPiP9HS275qqr4M1vFkQR/OUvpnTqjjsg/bmvcChreTk/5fN8YJf7kLHmqGMeGy88gb/97W8TxiaK1THbzZyYi6KIIDDieWhLno5eE12dzLRm4cKFbC+ECKF36VY7Vmw+XVNt6060ueI/XsRzf1CpVIx5VC2NM5/Pz2h96pPNfffdh+/7DAwMcNPYfplj0FozODjI4OAg3d3drFixgqampnFjtm3bNu5G3u5Sd+vr9ybFVwjBvHndvHJeN5u7Mzzwsr9zhJ7PtZet4JprJJ3zIuafu5UT3p5g+HXbaaeblF/klu0Rr3vrYzw8dy4HzdmI/4jiB1nF4Wf0sTT9GFsSOQaeyPHr+zfS2j/ID69uRkrNu9/dybvfDS984TaWHncd//3RNzbm8oUvDHPBBW/loA+9l2pVsGJFnle/FS447mZu+FaVv/2xSs/ZRV70IsE73xkYoblwIfGWrciwCt3d5nH//YSpLOUvfZNU/V/Bd74TrrvOvE4mjQDt6DCuvh0d8I1vjKYTP/qo6U/V0QFtbbaFjMVisVgs02BGhedY2tra+MAHPsAHPvABbr75Zr71rW/tr0NZxvCzn5nnKILzzoNSCTrYwb/yZQA+xSXs3LReBDG6NJqCKyNNS8uoaU1dUO4cJdRa40QRItZoKVDhzEYRPc8Iz+HtRSr3DhD4k39hLpfLpLwCAr3LSOZYQ5qna8SzLjyTpQqof/40dq01PT09tLe3A0wa8X46USwW96iVT7FYbIjVarU6LgJcrVZpbm4mk8lMKSZ3TtEdO25vROic/mHafrea7s5tLP7qJjbPaqJyquagBzZRVgEHreyifXBMND4Nywa3wyAgIb6j9rdiYZGF7ICO2rh+eP+//xgRwxPdnTxw9xIqqsimB+dz2NGPs3XDLHp7mtg29FP+34fm4rqnceZ593DvnQdz+ecyPHCh4trfP5dKxYU/gZ+6mVzTJu5pfSdrDruC320IaKGPb13+d7rue5Q7ftlMkC/xm4NHePe7FYsW/5FTekM6ggBRKpkI6vr15gEm1ffKK0fP633vM2ZI5qKaGtOWFmhtNY/f/Ga05vT6600/1Pr6+nM6betSLRaLxfKMYr8Jz7GceuqpnHrqqU/GoZ6xFIvwpS9BPQvxHe/QlErmS81/8hGyDHMXx0zaQkW1h4SbRtPT3Go0TpqKePKUxziOkVojtEYjJrjh7iuplJlFebjK0D2D+K2Tf0l74oknWLy8BVdGu/wiPVZ4Pt0jnq4Se9yn8unK4OBgQ3gODw8/xbN58onjeFKxOrYlUCaTobW1lba2NlavXk0qlaK9vZ3u7m5c12Xbtm20t7ezfPlof949EZ1jRaqnY+YODTGXIXrjJPFPIRNWCMoT/0bsjBRiXF34uHUaELCkYztLzhttyXTSSQ9TLjuEWpAKqnQe/yjHH/cYUsLzTrubOBYopTl4xXqG+1I8sW4ep516H1098OADy3j4oRIrDt1GteJw1b1JFiyYT/6VLch0nv4fCz74wQRve3uVd238CQsPg4+932XlX7s5tKOLs47oQnTtoFqocMuf4bTTasHNdBra2tC9vcZ4rbfXPFavNiJ17LX9r/+CP/1p4gm7romWbto0GjH96ldNi5qmJrOfXG70dVMTPOtZE02cLBaLxWJ5mvCkCE/L/uWhh+Dww8cvu/JK88XnaO7mzZho87/xJfQU5kKDV7U23rvViIMPXtd4L7XmtnoLhJ2QwowvKzmp8+3eEscxnme+v0WhZnhNCX/O1GOliHFVddoRsaer8AQarUsmuxnwz8jAwEBD9OyqhveZzPDwMMPDw6yvRegGBwfZunXruDFbtmxhZGQEKSUjIyMsX76cgYEBhoaGmDdvHuVymeHhYdrb23Ech4GBAdra2sjlclOK1Nbd/L6NjbiOTfetM13x6/tho/mTErKRtCGEqUUFaEoVaEoVmD+/26zT8MIX3MoLX3ArAMWiz+bN7SxatI1Fi0xN7Uc/+m26djQzb34Pp5xyP7/4xRmcd5HEDxIccWSZ5vM8VuWfx0c+HbB+PWQyVU4/PeQXv7iab3/7Ni754DFcfEEPC5LDrOjsY2G6lwNmlyiXSuzYEbBhA8xKHEnns8rkqr3Q12fEarkM1Sq6WKRcVcRlk93LtddOLlLrJzv2d/71rzfR1J0Fav31Jz5hGrMC3HWXaWmTyZhHOj36nEjYyKvFYrFYnhSs8Hwacf758Ktfwf33a444YvSLwlReKwkK/IDXINH8kFdxOydPOi7zkoFxwjM9VELK0S+HTjXapchxyxHlwJ3S/XZvUUrgOCZtuLCjQhBMboYShiFKRHgqJC5PL4X26ZpqCya12HXdRvTzn52xNxP2tm+pxTDWmOmhhx6adPlYV+ItW7aQy+XQWnPkkUcCk6foTuW6Ozw8TCaTIYoihoaGaG5uRmvNtm3bmDNn8jtJ00kB3lm8TmebRKLM0qWbxy3zvIh583sAOPnkh3jWs1ZRLnskk0VcN+Smv8DDj25jeORkXvCiBymVAm69bTmf+u+fUBrRHHxEhv/92TLieBbFos+ixb28891Xs/4/ruF///tVtaN8FoATThjgiu+tY+WjOd7ymk4WJHfQLodYe2DI+9+/mjPOKKMPeTlRfBTDWwbI6SHEUB+Hzh9CDPZRHNFc9wtJby+sWAHP3rYduW0b1Iypxl0fIShe+mkSnuaGGyoc94XPkfvjzye/MFJCfz9ks+b9Jz8JN944UaDWReu//Muo+dLKldDVZVRzKmWe649Ewta+WiwWi2UcVng+jfjVr8zzkUcKvv1teNWrTLbWypWTjz+CB1jEOrYwh/fwlSn3mzhptCZLeDGJYhVRy4cLjirstnYzKFYZyQWoGUy1rX+xTCYF5bKm2F3F9ydvhRCGIa6MaEsMkNCJPdr/05F8Pk86nX7GCM8wDHnooYfo6+sbly5t2f9Uq1V6eowwu/HGG5k1axYtLS3Mnj17nNATQrBlyxZ6enro7+8nCAIKBdMv1/M8tNZUq1U8z0NKSalUYs2aNSiliCKTIr9w4UKiKCKTyZDJZPB9n+7ubtra2oiiaFxvaCEEhUKhMX5XNa67E6ZjxySTZZLJURM8T8JRR6zhqCNG+9I+/1xjspbz4aUX/JWXXvBXtIahoRSOE6GoMLe9n9e87rd0dAzR0jTEE2tnM1II+PNNK4kUnPvqQ+ntybFp7WxkuYeu0h38/u9Frvnbadx755tobRsknw9IJMpc9OybWLBwA1/4z9fR83IQQqO1YL68kis/dzvJ8Alu/Pks1t7rkGOQZvoJdIlH3nYLxz5nNV/5zLm8bfUBnOseSRCOkJUjtLjDOKXa3/045ge/9PjVb2Ne8pIqr3rwQZjC+AqAN7+ZUskEUOd98YvwzW9OPXbTJtPiBuAzn4Gf/GS8OB0rWD/6UWPWBHDnnSaVJwgmfxxyiBG2YOpMwtAsdxwbvbVYLJZ/YKzwfJrypjeZx664gxN5LjfgUqWP1inHOW0R7pIS1TUBuiJJ5iuj/3a7Gre66y/7XsVEQ8V+EJ6ZjKS/P6IyFOFO0f3BfHGFBdntdA9PT5g8nYVnPQL4THKJfqbUs/6js2PHDnbs2MG6deuI45hMJoNSinw+Tz4/egNr7OuxTtRjX4dhOC6TYs2aUXEHo5HMujhtbm4mCAKKxSJDQ0ONrIW2tjaGh4dZvnw5a9euxfM88vk87e3tpFIpUqlUYz5z586lr6+PTZs2Eccxvu+zfPlytNbk83kymcyUqcA7R3XHvhcCcrnRcw6CKkc/a/R8Dj9stHRBaTjlmIfH7LemlQrwmouu5xUX3ojj1A3dQGuBijQf+PcfEIYOQVBh8+YOVq2ez42DRWZ1pNh8TCvZU6t0dgyD7mDz1jaOOOB+9BD8y1uu5a77zuGa7a+lpXWI4f4kJ57xAE1NQ/z8iucxsMFn7RsCWtuGcbO34SxfwsC5l3P/jR34lQIZhkkzwiELunjW0s387id/413veR6ViuQbHU2clVhGRuVpcvOIQglZHq1H/vZPEqzp01x+OTjr18MDDzAVmy56Pz5Ge0Y/+RnqS1+YciwPP2zEJ5imsR/7mHktpUkvHitSf/lLOOIIs/5nP4PvfGd03c5j3/pWWLTIjH3wQbj9dtP7dbLHs55lTKLARIy7uqYeayO/FovFAvyTCs8rrriCz3/+82zbto1DDjmEL33pS5xyyilP9bSeFAQx89jMJkyj+luZ3nknjitQXRMAEBSM8Dz3Wzex5gVtyF/uWqTVI6Iz6Wpb/1KZzZoveUqClJPfya5HwVJumUe7u6e1/6ez8BwZGQEm9lW1WJ4s6p+93t7e/XaM+u9o/fe7v79/0nH1iOwDO4maTZs2TRi7YcMGyuXyuFT77u5u4tj09p01axapVIqWlhYqlQptbW0NoRnHMf39/Y1o7PDwMIVCgdbWVhzHQWvjqj0yMoLjOKTT6cZ2RpxOTE82y2quSrWxSumaGBUIoWsRTnDdEM+L0Fozf/52FiyopUTHcMjB68dcsypLl2xpvHecCice9yAn7XT8uAKnv/AeWpsHue/+5Rx11EoSXshW2uk+KscqsYK21gG6lEZEITvmDvHY3C42rurnjDP/RlNTnv979C1cMfdNzF+4ndNPv4/CiM+3vvEimpODDG1Lsu4DOQ5YvJX5865h6WEHc0Xqt8T5KkkKpMiToMgbXrqOuc153vT+iBvuhEwm5KL8wbyQF3LQ/D7mthXo31aBIsSFCglR5IufcHjn5yN8v5ehhwdZ0jip2ERAx5hxffazMdUVMfPnS571+1Uc/oc/TPo5Avh7xws49F8WkUzClu//mbn/9d4pxz7+lT8SP/d5RBE4V/2C5V+4eMqx1Z/8AveiC8yba66Bd71rapH60Y/CWWeZsXfdBf/93yaS6zgmxan+2nHg5S+HE04wY9etg6uvnji2/nzccVA3F+vvN33Wxu5v7Os5c0wrIoBKxRhn7Xxs1zVC30aYLRbLHjBjwjOOY37wgx/wute9bqZ2uVdcffXV/Nu//RtXXHEFJ598Ml//+tc599xzefTRR1mwYMFTOrf9zUE8zld5O4fxECdzG6s5aLfbtLymF7Qm+7IBhn7UAoBfc6ecP28H6zuadutWmx4ykTepNffcc88+noWh/sWwqcmYIWUyU//jNjaKMl1B+XQWnl1dXfz9739/Rjq8Wiz7wmTuwGOjrvX61rVr1wKQSCQQQhBFEVJKisUiQohG5LWO45h/SrXWDaHsui5SSsrlMlJKmpqakFJSqVTwfR/P84iiiNmzZ7NlixGK2WyWIAgQQpDL5RgYGMB1Xfr6+nAch/b2doaHh/F9n0wm09iflBKlFL29vQghGunNhUKBUqlEW1sb2XoNZw0pYV6nuVF30vEP1paaKHNb6wAvOOtvaK3HpSnriuaAeds5YJ5xHT7+2EdGDaNiSKcq/Ot7f17bDsLQwfMiyhV4SMcc9K9dJPwy6VSFSsVl9RMLueHQCMcNOabzJmYvf5T+3hwDiYX8vvO13Dunh0yqxO+ufTb33bucpUdvoFTy2PGXVrZd+geOPGoVvx94Gbfxn/iUSckCc1p3cMzhj3DW6Wvp2dDMFdd20PPrMoV8gkN5Ec9ry3L+uXfhas21PzwEX5cJKBFQ4svvm8u/O7+lvSPPPb9NcwIX4FFpPOa09dGSHqI06PG69+T4e+2qvQHFl1WOhCoiwxgVj/dEuORjJS5ceAf5fAt/ekc/n90xsS63zlcu287vv1Bl5Up4TbCKT676yZRjb+lbyknHHsvvf1/gjo89wCfv/fCUY/964ZfZeuFyUilof+xhTvx/5005dviST5H55IeJIojufRjvxKOnHFv90EepfvRyc7NkzWoSZxyPUAqtFFoqcBxioYhQjFz0Zlo/9yFKJXji9h0sfOfziVAIpRCOItOkQCmKVUXxOS+g/C/vwXXBKw+T/tc3IR2zfsLjxBPhDW8wEwpD08B85zFSmueDDzaGGXW+9rW6S9nEx9y5MDZo8fvfm5sbUk58NDebKHide+4xBhWTjU0mYfHi0bGbNk091nXNvuvUs0nGjlHKnIO9CWB5GiD0DH0DL5fLJJPJp7wG6/jjj+eoo47iq1/9amPZwQcfzPnnn8+nP/3p3W4/NDRELpdjcHBwwj/UTzUT/6Zo5rGZZ3MrL+NnnM+1SDR5kryBq/g5L9vtPlfoe1AVTf6JJGtXrADgQz/+FrO2D7NuUSvXv/hQ3nDFrdzyh8mdFs8880zWH9DKDS9Ywak3PE7XldfMSApoJpPhuOOO4/bbK1x7bZFcTvIf/5HhxhtvnHIewG4FWWtrK9lslnXr1k05xmKxWJ5u7OwYvDPJZBKtNVJKfN8nDMOGcVRTUxNRFLFlyxay2SzZbJZUKoWUkp6enkZEt1qtNupq8/k8yWSSZDJJqVQiDEMStbrLMAwRQpDNZqlWq/i+P65GF0z9sOM44yLBcRwj96JdTLnsUiq5JBIVPG9yI7xKRRHHCs+rIKXRD909TQR+haHhFMqJCLwqudwISmny+YAokiQSZTZv6aBcclm0eAu+FzE0nGRoKEW57KJkTDJZoqVlCOXEVCsOW7e0sWNzE307Mszv2EZ2dokFS3sol13+eu2hLGAjugTDvQnKww6veukfcXWVu25bxo0jp9OfbmfWrD7S2/p4fcePmNXSy+qV89i0vp1MIk/SKSHjiCeOPIw5Ly7T35/hnu/M5+XbfkDCKZNwSiTcEgvnbUNGEU88Pof/0+/gV/FLAON2/+vZLydQVYb6AioFgUsVhxCXKr898m3M/s9jWbmyg6s/AHdwAorJM5ou42NczmUArOARHuHQKX9OPz/grQx85jTWPZHlh/9xKOtZNOXYK3g77+QKANroprvR9Hcifz3gFcz6w2V0dTm8+ZXtrN7aNOXYa50LeGP6apLJmGQiZvUTU/tCrDnobNr/fjWVCnz4wwn+77vN+OHkTt6PdZzCp8+5oaYBBVf8Yi7+4OQZWNvmHUPb2tsB+PznFW///IE0D6yfdGxP+8F8+V8eoVDQuK7mIz85nPSGRycdW+hYSHKH2c+PfwynffRU2jfdgxZGoDqeRCpJqCWFZBt/uWJlI4B9/JdeQW7lnURI8gUzXktJrCWhm6B9w91TXqcni3/k7+e7oj7vmwcPIp3dv2n3I0MRp+ZW/UNfoz2KeF5++eVTrvtHMDqpVCrcc889fOhDHxq3/HnPex633377pNuUy+VxQukfxTVz+/evJ/p6rWG5jpFhlSuf9Vzect+7AJjNVh5lBU2Mt7T9NS/kffw3T4wmH00ge8owui1i+JdNaCS5wRHUopgD1z2MCkLknzWbN28mNSuDiPVuI57t24fIDRSRMxhErH+Bmj9fkckIvMl9hRrUv7Ds7j5Kb2/vfk0PtFgslqeC3f3tG+sOXa95rf97t3nzqNvv/vo30HXdRvS4npZcT0mWUpLP56lUKmSz2UaktR5JDsOQ9vZ2tNaUSiVc1yWdTjcMpoIgqBlaRQ0jqrq4rpcm+L6PlLKRVm0iuVsoRBLfUwghCENNT4+5jvVI78BAGcETzJqVo5CHkeEYrXtwnYhkwsXzPEZGRujqikkmkzQ1NTF33iZmda4bJ8TBwferPO+i+yZcm00sA6D1UHg541uXbeFwtgCcAU0ANFH/xjKn9qq5eZjnvu9R+jhq3LYPjXl9mt7ACeX/o1p1iSLBjzP/hlIxO3Y0MzSUaqR1S6nJ5kYYXr2a4ZGtLH3VAl4rfodAo+KQwC2z4rC1CB2z5v65jJDmIv9PgMaLynyl7XJSyQI923IMdqeROsL3KiS9Esz2kNu6yKo+LnxPH9/c9n4CKhBrdAzNTcOgNX070gwm5/Pm1l8RxRJZqvKbgVeR9EsM9yYZ7E1BpCHSiFjTN78Tfd11FIseSw8+jJ+5r0QRo3SEJGbBnO1IYvq6M2xLLOCkBXdSqbhUSooHOJZ0okh+0GegNw217zxCxKwrzKbnO9+hUnH44x9fxr3yCAK3hBIRipi2lgGkiBgZSvBIvpO//KUPrU1d9hbVRGtLRFSRlIoeUscIYiSatQMBV//f/wHwhS+8kQuGfDySSGIkMY6MENr0Se/ugf/932FcNyQMHd5ZyZOe4nesu0fzyy9+EdB8/PKL+dNAkTmMEcq1JA0HKPULXvzi0VX3ZtbwrOH1KGBnuZInyZe++EUufutbSdUdrS2WvWSPIp6e5/GSl7xkUhUdRRHf/e53n9KI59atW5k7dy633XYbJ510UmP5pz71Kb773e/y+OOPT9jmYx/7GB//+McnLH+q7xZ88+h/4+J7vzxu2W9nvZh3eF/DdatsW9tKgRQhikc4hN/xfH7MK3mYw3a77+P6b6ESu1Q3e/iLy8zb2I/Wms0LmhFCcObvV7Ly+r9y9Mkn8tezlnHGHx/jztuNkyNao8IqOoYYj4OXHUKmOUvP/DROJWbNdTcR1++Mag0aRBShnVoqSN2QI9ZoBCIKEWGMrFSIlUIKgYgihJPk8IOOIo41q1aFbB8SPOdUjztX3mX2qQQIiIYddNVh2bJlNLc0c/fddxNWd9XfUiOcGOGaOQoVIgKJrghEvVGg1vi6TCV2cKMyrqvREcTlGOUK8iTBkygd4usKQmgq2qVaAu2O3ssR3uidLV0M0RKIBZQiU9KlBOYkYoQn0REIBTiCeDhEOLJxveKRCFyFDkFIiUiYnn5xGRAagR7d3vPQxQoyKc11UhLhgCqWcH0oJ1N7npJTm0c9La1EQEIUUSJmJE5RxSWWM3gnr+G4stPiWla12NWNiCm29YZGUArcUpm8n0DJGBlIgrhEWBVICXokRvuScsmci876pCojIDVlN4kTlnE9TTRsvqQVqi4eMTID1aokrEhE0iVZHiFUDtIX5MOA6rDGTcRUShkcOUyQ1RSGHdMIV3moOCKOBXFcxfEFSIlQGiUiisMpXLeCSEM2HmRANOEODkMsiJIu6TgPriDsMp97GQjiqkYGkjg/MUrhOCAkVCOg9uc6rqeKAmEqIA5cZDXGHRym3NJEdThAJiKioTIyFSDqwSuh0VGELgqcNkE8UEUECl2OkLnaICEIt5VwU5o4CCAWuMURwlwKlS8RpQMIIS5LZFojy1UcIrQQxNUYWaxS1UlE7e+KI0pE2iXCR6WriGqErIQkRJWidghESFk7aEY/A2lRIa/dccvGf2QkJZoI6Gt8dOLAfMhkqTJhfJQK0Eqi8iXC5jSiewQdArNSqEK5sU2YSxGVHXShhJMCVSwTZxJoKVEjRbNtFCPzZaJMgL+9D6E1lZYsWoDKl4kTPrJY3iPztigVIKKIOOGjlYkgykIZVSijAe27+FEZHUIVOcVVqV0b2OX6fSH2XYhj5E4GdlHCM9elsve9irUwf49FGI27drpWVivQSGUin2bhzDA28iyEIBaAFIhI46i6wA0bwjqRSBDHMZVKZcL3JyklUspGOngQBERRRKVSQQjRSLEOw5AoinAcpzG+bszleR6O49TqhxWu6zaEvxACpRRKqUakeVSUj+5fSolQkkrCI1QCylVUJURF5oavrNd7KoGKQcaaahwRxTFxNUTHMbEURHFEWCpDXLupICWhpCb2YiSicbz6sVXNmKlefiOlJHYd4lSABLxSddzN8frNhXK5TBRFjfOp77NO/eaE67q1myISkLiug1Ky4SkxVY12/QbGzvudcXb690xVq4g4NtcMbUR7JIiqEGsFbebvVhQJsiMDqDBE6Nr3Ax0jhSYsC6qRQ3fbXOJYEkWSOUMbyYkhiE3afByaXsiurCKkYN3Chbz85S9n4cKF++9cd4ONeO6ep0PEc4+E59FHH83ll1/O85///AnrSqUSyWTyKe2PWBeet99+OyeeeGJj+Sc/+Um+//3v89hjj03YZrKI5/z585/yH9pvP/NFnFvvBMy/ibFU9He20bVikekhHkpatm+hOzeHOOESujFxU4ynSqRT/RRzPr3tWfpTOVLhCN2pNja0ziXvJ6goB6FB1/5YiljjVMw/eNXAfAEVUdz4shHL0doBVQ054b5HKQY5NrR0oAciwjbFUGcCtxxS9dToH0mtyQyXyQ0V6WrPUPEdnFKFyHVo78kz4isWP7aN2fmIeRu3MRJkCZtiWvsK/PmJFL+7zNyFFwJO+OlhaE+QP3c+wVCFUsZDxpr421keum7ZtK9r06xBDjx9I4kDioSLI2IXhtsCkkMVCtlRNfPBVV9mR9zJ4q7NtC3dRrwmxeDqVpRfYWXzgdz2rKN5ceE6Znnbacp1c+e2U1hVWcgDsw6j2OzjliOK6dH9pfpLCKCccsk8MYiMY8rtSbQjiFyJVhJ/oIysRhRbEySv30Z4YhvuIwNUD2ki/v4m/PPnUf2jg/PCiGpa4t3ciy5FiCNzxNePIDIhpU155LsOhKvWkX55O9VujZ7nUU56fGLwEwROnp+IV3Nz+3GTXp/jex+gz83hxiGrMwuYXewhG+ZJRgVOLN3NkS134PpFhofaSGd6kSqmMJzjoaFj+FXubDak50663wOHN7IhOZtQucg4aojUdCXPiJdifl8X8zd2M7u7yi3HHsBRD6zjoWNm0+s1UXKDxn7qXyD1JEZT6UqeKJR0DPazo7mFox5ZS+wIjn3kIVoGy4zEc2iP19Ijl1DwUkROTFPrFtwdCgqSSotLeSQHIkZjRNOWRT5z1xZRahjph0TVLI5fojKSxokrhMrcrZh9xD10P34wYSkJokqimqfkZQAoZmJ0FWTV5561SzlqxWMsXPAgd/U/G6cSs63aTMeOIWYHQ5RaRwiGU4Q4oDTKrfAreTQvrv6dthMfInxgPv1+Gm+zuescESK3rUa0H8DIH24gf/31+AcfTPmhhwiOPZaop4eou5vUC15AZdUqUmeeiR4eRCZTiJZWyvffj7diBTKXMxexWsUNBNU1j1I+/AT8oTzVbIodXomVczPEs+ezaXEOjaAcOLT0FZCx5tB1W+hJJ8mnJBXf5YiHekgOR0SOpq2nwm1HzOHZD2xhqL8bvXA+s7rzlF2FX43ozwUkilWqriJyBIlCiFcxX5xFFCFcd6p7CTMqiiqRwlPTu3FacSSRI0mUQoZTHpm8EZqDGZ9UvoJT+5wOZH1KgUN7T6HRbqoQOISOJDtSobs1SW6ohBocobBpPemDDgat0fUv2c6+2TBUHclw2sOtxqTzlf0mIC3PHGLBjGY3WZ5a4jhuCPR6XfpkpFIpXvWqVzVuBDwVWOG5e54OwnOP/lW7+OKLp4xouq7LZZddNiOT2lva2tpQSrF9+/Zxy7u6upg1a9ak2/i+j+/7k657KnnBhya66cU6NlEAHeMgiKMCWgVoIShEQ/SKEXTYSxg+Tp/bwnpZYqtOkBR5smQZUQEhihBj/1//SUohcGs9MkNiNMZBVtSdFuvfVrQmKSXHzp7LUPs8CkoQtWv63BCooj3X3CKr4QjBonQTHdlZRBTYRpVMkKSsI5a3zyY/2MspSw/hkKZZHFKKGY4ctssBFsSK8A9d/A4jPNMdCTrOXErkSQbdClFLCk1MBMxb2D4unWh3LD3CYd7zk0CSx5qGcGrTHSs6FTEHDCQ4qlOSnHcoLYkzScxRFDpnwY6HOKx7NcujEVZkSpBMkXfXc9qciHkyZI2XZdgNCd3xf1zyzQFzdZktQtGy3CcRSlY5ScpjrtfsQHN0uZtbEj7Oi+azQyrm5HL0Jkrk3reMXhXTtDjBDmW+5KbOX0AmlPQ7EYvmdLIlW2V+H6xpjki89xAWii6G5iQ5nB08KFuZ7W4j9iq8grXczHEINAkiXDQ5qmSp8K+tv6ZEJwk2sobTmZ3ewDCtdPJnNM3oWtpOtmm0diWZGeSEzK3Mp5MPMNdEX2ufHYeYEMmHM9dzJ8eTxyOUklbyrKSD87wt/IJlvL+llUOcI+HIWdxJH53HHMRKtZK/uCV+zajw3FlwBkTMBp6bfwI1ELI9NReZncXi3o28sGku2abZ+IddBHsZja3F5ac3+MTdDzGcgSbmJUzvLvmbKhVS3olmLkt2MZd3vZc/vOc9PPczn+Hur3+dE9/7XnQck+/uJj3F37/pEkcRhZ4e1s/yWEuJTtL0EnJMS5ocCq9NEqGJ4hBPurDT4d4L7JQFaLFYLBaL5RnGHgnPt73tbVOuU0o95cLT8zyOPvporr/+el7ykpc0ll9//fW8eGwy+z8lAoUgqn2ZlQh8ND4RARolIEdIHw5yjDAw6HGRg0akU4zTkQjAjyGnHTQOSaEZVBWKwkjYUIy/DaoQdGjFHDw2UWYbVRxAIunUDl6QpSNO0xFqUk5g0gtJktIxfjw6v8NetZjunAZtjlMhJqqtdoI9u/U6a3mJvkyRTMWtzXnimPlhAbVZkl20CA9IpI7DrwgqGlLlEnLtPRwuKjhVhZ+fQ9nfjOeMMIetE67BWM4Wa9hAM1pAwfEoiwSKmICIPC4HyS6WeJtxKXC/6mArWcqu4Bw2co/qIItLlxxN+8uLmNDRlIXm8WyJpKiyvTkgFpoRYpbQR0k6PJe/0s6RpAayDLf3kBWbOJhB+vBJErKIIbJUOJO7EXSToJeYbpZg6rZTOMT0AJO3swDQlGlhKwLNYkbow6cfj9MxtTVJ/sapDOHQg2Y2JZayjA3MZxkv0ylWiKOg1jrneFrQ4RZyVQ832MGva5VNO/NKvYojRJJyNI8zvWO5bo7D0ZsfognNjsQ82tPzITd71x+I3TBt0bnH+51+alaqVuA8nbmc+5WvAHDie82NKyHlPotOAKkU6VmzWEGVA/FIMLHORyFQcopmuxaLxWKxWJ7x/NP18Xzf+97Ha1/7Wo455hhOPPFEvvGNb7Bx48ZdiuZ/BgQCiTTCUxh55wlQWuKicYAkIV4tgjNWHmkg3kmKNuzzxyxTWpPQ4Ac50tKhKY6pOILSFG53DoJOHObFLg/XIk4+Ek9rWrRDUgbkVICLhyscUq4LlTKeqOKPSfeY98ZFrBZxQxFHYyaqgj1L7U4clGebjPB2kS6yJD+MFM0kmIUY9nFampEdc3AHN6OaDsBTCVpUOwWZIah0wmAH/pwS7X3dRHNNFK7E+P0LNLMYpIzLrHiQSEseUia98Rh6eYAWlrOD+WojSQokqaCYiyMFJzCaIv4LsXjcfusRUy1gCSOsF3AMg5RQLKKLfBiQdu7nWToity5LoSmP9nppooxHzGxGOJRtJCnSwmpijPGSjANiuQlJOw1HAnadhphkPcfSyyF0UcHhhxzEyawmwzDNPI8B7iDFyThUSHMOD7OaDO0sjxTCGS+qRG4uTcxlDn8ct9wn4lAGuJ9mzuvZylHt72aIleTcxVzYdz/F5BJkupPmQs8+i07LRCQuCay4tFgsFovFsufss/C87LLLJjXneaq46KKL6O3t5fLLL2fbtm0ceuih/P73v39KC6L3P6YGUwAIicBFoFBa4hDjEqMQeGgCdE12inGbT6z0NTGZaIz0dOKYQEuSjk8sXDxdISEk8RSBmFykSaKYFwmSUuIAiUjTEcEc4SIR5JSHKz2IFUpKXOmgREzaH/1yW5njUSbEmSTiI909E56l1hJFYkpT1HK5RCRKVbyghSC5EEkOKU2tnutnEaUCQboFlwxhJY1yZpNsmYNURcSmAs7cKp0UWT/Gd84n5CCGadb9xEIwq9KFr0ssSSxiC0kOYQdbSLGITaTpwqXIEClexd+5Ry6hjU0cDwyQI8kCClP82p7GOpri2RzCDirKoSPqolxKkRlIITruoOVmTdeRKUIGSVNlCX00UWAxa8myEcETtdsPmkR5HgVvI6gQTRlQ7E54arbxem6ki05MhfBBHMBKPAZo4d+QzGY2r6PMJnwOJEeBHO14U32AgHYgRUiEoISiiZAXa4UrBkjmXo3AIaeMoZafWY7v+CAEWT+3y7laLBaLxWKxWJ5c9ll4fv7zn/+HEp4A73jHO3jHO97xVE9jvyF2ej3Gr8/UZyIROEgkSjgoJK6QBIT4RFCLxtW3NRFPGjE6gYluaiHQYvR4XqxJafCVB9IliKp4tfjpZG6RbaUyWU+QQZJE4SNJxNCEIqclPoqUdPGEBOUgAV+4KBHhqdGIZyUwEb1oEh8ssQele4lUzGBQQQsoyVHBOnb+CSKSrsDPtuE6GZx0C6L2a+L6GXAHkbOPIHayZLa3IzrbCYIW3P48I0N55jOEJBwnPNOEHEIv6ZE+REbQXthM1dW0VUsMuh7z2cFC2ungPkDgMMwBJGhiPVkGCdjILCoE0TwWqjwrGRVVARGLGGEdaQ4triJZiWjVA5SaJJ09XQyoDpK9gthNo4Y244SdVPyNHEQ3R/AgkioB/SjWEVMkWV1K2d1BqrqM0BkhVBU04NJOlfG10zujKdHEE8QoJDHtlEmyGp9DSHEUHvNxzE8fgCytJJnDJD5BDZLM53S2sI0kq3BpQnB2fjGL01cwxzlm/OAxJkQWi8VisVgsln8s9ll47oEprmWGmKzWS9T+k0IRI1FIfK1xBDgonFrE0yPGQdciUuMTbuviy0ROx1eh1Rp/kIsEnuMicUgKQYDAQVOdZE6pCJqjGFeCjyBAktCCRSVNm6cQcZW0UOMimY6QICSeU3PcFTBcq+OcLDAm1PQ/fy2zIoaksaUvjIneucRUarI7QYyf8vDcJJQyeLksuj5WCFAuItWO9HLIzKFotwW/1Ia3RVMQPgeLbrpJkKPKYC0lMUvIcjaT6u9DJB3coQpxJmJeZYiu1gSt4Q4OlW0gNyBIA+00sR5FDy0U0WzG033keto4qL2flXJUeLZSZuHwMCenNrNg7RYy7ZJoUFBMFUiVe4jCFNlVEYmuLMw9nHR/jkj3cFz6FjzWYKKYAXE8DBIc0Q4kcFJLUKxHUCEmhces3QpPAMFaUrTh08t/8U0UHooQiY/PvNoY84PMkUORYleZmwmWMZ9Bns0qruQQjidPJr2QuSwjK/8xHdssFovFYrFYLBPZ5+ZDO/c2sjxVGNGphEQLF4VCCoWjQQljPOSiSRI14p3j7YVGZWhdZI6jFgFNakgicYQkISQJFBkmF3/pMCYpPXwgQOJHEalIMEclyWlFIF0CLcan0AoJUuHWIp5+xqW6i4+Y9KafapvMRo1q1LEi1hkz/wQh0nVxqg5BttVMaWy9pnIg0YSUaWRmNjJoRRRa6d8a4M2azWHiCeYxyOya+6tDzMlsoUNvwxkYIVMcRqwMUYOCgwZ20EmB1Eg/C/M70FSJGUAQougG8ojqekRF4A8I0v0lTi8+PO6cmqslFm3vYU7vIKm+gLQo07JpmNb+Htx8lZaeAfxNPQTbKnDUy0h3NREMNeGxkpgeNHl03INfMGYxjm4miBeR0ccRcCBBeABJfQg+C5EkdnuNNUV8+pGsw+FvOLRNuV3TlG2wRxEoluPTyhKa0Bxbc7ht51X7zfjHYrFYLBaLxTLz/NOZCz2z2MmQRTiAQmLSVmUt8ilwaks1LhpVE1qyEfncaa/apN3Gop5Ga44kgQCBjyREksNhiJBgEuEp45hkJPCVjx8rkkgCJEkh6ZQJEkIihYOvxfj+fEKCcnFqwjPR5NdmMMUVSEyv7x5AkI4n3Zc7xhzJJ0aVYhwngRST3JdRLgQZc60TbeD6RLQS+iXcQsws1hNQYg2tPEaO89jIgWwnMdxDNBQS7ChQ2FrGHczSeVAvi2jBKwyRUEOQASP/S2hMuxKn6qCqLukdOeKhAnPCNeOmMyufZ35hgGy5RCJuoqRdZCRw+gZIbwFdLaATOZzeYTjqpbh/v5dEqYNBNqMqPpFXJt3bCcSU0yO4cRNSNhHoJQRyCSpKUIk24PlLCBlikOsx/qUCByPMq+wYNydJPzF5M39a8Vkw6c/DmYbwBFhElohWZrGNI2oOtxKbVmuxWCwWi8XydGKfI56Wfwzq0R8BSOmghEKi8KFWOylqElTXInyiJh9obMeY154WjcinAKTWOBo8JI4GF0GzcEkJl+QkYs6JYgLAk0b0plAEEaS0IIOD6zj4KKTeST5LCY6HUrWay2ZvF7IT9B642iYz8Th7HFUTnG5NfvuE+DokWSwT5Jom34l0INlknoMsQvgE2Q5Uup2omiExUqSJrbRSxCeihSIt9CJHthMOSMKtMZV+TWl7GboLNMcjFLpGECODYw4yjI5itK6iqi7BUJJEj0tyeITkyNC46SzMD5FMJmjpGcLxO8l0efhBG7knYhLbyriVCDoPQuKC4yFVllSXiW4GhRZzXcLFdDw2F1VN4sQ5/GguUqVQIkfSOQJHdODSicccABxaUDShSKOMWq5fHPMzoad2fTO4tNDCC6f9M5qMdubQRpqljNDK0n3al8VisVgsFovlqWGfhaet8fzHoVHnqY21kCMkjlA1j1twaxFPWUuslTChsUpdaDq19Fyoi0+BrzXZMMYVCk9IsihyKNKTRE29akQqBl8oPDka8WyKBV4MrpD4yjNGRpNs79TaqQQ5bxfnC7E3/YhnqiVC1w4lMSm2Kopw0KSjCgExGaq0hAVS7R2T70SaVFuUA65fE8kZkq2z8Jrn44+EePQxj36ShMymjzT9UCxS7tLoHkG1N6LUX6G4rUi2mCfa4SHLpdFj6BCn6iKrHsFwEn/IJ+iq4guPxKCoGUSZn92K4nbceXNJl0Ok24Q/KAmalpBcuRXS7XgjFfCbEJ2HmG3al+GOmMOkB+cgqw6O00k6Ppps33yU00YqPgSkROkkruzAE514zEKRND8TDsShFUWmlkYrUeTw6DTTp4hDKx7zSLCCgAOn/TOajCaSNJNiCYXxac8Wi8VisVgslqcN+yw8Tz755JmYh2Uv2Lm+tm4upIQyNZ64CCEbQtQIzxjFaOpsfQ8aqMcWBaCExK2tlRo8bXqBpmNwkfhIMkKRiQWZSdpsyDgmE2o8TA1nUgsC6dCiPJxYm8ip4yKlnLRSb2yN51TdNiQQ7UE7lURLOGZbgUtMUK7iommqlMgQkomruF4CN0hOvhPlgJswKbdgTJhEkiDdQtC2kGJ/jGCEFobppESSEgm2Eo0I4i7B0JqI/tXDDG8dIeoeoW1wiLBLIcoaih5okDpAah8ZSRI9CbwhhdubxwkTJHoVF7GaWZRopkpaKlrjAm66FZHqwClKkk4zeFnwc6iKRLhpWHGOmb7sQJFFhoqmdVkSAzm8qAN1yEtoLh6PdJrwPNMrVIkckiS+XohDC5IkLp108NqaEG0mwXJc2nHI4dXMgwBynI4kQZJDpv3z2RWKgCUsn5F9WSwWi8VisViefPZZeN5www0zMQ/LXjKaKisaYhIcZK3eU6FwqAk9jOAaTcvV4/YjGhFPk5YbaCPqnDhCAl5snGodx8NHkkaRQkxqHeNIiSddPCQKQVJLPGXSa00EVlOXnJNJT6fmapvqTEyZaisQhM70haeXHhXICnDDCL8c4RDTXCmTpUqyWCY1ub2SQSqTDixHy6PdIIWfbcFNNlPZ4SJD4xx8KhtJUMQfKiC7kxS3lxlYn6dvXTelvhJR7whicJj8EwIvCIi35nB6mhD4OJUUMpYEvQJ3BIT0UaFLetUwZ1Ru5Ri6aK8W8UnjA16uE7HgBJROgJ+GuYch/CwiaEEELTDHCEBHNSObDiKzox0vaifVlyHVnYK2RSSDU3FEzpwj4IhWBA6BWITAQ5Igy8n4LMRjNikOo41XIAkQNZupOrN5T62Fytxp/3x2xxxaZmxfFovFYrFYLJYnF1vj+U+CrIkliTJRzprFkIzNMmqRxwRxLdV2TOsURms56w8XgRKyFkWVtQghZGITDXWExBeClNZ4k6Q/SgGeVPg11RjUoqS+cFBS4cRjRe5EVC3imV2YpjKF9FRAqKYvPF1vdD8SQVCJSRXKuFFIa7lAqlwmUwjJDuURU4VZ64yJNkvHw8tmEQSochNOQSG1ZhZ9+GGFeBD0Zo/89iKVviLlkQLV4Qoj6wZRvQUK22PEYBZxdxv+1jYkAV4xiYgV3pDEGQ7RTbNRRY0zOEym0MOhbGVJOEgq1PjaoSmZRSY7YPYh4AaIOYcjVBqR7EAueja019Jds7MQqXbS3U2o9IE0bWpFtphIoptdgUNb47x85iPwEW669tNL4NCCQxuKZrKcSsAiPObUEqlTo5cHrxEJnSncMXOzWCwWi8VisTy9mDHh+ec//5mf/exnjfc7duzgvPPOo7Ozk9e97nWUSqVdbG3Ze8ZLR5NiK3G1qdFUOEi8msutS0A8zlSojmK8FHUQKC1Q2vT3lGhUFJNQnjEvwojTINbIST5GEoErHFwhcRCkESS0wJUKRzlIN5jU2KixvTRLvSaXUEwuPB0E1T0QnkmnMu58k+WQZKFCohxx4GAX2aESmUiiNhQRevq1hAJVS3v2SLbOQeQ9lIZmBvDKZap9guLmmHCkSqm/QHmkQGWwwPDWAdzhKq6bIu4TDK4XlCoBUnt4pSROxcUpe/hb+iFoQsoUOGmCoZgDKhs5orqVVKjJDGg8ms1k5h9phGf7Cpwoi2iaD83zRyebmQXpdpLdAaJlIa6/BNV6cO0CNRGweMw1So+JjteFZzMOWZKsIGAxkoAkh+CzgAzH4tBcq/30cOpzslgsFovFYrE845kx4XnppZfy6KOPNt5/8IMf5JZbbuGkk07i5z//OZ///Odn6lCWKZC16CQ1B1tRF49IFA4OCrfRSmWs4KtbttSij8JERwMUUkh8DW4MyShCCQdXOjVRKwikIphEOkqtcZWDU9tnCkVaC1whkUo15luPeu5MPaDopKbu+OMhiXaKhta3U7JWr1oTrcrReO6omJQIEpWI1HCBRKlK244uMsMlmnWMDBVSTr/TUP3qKcfFkSnCksDVEdm4G1GKUf0u/WvyFAfyFHoGqeQLlIeLlAbyVAc06fZO5LBDterhNeVQUYJgwKd5bTNSppD5EiLVhnASiNaFNK2JaCr2ky6VSCBJlQUqMctMJt0Ojo9ItkDHUnTLQkiNEYBSIhJtBF0xNM1FHnAGwmuacC4Tz1GY2tBaYnWGExstTZo4hwzHkeVUEhxCKxci8BpmQxaLxWKxWCwWy4wJz1WrVnHUUUcBEIYhv/zlL/nsZz/LNddcw+WXX86Pf/zjmTqUpcY4wSZGk1aF8GqurQpVS5M10tPUVjrUhYQeu3mjzlNiopl+bZ0jJJ6G5mqFwKmZF2njRqukh4M7YW4OgkCZPp0m7dfEzDypcBrCc8pKykbE081MLQD9yQRv3bVWjp4X1NJsU6PnqxBkqhGZoQKqWIXtRXLDRTJRSOu8eQi15y1ule8jq0mi/oQxLoqG8asFoh6Hoc1DlAbyFPuHqBaKFIdGCMtVwv6YbG42OnbJzG4nkWnBraRwhyWpTRqd6QAtwEuD48Gcw/AHK7hVQbpaIVl1UEEbdNTajCgH3ACSTYj2AxEHnT7xGgWtKLcd/DTKnz16sXaDQ1NDeKoxabUunTi04tCGSxsJliNQ+GOipxaLxWKxWCyWZzYzJjyHhoZoamoC4J577iGfz/OiF70IgOOOO46NGzfO1KEsk2BEnLEXMrFIIzedWhWli8DYDekxUc/RXp2yJjlNz07wNAS1Xp6qli7raYlbS6wVmJYojjDLdsYBApSZiTDWNH4t6VeKmkCeNNZpqNd4OqmJorZOMMn2daEpay/qmspxNGFTpTFTF0FLXKWpWMQpVIi35mkqFEmWqyTSOYTns6co1yU792CcoVY8HeHoPH6+n9Ig5HcMUOofQochcbVKtVgkKlWoDkTIbBsCSdvypThdGbxiCqfq42/uh0QOIRyE8E3K7OwVuP0lnBDaSnkcmUDJzKjLLoATQJCDdBv4qckmatqreElEMP0aTIfWRpRz/HLjfisQOLSS4eTaNbZmQBaLxWKxWCwWw56Hdaago6OD1atXc8opp3DDDTewcOFC5s0z7RWGh4dx3akFhGX6CBiNUwo5YZ1AILTA0bLWWsVYBXnoRnqsg0YxWhupalFOXYuZOhoSoi5FzXsVa3xtBKQQAqG1EaNAIlYTbmEIBJ4QtdpS42zrCxN1rYvCXXjHjqbaBlOlfpqIZ2Wn5bKRalt7VhCG4LkhsWPau5TQuAhy+SKqWKBQzhBuyZM9sooXJggcp5EOvKck22ahHm3HiSJkXKAyIigOxVSHClTyRbxMEuV5xGFEHEVUBkIyx86jWB0hle1AlEJkPIiQDkgPoVzwM0jtQOtC8FOowWHcUNNe6EIGJyBVZvwkXB8c3zxPevEUHHAceEkzbpq4tCAmEZ6mstgI3Awn4tI67X1aLBaLxWKx/LPzFq5Ekd6vx4gYAU7dr8fYV2ZMeJ5zzjlccsklPPLII1x11VW8/vWvb6x77LHHOOCAA2bqUM94duW3KgChBUKa2KQrNLH2jBAkbohMoCE2VU2URmPqPwMkiJqQrQlIR0hcqcZFSj0k2dr7sdWWcS266SKQUuJqSUY4OPHUUc6x1FNtHW/yoLwRnpLiFDWeUhrnXuVoKJs+pkhNMEZ4ZnYMoyol+l0HFcb4QuBEHlLtebSzjuslyOQWMKQ3o6MC0UiCke4K+d5+4jDETSVoO/hgKkNDqCCgOlAm0dKCjNuh4uEHnVRlL9JLICsRkZNEJJoQkYBMu4lWhjFuCKnBMk5TBvydhKcT1EK9U4hnLwGti2qtYfbERMltpNruTN3R1qV92vuzWCwWi8VisTxzmDHh+alPfYqNGzfyzW9+k+OOO46PfOQjjXU/+tGPOOmkk2bqUJYpMHFFau1TjPCQIkboUXEptCAQuiEe6908XQRxTcTV02jra2WtntOPBZ6o2RBpE100jrXxOOEpiXGFwK856wogEAofiVMzPqofR4rJhWXD1dZVKCDaeT2CBJKhndaISSKemCuBlpo0igFiE8XtL5OOq6RijQxjko7AJUDGey88hVY4KmXqaKsKXYKwEBFVKqA1aI3yPPxcFj/XRHmwjJ/JEIdpgmIGtzlN6K5DqSaEm0Q4KcjOMqm0mQ5z1ZrnkeoFN68Rfg6CnYTn7mo2pZq4zTSZyqm2HvGcyfYpFovFYrFYLJZ/HmZMeLa1tXHddddNuu6mm24iCCam6FlmhtF+mBoa4lPUUlxjosZ7szZFWBOio+mu9XUSjayZCUU1kagQOFrgao1bU5eS0WrSoOaWW64JSoUmGcakpFMzPTLHTiJx9Kg4FIDWutaKZDz1Gk/fVbW9jo9sujXhKWspw0IYXdcwFxojQKXUeI4AqWlCsBkI4phguIhfKJEdKeKEISmp8IIMwvH24WfhIGUapWOckQRxQRMVY+JqFSElOo4RUqL8gLblyxnevArl+/hBjjBqIp1ro+ikkcnZ0LIA4aag9QCTEptsgWoR0bYEb2gIFUlEqmPyOs7dsRfmSQAecyZdXo94Kis8LRaLxWKxWCyTMGPmQrsim83ieXv/Zd6ya0TDpsdIPClMNSfCaURAXUx00hgMiVqcU6PQDaHqENdasph6TD82dZxBFCGFEZ+qEQk1DweJrxV1eQgmwdPXMY4YXSoRBEg8PbaJC5OKThgVp0nXmfTuiMKk2io9fvt6sE8KkI7G8yOU1ASuQAtBS+0jP7dniKBnkHhND51DPUZUl12CRBrl7v1NEoFEOknj+jscoPMQ5UMAdByjY41UCjeZxMtkCCsxThDgeEmC5mbcoAkpAkT7UmhZiHAy0LzARDulND062w7CHaoiZAIpJ0993V8okrtcLtn7aLHFYrFYLBaL5Z+XJ0V4Wp48pMC4oFJvoTI+Oilx8Igbibb1qKeJiDbsiXC1IIhNlDMZxnjSONvWI54mkmmim55wx1SOgog1ComUDkKbDRwESWQjhXd31COeKaZyzTVC1q2n7Y6JdEoZoxS4rsZzYxwVE/gmbbguPP0qeAMjsH2QdG8/nuMh8wqpXdO+ZC8RWiG9NCoG0Z0gLkLUN2qBVI94uskkfjaLk0jiBAGirEi0tCCcAIQPiWZomodI5CA320Q9waTJti5CDRURbhPsQb9Ri8VisVgsFovlqcJ+a32aU49djlr21BJbhWi42Ma1qk8XgVt7Hk2xrW8tGiJUAR6CWJqqUU8IEBJXubhxbISs1uhaj1B3XLzTCE8vjnDU+Cioh0QJOW6mU+F5CikFTaGc1CLHReAhGhHUxj4FOErjuAI/inGVJhFEpFMCKSCDMn1KkylUKUIHAap7BK+1Fd/xkFIxpSnPtFAoL4sYUMTbFdU8FHpHRtc6rhGeiQTJtjaSLa24iQTDmyTuwlrUUCQQXspEOZ0A2g6EZNPoIVItyFIVnZi1R660FovFYrFYLBbLU4WNeD5NGZWO9fdj/68bsUuFRNZcXCV1R9rRxFxjCmREZ90IaGw0USFxpIMXa1wkStf2reNGpNTD2Ul6ghcbQVoPRToII3zHl2pOiecpfF+RFqoR1RyLaeUiSWqFVHqcqZCrNEpqfEfje5qMXyWZjklphwQSB/ATPl42i+N66P4imdmzSLfmkI43peHRdBDawU1l0PkEQ1ug3KXJd/c31kvPCE8vmyXZ2kqirQ3leWgxKnZl7IObMMJTOZBshrHpv6kWRMcyZGrOXtdqWiwWi8VisVgsTyb2W+s/CWMlaF2S1oVl/f6CwsGpRUHNUuN2WzcfMtZEpm+nVxNfjhCmF6gQOEKiNOOOJBF4wqTcjp2LiutCdrzwdKYpPH1fEQQOrpSmtcsYZG1/PoKUVihXo6ujojuTDHE9Fy/QpJNV0rJKuslFCUjEEk8KAi9JqqMDL46JdTeZzk6CZIrY8RB72cMTQEiFSmUJKs1056G4rUJ5cEzE03URStG2fDleOk2iuaWxvDEm9MFzINcJhYGJLU+CHLQtRjYv3ut5WiwWi8VisVgsTyY24vlPhXGyRdTjmg4SF6cmMeWYmKcZG9eeNU6t7lMArjaptq6uGxJJPARKi4YZkGDUZMgTatwdDAEEWiPi2FjN1pa5CJzdtfqo4TiSXHOArxSZMccC86H1kHhARiscY56LEEajZVMRiURIKq1J+yHpoEK6KUIBKW1MiTyhCHI5mhcdgJIOjuejZAKhJWKac5wU6RizIJnGb2qhsKNIWB5b46kRUhrRm8mQbGkDQI0x35K61ofTT5sazp0jsG4AzfOgzQpPi8VisVgsFsvTAys8n9aMSUGtixM9WjtZj3PWW6WomjB1asm4deMhAXhonHq6qjBRRq82xsNEQR0EYowIqteDuox3tUVAQusJKasuYlwUdFcoJcm2J/DlRHMhWa/vRJHQEseppf8KcBxI+REJ36TZ+p7GUzHpbBVHaFJIAqA5quInkySbmnBdHy9Io1QSJRRyHyKeCIF0HHydIzV3HqWhAmGpNLpaGmEb5HIm4tnWDoCfzY6eu6451XpJ079zZyHsBpCbM77u02KxWCwWi8Vi+QdmxoSnlBKl1KQPx3Foa2vjnHPO4aabbpqpQ1omEXCjSbSjcc26BZFEoYSq1XOOmgqJmumQU/OmlULga4EnTJTUj8GJYiMad2p/ItGoWruW0TkIkrFGStGo8TTHkNNOtRUCsk0BvhrjXFtbpxCkUfi1WlTHMzWeAnBrdZ2eZ2o8k36EqxTNQQkXyApIaMhF4AYJlO/hOAFeKoWIFELtY8SzhqNTpNo7icMYHUWN5W4yiZASP5vFS6dJthvhGTQ3j557vUWKcmGynqJCGOFpsVgsFovFYrE8TZgx4XnppZeycOFCWlpaeP3rX88HP/hBXvva19LS0sKCBQt4zWtew+bNmznrrLO4/vrrZ+qwFsbLTxP5U2MkZb2xihGcJt223s+z3kDFLDfi0wg7J9a4sYkketrUUzpaNFJsR4/nGtOhMTWeUms8FI47XjQ5mL6d02lWIqVx0/VdNa4zZN38KIUkhcJ1wPNr6bzCBAcDR5DwIHBCHAmuq2hKVkgLQRIfD0EaF9/1cIMAqVzjcuskzNWZAeEZSx8pg3qmcQM/lzOtVHI5/EyGVE14jhX0TjSmV+ZUrrXp1n2eo8VisVgsFovF8mQxY+ZCLS0tdHZ28tBDD5FKpRrLR0ZGOOuss5g7dy73338/Z511Fp/85Cc566yzZurQlhqi/j9df193stW1hxFtkghJjMdoWq5A4OkYVyjjGCsVUaxRGnwNoZC4OgKtzTbCSFBdqwE1Cb0RIPDQuEqys8Qc7RU6jXMRAldJPFfhjYl41g2QfCQJFI4b4Sc0QmikECgJvhPgBhWkDkErXCVJJSJSIsLXLgGSND6+5yNdEMJDuT5eJkMsqvv4UzAoJ4GbzECska5LXK2CEARNTSTb2hoRz3hMNLSxrcqNvpks4mmxWCwWi8VisTzNmLGI51e+8hXe//73jxOdAOl0mve///1cccUVOI7D2972Nu69996ZOqxlJ0bFXl3mmbrOep9OxejdBtUYCS4aD2otU4y5kKMclJA4UtVMhsbWeI4KSFlr39KgatJy2Sktt96uZTrS0/TjlPiuGlfjKWr78ak52wpBkIqRtWinUtCcjkj6MWm/Qhim8ANB0o/J4OEjSCJIa4nn+SSbm00SsOOYh5qZXwnXT+BnWtG6ZhxUq/1MtLSQbG/HS6VQvo+fyUzc2B+zzPbptFgsFovFYrH8EzBjwnPz5s24rjvpOsdx2L59OwCzZ8+mWp2ZqNIzlZ3rLKcYBTWxaVJp6zWcdRlqhCSMilFP0zAc8rSotVaJ8Wq1mZ5UiDGtPeoi0qntsy57tTDv5QThOT3RWT/HRNolIeW4Pp71c3Ex/T09BH4yNmm2AhwJqURI4GkSXogQDoGvSaRytTRbSRZNWkuk4+AkEkjpIZWHVGrarru7Q7ouqfY5OH5gWqhIczwvk6HzyCMRypgYOYnExI3dMWLTCk+LxWKxWCwWyz8BMyY8ly1bxpe//GXCMBy3PAxDvvzlL7Ns2TIAtm3bRnutrs2yPxiNcxop6DSMhhR6p3WilnoranWfow8lTE2nqq13hcKRwrT3gDGmQUbe1t1xJRqUMSXamYnJt7smlfZwpcIZ005lNOJpDJECJF5C1+o7Na6EbKpqenk6pvbU9wWBq8jhk0CTQeMrx4g/oXATKVMXq9SM/UJI6eJn2k10M5NBSInyPLxUinRnJ1KZWlI1xc2aBtO6yWCxWCwWi8VisfxjM2M1npdffjkXXnghS5Ys4fzzz2fWrFns2LGDa6+9li1btvCLX/wCgOuvv54TTzxxpg5rqTFe0o0Ky9FnbSo+tYkmjtoPxUZYAtTaorgIZC366QmFJ2qeuHX3np1QjeNrZGzErYOcIJokwDTNhQASnsJ1JD7j03slwvTiRBp326RJtdVa4LmQTYcUAenHDAxBKqfxpSSLJBlVSGgH13GJHAchHZJtbQhh3GxFPM3J7QYpjKRPNreSaGmh0N1N0NRkene2tSH2pWWLxWKxWCwWi8XyNGPGhOeLX/xifvvb33LppZfyP//zP2itEUJwzDHH8PWvf52zzz4bgCuvvHKmDmmZxLxn7HKJQou4kVoraom2pq/naARSovEx4tLBGPeoWtTT1+DqWt/PWCPl+I9MfXuXGBNtNWmvrp6YVlufhWZ6PVWCwMEVjBGeNOYeIPERZFGNiKcQ4DmQSVaRkYNyy8zuLJPJRgTKpOZKPBwvidISrRRSuTQvWmRSYZVCVmco5ikUSEmitY1ESwtCShLNzfg14blPvUItFovFYrFYLJanGTMmPAHOOecczjnnHAqFAv39/TQ3N5NMJne/oWWPqEcXJy4f+0o0ZKVZYpJhTeJqpdFaRRAjMGZCsTDPvjbGPY6GQCiToitqzVcmSf2UY+pFZc311tOwcy8RsdPz7ggCx6T9Ypxy49pZ1M2FXKQxQXKr5mwFuFKT8mOiUOClFJoKbU1FEsLDRSOdAB+NEhLtmbrOZHu7afOiFFRnRhBKYWLIyfZ2/EwGoRSJlha8dBrleTPSssVisVgsFovFYnm6MCPffovFInPnzuU3v/kNAMlkkrlz51rR+ZQgEHo0uigaktNUciqhauJNwxhp6tRG+ghcLXCEwNcCTxh33GQU4yg5KpjG1XiaJif1HptSS9w4niAw9/TDFgQOQsma+KwLaNFIB/YQBAiU0kipURJSAaT8CE85ZDIOgRfS6g/iK4WLEcbJWmsZ5fsIpQiamhpmPzv3Kd1bhDACNsjl8DIZlOcRNDcb4em6NuJpsVgsFovFYnlGMSPfshOJBMVicUIrFcv+ZedqycY7IRkfVzT2PPUGKy7xuFTbesTTrNMmzRaBIySJmhTzYxP1HG2nMr4HqNl/jIg1SIkr1QT33T21ycl1JFGiHpYfnauDwK2lBTsIlAO+FyOFJulpAhcSjiIZgCsi/DIECQgwbV46KJoore+belfPQzrmKPVI5b4ihNmfEwS4qRTKdcnOm4efyyFd19Z4WiwWi8VisVieUcxYvt+ZZ57JDTfcMFO7s0yHKR1PR8146g629TrPeo1kXfaY16JR0+nVlgmt8YTE0xIpTCqtG+sJx6zHTCWmDlQRg1S4UqL19Go5pyLjO0gparHU0VYqCmrCs9ab1NEk/BilIOWDozxcR5HwwY0l0m8jcAWBNvWuifp+fN/011SqITxnShBKaQSsk0jgJhIoz6Np0SJSHR3meFZ4WiwWi8VisVieQcxYjecll1zChRdeSBAEXHDBBcyePXtCxKulpWWmDmeZgtEoaD0+GDOaeGusheotUoSxH6otMxFEV8e4UuFqSQQktanrVELhiMmFpGnFomtpsODHMUK4k0Q8xbjn3ZFMuCBlQ3iCEdAeprenC8QIlAI/EFQrELgSXwX4QuIpgXACXLeEJyQqNoZXQeQglEB6HlG5bPZbj3g6M/QrUUtJrotO6bokWlpIz5plroGt8bRYLBaLxWKxPIOYMeF59NFHA/Cxj32Mj3/845OOiaJopg5nmZSxorNewymNcU6jelE2zIAUmnpfTFWLILo6whdG2Gk0aS1Naq4QUwpGQdxIh5Va4wJK7LuwSqe9Rv2pBKLambkII5KBKiCFxvM0jgLHAc/xCVxwHYEjBVlh2rsIHISQpKuOMZ1ViqgmjusRyJmORDqJBNJxkI5DorkZubu+nRaLxWKxWCwWyz8hMyY8L7300gkRLstTiBiNLmph6jajMa1UjLwy/3dqZjtOLfrpYkyFIh3hIPBjjVIOQk6VOitr4lCD1vjVXQvV6ZJKuci6ERCCEN0QzW7tOUIjFQQeFBR4CjzlkEtU8BS4QuI6Pq6xVkIASTdjDJekbHxm91fNpRMESMdBuS5eJmN/RywWi8VisVgsz0hmTHh+7GMfm6ldWfYRISTo2LyuRQt1TW6apFrZSMQVjdRbI0p9YlStlNMREqUFqhYBRU7+calLXKVjpIZENcLV7KIGdXr4ntOoL63HT80cR5dJBMrVOErgKnAdI0IzbomidHAcgXId6g1mJOAKr9HztJ7yur9qLhMtLUjXRfk+biKxz9fEYrFYLBaLxWJ5OjKjfTwBBgcHueOOO+jp6eG8886jubl5pg/xjEUgJuneOX69ea5XdWp0rfZy1GBIjXG4BYnGA3wg0uBqcOrpp9q43SqhkLFGjYkQjqd2DI0Rqrruo7tvBIFp8uLUHvW0YLdmhmTOJUJ5oCR4DrgKUkEVz1WEMsJ1FK6MatFcE/H0GJPuup+FYGbOHJTnGXdb217IYrFYLBaLxfIMZUYdTj7xiU8wZ84czj33XF73utexbt06wDjefuYzn5nJQ1mmoC4w669FrdbTiMy6IFW1KOdo6xQPYYSmkI1opRKmpUrd+VbEU6fa1oUssuaKy75HEH1f8f/bu/P4qKqD/+Pfc+8sCSFEMGIMBKSoLRhBg4hUK8QNFNzqY12oBaH2Zy2tLd20G+irT/GldPGxbrUK+Niqz6taqrWiaEGt0FYWK4siCCiVTZYsVQkhc35/3JmbmWSykblJyHzevgaYe+/ce+bkJs43Z3OMFI13/42fXmEpHj69KBnK8br2RsJSOGQVDRlFnKgijqOQkcKOVdjvRGxSJisKuuurG4konJurUE6OQrm5dLUFAABAVspY8Lz33nt16623atq0aXr22WdTltKYOHGinn322UxdCs2KB5v4Wp6JMZJSfTxMdDkNySoiG++2mliexPHX+HRN/Qy4YWubvFkS3XRdyQuAjuuN8WxnyAqFvKVcEsunGD8kO36bpZFVKPegXEfKz7UKu14bqetaRVyjVRDbqgAAODpJREFUUPwRkevPNJtSqoCDoOO6yu3Tx2vxpKstAAAAslTGutr++te/1owZM3THHXc0mr32+OOP14YNGzJ1KUhegGnFOpmOP7utpHhnVS8kWoXjLZ6OrEJyFHEc1Sqm8MH6aYFCxgt7Idv0zWLjI0hlvSuFZRTOwHIh4bDX4ukktZ9G/ImFEnP0GoVDUsi1ys01Coe8dTnDoVqFHCcePFOXcHGT6i3oFkgnFFLe0UcrnJurcI8esrFYoNcDAAAAuqKMtXhu2rRJ48aNS7svPz9fFRUVmbpUVms5JiW+pEndSf02zsTYSOO3HrqK+e2gTrxVMewf7U0wFDKOnJhkmgm6IXkjPR0bk5Gj3FaE4pZEQo7X5dfaeMnkh05XiW7FVpGoVdixysuxioat/17Cie63bmqtpdz0HdACGcnLUyje3ZYWTwAAAGSjjLV4FhQUaOfOnWn3bdmyRX379s3UpdBAS1HGC2hOvOUwFo+h9WthulJ8DU+rmLzuso6MYvHlS7zniq/6mZ4jyTFGskYRJyRj2x+wolHX6+5rjRKz8IbigTkx5tOVUdgxys2T8hxvciFjrFzHVcg9qEg0plCDX6+EOnCMpyQ54bA3vtN1A5s9FwAAAOjKMtbiec455+iOO+7QRx995G8zxujgwYO67777mmwNRWaZBl/SRBfT+hltTXzUp/HHSSYmGArFF1txk2aQDSVGhRpXxmk6NBlJIWtl5ChiJde0/9aKRkIy1qYsnRKJj0WNKDFXr5HjxNQrV+qZW6dISHKM975dx1E0YhVqkC2dpODZES2QbjiscI8eclxXTijjE0kDAAAAXV7GPgXfdtttGjlypIYOHarLLrtMxhj9+te/1qpVq/T+++/r//7v/zJ1KSRpbokVr6VTSUckJhaKB0vrzQabWK4k0QIasfUjQkOS3+LpNBEmEx15HestABqR4pMbtU9icqFQUotnJB46E8urSEYKST0ijvLDNQqHwjLGeOuQOkaO8ZZaSZYcijuixdONRBTp2VPGcWRo8QQAAEAWyliL53HHHafXXntNQ4YM0b333itrrR555BEVFhbq1Vdf1YABAzJ1KbQoEdQScc3jzWSb+DsWX04lpp7xyYWMMYomdWNNtHomurg2P8bTyMQkE1+OJRM3VijeRzZk61tuQzLKkaOoP92QI+PE1KNnTD17HFTYjXf5NVLI8SZHajjPkWs6Nvw54bCOGjrU+zfBEwAAAFkoo/3+hg4dqoULF6qmpkZ79uxR7969lZubm8lLoA0S4dM02OYqsUSJ1zrpKLGMykFJ8iNd/ZqX3hqfrppuHYzIKKo6GYUUkSPXbf+t5TjGP3ck/j4ifvCsb/E0rlUkJ6YeTkzhkPztrpwmWjw7doIfNxxWfnGxJNHVFgAAAFkpkE/B0WhUxfEP2ug8fjDz/7bx1ksbn6BHyvHmoo2Pn5QO+nPbJrrOei2OjoxMwwSXxJWRa72lMnOMI7eu/bPaGmPis+xKPeLl8lo8ldIq6xgpNxxTD9dr5XQcbzKlsByFTOpSKsk10lHcSEQ9Cgs79JoAAABAV0Lzy+HOGCntKM9EN1ur+vlovfDpyguV4fistd7MttYLc8ablKfhWRzjKNRMK2ZIjpyYUY4TUzhm5cQyETzj57ZSVI4/wVBu0gRI3nFW0bCUFwl5a3bGu9q6xpFjbCaGm7ZLKCeH4AkAAICs1q6P5MOGDdOaNWtafXwsFtOwYcP01ltvteeySKNxh9qG++qnFUosrRI2RiHrxNe9lBxrFJatn7jHJE1EZJxmx3iGZRWuq5NjXEXqYjINB1YeynsySUumqH4dz7BchZJuXccYRcJGPaMRuU5iuiUrJxFQO3ntzFBurqL5+Z1aBgAAAKAztSsdrFmzRp988kmrj7fWtvk1aLumQqjxF0jxxjkauXKt1+Lp+LHUel1mE91tJYWMo1A40mwHVdd6E/84Ma8br5OBdTwTeTFs3JTg6Sr1xnVllRO2yo2EFQ5ZGeOFUSMTX9ezk4NnNConHG75QAAAAKCLMMZowYIFGTtfu7vaXnrppYpGo60+vrNDQHaob5l0/Pho/D9DcuVK/tqdsk48qEkhm4imkqyVY7xutK7jNB885SoSi8mJSRHHlclIV1vvimHHVTTeepkTX8fT8ds1rSLWKDcaU8jxut16XW1jMnLUo2eNjFp/fwbBOE5K92UAAACgs+3atUs//vGP9dxzz2nnzp3q3bu3hg8frlmzZmn06NHavn27evfunbHrtSt4Tp48+ZBeV8h4tw6UOq9toqut1/nWxsd3ekEuZBzVWW9Mpc964zuNbdiSmsqRo0gsJmOteigmk8GBlRE58S62Rvly48FTinnFU8QYRSNS2JFMzJtsKFHSaOigjMnJWFkAAACA7uDyyy9XbW2t5s+fr0996lPauXOnXnrpJe3du1eSVFRUlNHrtSt4zp07N1PlQACS1/E0/n+J5VOMQrLeup7Gm7jHjbcjhpJe4xgjxWyLM8GG4ud0HW/pEyeDLXyOEuHT0RHx4OlPLJSY5TZqFYp5M9oaY+P7JMc1ar6tFgAAAMguFRUV+tvf/qYlS5ZozJgxkqSBAwfqtNNO848xxuiPf/yjLr300oxck/5/3Vpqd9dE/PKWJZE/o21ittiIjMJWflBz4q9xrZWxttlu0iFJbp23pEq4wcy47eXIKNqgxTO5HTfHxhSNSiHHxNfxNP740PrpkQAAAIDuraqqKuVRU1OT9riePXuqZ8+eWrBgQZPHZBrBs5urD16pLZ+uvJlovYl6jP/wJhaqX0bFm93Wu02ctMu2xK9jjKJ1ViFrFXbCXktpxt5DokVVisRbVpNF5Sg37CjkygueJj7WM/VdAAAAAN1aSUmJCgoK/Mfs2bPTHhcKhTRv3jzNnz9fRxxxhM444wz94Ac/0JtvvhlY2Qie3V5ye58bD52OQrLxf4f8NTHdeJfa5DbCRHdbY4zkND0za0hSOBxSOCZFjRtfdTMzvK62XuD0JhlKlM175MhVTigePF3JceoDsklZ8RMAAADovrZu3arKykr/ccsttzR57OWXX65t27bp6aef1rhx47RkyRKVlZVp3rx5gZSN4NktmaQ/03HkyMZnt01MNuR1jw1JfrBLnMN/NDNhUERWso5CMauQjQ+2zBBXRpF4lM3xp0aqf3/5chR2YjJGirhOSkfcaOxgxsoBAAAAdGW9evVKebS0+khOTo7OO+88/eQnP9HSpUs1ZcoUzZw5M5CyETy7reTutYmHk9St1uu+Go4HusSkQyF563Z6rzMy1no3iTHNthyGJDmOq1CdNytuJme1dSTlxZdTicaXU0nWU8YLnpJc1/pdbSUpHGv3ikEAAABAVhg6dKg++uijQM7Np/JuoOWupF5otP4YTW+tTmOtJFeh+FhPL2gauUmnc2S8pVSslZoZt2msq4iNyTWO3Fis2WPbKtHiGZK3jmfDFtmQDSkarpPjeN1sHZPYI5lQbsbKAQAAAHQHe/bs0RVXXKGpU6dq2LBhys/P1/Lly3XHHXfokksuCeSaHRI8KyoqtGbNGq1evVpvvvmm7rvvvo64bNYzSS2eicjpxB+ujFxjFDaubHzinsT4T8fxop0xiRGSRk7MyjTTfdYxjnrEYqoNRaRYZhcwScy4mxjrGVV9i6xk5TqOIo5kjeQab1sGcy8AAADQrfTs2VOjRo3SL3/5S7377ruqra1VSUmJrr/+ev3gBz8I5JoZD567du3SokWLtHr1av/xwQcfaMCAARo2bJiGDx+e6UuiCV73WtNomyMrJz6+MyKrunh3W0deN1vXyh9AaeLbva6zTac5V0ahmKSYN0tWJpOnq/p1QsPx1s9kYTmKOEa1TmIdT8k0MwMvAAAAkM2i0ahmz57d5Ky3kmRtZj9PZzx4fu5zn1NOTo5OP/10vfHGGzrhhBO0YsUKHXXUUZm+FBpIDpkmJXolQqOXKBNjPMOqi89pa+OTDMUUMomj65dfSRzfHG/MqNfSadxoRmeSTQTOkP+oZ+SF5bBxdVCJGW1p7gQAAAC6koxPLlRdXa2VK1fqgQce0Lp163TcccfprLPO0tKlSzN9KTSpmbGY8lo8vYVUvOeJbrZOosutSVpOxdr6szXTfzUkKRQKKxwJy3Uz+/sMLyTXh8+G63iGHKOwceQ4kpto8SR7AgAAAF1GxoPntm3b5LreGMGCggI9+OCDuueeezRlyhTddNNN+vjjjzN9SbRK8gy3Rq6sHH9pEjcePBUf5Zm68qcr02JTe0hWITekUMzWr/uZQYng2bD1NdHiGTKOPzOvkaXREwAAAOhCOmQ5lbPPPlv/+te/FA6HdfLJJ3fEJbNS46yVPn15Yya9kOkmtR+m61brj/FsYV3OsLUyxlVO7UG5jtvssW1l4hMKufEW2eRgbGQUMt5ESY6TOqESAAAAgK6hQ2a13bdvnz7++GPNmTNHV199dUdcEmnUj/tMtHNKrlzVxfe6snKs5BijWPLrjFFLc/WEjCvXOIrGDsqaxFRFmZMY3+mmGW/qulaOceSYRDdbI4e+tgAAAECX0SEtnq+88ooGDBggSRoxYkRHXDIrNR21EsuPJCbeMXKs5MoqKiuvZ2oi1DWeBdeo5a6zjhy5NqaQCQUyvjIsozy5jVo8JSnixDsIG0nGW0qFOW0BAACArqNDgic6RnKX2eTnjf/tyI3PQRuS5BrvNnBszO9am+AYI7ViKmVXjiKOo7BxvHBoMtvd1pGUGx+T2jDXOq63rT7w2ozOqgsAAACgfQie3VTq0ioN9yWWKInFWzmTJ+ZJfZ1RUrfVZpoyjYwiMopYG0hzY2JJlUQ5k4Xi78Axye+bNk8AAACgq2hX8Hz88ce1fv36TJUFGddUULRyZRVRrRyFZGTlKCbFu9SmC6otd7U18RZURyaAzBeSUTRpyZfUa8fL6Xe1NWJ6IQAAAKDraNfkQtdcc42MMcrLy9Pw4cNVVlamU045RWVlZTrxxBP9ZVXQNZjkh3HjAc4LaN5SKk59iGvwd4vnjkmudbz1PANInk58ZluvnKmlSszLmxo3CZ4AAABAV9Gu4LlgwQKtWrVKK1eu1MqVK/Xaa69J8lrHotGoSktLVVZWprq6uowUFpmRGCdpEit52jrJeK2JxtavgdlwzGhzQkZyrZVrXIVkmu2We2hl9iYYSi5XQiJ4Ok7GLwsAAAAgA9oVPC+++GJdfPHF/vPdu3drxYoVfhBduXKlfvOb30hquasmgufPUBtfkiTR4pmIc06aSXla2/LpWCnkSJGD3vy4mQ+e9Wt5NlpOJXFM0iWZXAgAAADoOjK6jmdhYaHGjRuncePG+dsqKyu1cuVKrVq1KpOXQhulzmpr/DGZybPY+nMIJU/RE28BtdY2+8sDxxpFXMebzTaAXzKE5LV4eiuENgzH8Vl54y2ehE4AAACga8lo8EynoKBA5eXlKi8vD/pSUGtCV6LF08a/+EauSR7p2Xh8ZusmF7IKO67CocZrgWaCE581N53Ee3ZMyy2zAAAAADoey6lkhdRFUrxWTivJkSubsr9hcG1tiHTkyI3ZeHfXICYXkiL+wi+N90mS6wRzbQAAAADtQ/A8nLWyS2tiiZT6KYXqu9iG4+M6TVKbZ3LrptPKa7hyFLKSa+VNUJRhrr+cSmOJbV7opc0TAAAA6GoInlnA+tMH1S+oEpKVkSs3PpOtkwifxokfW681N4kxRk7MKmRNq8NqWxhJkSY6Eie2um7rgzIAAACAjhP4GE90HTapG6oXPEOKz0Erk9jnpMbM1sY4xxjJunKdYGYwdmUUbk0ENl7XYQAAAABdBy2eWSTRodaVTZod1uu+alS/vmcya21SN93mzx1yHIXdkJf9Ml72RIsnoRIAAAA43NDimTWMEhPv1C+moqTxnl6gsw0m5zHGeEuqqPmWTNcYhVyjsBP1j88kV0Y9/RU7m0YsBQAAALoeWjyzQv3YTpOyzU16ZlNCW1sDnCMr18RXCA1ojGcOtysAAABwWOKTfBaqj6HxVk9TH0hdpU4u1Jo1PCWvxdSJWTkx658j02Xu0YrzMrcQAAAA0PUQPLuRhpmr8XjI1DbNRDdb43e0PfTU5sibsMiNBbeOptuK8pkgBpgCAAAAaBeCZ7eWPoR5Yzodfybb1PCZXkutniEZucbrZhtEV9vENVpGkycAAADQ1RA8DyOZCnRG1m89TG4Vbc+kQEZGjk3Evsy3OqabcRcAAADA4aHbBM8tW7Zo2rRpGjRokHJzczV48GDNnDlTBw4c6OyidTnJnWwTf7b3RnDlyLXGW88zgOCZGJfa4nGkUwAAAKDL6TbLqbz99tuKxWJ64IEHdNxxx2nNmjW6/vrr9dFHH2nOnDmdXbwuI9HO6cqVFEvqvmrTrpLZ2nZGR1LYDcmpzVRJAQAAAHQX3SZ4jh8/XuPHj/eff+pTn9L69et13333davg2b4Op95rHVkZ603EE1JM9aM8Gy+p0tqrGUkm3uIZ1BhPGjMBAACAw1O3CZ7pVFZWqk+fPs0eU1NTo5qaGv95VVVV0MUKTOtDqfEXTTEy8dU8jYysrKwOJeI58tpR5bZm7tlD07qphYinAAAAQFfTbcZ4NvTuu+/q7rvv1g033NDscbNnz1ZBQYH/KCkp6aASdp7EmE6nPi5629O0VNaPBG2eI8l1QnKNadckRQAAAAC6ny4fPGfNmuUv0dHUY/ny5Smv2bZtm8aPH68rrrhCX/7yl5s9/y233KLKykr/sXXr1iDfTqdLXrnTxIOn0+JiKq07r2NtPLxmvtWRdkwAAADg8NXlu9pOnz5dV111VbPHHHvssf6/t23bpvLyco0ePVq/+c1vWjx/NBpVNBptbzG7NOP/af3gmehma/wtVul+D9GmwGcTXW4JngAAAADqdfngWVhYqMLCwlYd+8EHH6i8vFwjRozQ3Llz5ThdvkG3gyS6viYmF3Liy54YOXLkKuYf2Z7QaKyV4wZX54zfBAAAAA5PXT54tta2bds0duxYDRgwQHPmzNGHH37o7ysqKurEknUmk/QvE584yJPoZFv/Z/u6yBoZOY6RYxw5hsAPAAAAoF63CZ4vvPCCNm7cqI0bN6p///4p+7J9spumWwqd+FhPGz+uLa9t4kqOI+t36QUAAACAw2ByodaaMmWKrLVpH2jMa+WUlLR8SnvDojEOgRMAAABAI90meKK1vGjoNOiGm/y39+9DOHOa5VgAAAAAgOCZdRqHTG9yofaHRhPgZE60pAIAAACHr24zxhOt1XgaoeaWQGnTCE/j0OoJAAAAJHnnyuFSuFewF6mtCvb8GUCLZzeWPkzWz25r5Mqb1dY2GTDbOLUQ7ZIAAAAAGiF4ZrXG4z3beToAAAAAaITgmYVM0sMTS3NM21Ok8f8mgQIAAACoR/DMCg0nFLKNtmQCi6kAAAAASIfgmZXqJxhyZWS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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plotter.generate_plot_mpl()" + ] + }, + { + "cell_type": "markdown", + "id": "18d60457-ad52-4014-b04f-14f71333f388", + "metadata": {}, + "source": [ + "## Let's now work on the abundance vs velocity plot\n", + "For the next few cells, we'll modify the abundace dataframe so as to plot it easily" + ] + }, + { + "cell_type": "markdown", + "id": "7e316952-62d5-41ba-85f1-fef31265ba72", + "metadata": {}, + "source": [ + "### Get the abundance data from the simulation_state" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f2d15d55-a59b-44aa-b6c2-0a71aad1775f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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012345678910111213141516171819
atomic_number
80.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.19
120.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.03
140.520.520.520.520.520.520.520.520.520.520.520.520.520.520.520.520.520.520.520.52
160.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.19
180.040.040.040.040.040.040.040.040.040.040.040.040.040.040.040.040.040.040.040.04
200.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.03
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" + ], + "text/plain": [ + " 0 1 2 3 4 5 6 7 8 9 \\\n", + "atomic_number \n", + "8 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 \n", + "12 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 \n", + "14 0.52 0.52 0.52 0.52 0.52 0.52 0.52 0.52 0.52 0.52 \n", + "16 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 \n", + "18 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 \n", + "20 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 \n", + "\n", + " 10 11 12 13 14 15 16 17 18 19 \n", + "atomic_number \n", + "8 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 \n", + "12 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 \n", + "14 0.52 0.52 0.52 0.52 0.52 0.52 0.52 0.52 0.52 0.52 \n", + "16 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 \n", + "18 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 \n", + "20 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "abundance = sim.simulation_state.abundance\n", + "abundance" + ] + }, + { + "cell_type": "markdown", + "id": "86aac0e5-3f2a-45c3-8a1e-be2bd03ae947", + "metadata": {}, + "source": [ + "### Transpose the abundance dataframe so to get atomic number as columns heads" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3398110e-e982-489e-948c-3b1643360e02", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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atomic_number81214161820
00.190.030.520.190.040.03
10.190.030.520.190.040.03
20.190.030.520.190.040.03
30.190.030.520.190.040.03
40.190.030.520.190.040.03
50.190.030.520.190.040.03
60.190.030.520.190.040.03
70.190.030.520.190.040.03
80.190.030.520.190.040.03
90.190.030.520.190.040.03
100.190.030.520.190.040.03
110.190.030.520.190.040.03
120.190.030.520.190.040.03
130.190.030.520.190.040.03
140.190.030.520.190.040.03
150.190.030.520.190.040.03
160.190.030.520.190.040.03
170.190.030.520.190.040.03
180.190.030.520.190.040.03
190.190.030.520.190.040.03
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" + ], + "text/plain": [ + "atomic_number 8 12 14 16 18 20\n", + "0 0.19 0.03 0.52 0.19 0.04 0.03\n", + "1 0.19 0.03 0.52 0.19 0.04 0.03\n", + "2 0.19 0.03 0.52 0.19 0.04 0.03\n", + "3 0.19 0.03 0.52 0.19 0.04 0.03\n", + "4 0.19 0.03 0.52 0.19 0.04 0.03\n", + "5 0.19 0.03 0.52 0.19 0.04 0.03\n", + "6 0.19 0.03 0.52 0.19 0.04 0.03\n", + "7 0.19 0.03 0.52 0.19 0.04 0.03\n", + "8 0.19 0.03 0.52 0.19 0.04 0.03\n", + "9 0.19 0.03 0.52 0.19 0.04 0.03\n", + "10 0.19 0.03 0.52 0.19 0.04 0.03\n", + "11 0.19 0.03 0.52 0.19 0.04 0.03\n", + "12 0.19 0.03 0.52 0.19 0.04 0.03\n", + "13 0.19 0.03 0.52 0.19 0.04 0.03\n", + "14 0.19 0.03 0.52 0.19 0.04 0.03\n", + "15 0.19 0.03 0.52 0.19 0.04 0.03\n", + "16 0.19 0.03 0.52 0.19 0.04 0.03\n", + "17 0.19 0.03 0.52 0.19 0.04 0.03\n", + "18 0.19 0.03 0.52 0.19 0.04 0.03\n", + "19 0.19 0.03 0.52 0.19 0.04 0.03" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "abundance = abundance.transpose()\n", + "abundance" + ] + }, + { + "cell_type": "markdown", + "id": "0b5523a2-7965-488d-9b4f-613cf7fb9057", + "metadata": {}, + "source": [ + "### Get the velocity at the middle of each shell" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7508164f-4d26-4c8e-a08c-f4ccda867218", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$[1.1225 \\times 10^{9},~1.1675 \\times 10^{9},~1.2125 \\times 10^{9},~1.2575 \\times 10^{9},~1.3025 \\times 10^{9},~1.3475 \\times 10^{9},~1.3925 \\times 10^{9},~1.4375 \\times 10^{9},~1.4825 \\times 10^{9},~1.5275 \\times 10^{9},~1.5725 \\times 10^{9},~1.6175 \\times 10^{9},~1.6625 \\times 10^{9},~1.7075 \\times 10^{9},~1.7525 \\times 10^{9},~1.7975 \\times 10^{9},~1.8425 \\times 10^{9},~1.8875 \\times 10^{9},~1.9325 \\times 10^{9},~1.9775 \\times 10^{9}] \\; \\mathrm{\\frac{cm}{s}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "v_middle = sim.simulation_state.v_middle\n", + "v_middle" + ] + }, + { + "cell_type": "markdown", + "id": "bb113dc0-3c83-46cc-81e0-aeb0860433ca", + "metadata": {}, + "source": [ + "### Renaming the abundance columns( atomic number to atomic symbol )" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b2dbebb8-832c-4689-91a9-fbe9391e3222", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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atomic_numberOMgSiSArCa
00.190.030.520.190.040.03
10.190.030.520.190.040.03
20.190.030.520.190.040.03
30.190.030.520.190.040.03
40.190.030.520.190.040.03
50.190.030.520.190.040.03
60.190.030.520.190.040.03
70.190.030.520.190.040.03
80.190.030.520.190.040.03
90.190.030.520.190.040.03
100.190.030.520.190.040.03
110.190.030.520.190.040.03
120.190.030.520.190.040.03
130.190.030.520.190.040.03
140.190.030.520.190.040.03
150.190.030.520.190.040.03
160.190.030.520.190.040.03
170.190.030.520.190.040.03
180.190.030.520.190.040.03
190.190.030.520.190.040.03
\n", + "
" + ], + "text/plain": [ + "atomic_number O Mg Si S Ar Ca\n", + "0 0.19 0.03 0.52 0.19 0.04 0.03\n", + "1 0.19 0.03 0.52 0.19 0.04 0.03\n", + "2 0.19 0.03 0.52 0.19 0.04 0.03\n", + "3 0.19 0.03 0.52 0.19 0.04 0.03\n", + "4 0.19 0.03 0.52 0.19 0.04 0.03\n", + "5 0.19 0.03 0.52 0.19 0.04 0.03\n", + "6 0.19 0.03 0.52 0.19 0.04 0.03\n", + "7 0.19 0.03 0.52 0.19 0.04 0.03\n", + "8 0.19 0.03 0.52 0.19 0.04 0.03\n", + "9 0.19 0.03 0.52 0.19 0.04 0.03\n", + "10 0.19 0.03 0.52 0.19 0.04 0.03\n", + "11 0.19 0.03 0.52 0.19 0.04 0.03\n", + "12 0.19 0.03 0.52 0.19 0.04 0.03\n", + "13 0.19 0.03 0.52 0.19 0.04 0.03\n", + "14 0.19 0.03 0.52 0.19 0.04 0.03\n", + "15 0.19 0.03 0.52 0.19 0.04 0.03\n", + "16 0.19 0.03 0.52 0.19 0.04 0.03\n", + "17 0.19 0.03 0.52 0.19 0.04 0.03\n", + "18 0.19 0.03 0.52 0.19 0.04 0.03\n", + "19 0.19 0.03 0.52 0.19 0.04 0.03" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from tardis.util.base import atomic_number2element_symbol\n", + "columns = {head: atomic_number2element_symbol(head) for head in abundance.columns}\n", + "abundance.rename(columns = columns, inplace = True)\n", + "abundance" + ] + }, + { + "cell_type": "markdown", + "id": "5be54c4d-5004-41c2-94f5-f04bdf5d4765", + "metadata": {}, + "source": [ + "### Add a new column of ```v_middle``` in units of km/s" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "43f16cac-9591-47a2-9cc7-2d3c211d19fb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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atomic_numberOMgSiSArCav_middle
00.190.030.520.190.040.0311225.0
10.190.030.520.190.040.0311675.0
20.190.030.520.190.040.0312125.0
30.190.030.520.190.040.0312575.0
40.190.030.520.190.040.0313025.0
50.190.030.520.190.040.0313475.0
60.190.030.520.190.040.0313925.0
70.190.030.520.190.040.0314375.0
80.190.030.520.190.040.0314825.0
90.190.030.520.190.040.0315275.0
100.190.030.520.190.040.0315725.0
110.190.030.520.190.040.0316175.0
120.190.030.520.190.040.0316625.0
130.190.030.520.190.040.0317075.0
140.190.030.520.190.040.0317525.0
150.190.030.520.190.040.0317975.0
160.190.030.520.190.040.0318425.0
170.190.030.520.190.040.0318875.0
180.190.030.520.190.040.0319325.0
190.190.030.520.190.040.0319775.0
\n", + "
" + ], + "text/plain": [ + "atomic_number O Mg Si S Ar Ca v_middle\n", + "0 0.19 0.03 0.52 0.19 0.04 0.03 11225.0\n", + "1 0.19 0.03 0.52 0.19 0.04 0.03 11675.0\n", + "2 0.19 0.03 0.52 0.19 0.04 0.03 12125.0\n", + "3 0.19 0.03 0.52 0.19 0.04 0.03 12575.0\n", + "4 0.19 0.03 0.52 0.19 0.04 0.03 13025.0\n", + "5 0.19 0.03 0.52 0.19 0.04 0.03 13475.0\n", + "6 0.19 0.03 0.52 0.19 0.04 0.03 13925.0\n", + "7 0.19 0.03 0.52 0.19 0.04 0.03 14375.0\n", + "8 0.19 0.03 0.52 0.19 0.04 0.03 14825.0\n", + "9 0.19 0.03 0.52 0.19 0.04 0.03 15275.0\n", + "10 0.19 0.03 0.52 0.19 0.04 0.03 15725.0\n", + "11 0.19 0.03 0.52 0.19 0.04 0.03 16175.0\n", + "12 0.19 0.03 0.52 0.19 0.04 0.03 16625.0\n", + "13 0.19 0.03 0.52 0.19 0.04 0.03 17075.0\n", + "14 0.19 0.03 0.52 0.19 0.04 0.03 17525.0\n", + "15 0.19 0.03 0.52 0.19 0.04 0.03 17975.0\n", + "16 0.19 0.03 0.52 0.19 0.04 0.03 18425.0\n", + "17 0.19 0.03 0.52 0.19 0.04 0.03 18875.0\n", + "18 0.19 0.03 0.52 0.19 0.04 0.03 19325.0\n", + "19 0.19 0.03 0.52 0.19 0.04 0.03 19775.0" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import astropy.units as u\n", + "abundance['v_middle'] = [u.Quantity(vel, u.km/u.s).value for vel in v_middle]\n", + "abundance" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "38ac8143-88a0-4776-b7cd-280ecaa3939b", + "metadata": {}, + "outputs": [], + "source": [ + "abundance.columns.name = 'atomic symbol'" + ] + }, + { + "cell_type": "markdown", + "id": "1d5ae678-af08-4a68-b378-7bd980f1f4c4", + "metadata": {}, + "source": [ + "## Plot of Abundance vs velocity" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "5fed4c8d-fceb-4d1a-b8d5-f23da51a3903", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "abundance.plot(x = 'v_middle', xlabel = \"$v_{middle}$ in km/s\", ylabel = \"Fractional Abundance\", title = \"Abundace vs velocity\").legend(loc = 'upper right')" + ] + }, + { + "cell_type": "markdown", + "id": "ef91834e-3aed-4864-95ab-f60f9284229a", + "metadata": {}, + "source": [ + "### Things to note in above graph\n", + "1. Since data overlap, we can only see four out of six line plots\n", + "2. Fractional abundance is uniform throughout the ejecta." + ] + }, + { + "cell_type": "markdown", + "id": "138ac797-80ae-46ee-8fab-068ff07d4c18", + "metadata": {}, + "source": [ + "## Our final task was to plot the total number of interactions that escape the simulation from the different elements\n", + "### I have worked it out for virtual packets, similar thing could be applied for the real packets as well\n", + "\n", + "I found out that most of the necessary data was alreadly pre computed during plotting of SDEC and available in the plotter object.\n" + ] + }, + { + "cell_type": "markdown", + "id": "c5fdf21b-f896-4b20-ae5b-c5125365db2b", + "metadata": {}, + "source": [ + "Get the line interaction data" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "418fd4a8-3f59-4933-a06e-da17404b1371", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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nuslambdasenergieslast_interaction_typelast_line_interaction_out_idlast_line_interaction_in_idlast_line_interaction_in_nulast_line_interaction_atomlast_line_interaction_species
102.610913e+151148.2283610.000000e+002355153431.736483e+15141402
112.623633e+151142.6616430.000000e+002355153431.736483e+15141402
122.635277e+151137.6127830.000000e+002355153431.736483e+15141402
132.652415e+151130.2620870.000000e+002355153431.736483e+15141402
142.666043e+151124.4845570.000000e+002355153431.736483e+15141402
..............................
26527351.086349e+152759.6323376.346043e-072769776971.109094e+15121201
26527361.092227e+152744.7820746.591144e-072769776971.109094e+15121201
26527371.099582e+152726.4225686.837456e-072769776971.109094e+15121201
26527381.109661e+152701.6577697.107076e-072769776971.109094e+15121201
26527391.115506e+152687.5019447.237699e-072769776971.109094e+15121201
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1071430 rows × 9 columns

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" + ], + "text/plain": [ + " nus lambdas energies last_interaction_type \\\n", + "10 2.610913e+15 1148.228361 0.000000e+00 2 \n", + "11 2.623633e+15 1142.661643 0.000000e+00 2 \n", + "12 2.635277e+15 1137.612783 0.000000e+00 2 \n", + "13 2.652415e+15 1130.262087 0.000000e+00 2 \n", + "14 2.666043e+15 1124.484557 0.000000e+00 2 \n", + "... ... ... ... ... \n", + "2652735 1.086349e+15 2759.632337 6.346043e-07 2 \n", + "2652736 1.092227e+15 2744.782074 6.591144e-07 2 \n", + "2652737 1.099582e+15 2726.422568 6.837456e-07 2 \n", + "2652738 1.109661e+15 2701.657769 7.107076e-07 2 \n", + "2652739 1.115506e+15 2687.501944 7.237699e-07 2 \n", + "\n", + " last_line_interaction_out_id last_line_interaction_in_id \\\n", + "10 3551 5343 \n", + "11 3551 5343 \n", + "12 3551 5343 \n", + "13 3551 5343 \n", + "14 3551 5343 \n", + "... ... ... \n", + "2652735 7697 7697 \n", + "2652736 7697 7697 \n", + "2652737 7697 7697 \n", + "2652738 7697 7697 \n", + "2652739 7697 7697 \n", + "\n", + " last_line_interaction_in_nu last_line_interaction_atom \\\n", + "10 1.736483e+15 14 \n", + "11 1.736483e+15 14 \n", + "12 1.736483e+15 14 \n", + "13 1.736483e+15 14 \n", + "14 1.736483e+15 14 \n", + "... ... ... \n", + "2652735 1.109094e+15 12 \n", + "2652736 1.109094e+15 12 \n", + "2652737 1.109094e+15 12 \n", + "2652738 1.109094e+15 12 \n", + "2652739 1.109094e+15 12 \n", + "\n", + " last_line_interaction_species \n", + "10 1402 \n", + "11 1402 \n", + "12 1402 \n", + "13 1402 \n", + "14 1402 \n", + "... ... \n", + "2652735 1201 \n", + "2652736 1201 \n", + "2652737 1201 \n", + "2652738 1201 \n", + "2652739 1201 \n", + "\n", + "[1071430 rows x 9 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "line_interaction_df = plotter.data[\"virtual\"].packets_df_line_interaction\n", + "line_interaction_df" + ] + }, + { + "cell_type": "markdown", + "id": "b064e5da-b865-45ab-84d7-797d9a0fb16e", + "metadata": {}, + "source": [ + "### Some slight pre processing ( refinement, counting )" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f31be200-6849-4177-91c7-d59f90da046a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
atomic_numbersymbolcount
214Si665620
316S219410
112Mg75800
08O39400
520Ca37650
418Ar33550
\n", + "
" + ], + "text/plain": [ + " atomic_number symbol count\n", + "2 14 Si 665620\n", + "3 16 S 219410\n", + "1 12 Mg 75800\n", + "0 8 O 39400\n", + "5 20 Ca 37650\n", + "4 18 Ar 33550" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Adding a new column to get the count of interactions\n", + "line_interaction_df['count'] = 1\n", + "\n", + "#Since only count is required, let's use only count and atomic_number columns\n", + "line_interaction_count_df = line_interaction_df[['last_line_interaction_atom', 'count']]\n", + "\n", + "#Group by the last_line_interaction_atom\n", + "line_interaction_count_df = line_interaction_count_df.groupby(['last_line_interaction_atom']).count()[['count']]\n", + "line_interaction_count_df['atomic_number'] = line_interaction_count_df.index\n", + "line_interaction_count_df.reset_index(drop = True, inplace = True)\n", + "\n", + "#Add a new column with the correspong atomic symbols\n", + "line_interaction_count_df['symbol'] = line_interaction_count_df['atomic_number'].apply(atomic_number2element_symbol)\n", + "\n", + "#Rearranging the columns\n", + "line_interaction_count_df = line_interaction_count_df[['atomic_number', 'symbol', 'count']]\n", + "\n", + "#Sorting according to the count value\n", + "line_interaction_count_df = line_interaction_count_df.sort_values('count', ascending = False)\n", + "line_interaction_count_df" + ] + }, + { + "cell_type": "markdown", + "id": "4006f519-f11c-48b4-bffc-b22737729593", + "metadata": {}, + "source": [ + "## Finally, the required plot." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "837d9f0e-e01a-4e91-95e4-d1a822042d79", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "line_interaction_count_df.plot.bar(x = 'symbol' , y = 'count', rot = 0)" + ] + }, + { + "cell_type": "markdown", + "id": "a36f4f12-3970-42d6-994f-6caa23441142", + "metadata": {}, + "source": [ + "## Thanks for giving your time. Please suggest any impovements or any mistakes I made." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ee25854-b8d4-4229-a908-11d11ad3ba49", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tardis", + "language": "python", + "name": "tardis" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From cf730830c6d29cbc68515e788eacb161923903b5 Mon Sep 17 00:00:00 2001 From: Sumit112192 Date: Sun, 24 Mar 2024 23:28:51 +0530 Subject: [PATCH 2/6] Changed as suggested --- .mailmap | 2 + .../VelocityPacketTrackerFirstObjective.md | 2798 ------------ FirstObjectiveMarkdown/output_10_1.png | Bin 80089 -> 0 bytes FirstObjectiveMarkdown/output_24_1.png | Bin 19009 -> 0 bytes FirstObjectiveMarkdown/output_32_1.png | Bin 14925 -> 0 bytes VelocityPacketTrackerFirstObjective.ipynb | 3956 +++++++++++++---- 6 files changed, 3156 insertions(+), 3600 deletions(-) delete mode 100644 FirstObjectiveMarkdown/VelocityPacketTrackerFirstObjective.md delete mode 100644 FirstObjectiveMarkdown/output_10_1.png delete mode 100644 FirstObjectiveMarkdown/output_24_1.png delete mode 100644 FirstObjectiveMarkdown/output_32_1.png diff --git a/.mailmap b/.mailmap index 77a23a6afca..b4a69963c7d 100644 --- a/.mailmap +++ b/.mailmap @@ -263,3 +263,5 @@ Sarthak Srivastava Sarthak Srivastava kimsina Kim Lingemann kim + +Sumit Gupta diff --git a/FirstObjectiveMarkdown/VelocityPacketTrackerFirstObjective.md b/FirstObjectiveMarkdown/VelocityPacketTrackerFirstObjective.md deleted file mode 100644 index c5110c7728d..00000000000 --- a/FirstObjectiveMarkdown/VelocityPacketTrackerFirstObjective.md +++ /dev/null @@ -1,2798 +0,0 @@ -# Velocity Packet Tracker Visulization - -## In the below cell, we import necessaries libraries and download the required dataset - - -```python -from tardis import run_tardis -from tardis.io.atom_data.util import download_atom_data - -download_atom_data('kurucz_cd23_chianti_H_He') -``` - - - Iterations: 0/? [00:00 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
09.93e+03 K1.01e+04 K0.40.507
59.85e+03 K1.02e+04 K0.2110.197
109.78e+03 K1.01e+04 K0.1430.117
159.71e+03 K9.87e+03 K0.1050.0869
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 9933.952 K - Expected t_inner for next iteration = 10703.212 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 2 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.071e+43 erg / s - Luminosity absorbed = 3.576e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.01e+04 K1.08e+04 K0.5070.525
51.02e+04 K1.1e+04 K0.1970.203
101.01e+04 K1.08e+04 K0.1170.125
159.87e+03 K1.05e+04 K0.08690.0933
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10703.212 K - Expected t_inner for next iteration = 10673.712 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 3 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.074e+43 erg / s - Luminosity absorbed = 3.391e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.08e+04 K1.1e+04 K0.5250.483
51.1e+04 K1.12e+04 K0.2030.189
101.08e+04 K1.1e+04 K0.1250.118
151.05e+04 K1.06e+04 K0.09330.0895
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10673.712 K - Expected t_inner for next iteration = 10635.953 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 4 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.058e+43 erg / s - Luminosity absorbed = 3.352e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4830.469
51.12e+04 K1.12e+04 K0.1890.182
101.1e+04 K1.1e+04 K0.1180.113
151.06e+04 K1.07e+04 K0.08950.0861
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10635.953 K - Expected t_inner for next iteration = 10638.407 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 5 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.055e+43 erg / s - Luminosity absorbed = 3.399e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - Iteration converged 1/4 consecutive times. (base.py:261) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4690.479
51.12e+04 K1.13e+04 K0.1820.178
101.1e+04 K1.1e+04 K0.1130.113
151.07e+04 K1.07e+04 K0.08610.0839
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10638.407 K - Expected t_inner for next iteration = 10650.202 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 6 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.061e+43 erg / s - Luminosity absorbed = 3.398e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - Iteration converged 2/4 consecutive times. (base.py:261) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4790.47
51.13e+04 K1.12e+04 K0.1780.185
101.1e+04 K1.11e+04 K0.1130.112
151.07e+04 K1.07e+04 K0.08390.0856
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10650.202 K - Expected t_inner for next iteration = 10645.955 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 7 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.061e+43 erg / s - Luminosity absorbed = 3.382e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - Iteration converged 3/4 consecutive times. (base.py:261) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.470.47
51.12e+04 K1.13e+04 K0.1850.178
101.11e+04 K1.11e+04 K0.1120.112
151.07e+04 K1.07e+04 K0.08560.086
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10645.955 K - Expected t_inner for next iteration = 10642.050 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 8 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.062e+43 erg / s - Luminosity absorbed = 3.350e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - Iteration converged 4/4 consecutive times. (base.py:261) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.11e+04 K0.470.472
51.13e+04 K1.14e+04 K0.1780.175
101.11e+04 K1.11e+04 K0.1120.111
151.07e+04 K1.07e+04 K0.0860.084
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10642.050 K - Expected t_inner for next iteration = 10636.106 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 9 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.052e+43 erg / s - Luminosity absorbed = 3.411e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - Iteration converged 5/4 consecutive times. (base.py:261) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4720.469
51.14e+04 K1.15e+04 K0.1750.17
101.11e+04 K1.11e+04 K0.1110.109
151.07e+04 K1.08e+04 K0.0840.0822
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10636.106 K - Expected t_inner for next iteration = 10654.313 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 10 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.070e+43 erg / s - Luminosity absorbed = 3.335e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.1e+04 K0.4690.475
51.15e+04 K1.14e+04 K0.170.177
101.11e+04 K1.11e+04 K0.1090.112
151.08e+04 K1.06e+04 K0.08220.0878
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10654.313 K - Expected t_inner for next iteration = 10628.190 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 11 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.053e+43 erg / s - Luminosity absorbed = 3.363e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - Iteration converged 1/4 consecutive times. (base.py:261) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4750.472
51.14e+04 K1.12e+04 K0.1770.184
101.11e+04 K1.1e+04 K0.1120.114
151.06e+04 K1.06e+04 K0.08780.0859
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10628.190 K - Expected t_inner for next iteration = 10644.054 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 12 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.056e+43 erg / s - Luminosity absorbed = 3.420e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.11e+04 K0.4720.467
51.12e+04 K1.13e+04 K0.1840.176
101.1e+04 K1.11e+04 K0.1140.11
151.06e+04 K1.08e+04 K0.08590.0821
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10644.054 K - Expected t_inner for next iteration = 10653.543 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 13 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.062e+43 erg / s - Luminosity absorbed = 3.406e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - Iteration converged 1/4 consecutive times. (base.py:261) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4670.466
51.13e+04 K1.13e+04 K0.1760.18
101.11e+04 K1.11e+04 K0.110.111
151.08e+04 K1.08e+04 K0.08210.0841
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10653.543 K - Expected t_inner for next iteration = 10647.277 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 14 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.063e+43 erg / s - Luminosity absorbed = 3.369e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - Iteration converged 2/4 consecutive times. (base.py:261) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4660.469
51.13e+04 K1.13e+04 K0.180.182
101.11e+04 K1.1e+04 K0.1110.113
151.08e+04 K1.07e+04 K0.08410.0854
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10647.277 K - Expected t_inner for next iteration = 10638.875 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 15 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.053e+43 erg / s - Luminosity absorbed = 3.417e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - Iteration converged 3/4 consecutive times. (base.py:261) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.1e+04 K0.4690.484
51.13e+04 K1.13e+04 K0.1820.181
101.1e+04 K1.1e+04 K0.1130.113
151.07e+04 K1.07e+04 K0.08540.0858
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10638.875 K - Expected t_inner for next iteration = 10655.125 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 16 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.059e+43 erg / s - Luminosity absorbed = 3.445e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - Iteration converged 4/4 consecutive times. (base.py:261) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4840.472
51.13e+04 K1.13e+04 K0.1810.177
101.1e+04 K1.1e+04 K0.1130.113
151.07e+04 K1.06e+04 K0.08580.0858
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10655.125 K - Expected t_inner for next iteration = 10655.561 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 17 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.067e+43 erg / s - Luminosity absorbed = 3.372e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.11e+04 K0.4720.468
51.13e+04 K1.14e+04 K0.1770.175
101.1e+04 K1.11e+04 K0.1130.11
151.06e+04 K1.08e+04 K0.08580.0816
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10655.561 K - Expected t_inner for next iteration = 10636.536 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 18 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.057e+43 erg / s - Luminosity absorbed = 3.365e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - Iteration converged 1/4 consecutive times. (base.py:261) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4680.464
51.14e+04 K1.13e+04 K0.1750.177
101.11e+04 K1.1e+04 K0.110.113
151.08e+04 K1.07e+04 K0.08160.0848
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10636.536 K - Expected t_inner for next iteration = 10641.692 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 19 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.056e+43 erg / s - Luminosity absorbed = 3.405e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - Iteration converged 2/4 consecutive times. (base.py:261) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4640.466
51.13e+04 K1.13e+04 K0.1770.177
101.1e+04 K1.11e+04 K0.1130.111
151.07e+04 K1.07e+04 K0.08480.0853
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10641.692 K - Expected t_inner for next iteration = 10650.463 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Simulation finished in 19 iterations - Simulation took 54.57 s - (base.py:469) - [tardis.simulation.base][INFO ] - - Starting iteration 20 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.061e+43 erg / s - Luminosity absorbed = 3.401e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - - -## Import the SDECPlotter class for plotting spectral element Decomposition Plot and obtain the necessary data from the simulation we just ran above. - - -```python -from tardis.visualization import SDECPlotter -plotter = SDECPlotter.from_simulation(sim) -``` - -## Let's now plot the SDEC Plot ( Second part of our First Objective ) - - -```python -plotter.generate_plot_mpl() -``` - - - - - - - - - - -![png](output_10_1.png) - - - -## Let's now work on the abundance vs velocity plot -For the next few cells, we'll modify the abundace dataframe so as to plot it easily - -### Get the abundance data from the simulation_state - - -```python -abundance = sim.simulation_state.abundance -abundance -``` - - - - -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
012345678910111213141516171819
atomic_number
80.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.19
120.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.03
140.520.520.520.520.520.520.520.520.520.520.520.520.520.520.520.520.520.520.520.52
160.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.19
180.040.040.040.040.040.040.040.040.040.040.040.040.040.040.040.040.040.040.040.04
200.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.03
-
- - - -### Transpose the abundance dataframe so to get atomic number as columns heads - - -```python -abundance = abundance.transpose() -abundance -``` - - - - -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
atomic_number81214161820
00.190.030.520.190.040.03
10.190.030.520.190.040.03
20.190.030.520.190.040.03
30.190.030.520.190.040.03
40.190.030.520.190.040.03
50.190.030.520.190.040.03
60.190.030.520.190.040.03
70.190.030.520.190.040.03
80.190.030.520.190.040.03
90.190.030.520.190.040.03
100.190.030.520.190.040.03
110.190.030.520.190.040.03
120.190.030.520.190.040.03
130.190.030.520.190.040.03
140.190.030.520.190.040.03
150.190.030.520.190.040.03
160.190.030.520.190.040.03
170.190.030.520.190.040.03
180.190.030.520.190.040.03
190.190.030.520.190.040.03
-
- - - -### Get the velocity at the middle of each shell - - -```python -v_middle = sim.simulation_state.v_middle -v_middle -``` - - - - -$[1.1225 \times 10^{9},~1.1675 \times 10^{9},~1.2125 \times 10^{9},~1.2575 \times 10^{9},~1.3025 \times 10^{9},~1.3475 \times 10^{9},~1.3925 \times 10^{9},~1.4375 \times 10^{9},~1.4825 \times 10^{9},~1.5275 \times 10^{9},~1.5725 \times 10^{9},~1.6175 \times 10^{9},~1.6625 \times 10^{9},~1.7075 \times 10^{9},~1.7525 \times 10^{9},~1.7975 \times 10^{9},~1.8425 \times 10^{9},~1.8875 \times 10^{9},~1.9325 \times 10^{9},~1.9775 \times 10^{9}] \; \mathrm{\frac{cm}{s}}$ - - - -### Renaming the abundance columns( atomic number to atomic symbol ) - - -```python -from tardis.util.base import atomic_number2element_symbol -columns = {head: atomic_number2element_symbol(head) for head in abundance.columns} -abundance.rename(columns = columns, inplace = True) -abundance -``` - - - - -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
atomic_numberOMgSiSArCa
00.190.030.520.190.040.03
10.190.030.520.190.040.03
20.190.030.520.190.040.03
30.190.030.520.190.040.03
40.190.030.520.190.040.03
50.190.030.520.190.040.03
60.190.030.520.190.040.03
70.190.030.520.190.040.03
80.190.030.520.190.040.03
90.190.030.520.190.040.03
100.190.030.520.190.040.03
110.190.030.520.190.040.03
120.190.030.520.190.040.03
130.190.030.520.190.040.03
140.190.030.520.190.040.03
150.190.030.520.190.040.03
160.190.030.520.190.040.03
170.190.030.520.190.040.03
180.190.030.520.190.040.03
190.190.030.520.190.040.03
-
- - - -### Add a new column of ```v_middle``` in units of km/s - - -```python -import astropy.units as u -abundance['v_middle'] = [u.Quantity(vel, u.km/u.s).value for vel in v_middle] -abundance -``` - - - - -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
atomic_numberOMgSiSArCav_middle
00.190.030.520.190.040.0311225.0
10.190.030.520.190.040.0311675.0
20.190.030.520.190.040.0312125.0
30.190.030.520.190.040.0312575.0
40.190.030.520.190.040.0313025.0
50.190.030.520.190.040.0313475.0
60.190.030.520.190.040.0313925.0
70.190.030.520.190.040.0314375.0
80.190.030.520.190.040.0314825.0
90.190.030.520.190.040.0315275.0
100.190.030.520.190.040.0315725.0
110.190.030.520.190.040.0316175.0
120.190.030.520.190.040.0316625.0
130.190.030.520.190.040.0317075.0
140.190.030.520.190.040.0317525.0
150.190.030.520.190.040.0317975.0
160.190.030.520.190.040.0318425.0
170.190.030.520.190.040.0318875.0
180.190.030.520.190.040.0319325.0
190.190.030.520.190.040.0319775.0
-
- - - - -```python -abundance.columns.name = 'atomic symbol' -``` - -## Plot of Abundance vs velocity - - -```python -abundance.plot(x = 'v_middle', xlabel = "$v_{middle}$ in km/s", ylabel = "Fractional Abundance", title = "Abundace vs velocity").legend(loc = 'upper right') -``` - - - - - - - - - - -![png](output_24_1.png) - - - -### Things to note in above graph -1. Since data overlap, we can only see four out of six line plots -2. Fractional abundance is uniform throughout the ejecta. - -## Our final task was to plot the total number of interactions that escape the simulation from the different elements -### I have worked it out for virtual packets, similar thing could be applied for the real packets as well - -I found out that most of the necessary data was alreadly pre computed during plotting of SDEC and available in the plotter object. - - -Get the line interaction data - - -```python -line_interaction_df = plotter.data["virtual"].packets_df_line_interaction -line_interaction_df -``` - - - - -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
nuslambdasenergieslast_interaction_typelast_line_interaction_out_idlast_line_interaction_in_idlast_line_interaction_in_nulast_line_interaction_atomlast_line_interaction_species
102.610913e+151148.2283610.000000e+002355153431.736483e+15141402
112.623633e+151142.6616430.000000e+002355153431.736483e+15141402
122.635277e+151137.6127830.000000e+002355153431.736483e+15141402
132.652415e+151130.2620870.000000e+002355153431.736483e+15141402
142.666043e+151124.4845570.000000e+002355153431.736483e+15141402
..............................
26527351.086349e+152759.6323376.346043e-072769776971.109094e+15121201
26527361.092227e+152744.7820746.591144e-072769776971.109094e+15121201
26527371.099582e+152726.4225686.837456e-072769776971.109094e+15121201
26527381.109661e+152701.6577697.107076e-072769776971.109094e+15121201
26527391.115506e+152687.5019447.237699e-072769776971.109094e+15121201
-

1071430 rows × 9 columns

-
- - - -### Some slight pre processing ( refinement, counting ) - - -```python -#Adding a new column to get the count of interactions -line_interaction_df['count'] = 1 - -#Since only count is required, let's use only count and atomic_number columns -line_interaction_count_df = line_interaction_df[['last_line_interaction_atom', 'count']] - -#Group by the last_line_interaction_atom -line_interaction_count_df = line_interaction_count_df.groupby(['last_line_interaction_atom']).count()[['count']] -line_interaction_count_df['atomic_number'] = line_interaction_count_df.index -line_interaction_count_df.reset_index(drop = True, inplace = True) - -#Add a new column with the correspong atomic symbols -line_interaction_count_df['symbol'] = line_interaction_count_df['atomic_number'].apply(atomic_number2element_symbol) - -#Rearranging the columns -line_interaction_count_df = line_interaction_count_df[['atomic_number', 'symbol', 'count']] - -#Sorting according to the count value -line_interaction_count_df = line_interaction_count_df.sort_values('count', ascending = False) -line_interaction_count_df -``` - - - - -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
atomic_numbersymbolcount
214Si665620
316S219410
112Mg75800
08O39400
520Ca37650
418Ar33550
-
- - - -## Finally, the required plot. - - -```python -line_interaction_count_df.plot.bar(x = 'symbol' , y = 'count', rot = 0) -``` - - - - - - - - - - -![png](output_32_1.png) - - - -## Thanks for giving your time. Please suggest any impovements or any mistakes I made. - - -```python - -``` diff --git a/FirstObjectiveMarkdown/output_10_1.png b/FirstObjectiveMarkdown/output_10_1.png deleted file mode 100644 index d9c447be6af2432d8893984fdad004571d9115ee..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 80089 zcma%jcOaGj+dmYR-1mE2*K57ct49jb=gv}}#lpfm_wd0zWh^Y5cr2__4rfloPjY-1 zvfzJ$PEt>t9^0BYx#~L@W6A3~*;&~-S(zJLb}@EvG`F?k;Sk{9V!v$W zUoYUWbui`REhx!`ixAj7&~U`UBGE_w!_E*-H^;)p!g_d5Ld7k1dDPX7tgpX*ebuTJ zop$NM>J5WndYCb z$K2dJzBMS%S7lBrGvO6~9fj^3iLT(e_UXOjYDI8X7OUi$E6+*ck0z$%svbJzZ^%FG zt>4!_{;%IK`KSN;#YAKI7 z&vnE|D=S|(M}7Ng&*z6u3tdU|o1wIV@2rPQ*AG_faJq7Jgl#GpnaAAc>0DzbzXla~ zAGq*Y4cKMcNdFsd4mK(aPk(y%ne)fH(I; zZ6Atl$H!_qEJ_UcVnl~D*eObFMt}2OQ~!6JHdX==&U*NWy}20M-JN0EirJ4^85%{* zDbnF{@FSC*;Hat&)_MyUnRGOo z6rE>a@U5_w{m*R@L{Gb8yP!v2Wo7-$)fqOd>a$?FdbQDTxG|9Mp}f2?20bcra=2N2 zfUc@uYb3;~hB=BA_58?ZJ(QTmI{PEiv@20UE?$JHM%Q-`{a;Vd1b6M%yLd0JzsRtK 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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
09.93e+03 K1.01e+04 K0.40.50709.94e+03 K1.01e+04 K0.40.506
59.85e+03 K1.02e+04 K0.2110.19751e+04 K1.03e+04 K0.2110.191
109.78e+03 K1.01e+04 K0.1430.117101.01e+04 K1.02e+04 K0.1430.115
159.71e+03 K9.87e+03 K0.1050.0869151.02e+04 K9.9e+03 K0.1050.086
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -299,24 +299,24 @@ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tCurrent t_inner = 9933.952 K\n", - "\tExpected t_inner for next iteration = 10703.212 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tExpected t_inner for next iteration = 10697.222 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 2 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 2 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tLuminosity emitted = 1.071e+43 erg / s\n", - "\tLuminosity absorbed = 3.576e+42 erg / s\n", + "\tLuminosity absorbed = 3.548e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -324,50 +324,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.01e+04 K1.08e+04 K0.5070.52501.01e+04 K1.08e+04 K0.5060.517
51.02e+04 K1.1e+04 K0.1970.20351.03e+04 K1.1e+04 K0.1910.198
101.01e+04 K1.08e+04 K0.1170.125101.02e+04 K1.07e+04 K0.1150.127
159.87e+03 K1.05e+04 K0.08690.0933159.9e+03 K1.05e+04 K0.0860.0928
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -379,25 +379,25 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10703.212 K\n", - "\tExpected t_inner for next iteration = 10673.712 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10697.222 K\n", + "\tExpected t_inner for next iteration = 10668.196 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 3 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 3 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.074e+43 erg / s\n", - "\tLuminosity absorbed = 3.391e+42 erg / s\n", + "\tLuminosity emitted = 1.072e+43 erg / s\n", + "\tLuminosity absorbed = 3.383e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -405,50 +405,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.08e+04 K1.1e+04 K0.5250.48301.08e+04 K1.1e+04 K0.5170.482
51.1e+04 K1.12e+04 K0.2030.18951.1e+04 K1.12e+04 K0.1980.188
101.08e+04 K1.1e+04 K0.1250.118101.07e+04 K1.1e+04 K0.1270.116
151.05e+04 K1.06e+04 K0.09330.0895151.05e+04 K1.06e+04 K0.09280.0896
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -460,25 +460,27 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10673.712 K\n", - "\tExpected t_inner for next iteration = 10635.953 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10668.196 K\n", + "\tExpected t_inner for next iteration = 10635.748 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 4 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 4 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.058e+43 erg / s\n", - "\tLuminosity absorbed = 3.352e+42 erg / s\n", + "\tLuminosity emitted = 1.060e+43 erg / s\n", + "\tLuminosity absorbed = 3.336e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\tIteration converged 1/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -486,50 +488,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4830.46901.1e+04 K1.1e+04 K0.4820.467
51.12e+04 K1.12e+04 K0.1890.18251.12e+04 K1.12e+04 K0.1880.183
101.1e+04 K1.1e+04 K0.1180.113101.1e+04 K1.1e+04 K0.1160.115
151.06e+04 K1.07e+04 K0.08950.0861151.06e+04 K1.07e+04 K0.08960.0859
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -541,27 +543,27 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10635.953 K\n", - "\tExpected t_inner for next iteration = 10638.407 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10635.748 K\n", + "\tExpected t_inner for next iteration = 10634.292 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 5 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 5 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.055e+43 erg / s\n", - "\tLuminosity absorbed = 3.399e+42 erg / s\n", + "\tLuminosity emitted = 1.054e+43 erg / s\n", + "\tLuminosity absorbed = 3.380e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 1/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "\tIteration converged 2/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -569,50 +571,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4690.47901.1e+04 K1.1e+04 K0.4670.479
51.12e+04 K1.13e+04 K0.1820.17851.12e+04 K1.13e+04 K0.1830.179
101.1e+04 K1.1e+04 K0.1130.113101.1e+04 K1.11e+04 K0.1150.111
151.07e+04 K1.07e+04 K0.08610.0839151.07e+04 K1.07e+04 K0.08590.0844
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -624,27 +626,27 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10638.407 K\n", - "\tExpected t_inner for next iteration = 10650.202 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10634.292 K\n", + "\tExpected t_inner for next iteration = 10646.785 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 6 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 6 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.061e+43 erg / s\n", - "\tLuminosity absorbed = 3.398e+42 erg / s\n", + "\tLuminosity emitted = 1.060e+43 erg / s\n", + "\tLuminosity absorbed = 3.394e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 2/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "\tIteration converged 3/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -652,50 +654,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4790.4701.1e+04 K1.11e+04 K0.4790.469
51.13e+04 K1.12e+04 K0.1780.18551.13e+04 K1.13e+04 K0.1790.183
101.1e+04 K1.11e+04 K0.1130.112101.11e+04 K1.11e+04 K0.1110.113
151.07e+04 K1.07e+04 K0.08390.0856151.07e+04 K1.07e+04 K0.08440.0855
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -707,27 +709,27 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10650.202 K\n", - "\tExpected t_inner for next iteration = 10645.955 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10646.785 K\n", + "\tExpected t_inner for next iteration = 10645.882 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 7 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 7 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tLuminosity emitted = 1.061e+43 erg / s\n", - "\tLuminosity absorbed = 3.382e+42 erg / s\n", + "\tLuminosity absorbed = 3.380e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 3/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "\tIteration converged 4/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -735,50 +737,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.470.4701.11e+04 K1.1e+04 K0.4690.471
51.12e+04 K1.13e+04 K0.1850.17851.13e+04 K1.13e+04 K0.1830.175
101.11e+04 K1.11e+04 K0.1120.112101.11e+04 K1.11e+04 K0.1130.111
151.07e+04 K1.07e+04 K0.08560.086151.07e+04 K1.06e+04 K0.08550.0863
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -790,27 +792,27 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10645.955 K\n", - "\tExpected t_inner for next iteration = 10642.050 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10645.882 K\n", + "\tExpected t_inner for next iteration = 10641.685 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 8 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 8 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.062e+43 erg / s\n", - "\tLuminosity absorbed = 3.350e+42 erg / s\n", + "\tLuminosity emitted = 1.061e+43 erg / s\n", + "\tLuminosity absorbed = 3.358e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 4/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "\tIteration converged 5/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -818,50 +820,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.11e+04 K0.470.47201.1e+04 K1.11e+04 K0.4710.468
51.13e+04 K1.14e+04 K0.1780.17551.13e+04 K1.14e+04 K0.1750.174
101.11e+04 K1.11e+04 K0.1120.111101.11e+04 K1.11e+04 K0.1110.109
151.07e+04 K1.07e+04 K0.0860.084151.06e+04 K1.08e+04 K0.08630.0826
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -873,27 +875,27 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10642.050 K\n", - "\tExpected t_inner for next iteration = 10636.106 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10641.685 K\n", + "\tExpected t_inner for next iteration = 10638.233 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 9 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 9 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.052e+43 erg / s\n", - "\tLuminosity absorbed = 3.411e+42 erg / s\n", + "\tLuminosity emitted = 1.053e+43 erg / s\n", + "\tLuminosity absorbed = 3.412e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 5/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "\tIteration converged 6/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -901,50 +903,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4720.46901.11e+04 K1.11e+04 K0.4680.467
51.14e+04 K1.15e+04 K0.1750.1751.14e+04 K1.15e+04 K0.1740.17
101.11e+04 K1.11e+04 K0.1110.109101.11e+04 K1.11e+04 K0.1090.109
151.07e+04 K1.08e+04 K0.0840.0822151.08e+04 K1.08e+04 K0.08260.0821
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -956,25 +958,25 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10636.106 K\n", - "\tExpected t_inner for next iteration = 10654.313 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10638.233 K\n", + "\tExpected t_inner for next iteration = 10654.289 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 10 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 10 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.070e+43 erg / s\n", - "\tLuminosity absorbed = 3.335e+42 erg / s\n", + "\tLuminosity emitted = 1.069e+43 erg / s\n", + "\tLuminosity absorbed = 3.338e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -982,50 +984,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.11e+04 K1.1e+04 K0.4690.47501.11e+04 K1.1e+04 K0.4670.473
51.15e+04 K1.14e+04 K0.170.17751.15e+04 K1.14e+04 K0.170.178
101.11e+04 K1.11e+04 K0.1090.112101.11e+04 K1.11e+04 K0.1090.112
151.08e+04 K1.06e+04 K0.08220.0878151.08e+04 K1.06e+04 K0.08210.0879
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1037,27 +1039,27 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10654.313 K\n", - "\tExpected t_inner for next iteration = 10628.190 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10654.289 K\n", + "\tExpected t_inner for next iteration = 10628.970 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 11 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 11 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.053e+43 erg / s\n", - "\tLuminosity absorbed = 3.363e+42 erg / s\n", + "\tLuminosity emitted = 1.052e+43 erg / s\n", + "\tLuminosity absorbed = 3.372e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 1/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "\tIteration converged 1/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -1065,50 +1067,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4750.47201.1e+04 K1.1e+04 K0.4730.477
51.14e+04 K1.12e+04 K0.1770.18451.14e+04 K1.12e+04 K0.1780.183
101.11e+04 K1.1e+04 K0.1120.114101.11e+04 K1.1e+04 K0.1120.115
151.06e+04 K1.06e+04 K0.08780.0859151.06e+04 K1.06e+04 K0.08790.0867
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1120,25 +1122,27 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10628.190 K\n", - "\tExpected t_inner for next iteration = 10644.054 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10628.970 K\n", + "\tExpected t_inner for next iteration = 10646.280 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 12 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 12 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.056e+43 erg / s\n", - "\tLuminosity absorbed = 3.420e+42 erg / s\n", + "\tLuminosity emitted = 1.055e+43 erg / s\n", + "\tLuminosity absorbed = 3.435e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\tIteration converged 2/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -1146,50 +1150,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.11e+04 K0.4720.46701.1e+04 K1.11e+04 K0.4770.463
51.12e+04 K1.13e+04 K0.1840.17651.12e+04 K1.13e+04 K0.1830.18
101.1e+04 K1.11e+04 K0.1140.11101.1e+04 K1.11e+04 K0.1150.112
151.06e+04 K1.08e+04 K0.08590.0821151.06e+04 K1.07e+04 K0.08670.0843
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1201,27 +1205,27 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10644.054 K\n", - "\tExpected t_inner for next iteration = 10653.543 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10646.280 K\n", + "\tExpected t_inner for next iteration = 10656.684 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 13 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 13 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.062e+43 erg / s\n", - "\tLuminosity absorbed = 3.406e+42 erg / s\n", + "\tLuminosity emitted = 1.065e+43 erg / s\n", + "\tLuminosity absorbed = 3.396e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 1/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "\tIteration converged 3/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -1229,50 +1233,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4670.46601.11e+04 K1.11e+04 K0.4630.463
51.13e+04 K1.13e+04 K0.1760.1851.13e+04 K1.13e+04 K0.180.179
101.11e+04 K1.11e+04 K0.110.111101.11e+04 K1.1e+04 K0.1120.114
151.08e+04 K1.08e+04 K0.08210.0841151.07e+04 K1.07e+04 K0.08430.0869
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1284,27 +1288,27 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10653.543 K\n", - "\tExpected t_inner for next iteration = 10647.277 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10656.684 K\n", + "\tExpected t_inner for next iteration = 10643.209 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 14 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 14 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.063e+43 erg / s\n", - "\tLuminosity absorbed = 3.369e+42 erg / s\n", + "\tLuminosity emitted = 1.061e+43 erg / s\n", + "\tLuminosity absorbed = 3.360e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 2/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "\tIteration converged 4/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -1312,50 +1316,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4660.46901.11e+04 K1.11e+04 K0.4630.467
51.13e+04 K1.13e+04 K0.180.18251.13e+04 K1.13e+04 K0.1790.181
101.11e+04 K1.1e+04 K0.1110.113101.1e+04 K1.1e+04 K0.1140.114
151.08e+04 K1.07e+04 K0.08410.0854151.07e+04 K1.06e+04 K0.08690.0866
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1367,27 +1371,27 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10647.277 K\n", - "\tExpected t_inner for next iteration = 10638.875 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10643.209 K\n", + "\tExpected t_inner for next iteration = 10637.728 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 15 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 15 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.053e+43 erg / s\n", - "\tLuminosity absorbed = 3.417e+42 erg / s\n", + "\tLuminosity emitted = 1.054e+43 erg / s\n", + "\tLuminosity absorbed = 3.401e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 3/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "\tIteration converged 5/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -1395,50 +1399,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.11e+04 K1.1e+04 K0.4690.48401.11e+04 K1.1e+04 K0.4670.482
51.13e+04 K1.13e+04 K0.1820.18151.13e+04 K1.13e+04 K0.1810.18
101.1e+04 K1.1e+04 K0.1130.113101.1e+04 K1.11e+04 K0.1140.111
151.07e+04 K1.07e+04 K0.08540.0858151.06e+04 K1.07e+04 K0.08660.0845
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1450,27 +1454,27 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10638.875 K\n", - "\tExpected t_inner for next iteration = 10655.125 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10637.728 K\n", + "\tExpected t_inner for next iteration = 10651.277 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 16 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 16 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.059e+43 erg / s\n", - "\tLuminosity absorbed = 3.445e+42 erg / s\n", + "\tLuminosity emitted = 1.057e+43 erg / s\n", + "\tLuminosity absorbed = 3.448e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 4/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "\tIteration converged 6/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -1478,50 +1482,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4840.47201.1e+04 K1.1e+04 K0.4820.473
51.13e+04 K1.13e+04 K0.1810.17751.13e+04 K1.14e+04 K0.180.172
101.1e+04 K1.1e+04 K0.1130.113101.11e+04 K1.1e+04 K0.1110.113
151.07e+04 K1.06e+04 K0.08580.0858151.07e+04 K1.08e+04 K0.08450.0824
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1533,25 +1537,25 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10655.125 K\n", - "\tExpected t_inner for next iteration = 10655.561 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10651.277 K\n", + "\tExpected t_inner for next iteration = 10658.182 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 17 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 17 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.067e+43 erg / s\n", - "\tLuminosity absorbed = 3.372e+42 erg / s\n", + "\tLuminosity emitted = 1.066e+43 erg / s\n", + "\tLuminosity absorbed = 3.396e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -1559,50 +1563,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.11e+04 K0.4720.46801.1e+04 K1.11e+04 K0.4730.463
51.13e+04 K1.14e+04 K0.1770.17551.14e+04 K1.14e+04 K0.1720.172
101.1e+04 K1.11e+04 K0.1130.11101.1e+04 K1.12e+04 K0.1130.106
151.06e+04 K1.08e+04 K0.08580.0816151.08e+04 K1.08e+04 K0.08240.0809
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1614,27 +1618,27 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10655.561 K\n", - "\tExpected t_inner for next iteration = 10636.536 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10658.182 K\n", + "\tExpected t_inner for next iteration = 10642.273 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 18 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 18 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.057e+43 erg / s\n", - "\tLuminosity absorbed = 3.365e+42 erg / s\n", + "\tLuminosity emitted = 1.058e+43 erg / s\n", + "\tLuminosity absorbed = 3.382e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 1/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "\tIteration converged 1/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -1642,50 +1646,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4680.46401.11e+04 K1.11e+04 K0.4630.462
51.14e+04 K1.13e+04 K0.1750.17751.14e+04 K1.14e+04 K0.1720.174
101.11e+04 K1.1e+04 K0.110.113101.12e+04 K1.11e+04 K0.1060.109
151.08e+04 K1.07e+04 K0.08160.0848151.08e+04 K1.07e+04 K0.08090.0829
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1697,27 +1701,27 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10636.536 K\n", - "\tExpected t_inner for next iteration = 10641.692 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10642.273 K\n", + "\tExpected t_inner for next iteration = 10644.386 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 19 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 19 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.056e+43 erg / s\n", - "\tLuminosity absorbed = 3.405e+42 erg / s\n", + "\tLuminosity emitted = 1.057e+43 erg / s\n", + "\tLuminosity absorbed = 3.403e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 2/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "\tIteration converged 2/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -1725,50 +1729,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4640.46601.11e+04 K1.11e+04 K0.4620.462
51.13e+04 K1.13e+04 K0.1770.17751.14e+04 K1.14e+04 K0.1740.173
101.1e+04 K1.11e+04 K0.1130.111101.11e+04 K1.11e+04 K0.1090.111
151.07e+04 K1.07e+04 K0.08480.0853151.07e+04 K1.07e+04 K0.08290.0845
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1780,9 +1784,9 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10641.692 K\n", - "\tExpected t_inner for next iteration = 10650.463 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10644.386 K\n", + "\tExpected t_inner for next iteration = 10649.220 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", @@ -1790,17 +1794,17 @@ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tSimulation finished in 19 iterations \n", - "\tSimulation took 54.57 s\n", - " (\u001b[1mbase.py\u001b[0m:469)\n", + "\tSimulation took 53.10 s\n", + " (\u001b[1mbase.py\u001b[0m:476)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 20 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 20 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.061e+43 erg / s\n", - "\tLuminosity absorbed = 3.401e+42 erg / s\n", + "\tLuminosity emitted = 1.060e+43 erg / s\n", + "\tLuminosity absorbed = 3.406e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n" + " (\u001b[1mbase.py\u001b[0m:580)\n" ] } ], @@ -1853,7 +1857,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -2684,7 +2688,7 @@ } ], "source": [ - "from tardis.util.base import atomic_number2element_symbol\n", + "from tardis.util.base import atomic_number2element_symbol, species_tuple_to_string\n", "columns = {head: atomic_number2element_symbol(head) for head in abundance.columns}\n", "abundance.rename(columns = columns, inplace = True)\n", "abundance" @@ -3001,7 +3005,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 12, @@ -3039,7 +3043,7 @@ "metadata": {}, "source": [ "## Our final task was to plot the total number of interactions that escape the simulation from the different elements\n", - "### I have worked it out for virtual packets, similar thing could be applied for the real packets as well\n", + "### I have worked it out for virtual mode, similar thing could be applied for the real packets as well\n", "\n", "I found out that most of the necessary data was alreadly pre computed during plotting of SDEC and available in the plotter object.\n" ] @@ -3093,61 +3097,61 @@ " \n", " \n", " 10\n", - " 2.610913e+15\n", - " 1148.228361\n", + " 2.612094e+15\n", + " 1147.709110\n", " 0.000000e+00\n", " 2\n", " 3551\n", " 5343\n", - " 1.736483e+15\n", + " 1.736280e+15\n", " 14\n", " 1402\n", " \n", " \n", " 11\n", - " 2.623633e+15\n", - " 1142.661643\n", + " 2.624666e+15\n", + " 1142.211688\n", " 0.000000e+00\n", " 2\n", " 3551\n", " 5343\n", - " 1.736483e+15\n", + " 1.736280e+15\n", " 14\n", " 1402\n", " \n", " \n", " 12\n", - " 2.635277e+15\n", - " 1137.612783\n", + " 2.636174e+15\n", + " 1137.225677\n", " 0.000000e+00\n", " 2\n", " 3551\n", " 5343\n", - " 1.736483e+15\n", + " 1.736280e+15\n", " 14\n", " 1402\n", " \n", " \n", " 13\n", - " 2.652415e+15\n", - " 1130.262087\n", + " 2.653109e+15\n", + " 1129.966483\n", " 0.000000e+00\n", " 2\n", " 3551\n", " 5343\n", - " 1.736483e+15\n", + " 1.736280e+15\n", " 14\n", " 1402\n", " \n", " \n", " 14\n", - " 2.666043e+15\n", - " 1124.484557\n", + " 2.666574e+15\n", + " 1124.260873\n", " 0.000000e+00\n", " 2\n", " 3551\n", " 5343\n", - " 1.736483e+15\n", + " 1.736280e+15\n", " 14\n", " 1402\n", " \n", @@ -3164,83 +3168,83 @@ " ...\n", " \n", " \n", - " 2652735\n", - " 1.086349e+15\n", - " 2759.632337\n", - " 6.346043e-07\n", + " 2660385\n", + " 1.081308e+15\n", + " 2772.499444\n", + " 4.970635e-07\n", " 2\n", " 7697\n", - " 7697\n", - " 1.109094e+15\n", + " 7672\n", + " 1.108046e+15\n", " 12\n", " 1201\n", " \n", " \n", - " 2652736\n", - " 1.092227e+15\n", - " 2744.782074\n", - " 6.591144e-07\n", + " 2660386\n", + " 1.093554e+15\n", + " 2741.449794\n", + " 5.658597e-07\n", " 2\n", " 7697\n", - " 7697\n", - " 1.109094e+15\n", + " 7672\n", + " 1.108046e+15\n", " 12\n", " 1201\n", " \n", " \n", - " 2652737\n", - " 1.099582e+15\n", - " 2726.422568\n", - " 6.837456e-07\n", + " 2660387\n", + " 1.098036e+15\n", + " 2730.260631\n", + " 5.847772e-07\n", " 2\n", " 7697\n", - " 7697\n", - " 1.109094e+15\n", + " 7672\n", + " 1.108046e+15\n", " 12\n", " 1201\n", " \n", " \n", - " 2652738\n", - " 1.109661e+15\n", - " 2701.657769\n", - " 7.107076e-07\n", + " 2660388\n", + " 1.104022e+15\n", + " 2715.457636\n", + " 6.066446e-07\n", " 2\n", " 7697\n", - " 7697\n", - " 1.109094e+15\n", + " 7672\n", + " 1.108046e+15\n", " 12\n", " 1201\n", " \n", " \n", - " 2652739\n", - " 1.115506e+15\n", - " 2687.501944\n", - " 7.237699e-07\n", + " 2660389\n", + " 1.112540e+15\n", + " 2694.667837\n", + " 6.325031e-07\n", " 2\n", " 7697\n", - " 7697\n", - " 1.109094e+15\n", + " 7672\n", + " 1.108046e+15\n", " 12\n", " 1201\n", " \n", " \n", "\n", - "

1071430 rows × 9 columns

\n", + "

1079320 rows × 9 columns

\n", "" ], "text/plain": [ " nus lambdas energies last_interaction_type \\\n", - "10 2.610913e+15 1148.228361 0.000000e+00 2 \n", - "11 2.623633e+15 1142.661643 0.000000e+00 2 \n", - "12 2.635277e+15 1137.612783 0.000000e+00 2 \n", - "13 2.652415e+15 1130.262087 0.000000e+00 2 \n", - "14 2.666043e+15 1124.484557 0.000000e+00 2 \n", + "10 2.612094e+15 1147.709110 0.000000e+00 2 \n", + "11 2.624666e+15 1142.211688 0.000000e+00 2 \n", + "12 2.636174e+15 1137.225677 0.000000e+00 2 \n", + "13 2.653109e+15 1129.966483 0.000000e+00 2 \n", + "14 2.666574e+15 1124.260873 0.000000e+00 2 \n", "... ... ... ... ... \n", - "2652735 1.086349e+15 2759.632337 6.346043e-07 2 \n", - "2652736 1.092227e+15 2744.782074 6.591144e-07 2 \n", - "2652737 1.099582e+15 2726.422568 6.837456e-07 2 \n", - "2652738 1.109661e+15 2701.657769 7.107076e-07 2 \n", - "2652739 1.115506e+15 2687.501944 7.237699e-07 2 \n", + "2660385 1.081308e+15 2772.499444 4.970635e-07 2 \n", + "2660386 1.093554e+15 2741.449794 5.658597e-07 2 \n", + "2660387 1.098036e+15 2730.260631 5.847772e-07 2 \n", + "2660388 1.104022e+15 2715.457636 6.066446e-07 2 \n", + "2660389 1.112540e+15 2694.667837 6.325031e-07 2 \n", "\n", " last_line_interaction_out_id last_line_interaction_in_id \\\n", "10 3551 5343 \n", @@ -3249,24 +3253,24 @@ "13 3551 5343 \n", "14 3551 5343 \n", "... ... ... \n", - "2652735 7697 7697 \n", - "2652736 7697 7697 \n", - "2652737 7697 7697 \n", - "2652738 7697 7697 \n", - "2652739 7697 7697 \n", + "2660385 7697 7672 \n", + "2660386 7697 7672 \n", + "2660387 7697 7672 \n", + "2660388 7697 7672 \n", + "2660389 7697 7672 \n", "\n", " last_line_interaction_in_nu last_line_interaction_atom \\\n", - "10 1.736483e+15 14 \n", - "11 1.736483e+15 14 \n", - "12 1.736483e+15 14 \n", - "13 1.736483e+15 14 \n", - "14 1.736483e+15 14 \n", + "10 1.736280e+15 14 \n", + "11 1.736280e+15 14 \n", + "12 1.736280e+15 14 \n", + "13 1.736280e+15 14 \n", + "14 1.736280e+15 14 \n", "... ... ... \n", - "2652735 1.109094e+15 12 \n", - "2652736 1.109094e+15 12 \n", - "2652737 1.109094e+15 12 \n", - "2652738 1.109094e+15 12 \n", - "2652739 1.109094e+15 12 \n", + "2660385 1.108046e+15 12 \n", + "2660386 1.108046e+15 12 \n", + "2660387 1.108046e+15 12 \n", + "2660388 1.108046e+15 12 \n", + "2660389 1.108046e+15 12 \n", "\n", " last_line_interaction_species \n", "10 1402 \n", @@ -3275,13 +3279,13 @@ "13 1402 \n", "14 1402 \n", "... ... \n", - "2652735 1201 \n", - "2652736 1201 \n", - "2652737 1201 \n", - "2652738 1201 \n", - "2652739 1201 \n", + "2660385 1201 \n", + "2660386 1201 \n", + "2660387 1201 \n", + "2660388 1201 \n", + "2660389 1201 \n", "\n", - "[1071430 rows x 9 columns]" + "[1079320 rows x 9 columns]" ] }, "execution_count": 13, @@ -3329,60 +3333,130 @@ " \n", " \n", " \n", - " atomic_number\n", " symbol\n", + " species\n", " count\n", " \n", " \n", " \n", " \n", + " 0\n", + " O I\n", + " (8, 800)\n", + " 9330\n", + " \n", + " \n", + " 1\n", + " O II\n", + " (8, 801)\n", + " 1920\n", + " \n", + " \n", " 2\n", - " 14\n", - " Si\n", - " 665620\n", + " O III\n", + " (8, 802)\n", + " 27420\n", " \n", " \n", " 3\n", - " 16\n", - " S\n", - " 219410\n", + " Mg II\n", + " (12, 1201)\n", + " 73280\n", " \n", " \n", - " 1\n", - " 12\n", - " Mg\n", - " 75800\n", + " 4\n", + " Si II\n", + " (14, 1401)\n", + " 242340\n", " \n", " \n", - " 0\n", - " 8\n", - " O\n", - " 39400\n", + " 5\n", + " Si III\n", + " (14, 1402)\n", + " 415620\n", " \n", " \n", - " 5\n", - " 20\n", - " Ca\n", - " 37650\n", + " 6\n", + " Si IV\n", + " (14, 1403)\n", + " 17150\n", " \n", " \n", - " 4\n", - " 18\n", - " Ar\n", - " 33550\n", + " 7\n", + " S I\n", + " (16, 1600)\n", + " 50\n", + " \n", + " \n", + " 8\n", + " S II\n", + " (16, 1601)\n", + " 165050\n", + " \n", + " \n", + " 9\n", + " S III\n", + " (16, 1602)\n", + " 50950\n", + " \n", + " \n", + " 10\n", + " S IV\n", + " (16, 1603)\n", + " 2980\n", + " \n", + " \n", + " 11\n", + " Ar I\n", + " (18, 1800)\n", + " 470\n", + " \n", + " \n", + " 12\n", + " Ar II\n", + " (18, 1801)\n", + " 31250\n", + " \n", + " \n", + " 13\n", + " Ar III\n", + " (18, 1802)\n", + " 2790\n", + " \n", + " \n", + " 14\n", + " Ar IV\n", + " (18, 1803)\n", + " 10\n", + " \n", + " \n", + " 15\n", + " Ca II\n", + " (20, 2001)\n", + " 38710\n", " \n", " \n", "\n", "" ], "text/plain": [ - " atomic_number symbol count\n", - "2 14 Si 665620\n", - "3 16 S 219410\n", - "1 12 Mg 75800\n", - "0 8 O 39400\n", - "5 20 Ca 37650\n", - "4 18 Ar 33550" + " symbol species count\n", + "0 O I (8, 800) 9330\n", + "1 O II (8, 801) 1920\n", + "2 O III (8, 802) 27420\n", + "3 Mg II (12, 1201) 73280\n", + "4 Si II (14, 1401) 242340\n", + "5 Si III (14, 1402) 415620\n", + "6 Si IV (14, 1403) 17150\n", + "7 S I (16, 1600) 50\n", + "8 S II (16, 1601) 165050\n", + "9 S III (16, 1602) 50950\n", + "10 S IV (16, 1603) 2980\n", + "11 Ar I (18, 1800) 470\n", + "12 Ar II (18, 1801) 31250\n", + "13 Ar III (18, 1802) 2790\n", + "14 Ar IV (18, 1803) 10\n", + "15 Ca II (20, 2001) 38710" ] }, "execution_count": 14, @@ -3391,25 +3465,20 @@ } ], "source": [ + "import pandas as pd\n", + "\n", "#Adding a new column to get the count of interactions\n", "line_interaction_df['count'] = 1\n", "\n", - "#Since only count is required, let's use only count and atomic_number columns\n", - "line_interaction_count_df = line_interaction_df[['last_line_interaction_atom', 'count']]\n", - "\n", - "#Group by the last_line_interaction_atom\n", - "line_interaction_count_df = line_interaction_count_df.groupby(['last_line_interaction_atom']).count()[['count']]\n", - "line_interaction_count_df['atomic_number'] = line_interaction_count_df.index\n", - "line_interaction_count_df.reset_index(drop = True, inplace = True)\n", - "\n", - "#Add a new column with the correspong atomic symbols\n", - "line_interaction_count_df['symbol'] = line_interaction_count_df['atomic_number'].apply(atomic_number2element_symbol)\n", + "#Since only count is required, let's use only count, atomic_number, species columns\n", + "line_interaction_count_df = line_interaction_df[['last_line_interaction_atom', 'last_line_interaction_species', 'count']]\n", "\n", - "#Rearranging the columns\n", - "line_interaction_count_df = line_interaction_count_df[['atomic_number', 'symbol', 'count']]\n", - "\n", - "#Sorting according to the count value\n", - "line_interaction_count_df = line_interaction_count_df.sort_values('count', ascending = False)\n", + "line_interaction_count_df = line_interaction_count_df.groupby(['last_line_interaction_atom', 'last_line_interaction_species']).size()\n", + "line_interaction_count_df = pd.DataFrame({'species':line_interaction_count_df.index, 'count':line_interaction_count_df.values})\n", + "line_interaction_count_df['symbol'] = line_interaction_count_df.apply(lambda x: species_tuple_to_string(\n", + " (x.species[0], x.species[1] - 100*x.species[0])), axis = 1\n", + " )\n", + "line_interaction_count_df = line_interaction_count_df[['symbol', 'species', 'count']]\n", "line_interaction_count_df" ] }, @@ -3439,7 +3508,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -3449,24 +3518,2307 @@ } ], "source": [ - "line_interaction_count_df.plot.bar(x = 'symbol' , y = 'count', rot = 0)" + "line_interaction_count_df.plot.bar(x = 'symbol' , y = 'count', rot = 90, **{'legend': False})" ] }, { "cell_type": "markdown", - "id": "a36f4f12-3970-42d6-994f-6caa23441142", + "id": "9c02ef19-0071-4ffc-ade1-26acd3ca5c15", "metadata": {}, "source": [ - "## Thanks for giving your time. Please suggest any impovements or any mistakes I made." + "## We plot the above plots using plotly." ] }, { "cell_type": "code", - "execution_count": null, - "id": "2ee25854-b8d4-4229-a908-11d11ad3ba49", + "execution_count": 16, + "id": "2c5eb16a-5ec0-417b-9377-518e3f37baa0", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "atomic symbol=O
v_middle=%{x}
value=%{y}", + "legendgroup": "O", + "line": { + "color": "#636efa", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines", + "name": "O", + "orientation": "v", + "showlegend": true, + "type": "scatter", + "x": [ + 11225.000000000002, + 11675.000000000002, + 12125.000000000002, + 12575.000000000002, + 13025.000000000002, + 13475.000000000002, + 13925.000000000002, + 14375.000000000002, + 14825.000000000002, + 15275.000000000002, + 15725.000000000002, + 16175.000000000002, + 16625, + 17075, + 17525, + 17975, + 18425, + 18875, + 19325, + 19775 + ], + "xaxis": "x", + "y": [ + 0.19, + 0.19, + 0.19, + 0.19, + 0.19, + 0.19, + 0.19, + 0.19, + 0.19, + 0.19, + 0.19, + 0.19, + 0.19, + 0.19, + 0.19, + 0.19, + 0.19, + 0.19, + 0.19, + 0.19 + ], + "yaxis": "y" + }, + { + "hovertemplate": "atomic symbol=Mg
v_middle=%{x}
value=%{y}", + "legendgroup": "Mg", + "line": { + "color": "#EF553B", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines", + "name": "Mg", + "orientation": "v", + "showlegend": true, + "type": "scatter", + "x": [ + 11225.000000000002, + 11675.000000000002, + 12125.000000000002, + 12575.000000000002, + 13025.000000000002, + 13475.000000000002, + 13925.000000000002, + 14375.000000000002, + 14825.000000000002, + 15275.000000000002, + 15725.000000000002, + 16175.000000000002, + 16625, + 17075, + 17525, + 17975, + 18425, + 18875, + 19325, + 19775 + ], + "xaxis": "x", + "y": [ + 0.03, + 0.03, + 0.03, + 0.03, + 0.03, + 0.03, + 0.03, + 0.03, + 0.03, + 0.03, + 0.03, + 0.03, + 0.03, + 0.03, + 0.03, + 0.03, + 0.03, + 0.03, + 0.03, + 0.03 + ], + "yaxis": "y" + }, + { + "hovertemplate": "atomic symbol=Si
v_middle=%{x}
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", + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = px.bar(line_interaction_count_df, x='symbol', y='count')\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "a36f4f12-3970-42d6-994f-6caa23441142", + "metadata": {}, + "source": [ + "## Thanks for giving your time. Please suggest any impovements or any mistakes I made." + ] } ], "metadata": { From a067ca68d1f415b315d12608555f39c859830bd5 Mon Sep 17 00:00:00 2001 From: Sumit112192 Date: Mon, 11 Mar 2024 00:17:02 +0530 Subject: [PATCH 3/6] Added necessary files --- .../VelocityPacketTrackerFirstObjective.md | 2798 +++++++++++++ FirstObjectiveMarkdown/output_10_1.png | Bin 0 -> 80089 bytes FirstObjectiveMarkdown/output_24_1.png | Bin 0 -> 19009 bytes FirstObjectiveMarkdown/output_32_1.png | Bin 0 -> 14925 bytes VelocityPacketTrackerFirstObjective.ipynb | 3493 +++++++++++++++++ 5 files changed, 6291 insertions(+) create mode 100644 FirstObjectiveMarkdown/VelocityPacketTrackerFirstObjective.md create mode 100644 FirstObjectiveMarkdown/output_10_1.png create mode 100644 FirstObjectiveMarkdown/output_24_1.png create mode 100644 FirstObjectiveMarkdown/output_32_1.png create mode 100644 VelocityPacketTrackerFirstObjective.ipynb diff --git a/FirstObjectiveMarkdown/VelocityPacketTrackerFirstObjective.md b/FirstObjectiveMarkdown/VelocityPacketTrackerFirstObjective.md new file mode 100644 index 00000000000..c5110c7728d --- /dev/null +++ b/FirstObjectiveMarkdown/VelocityPacketTrackerFirstObjective.md @@ -0,0 +1,2798 @@ +# Velocity Packet Tracker Visulization + +## In the below cell, we import necessaries libraries and download the required dataset + + +```python +from tardis import run_tardis +from tardis.io.atom_data.util import download_atom_data + +download_atom_data('kurucz_cd23_chianti_H_He') +``` + + + Iterations: 0/? [00:00 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
09.93e+03 K1.01e+04 K0.40.507
59.85e+03 K1.02e+04 K0.2110.197
109.78e+03 K1.01e+04 K0.1430.117
159.71e+03 K9.87e+03 K0.1050.0869
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 9933.952 K + Expected t_inner for next iteration = 10703.212 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 2 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.071e+43 erg / s + Luminosity absorbed = 3.576e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.01e+04 K1.08e+04 K0.5070.525
51.02e+04 K1.1e+04 K0.1970.203
101.01e+04 K1.08e+04 K0.1170.125
159.87e+03 K1.05e+04 K0.08690.0933
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10703.212 K + Expected t_inner for next iteration = 10673.712 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 3 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.074e+43 erg / s + Luminosity absorbed = 3.391e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.08e+04 K1.1e+04 K0.5250.483
51.1e+04 K1.12e+04 K0.2030.189
101.08e+04 K1.1e+04 K0.1250.118
151.05e+04 K1.06e+04 K0.09330.0895
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10673.712 K + Expected t_inner for next iteration = 10635.953 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 4 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.058e+43 erg / s + Luminosity absorbed = 3.352e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4830.469
51.12e+04 K1.12e+04 K0.1890.182
101.1e+04 K1.1e+04 K0.1180.113
151.06e+04 K1.07e+04 K0.08950.0861
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10635.953 K + Expected t_inner for next iteration = 10638.407 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 5 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.055e+43 erg / s + Luminosity absorbed = 3.399e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + Iteration converged 1/4 consecutive times. (base.py:261) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4690.479
51.12e+04 K1.13e+04 K0.1820.178
101.1e+04 K1.1e+04 K0.1130.113
151.07e+04 K1.07e+04 K0.08610.0839
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10638.407 K + Expected t_inner for next iteration = 10650.202 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 6 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.061e+43 erg / s + Luminosity absorbed = 3.398e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + Iteration converged 2/4 consecutive times. (base.py:261) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4790.47
51.13e+04 K1.12e+04 K0.1780.185
101.1e+04 K1.11e+04 K0.1130.112
151.07e+04 K1.07e+04 K0.08390.0856
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10650.202 K + Expected t_inner for next iteration = 10645.955 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 7 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.061e+43 erg / s + Luminosity absorbed = 3.382e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + Iteration converged 3/4 consecutive times. (base.py:261) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.470.47
51.12e+04 K1.13e+04 K0.1850.178
101.11e+04 K1.11e+04 K0.1120.112
151.07e+04 K1.07e+04 K0.08560.086
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10645.955 K + Expected t_inner for next iteration = 10642.050 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 8 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.062e+43 erg / s + Luminosity absorbed = 3.350e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + Iteration converged 4/4 consecutive times. (base.py:261) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.11e+04 K0.470.472
51.13e+04 K1.14e+04 K0.1780.175
101.11e+04 K1.11e+04 K0.1120.111
151.07e+04 K1.07e+04 K0.0860.084
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10642.050 K + Expected t_inner for next iteration = 10636.106 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 9 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.052e+43 erg / s + Luminosity absorbed = 3.411e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + Iteration converged 5/4 consecutive times. (base.py:261) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4720.469
51.14e+04 K1.15e+04 K0.1750.17
101.11e+04 K1.11e+04 K0.1110.109
151.07e+04 K1.08e+04 K0.0840.0822
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10636.106 K + Expected t_inner for next iteration = 10654.313 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 10 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.070e+43 erg / s + Luminosity absorbed = 3.335e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.1e+04 K0.4690.475
51.15e+04 K1.14e+04 K0.170.177
101.11e+04 K1.11e+04 K0.1090.112
151.08e+04 K1.06e+04 K0.08220.0878
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10654.313 K + Expected t_inner for next iteration = 10628.190 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 11 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.053e+43 erg / s + Luminosity absorbed = 3.363e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + Iteration converged 1/4 consecutive times. (base.py:261) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4750.472
51.14e+04 K1.12e+04 K0.1770.184
101.11e+04 K1.1e+04 K0.1120.114
151.06e+04 K1.06e+04 K0.08780.0859
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10628.190 K + Expected t_inner for next iteration = 10644.054 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 12 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.056e+43 erg / s + Luminosity absorbed = 3.420e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.11e+04 K0.4720.467
51.12e+04 K1.13e+04 K0.1840.176
101.1e+04 K1.11e+04 K0.1140.11
151.06e+04 K1.08e+04 K0.08590.0821
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10644.054 K + Expected t_inner for next iteration = 10653.543 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 13 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.062e+43 erg / s + Luminosity absorbed = 3.406e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + Iteration converged 1/4 consecutive times. (base.py:261) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4670.466
51.13e+04 K1.13e+04 K0.1760.18
101.11e+04 K1.11e+04 K0.110.111
151.08e+04 K1.08e+04 K0.08210.0841
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10653.543 K + Expected t_inner for next iteration = 10647.277 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 14 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.063e+43 erg / s + Luminosity absorbed = 3.369e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + Iteration converged 2/4 consecutive times. (base.py:261) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4660.469
51.13e+04 K1.13e+04 K0.180.182
101.11e+04 K1.1e+04 K0.1110.113
151.08e+04 K1.07e+04 K0.08410.0854
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10647.277 K + Expected t_inner for next iteration = 10638.875 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 15 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.053e+43 erg / s + Luminosity absorbed = 3.417e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + Iteration converged 3/4 consecutive times. (base.py:261) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.1e+04 K0.4690.484
51.13e+04 K1.13e+04 K0.1820.181
101.1e+04 K1.1e+04 K0.1130.113
151.07e+04 K1.07e+04 K0.08540.0858
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10638.875 K + Expected t_inner for next iteration = 10655.125 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 16 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.059e+43 erg / s + Luminosity absorbed = 3.445e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + Iteration converged 4/4 consecutive times. (base.py:261) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4840.472
51.13e+04 K1.13e+04 K0.1810.177
101.1e+04 K1.1e+04 K0.1130.113
151.07e+04 K1.06e+04 K0.08580.0858
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10655.125 K + Expected t_inner for next iteration = 10655.561 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 17 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.067e+43 erg / s + Luminosity absorbed = 3.372e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.11e+04 K0.4720.468
51.13e+04 K1.14e+04 K0.1770.175
101.1e+04 K1.11e+04 K0.1130.11
151.06e+04 K1.08e+04 K0.08580.0816
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10655.561 K + Expected t_inner for next iteration = 10636.536 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 18 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.057e+43 erg / s + Luminosity absorbed = 3.365e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + Iteration converged 1/4 consecutive times. (base.py:261) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4680.464
51.14e+04 K1.13e+04 K0.1750.177
101.11e+04 K1.1e+04 K0.110.113
151.08e+04 K1.07e+04 K0.08160.0848
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10636.536 K + Expected t_inner for next iteration = 10641.692 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Starting iteration 19 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.056e+43 erg / s + Luminosity absorbed = 3.405e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + [tardis.simulation.base][INFO ] + Iteration converged 2/4 consecutive times. (base.py:261) + [tardis.simulation.base][INFO ] + + Plasma stratification: (base.py:541) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4640.466
51.13e+04 K1.13e+04 K0.1770.177
101.1e+04 K1.11e+04 K0.1130.111
151.07e+04 K1.07e+04 K0.08480.0853
+ + + + [tardis.simulation.base][INFO ] + + Current t_inner = 10641.692 K + Expected t_inner for next iteration = 10650.463 K + (base.py:568) + [py.warnings ][WARNING] + /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide + (g_lower * n_upper) / (g_upper * n_lower) + (warnings.py:109) + [tardis.simulation.base][INFO ] + + Simulation finished in 19 iterations + Simulation took 54.57 s + (base.py:469) + [tardis.simulation.base][INFO ] + + Starting iteration 20 of 20 (base.py:391) + [tardis.simulation.base][INFO ] + + Luminosity emitted = 1.061e+43 erg / s + Luminosity absorbed = 3.401e+42 erg / s + Luminosity requested = 1.059e+43 erg / s + (base.py:573) + + +## Import the SDECPlotter class for plotting spectral element Decomposition Plot and obtain the necessary data from the simulation we just ran above. + + +```python +from tardis.visualization import SDECPlotter +plotter = SDECPlotter.from_simulation(sim) +``` + +## Let's now plot the SDEC Plot ( Second part of our First Objective ) + + +```python +plotter.generate_plot_mpl() +``` + + + + + + + + + + +![png](output_10_1.png) + + + +## Let's now work on the abundance vs velocity plot +For the next few cells, we'll modify the abundace dataframe so as to plot it easily + +### Get the abundance data from the simulation_state + + +```python +abundance = sim.simulation_state.abundance +abundance +``` + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
012345678910111213141516171819
atomic_number
80.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.19
120.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.03
140.520.520.520.520.520.520.520.520.520.520.520.520.520.520.520.520.520.520.520.52
160.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.19
180.040.040.040.040.040.040.040.040.040.040.040.040.040.040.040.040.040.040.040.04
200.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.03
+
+ + + +### Transpose the abundance dataframe so to get atomic number as columns heads + + +```python +abundance = abundance.transpose() +abundance +``` + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
atomic_number81214161820
00.190.030.520.190.040.03
10.190.030.520.190.040.03
20.190.030.520.190.040.03
30.190.030.520.190.040.03
40.190.030.520.190.040.03
50.190.030.520.190.040.03
60.190.030.520.190.040.03
70.190.030.520.190.040.03
80.190.030.520.190.040.03
90.190.030.520.190.040.03
100.190.030.520.190.040.03
110.190.030.520.190.040.03
120.190.030.520.190.040.03
130.190.030.520.190.040.03
140.190.030.520.190.040.03
150.190.030.520.190.040.03
160.190.030.520.190.040.03
170.190.030.520.190.040.03
180.190.030.520.190.040.03
190.190.030.520.190.040.03
+
+ + + +### Get the velocity at the middle of each shell + + +```python +v_middle = sim.simulation_state.v_middle +v_middle +``` + + + + +$[1.1225 \times 10^{9},~1.1675 \times 10^{9},~1.2125 \times 10^{9},~1.2575 \times 10^{9},~1.3025 \times 10^{9},~1.3475 \times 10^{9},~1.3925 \times 10^{9},~1.4375 \times 10^{9},~1.4825 \times 10^{9},~1.5275 \times 10^{9},~1.5725 \times 10^{9},~1.6175 \times 10^{9},~1.6625 \times 10^{9},~1.7075 \times 10^{9},~1.7525 \times 10^{9},~1.7975 \times 10^{9},~1.8425 \times 10^{9},~1.8875 \times 10^{9},~1.9325 \times 10^{9},~1.9775 \times 10^{9}] \; \mathrm{\frac{cm}{s}}$ + + + +### Renaming the abundance columns( atomic number to atomic symbol ) + + +```python +from tardis.util.base import atomic_number2element_symbol +columns = {head: atomic_number2element_symbol(head) for head in abundance.columns} +abundance.rename(columns = columns, inplace = True) +abundance +``` + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
atomic_numberOMgSiSArCa
00.190.030.520.190.040.03
10.190.030.520.190.040.03
20.190.030.520.190.040.03
30.190.030.520.190.040.03
40.190.030.520.190.040.03
50.190.030.520.190.040.03
60.190.030.520.190.040.03
70.190.030.520.190.040.03
80.190.030.520.190.040.03
90.190.030.520.190.040.03
100.190.030.520.190.040.03
110.190.030.520.190.040.03
120.190.030.520.190.040.03
130.190.030.520.190.040.03
140.190.030.520.190.040.03
150.190.030.520.190.040.03
160.190.030.520.190.040.03
170.190.030.520.190.040.03
180.190.030.520.190.040.03
190.190.030.520.190.040.03
+
+ + + +### Add a new column of ```v_middle``` in units of km/s + + +```python +import astropy.units as u +abundance['v_middle'] = [u.Quantity(vel, u.km/u.s).value for vel in v_middle] +abundance +``` + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
atomic_numberOMgSiSArCav_middle
00.190.030.520.190.040.0311225.0
10.190.030.520.190.040.0311675.0
20.190.030.520.190.040.0312125.0
30.190.030.520.190.040.0312575.0
40.190.030.520.190.040.0313025.0
50.190.030.520.190.040.0313475.0
60.190.030.520.190.040.0313925.0
70.190.030.520.190.040.0314375.0
80.190.030.520.190.040.0314825.0
90.190.030.520.190.040.0315275.0
100.190.030.520.190.040.0315725.0
110.190.030.520.190.040.0316175.0
120.190.030.520.190.040.0316625.0
130.190.030.520.190.040.0317075.0
140.190.030.520.190.040.0317525.0
150.190.030.520.190.040.0317975.0
160.190.030.520.190.040.0318425.0
170.190.030.520.190.040.0318875.0
180.190.030.520.190.040.0319325.0
190.190.030.520.190.040.0319775.0
+
+ + + + +```python +abundance.columns.name = 'atomic symbol' +``` + +## Plot of Abundance vs velocity + + +```python +abundance.plot(x = 'v_middle', xlabel = "$v_{middle}$ in km/s", ylabel = "Fractional Abundance", title = "Abundace vs velocity").legend(loc = 'upper right') +``` + + + + + + + + + + +![png](output_24_1.png) + + + +### Things to note in above graph +1. Since data overlap, we can only see four out of six line plots +2. Fractional abundance is uniform throughout the ejecta. + +## Our final task was to plot the total number of interactions that escape the simulation from the different elements +### I have worked it out for virtual packets, similar thing could be applied for the real packets as well + +I found out that most of the necessary data was alreadly pre computed during plotting of SDEC and available in the plotter object. + + +Get the line interaction data + + +```python +line_interaction_df = plotter.data["virtual"].packets_df_line_interaction +line_interaction_df +``` + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
nuslambdasenergieslast_interaction_typelast_line_interaction_out_idlast_line_interaction_in_idlast_line_interaction_in_nulast_line_interaction_atomlast_line_interaction_species
102.610913e+151148.2283610.000000e+002355153431.736483e+15141402
112.623633e+151142.6616430.000000e+002355153431.736483e+15141402
122.635277e+151137.6127830.000000e+002355153431.736483e+15141402
132.652415e+151130.2620870.000000e+002355153431.736483e+15141402
142.666043e+151124.4845570.000000e+002355153431.736483e+15141402
..............................
26527351.086349e+152759.6323376.346043e-072769776971.109094e+15121201
26527361.092227e+152744.7820746.591144e-072769776971.109094e+15121201
26527371.099582e+152726.4225686.837456e-072769776971.109094e+15121201
26527381.109661e+152701.6577697.107076e-072769776971.109094e+15121201
26527391.115506e+152687.5019447.237699e-072769776971.109094e+15121201
+

1071430 rows × 9 columns

+
+ + + +### Some slight pre processing ( refinement, counting ) + + +```python +#Adding a new column to get the count of interactions +line_interaction_df['count'] = 1 + +#Since only count is required, let's use only count and atomic_number columns +line_interaction_count_df = line_interaction_df[['last_line_interaction_atom', 'count']] + +#Group by the last_line_interaction_atom +line_interaction_count_df = line_interaction_count_df.groupby(['last_line_interaction_atom']).count()[['count']] +line_interaction_count_df['atomic_number'] = line_interaction_count_df.index +line_interaction_count_df.reset_index(drop = True, inplace = True) + +#Add a new column with the correspong atomic symbols +line_interaction_count_df['symbol'] = line_interaction_count_df['atomic_number'].apply(atomic_number2element_symbol) + +#Rearranging the columns +line_interaction_count_df = line_interaction_count_df[['atomic_number', 'symbol', 'count']] + +#Sorting according to the count value +line_interaction_count_df = line_interaction_count_df.sort_values('count', ascending = False) +line_interaction_count_df +``` + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
atomic_numbersymbolcount
214Si665620
316S219410
112Mg75800
08O39400
520Ca37650
418Ar33550
+
+ + + +## Finally, the required plot. + + +```python +line_interaction_count_df.plot.bar(x = 'symbol' , y = 'count', rot = 0) +``` + + + + + + + + + + +![png](output_32_1.png) + + + +## Thanks for giving your time. Please suggest any impovements or any mistakes I made. + + +```python + +``` diff --git a/FirstObjectiveMarkdown/output_10_1.png b/FirstObjectiveMarkdown/output_10_1.png new file mode 100644 index 0000000000000000000000000000000000000000..d9c447be6af2432d8893984fdad004571d9115ee GIT binary patch literal 80089 zcma%jcOaGj+dmYR-1mE2*K57ct49jb=gv}}#lpfm_wd0zWh^Y5cr2__4rfloPjY-1 zvfzJ$PEt>t9^0BYx#~L@W6A3~*;&~-S(zJLb}@EvG`F?k;Sk{9V!v$W zUoYUWbui`REhx!`ixAj7&~U`UBGE_w!_E*-H^;)p!g_d5Ld7k1dDPX7tgpX*ebuTJ zop$NM>J5WndYCb z$K2dJzBMS%S7lBrGvO6~9fj^3iLT(e_UXOjYDI8X7OUi$E6+*ck0z$%svbJzZ^%FG zt>4!_{;%IK`KSN;#YAKI7 z&vnE|D=S|(M}7Ng&*z6u3tdU|o1wIV@2rPQ*AG_faJq7Jgl#GpnaAAc>0DzbzXla~ zAGq*Y4cKMcNdFsd4mK(aPk(y%ne)fH(I; zZ6Atl$H!_qEJ_UcVnl~D*eObFMt}2OQ~!6JHdX==&U*NWy}20M-JN0EirJ4^85%{* zDbnF{@FSC*;Hat&)_MyUnRGOo z6rE>a@U5_w{m*R@L{Gb8yP!v2Wo7-$)fqOd>a$?FdbQDTxG|9Mp}f2?20bcra=2N2 zfUc@uYb3;~hB=BA_58?ZJ(QTmI{PEiv@20UE?$JHM%Q-`{a;Vd1b6M%yLd0JzsRtK zI1lR<7gyJ`;|Eq%tPH(6G2)exVjbr@l?n``(i9UE 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Shell No.t_radnext_t_radwnext_w
09.93e+03 K1.01e+04 K0.40.507
59.85e+03 K1.02e+04 K0.2110.197
109.78e+03 K1.01e+04 K0.1430.117
159.71e+03 K9.87e+03 K0.1050.0869
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Shell No.t_radnext_t_radwnext_w
01.01e+04 K1.08e+04 K0.5070.525
51.02e+04 K1.1e+04 K0.1970.203
101.01e+04 K1.08e+04 K0.1170.125
159.87e+03 K1.05e+04 K0.08690.0933
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Shell No.t_radnext_t_radwnext_w
01.08e+04 K1.1e+04 K0.5250.483
51.1e+04 K1.12e+04 K0.2030.189
101.08e+04 K1.1e+04 K0.1250.118
151.05e+04 K1.06e+04 K0.09330.0895
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Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4830.469
51.12e+04 K1.12e+04 K0.1890.182
101.1e+04 K1.1e+04 K0.1180.113
151.06e+04 K1.07e+04 K0.08950.0861
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10635.953 K\n", + "\tExpected t_inner for next iteration = 10638.407 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 5 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.055e+43 erg / s\n", + "\tLuminosity absorbed = 3.399e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\tIteration converged 1/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4690.479
51.12e+04 K1.13e+04 K0.1820.178
101.1e+04 K1.1e+04 K0.1130.113
151.07e+04 K1.07e+04 K0.08610.0839
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10638.407 K\n", + "\tExpected t_inner for next iteration = 10650.202 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 6 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.061e+43 erg / s\n", + "\tLuminosity absorbed = 3.398e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\tIteration converged 2/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4790.47
51.13e+04 K1.12e+04 K0.1780.185
101.1e+04 K1.11e+04 K0.1130.112
151.07e+04 K1.07e+04 K0.08390.0856
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10650.202 K\n", + "\tExpected t_inner for next iteration = 10645.955 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 7 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.061e+43 erg / s\n", + "\tLuminosity absorbed = 3.382e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\tIteration converged 3/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.470.47
51.12e+04 K1.13e+04 K0.1850.178
101.11e+04 K1.11e+04 K0.1120.112
151.07e+04 K1.07e+04 K0.08560.086
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10645.955 K\n", + "\tExpected t_inner for next iteration = 10642.050 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 8 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.062e+43 erg / s\n", + "\tLuminosity absorbed = 3.350e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\tIteration converged 4/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.11e+04 K0.470.472
51.13e+04 K1.14e+04 K0.1780.175
101.11e+04 K1.11e+04 K0.1120.111
151.07e+04 K1.07e+04 K0.0860.084
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10642.050 K\n", + "\tExpected t_inner for next iteration = 10636.106 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 9 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.052e+43 erg / s\n", + "\tLuminosity absorbed = 3.411e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\tIteration converged 5/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4720.469
51.14e+04 K1.15e+04 K0.1750.17
101.11e+04 K1.11e+04 K0.1110.109
151.07e+04 K1.08e+04 K0.0840.0822
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10636.106 K\n", + "\tExpected t_inner for next iteration = 10654.313 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 10 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.070e+43 erg / s\n", + "\tLuminosity absorbed = 3.335e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.1e+04 K0.4690.475
51.15e+04 K1.14e+04 K0.170.177
101.11e+04 K1.11e+04 K0.1090.112
151.08e+04 K1.06e+04 K0.08220.0878
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10654.313 K\n", + "\tExpected t_inner for next iteration = 10628.190 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 11 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.053e+43 erg / s\n", + "\tLuminosity absorbed = 3.363e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\tIteration converged 1/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4750.472
51.14e+04 K1.12e+04 K0.1770.184
101.11e+04 K1.1e+04 K0.1120.114
151.06e+04 K1.06e+04 K0.08780.0859
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10628.190 K\n", + "\tExpected t_inner for next iteration = 10644.054 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 12 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.056e+43 erg / s\n", + "\tLuminosity absorbed = 3.420e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.11e+04 K0.4720.467
51.12e+04 K1.13e+04 K0.1840.176
101.1e+04 K1.11e+04 K0.1140.11
151.06e+04 K1.08e+04 K0.08590.0821
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10644.054 K\n", + "\tExpected t_inner for next iteration = 10653.543 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 13 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.062e+43 erg / s\n", + "\tLuminosity absorbed = 3.406e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\tIteration converged 1/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4670.466
51.13e+04 K1.13e+04 K0.1760.18
101.11e+04 K1.11e+04 K0.110.111
151.08e+04 K1.08e+04 K0.08210.0841
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10653.543 K\n", + "\tExpected t_inner for next iteration = 10647.277 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 14 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.063e+43 erg / s\n", + "\tLuminosity absorbed = 3.369e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\tIteration converged 2/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4660.469
51.13e+04 K1.13e+04 K0.180.182
101.11e+04 K1.1e+04 K0.1110.113
151.08e+04 K1.07e+04 K0.08410.0854
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10647.277 K\n", + "\tExpected t_inner for next iteration = 10638.875 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 15 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.053e+43 erg / s\n", + "\tLuminosity absorbed = 3.417e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\tIteration converged 3/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.1e+04 K0.4690.484
51.13e+04 K1.13e+04 K0.1820.181
101.1e+04 K1.1e+04 K0.1130.113
151.07e+04 K1.07e+04 K0.08540.0858
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10638.875 K\n", + "\tExpected t_inner for next iteration = 10655.125 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 16 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.059e+43 erg / s\n", + "\tLuminosity absorbed = 3.445e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\tIteration converged 4/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4840.472
51.13e+04 K1.13e+04 K0.1810.177
101.1e+04 K1.1e+04 K0.1130.113
151.07e+04 K1.06e+04 K0.08580.0858
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10655.125 K\n", + "\tExpected t_inner for next iteration = 10655.561 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 17 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.067e+43 erg / s\n", + "\tLuminosity absorbed = 3.372e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.11e+04 K0.4720.468
51.13e+04 K1.14e+04 K0.1770.175
101.1e+04 K1.11e+04 K0.1130.11
151.06e+04 K1.08e+04 K0.08580.0816
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10655.561 K\n", + "\tExpected t_inner for next iteration = 10636.536 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 18 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.057e+43 erg / s\n", + "\tLuminosity absorbed = 3.365e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\tIteration converged 1/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4680.464
51.14e+04 K1.13e+04 K0.1750.177
101.11e+04 K1.1e+04 K0.110.113
151.08e+04 K1.07e+04 K0.08160.0848
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10636.536 K\n", + "\tExpected t_inner for next iteration = 10641.692 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 19 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.056e+43 erg / s\n", + "\tLuminosity absorbed = 3.405e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\tIteration converged 2/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4640.466
51.13e+04 K1.13e+04 K0.1770.177
101.1e+04 K1.11e+04 K0.1130.111
151.07e+04 K1.07e+04 K0.08480.0853
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tCurrent t_inner = 10641.692 K\n", + "\tExpected t_inner for next iteration = 10650.463 K\n", + " (\u001b[1mbase.py\u001b[0m:568)\n", + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", + " (g_lower * n_upper) / (g_upper * n_lower)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tSimulation finished in 19 iterations \n", + "\tSimulation took 54.57 s\n", + " (\u001b[1mbase.py\u001b[0m:469)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tStarting iteration 20 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\t\n", + "\tLuminosity emitted = 1.061e+43 erg / s\n", + "\tLuminosity absorbed = 3.401e+42 erg / s\n", + "\tLuminosity requested = 1.059e+43 erg / s\n", + " (\u001b[1mbase.py\u001b[0m:573)\n" + ] + } + ], + "source": [ + "sim = run_tardis(\"tardis_example.yml\", virtual_packet_logging = True)" + ] + }, + { + "cell_type": "markdown", + "id": "062f5e2c-1a31-47ab-b4a7-2cbf49b9d168", + "metadata": {}, + "source": [ + "## Import the SDECPlotter class for plotting spectral element Decomposition Plot and obtain the necessary data from the simulation we just ran above." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "fcc3da4e-b70d-4960-bdca-4c47f8e1c763", + "metadata": {}, + "outputs": [], + "source": [ + "from tardis.visualization import SDECPlotter\n", + "plotter = SDECPlotter.from_simulation(sim)" + ] + }, + { + "cell_type": "markdown", + "id": "84a175b0-c284-4ba5-99c7-c409d480efe1", + "metadata": {}, + "source": [ + "## Let's now plot the SDEC Plot ( Second part of our First Objective )" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5d968c4c-8354-4907-9eb6-09ecbe233143", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plotter.generate_plot_mpl()" + ] + }, + { + "cell_type": "markdown", + "id": "18d60457-ad52-4014-b04f-14f71333f388", + "metadata": {}, + "source": [ + "## Let's now work on the abundance vs velocity plot\n", + "For the next few cells, we'll modify the abundace dataframe so as to plot it easily" + ] + }, + { + "cell_type": "markdown", + "id": "7e316952-62d5-41ba-85f1-fef31265ba72", + "metadata": {}, + "source": [ + "### Get the abundance data from the simulation_state" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f2d15d55-a59b-44aa-b6c2-0a71aad1775f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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012345678910111213141516171819
atomic_number
80.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.19
120.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.03
140.520.520.520.520.520.520.520.520.520.520.520.520.520.520.520.520.520.520.520.52
160.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.19
180.040.040.040.040.040.040.040.040.040.040.040.040.040.040.040.040.040.040.040.04
200.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.03
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" + ], + "text/plain": [ + " 0 1 2 3 4 5 6 7 8 9 \\\n", + "atomic_number \n", + "8 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 \n", + "12 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 \n", + "14 0.52 0.52 0.52 0.52 0.52 0.52 0.52 0.52 0.52 0.52 \n", + "16 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 \n", + "18 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 \n", + "20 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 \n", + "\n", + " 10 11 12 13 14 15 16 17 18 19 \n", + "atomic_number \n", + "8 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 \n", + "12 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 \n", + "14 0.52 0.52 0.52 0.52 0.52 0.52 0.52 0.52 0.52 0.52 \n", + "16 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 \n", + "18 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 \n", + "20 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "abundance = sim.simulation_state.abundance\n", + "abundance" + ] + }, + { + "cell_type": "markdown", + "id": "86aac0e5-3f2a-45c3-8a1e-be2bd03ae947", + "metadata": {}, + "source": [ + "### Transpose the abundance dataframe so to get atomic number as columns heads" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3398110e-e982-489e-948c-3b1643360e02", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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atomic_number81214161820
00.190.030.520.190.040.03
10.190.030.520.190.040.03
20.190.030.520.190.040.03
30.190.030.520.190.040.03
40.190.030.520.190.040.03
50.190.030.520.190.040.03
60.190.030.520.190.040.03
70.190.030.520.190.040.03
80.190.030.520.190.040.03
90.190.030.520.190.040.03
100.190.030.520.190.040.03
110.190.030.520.190.040.03
120.190.030.520.190.040.03
130.190.030.520.190.040.03
140.190.030.520.190.040.03
150.190.030.520.190.040.03
160.190.030.520.190.040.03
170.190.030.520.190.040.03
180.190.030.520.190.040.03
190.190.030.520.190.040.03
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" + ], + "text/plain": [ + "atomic_number 8 12 14 16 18 20\n", + "0 0.19 0.03 0.52 0.19 0.04 0.03\n", + "1 0.19 0.03 0.52 0.19 0.04 0.03\n", + "2 0.19 0.03 0.52 0.19 0.04 0.03\n", + "3 0.19 0.03 0.52 0.19 0.04 0.03\n", + "4 0.19 0.03 0.52 0.19 0.04 0.03\n", + "5 0.19 0.03 0.52 0.19 0.04 0.03\n", + "6 0.19 0.03 0.52 0.19 0.04 0.03\n", + "7 0.19 0.03 0.52 0.19 0.04 0.03\n", + "8 0.19 0.03 0.52 0.19 0.04 0.03\n", + "9 0.19 0.03 0.52 0.19 0.04 0.03\n", + "10 0.19 0.03 0.52 0.19 0.04 0.03\n", + "11 0.19 0.03 0.52 0.19 0.04 0.03\n", + "12 0.19 0.03 0.52 0.19 0.04 0.03\n", + "13 0.19 0.03 0.52 0.19 0.04 0.03\n", + "14 0.19 0.03 0.52 0.19 0.04 0.03\n", + "15 0.19 0.03 0.52 0.19 0.04 0.03\n", + "16 0.19 0.03 0.52 0.19 0.04 0.03\n", + "17 0.19 0.03 0.52 0.19 0.04 0.03\n", + "18 0.19 0.03 0.52 0.19 0.04 0.03\n", + "19 0.19 0.03 0.52 0.19 0.04 0.03" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "abundance = abundance.transpose()\n", + "abundance" + ] + }, + { + "cell_type": "markdown", + "id": "0b5523a2-7965-488d-9b4f-613cf7fb9057", + "metadata": {}, + "source": [ + "### Get the velocity at the middle of each shell" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7508164f-4d26-4c8e-a08c-f4ccda867218", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$[1.1225 \\times 10^{9},~1.1675 \\times 10^{9},~1.2125 \\times 10^{9},~1.2575 \\times 10^{9},~1.3025 \\times 10^{9},~1.3475 \\times 10^{9},~1.3925 \\times 10^{9},~1.4375 \\times 10^{9},~1.4825 \\times 10^{9},~1.5275 \\times 10^{9},~1.5725 \\times 10^{9},~1.6175 \\times 10^{9},~1.6625 \\times 10^{9},~1.7075 \\times 10^{9},~1.7525 \\times 10^{9},~1.7975 \\times 10^{9},~1.8425 \\times 10^{9},~1.8875 \\times 10^{9},~1.9325 \\times 10^{9},~1.9775 \\times 10^{9}] \\; \\mathrm{\\frac{cm}{s}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "v_middle = sim.simulation_state.v_middle\n", + "v_middle" + ] + }, + { + "cell_type": "markdown", + "id": "bb113dc0-3c83-46cc-81e0-aeb0860433ca", + "metadata": {}, + "source": [ + "### Renaming the abundance columns( atomic number to atomic symbol )" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b2dbebb8-832c-4689-91a9-fbe9391e3222", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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atomic_numberOMgSiSArCa
00.190.030.520.190.040.03
10.190.030.520.190.040.03
20.190.030.520.190.040.03
30.190.030.520.190.040.03
40.190.030.520.190.040.03
50.190.030.520.190.040.03
60.190.030.520.190.040.03
70.190.030.520.190.040.03
80.190.030.520.190.040.03
90.190.030.520.190.040.03
100.190.030.520.190.040.03
110.190.030.520.190.040.03
120.190.030.520.190.040.03
130.190.030.520.190.040.03
140.190.030.520.190.040.03
150.190.030.520.190.040.03
160.190.030.520.190.040.03
170.190.030.520.190.040.03
180.190.030.520.190.040.03
190.190.030.520.190.040.03
\n", + "
" + ], + "text/plain": [ + "atomic_number O Mg Si S Ar Ca\n", + "0 0.19 0.03 0.52 0.19 0.04 0.03\n", + "1 0.19 0.03 0.52 0.19 0.04 0.03\n", + "2 0.19 0.03 0.52 0.19 0.04 0.03\n", + "3 0.19 0.03 0.52 0.19 0.04 0.03\n", + "4 0.19 0.03 0.52 0.19 0.04 0.03\n", + "5 0.19 0.03 0.52 0.19 0.04 0.03\n", + "6 0.19 0.03 0.52 0.19 0.04 0.03\n", + "7 0.19 0.03 0.52 0.19 0.04 0.03\n", + "8 0.19 0.03 0.52 0.19 0.04 0.03\n", + "9 0.19 0.03 0.52 0.19 0.04 0.03\n", + "10 0.19 0.03 0.52 0.19 0.04 0.03\n", + "11 0.19 0.03 0.52 0.19 0.04 0.03\n", + "12 0.19 0.03 0.52 0.19 0.04 0.03\n", + "13 0.19 0.03 0.52 0.19 0.04 0.03\n", + "14 0.19 0.03 0.52 0.19 0.04 0.03\n", + "15 0.19 0.03 0.52 0.19 0.04 0.03\n", + "16 0.19 0.03 0.52 0.19 0.04 0.03\n", + "17 0.19 0.03 0.52 0.19 0.04 0.03\n", + "18 0.19 0.03 0.52 0.19 0.04 0.03\n", + "19 0.19 0.03 0.52 0.19 0.04 0.03" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from tardis.util.base import atomic_number2element_symbol\n", + "columns = {head: atomic_number2element_symbol(head) for head in abundance.columns}\n", + "abundance.rename(columns = columns, inplace = True)\n", + "abundance" + ] + }, + { + "cell_type": "markdown", + "id": "5be54c4d-5004-41c2-94f5-f04bdf5d4765", + "metadata": {}, + "source": [ + "### Add a new column of ```v_middle``` in units of km/s" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "43f16cac-9591-47a2-9cc7-2d3c211d19fb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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atomic_numberOMgSiSArCav_middle
00.190.030.520.190.040.0311225.0
10.190.030.520.190.040.0311675.0
20.190.030.520.190.040.0312125.0
30.190.030.520.190.040.0312575.0
40.190.030.520.190.040.0313025.0
50.190.030.520.190.040.0313475.0
60.190.030.520.190.040.0313925.0
70.190.030.520.190.040.0314375.0
80.190.030.520.190.040.0314825.0
90.190.030.520.190.040.0315275.0
100.190.030.520.190.040.0315725.0
110.190.030.520.190.040.0316175.0
120.190.030.520.190.040.0316625.0
130.190.030.520.190.040.0317075.0
140.190.030.520.190.040.0317525.0
150.190.030.520.190.040.0317975.0
160.190.030.520.190.040.0318425.0
170.190.030.520.190.040.0318875.0
180.190.030.520.190.040.0319325.0
190.190.030.520.190.040.0319775.0
\n", + "
" + ], + "text/plain": [ + "atomic_number O Mg Si S Ar Ca v_middle\n", + "0 0.19 0.03 0.52 0.19 0.04 0.03 11225.0\n", + "1 0.19 0.03 0.52 0.19 0.04 0.03 11675.0\n", + "2 0.19 0.03 0.52 0.19 0.04 0.03 12125.0\n", + "3 0.19 0.03 0.52 0.19 0.04 0.03 12575.0\n", + "4 0.19 0.03 0.52 0.19 0.04 0.03 13025.0\n", + "5 0.19 0.03 0.52 0.19 0.04 0.03 13475.0\n", + "6 0.19 0.03 0.52 0.19 0.04 0.03 13925.0\n", + "7 0.19 0.03 0.52 0.19 0.04 0.03 14375.0\n", + "8 0.19 0.03 0.52 0.19 0.04 0.03 14825.0\n", + "9 0.19 0.03 0.52 0.19 0.04 0.03 15275.0\n", + "10 0.19 0.03 0.52 0.19 0.04 0.03 15725.0\n", + "11 0.19 0.03 0.52 0.19 0.04 0.03 16175.0\n", + "12 0.19 0.03 0.52 0.19 0.04 0.03 16625.0\n", + "13 0.19 0.03 0.52 0.19 0.04 0.03 17075.0\n", + "14 0.19 0.03 0.52 0.19 0.04 0.03 17525.0\n", + "15 0.19 0.03 0.52 0.19 0.04 0.03 17975.0\n", + "16 0.19 0.03 0.52 0.19 0.04 0.03 18425.0\n", + "17 0.19 0.03 0.52 0.19 0.04 0.03 18875.0\n", + "18 0.19 0.03 0.52 0.19 0.04 0.03 19325.0\n", + "19 0.19 0.03 0.52 0.19 0.04 0.03 19775.0" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import astropy.units as u\n", + "abundance['v_middle'] = [u.Quantity(vel, u.km/u.s).value for vel in v_middle]\n", + "abundance" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "38ac8143-88a0-4776-b7cd-280ecaa3939b", + "metadata": {}, + "outputs": [], + "source": [ + "abundance.columns.name = 'atomic symbol'" + ] + }, + { + "cell_type": "markdown", + "id": "1d5ae678-af08-4a68-b378-7bd980f1f4c4", + "metadata": {}, + "source": [ + "## Plot of Abundance vs velocity" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "5fed4c8d-fceb-4d1a-b8d5-f23da51a3903", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "abundance.plot(x = 'v_middle', xlabel = \"$v_{middle}$ in km/s\", ylabel = \"Fractional Abundance\", title = \"Abundace vs velocity\").legend(loc = 'upper right')" + ] + }, + { + "cell_type": "markdown", + "id": "ef91834e-3aed-4864-95ab-f60f9284229a", + "metadata": {}, + "source": [ + "### Things to note in above graph\n", + "1. Since data overlap, we can only see four out of six line plots\n", + "2. Fractional abundance is uniform throughout the ejecta." + ] + }, + { + "cell_type": "markdown", + "id": "138ac797-80ae-46ee-8fab-068ff07d4c18", + "metadata": {}, + "source": [ + "## Our final task was to plot the total number of interactions that escape the simulation from the different elements\n", + "### I have worked it out for virtual packets, similar thing could be applied for the real packets as well\n", + "\n", + "I found out that most of the necessary data was alreadly pre computed during plotting of SDEC and available in the plotter object.\n" + ] + }, + { + "cell_type": "markdown", + "id": "c5fdf21b-f896-4b20-ae5b-c5125365db2b", + "metadata": {}, + "source": [ + "Get the line interaction data" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "418fd4a8-3f59-4933-a06e-da17404b1371", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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nuslambdasenergieslast_interaction_typelast_line_interaction_out_idlast_line_interaction_in_idlast_line_interaction_in_nulast_line_interaction_atomlast_line_interaction_species
102.610913e+151148.2283610.000000e+002355153431.736483e+15141402
112.623633e+151142.6616430.000000e+002355153431.736483e+15141402
122.635277e+151137.6127830.000000e+002355153431.736483e+15141402
132.652415e+151130.2620870.000000e+002355153431.736483e+15141402
142.666043e+151124.4845570.000000e+002355153431.736483e+15141402
..............................
26527351.086349e+152759.6323376.346043e-072769776971.109094e+15121201
26527361.092227e+152744.7820746.591144e-072769776971.109094e+15121201
26527371.099582e+152726.4225686.837456e-072769776971.109094e+15121201
26527381.109661e+152701.6577697.107076e-072769776971.109094e+15121201
26527391.115506e+152687.5019447.237699e-072769776971.109094e+15121201
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1071430 rows × 9 columns

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" + ], + "text/plain": [ + " nus lambdas energies last_interaction_type \\\n", + "10 2.610913e+15 1148.228361 0.000000e+00 2 \n", + "11 2.623633e+15 1142.661643 0.000000e+00 2 \n", + "12 2.635277e+15 1137.612783 0.000000e+00 2 \n", + "13 2.652415e+15 1130.262087 0.000000e+00 2 \n", + "14 2.666043e+15 1124.484557 0.000000e+00 2 \n", + "... ... ... ... ... \n", + "2652735 1.086349e+15 2759.632337 6.346043e-07 2 \n", + "2652736 1.092227e+15 2744.782074 6.591144e-07 2 \n", + "2652737 1.099582e+15 2726.422568 6.837456e-07 2 \n", + "2652738 1.109661e+15 2701.657769 7.107076e-07 2 \n", + "2652739 1.115506e+15 2687.501944 7.237699e-07 2 \n", + "\n", + " last_line_interaction_out_id last_line_interaction_in_id \\\n", + "10 3551 5343 \n", + "11 3551 5343 \n", + "12 3551 5343 \n", + "13 3551 5343 \n", + "14 3551 5343 \n", + "... ... ... \n", + "2652735 7697 7697 \n", + "2652736 7697 7697 \n", + "2652737 7697 7697 \n", + "2652738 7697 7697 \n", + "2652739 7697 7697 \n", + "\n", + " last_line_interaction_in_nu last_line_interaction_atom \\\n", + "10 1.736483e+15 14 \n", + "11 1.736483e+15 14 \n", + "12 1.736483e+15 14 \n", + "13 1.736483e+15 14 \n", + "14 1.736483e+15 14 \n", + "... ... ... \n", + "2652735 1.109094e+15 12 \n", + "2652736 1.109094e+15 12 \n", + "2652737 1.109094e+15 12 \n", + "2652738 1.109094e+15 12 \n", + "2652739 1.109094e+15 12 \n", + "\n", + " last_line_interaction_species \n", + "10 1402 \n", + "11 1402 \n", + "12 1402 \n", + "13 1402 \n", + "14 1402 \n", + "... ... \n", + "2652735 1201 \n", + "2652736 1201 \n", + "2652737 1201 \n", + "2652738 1201 \n", + "2652739 1201 \n", + "\n", + "[1071430 rows x 9 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "line_interaction_df = plotter.data[\"virtual\"].packets_df_line_interaction\n", + "line_interaction_df" + ] + }, + { + "cell_type": "markdown", + "id": "b064e5da-b865-45ab-84d7-797d9a0fb16e", + "metadata": {}, + "source": [ + "### Some slight pre processing ( refinement, counting )" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f31be200-6849-4177-91c7-d59f90da046a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
atomic_numbersymbolcount
214Si665620
316S219410
112Mg75800
08O39400
520Ca37650
418Ar33550
\n", + "
" + ], + "text/plain": [ + " atomic_number symbol count\n", + "2 14 Si 665620\n", + "3 16 S 219410\n", + "1 12 Mg 75800\n", + "0 8 O 39400\n", + "5 20 Ca 37650\n", + "4 18 Ar 33550" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Adding a new column to get the count of interactions\n", + "line_interaction_df['count'] = 1\n", + "\n", + "#Since only count is required, let's use only count and atomic_number columns\n", + "line_interaction_count_df = line_interaction_df[['last_line_interaction_atom', 'count']]\n", + "\n", + "#Group by the last_line_interaction_atom\n", + "line_interaction_count_df = line_interaction_count_df.groupby(['last_line_interaction_atom']).count()[['count']]\n", + "line_interaction_count_df['atomic_number'] = line_interaction_count_df.index\n", + "line_interaction_count_df.reset_index(drop = True, inplace = True)\n", + "\n", + "#Add a new column with the correspong atomic symbols\n", + "line_interaction_count_df['symbol'] = line_interaction_count_df['atomic_number'].apply(atomic_number2element_symbol)\n", + "\n", + "#Rearranging the columns\n", + "line_interaction_count_df = line_interaction_count_df[['atomic_number', 'symbol', 'count']]\n", + "\n", + "#Sorting according to the count value\n", + "line_interaction_count_df = line_interaction_count_df.sort_values('count', ascending = False)\n", + "line_interaction_count_df" + ] + }, + { + "cell_type": "markdown", + "id": "4006f519-f11c-48b4-bffc-b22737729593", + "metadata": {}, + "source": [ + "## Finally, the required plot." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "837d9f0e-e01a-4e91-95e4-d1a822042d79", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "line_interaction_count_df.plot.bar(x = 'symbol' , y = 'count', rot = 0)" + ] + }, + { + "cell_type": "markdown", + "id": "a36f4f12-3970-42d6-994f-6caa23441142", + "metadata": {}, + "source": [ + "## Thanks for giving your time. Please suggest any impovements or any mistakes I made." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ee25854-b8d4-4229-a908-11d11ad3ba49", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tardis", + "language": "python", + "name": "tardis" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 219744feba65cd5bf8f6a1e1a160ee974118bc03 Mon Sep 17 00:00:00 2001 From: Sumit112192 Date: Sun, 24 Mar 2024 23:28:51 +0530 Subject: [PATCH 4/6] Changed as suggested --- .../VelocityPacketTrackerFirstObjective.md | 2798 ------------ FirstObjectiveMarkdown/output_10_1.png | Bin 80089 -> 0 bytes FirstObjectiveMarkdown/output_24_1.png | Bin 19009 -> 0 bytes FirstObjectiveMarkdown/output_32_1.png | Bin 14925 -> 0 bytes VelocityPacketTrackerFirstObjective.ipynb | 3956 +++++++++++++---- 5 files changed, 3154 insertions(+), 3600 deletions(-) delete mode 100644 FirstObjectiveMarkdown/VelocityPacketTrackerFirstObjective.md delete mode 100644 FirstObjectiveMarkdown/output_10_1.png delete mode 100644 FirstObjectiveMarkdown/output_24_1.png delete mode 100644 FirstObjectiveMarkdown/output_32_1.png diff --git a/FirstObjectiveMarkdown/VelocityPacketTrackerFirstObjective.md b/FirstObjectiveMarkdown/VelocityPacketTrackerFirstObjective.md deleted file mode 100644 index c5110c7728d..00000000000 --- a/FirstObjectiveMarkdown/VelocityPacketTrackerFirstObjective.md +++ /dev/null @@ -1,2798 +0,0 @@ -# Velocity Packet Tracker Visulization - -## In the below cell, we import necessaries libraries and download the required dataset - - -```python -from tardis import run_tardis -from tardis.io.atom_data.util import download_atom_data - -download_atom_data('kurucz_cd23_chianti_H_He') -``` - - - Iterations: 0/? [00:00 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
09.93e+03 K1.01e+04 K0.40.507
59.85e+03 K1.02e+04 K0.2110.197
109.78e+03 K1.01e+04 K0.1430.117
159.71e+03 K9.87e+03 K0.1050.0869
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 9933.952 K - Expected t_inner for next iteration = 10703.212 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 2 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.071e+43 erg / s - Luminosity absorbed = 3.576e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.01e+04 K1.08e+04 K0.5070.525
51.02e+04 K1.1e+04 K0.1970.203
101.01e+04 K1.08e+04 K0.1170.125
159.87e+03 K1.05e+04 K0.08690.0933
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10703.212 K - Expected t_inner for next iteration = 10673.712 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 3 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.074e+43 erg / s - Luminosity absorbed = 3.391e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.08e+04 K1.1e+04 K0.5250.483
51.1e+04 K1.12e+04 K0.2030.189
101.08e+04 K1.1e+04 K0.1250.118
151.05e+04 K1.06e+04 K0.09330.0895
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10673.712 K - Expected t_inner for next iteration = 10635.953 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 4 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.058e+43 erg / s - Luminosity absorbed = 3.352e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4830.469
51.12e+04 K1.12e+04 K0.1890.182
101.1e+04 K1.1e+04 K0.1180.113
151.06e+04 K1.07e+04 K0.08950.0861
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10635.953 K - Expected t_inner for next iteration = 10638.407 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 5 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.055e+43 erg / s - Luminosity absorbed = 3.399e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - Iteration converged 1/4 consecutive times. (base.py:261) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4690.479
51.12e+04 K1.13e+04 K0.1820.178
101.1e+04 K1.1e+04 K0.1130.113
151.07e+04 K1.07e+04 K0.08610.0839
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10638.407 K - Expected t_inner for next iteration = 10650.202 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 6 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.061e+43 erg / s - Luminosity absorbed = 3.398e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - Iteration converged 2/4 consecutive times. (base.py:261) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4790.47
51.13e+04 K1.12e+04 K0.1780.185
101.1e+04 K1.11e+04 K0.1130.112
151.07e+04 K1.07e+04 K0.08390.0856
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10650.202 K - Expected t_inner for next iteration = 10645.955 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 7 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.061e+43 erg / s - Luminosity absorbed = 3.382e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - Iteration converged 3/4 consecutive times. (base.py:261) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.470.47
51.12e+04 K1.13e+04 K0.1850.178
101.11e+04 K1.11e+04 K0.1120.112
151.07e+04 K1.07e+04 K0.08560.086
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10645.955 K - Expected t_inner for next iteration = 10642.050 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 8 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.062e+43 erg / s - Luminosity absorbed = 3.350e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - Iteration converged 4/4 consecutive times. (base.py:261) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.11e+04 K0.470.472
51.13e+04 K1.14e+04 K0.1780.175
101.11e+04 K1.11e+04 K0.1120.111
151.07e+04 K1.07e+04 K0.0860.084
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10642.050 K - Expected t_inner for next iteration = 10636.106 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 9 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.052e+43 erg / s - Luminosity absorbed = 3.411e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - Iteration converged 5/4 consecutive times. (base.py:261) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4720.469
51.14e+04 K1.15e+04 K0.1750.17
101.11e+04 K1.11e+04 K0.1110.109
151.07e+04 K1.08e+04 K0.0840.0822
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10636.106 K - Expected t_inner for next iteration = 10654.313 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 10 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.070e+43 erg / s - Luminosity absorbed = 3.335e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.1e+04 K0.4690.475
51.15e+04 K1.14e+04 K0.170.177
101.11e+04 K1.11e+04 K0.1090.112
151.08e+04 K1.06e+04 K0.08220.0878
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10654.313 K - Expected t_inner for next iteration = 10628.190 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 11 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.053e+43 erg / s - Luminosity absorbed = 3.363e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - Iteration converged 1/4 consecutive times. (base.py:261) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4750.472
51.14e+04 K1.12e+04 K0.1770.184
101.11e+04 K1.1e+04 K0.1120.114
151.06e+04 K1.06e+04 K0.08780.0859
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10628.190 K - Expected t_inner for next iteration = 10644.054 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 12 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.056e+43 erg / s - Luminosity absorbed = 3.420e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.11e+04 K0.4720.467
51.12e+04 K1.13e+04 K0.1840.176
101.1e+04 K1.11e+04 K0.1140.11
151.06e+04 K1.08e+04 K0.08590.0821
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10644.054 K - Expected t_inner for next iteration = 10653.543 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 13 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.062e+43 erg / s - Luminosity absorbed = 3.406e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - Iteration converged 1/4 consecutive times. (base.py:261) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4670.466
51.13e+04 K1.13e+04 K0.1760.18
101.11e+04 K1.11e+04 K0.110.111
151.08e+04 K1.08e+04 K0.08210.0841
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10653.543 K - Expected t_inner for next iteration = 10647.277 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 14 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.063e+43 erg / s - Luminosity absorbed = 3.369e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - Iteration converged 2/4 consecutive times. (base.py:261) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4660.469
51.13e+04 K1.13e+04 K0.180.182
101.11e+04 K1.1e+04 K0.1110.113
151.08e+04 K1.07e+04 K0.08410.0854
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10647.277 K - Expected t_inner for next iteration = 10638.875 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 15 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.053e+43 erg / s - Luminosity absorbed = 3.417e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - Iteration converged 3/4 consecutive times. (base.py:261) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.1e+04 K0.4690.484
51.13e+04 K1.13e+04 K0.1820.181
101.1e+04 K1.1e+04 K0.1130.113
151.07e+04 K1.07e+04 K0.08540.0858
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10638.875 K - Expected t_inner for next iteration = 10655.125 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 16 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.059e+43 erg / s - Luminosity absorbed = 3.445e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - Iteration converged 4/4 consecutive times. (base.py:261) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4840.472
51.13e+04 K1.13e+04 K0.1810.177
101.1e+04 K1.1e+04 K0.1130.113
151.07e+04 K1.06e+04 K0.08580.0858
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10655.125 K - Expected t_inner for next iteration = 10655.561 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 17 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.067e+43 erg / s - Luminosity absorbed = 3.372e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.11e+04 K0.4720.468
51.13e+04 K1.14e+04 K0.1770.175
101.1e+04 K1.11e+04 K0.1130.11
151.06e+04 K1.08e+04 K0.08580.0816
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10655.561 K - Expected t_inner for next iteration = 10636.536 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 18 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.057e+43 erg / s - Luminosity absorbed = 3.365e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - Iteration converged 1/4 consecutive times. (base.py:261) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4680.464
51.14e+04 K1.13e+04 K0.1750.177
101.11e+04 K1.1e+04 K0.110.113
151.08e+04 K1.07e+04 K0.08160.0848
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10636.536 K - Expected t_inner for next iteration = 10641.692 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Starting iteration 19 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.056e+43 erg / s - Luminosity absorbed = 3.405e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - [tardis.simulation.base][INFO ] - Iteration converged 2/4 consecutive times. (base.py:261) - [tardis.simulation.base][INFO ] - - Plasma stratification: (base.py:541) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4640.466
51.13e+04 K1.13e+04 K0.1770.177
101.1e+04 K1.11e+04 K0.1130.111
151.07e+04 K1.07e+04 K0.08480.0853
- - - - [tardis.simulation.base][INFO ] - - Current t_inner = 10641.692 K - Expected t_inner for next iteration = 10650.463 K - (base.py:568) - [py.warnings ][WARNING] - /home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide - (g_lower * n_upper) / (g_upper * n_lower) - (warnings.py:109) - [tardis.simulation.base][INFO ] - - Simulation finished in 19 iterations - Simulation took 54.57 s - (base.py:469) - [tardis.simulation.base][INFO ] - - Starting iteration 20 of 20 (base.py:391) - [tardis.simulation.base][INFO ] - - Luminosity emitted = 1.061e+43 erg / s - Luminosity absorbed = 3.401e+42 erg / s - Luminosity requested = 1.059e+43 erg / s - (base.py:573) - - -## Import the SDECPlotter class for plotting spectral element Decomposition Plot and obtain the necessary data from the simulation we just ran above. - - -```python -from tardis.visualization import SDECPlotter -plotter = SDECPlotter.from_simulation(sim) -``` - -## Let's now plot the SDEC Plot ( Second part of our First Objective ) - - -```python -plotter.generate_plot_mpl() -``` - - - - - - - - - - -![png](output_10_1.png) - - - -## Let's now work on the abundance vs velocity plot -For the next few cells, we'll modify the abundace dataframe so as to plot it easily - -### Get the abundance data from the simulation_state - - -```python -abundance = sim.simulation_state.abundance -abundance -``` - - - - -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
012345678910111213141516171819
atomic_number
80.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.19
120.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.03
140.520.520.520.520.520.520.520.520.520.520.520.520.520.520.520.520.520.520.520.52
160.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.190.19
180.040.040.040.040.040.040.040.040.040.040.040.040.040.040.040.040.040.040.040.04
200.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.030.03
-
- - - -### Transpose the abundance dataframe so to get atomic number as columns heads - - -```python -abundance = abundance.transpose() -abundance -``` - - - - -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
atomic_number81214161820
00.190.030.520.190.040.03
10.190.030.520.190.040.03
20.190.030.520.190.040.03
30.190.030.520.190.040.03
40.190.030.520.190.040.03
50.190.030.520.190.040.03
60.190.030.520.190.040.03
70.190.030.520.190.040.03
80.190.030.520.190.040.03
90.190.030.520.190.040.03
100.190.030.520.190.040.03
110.190.030.520.190.040.03
120.190.030.520.190.040.03
130.190.030.520.190.040.03
140.190.030.520.190.040.03
150.190.030.520.190.040.03
160.190.030.520.190.040.03
170.190.030.520.190.040.03
180.190.030.520.190.040.03
190.190.030.520.190.040.03
-
- - - -### Get the velocity at the middle of each shell - - -```python -v_middle = sim.simulation_state.v_middle -v_middle -``` - - - - -$[1.1225 \times 10^{9},~1.1675 \times 10^{9},~1.2125 \times 10^{9},~1.2575 \times 10^{9},~1.3025 \times 10^{9},~1.3475 \times 10^{9},~1.3925 \times 10^{9},~1.4375 \times 10^{9},~1.4825 \times 10^{9},~1.5275 \times 10^{9},~1.5725 \times 10^{9},~1.6175 \times 10^{9},~1.6625 \times 10^{9},~1.7075 \times 10^{9},~1.7525 \times 10^{9},~1.7975 \times 10^{9},~1.8425 \times 10^{9},~1.8875 \times 10^{9},~1.9325 \times 10^{9},~1.9775 \times 10^{9}] \; \mathrm{\frac{cm}{s}}$ - - - -### Renaming the abundance columns( atomic number to atomic symbol ) - - -```python -from tardis.util.base import atomic_number2element_symbol -columns = {head: atomic_number2element_symbol(head) for head in abundance.columns} -abundance.rename(columns = columns, inplace = True) -abundance -``` - - - - -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
atomic_numberOMgSiSArCa
00.190.030.520.190.040.03
10.190.030.520.190.040.03
20.190.030.520.190.040.03
30.190.030.520.190.040.03
40.190.030.520.190.040.03
50.190.030.520.190.040.03
60.190.030.520.190.040.03
70.190.030.520.190.040.03
80.190.030.520.190.040.03
90.190.030.520.190.040.03
100.190.030.520.190.040.03
110.190.030.520.190.040.03
120.190.030.520.190.040.03
130.190.030.520.190.040.03
140.190.030.520.190.040.03
150.190.030.520.190.040.03
160.190.030.520.190.040.03
170.190.030.520.190.040.03
180.190.030.520.190.040.03
190.190.030.520.190.040.03
-
- - - -### Add a new column of ```v_middle``` in units of km/s - - -```python -import astropy.units as u -abundance['v_middle'] = [u.Quantity(vel, u.km/u.s).value for vel in v_middle] -abundance -``` - - - - -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
atomic_numberOMgSiSArCav_middle
00.190.030.520.190.040.0311225.0
10.190.030.520.190.040.0311675.0
20.190.030.520.190.040.0312125.0
30.190.030.520.190.040.0312575.0
40.190.030.520.190.040.0313025.0
50.190.030.520.190.040.0313475.0
60.190.030.520.190.040.0313925.0
70.190.030.520.190.040.0314375.0
80.190.030.520.190.040.0314825.0
90.190.030.520.190.040.0315275.0
100.190.030.520.190.040.0315725.0
110.190.030.520.190.040.0316175.0
120.190.030.520.190.040.0316625.0
130.190.030.520.190.040.0317075.0
140.190.030.520.190.040.0317525.0
150.190.030.520.190.040.0317975.0
160.190.030.520.190.040.0318425.0
170.190.030.520.190.040.0318875.0
180.190.030.520.190.040.0319325.0
190.190.030.520.190.040.0319775.0
-
- - - - -```python -abundance.columns.name = 'atomic symbol' -``` - -## Plot of Abundance vs velocity - - -```python -abundance.plot(x = 'v_middle', xlabel = "$v_{middle}$ in km/s", ylabel = "Fractional Abundance", title = "Abundace vs velocity").legend(loc = 'upper right') -``` - - - - - - - - - - -![png](output_24_1.png) - - - -### Things to note in above graph -1. Since data overlap, we can only see four out of six line plots -2. Fractional abundance is uniform throughout the ejecta. - -## Our final task was to plot the total number of interactions that escape the simulation from the different elements -### I have worked it out for virtual packets, similar thing could be applied for the real packets as well - -I found out that most of the necessary data was alreadly pre computed during plotting of SDEC and available in the plotter object. - - -Get the line interaction data - - -```python -line_interaction_df = plotter.data["virtual"].packets_df_line_interaction -line_interaction_df -``` - - - - -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
nuslambdasenergieslast_interaction_typelast_line_interaction_out_idlast_line_interaction_in_idlast_line_interaction_in_nulast_line_interaction_atomlast_line_interaction_species
102.610913e+151148.2283610.000000e+002355153431.736483e+15141402
112.623633e+151142.6616430.000000e+002355153431.736483e+15141402
122.635277e+151137.6127830.000000e+002355153431.736483e+15141402
132.652415e+151130.2620870.000000e+002355153431.736483e+15141402
142.666043e+151124.4845570.000000e+002355153431.736483e+15141402
..............................
26527351.086349e+152759.6323376.346043e-072769776971.109094e+15121201
26527361.092227e+152744.7820746.591144e-072769776971.109094e+15121201
26527371.099582e+152726.4225686.837456e-072769776971.109094e+15121201
26527381.109661e+152701.6577697.107076e-072769776971.109094e+15121201
26527391.115506e+152687.5019447.237699e-072769776971.109094e+15121201
-

1071430 rows × 9 columns

-
- - - -### Some slight pre processing ( refinement, counting ) - - -```python -#Adding a new column to get the count of interactions -line_interaction_df['count'] = 1 - -#Since only count is required, let's use only count and atomic_number columns -line_interaction_count_df = line_interaction_df[['last_line_interaction_atom', 'count']] - -#Group by the last_line_interaction_atom -line_interaction_count_df = line_interaction_count_df.groupby(['last_line_interaction_atom']).count()[['count']] -line_interaction_count_df['atomic_number'] = line_interaction_count_df.index -line_interaction_count_df.reset_index(drop = True, inplace = True) - -#Add a new column with the correspong atomic symbols -line_interaction_count_df['symbol'] = line_interaction_count_df['atomic_number'].apply(atomic_number2element_symbol) - -#Rearranging the columns -line_interaction_count_df = line_interaction_count_df[['atomic_number', 'symbol', 'count']] - -#Sorting according to the count value -line_interaction_count_df = line_interaction_count_df.sort_values('count', ascending = False) -line_interaction_count_df -``` - - - - -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
atomic_numbersymbolcount
214Si665620
316S219410
112Mg75800
08O39400
520Ca37650
418Ar33550
-
- - - -## Finally, the required plot. - - -```python -line_interaction_count_df.plot.bar(x = 'symbol' , y = 'count', rot = 0) -``` - - - - - - - - - - -![png](output_32_1.png) - - - -## Thanks for giving your time. Please suggest any impovements or any mistakes I made. - - -```python - -``` diff --git a/FirstObjectiveMarkdown/output_10_1.png b/FirstObjectiveMarkdown/output_10_1.png deleted file mode 100644 index d9c447be6af2432d8893984fdad004571d9115ee..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 80089 zcma%jcOaGj+dmYR-1mE2*K57ct49jb=gv}}#lpfm_wd0zWh^Y5cr2__4rfloPjY-1 zvfzJ$PEt>t9^0BYx#~L@W6A3~*;&~-S(zJLb}@EvG`F?k;Sk{9V!v$W zUoYUWbui`REhx!`ixAj7&~U`UBGE_w!_E*-H^;)p!g_d5Ld7k1dDPX7tgpX*ebuTJ zop$NM>J5WndYCb z$K2dJzBMS%S7lBrGvO6~9fj^3iLT(e_UXOjYDI8X7OUi$E6+*ck0z$%svbJzZ^%FG zt>4!_{;%IK`KSN;#YAKI7 z&vnE|D=S|(M}7Ng&*z6u3tdU|o1wIV@2rPQ*AG_faJq7Jgl#GpnaAAc>0DzbzXla~ zAGq*Y4cKMcNdFsd4mK(aPk(y%ne)fH(I; zZ6Atl$H!_qEJ_UcVnl~D*eObFMt}2OQ~!6JHdX==&U*NWy}20M-JN0EirJ4^85%{* zDbnF{@FSC*;Hat&)_MyUnRGOo z6rE>a@U5_w{m*R@L{Gb8yP!v2Wo7-$)fqOd>a$?FdbQDTxG|9Mp}f2?20bcra=2N2 zfUc@uYb3;~hB=BA_58?ZJ(QTmI{PEiv@20UE?$JHM%Q-`{a;Vd1b6M%yLd0JzsRtK 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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
09.93e+03 K1.01e+04 K0.40.50709.94e+03 K1.01e+04 K0.40.506
59.85e+03 K1.02e+04 K0.2110.19751e+04 K1.03e+04 K0.2110.191
109.78e+03 K1.01e+04 K0.1430.117101.01e+04 K1.02e+04 K0.1430.115
159.71e+03 K9.87e+03 K0.1050.0869151.02e+04 K9.9e+03 K0.1050.086
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -299,24 +299,24 @@ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tCurrent t_inner = 9933.952 K\n", - "\tExpected t_inner for next iteration = 10703.212 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tExpected t_inner for next iteration = 10697.222 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 2 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 2 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tLuminosity emitted = 1.071e+43 erg / s\n", - "\tLuminosity absorbed = 3.576e+42 erg / s\n", + "\tLuminosity absorbed = 3.548e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -324,50 +324,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.01e+04 K1.08e+04 K0.5070.52501.01e+04 K1.08e+04 K0.5060.517
51.02e+04 K1.1e+04 K0.1970.20351.03e+04 K1.1e+04 K0.1910.198
101.01e+04 K1.08e+04 K0.1170.125101.02e+04 K1.07e+04 K0.1150.127
159.87e+03 K1.05e+04 K0.08690.0933159.9e+03 K1.05e+04 K0.0860.0928
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -379,25 +379,25 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10703.212 K\n", - "\tExpected t_inner for next iteration = 10673.712 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10697.222 K\n", + "\tExpected t_inner for next iteration = 10668.196 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 3 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 3 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.074e+43 erg / s\n", - "\tLuminosity absorbed = 3.391e+42 erg / s\n", + "\tLuminosity emitted = 1.072e+43 erg / s\n", + "\tLuminosity absorbed = 3.383e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -405,50 +405,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.08e+04 K1.1e+04 K0.5250.48301.08e+04 K1.1e+04 K0.5170.482
51.1e+04 K1.12e+04 K0.2030.18951.1e+04 K1.12e+04 K0.1980.188
101.08e+04 K1.1e+04 K0.1250.118101.07e+04 K1.1e+04 K0.1270.116
151.05e+04 K1.06e+04 K0.09330.0895151.05e+04 K1.06e+04 K0.09280.0896
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -460,25 +460,27 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10673.712 K\n", - "\tExpected t_inner for next iteration = 10635.953 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10668.196 K\n", + "\tExpected t_inner for next iteration = 10635.748 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 4 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 4 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.058e+43 erg / s\n", - "\tLuminosity absorbed = 3.352e+42 erg / s\n", + "\tLuminosity emitted = 1.060e+43 erg / s\n", + "\tLuminosity absorbed = 3.336e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\tIteration converged 1/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -486,50 +488,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4830.46901.1e+04 K1.1e+04 K0.4820.467
51.12e+04 K1.12e+04 K0.1890.18251.12e+04 K1.12e+04 K0.1880.183
101.1e+04 K1.1e+04 K0.1180.113101.1e+04 K1.1e+04 K0.1160.115
151.06e+04 K1.07e+04 K0.08950.0861151.06e+04 K1.07e+04 K0.08960.0859
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -541,27 +543,27 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10635.953 K\n", - "\tExpected t_inner for next iteration = 10638.407 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10635.748 K\n", + "\tExpected t_inner for next iteration = 10634.292 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 5 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 5 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.055e+43 erg / s\n", - "\tLuminosity absorbed = 3.399e+42 erg / s\n", + "\tLuminosity emitted = 1.054e+43 erg / s\n", + "\tLuminosity absorbed = 3.380e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 1/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "\tIteration converged 2/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -569,50 +571,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4690.47901.1e+04 K1.1e+04 K0.4670.479
51.12e+04 K1.13e+04 K0.1820.17851.12e+04 K1.13e+04 K0.1830.179
101.1e+04 K1.1e+04 K0.1130.113101.1e+04 K1.11e+04 K0.1150.111
151.07e+04 K1.07e+04 K0.08610.0839151.07e+04 K1.07e+04 K0.08590.0844
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -624,27 +626,27 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10638.407 K\n", - "\tExpected t_inner for next iteration = 10650.202 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10634.292 K\n", + "\tExpected t_inner for next iteration = 10646.785 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 6 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 6 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.061e+43 erg / s\n", - "\tLuminosity absorbed = 3.398e+42 erg / s\n", + "\tLuminosity emitted = 1.060e+43 erg / s\n", + "\tLuminosity absorbed = 3.394e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 2/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "\tIteration converged 3/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -652,50 +654,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4790.4701.1e+04 K1.11e+04 K0.4790.469
51.13e+04 K1.12e+04 K0.1780.18551.13e+04 K1.13e+04 K0.1790.183
101.1e+04 K1.11e+04 K0.1130.112101.11e+04 K1.11e+04 K0.1110.113
151.07e+04 K1.07e+04 K0.08390.0856151.07e+04 K1.07e+04 K0.08440.0855
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -707,27 +709,27 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10650.202 K\n", - "\tExpected t_inner for next iteration = 10645.955 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10646.785 K\n", + "\tExpected t_inner for next iteration = 10645.882 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 7 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 7 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tLuminosity emitted = 1.061e+43 erg / s\n", - "\tLuminosity absorbed = 3.382e+42 erg / s\n", + "\tLuminosity absorbed = 3.380e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 3/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "\tIteration converged 4/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -735,50 +737,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.470.4701.11e+04 K1.1e+04 K0.4690.471
51.12e+04 K1.13e+04 K0.1850.17851.13e+04 K1.13e+04 K0.1830.175
101.11e+04 K1.11e+04 K0.1120.112101.11e+04 K1.11e+04 K0.1130.111
151.07e+04 K1.07e+04 K0.08560.086151.07e+04 K1.06e+04 K0.08550.0863
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -790,27 +792,27 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10645.955 K\n", - "\tExpected t_inner for next iteration = 10642.050 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10645.882 K\n", + "\tExpected t_inner for next iteration = 10641.685 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 8 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 8 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.062e+43 erg / s\n", - "\tLuminosity absorbed = 3.350e+42 erg / s\n", + "\tLuminosity emitted = 1.061e+43 erg / s\n", + "\tLuminosity absorbed = 3.358e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 4/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "\tIteration converged 5/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -818,50 +820,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.11e+04 K0.470.47201.1e+04 K1.11e+04 K0.4710.468
51.13e+04 K1.14e+04 K0.1780.17551.13e+04 K1.14e+04 K0.1750.174
101.11e+04 K1.11e+04 K0.1120.111101.11e+04 K1.11e+04 K0.1110.109
151.07e+04 K1.07e+04 K0.0860.084151.06e+04 K1.08e+04 K0.08630.0826
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -873,27 +875,27 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10642.050 K\n", - "\tExpected t_inner for next iteration = 10636.106 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10641.685 K\n", + "\tExpected t_inner for next iteration = 10638.233 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 9 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 9 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.052e+43 erg / s\n", - "\tLuminosity absorbed = 3.411e+42 erg / s\n", + "\tLuminosity emitted = 1.053e+43 erg / s\n", + "\tLuminosity absorbed = 3.412e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 5/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "\tIteration converged 6/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -901,50 +903,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4720.46901.11e+04 K1.11e+04 K0.4680.467
51.14e+04 K1.15e+04 K0.1750.1751.14e+04 K1.15e+04 K0.1740.17
101.11e+04 K1.11e+04 K0.1110.109101.11e+04 K1.11e+04 K0.1090.109
151.07e+04 K1.08e+04 K0.0840.0822151.08e+04 K1.08e+04 K0.08260.0821
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -956,25 +958,25 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10636.106 K\n", - "\tExpected t_inner for next iteration = 10654.313 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10638.233 K\n", + "\tExpected t_inner for next iteration = 10654.289 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 10 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 10 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.070e+43 erg / s\n", - "\tLuminosity absorbed = 3.335e+42 erg / s\n", + "\tLuminosity emitted = 1.069e+43 erg / s\n", + "\tLuminosity absorbed = 3.338e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -982,50 +984,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.11e+04 K1.1e+04 K0.4690.47501.11e+04 K1.1e+04 K0.4670.473
51.15e+04 K1.14e+04 K0.170.17751.15e+04 K1.14e+04 K0.170.178
101.11e+04 K1.11e+04 K0.1090.112101.11e+04 K1.11e+04 K0.1090.112
151.08e+04 K1.06e+04 K0.08220.0878151.08e+04 K1.06e+04 K0.08210.0879
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1037,27 +1039,27 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10654.313 K\n", - "\tExpected t_inner for next iteration = 10628.190 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10654.289 K\n", + "\tExpected t_inner for next iteration = 10628.970 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 11 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 11 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.053e+43 erg / s\n", - "\tLuminosity absorbed = 3.363e+42 erg / s\n", + "\tLuminosity emitted = 1.052e+43 erg / s\n", + "\tLuminosity absorbed = 3.372e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 1/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "\tIteration converged 1/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -1065,50 +1067,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4750.47201.1e+04 K1.1e+04 K0.4730.477
51.14e+04 K1.12e+04 K0.1770.18451.14e+04 K1.12e+04 K0.1780.183
101.11e+04 K1.1e+04 K0.1120.114101.11e+04 K1.1e+04 K0.1120.115
151.06e+04 K1.06e+04 K0.08780.0859151.06e+04 K1.06e+04 K0.08790.0867
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1120,25 +1122,27 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10628.190 K\n", - "\tExpected t_inner for next iteration = 10644.054 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10628.970 K\n", + "\tExpected t_inner for next iteration = 10646.280 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 12 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 12 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.056e+43 erg / s\n", - "\tLuminosity absorbed = 3.420e+42 erg / s\n", + "\tLuminosity emitted = 1.055e+43 erg / s\n", + "\tLuminosity absorbed = 3.435e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", + "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", + "\tIteration converged 2/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -1146,50 +1150,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.11e+04 K0.4720.46701.1e+04 K1.11e+04 K0.4770.463
51.12e+04 K1.13e+04 K0.1840.17651.12e+04 K1.13e+04 K0.1830.18
101.1e+04 K1.11e+04 K0.1140.11101.1e+04 K1.11e+04 K0.1150.112
151.06e+04 K1.08e+04 K0.08590.0821151.06e+04 K1.07e+04 K0.08670.0843
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1201,27 +1205,27 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10644.054 K\n", - "\tExpected t_inner for next iteration = 10653.543 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10646.280 K\n", + "\tExpected t_inner for next iteration = 10656.684 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 13 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 13 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.062e+43 erg / s\n", - "\tLuminosity absorbed = 3.406e+42 erg / s\n", + "\tLuminosity emitted = 1.065e+43 erg / s\n", + "\tLuminosity absorbed = 3.396e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 1/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "\tIteration converged 3/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -1229,50 +1233,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4670.46601.11e+04 K1.11e+04 K0.4630.463
51.13e+04 K1.13e+04 K0.1760.1851.13e+04 K1.13e+04 K0.180.179
101.11e+04 K1.11e+04 K0.110.111101.11e+04 K1.1e+04 K0.1120.114
151.08e+04 K1.08e+04 K0.08210.0841151.07e+04 K1.07e+04 K0.08430.0869
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1284,27 +1288,27 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10653.543 K\n", - "\tExpected t_inner for next iteration = 10647.277 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10656.684 K\n", + "\tExpected t_inner for next iteration = 10643.209 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 14 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 14 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.063e+43 erg / s\n", - "\tLuminosity absorbed = 3.369e+42 erg / s\n", + "\tLuminosity emitted = 1.061e+43 erg / s\n", + "\tLuminosity absorbed = 3.360e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 2/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "\tIteration converged 4/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -1312,50 +1316,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4660.46901.11e+04 K1.11e+04 K0.4630.467
51.13e+04 K1.13e+04 K0.180.18251.13e+04 K1.13e+04 K0.1790.181
101.11e+04 K1.1e+04 K0.1110.113101.1e+04 K1.1e+04 K0.1140.114
151.08e+04 K1.07e+04 K0.08410.0854151.07e+04 K1.06e+04 K0.08690.0866
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1367,27 +1371,27 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10647.277 K\n", - "\tExpected t_inner for next iteration = 10638.875 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10643.209 K\n", + "\tExpected t_inner for next iteration = 10637.728 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 15 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 15 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.053e+43 erg / s\n", - "\tLuminosity absorbed = 3.417e+42 erg / s\n", + "\tLuminosity emitted = 1.054e+43 erg / s\n", + "\tLuminosity absorbed = 3.401e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 3/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "\tIteration converged 5/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -1395,50 +1399,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.11e+04 K1.1e+04 K0.4690.48401.11e+04 K1.1e+04 K0.4670.482
51.13e+04 K1.13e+04 K0.1820.18151.13e+04 K1.13e+04 K0.1810.18
101.1e+04 K1.1e+04 K0.1130.113101.1e+04 K1.11e+04 K0.1140.111
151.07e+04 K1.07e+04 K0.08540.0858151.06e+04 K1.07e+04 K0.08660.0845
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1450,27 +1454,27 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10638.875 K\n", - "\tExpected t_inner for next iteration = 10655.125 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10637.728 K\n", + "\tExpected t_inner for next iteration = 10651.277 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 16 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 16 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.059e+43 erg / s\n", - "\tLuminosity absorbed = 3.445e+42 erg / s\n", + "\tLuminosity emitted = 1.057e+43 erg / s\n", + "\tLuminosity absorbed = 3.448e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 4/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "\tIteration converged 6/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -1478,50 +1482,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4840.47201.1e+04 K1.1e+04 K0.4820.473
51.13e+04 K1.13e+04 K0.1810.17751.13e+04 K1.14e+04 K0.180.172
101.1e+04 K1.1e+04 K0.1130.113101.11e+04 K1.1e+04 K0.1110.113
151.07e+04 K1.06e+04 K0.08580.0858151.07e+04 K1.08e+04 K0.08450.0824
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1533,25 +1537,25 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10655.125 K\n", - "\tExpected t_inner for next iteration = 10655.561 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10651.277 K\n", + "\tExpected t_inner for next iteration = 10658.182 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 17 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 17 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.067e+43 erg / s\n", - "\tLuminosity absorbed = 3.372e+42 erg / s\n", + "\tLuminosity emitted = 1.066e+43 erg / s\n", + "\tLuminosity absorbed = 3.396e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -1559,50 +1563,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.11e+04 K0.4720.46801.1e+04 K1.11e+04 K0.4730.463
51.13e+04 K1.14e+04 K0.1770.17551.14e+04 K1.14e+04 K0.1720.172
101.1e+04 K1.11e+04 K0.1130.11101.1e+04 K1.12e+04 K0.1130.106
151.06e+04 K1.08e+04 K0.08580.0816151.08e+04 K1.08e+04 K0.08240.0809
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1614,27 +1618,27 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10655.561 K\n", - "\tExpected t_inner for next iteration = 10636.536 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10658.182 K\n", + "\tExpected t_inner for next iteration = 10642.273 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 18 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 18 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.057e+43 erg / s\n", - "\tLuminosity absorbed = 3.365e+42 erg / s\n", + "\tLuminosity emitted = 1.058e+43 erg / s\n", + "\tLuminosity absorbed = 3.382e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 1/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "\tIteration converged 1/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -1642,50 +1646,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4680.46401.11e+04 K1.11e+04 K0.4630.462
51.14e+04 K1.13e+04 K0.1750.17751.14e+04 K1.14e+04 K0.1720.174
101.11e+04 K1.1e+04 K0.110.113101.12e+04 K1.11e+04 K0.1060.109
151.08e+04 K1.07e+04 K0.08160.0848151.08e+04 K1.07e+04 K0.08090.0829
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1697,27 +1701,27 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10636.536 K\n", - "\tExpected t_inner for next iteration = 10641.692 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10642.273 K\n", + "\tExpected t_inner for next iteration = 10644.386 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 19 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 19 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.056e+43 erg / s\n", - "\tLuminosity absorbed = 3.405e+42 erg / s\n", + "\tLuminosity emitted = 1.057e+43 erg / s\n", + "\tLuminosity absorbed = 3.403e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n", + " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 2/4 consecutive times. (\u001b[1mbase.py\u001b[0m:261)\n", + "\tIteration converged 2/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:541)\n" + "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] }, { @@ -1725,50 +1729,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4640.46601.11e+04 K1.11e+04 K0.4620.462
51.13e+04 K1.13e+04 K0.1770.17751.14e+04 K1.14e+04 K0.1740.173
101.1e+04 K1.11e+04 K0.1130.111101.11e+04 K1.11e+04 K0.1090.111
151.07e+04 K1.07e+04 K0.08480.0853151.07e+04 K1.07e+04 K0.08290.0845
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1780,9 +1784,9 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10641.692 K\n", - "\tExpected t_inner for next iteration = 10650.463 K\n", - " (\u001b[1mbase.py\u001b[0m:568)\n", + "\tCurrent t_inner = 10644.386 K\n", + "\tExpected t_inner for next iteration = 10649.220 K\n", + " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", " (g_lower * n_upper) / (g_upper * n_lower)\n", @@ -1790,17 +1794,17 @@ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tSimulation finished in 19 iterations \n", - "\tSimulation took 54.57 s\n", - " (\u001b[1mbase.py\u001b[0m:469)\n", + "\tSimulation took 53.10 s\n", + " (\u001b[1mbase.py\u001b[0m:476)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tStarting iteration 20 of 20 (\u001b[1mbase.py\u001b[0m:391)\n", + "\tStarting iteration 20 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.061e+43 erg / s\n", - "\tLuminosity absorbed = 3.401e+42 erg / s\n", + "\tLuminosity emitted = 1.060e+43 erg / s\n", + "\tLuminosity absorbed = 3.406e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:573)\n" + " (\u001b[1mbase.py\u001b[0m:580)\n" ] } ], @@ -1853,7 +1857,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -2684,7 +2688,7 @@ } ], "source": [ - "from tardis.util.base import atomic_number2element_symbol\n", + "from tardis.util.base import atomic_number2element_symbol, species_tuple_to_string\n", "columns = {head: atomic_number2element_symbol(head) for head in abundance.columns}\n", "abundance.rename(columns = columns, inplace = True)\n", "abundance" @@ -3001,7 +3005,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 12, @@ -3039,7 +3043,7 @@ "metadata": {}, "source": [ "## Our final task was to plot the total number of interactions that escape the simulation from the different elements\n", - "### I have worked it out for virtual packets, similar thing could be applied for the real packets as well\n", + "### I have worked it out for virtual mode, similar thing could be applied for the real packets as well\n", "\n", "I found out that most of the necessary data was alreadly pre computed during plotting of SDEC and available in the plotter object.\n" ] @@ -3093,61 +3097,61 @@ " \n", " \n", " 10\n", - " 2.610913e+15\n", - " 1148.228361\n", + " 2.612094e+15\n", + " 1147.709110\n", " 0.000000e+00\n", " 2\n", " 3551\n", " 5343\n", - " 1.736483e+15\n", + " 1.736280e+15\n", " 14\n", " 1402\n", " \n", " \n", " 11\n", - " 2.623633e+15\n", - " 1142.661643\n", + " 2.624666e+15\n", + " 1142.211688\n", " 0.000000e+00\n", " 2\n", " 3551\n", " 5343\n", - " 1.736483e+15\n", + " 1.736280e+15\n", " 14\n", " 1402\n", " \n", " \n", " 12\n", - " 2.635277e+15\n", - " 1137.612783\n", + " 2.636174e+15\n", + " 1137.225677\n", " 0.000000e+00\n", " 2\n", " 3551\n", " 5343\n", - " 1.736483e+15\n", + " 1.736280e+15\n", " 14\n", " 1402\n", " \n", " \n", " 13\n", - " 2.652415e+15\n", - " 1130.262087\n", + " 2.653109e+15\n", + " 1129.966483\n", " 0.000000e+00\n", " 2\n", " 3551\n", " 5343\n", - " 1.736483e+15\n", + " 1.736280e+15\n", " 14\n", " 1402\n", " \n", " \n", " 14\n", - " 2.666043e+15\n", - " 1124.484557\n", + " 2.666574e+15\n", + " 1124.260873\n", " 0.000000e+00\n", " 2\n", " 3551\n", " 5343\n", - " 1.736483e+15\n", + " 1.736280e+15\n", " 14\n", " 1402\n", " \n", @@ -3164,83 +3168,83 @@ " ...\n", " \n", " \n", - " 2652735\n", - " 1.086349e+15\n", - " 2759.632337\n", - " 6.346043e-07\n", + " 2660385\n", + " 1.081308e+15\n", + " 2772.499444\n", + " 4.970635e-07\n", " 2\n", " 7697\n", - " 7697\n", - " 1.109094e+15\n", + " 7672\n", + " 1.108046e+15\n", " 12\n", " 1201\n", " \n", " \n", - " 2652736\n", - " 1.092227e+15\n", - " 2744.782074\n", - " 6.591144e-07\n", + " 2660386\n", + " 1.093554e+15\n", + " 2741.449794\n", + " 5.658597e-07\n", " 2\n", " 7697\n", - " 7697\n", - " 1.109094e+15\n", + " 7672\n", + " 1.108046e+15\n", " 12\n", " 1201\n", " \n", " \n", - " 2652737\n", - " 1.099582e+15\n", - " 2726.422568\n", - " 6.837456e-07\n", + " 2660387\n", + " 1.098036e+15\n", + " 2730.260631\n", + " 5.847772e-07\n", " 2\n", " 7697\n", - " 7697\n", - " 1.109094e+15\n", + " 7672\n", + " 1.108046e+15\n", " 12\n", " 1201\n", " \n", " \n", - " 2652738\n", - " 1.109661e+15\n", - " 2701.657769\n", - " 7.107076e-07\n", + " 2660388\n", + " 1.104022e+15\n", + " 2715.457636\n", + " 6.066446e-07\n", " 2\n", " 7697\n", - " 7697\n", - " 1.109094e+15\n", + " 7672\n", + " 1.108046e+15\n", " 12\n", " 1201\n", " \n", " \n", - " 2652739\n", - " 1.115506e+15\n", - " 2687.501944\n", - " 7.237699e-07\n", + " 2660389\n", + " 1.112540e+15\n", + " 2694.667837\n", + " 6.325031e-07\n", " 2\n", " 7697\n", - " 7697\n", - " 1.109094e+15\n", + " 7672\n", + " 1.108046e+15\n", " 12\n", " 1201\n", " \n", " \n", "\n", - "

1071430 rows × 9 columns

\n", + "

1079320 rows × 9 columns

\n", "" ], "text/plain": [ " nus lambdas energies last_interaction_type \\\n", - "10 2.610913e+15 1148.228361 0.000000e+00 2 \n", - "11 2.623633e+15 1142.661643 0.000000e+00 2 \n", - "12 2.635277e+15 1137.612783 0.000000e+00 2 \n", - "13 2.652415e+15 1130.262087 0.000000e+00 2 \n", - "14 2.666043e+15 1124.484557 0.000000e+00 2 \n", + "10 2.612094e+15 1147.709110 0.000000e+00 2 \n", + "11 2.624666e+15 1142.211688 0.000000e+00 2 \n", + "12 2.636174e+15 1137.225677 0.000000e+00 2 \n", + "13 2.653109e+15 1129.966483 0.000000e+00 2 \n", + "14 2.666574e+15 1124.260873 0.000000e+00 2 \n", "... ... ... ... ... \n", - "2652735 1.086349e+15 2759.632337 6.346043e-07 2 \n", - "2652736 1.092227e+15 2744.782074 6.591144e-07 2 \n", - "2652737 1.099582e+15 2726.422568 6.837456e-07 2 \n", - "2652738 1.109661e+15 2701.657769 7.107076e-07 2 \n", - "2652739 1.115506e+15 2687.501944 7.237699e-07 2 \n", + "2660385 1.081308e+15 2772.499444 4.970635e-07 2 \n", + "2660386 1.093554e+15 2741.449794 5.658597e-07 2 \n", + "2660387 1.098036e+15 2730.260631 5.847772e-07 2 \n", + "2660388 1.104022e+15 2715.457636 6.066446e-07 2 \n", + "2660389 1.112540e+15 2694.667837 6.325031e-07 2 \n", "\n", " last_line_interaction_out_id last_line_interaction_in_id \\\n", "10 3551 5343 \n", @@ -3249,24 +3253,24 @@ "13 3551 5343 \n", "14 3551 5343 \n", "... ... ... \n", - "2652735 7697 7697 \n", - "2652736 7697 7697 \n", - "2652737 7697 7697 \n", - "2652738 7697 7697 \n", - "2652739 7697 7697 \n", + "2660385 7697 7672 \n", + "2660386 7697 7672 \n", + "2660387 7697 7672 \n", + "2660388 7697 7672 \n", + "2660389 7697 7672 \n", "\n", " last_line_interaction_in_nu last_line_interaction_atom \\\n", - "10 1.736483e+15 14 \n", - "11 1.736483e+15 14 \n", - "12 1.736483e+15 14 \n", - "13 1.736483e+15 14 \n", - "14 1.736483e+15 14 \n", + "10 1.736280e+15 14 \n", + "11 1.736280e+15 14 \n", + "12 1.736280e+15 14 \n", + "13 1.736280e+15 14 \n", + "14 1.736280e+15 14 \n", "... ... ... \n", - "2652735 1.109094e+15 12 \n", - "2652736 1.109094e+15 12 \n", - "2652737 1.109094e+15 12 \n", - "2652738 1.109094e+15 12 \n", - "2652739 1.109094e+15 12 \n", + "2660385 1.108046e+15 12 \n", + "2660386 1.108046e+15 12 \n", + "2660387 1.108046e+15 12 \n", + "2660388 1.108046e+15 12 \n", + "2660389 1.108046e+15 12 \n", "\n", " last_line_interaction_species \n", "10 1402 \n", @@ -3275,13 +3279,13 @@ "13 1402 \n", "14 1402 \n", "... ... \n", - "2652735 1201 \n", - "2652736 1201 \n", - "2652737 1201 \n", - "2652738 1201 \n", - "2652739 1201 \n", + "2660385 1201 \n", + "2660386 1201 \n", + "2660387 1201 \n", + "2660388 1201 \n", + "2660389 1201 \n", "\n", - "[1071430 rows x 9 columns]" + "[1079320 rows x 9 columns]" ] }, "execution_count": 13, @@ -3329,60 +3333,130 @@ " \n", " \n", " \n", - " atomic_number\n", " symbol\n", + " species\n", " count\n", " \n", " \n", " \n", " \n", + " 0\n", + " O I\n", + " (8, 800)\n", + " 9330\n", + " \n", + " \n", + " 1\n", + " O II\n", + " (8, 801)\n", + " 1920\n", + " \n", + " \n", " 2\n", - " 14\n", - " Si\n", - " 665620\n", + " O III\n", + " (8, 802)\n", + " 27420\n", " \n", " \n", " 3\n", - " 16\n", - " S\n", - " 219410\n", + " Mg II\n", + " (12, 1201)\n", + " 73280\n", " \n", " \n", - " 1\n", - " 12\n", - " Mg\n", - " 75800\n", + " 4\n", + " Si II\n", + " (14, 1401)\n", + " 242340\n", " \n", " \n", - " 0\n", - " 8\n", - " O\n", - " 39400\n", + " 5\n", + " Si III\n", + " (14, 1402)\n", + " 415620\n", " \n", " \n", - " 5\n", - " 20\n", - " Ca\n", - " 37650\n", + " 6\n", + " Si IV\n", + " (14, 1403)\n", + " 17150\n", " \n", " \n", - " 4\n", - " 18\n", - " Ar\n", - " 33550\n", + " 7\n", + " S I\n", + " (16, 1600)\n", + " 50\n", + " \n", + " \n", + " 8\n", + " S II\n", + " (16, 1601)\n", + " 165050\n", + " \n", + " \n", + " 9\n", + " S III\n", + " (16, 1602)\n", + " 50950\n", + " \n", + " \n", + " 10\n", + " S IV\n", + " (16, 1603)\n", + " 2980\n", + " \n", + " \n", + " 11\n", + " Ar I\n", + " (18, 1800)\n", + " 470\n", + " \n", + " \n", + " 12\n", + " Ar II\n", + " (18, 1801)\n", + " 31250\n", + " \n", + " \n", + " 13\n", + " Ar III\n", + " (18, 1802)\n", + " 2790\n", + " \n", + " \n", + " 14\n", + " Ar IV\n", + " (18, 1803)\n", + " 10\n", + " \n", + " \n", + " 15\n", + " Ca II\n", + " (20, 2001)\n", + " 38710\n", " \n", " \n", "\n", "" ], "text/plain": [ - " atomic_number symbol count\n", - "2 14 Si 665620\n", - "3 16 S 219410\n", - "1 12 Mg 75800\n", - "0 8 O 39400\n", - "5 20 Ca 37650\n", - "4 18 Ar 33550" + " symbol species count\n", + "0 O I (8, 800) 9330\n", + "1 O II (8, 801) 1920\n", + "2 O III (8, 802) 27420\n", + "3 Mg II (12, 1201) 73280\n", + "4 Si II (14, 1401) 242340\n", + "5 Si III (14, 1402) 415620\n", + "6 Si IV (14, 1403) 17150\n", + "7 S I (16, 1600) 50\n", + "8 S II (16, 1601) 165050\n", + "9 S III (16, 1602) 50950\n", + "10 S IV (16, 1603) 2980\n", + "11 Ar I (18, 1800) 470\n", + "12 Ar II (18, 1801) 31250\n", + "13 Ar III (18, 1802) 2790\n", + "14 Ar IV (18, 1803) 10\n", + "15 Ca II (20, 2001) 38710" ] }, "execution_count": 14, @@ -3391,25 +3465,20 @@ } ], "source": [ + "import pandas as pd\n", + "\n", "#Adding a new column to get the count of interactions\n", "line_interaction_df['count'] = 1\n", "\n", - "#Since only count is required, let's use only count and atomic_number columns\n", - "line_interaction_count_df = line_interaction_df[['last_line_interaction_atom', 'count']]\n", - "\n", - "#Group by the last_line_interaction_atom\n", - "line_interaction_count_df = line_interaction_count_df.groupby(['last_line_interaction_atom']).count()[['count']]\n", - "line_interaction_count_df['atomic_number'] = line_interaction_count_df.index\n", - "line_interaction_count_df.reset_index(drop = True, inplace = True)\n", - "\n", - "#Add a new column with the correspong atomic symbols\n", - "line_interaction_count_df['symbol'] = line_interaction_count_df['atomic_number'].apply(atomic_number2element_symbol)\n", + "#Since only count is required, let's use only count, atomic_number, species columns\n", + "line_interaction_count_df = line_interaction_df[['last_line_interaction_atom', 'last_line_interaction_species', 'count']]\n", "\n", - "#Rearranging the columns\n", - "line_interaction_count_df = line_interaction_count_df[['atomic_number', 'symbol', 'count']]\n", - "\n", - "#Sorting according to the count value\n", - "line_interaction_count_df = line_interaction_count_df.sort_values('count', ascending = False)\n", + "line_interaction_count_df = line_interaction_count_df.groupby(['last_line_interaction_atom', 'last_line_interaction_species']).size()\n", + "line_interaction_count_df = pd.DataFrame({'species':line_interaction_count_df.index, 'count':line_interaction_count_df.values})\n", + "line_interaction_count_df['symbol'] = line_interaction_count_df.apply(lambda x: species_tuple_to_string(\n", + " (x.species[0], x.species[1] - 100*x.species[0])), axis = 1\n", + " )\n", + "line_interaction_count_df = line_interaction_count_df[['symbol', 'species', 'count']]\n", "line_interaction_count_df" ] }, @@ -3439,7 +3508,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -3449,24 +3518,2307 @@ } ], "source": [ - "line_interaction_count_df.plot.bar(x = 'symbol' , y = 'count', rot = 0)" + "line_interaction_count_df.plot.bar(x = 'symbol' , y = 'count', rot = 90, **{'legend': False})" ] }, { "cell_type": "markdown", - "id": "a36f4f12-3970-42d6-994f-6caa23441142", + "id": "9c02ef19-0071-4ffc-ade1-26acd3ca5c15", "metadata": {}, "source": [ - "## Thanks for giving your time. Please suggest any impovements or any mistakes I made." + "## We plot the above plots using plotly." ] }, { "cell_type": "code", - "execution_count": null, - "id": "2ee25854-b8d4-4229-a908-11d11ad3ba49", + "execution_count": 16, + "id": "2c5eb16a-5ec0-417b-9377-518e3f37baa0", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "atomic symbol=O
v_middle=%{x}
value=%{y}", + "legendgroup": "O", + "line": { + "color": "#636efa", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines", + "name": "O", + "orientation": "v", + "showlegend": true, + "type": "scatter", + "x": [ + 11225.000000000002, + 11675.000000000002, + 12125.000000000002, + 12575.000000000002, + 13025.000000000002, + 13475.000000000002, + 13925.000000000002, + 14375.000000000002, + 14825.000000000002, + 15275.000000000002, + 15725.000000000002, + 16175.000000000002, + 16625, + 17075, + 17525, + 17975, + 18425, + 18875, + 19325, + 19775 + ], + "xaxis": "x", + "y": [ + 0.19, + 0.19, + 0.19, + 0.19, + 0.19, + 0.19, + 0.19, + 0.19, + 0.19, + 0.19, + 0.19, + 0.19, + 0.19, + 0.19, + 0.19, + 0.19, + 0.19, + 0.19, + 0.19, + 0.19 + ], + "yaxis": "y" + }, + { + "hovertemplate": "atomic symbol=Mg
v_middle=%{x}
value=%{y}", + "legendgroup": "Mg", + "line": { + "color": "#EF553B", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines", + "name": "Mg", + "orientation": "v", + "showlegend": true, + "type": "scatter", + "x": [ + 11225.000000000002, + 11675.000000000002, + 12125.000000000002, + 12575.000000000002, + 13025.000000000002, + 13475.000000000002, + 13925.000000000002, + 14375.000000000002, + 14825.000000000002, + 15275.000000000002, + 15725.000000000002, + 16175.000000000002, + 16625, + 17075, + 17525, + 17975, + 18425, + 18875, + 19325, + 19775 + ], + "xaxis": "x", + "y": [ + 0.03, + 0.03, + 0.03, + 0.03, + 0.03, + 0.03, + 0.03, + 0.03, + 0.03, + 0.03, + 0.03, + 0.03, + 0.03, + 0.03, + 0.03, + 0.03, + 0.03, + 0.03, + 0.03, + 0.03 + ], + "yaxis": "y" + }, + { + "hovertemplate": "atomic symbol=Si
v_middle=%{x}
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = px.bar(line_interaction_count_df, x='symbol', y='count')\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "a36f4f12-3970-42d6-994f-6caa23441142", + "metadata": {}, + "source": [ + "## Thanks for giving your time. Please suggest any impovements or any mistakes I made." + ] } ], "metadata": { From 4b5187206e6ef828d945e17de44faf10a5045446 Mon Sep 17 00:00:00 2001 From: Sumit112192 Date: Sat, 30 Mar 2024 10:39:06 +0530 Subject: [PATCH 5/6] Updated y-axis scale to be log scale --- VelocityPacketTrackerFirstObjective.ipynb | 3715 +++++---------------- 1 file changed, 792 insertions(+), 2923 deletions(-) diff --git a/VelocityPacketTrackerFirstObjective.ipynb b/VelocityPacketTrackerFirstObjective.ipynb index 3dd6a0240ad..7af8a10e8bc 100644 --- a/VelocityPacketTrackerFirstObjective.ipynb +++ b/VelocityPacketTrackerFirstObjective.ipynb @@ -25,7 +25,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6fc9c39e8c5c403da69e36124df48e77", + "model_id": "", "version_major": 2, "version_minor": 0 }, @@ -39,7 +39,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f2b888ffcf934cce9b28388fd5c2f6dc", + "model_id": "", "version_major": 2, "version_minor": 0 }, @@ -123,6 +123,8 @@ " no_of_packets: 4.0e+4\n", " iterations: 20\n", " nthreads: 1\n", + " tracking:\n", + " track_rpacket: true\n", "\n", " last_no_of_packets: 1.e+5\n", " no_of_virtual_packets: 10\n", @@ -136,6 +138,7 @@ " t_inner:\n", " damping_constant: 0.5\n", "\n", + "\n", "spectrum:\n", " start: 500 angstrom\n", " stop: 20000 angstrom\n", @@ -179,7 +182,7 @@ "[\u001b[1mtardis.io.atom_data.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\tNon provided Atomic Data: synpp_refs, photoionization_data, yg_data, two_photon_data, linelist (\u001b[1mbase.py\u001b[0m:262)\n", "[\u001b[1mtardis.model.parse_input\u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", - "\tNumber of density points larger than number of shells. Assuming inner point irrelevant (\u001b[1mparse_input.py\u001b[0m:107)\n", + "\tNumber of density points larger than number of shells. Assuming inner point irrelevant (\u001b[1mparse_input.py\u001b[0m:143)\n", "[\u001b[1mtardis.model.matter.decay\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\tDecaying abundances for 1123200.0 seconds (\u001b[1mdecay.py\u001b[0m:101)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", @@ -198,7 +201,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c2ae94316d3d41839e9b6d82ae00a2f6", + "model_id": "a3baaa96e7604e8a866363b59d4ab704", "version_major": 2, "version_minor": 0 }, @@ -212,7 +215,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2bd58717056a4bdcaa5aec4568e67dfd", + "model_id": "8911ccdda6ce418f8f5a9f529d77a776", "version_major": 2, "version_minor": 0 }, @@ -229,8 +232,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 7.959e+42 erg / s\n", - "\tLuminosity absorbed = 2.644e+42 erg / s\n", + "\tLuminosity emitted = 7.942e+42 erg / s\n", + "\tLuminosity absorbed = 2.659e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", @@ -243,50 +246,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
09.94e+03 K1.01e+04 K0.40.50609.93e+03 K1.01e+04 K0.40.507
51e+04 K1.03e+04 K0.2110.19159.85e+03 K1.02e+04 K0.2110.197
101.01e+04 K1.02e+04 K0.1430.115109.78e+03 K1.01e+04 K0.1430.117
151.02e+04 K9.9e+03 K0.1050.086159.71e+03 K9.87e+03 K0.1050.0869
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -299,7 +302,7 @@ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tCurrent t_inner = 9933.952 K\n", - "\tExpected t_inner for next iteration = 10697.222 K\n", + "\tExpected t_inner for next iteration = 10703.212 K\n", " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -311,7 +314,7 @@ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tLuminosity emitted = 1.071e+43 erg / s\n", - "\tLuminosity absorbed = 3.548e+42 erg / s\n", + "\tLuminosity absorbed = 3.576e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", @@ -324,50 +327,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.01e+04 K1.08e+04 K0.5060.51701.01e+04 K1.08e+04 K0.5070.525
51.03e+04 K1.1e+04 K0.1910.19851.02e+04 K1.1e+04 K0.1970.203
101.02e+04 K1.07e+04 K0.1150.127101.01e+04 K1.08e+04 K0.1170.125
159.9e+03 K1.05e+04 K0.0860.0928159.87e+03 K1.05e+04 K0.08690.0933
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -379,8 +382,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10697.222 K\n", - "\tExpected t_inner for next iteration = 10668.196 K\n", + "\tCurrent t_inner = 10703.212 K\n", + "\tExpected t_inner for next iteration = 10673.712 K\n", " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -391,8 +394,8 @@ "\tStarting iteration 3 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.072e+43 erg / s\n", - "\tLuminosity absorbed = 3.383e+42 erg / s\n", + "\tLuminosity emitted = 1.074e+43 erg / s\n", + "\tLuminosity absorbed = 3.391e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", @@ -405,50 +408,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.08e+04 K1.1e+04 K0.5170.48201.08e+04 K1.1e+04 K0.5250.483
51.1e+04 K1.12e+04 K0.1980.18851.1e+04 K1.12e+04 K0.2030.189
101.07e+04 K1.1e+04 K0.1270.116101.08e+04 K1.1e+04 K0.1250.118
151.05e+04 K1.06e+04 K0.09280.0896151.05e+04 K1.06e+04 K0.09330.0895
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -460,8 +463,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10668.196 K\n", - "\tExpected t_inner for next iteration = 10635.748 K\n", + "\tCurrent t_inner = 10673.712 K\n", + "\tExpected t_inner for next iteration = 10635.953 K\n", " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -472,13 +475,11 @@ "\tStarting iteration 4 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.060e+43 erg / s\n", - "\tLuminosity absorbed = 3.336e+42 erg / s\n", + "\tLuminosity emitted = 1.058e+43 erg / s\n", + "\tLuminosity absorbed = 3.352e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 1/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", - "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] @@ -488,50 +489,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4820.46701.1e+04 K1.1e+04 K0.4830.469
51.12e+04 K1.12e+04 K0.1880.18351.12e+04 K1.12e+04 K0.1890.182
101.1e+04 K1.1e+04 K0.1160.115101.1e+04 K1.1e+04 K0.1180.113
151.06e+04 K1.07e+04 K0.08960.0859151.06e+04 K1.07e+04 K0.08950.0861
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -543,8 +544,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10635.748 K\n", - "\tExpected t_inner for next iteration = 10634.292 K\n", + "\tCurrent t_inner = 10635.953 K\n", + "\tExpected t_inner for next iteration = 10638.407 K\n", " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -555,12 +556,12 @@ "\tStarting iteration 5 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.054e+43 erg / s\n", - "\tLuminosity absorbed = 3.380e+42 erg / s\n", + "\tLuminosity emitted = 1.055e+43 erg / s\n", + "\tLuminosity absorbed = 3.399e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 2/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", + "\tIteration converged 1/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" @@ -571,50 +572,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4670.47901.1e+04 K1.1e+04 K0.4690.479
51.12e+04 K1.13e+04 K0.1830.17951.12e+04 K1.13e+04 K0.1820.178
101.1e+04 K1.11e+04 K0.1150.111101.1e+04 K1.1e+04 K0.1130.113
151.07e+04 K1.07e+04 K0.08590.0844151.07e+04 K1.07e+04 K0.08610.0839
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -626,8 +627,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10634.292 K\n", - "\tExpected t_inner for next iteration = 10646.785 K\n", + "\tCurrent t_inner = 10638.407 K\n", + "\tExpected t_inner for next iteration = 10650.202 K\n", " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -638,12 +639,12 @@ "\tStarting iteration 6 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.060e+43 erg / s\n", - "\tLuminosity absorbed = 3.394e+42 erg / s\n", + "\tLuminosity emitted = 1.061e+43 erg / s\n", + "\tLuminosity absorbed = 3.398e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 3/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", + "\tIteration converged 2/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" @@ -654,50 +655,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.11e+04 K0.4790.46901.1e+04 K1.1e+04 K0.4790.47
51.13e+04 K1.13e+04 K0.1790.18351.13e+04 K1.12e+04 K0.1780.185
101.11e+04 K1.11e+04 K0.1110.113101.1e+04 K1.11e+04 K0.1130.112
151.07e+04 K1.07e+04 K0.08440.0855151.07e+04 K1.07e+04 K0.08390.0856
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -709,8 +710,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10646.785 K\n", - "\tExpected t_inner for next iteration = 10645.882 K\n", + "\tCurrent t_inner = 10650.202 K\n", + "\tExpected t_inner for next iteration = 10645.955 K\n", " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -722,11 +723,11 @@ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tLuminosity emitted = 1.061e+43 erg / s\n", - "\tLuminosity absorbed = 3.380e+42 erg / s\n", + "\tLuminosity absorbed = 3.382e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 4/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", + "\tIteration converged 3/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" @@ -737,50 +738,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.11e+04 K1.1e+04 K0.4690.47101.1e+04 K1.1e+04 K0.470.47
51.13e+04 K1.13e+04 K0.1830.17551.12e+04 K1.13e+04 K0.1850.178
101.11e+04 K1.11e+04 K0.1130.111101.11e+04 K1.11e+04 K0.1120.112
151.07e+04 K1.06e+04 K0.08550.0863151.07e+04 K1.07e+04 K0.08560.086
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -792,8 +793,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10645.882 K\n", - "\tExpected t_inner for next iteration = 10641.685 K\n", + "\tCurrent t_inner = 10645.955 K\n", + "\tExpected t_inner for next iteration = 10642.050 K\n", " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -804,12 +805,12 @@ "\tStarting iteration 8 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.061e+43 erg / s\n", - "\tLuminosity absorbed = 3.358e+42 erg / s\n", + "\tLuminosity emitted = 1.062e+43 erg / s\n", + "\tLuminosity absorbed = 3.350e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 5/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", + "\tIteration converged 4/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" @@ -820,50 +821,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.11e+04 K0.4710.46801.1e+04 K1.11e+04 K0.470.472
51.13e+04 K1.14e+04 K0.1750.17451.13e+04 K1.14e+04 K0.1780.175
101.11e+04 K1.11e+04 K0.1110.109101.11e+04 K1.11e+04 K0.1120.111
151.06e+04 K1.08e+04 K0.08630.0826151.07e+04 K1.07e+04 K0.0860.084
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -875,8 +876,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10641.685 K\n", - "\tExpected t_inner for next iteration = 10638.233 K\n", + "\tCurrent t_inner = 10642.050 K\n", + "\tExpected t_inner for next iteration = 10636.106 K\n", " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -887,12 +888,12 @@ "\tStarting iteration 9 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.053e+43 erg / s\n", - "\tLuminosity absorbed = 3.412e+42 erg / s\n", + "\tLuminosity emitted = 1.052e+43 erg / s\n", + "\tLuminosity absorbed = 3.411e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 6/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", + "\tIteration converged 5/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" @@ -903,50 +904,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4680.46701.11e+04 K1.11e+04 K0.4720.469
51.14e+04 K1.15e+04 K0.1740.1751.14e+04 K1.15e+04 K0.1750.17
101.11e+04 K1.11e+04 K0.1090.109101.11e+04 K1.11e+04 K0.1110.109
151.08e+04 K1.08e+04 K0.08260.0821151.07e+04 K1.08e+04 K0.0840.0822
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -958,8 +959,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10638.233 K\n", - "\tExpected t_inner for next iteration = 10654.289 K\n", + "\tCurrent t_inner = 10636.106 K\n", + "\tExpected t_inner for next iteration = 10654.313 K\n", " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -970,8 +971,8 @@ "\tStarting iteration 10 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.069e+43 erg / s\n", - "\tLuminosity absorbed = 3.338e+42 erg / s\n", + "\tLuminosity emitted = 1.070e+43 erg / s\n", + "\tLuminosity absorbed = 3.335e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", @@ -984,50 +985,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.11e+04 K1.1e+04 K0.4670.47301.11e+04 K1.1e+04 K0.4690.475
51.15e+04 K1.14e+04 K0.170.17851.15e+04 K1.14e+04 K0.170.177
101.11e+04 K1.11e+04 K0.1090.112101.11e+04 K1.11e+04 K0.1090.112
151.08e+04 K1.06e+04 K0.08210.0879151.08e+04 K1.06e+04 K0.08220.0878
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1039,8 +1040,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10654.289 K\n", - "\tExpected t_inner for next iteration = 10628.970 K\n", + "\tCurrent t_inner = 10654.313 K\n", + "\tExpected t_inner for next iteration = 10628.190 K\n", " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -1051,8 +1052,8 @@ "\tStarting iteration 11 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.052e+43 erg / s\n", - "\tLuminosity absorbed = 3.372e+42 erg / s\n", + "\tLuminosity emitted = 1.053e+43 erg / s\n", + "\tLuminosity absorbed = 3.363e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", @@ -1067,50 +1068,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4730.47701.1e+04 K1.1e+04 K0.4750.472
51.14e+04 K1.12e+04 K0.1780.18351.14e+04 K1.12e+04 K0.1770.184
101.11e+04 K1.1e+04 K0.1120.115101.11e+04 K1.1e+04 K0.1120.114
151.06e+04 K1.06e+04 K0.08790.0867151.06e+04 K1.06e+04 K0.08780.0859
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1122,8 +1123,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10628.970 K\n", - "\tExpected t_inner for next iteration = 10646.280 K\n", + "\tCurrent t_inner = 10628.190 K\n", + "\tExpected t_inner for next iteration = 10644.054 K\n", " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -1134,13 +1135,11 @@ "\tStarting iteration 12 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.055e+43 erg / s\n", - "\tLuminosity absorbed = 3.435e+42 erg / s\n", + "\tLuminosity emitted = 1.056e+43 erg / s\n", + "\tLuminosity absorbed = 3.420e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 2/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", - "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" ] @@ -1150,50 +1149,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.11e+04 K0.4770.46301.1e+04 K1.11e+04 K0.4720.467
51.12e+04 K1.13e+04 K0.1830.1851.12e+04 K1.13e+04 K0.1840.176
101.1e+04 K1.11e+04 K0.1150.112101.1e+04 K1.11e+04 K0.1140.11
151.06e+04 K1.07e+04 K0.08670.0843151.06e+04 K1.08e+04 K0.08590.0821
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1205,8 +1204,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10646.280 K\n", - "\tExpected t_inner for next iteration = 10656.684 K\n", + "\tCurrent t_inner = 10644.054 K\n", + "\tExpected t_inner for next iteration = 10653.543 K\n", " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -1217,12 +1216,12 @@ "\tStarting iteration 13 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.065e+43 erg / s\n", - "\tLuminosity absorbed = 3.396e+42 erg / s\n", + "\tLuminosity emitted = 1.062e+43 erg / s\n", + "\tLuminosity absorbed = 3.406e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 3/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", + "\tIteration converged 1/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" @@ -1233,50 +1232,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4630.46301.11e+04 K1.11e+04 K0.4670.466
51.13e+04 K1.13e+04 K0.180.17951.13e+04 K1.13e+04 K0.1760.18
101.11e+04 K1.1e+04 K0.1120.114101.11e+04 K1.11e+04 K0.110.111
151.07e+04 K1.07e+04 K0.08430.0869151.08e+04 K1.08e+04 K0.08210.0841
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1288,8 +1287,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10656.684 K\n", - "\tExpected t_inner for next iteration = 10643.209 K\n", + "\tCurrent t_inner = 10653.543 K\n", + "\tExpected t_inner for next iteration = 10647.277 K\n", " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -1300,12 +1299,12 @@ "\tStarting iteration 14 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.061e+43 erg / s\n", - "\tLuminosity absorbed = 3.360e+42 erg / s\n", + "\tLuminosity emitted = 1.063e+43 erg / s\n", + "\tLuminosity absorbed = 3.369e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 4/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", + "\tIteration converged 2/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" @@ -1316,50 +1315,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4630.46701.11e+04 K1.11e+04 K0.4660.469
51.13e+04 K1.13e+04 K0.1790.18151.13e+04 K1.13e+04 K0.180.182
101.1e+04 K1.1e+04 K0.1140.114101.11e+04 K1.1e+04 K0.1110.113
151.07e+04 K1.06e+04 K0.08690.0866151.08e+04 K1.07e+04 K0.08410.0854
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1371,8 +1370,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10643.209 K\n", - "\tExpected t_inner for next iteration = 10637.728 K\n", + "\tCurrent t_inner = 10647.277 K\n", + "\tExpected t_inner for next iteration = 10638.875 K\n", " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -1383,12 +1382,12 @@ "\tStarting iteration 15 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.054e+43 erg / s\n", - "\tLuminosity absorbed = 3.401e+42 erg / s\n", + "\tLuminosity emitted = 1.053e+43 erg / s\n", + "\tLuminosity absorbed = 3.417e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 5/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", + "\tIteration converged 3/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" @@ -1399,50 +1398,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.11e+04 K1.1e+04 K0.4670.48201.11e+04 K1.1e+04 K0.4690.484
51.13e+04 K1.13e+04 K0.1810.1851.13e+04 K1.13e+04 K0.1820.181
101.1e+04 K1.11e+04 K0.1140.111101.1e+04 K1.1e+04 K0.1130.113
151.06e+04 K1.07e+04 K0.08660.0845151.07e+04 K1.07e+04 K0.08540.0858
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1454,8 +1453,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10637.728 K\n", - "\tExpected t_inner for next iteration = 10651.277 K\n", + "\tCurrent t_inner = 10638.875 K\n", + "\tExpected t_inner for next iteration = 10655.125 K\n", " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -1466,12 +1465,12 @@ "\tStarting iteration 16 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.057e+43 erg / s\n", - "\tLuminosity absorbed = 3.448e+42 erg / s\n", + "\tLuminosity emitted = 1.059e+43 erg / s\n", + "\tLuminosity absorbed = 3.445e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 6/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", + "\tIteration converged 4/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" @@ -1482,50 +1481,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4820.47301.1e+04 K1.1e+04 K0.4840.472
51.13e+04 K1.14e+04 K0.180.17251.13e+04 K1.13e+04 K0.1810.177
101.11e+04 K1.1e+04 K0.1110.113101.1e+04 K1.1e+04 K0.1130.113
151.07e+04 K1.08e+04 K0.08450.0824151.07e+04 K1.06e+04 K0.08580.0858
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1537,8 +1536,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10651.277 K\n", - "\tExpected t_inner for next iteration = 10658.182 K\n", + "\tCurrent t_inner = 10655.125 K\n", + "\tExpected t_inner for next iteration = 10655.561 K\n", " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -1549,8 +1548,8 @@ "\tStarting iteration 17 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.066e+43 erg / s\n", - "\tLuminosity absorbed = 3.396e+42 erg / s\n", + "\tLuminosity emitted = 1.067e+43 erg / s\n", + "\tLuminosity absorbed = 3.372e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", @@ -1563,50 +1562,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.11e+04 K0.4730.46301.1e+04 K1.11e+04 K0.4720.468
51.14e+04 K1.14e+04 K0.1720.17251.13e+04 K1.14e+04 K0.1770.175
101.1e+04 K1.12e+04 K0.1130.106101.1e+04 K1.11e+04 K0.1130.11
151.08e+04 K1.08e+04 K0.08240.0809151.06e+04 K1.08e+04 K0.08580.0816
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1618,8 +1617,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10658.182 K\n", - "\tExpected t_inner for next iteration = 10642.273 K\n", + "\tCurrent t_inner = 10655.561 K\n", + "\tExpected t_inner for next iteration = 10636.536 K\n", " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -1630,8 +1629,8 @@ "\tStarting iteration 18 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.058e+43 erg / s\n", - "\tLuminosity absorbed = 3.382e+42 erg / s\n", + "\tLuminosity emitted = 1.057e+43 erg / s\n", + "\tLuminosity absorbed = 3.365e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", @@ -1646,50 +1645,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4630.46201.11e+04 K1.11e+04 K0.4680.464
51.14e+04 K1.14e+04 K0.1720.17451.14e+04 K1.13e+04 K0.1750.177
101.12e+04 K1.11e+04 K0.1060.109101.11e+04 K1.1e+04 K0.110.113
151.08e+04 K1.07e+04 K0.08090.0829151.08e+04 K1.07e+04 K0.08160.0848
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1701,8 +1700,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10642.273 K\n", - "\tExpected t_inner for next iteration = 10644.386 K\n", + "\tCurrent t_inner = 10636.536 K\n", + "\tExpected t_inner for next iteration = 10641.692 K\n", " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -1713,8 +1712,8 @@ "\tStarting iteration 19 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.057e+43 erg / s\n", - "\tLuminosity absorbed = 3.403e+42 erg / s\n", + "\tLuminosity emitted = 1.056e+43 erg / s\n", + "\tLuminosity absorbed = 3.405e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", @@ -1729,50 +1728,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4620.46201.11e+04 K1.11e+04 K0.4640.466
51.14e+04 K1.14e+04 K0.1740.17351.13e+04 K1.13e+04 K0.1770.177
101.11e+04 K1.11e+04 K0.1090.111101.1e+04 K1.11e+04 K0.1130.111
151.07e+04 K1.07e+04 K0.08290.0845151.07e+04 K1.07e+04 K0.08480.0853
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1784,8 +1783,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tCurrent t_inner = 10644.386 K\n", - "\tExpected t_inner for next iteration = 10649.220 K\n", + "\tCurrent t_inner = 10641.692 K\n", + "\tExpected t_inner for next iteration = 10650.463 K\n", " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -1794,15 +1793,15 @@ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tSimulation finished in 19 iterations \n", - "\tSimulation took 53.10 s\n", + "\tSimulation took 113.21 s\n", " (\u001b[1mbase.py\u001b[0m:476)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tStarting iteration 20 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tLuminosity emitted = 1.060e+43 erg / s\n", - "\tLuminosity absorbed = 3.406e+42 erg / s\n", + "\tLuminosity emitted = 1.061e+43 erg / s\n", + "\tLuminosity absorbed = 3.401e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n" ] @@ -1857,7 +1856,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -2999,22 +2998,58 @@ { "cell_type": "code", "execution_count": 12, + "id": "b7e0d48e-2e25-4993-8bee-c2adbeaa403e", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "def forward(x):\n", + " return np.log(x)\n", + "\n", + "\n", + "def inverse(x):\n", + " return np.exp(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, "id": "5fed4c8d-fceb-4d1a-b8d5-f23da51a3903", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/tmp/ipykernel_6420/2943747606.py:3: RuntimeWarning: divide by zero encountered in log\n", + " return np.log(x)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n" + ] + }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", + "\t/tmp/ipykernel_6420/2943747606.py:3: RuntimeWarning: divide by zero encountered in log\n", + " return np.log(x)\n", + " (\u001b[1mwarnings.py\u001b[0m:109)\n" + ] + }, { "data": { - "image/png": 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kG/7+/vL19c3TYxKEAADwgLNnz+rvv/8Wj/z8H4vForJlyyokJCTPjkkQAgAgj2VmZurvv/9WoUKFVLJkSRb41aUFE48dO6a///5blSpVyrORIYIQAAB5LD09XcYYlSxZUkFBQZ4uJ98oWbKk9u/fr/T09DwLQkyWBgDAQxgJsueJ74MgBAAAvBZBCAAAeC2CEAAA8FoEIQAA4JRDhw7p6aefVpkyZRQQEKCoqCj1799fJ06c8HRpTuOuMRcxxuhCxgVPlwEAKABS01OVZbKUmZWpzKxMT5fjlL/++ktNGjdRpdsq6fMvPld0dLR27typV195VT/++KNWr1mt4sWLy8fiUyAmgxOEXORCxgXV/7K+p8sAABQApQNK65WKrygrOUs+531kjFFqumcWVrT6W5wKLL169pLFz6L3v3xfgUGBOq/ziq4frY9mfqR29dqpz6A+Gvqfobq9+O3yteTtKtHXgyAEAICHpaYbdf7goEeOPbtfOQUGOBaEkk8la/Wy1er3Wj8FBgXabQsrFab2ndtr4ZyFGjJ6iDtKdQuCkIsE+QVp/WPrPV0GAKAASL2YqiOHjqh8aHkFBgbqfFqGJM8EoduK36ZCAY7FgfV718sYozvr3Knbi9+ebXuDWg307WffqkRmCflYCsY0ZIKQi1gsFhXyL+TpMgAABYBPpo98LD7y9fGVr4+vQqw+2jWyrUdqCfL3dfjSmK/PpUtdPj4+tn//k0WX9uPn61cg5gdJBCEAADzOYrE4PCrjSRUrVpTFYtGuXbvUqVOnbNv37NmjYsWKKSwsLO+Lu04FY9wKAAB4XIkSJdS6dWtNmDBBFy7Y3ymdmJioL774Ql26dCkwo0ESQQgAADhh/PjxSk1NVdu2bbVixQodOnRICxcuVOvWrXXLLbforbfe8nSJTiEIAQAAh1WqVEm//PKLKlSooC5duqhChQp67rnn1KJFC61du1bFixf3dIlOyf8XJAEAQL4SFRWlqVOneroMl2BECAAAeC2PB6EJEyYoOjpagYGBio2N1cqVK3Ptu3z5clkslmyvPXv25GHFAADgZuHRIDRz5kwNGDBAr7/+urZs2aKmTZuqXbt2Onjw6otK/fbbb0pISLC9KlWqlEcVAwCAm4lHg9CYMWP09NNP65lnnlGVKlU0btw4RUZGauLEiVd9X3h4uCIiImwvX9/8/ywTAACQ/3gsCKWlpWnTpk1q06aNXXubNm20Zs2aq763du3aKl26tFq2bKlly5ZdtW9qaqpSUlLsXgAAAJIHg9Dx48eVmZmpUqVK2bWXKlVKiYmJOb6ndOnSmjRpkmbPnq24uDhVrlxZLVu21IoVK3I9zqhRoxQaGmp7RUZGuvRzAACAgsvjt89fufqkMSbXFSkrV66sypUr2/5u2LChDh06pHfffVd33nlnju8ZPHiwBg4caPs7JSWFMAQAACR5cEQoLCxMvr6+2UZ/kpKSso0SXU2DBg20d+/eXLdbrVYVKVLE7gUAACB5MAgFBAQoNjZW8fHxdu3x8fFq1KiRw/vZsmWLSpcu7eryAACAF/DopbGBAweqa9euqlu3rho2bKhJkybp4MGD6tWrl6RLl7UOHz6sGTNmSJLGjRun8uXLq1q1akpLS9Pnn3+u2bNna/bs2Z78GAAAoIDy6O3zXbp00bhx4zRy5EjVqlVLK1as0IIFCxQVFSVJSkhIsFtTKC0tTf/6179Uo0YNNW3aVKtWrdL8+fP1wAMPeOojAADgNbp37y6LxWIbsPin3r17y2KxqHv37nlf2A2wGGOMp4vISykpKQoNDVVycjLzhQAAHnHx4kXt27fP9mSFgqJ79+5aunSpUlJSlJCQoKCgIEmXPk/p0qVVpEgRtWjRQtOmTbuu/V/te3HX77fHH7EBAIDXM0ZKO+eZl5PjIXXq1FG5cuUUFxdna4uLi1NkZKRq165taztz5owef/xxBQcHq3Tp0ho7dqyaN2+uAQMGuOpbcwmP3z4PAIDXSz8v/buMZ4792hEpINiptzz11FOaOnWqHn/8cUnSp59+qh49emj58uW2PgMHDtTq1as1d+5clSpVSkOHDtXmzZtVq1YtFxZ/4xgRAgAATunatatWrVql/fv368CBA1q9erWeeOIJ2/YzZ85o+vTpevfdd9WyZUtVr15dU6dOVWZmpgerzhkjQgAAeJp/oUsjM546tpPCwsLUvn17TZ8+XcYYtW/fXmFhYbbtf/31l9LT01WvXj1bW2hoqN2iyPkFQQgAAE+zWJy+POVpPXr0UJ8+fSRJH330kd22y/dh5fT0iPyGS2MAAMBpd999t9LS0pSWlqa2bdvabatQoYL8/f21YcMGW1tKSspVnwThKYwIAQAAp/n6+mr37t22f/9T4cKF9eSTT2rQoEEqXry4wsPDNWzYMPn4+OT6PFFPYUQIAABcl6s9w3PMmDFq2LCh7r33XrVq1UqNGzdWlSpV8t26SYwIAQAAh1xrocQ5c+bY/l24cGF98cUXtr/PnTunESNG6LnnnnNTddeHIAQAAFxuy5Yt2rNnj+rVq6fk5GSNHDlSknTfffd5uDJ7BCEAAOAW7777rn777TcFBAQoNjZWK1eutLvNPj8gCAEAAJerXbu2Nm3a5OkyronJ0gAAwGsRhAAAgNciCAEAAK9FEAIAAF6LIAQAALwWQQgAAHgtghAAAHAZi8Vit8J0fkcQAgAADktKSlLPnj1Vrlw5Wa1WRUREqG3btlq7dq0kKSEhQe3atfNwlY5jQUUAAOCwzp07Kz09XdOnT9ett96qo0ePasmSJTp58qQkKSIiwsMVOocgBACAhxljdCHjgkeOHeQXJIvF4lDf06dPa9WqVVq+fLmaNWsmSYqKilK9evVsfSwWi7777jt16tTJHeW6HEEIAAAPu5BxQfW/rO+RY69/bL0K+RdyqG9ISIhCQkI0Z84cNWjQQFar1c3VuR9zhAAAgEP8/Pw0bdo0TZ8+XUWLFlXjxo312muvadu2bZ4u7boxIgQAgIcF+QVp/WPrPXZsZ3Tu3Fnt27fXypUrtXbtWi1cuFCjR4/W5MmT1b17d/cU6UYEIQAAPMxisTh8eSo/CAwMVOvWrdW6dWsNHTpUzzzzjIYNG1YggxCXxgAAwA2pWrWqzp075+kyrgsjQgAAwCEnTpzQQw89pB49eqhGjRoqXLiwfvnlF40ePVr33Xefp8u7LgQhAADgkJCQENWvX19jx47Vn3/+qfT0dEVGRurZZ5/Va6+95unyrgtBCAAAOMRqtWrUqFEaNWpUrn2MMXlY0Y1jjhAAAPBaBCEAAOC1CEIAAMBrEYQAAIDXIggBAACvRRACAABeiyAEAAC8FkEIAAB4LYIQAADwWgQhAADgtQhCAADAYUlJSerZs6fKlSsnq9WqiIgItW3bVmvXrvV0adeFZ40BAACHde7cWenp6Zo+fbpuvfVWHT16VEuWLNHJkyc9Xdp1IQgBAOBhxhiZCxc8cmxLUJAsFotDfU+fPq1Vq1Zp+fLlatasmSQpKipK9erVc2eJbkUQAgDAw8yFC/qtTqxHjl158yZZChVyqG9ISIhCQkI0Z84cNWjQQFar1c3VuR9zhAAAgEP8/Pw0bdo0TZ8+XUWLFlXjxo312muvadu2bZ4u7boxIgQAgIdZgoJUefMmjx3bGZ07d1b79u21cuVKrV27VgsXLtTo0aM1efJkde/e3T1FupHFGGM8XUReSklJUWhoqJKTk1WkSBFPlwMA8EIXL17Uvn37FB0drcDAQE+Xc8OeeeYZxcfH68CBAze0n6t9L+76/ebSGAAAuCFVq1bVuXPnPF3GdeHSGAAAcMiJEyf00EMPqUePHqpRo4YKFy6sX375RaNHj9Z9993n6fKuC0EIAAA4JCQkRPXr19fYsWP1559/Kj09XZGRkXr22Wf12muvebq860IQAgAADrFarRo1apRGjRrl6VJchjlCAADAaxGEAACA1yIIAQAAr0UQAgAAXosgBAAAvBZBCAAAeC2CEAAA8FoeD0ITJkywPVMkNjZWK1eudOh9q1evlp+fn2rVquXeAgEAwE3Lo0Fo5syZGjBggF5//XVt2bJFTZs2Vbt27XTw4MGrvi85OVndunVTy5Yt86hSAABwM/JoEBozZoyefvppPfPMM6pSpYrGjRunyMhITZw48arv69mzpx577DE1bNgwjyoFAAA3I48FobS0NG3atElt2rSxa2/Tpo3WrFmT6/umTp2qP//8U8OGDXPoOKmpqUpJSbF7AQCA67dmzRr5+vrq7rvv9nQpN8xjQej48ePKzMxUqVKl7NpLlSqlxMTEHN+zd+9evfrqq/riiy/k5+fYY9JGjRql0NBQ2ysyMvKGawcAwJt9+umn6tu3r1atWnXV6SzGGGVkZORhZc7z+GRpi8Vi97cxJlubJGVmZuqxxx7TiBEjdNtttzm8/8GDBys5Odn2OnTo0A3XDACAKxljlJ6a6ZGXMcapWs+dO6dZs2bp+eef17333qtp06bZti1fvlwWi0WLFi1S3bp1ZbVaHb4JylM89vT5sLAw+fr6Zhv9SUpKyjZKJElnzpzRL7/8oi1btqhPnz6SpKysLBlj5Ofnp8WLF+uuu+7K9j6r1Sqr1eqeDwEAgAtkpGVpUv+fPXLs595vJn+rr8P9Z86cqcqVK6ty5cp64okn1LdvXw0ZMsRuEOPll1/Wu+++q1tvvVVFixZ1Q9Wu47ERoYCAAMXGxio+Pt6uPT4+Xo0aNcrWv0iRItq+fbu2bt1qe/Xq1UuVK1fW1q1bVb9+/bwqHQAArzVlyhQ98cQTkqS7775bZ8+e1ZIlS+z6jBw5Uq1bt1aFChVUokQJT5TpMI+NCEnSwIED1bVrV9WtW1cNGzbUpEmTdPDgQfXq1UvSpctahw8f1owZM+Tj46Pq1avbvT88PFyBgYHZ2gEAKEj8Anz03PvNPHZsR/3222/asGGD4uLiLr3Xz09dunTRp59+qlatWtn61a1b1+V1uotHg1CXLl104sQJjRw5UgkJCapevboWLFigqKgoSVJCQsI11xQCAKCgs1gsTl2e8pQpU6YoIyNDt9xyi63NGCN/f3+dOnXK1hYcHOyJ8q6LxTg7S6qAS0lJUWhoqJKTk1WkSBFPlwMA8EIXL17Uvn37bE9WKAgyMjJUtmxZvfzyy9mWvuncubP69u2r6tWrq0WLFjp16tR1zQ262vfirt9vj44IAQCAguGHH37QqVOn9PTTTys0NNRu24MPPqgpU6Zo7NixHqru+nn89nkAAJD/TZkyRa1atcoWgqRLI0Jbt27V5s2bPVDZjWFECAAAXNO8efNy3VanTh3bekQDBw7Mq5JcghEhAADgtQhCAADAa91QELp48aKr6gAAAMhzTgehrKwsvfHGG7rlllsUEhKiv/76S5I0ZMgQTZkyxeUFAgAAuIvTQejNN9/UtGnTNHr0aAUEBNjaY2JiNHnyZJcWBwAA4E5OB6EZM2Zo0qRJevzxx+Xr+79VMGvUqKE9e/a4tDgAAAB3cjoIHT58WBUrVszWnpWVpfT0dJcUBQAAkBecDkLVqlXTypUrs7V/8803ql27tkuKAgAAyAtOL6g4bNgwde3aVYcPH1ZWVpbi4uL022+/acaMGfrhhx/cUSMAAIBbOD0i1KFDB82cOVMLFiyQxWLR0KFDtXv3bs2bN0+tW7d2R40AAABucV3rCLVt21Y///yzzp49q/Pnz2vVqlXZnkQLAABuTomJierbt69uvfVWWa1WRUZGqkOHDlqyZImnS3Oa05fGNm7cqKysLNWvX9+uff369fL19VXdunVdVhwAAMhf9u/fr8aNG6to0aIaPXq0atSoofT0dC1atEgvvPBCgbuD3Okg9MILL+jll1/OFoQOHz6sd955R+vXr3dZcQAAeANjjDJSUz1ybD+rVRaLxeH+vXv3lsVi0YYNGxQcHGxrr1atmnr06CFJGjNmjKZOnaq//vpLxYsXV4cOHTR69GiFhIS4vP4b5XQQ2rVrl+rUqZOtvXbt2tq1a5dLigIAwJtkpKbqgycf9Mix+03/Vv6BgQ71PXnypBYuXKi33nrLLgRdVrRoUUmSj4+PPvjgA5UvX1779u1T79699fLLL2vChAmuLN0lnJ4jZLVadfTo0WztCQkJ8vNzOlcBAIAC4o8//pAxRrfffvtV+w0YMEAtWrRQdHS07rrrLr3xxhuaNWtWHlXpHKeTS+vWrTV48GB9//33Cg0NlSSdPn1ar732GneNAQBwHfysVvWb/q3Hju0oY4wkXfNS2rJly/Tvf/9bu3btUkpKijIyMnTx4kWdO3cux5EkT3I6CL333nu68847FRUVZVtAcevWrSpVqpQ+++wzlxcIAMDNzmKxOHx5ypMqVaoki8Wi3bt3q1OnTjn2OXDggO655x716tVLb7zxhooXL65Vq1bp6aefzpdPoHD60tgtt9yibdu2afTo0apatapiY2P1/vvva/v27YqMjHRHjQAAIB8oXry42rZtq48++kjnzp3Ltv306dP65ZdflJGRoffee08NGjTQbbfdpiNHjnigWsdc16Se4OBgPffcc66uBQAA5HMTJkxQo0aNVK9ePY0cOVI1atRQRkaG4uPjNXHiRH311VfKyMjQhx9+qA4dOmj16tX6+OOPPV12rq4rCP3+++9avny5kpKSlJWVZbdt6NChLikMAADkP9HR0dq8ebPeeustvfTSS0pISFDJkiUVGxuriRMnqlatWhozZozeeecdDR48WHfeeadGjRqlbt26ebr0HFnM5ZlPDvrkk0/0/PPPKywsTBEREXYTpiwWizZv3uzyIl0pJSVFoaGhSk5OVpEiRTxdDgDAC128eFH79u1TdHS0AgvA3KC8crXvxV2/306PCL355pt666239Morr7isCAAAAE9werL0qVOn9NBDD7mjFgAAgDzldBB66KGHtHjxYnfUAgAAkKecvjRWsWJFDRkyROvWrVNMTIz8/f3ttvfr189lxQEAALiT05Olo6Ojc9+ZxaK//vrrhotyJyZLAwA87fKk4PLlyysoKMjT5eQbFy5c0P79+/P3ZOl9+/a57OAAAHgjX19fSVJaWhpB6B/S0tIk/e/7yQs8JRUAgDzm5+enQoUK6dixY/L395ePj9NTdm86WVlZOnbsmAoVKpSnD3G/riP9/fffmjt3rg4ePGhLb5eNGTPGJYUBAHCzslgsKl26tPbt26cDBw54upx8w8fHR+XKlbvmQ11dyekgtGTJEnXs2FHR0dH67bffVL16de3fv1/GGNWpU8cdNQIAcNMJCAhQpUqVsg0oeLOAgIA8Hx1zOggNHjxYL730kkaOHKnChQtr9uzZCg8P1+OPP667777bHTUCAHBT8vHxYWVpD3M6du3evVtPPvmkpEvXOC9cuKCQkBCNHDlS77zzjssLBAAAcBeng1BwcLBSU1MlSWXKlNGff/5p23b8+HHXVQYAAOBmTl8aa9CggVavXq2qVauqffv2eumll7R9+3bFxcWpQYMG7qgRAADALZwOQmPGjNHZs2clScOHD9fZs2c1c+ZMVaxYUWPHjnV5gQAAAO7i9MrSBR0rSwMAUPC46/ebFZwAAIDXcujSWLFixRxe3OjkyZM3VBAAAEBecSgIjRs3zvbvEydO6M0331Tbtm3VsGFDSdLatWu1aNEiDRkyxC1FAgAAuIPTc4Q6d+6sFi1aqE+fPnbt48eP108//aQ5c+a4sj6XY44QAAAFT76ZI7Ro0aIcV5Bu27atfvrpJ5cUBQAAkBecDkIlSpTQd999l619zpw5KlGihEuKAgAAyAtOryM0YsQIPf3001q+fLltjtC6deu0cOFCTZ482eUFAgAAuIvTQah79+6qUqWKPvjgA8XFxckYo6pVq2r16tWqX7++O2oEAABwCxZUBAAA+Z67fr+dHhGSpKysLP3xxx9KSkpSVlaW3bY777zTJYUBAAC4m9NBaN26dXrsscd04MABXTmYZLFYlJmZ6bLiAAAA3MnpINSrVy/VrVtX8+fPV+nSpR1ecRoAACC/cToI7d27V99++60qVqzojnoAAADyjNPrCNWvX19//PGHO2oBAADIU06PCPXt21cvvfSSEhMTFRMTI39/f7vtNWrUcFlxAAAA7uT07fM+PtkHkSwWi4wxBWKyNLfPAwBQ8OSb2+f37dvnsoMDAAB4ktNBKCoqyh11AAAA5Dmng9CMGTOuur1bt25O7W/ChAn6z3/+o4SEBFWrVk3jxo1T06ZNc+y7atUqvfLKK9qzZ4/Onz+vqKgo9ezZUy+++KJTxwQAAJCuIwj179/f7u/09HSdP39eAQEBKlSokFNBaObMmRowYIAmTJigxo0b67///a/atWunXbt2qVy5ctn6BwcHq0+fPqpRo4aCg4O1atUq9ezZU8HBwXruueec/SgAAMDLueRZY3v37tXzzz+vQYMGqW3btg6/r379+qpTp44mTpxoa6tSpYo6deqkUaNGObSPBx54QMHBwfrss88c6s9kaQAACh53/X47vY5QTipVqqS3334722jR1aSlpWnTpk1q06aNXXubNm20Zs0ah/axZcsWrVmzRs2aNcu1T2pqqlJSUuxeAAAAkouCkCT5+vrqyJEjDvc/fvy4MjMzVapUKbv2UqVKKTEx8arvLVu2rKxWq+rWrasXXnhBzzzzTK59R40apdDQUNsrMjLS4RoBAMDNzek5QnPnzrX72xijhIQEjR8/Xo0bN3a6gCufVXZ5PaKrWblypc6ePat169bp1VdfVcWKFfXoo4/m2Hfw4MEaOHCg7e+UlBTCEAAAkHQdQahTp052f1ssFpUsWVJ33XWX3nvvPYf3ExYWJl9f32yjP0lJSdlGia4UHR0tSYqJidHRo0c1fPjwXIOQ1WqV1Wp1uC4AAOA9nA5CWVlZLjlwQECAYmNjFR8fr/vvv9/WHh8fr/vuu8/h/RhjlJqa6pKaAACAd3E6CP3T5RvOrnUpKzcDBw5U165dVbduXTVs2FCTJk3SwYMH1atXL0mXLmsdPnzYtnbRRx99pHLlyun222+XdGldoXfffVd9+/a9kY8BAAC81HUFoSlTpmjs2LHau3evpEt3jQ0YMOCqk5Zz0qVLF504cUIjR45UQkKCqlevrgULFthWr05ISNDBgwdt/bOysjR48GDt27dPfn5+qlChgt5++2317Nnzej4GAADwck6vIzRkyBCNHTtWffv2VcOGDSVJa9eu1fjx49W/f3+9+eabbinUVVhHCACAgsddv99OB6GwsDB9+OGH2SYnf/XVV+rbt6+OHz/usuLcgSAEAEDBk28WVMzMzFTdunWztcfGxiojI8MlRQEAAOQFp4PQE088YfdIjMsmTZqkxx9/3CVFAQAA5AWHJkv/c0FCi8WiyZMna/HixWrQoIEkad26dTp06JDTT54HAADwJIeC0JYtW+z+jo2NlST9+eefkqSSJUuqZMmS2rlzp4vLAwAAcB+HgtCyZcvcXQcAAECec8lDV7OysjRv3rxsj98AAADIz24oCO3du1eDBw9W2bJl9fDDD7uqJgAAgDzh9MrSFy5c0KxZszRlyhStW7dOmZmZGjt2rHr06KGQkBB31AgAAOAWDo8IbdiwQc8995wiIiI0fvx4de7cWYcOHZKPj49atWpFCAIAAAWOwyNCjRo1Ut++fbVhwwZVrlzZnTUBAADkCYeD0F133aUpU6YoKSlJXbt2Vdu2ba/7qfMAAAD5gcOXxhYvXqydO3eqcuXKev7551W6dGn1799fkghEAACgQHLqrrHIyEgNHTpU+/bt02effaakpCT5+fnpvvvu02uvvabNmze7q04AAACXc/rp81c6deqUPv/8c3366afatm2bMjMzXVWbW/D0eQAACh53/X7fcBD6p82bN6tOnTqu2p1bEIQAACh43PX77ZKVpS/L7yEIAADgn1wahAAAAAoSghAAAPBaBCEAAOC1CEIAAMBrObSydO3atR1eNJG1hAAAQEHhUBDq1KmTm8sAAADIey5dR6ggYB0hAAAKngKxjhAAAEBB4vDT5y/LzMzU2LFjNWvWLB08eFBpaWl220+ePOmy4gAAANzJ6RGhESNGaMyYMXr44YeVnJysgQMH6oEHHpCPj4+GDx/uhhIBAADcw+kg9MUXX+iTTz7Rv/71L/n5+enRRx/V5MmTNXToUK1bt84dNQIAALiF00EoMTFRMTExkqSQkBAlJydLku69917Nnz/ftdUBAAC4kdNBqGzZskpISJAkVaxYUYsXL5Ykbdy4UVar1bXVAQAAuJHTQej+++/XkiVLJEn9+/fXkCFDVKlSJXXr1k09evRweYEAAADucsPrCK1bt05r1qxRxYoV1bFjR1fV5TasIwQAQMHjrt9vp2+fv1KDBg3UoEEDV9QCAACQp64rCP3+++9avny5kpKSlJWVZbdt6NChLikMAADA3ZwOQp988omef/55hYWFKSIiwu5hrBaLhSAEAAAKDKeD0Jtvvqm33npLr7zyijvqAQAAyDNO3zV26tQpPfTQQ+6oBQAAIE85HYQeeugh29pBAAAABZnTl8YqVqyoIUOGaN26dYqJiZG/v7/d9n79+rmsOAAAAHdyeh2h6Ojo3Hdmseivv/664aLciXWEAAAoePLNOkL79u1z2cEBAAA8yek5Qv9kjNENLkwNAADgMdcVhGbMmKGYmBgFBQUpKChINWrU0Geffebq2gAAANzK6UtjY8aM0ZAhQ9SnTx81btxYxhitXr1avXr10vHjx/Xiiy+6o04AAACXu67J0iNGjFC3bt3s2qdPn67hw4fn+zlETJYGAKDgcdfvt9OXxhISEtSoUaNs7Y0aNVJCQoJLigIAAMgLTgehihUratasWdnaZ86cqUqVKrmkKAAAgLzg9ByhESNGqEuXLlqxYoUaN24si8WiVatWacmSJTkGJAAAgPzK6RGhzp07a/369QoLC9OcOXMUFxensLAwbdiwQffff787agQAAHALpydLF3RMlgYAoODx6MrSKSkptoOmpKRctS/hAgAAFBQOBaFixYopISFB4eHhKlq0qCwWS7Y+xhhZLBZlZma6vEgAAAB3cCgILV26VMWLF5ckLVu2zK0FAQAA5BWHglCzZs1s/46OjlZkZGS2USFjjA4dOuTa6gAAANzI6bvGoqOjdezYsWztJ0+eVHR0tEuKAgAAyAtOB6HLc4GudPbsWQUGBrqkKAAAgLzg8IKKAwcOlCRZLBYNGTJEhQoVsm3LzMzU+vXrVatWLZcXCAAA4C4OB6EtW7ZIujQitH37dgUEBNi2BQQEqGbNmvrXv/7l+goBAADcxOEgdPlusaeeekrvv/++y9YLmjBhgv7zn/8oISFB1apV07hx49S0adMc+8bFxWnixInaunWrUlNTVa1aNQ0fPlxt27Z1SS0AAMC7OD1HaNy4ccrIyMjWfvLkyWsutnilmTNnasCAAXr99de1ZcsWNW3aVO3atdPBgwdz7L9ixQq1bt1aCxYs0KZNm9SiRQt16NDBNloFAADgDKcfsdGuXTt16NBBvXv3tmv/+OOPNXfuXC1YsMDhfdWvX1916tTRxIkTbW1VqlRRp06dNGrUKIf2Ua1aNXXp0kVDhw51qD+P2AAAoOBx1++30yNC69evV4sWLbK1N2/eXOvXr3d4P2lpadq0aZPatGlj196mTRutWbPGoX1kZWXpzJkztsUec5KamqqUlBS7FwAAgHQdQSg1NTXHS2Pp6em6cOGCw/s5fvy4MjMzVapUKbv2UqVKKTEx0aF9vPfeezp37pwefvjhXPuMGjVKoaGhtldkZKTDNQIAgJub00Hojjvu0KRJk7K1f/zxx4qNjXW6gJxWqM5pnaIrffXVVxo+fLhmzpyp8PDwXPsNHjxYycnJtherXwMAgMscvmvssrfeekutWrXSr7/+qpYtW0qSlixZoo0bN2rx4sUO7ycsLEy+vr7ZRn+SkpKyjRJdaebMmXr66af1zTffqFWrVlfta7VaZbVaHa4LAAB4D6dHhBo3bqy1a9cqMjJSs2bN0rx581SxYkVt27Yt19vecxIQEKDY2FjFx8fbtcfHx6tRo0a5vu+rr75S9+7d9eWXX6p9+/bOlg8AAGDj9IiQJNWqVUtffPHFDR984MCB6tq1q+rWrauGDRtq0qRJOnjwoHr16iXp0mWtw4cPa8aMGZIuhaBu3brp/fffV4MGDWyjSUFBQQoNDb3hegAAgHe5riB02YULF5Senm7X5swtbV26dNGJEyc0cuRIJSQkqHr16lqwYIGioqIkSQkJCXZrCv33v/9VRkaGXnjhBb3wwgu29ieffFLTpk27kY9yw7KysnQ+5axHawAAIL8oVCREPj5OX3jKc06vI3T+/Hm9/PLLmjVrlk6cOJFte2ZmpsuKcwd3rUNw9nSKDjWo77L9AQBQkEWuW6+Qoq77nc036wgNGjRIS5cu1YQJE2S1WjV58mSNGDFCZcqUsV3CAgAAKAicHhEqV66cZsyYoebNm6tIkSLavHmzKlasqM8++0xfffWVUytLe4K7EiWXxgAA+B9XXxpz1++303OETp48qejoaEmX5gOdPHlSktSkSRM9//zzLiusoPHx8XHpECAAAHA/p6Parbfeqv3790uSqlatqlmzZkmS5s2bp6JFi7qyNgAAALdyOgg99dRT+vXXXyVdur398lyhF198UYMGDXJ5gQAAAO7i9ByhKx08eFC//PKLKlSooJo1a7qqLrfh6fMAABQ8+eKusfT0dLVo0UK///67ra1cuXJ64IEHCkQIAgAA+CengpC/v7927Njh0ENRAQAA8jun5wh169ZNU6ZMcUctAAAAecrp2+fT0tI0efJkxcfHq27dugoODrbbPmbMGJcVBwAA4E5OB6EdO3aoTp06kmQ3V0gSl8wAAECB4nAQ+uuvvxQdHa1ly5a5sx4AAIA84/AcoUqVKunYsWO2v7t06aKjR4+6pSgAAIC84HAQunK5oQULFujcuXMuLwgAACCvuO5paAAAAAWMw0HIYrFkmwzN5GgAAFCQOTxZ2hij7t27y2q1SpIuXryoXr16Zbt9Pi4uzrUVAgAAuInDQejJJ5+0+/uJJ55weTEAAAB5yeEgNHXqVHfWAQAAkOeYLA0AALwWQQgAAHgtghAAAPBaBCEAAOC1CEIAAMBrEYQAAIDXIggBAACvRRACAABeiyAEAAC8FkEIAAB4LYIQAADwWgQhAADgtQhCAADAaxGEAACA1yIIAQAAr0UQAgAAXosgBAAAvBZBCAAAeC2CEAAA8FoEIQAA4LUIQgAAwGsRhAAAgNciCAEAAK9FEAIAAF6LIAQAALwWQQgAAHgtghAAAPBaBCEAAOC1CEIAAMBrEYQAAIDXIggBAACvRRACAABeiyAEAAC8FkEIAAB4LYIQAADwWgQhAADgtQhCAADAaxGEAACA1yIIAQAAr+XxIDRhwgRFR0crMDBQsbGxWrlyZa59ExIS9Nhjj6ly5cry8fHRgAED8q5QAABw0/FoEJo5c6YGDBig119/XVu2bFHTpk3Vrl07HTx4MMf+qampKlmypF5//XXVrFkzj6sFAAA3G4sxxnjq4PXr11edOnU0ceJEW1uVKlXUqVMnjRo16qrvbd68uWrVqqVx48Y5dcyUlBSFhoYqOTlZRYoUuZ6yAQBAHnPX77fHRoTS0tK0adMmtWnTxq69TZs2WrNmjcuOk5qaqpSUFLsXAACA5MEgdPz4cWVmZqpUqVJ27aVKlVJiYqLLjjNq1CiFhobaXpGRkS7bNwAAKNg8PlnaYrHY/W2MydZ2IwYPHqzk5GTb69ChQy7bNwAAKNj8PHXgsLAw+fr6Zhv9SUpKyjZKdCOsVqusVqvL9gcAAG4eHhsRCggIUGxsrOLj4+3a4+Pj1ahRIw9VBQAAvInHRoQkaeDAgeratavq1q2rhg0batKkSTp48KB69eol6dJlrcOHD2vGjBm292zdulWSdPbsWR07dkxbt25VQECAqlat6omPAAAACjCPBqEuXbroxIkTGjlypBISElS9enUtWLBAUVFRki4toHjlmkK1a9e2/XvTpk368ssvFRUVpf379+dl6QAA4Cbg0XWEPIF1hAAAKHhuunWEAAAAPI0gBAAAvBZBCAAAeC2CEAAA8FoEIQAA4LUIQgAAwGsRhAAAgNciCAEAAK9FEAIAAF6LIAQAALwWQQgAAHgtghAAAPBaBCEAAOC1CEIAAMBrEYQAAIDXIggBAACvRRACAABeiyAEAAC8FkEIAAB4LYIQAADwWgQhAADgtQhCAADAaxGEAACA1yIIAQAAr0UQAgAAXosgBAAAvBZBCAAAeC2CEAAA8FoEIQAA4LUIQgAAwGsRhAAAgNciCAEAAK/l5+kCbhbGGGWkZXm6DAAA8gW/AB9ZLBZPl3FNBCEXyUjL0qT+P3u6DAAA8oXn3m8mf6uvp8u4Ji6NAQAAr8WIkIv4BfjoufebeboMAADyBb+AgjHWQhByEYvFUiCGAAEAwP8UjLgGAADgBgQhAADgtbg05iImK0sZZ095ugwAAPIFv5Bisvjk//EWgpCLZJw9pQ+efdLTZQAAkC/0+2S6/IuU8HQZ15T/oxoAAICbMCLkIn4hxdTvk+meLgMAgHzBL6SYp0twCEHIRSw+PgViCBAAAPwPl8YAAIDXIggBAACvRRACAABeiyAEAAC8FkEIAAB4LYIQAADwWgQhAADgtQhCAADAaxGEAACA1yIIAQAAr0UQAgAAXosgBAAAvJbXPXTVGCNJSklJ8XAlAADAUZd/ty//jruK1wWhM2fOSJIiIyM9XAkAAHDWmTNnFBoa6rL9WYyro1U+l5WVpSNHjqhw4cKyWCyeLifPpaSkKDIyUocOHVKRIkU8XQ5ywDkqGDhP+R/nqGBw9DwZY3TmzBmVKVNGPj6um9njdSNCPj4+Klu2rKfL8LgiRYrwPwz5HOeoYOA85X+co4LBkfPkypGgy5gsDQAAvBZBCAAAeC2CkJexWq0aNmyYrFarp0tBLjhHBQPnKf/jHBUMnj5PXjdZGgAA4DJGhAAAgNciCAEAAK9FEAIAAF6LIAQAALwWQQgAAHgtglA+t2LFCnXo0EFlypSRxWLRnDlzbNvS09P1yiuvKCYmRsHBwSpTpoy6deumI0eO2O0jNTVVffv2VVhYmIKDg9WxY0f9/fffdn1OnTqlrl27KjQ0VKGhoeratatOnz5t1+fgwYPq0KGDgoODFRYWpn79+iktLc1dH71Audp5ulLPnj1lsVg0btw4u3bOk3s5co52796tjh07KjQ0VIULF1aDBg108OBB23bOkftd6zydPXtWffr0UdmyZRUUFKQqVapo4sSJdn04T+41atQo3XHHHSpcuLDCw8PVqVMn/fbbb3Z9jDEaPny4ypQpo6CgIDVv3lw7d+6065NfzhNBKJ87d+6catasqfHjx2fbdv78eW3evFlDhgzR5s2bFRcXp99//10dO3a06zdgwAB99913+vrrr7Vq1SqdPXtW9957rzIzM219HnvsMW3dulULFy7UwoULtXXrVnXt2tW2PTMzU+3bt9e5c+e0atUqff3115o9e7Zeeukl9334AuRq5+mf5syZo/Xr16tMmTLZtnGe3Ota5+jPP/9UkyZNdPvtt2v58uX69ddfNWTIEAUGBtr6cI7c71rn6cUXX9TChQv1+eefa/fu3XrxxRfVt29fff/997Y+nCf3+vnnn/XCCy9o3bp1io+PV0ZGhtq0aaNz587Z+owePVpjxozR+PHjtXHjRkVERKh169a2B59L+eg8GRQYksx333131T4bNmwwksyBAweMMcacPn3a+Pv7m6+//trW5/Dhw8bHx8csXLjQGGPMrl27jCSzbt06W5+1a9caSWbPnj3GGGMWLFhgfHx8zOHDh219vvrqK2O1Wk1ycrKrPuJNIbfz9Pfff5tbbrnF7Nixw0RFRZmxY8fatnGe8lZO56hLly7miSeeyPU9nKO8l9N5qlatmhk5cqRdW506dcz//d//GWM4T56QlJRkJJmff/7ZGGNMVlaWiYiIMG+//batz8WLF01oaKj5+OOPjTH56zwxInSTSU5OlsViUdGiRSVJmzZtUnp6utq0aWPrU6ZMGVWvXl1r1qyRJK1du1ahoaGqX7++rU+DBg0UGhpq16d69ep2Ixlt27ZVamqqNm3alAefrGDLyspS165dNWjQIFWrVi3bds6TZ2VlZWn+/Pm67bbb1LZtW4WHh6t+/fp2l2U4R/lDkyZNNHfuXB0+fFjGGC1btky///672rZtK4nz5AnJycmSpOLFi0uS9u3bp8TERLtzYLVa1axZM9v3m5/OE0HoJnLx4kW9+uqreuyxx2xP8E1MTFRAQICKFStm17dUqVJKTEy09QkPD8+2v/DwcLs+pUqVstterFgxBQQE2Pogd++88478/PzUr1+/HLdznjwrKSlJZ8+e1dtvv627775bixcv1v33368HHnhAP//8syTOUX7xwQcfqGrVqipbtqwCAgJ09913a8KECWrSpIkkzlNeM8Zo4MCBatKkiapXry5Jtu/nyu/vynOQX86Tn0O9kO+lp6frkUceUVZWliZMmHDN/sYYWSwW29///PeN9EF2mzZt0vvvv6/Nmzc7/V1xnvJGVlaWJOm+++7Tiy++KEmqVauW1qxZo48//ljNmjXL9b2co7z1wQcfaN26dZo7d66ioqK0YsUK9e7dW6VLl1arVq1yfR/nyT369Omjbdu2adWqVdm2Xfk9OfLdeeI8MSJ0E0hPT9fDDz+sffv2KT4+3jYaJEkRERFKS0vTqVOn7N6TlJRkS9ERERE6evRotv0eO3bMrs+V6frUqVNKT0/PlsZhb+XKlUpKSlK5cuXk5+cnPz8/HThwQC+99JLKly8vifPkaWFhYfLz81PVqlXt2qtUqWK7a4xz5HkXLlzQa6+9pjFjxqhDhw6qUaOG+vTpoy5duujdd9+VxHnKS3379tXcuXO1bNkylS1b1tYeEREhSdm+vyvPQX45TwShAu5yCNq7d69++uknlShRwm57bGys/P39FR8fb2tLSEjQjh071KhRI0lSw4YNlZycrA0bNtj6rF+/XsnJyXZ9duzYoYSEBFufxYsXy2q1KjY21p0fscDr2rWrtm3bpq1bt9peZcqU0aBBg7Ro0SJJnCdPCwgI0B133JHtFuDff/9dUVFRkjhH+UF6errS09Pl42P/0+Xr62sb1eM8uZ8xRn369FFcXJyWLl2q6Ohou+3R0dGKiIiwOwdpaWn6+eefbd9vvjpPDk2phsecOXPGbNmyxWzZssVIMmPGjDFbtmwxBw4cMOnp6aZjx46mbNmyZuvWrSYhIcH2Sk1Nte2jV69epmzZsuann34ymzdvNnfddZepWbOmycjIsPW5++67TY0aNczatWvN2rVrTUxMjLn33ntt2zMyMkz16tVNy5YtzebNm81PP/1kypYta/r06ZOn30d+dbXzlJMr7xozhvPkbtc6R3Fxccbf399MmjTJ7N2713z44YfG19fXrFy50rYPzpH7Xes8NWvWzFSrVs0sW7bM/PXXX2bq1KkmMDDQTJgwwbYPzpN7Pf/88yY0NNQsX77c7nfn/Pnztj5vv/22CQ0NNXFxcWb79u3m0UcfNaVLlzYpKSm2PvnlPBGE8rlly5YZSdleTz75pNm3b1+O2ySZZcuW2fZx4cIF06dPH1O8eHETFBRk7r33XnPw4EG745w4ccI8/vjjpnDhwqZw4cLm8ccfN6dOnbLrc+DAAdO+fXsTFBRkihcvbvr06WMuXryYB99C/ne185STnIIQ58m9HDlHU6ZMMRUrVjSBgYGmZs2aZs6cOXb74By537XOU0JCgunevbspU6aMCQwMNJUrVzbvvfeeycrKsu2D8+Reuf3uTJ061dYnKyvLDBs2zERERBir1WruvPNOs337drv95JfzZPn/HwoAAMDrMEcIAAB4LYIQAADwWgQhAADgtQhCAADAaxGEAACA1yIIAQAAr0UQAgAAXosgBAAAvBZBCAAAeC2CEICbRvPmzTVgwACP7wNAweHn6QIAwFXi4uLk7+/v6TJy1b17d0VEROjtt9/2dCkA/j+CEICbRvHixT1dQq6ysrI0f/58zZ0719OlAPgHLo0ByCY8PFyTJ0+2a9u4caOsVqv+/PNPD1V1bVde1mrevLn69eunl19+WcWLF1dERISGDx/u1D4XLlyo0NBQzZgxw7bPvn37asCAASpWrJhKlSqlSZMm6dy5c3rqqadUuHBhVahQQT/++KPdflavXi0fHx/Vr19fkvTtt98qJiZGQUFBKlGihFq1aqVz587d0OcH4DyCEIBsqlevrp07d9q1DR48WM8995wqVKjgoaquz/Tp0xUcHKz169dr9OjRGjlypOLj4x1679dff62HH35YM2bMULdu3ez2GRYWpg0bNqhv3756/vnn9dBDD6lRo0bavHmz2rZtq65du+r8+fO298ydO1cdOnSQj4+PEhIS9Oijj6pHjx7avXu3li9frgceeEDGGJd/fgBXRxACkE1MTIx27dpl+3vx4sVav369hgwZ4vA+fvrpJ40dOzbHbc2aNdP+/ftzbZ8zZ47LJizXqFFDw4YNU6VKldStWzfVrVtXS5Ysueb7JkyYoF69eun777/XfffdZ7etZs2a+r//+z9VqlRJgwcPVlBQkMLCwvTss8+qUqVKGjp0qE6cOKFt27bZ3jN37lzbfhISEpSRkaEHHnhA5cuXV0xMjHr37q2QkBCXfGYAjmOOEIBsqlevrtmzZ0uSjDF67bXXNGjQIIWHhzu8j1atWqlVq1Y5btu/f7/Kly+fa/uMGTNUo0aN66r9Slfup3Tp0kpKSrrqe2bPnq2jR49q1apVqlev3lX36evrqxIlSigmJsbWVqpUKUmyHWf37t36+++/bd9HzZo11bJlS8XExKht27Zq06aNHnzwQRUrVuz6PiSA68aIEIBsYmJidPjwYaWkpGjWrFn6+++/NXDgQNv2du3aaeDAgWrQoIFuv/12bdy4UR07dlRUVJQmTZpk67N7925J0s6dO9WgQQPVrFlTY8aMUWRk5FXbt23bZgsbv/32m+655x7FxsaqefPmOn78uFOf5cq7yCwWi7Kysq76nlq1aqlkyZKaOnVqjperctrnP9ssFosk2Y4zd+5ctW7dWkFBQZIuhaf4+Hj9+OOPqlq1qj788ENVrlxZ+/btc+qzAbhxBCEA2VSvXl0Wi0W//vqrhgwZomHDhtldttmxY4dq1KihdevWqV69enrllVf01Vdf6fvvv9fUqVMlSXv37lWlSpV04cIFPfLII/r000/166+/avny5apRo0au7dKlgFStWjWlpqbqhRde0KRJk7Rp0yY9+OCD2SZxu0OFChW0bNkyff/99+rbt+8N7+/7779Xx44d7dosFosaN26sESNGaMuWLQoICNB33313w8cC4BwujQHIJiQkRFFRUXr55ZclSc8++6xtW3JysgICAtS9e3dJUmBgoPr376/g4GBZrVaFhoYqOTlZISEh8vPz06xZs9S8eXNVrVpVklS5cmVVqFBB3333XY7t58+fl4+Pj4KCgjRz5kzt2rVL9957ryQpNTVVzzzzTJ58B7fddpuWLVum5s2by8/PT+PGjbuu/SQlJWnjxo2aM2eOrW39+vVasmSJ2rRpo/DwcK1fv17Hjh1TlSpVXFM8AIcRhADkKCYmRvPmzdOsWbPk5/e//6nYsWOH7rjjDtvf27dv18iRI23/rl69unbs2KFq1apJujS68885NZs3b9b999+v+fPn59j+z/du375d7733nh599FG3ftbcVK5cWUuXLlXz5s3l6+ur9957z+l9zJs3T/Xr17ebX1WkSBGtWLFC48aNU0pKiqKiovTee++pXbt2riwfgAMIQgBylNvCfzt27LBNDDbG6OjRo4qIiLDbtmPHDlWvXl2SVKJECdsdaIsXL9by5csVExOjdevW5dg+c+ZMW0CKiIjQokWLbEFo+/btdpOSr7R8+fKr/i3JbmTGkX1UqVJFR48eveo+c7oD7vLcoo4dO2a7LFalShUtXLjwqnUAyBvMEQLglJ07d9rCyJV3f10OQjt37rQFoSeeeEIrVqxQnTp19Pnnnys6OlqFCxfOtX379u22IPTUU0/p9OnTuv3221WzZk19+eWXef55b1STJk08NqIF4NoshhW8AACAl2JECAAAeC2CEAAA8FoEIQAA4LUIQgAAwGsRhAAAgNciCAEAAK9FEAIAAF6LIAQAALwWQQgAAHgtghAAAPBaBCEAAOC1/h8qZzhDn6WA9gAAAABJRU5ErkJggg==", + "image/png": 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", "text/plain": [ "
" ] @@ -3024,7 +3059,17 @@ } ], "source": [ - "abundance.plot(x = 'v_middle', xlabel = \"$v_{middle}$ in km/s\", ylabel = \"Fractional Abundance\", title = \"Abundace vs velocity\").legend(loc = 'upper right')" + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.set_yscale('function', functions = (forward, inverse))\n", + "plt.yticks([0.03, 0.04, 0.1, 0.2, 0.3, 0.4, 0.5])\n", + "abundance.plot(x = 'v_middle', xlabel = \"$v_{middle}$ in km/s\", ylabel = \"Fractional Abundance\",\n", + " title = \"Abundace vs velocity\", ax = ax)\n", + "\n", + "ax.legend(loc = \"upper right\")\n", + "\n" ] }, { @@ -3058,7 +3103,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "id": "418fd4a8-3f59-4933-a06e-da17404b1371", "metadata": {}, "outputs": [ @@ -3097,61 +3142,61 @@ " \n", " \n", " 10\n", - " 2.612094e+15\n", - " 1147.709110\n", + " 2.610913e+15\n", + " 1148.228361\n", " 0.000000e+00\n", " 2\n", " 3551\n", " 5343\n", - " 1.736280e+15\n", + " 1.736483e+15\n", " 14\n", " 1402\n", " \n", " \n", " 11\n", - " 2.624666e+15\n", - " 1142.211688\n", + " 2.623633e+15\n", + " 1142.661643\n", " 0.000000e+00\n", " 2\n", " 3551\n", " 5343\n", - " 1.736280e+15\n", + " 1.736483e+15\n", " 14\n", " 1402\n", " \n", " \n", " 12\n", - " 2.636174e+15\n", - " 1137.225677\n", + " 2.635277e+15\n", + " 1137.612783\n", " 0.000000e+00\n", " 2\n", " 3551\n", " 5343\n", - " 1.736280e+15\n", + " 1.736483e+15\n", " 14\n", " 1402\n", " \n", " \n", " 13\n", - " 2.653109e+15\n", - " 1129.966483\n", + " 2.652415e+15\n", + " 1130.262087\n", " 0.000000e+00\n", " 2\n", " 3551\n", " 5343\n", - " 1.736280e+15\n", + " 1.736483e+15\n", " 14\n", " 1402\n", " \n", " \n", " 14\n", - " 2.666574e+15\n", - " 1124.260873\n", + " 2.666043e+15\n", + " 1124.484557\n", " 0.000000e+00\n", " 2\n", " 3551\n", " 5343\n", - " 1.736280e+15\n", + " 1.736483e+15\n", " 14\n", " 1402\n", " \n", @@ -3168,83 +3213,83 @@ " ...\n", " \n", " \n", - " 2660385\n", - " 1.081308e+15\n", - " 2772.499444\n", - " 4.970635e-07\n", + " 2652735\n", + " 1.086349e+15\n", + " 2759.632337\n", + " 6.346043e-07\n", " 2\n", " 7697\n", - " 7672\n", - " 1.108046e+15\n", + " 7697\n", + " 1.109094e+15\n", " 12\n", " 1201\n", " \n", " \n", - " 2660386\n", - " 1.093554e+15\n", - " 2741.449794\n", - " 5.658597e-07\n", + " 2652736\n", + " 1.092227e+15\n", + " 2744.782074\n", + " 6.591144e-07\n", " 2\n", " 7697\n", - " 7672\n", - " 1.108046e+15\n", + " 7697\n", + " 1.109094e+15\n", " 12\n", " 1201\n", " \n", " \n", - " 2660387\n", - " 1.098036e+15\n", - " 2730.260631\n", - " 5.847772e-07\n", + " 2652737\n", + " 1.099582e+15\n", + " 2726.422568\n", + " 6.837456e-07\n", " 2\n", " 7697\n", - " 7672\n", - " 1.108046e+15\n", + " 7697\n", + " 1.109094e+15\n", " 12\n", " 1201\n", " \n", " \n", - " 2660388\n", - " 1.104022e+15\n", - " 2715.457636\n", - " 6.066446e-07\n", + " 2652738\n", + " 1.109661e+15\n", + " 2701.657769\n", + " 7.107076e-07\n", " 2\n", " 7697\n", - " 7672\n", - " 1.108046e+15\n", + " 7697\n", + " 1.109094e+15\n", " 12\n", " 1201\n", " \n", " \n", - " 2660389\n", - " 1.112540e+15\n", - " 2694.667837\n", - " 6.325031e-07\n", + " 2652739\n", + " 1.115506e+15\n", + " 2687.501944\n", + " 7.237699e-07\n", " 2\n", " 7697\n", - " 7672\n", - " 1.108046e+15\n", + " 7697\n", + " 1.109094e+15\n", " 12\n", " 1201\n", " \n", " \n", "\n", - "

1079320 rows × 9 columns

\n", + "

1071430 rows × 9 columns

\n", "" ], "text/plain": [ " nus lambdas energies last_interaction_type \\\n", - "10 2.612094e+15 1147.709110 0.000000e+00 2 \n", - "11 2.624666e+15 1142.211688 0.000000e+00 2 \n", - "12 2.636174e+15 1137.225677 0.000000e+00 2 \n", - "13 2.653109e+15 1129.966483 0.000000e+00 2 \n", - "14 2.666574e+15 1124.260873 0.000000e+00 2 \n", + "10 2.610913e+15 1148.228361 0.000000e+00 2 \n", + "11 2.623633e+15 1142.661643 0.000000e+00 2 \n", + "12 2.635277e+15 1137.612783 0.000000e+00 2 \n", + "13 2.652415e+15 1130.262087 0.000000e+00 2 \n", + "14 2.666043e+15 1124.484557 0.000000e+00 2 \n", "... ... ... ... ... \n", - "2660385 1.081308e+15 2772.499444 4.970635e-07 2 \n", - "2660386 1.093554e+15 2741.449794 5.658597e-07 2 \n", - "2660387 1.098036e+15 2730.260631 5.847772e-07 2 \n", - "2660388 1.104022e+15 2715.457636 6.066446e-07 2 \n", - "2660389 1.112540e+15 2694.667837 6.325031e-07 2 \n", + "2652735 1.086349e+15 2759.632337 6.346043e-07 2 \n", + "2652736 1.092227e+15 2744.782074 6.591144e-07 2 \n", + "2652737 1.099582e+15 2726.422568 6.837456e-07 2 \n", + "2652738 1.109661e+15 2701.657769 7.107076e-07 2 \n", + "2652739 1.115506e+15 2687.501944 7.237699e-07 2 \n", "\n", " last_line_interaction_out_id last_line_interaction_in_id \\\n", "10 3551 5343 \n", @@ -3253,24 +3298,24 @@ "13 3551 5343 \n", "14 3551 5343 \n", "... ... ... \n", - "2660385 7697 7672 \n", - "2660386 7697 7672 \n", - "2660387 7697 7672 \n", - "2660388 7697 7672 \n", - "2660389 7697 7672 \n", + "2652735 7697 7697 \n", + "2652736 7697 7697 \n", + "2652737 7697 7697 \n", + "2652738 7697 7697 \n", + "2652739 7697 7697 \n", "\n", " last_line_interaction_in_nu last_line_interaction_atom \\\n", - "10 1.736280e+15 14 \n", - "11 1.736280e+15 14 \n", - "12 1.736280e+15 14 \n", - "13 1.736280e+15 14 \n", - "14 1.736280e+15 14 \n", + "10 1.736483e+15 14 \n", + "11 1.736483e+15 14 \n", + "12 1.736483e+15 14 \n", + "13 1.736483e+15 14 \n", + "14 1.736483e+15 14 \n", "... ... ... \n", - "2660385 1.108046e+15 12 \n", - "2660386 1.108046e+15 12 \n", - "2660387 1.108046e+15 12 \n", - "2660388 1.108046e+15 12 \n", - "2660389 1.108046e+15 12 \n", + "2652735 1.109094e+15 12 \n", + "2652736 1.109094e+15 12 \n", + "2652737 1.109094e+15 12 \n", + "2652738 1.109094e+15 12 \n", + "2652739 1.109094e+15 12 \n", "\n", " last_line_interaction_species \n", "10 1402 \n", @@ -3279,16 +3324,16 @@ "13 1402 \n", "14 1402 \n", "... ... \n", - "2660385 1201 \n", - "2660386 1201 \n", - "2660387 1201 \n", - "2660388 1201 \n", - "2660389 1201 \n", + "2652735 1201 \n", + "2652736 1201 \n", + "2652737 1201 \n", + "2652738 1201 \n", + "2652739 1201 \n", "\n", - "[1079320 rows x 9 columns]" + "[1071430 rows x 9 columns]" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -3308,7 +3353,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "id": "f31be200-6849-4177-91c7-d59f90da046a", "metadata": {}, "outputs": [ @@ -3343,85 +3388,85 @@ " 0\n", " O I\n", " (8, 800)\n", - " 9330\n", + " 9560\n", " \n", " \n", " 1\n", " O II\n", " (8, 801)\n", - " 1920\n", + " 2200\n", " \n", " \n", " 2\n", " O III\n", " (8, 802)\n", - " 27420\n", + " 27640\n", " \n", " \n", " 3\n", " Mg II\n", " (12, 1201)\n", - " 73280\n", + " 75800\n", " \n", " \n", " 4\n", " Si II\n", " (14, 1401)\n", - " 242340\n", + " 241460\n", " \n", " \n", " 5\n", " Si III\n", " (14, 1402)\n", - " 415620\n", + " 407050\n", " \n", " \n", " 6\n", " Si IV\n", " (14, 1403)\n", - " 17150\n", + " 17110\n", " \n", " \n", " 7\n", " S I\n", " (16, 1600)\n", - " 50\n", + " 80\n", " \n", " \n", " 8\n", " S II\n", " (16, 1601)\n", - " 165050\n", + " 165550\n", " \n", " \n", " 9\n", " S III\n", " (16, 1602)\n", - " 50950\n", + " 51000\n", " \n", " \n", " 10\n", " S IV\n", " (16, 1603)\n", - " 2980\n", + " 2780\n", " \n", " \n", " 11\n", " Ar I\n", " (18, 1800)\n", - " 470\n", + " 480\n", " \n", " \n", " 12\n", " Ar II\n", " (18, 1801)\n", - " 31250\n", + " 30180\n", " \n", " \n", " 13\n", " Ar III\n", " (18, 1802)\n", - " 2790\n", + " 2880\n", " \n", " \n", " 14\n", @@ -3433,7 +3478,7 @@ " 15\n", " Ca II\n", " (20, 2001)\n", - " 38710\n", + " 37650\n", " \n", " \n", "\n", @@ -3441,25 +3486,25 @@ ], "text/plain": [ " symbol species count\n", - "0 O I (8, 800) 9330\n", - "1 O II (8, 801) 1920\n", - "2 O III (8, 802) 27420\n", - "3 Mg II (12, 1201) 73280\n", - "4 Si II (14, 1401) 242340\n", - "5 Si III (14, 1402) 415620\n", - "6 Si IV (14, 1403) 17150\n", - "7 S I (16, 1600) 50\n", - "8 S II (16, 1601) 165050\n", - "9 S III (16, 1602) 50950\n", - "10 S IV (16, 1603) 2980\n", - "11 Ar I (18, 1800) 470\n", - "12 Ar II (18, 1801) 31250\n", - "13 Ar III (18, 1802) 2790\n", + "0 O I (8, 800) 9560\n", + "1 O II (8, 801) 2200\n", + "2 O III (8, 802) 27640\n", + "3 Mg II (12, 1201) 75800\n", + "4 Si II (14, 1401) 241460\n", + "5 Si III (14, 1402) 407050\n", + "6 Si IV (14, 1403) 17110\n", + "7 S I (16, 1600) 80\n", + "8 S II (16, 1601) 165550\n", + "9 S III (16, 1602) 51000\n", + "10 S IV (16, 1603) 2780\n", + "11 Ar I (18, 1800) 480\n", + "12 Ar II (18, 1801) 30180\n", + "13 Ar III (18, 1802) 2880\n", "14 Ar IV (18, 1803) 10\n", - "15 Ca II (20, 2001) 38710" + "15 Ca II (20, 2001) 37650" ] }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -3492,7 +3537,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "id": "837d9f0e-e01a-4e91-95e4-d1a822042d79", "metadata": {}, "outputs": [ @@ -3502,13 +3547,13 @@ "" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -3518,7 +3563,13 @@ } ], "source": [ - "line_interaction_count_df.plot.bar(x = 'symbol' , y = 'count', rot = 90, **{'legend': False})" + "fig, ax = plt.subplots()\n", + "ax.set_yscale('log')\n", + "line_interaction_count_df.plot.bar(x = 'symbol' , y = 'count', rot = 90, **{'legend': False}, ax = ax)\n", + "\n", + "\n", + "\n", + "\n" ] }, { @@ -3531,7 +3582,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "id": "2c5eb16a-5ec0-417b-9377-518e3f37baa0", "metadata": {}, "outputs": [ @@ -3566,1272 +3617,15 @@ }, { "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "hovertemplate": "atomic symbol=O
v_middle=%{x}
value=%{y}", - "legendgroup": "O", - "line": { - "color": "#636efa", - "dash": "solid" - }, - "marker": { - "symbol": "circle" - }, - "mode": "lines", - "name": "O", - "orientation": "v", - "showlegend": true, - "type": "scatter", - "x": [ - 11225.000000000002, - 11675.000000000002, - 12125.000000000002, - 12575.000000000002, - 13025.000000000002, - 13475.000000000002, - 13925.000000000002, - 14375.000000000002, - 14825.000000000002, - 15275.000000000002, - 15725.000000000002, - 16175.000000000002, - 16625, - 17075, - 17525, - 17975, - 18425, - 18875, - 19325, - 19775 - ], - "xaxis": "x", - "y": [ - 0.19, - 0.19, - 0.19, - 0.19, - 0.19, - 0.19, - 0.19, - 0.19, - 0.19, - 0.19, - 0.19, - 0.19, - 0.19, - 0.19, - 0.19, - 0.19, - 0.19, - 0.19, - 0.19, - 0.19 - ], - "yaxis": "y" - }, - { - "hovertemplate": "atomic symbol=Mg
v_middle=%{x}
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", "text/html": [ - "
" + "\n" ] }, "metadata": {}, @@ -5808,7 +3669,7 @@ } ], "source": [ - "fig = px.bar(line_interaction_count_df, x='symbol', y='count')\n", + "fig = px.bar(line_interaction_count_df, x='symbol', y='count', log_y = True)\n", "fig.show()" ] }, @@ -5819,6 +3680,14 @@ "source": [ "## Thanks for giving your time. Please suggest any impovements or any mistakes I made." ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "59006d1f-96a9-4091-935a-2c34f0709c2e", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 97ad9562902da16afa12ba217a31ea0932ed0824 Mon Sep 17 00:00:00 2001 From: Sumit112192 Date: Sat, 30 Mar 2024 11:08:59 +0530 Subject: [PATCH 6/6] Updated y axis scale to scale as log --- VelocityPacketTrackerFirstObjective.ipynb | 4750 +++------------------ 1 file changed, 503 insertions(+), 4247 deletions(-) diff --git a/VelocityPacketTrackerFirstObjective.ipynb b/VelocityPacketTrackerFirstObjective.ipynb index 402eb87fd27..015072c06ee 100644 --- a/VelocityPacketTrackerFirstObjective.ipynb +++ b/VelocityPacketTrackerFirstObjective.ipynb @@ -25,11 +25,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { -<<<<<<< HEAD "model_id": "", -======= - "model_id": "6fc9c39e8c5c403da69e36124df48e77", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "version_major": 2, "version_minor": 0 }, @@ -43,11 +39,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { -<<<<<<< HEAD "model_id": "", -======= - "model_id": "f2b888ffcf934cce9b28388fd5c2f6dc", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "version_major": 2, "version_minor": 0 }, @@ -91,72 +83,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "# Example YAML configuration for TARDIS\n", - "tardis_config_version: v1.0\n", - "\n", - "supernova:\n", - " luminosity_requested: 9.44 log_lsun\n", - " time_explosion: 13 day\n", - "\n", - "atom_data: kurucz_cd23_chianti_H_He.h5\n", - "\n", - "model:\n", - " structure:\n", - " type: specific\n", - " velocity:\n", - " start: 1.1e4 km/s\n", - " stop: 20000 km/s\n", - " num: 20\n", - " density:\n", - " type: branch85_w7\n", - "\n", - " abundances:\n", - " type: uniform\n", - " O: 0.19\n", - " Mg: 0.03\n", - " Si: 0.52\n", - " S: 0.19\n", - " Ar: 0.04\n", - " Ca: 0.03\n", - "\n", - "plasma:\n", - " disable_electron_scattering: no\n", - " ionization: lte\n", - " excitation: lte\n", - " radiative_rates_type: dilute-blackbody\n", - " line_interaction_type: macroatom\n", - "\n", - "montecarlo:\n", - " seed: 23111963\n", - " no_of_packets: 4.0e+4\n", - " iterations: 20\n", - " nthreads: 1\n", -<<<<<<< HEAD - " tracking:\n", - " track_rpacket: true\n", -======= ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a - "\n", - " last_no_of_packets: 1.e+5\n", - " no_of_virtual_packets: 10\n", - "\n", - " convergence_strategy:\n", - " type: damped\n", - " damping_constant: 1.0\n", - " threshold: 0.05\n", - " fraction: 0.8\n", - " hold_iterations: 3\n", - " t_inner:\n", - " damping_constant: 0.5\n", - "\n", -<<<<<<< HEAD - "\n", -======= ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a - "spectrum:\n", - " start: 500 angstrom\n", - " stop: 20000 angstrom\n", - " num: 10000\n" + "cat: tardis_example.yml: No such file or directory\n" ] } ], @@ -186,7 +113,7 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", - "\tReading Atomic Data from kurucz_cd23_chianti_H_He.h5 (\u001b[1mbase.py\u001b[0m:681)\n", + "\tReading Atomic Data from docs/physics/setup/kurucz_cd23_chianti_H_He.h5 (\u001b[1mbase.py\u001b[0m:681)\n", "[\u001b[1mtardis.io.atom_data.util\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tAtom Data kurucz_cd23_chianti_H_He.h5 not found in local path.\n", @@ -196,11 +123,7 @@ "[\u001b[1mtardis.io.atom_data.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\tNon provided Atomic Data: synpp_refs, photoionization_data, yg_data, two_photon_data, linelist (\u001b[1mbase.py\u001b[0m:262)\n", "[\u001b[1mtardis.model.parse_input\u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", -<<<<<<< HEAD "\tNumber of density points larger than number of shells. Assuming inner point irrelevant (\u001b[1mparse_input.py\u001b[0m:143)\n", -======= - "\tNumber of density points larger than number of shells. Assuming inner point irrelevant (\u001b[1mparse_input.py\u001b[0m:107)\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "[\u001b[1mtardis.model.matter.decay\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\tDecaying abundances for 1123200.0 seconds (\u001b[1mdecay.py\u001b[0m:101)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", @@ -219,11 +142,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { -<<<<<<< HEAD - "model_id": "a3baaa96e7604e8a866363b59d4ab704", -======= - "model_id": "c2ae94316d3d41839e9b6d82ae00a2f6", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + "model_id": "b879bc73b60a4e0cb7accf635bced18e", "version_major": 2, "version_minor": 0 }, @@ -237,11 +156,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { -<<<<<<< HEAD - "model_id": "8911ccdda6ce418f8f5a9f529d77a776", -======= - "model_id": "2bd58717056a4bdcaa5aec4568e67dfd", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + "model_id": "e67ab6c8b4e74c1fba11f429630838cc", "version_major": 2, "version_minor": 0 }, @@ -258,13 +173,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", -<<<<<<< HEAD "\tLuminosity emitted = 7.942e+42 erg / s\n", "\tLuminosity absorbed = 2.659e+42 erg / s\n", -======= - "\tLuminosity emitted = 7.959e+42 erg / s\n", - "\tLuminosity absorbed = 2.644e+42 erg / s\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", @@ -277,94 +187,50 @@ "text/html": [ "\n", -<<<<<<< HEAD - "\n", + "
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Shell No.t_radnext_t_radwnext_w
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
09.93e+03 K1.01e+04 K0.40.507
59.85e+03 K1.02e+04 K0.2110.197
109.78e+03 K1.01e+04 K0.1430.117
159.71e+03 K9.87e+03 K0.1050.086909.94e+03 K1.01e+04 K0.40.50609.93e+03 K1.01e+04 K0.40.507
51e+04 K1.03e+04 K0.2110.19159.85e+03 K1.02e+04 K0.2110.197
101.01e+04 K1.02e+04 K0.1430.115109.78e+03 K1.01e+04 K0.1430.117
151.02e+04 K9.9e+03 K0.1050.086159.71e+03 K9.87e+03 K0.1050.0869
\n" ], "text/plain": [ -<<<<<<< HEAD - "" -======= - "" ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + "" ] }, "metadata": {}, @@ -377,11 +243,7 @@ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tCurrent t_inner = 9933.952 K\n", -<<<<<<< HEAD "\tExpected t_inner for next iteration = 10703.212 K\n", -======= - "\tExpected t_inner for next iteration = 10697.222 K\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -393,11 +255,7 @@ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tLuminosity emitted = 1.071e+43 erg / s\n", -<<<<<<< HEAD "\tLuminosity absorbed = 3.576e+42 erg / s\n", -======= - "\tLuminosity absorbed = 3.548e+42 erg / s\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", @@ -410,94 +268,50 @@ "text/html": [ "\n", -<<<<<<< HEAD - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", -======= - "
Shell No.t_radnext_t_radwnext_w
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.01e+04 K1.08e+04 K0.5070.52501.01e+04 K1.08e+04 K0.5070.525
51.02e+04 K1.1e+04 K0.1970.20351.02e+04 K1.1e+04 K0.1970.203
101.01e+04 K1.08e+04 K0.1170.125101.01e+04 K1.08e+04 K0.1170.125
159.87e+03 K1.05e+04 K0.08690.093301.01e+04 K1.08e+04 K0.5060.517
51.03e+04 K1.1e+04 K0.1910.198
101.02e+04 K1.07e+04 K0.1150.127
159.9e+03 K1.05e+04 K0.0860.0928159.87e+03 K1.05e+04 K0.08690.0933
\n" ], "text/plain": [ -<<<<<<< HEAD - "" -======= - "" ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + "" ] }, "metadata": {}, @@ -509,13 +323,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", -<<<<<<< HEAD "\tCurrent t_inner = 10703.212 K\n", "\tExpected t_inner for next iteration = 10673.712 K\n", -======= - "\tCurrent t_inner = 10697.222 K\n", - "\tExpected t_inner for next iteration = 10668.196 K\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -526,13 +335,8 @@ "\tStarting iteration 3 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", -<<<<<<< HEAD "\tLuminosity emitted = 1.074e+43 erg / s\n", "\tLuminosity absorbed = 3.391e+42 erg / s\n", -======= - "\tLuminosity emitted = 1.072e+43 erg / s\n", - "\tLuminosity absorbed = 3.383e+42 erg / s\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", @@ -545,94 +349,50 @@ "text/html": [ "\n", -<<<<<<< HEAD - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.08e+04 K1.1e+04 K0.5250.48301.08e+04 K1.1e+04 K0.5250.483
51.1e+04 K1.12e+04 K0.2030.18951.1e+04 K1.12e+04 K0.2030.189
101.08e+04 K1.1e+04 K0.1250.118101.08e+04 K1.1e+04 K0.1250.118
151.05e+04 K1.06e+04 K0.09330.089501.08e+04 K1.1e+04 K0.5170.482
51.1e+04 K1.12e+04 K0.1980.188
101.07e+04 K1.1e+04 K0.1270.116
151.05e+04 K1.06e+04 K0.09280.0896151.05e+04 K1.06e+04 K0.09330.0895
\n" ], "text/plain": [ -<<<<<<< HEAD - "" -======= - "" ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + "" ] }, "metadata": {}, @@ -644,13 +404,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", -<<<<<<< HEAD "\tCurrent t_inner = 10673.712 K\n", "\tExpected t_inner for next iteration = 10635.953 K\n", -======= - "\tCurrent t_inner = 10668.196 K\n", - "\tExpected t_inner for next iteration = 10635.748 K\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -661,7 +416,6 @@ "\tStarting iteration 4 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", -<<<<<<< HEAD "\tLuminosity emitted = 1.058e+43 erg / s\n", "\tLuminosity absorbed = 3.352e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", @@ -676,50 +430,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4830.46901.1e+04 K1.1e+04 K0.4830.469
51.12e+04 K1.12e+04 K0.1890.18251.12e+04 K1.12e+04 K0.1890.182
101.1e+04 K1.1e+04 K0.1180.113101.1e+04 K1.1e+04 K0.1180.113
151.06e+04 K1.07e+04 K0.08950.0861151.06e+04 K1.07e+04 K0.08950.0861
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Shell No.t_radnext_t_radwnext_w
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4690.479
51.12e+04 K1.13e+04 K0.1820.178
101.1e+04 K1.1e+04 K0.1130.113
151.07e+04 K1.07e+04 K0.08610.083901.1e+04 K1.1e+04 K0.4820.46701.1e+04 K1.1e+04 K0.4690.479
51.12e+04 K1.12e+04 K0.1880.18351.12e+04 K1.13e+04 K0.1820.178
101.1e+04 K1.1e+04 K0.1160.115101.1e+04 K1.1e+04 K0.1130.113
151.06e+04 K1.07e+04 K0.08960.0859151.07e+04 K1.07e+04 K0.08610.0839
\n" ], "text/plain": [ -<<<<<<< HEAD - "" -======= - "" ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + "" ] }, "metadata": {}, @@ -862,13 +568,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", -<<<<<<< HEAD "\tCurrent t_inner = 10638.407 K\n", "\tExpected t_inner for next iteration = 10650.202 K\n", -======= - "\tCurrent t_inner = 10635.748 K\n", - "\tExpected t_inner for next iteration = 10634.292 K\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -876,19 +577,11 @@ " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", -<<<<<<< HEAD "\tStarting iteration 6 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tLuminosity emitted = 1.061e+43 erg / s\n", "\tLuminosity absorbed = 3.398e+42 erg / s\n", -======= - "\tStarting iteration 5 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", - "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\t\n", - "\tLuminosity emitted = 1.054e+43 erg / s\n", - "\tLuminosity absorbed = 3.380e+42 erg / s\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", @@ -903,94 +596,50 @@ "text/html": [ "\n", -<<<<<<< HEAD - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", -======= - "
Shell No.t_radnext_t_radwnext_w
\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4790.47
51.13e+04 K1.12e+04 K0.1780.185
101.1e+04 K1.11e+04 K0.1130.112
151.07e+04 K1.07e+04 K0.08390.085601.1e+04 K1.1e+04 K0.4670.47901.1e+04 K1.1e+04 K0.4790.47
51.12e+04 K1.13e+04 K0.1830.17951.13e+04 K1.12e+04 K0.1780.185
101.1e+04 K1.11e+04 K0.1150.111101.1e+04 K1.11e+04 K0.1130.112
151.07e+04 K1.07e+04 K0.08590.0844151.07e+04 K1.07e+04 K0.08390.0856
\n" ], "text/plain": [ -<<<<<<< HEAD - "" -======= - "" ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + "" ] }, "metadata": {}, @@ -1002,13 +651,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", -<<<<<<< HEAD "\tCurrent t_inner = 10650.202 K\n", "\tExpected t_inner for next iteration = 10645.955 K\n", -======= - "\tCurrent t_inner = 10634.292 K\n", - "\tExpected t_inner for next iteration = 10646.785 K\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -1016,19 +660,11 @@ " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", -<<<<<<< HEAD "\tStarting iteration 7 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tLuminosity emitted = 1.061e+43 erg / s\n", "\tLuminosity absorbed = 3.382e+42 erg / s\n", -======= - "\tStarting iteration 6 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", - "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\t\n", - "\tLuminosity emitted = 1.060e+43 erg / s\n", - "\tLuminosity absorbed = 3.394e+42 erg / s\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", @@ -1043,94 +679,50 @@ "text/html": [ "\n", -<<<<<<< HEAD - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", -======= - "
Shell No.t_radnext_t_radwnext_w
\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.470.47
51.12e+04 K1.13e+04 K0.1850.178
101.11e+04 K1.11e+04 K0.1120.112
151.07e+04 K1.07e+04 K0.08560.08601.1e+04 K1.11e+04 K0.4790.46901.1e+04 K1.1e+04 K0.470.47
51.13e+04 K1.13e+04 K0.1790.18351.12e+04 K1.13e+04 K0.1850.178
101.11e+04 K1.11e+04 K0.1110.113101.11e+04 K1.11e+04 K0.1120.112
151.07e+04 K1.07e+04 K0.08440.0855151.07e+04 K1.07e+04 K0.08560.086
\n" ], "text/plain": [ -<<<<<<< HEAD - "" -======= - "" ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + "" ] }, "metadata": {}, @@ -1142,13 +734,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", -<<<<<<< HEAD "\tCurrent t_inner = 10645.955 K\n", "\tExpected t_inner for next iteration = 10642.050 K\n", -======= - "\tCurrent t_inner = 10646.785 K\n", - "\tExpected t_inner for next iteration = 10645.882 K\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -1156,19 +743,11 @@ " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", -<<<<<<< HEAD "\tStarting iteration 8 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tLuminosity emitted = 1.062e+43 erg / s\n", "\tLuminosity absorbed = 3.350e+42 erg / s\n", -======= - "\tStarting iteration 7 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", - "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\t\n", - "\tLuminosity emitted = 1.061e+43 erg / s\n", - "\tLuminosity absorbed = 3.380e+42 erg / s\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", @@ -1183,94 +762,50 @@ "text/html": [ "\n", -<<<<<<< HEAD - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", -======= - "
Shell No.t_radnext_t_radwnext_w
\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", -<<<<<<< HEAD - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", -======= - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.11e+04 K0.470.472
51.13e+04 K1.14e+04 K0.1780.175
101.11e+04 K1.11e+04 K0.1120.11101.1e+04 K1.11e+04 K0.470.472
151.07e+04 K1.07e+04 K0.0860.08401.11e+04 K1.1e+04 K0.4690.47151.13e+04 K1.14e+04 K0.1780.175
51.13e+04 K1.13e+04 K0.1830.175101.11e+04 K1.11e+04 K0.1120.111
101.11e+04 K1.11e+04 K0.1130.111
151.07e+04 K1.06e+04 K0.08550.0863151.07e+04 K1.07e+04 K0.0860.084
\n" ], "text/plain": [ -<<<<<<< HEAD - "" -======= - "" ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + "" ] }, "metadata": {}, @@ -1282,13 +817,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", -<<<<<<< HEAD "\tCurrent t_inner = 10642.050 K\n", "\tExpected t_inner for next iteration = 10636.106 K\n", -======= - "\tCurrent t_inner = 10645.882 K\n", - "\tExpected t_inner for next iteration = 10641.685 K\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -1296,19 +826,11 @@ " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", -<<<<<<< HEAD "\tStarting iteration 9 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tLuminosity emitted = 1.052e+43 erg / s\n", "\tLuminosity absorbed = 3.411e+42 erg / s\n", -======= - "\tStarting iteration 8 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", - "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\t\n", - "\tLuminosity emitted = 1.061e+43 erg / s\n", - "\tLuminosity absorbed = 3.358e+42 erg / s\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", @@ -1323,94 +845,50 @@ "text/html": [ "\n", -<<<<<<< HEAD - "\n", + "
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Shell No.t_radnext_t_radwnext_w
\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", -<<<<<<< HEAD - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", -======= - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4720.46901.11e+04 K1.11e+04 K0.4720.469
51.14e+04 K1.15e+04 K0.1750.1751.14e+04 K1.15e+04 K0.1750.17
101.11e+04 K1.11e+04 K0.1110.109101.11e+04 K1.11e+04 K0.1110.109
151.07e+04 K1.08e+04 K0.0840.082201.1e+04 K1.11e+04 K0.4710.468
51.13e+04 K1.14e+04 K0.1750.174
101.11e+04 K1.11e+04 K0.1110.109
151.06e+04 K1.08e+04 K0.08630.0826151.07e+04 K1.08e+04 K0.0840.0822
\n" ], "text/plain": [ -<<<<<<< HEAD - "" -======= - "" ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + "" ] }, "metadata": {}, @@ -1422,96 +900,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", -<<<<<<< HEAD "\tCurrent t_inner = 10636.106 K\n", "\tExpected t_inner for next iteration = 10654.313 K\n", -======= - "\tCurrent t_inner = 10641.685 K\n", - "\tExpected t_inner for next iteration = 10638.233 K\n", - " (\u001b[1mbase.py\u001b[0m:575)\n", - "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", - "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", - " (g_lower * n_upper) / (g_upper * n_lower)\n", - " (\u001b[1mwarnings.py\u001b[0m:109)\n", - "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\t\n", - "\tStarting iteration 9 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", - "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\t\n", - "\tLuminosity emitted = 1.053e+43 erg / s\n", - "\tLuminosity absorbed = 3.412e+42 erg / s\n", - "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:580)\n", - "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 6/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", - "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4680.467
51.14e+04 K1.15e+04 K0.1740.17
101.11e+04 K1.11e+04 K0.1090.109
151.08e+04 K1.08e+04 K0.08260.0821
\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\t\n", - "\tCurrent t_inner = 10638.233 K\n", - "\tExpected t_inner for next iteration = 10654.289 K\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -1522,13 +912,8 @@ "\tStarting iteration 10 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", -<<<<<<< HEAD "\tLuminosity emitted = 1.070e+43 erg / s\n", "\tLuminosity absorbed = 3.335e+42 erg / s\n", -======= - "\tLuminosity emitted = 1.069e+43 erg / s\n", - "\tLuminosity absorbed = 3.338e+42 erg / s\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", @@ -1541,94 +926,50 @@ "text/html": [ "\n", -<<<<<<< HEAD - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", -======= - "
Shell No.t_radnext_t_radwnext_w
\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", -<<<<<<< HEAD - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", -======= - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.11e+04 K1.1e+04 K0.4690.475
51.15e+04 K1.14e+04 K0.170.177
101.11e+04 K1.11e+04 K0.1090.112
151.08e+04 K1.06e+04 K0.08220.087801.11e+04 K1.1e+04 K0.4670.47301.11e+04 K1.1e+04 K0.4690.475
51.15e+04 K1.14e+04 K0.170.17851.15e+04 K1.14e+04 K0.170.177
101.11e+04 K1.11e+04 K0.1090.112101.11e+04 K1.11e+04 K0.1090.112
151.08e+04 K1.06e+04 K0.08210.0879151.08e+04 K1.06e+04 K0.08220.0878
\n" ], "text/plain": [ -<<<<<<< HEAD - "" -======= - "" ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + "" ] }, "metadata": {}, @@ -1640,13 +981,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", -<<<<<<< HEAD "\tCurrent t_inner = 10654.313 K\n", "\tExpected t_inner for next iteration = 10628.190 K\n", -======= - "\tCurrent t_inner = 10654.289 K\n", - "\tExpected t_inner for next iteration = 10628.970 K\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -1657,13 +993,8 @@ "\tStarting iteration 11 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", -<<<<<<< HEAD "\tLuminosity emitted = 1.053e+43 erg / s\n", "\tLuminosity absorbed = 3.363e+42 erg / s\n", -======= - "\tLuminosity emitted = 1.052e+43 erg / s\n", - "\tLuminosity absorbed = 3.372e+42 erg / s\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", @@ -1678,94 +1009,50 @@ "text/html": [ "\n", -<<<<<<< HEAD - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", -======= - "
Shell No.t_radnext_t_radwnext_w
\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", -<<<<<<< HEAD - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", -======= - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4750.47201.1e+04 K1.1e+04 K0.4750.472
51.14e+04 K1.12e+04 K0.1770.18451.14e+04 K1.12e+04 K0.1770.184
101.11e+04 K1.1e+04 K0.1120.114101.11e+04 K1.1e+04 K0.1120.114
151.06e+04 K1.06e+04 K0.08780.085901.1e+04 K1.1e+04 K0.4730.477
51.14e+04 K1.12e+04 K0.1780.183
101.11e+04 K1.1e+04 K0.1120.115
151.06e+04 K1.06e+04 K0.08790.0867151.06e+04 K1.06e+04 K0.08780.0859
\n" ], "text/plain": [ -<<<<<<< HEAD - "" -======= - "" ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + "" ] }, "metadata": {}, @@ -1777,13 +1064,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", -<<<<<<< HEAD "\tCurrent t_inner = 10628.190 K\n", "\tExpected t_inner for next iteration = 10644.054 K\n", -======= - "\tCurrent t_inner = 10628.970 K\n", - "\tExpected t_inner for next iteration = 10646.280 K\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -1794,7 +1076,6 @@ "\tStarting iteration 12 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", -<<<<<<< HEAD "\tLuminosity emitted = 1.056e+43 erg / s\n", "\tLuminosity absorbed = 3.420e+42 erg / s\n", "\tLuminosity requested = 1.059e+43 erg / s\n", @@ -1809,50 +1090,50 @@ "text/html": [ "\n", - "\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.11e+04 K0.4720.46701.1e+04 K1.11e+04 K0.4720.467
51.12e+04 K1.13e+04 K0.1840.17651.12e+04 K1.13e+04 K0.1840.176
101.1e+04 K1.11e+04 K0.1140.11101.1e+04 K1.11e+04 K0.1140.11
151.06e+04 K1.08e+04 K0.08590.0821151.06e+04 K1.08e+04 K0.08590.0821
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4670.46601.11e+04 K1.11e+04 K0.4670.466
51.13e+04 K1.13e+04 K0.1760.1851.13e+04 K1.13e+04 K0.1760.18
101.11e+04 K1.11e+04 K0.110.111101.11e+04 K1.11e+04 K0.110.111
151.08e+04 K1.08e+04 K0.08210.0841151.08e+04 K1.08e+04 K0.08210.0841
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1961,10 +1242,6 @@ "\t\n", "\tLuminosity emitted = 1.063e+43 erg / s\n", "\tLuminosity absorbed = 3.369e+42 erg / s\n", -======= - "\tLuminosity emitted = 1.055e+43 erg / s\n", - "\tLuminosity absorbed = 3.435e+42 erg / s\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", @@ -1979,94 +1256,50 @@ "text/html": [ "\n", -<<<<<<< HEAD - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", -======= - "
Shell No.t_radnext_t_radwnext_w
\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", -<<<<<<< HEAD - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", -======= - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4660.469
51.13e+04 K1.13e+04 K0.180.18201.11e+04 K1.11e+04 K0.4660.469
101.11e+04 K1.1e+04 K0.1110.11351.13e+04 K1.13e+04 K0.180.182
151.08e+04 K1.07e+04 K0.08410.085401.1e+04 K1.11e+04 K0.4770.463101.11e+04 K1.1e+04 K0.1110.113
51.12e+04 K1.13e+04 K0.1830.18
101.1e+04 K1.11e+04 K0.1150.112
151.06e+04 K1.07e+04 K0.08670.0843151.08e+04 K1.07e+04 K0.08410.0854
\n" ], "text/plain": [ -<<<<<<< HEAD - "" -======= - "" ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + "" ] }, "metadata": {}, @@ -2078,13 +1311,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", -<<<<<<< HEAD "\tCurrent t_inner = 10647.277 K\n", "\tExpected t_inner for next iteration = 10638.875 K\n", -======= - "\tCurrent t_inner = 10646.280 K\n", - "\tExpected t_inner for next iteration = 10656.684 K\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -2092,19 +1320,11 @@ " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", -<<<<<<< HEAD "\tStarting iteration 15 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tLuminosity emitted = 1.053e+43 erg / s\n", "\tLuminosity absorbed = 3.417e+42 erg / s\n", -======= - "\tStarting iteration 13 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", - "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\t\n", - "\tLuminosity emitted = 1.065e+43 erg / s\n", - "\tLuminosity absorbed = 3.396e+42 erg / s\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", @@ -2119,94 +1339,50 @@ "text/html": [ "\n", -<<<<<<< HEAD - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", -======= - "
Shell No.t_radnext_t_radwnext_w
\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", -<<<<<<< HEAD - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", -======= - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.11e+04 K1.1e+04 K0.4690.484
51.13e+04 K1.13e+04 K0.1820.18101.11e+04 K1.1e+04 K0.4690.484
101.1e+04 K1.1e+04 K0.1130.11351.13e+04 K1.13e+04 K0.1820.181
151.07e+04 K1.07e+04 K0.08540.085801.11e+04 K1.11e+04 K0.4630.463101.1e+04 K1.1e+04 K0.1130.113
51.13e+04 K1.13e+04 K0.180.179
101.11e+04 K1.1e+04 K0.1120.114
151.07e+04 K1.07e+04 K0.08430.0869151.07e+04 K1.07e+04 K0.08540.0858
\n" ], "text/plain": [ -<<<<<<< HEAD - "" -======= - "" ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + "" ] }, "metadata": {}, @@ -2218,13 +1394,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", -<<<<<<< HEAD "\tCurrent t_inner = 10638.875 K\n", "\tExpected t_inner for next iteration = 10655.125 K\n", -======= - "\tCurrent t_inner = 10656.684 K\n", - "\tExpected t_inner for next iteration = 10643.209 K\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -2232,19 +1403,11 @@ " (\u001b[1mwarnings.py\u001b[0m:109)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", -<<<<<<< HEAD "\tStarting iteration 16 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tLuminosity emitted = 1.059e+43 erg / s\n", "\tLuminosity absorbed = 3.445e+42 erg / s\n", -======= - "\tStarting iteration 14 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", - "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\t\n", - "\tLuminosity emitted = 1.061e+43 erg / s\n", - "\tLuminosity absorbed = 3.360e+42 erg / s\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", @@ -2259,94 +1422,50 @@ "text/html": [ "\n", -<<<<<<< HEAD - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", -======= - "
Shell No.t_radnext_t_radwnext_w
\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4840.47201.1e+04 K1.1e+04 K0.4840.472
51.13e+04 K1.13e+04 K0.1810.17751.13e+04 K1.13e+04 K0.1810.177
101.1e+04 K1.1e+04 K0.1130.113101.1e+04 K1.1e+04 K0.1130.113
151.07e+04 K1.06e+04 K0.08580.085801.11e+04 K1.11e+04 K0.4630.467
51.13e+04 K1.13e+04 K0.1790.181
101.1e+04 K1.1e+04 K0.1140.114
151.07e+04 K1.06e+04 K0.08690.0866151.07e+04 K1.06e+04 K0.08580.0858
\n" ], "text/plain": [ -<<<<<<< HEAD - "" -======= - "" ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + "" ] }, "metadata": {}, @@ -2358,179 +1477,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", -<<<<<<< HEAD "\tCurrent t_inner = 10655.125 K\n", "\tExpected t_inner for next iteration = 10655.561 K\n", -======= - "\tCurrent t_inner = 10643.209 K\n", - "\tExpected t_inner for next iteration = 10637.728 K\n", - " (\u001b[1mbase.py\u001b[0m:575)\n", - "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", - "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", - " (g_lower * n_upper) / (g_upper * n_lower)\n", - " (\u001b[1mwarnings.py\u001b[0m:109)\n", - "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\t\n", - "\tStarting iteration 15 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", - "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\t\n", - "\tLuminosity emitted = 1.054e+43 erg / s\n", - "\tLuminosity absorbed = 3.401e+42 erg / s\n", - "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:580)\n", - "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 5/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", - "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Shell No.t_radnext_t_radwnext_w
01.11e+04 K1.1e+04 K0.4670.482
51.13e+04 K1.13e+04 K0.1810.18
101.1e+04 K1.11e+04 K0.1140.111
151.06e+04 K1.07e+04 K0.08660.0845
\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\t\n", - "\tCurrent t_inner = 10637.728 K\n", - "\tExpected t_inner for next iteration = 10651.277 K\n", - " (\u001b[1mbase.py\u001b[0m:575)\n", - "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", - "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", - " (g_lower * n_upper) / (g_upper * n_lower)\n", - " (\u001b[1mwarnings.py\u001b[0m:109)\n", - "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\t\n", - "\tStarting iteration 16 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", - "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\t\n", - "\tLuminosity emitted = 1.057e+43 erg / s\n", - "\tLuminosity absorbed = 3.448e+42 erg / s\n", - "\tLuminosity requested = 1.059e+43 erg / s\n", - " (\u001b[1mbase.py\u001b[0m:580)\n", - "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\tIteration converged 6/4 consecutive times. (\u001b[1mbase.py\u001b[0m:268)\n", - "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\t\n", - "\tPlasma stratification: (\u001b[1mbase.py\u001b[0m:548)\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Shell No.t_radnext_t_radwnext_w
01.1e+04 K1.1e+04 K0.4820.473
51.13e+04 K1.14e+04 K0.180.172
101.11e+04 K1.1e+04 K0.1110.113
151.07e+04 K1.08e+04 K0.08450.0824
\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", - "\t\n", - "\tCurrent t_inner = 10651.277 K\n", - "\tExpected t_inner for next iteration = 10658.182 K\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -2541,13 +1489,8 @@ "\tStarting iteration 17 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", -<<<<<<< HEAD "\tLuminosity emitted = 1.067e+43 erg / s\n", "\tLuminosity absorbed = 3.372e+42 erg / s\n", -======= - "\tLuminosity emitted = 1.066e+43 erg / s\n", - "\tLuminosity absorbed = 3.396e+42 erg / s\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", @@ -2560,94 +1503,50 @@ "text/html": [ "\n", -<<<<<<< HEAD - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", -======= - "
Shell No.t_radnext_t_radwnext_w
\n", + "
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Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.1e+04 K1.11e+04 K0.4720.468
51.13e+04 K1.14e+04 K0.1770.17501.1e+04 K1.11e+04 K0.4720.468
101.1e+04 K1.11e+04 K0.1130.1151.13e+04 K1.14e+04 K0.1770.175
151.06e+04 K1.08e+04 K0.08580.081601.1e+04 K1.11e+04 K0.4730.463101.1e+04 K1.11e+04 K0.1130.11
51.14e+04 K1.14e+04 K0.1720.172
101.1e+04 K1.12e+04 K0.1130.106
151.08e+04 K1.08e+04 K0.08240.0809151.06e+04 K1.08e+04 K0.08580.0816
\n" ], "text/plain": [ -<<<<<<< HEAD - "" -======= - "" ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + "" ] }, "metadata": {}, @@ -2659,13 +1558,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", -<<<<<<< HEAD "\tCurrent t_inner = 10655.561 K\n", "\tExpected t_inner for next iteration = 10636.536 K\n", -======= - "\tCurrent t_inner = 10658.182 K\n", - "\tExpected t_inner for next iteration = 10642.273 K\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -2676,13 +1570,8 @@ "\tStarting iteration 18 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", -<<<<<<< HEAD "\tLuminosity emitted = 1.057e+43 erg / s\n", "\tLuminosity absorbed = 3.365e+42 erg / s\n", -======= - "\tLuminosity emitted = 1.058e+43 erg / s\n", - "\tLuminosity absorbed = 3.382e+42 erg / s\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", @@ -2697,94 +1586,50 @@ "text/html": [ "\n", -<<<<<<< HEAD - "\n", + "
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Shell No.t_radnext_t_radwnext_w
\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", -<<<<<<< HEAD - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", -======= - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4680.46401.11e+04 K1.11e+04 K0.4680.464
51.14e+04 K1.13e+04 K0.1750.17751.14e+04 K1.13e+04 K0.1750.177
101.11e+04 K1.1e+04 K0.110.113101.11e+04 K1.1e+04 K0.110.113
151.08e+04 K1.07e+04 K0.08160.084801.11e+04 K1.11e+04 K0.4630.462
51.14e+04 K1.14e+04 K0.1720.174
101.12e+04 K1.11e+04 K0.1060.109
151.08e+04 K1.07e+04 K0.08090.0829151.08e+04 K1.07e+04 K0.08160.0848
\n" ], "text/plain": [ -<<<<<<< HEAD - "" -======= - "" ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + "" ] }, "metadata": {}, @@ -2796,13 +1641,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", -<<<<<<< HEAD "\tCurrent t_inner = 10636.536 K\n", "\tExpected t_inner for next iteration = 10641.692 K\n", -======= - "\tCurrent t_inner = 10642.273 K\n", - "\tExpected t_inner for next iteration = 10644.386 K\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -2813,13 +1653,8 @@ "\tStarting iteration 19 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", -<<<<<<< HEAD "\tLuminosity emitted = 1.056e+43 erg / s\n", "\tLuminosity absorbed = 3.405e+42 erg / s\n", -======= - "\tLuminosity emitted = 1.057e+43 erg / s\n", - "\tLuminosity absorbed = 3.403e+42 erg / s\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", @@ -2834,94 +1669,50 @@ "text/html": [ "\n", -<<<<<<< HEAD - "\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", -======= - "
Shell No.t_radnext_t_radwnext_w
\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", -<<<<<<< HEAD - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", -======= - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
Shell No.t_radnext_t_radwnext_wt_radnext_t_radwnext_w
01.11e+04 K1.11e+04 K0.4640.466
51.13e+04 K1.13e+04 K0.1770.177
101.1e+04 K1.11e+04 K0.1130.11101.11e+04 K1.11e+04 K0.4640.466
151.07e+04 K1.07e+04 K0.08480.085301.11e+04 K1.11e+04 K0.4620.46251.13e+04 K1.13e+04 K0.1770.177
51.14e+04 K1.14e+04 K0.1740.173101.1e+04 K1.11e+04 K0.1130.111
101.11e+04 K1.11e+04 K0.1090.111
151.07e+04 K1.07e+04 K0.08290.0845151.07e+04 K1.07e+04 K0.08480.0853
\n" ], "text/plain": [ -<<<<<<< HEAD - "" -======= - "" ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + "" ] }, "metadata": {}, @@ -2933,13 +1724,8 @@ "text": [ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", -<<<<<<< HEAD "\tCurrent t_inner = 10641.692 K\n", "\tExpected t_inner for next iteration = 10650.463 K\n", -======= - "\tCurrent t_inner = 10644.386 K\n", - "\tExpected t_inner for next iteration = 10649.220 K\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " (\u001b[1mbase.py\u001b[0m:575)\n", "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/sam/tardis/tardis/tardis/plasma/properties/radiative_properties.py:93: RuntimeWarning: invalid value encountered in divide\n", @@ -2948,31 +1734,22 @@ "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tSimulation finished in 19 iterations \n", -<<<<<<< HEAD - "\tSimulation took 113.21 s\n", -======= - "\tSimulation took 53.10 s\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a + "\tSimulation took 86.94 s\n", " (\u001b[1mbase.py\u001b[0m:476)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", "\tStarting iteration 20 of 20 (\u001b[1mbase.py\u001b[0m:398)\n", "[\u001b[1mtardis.simulation.base\u001b[0m][\u001b[1;37mINFO\u001b[0m ] \n", "\t\n", -<<<<<<< HEAD "\tLuminosity emitted = 1.061e+43 erg / s\n", "\tLuminosity absorbed = 3.401e+42 erg / s\n", -======= - "\tLuminosity emitted = 1.060e+43 erg / s\n", - "\tLuminosity absorbed = 3.406e+42 erg / s\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "\tLuminosity requested = 1.059e+43 erg / s\n", " (\u001b[1mbase.py\u001b[0m:580)\n" ] } ], "source": [ - "sim = run_tardis(\"tardis_example.yml\", virtual_packet_logging = True)" + "sim = run_tardis(\"docs/physics/setup/tardis_example.yml\", virtual_packet_logging = True)" ] }, { @@ -3020,11 +1797,7 @@ }, { "data": { -<<<<<<< HEAD "image/png": 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", -======= - "image/png": 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", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "text/plain": [ "
" ] @@ -4166,7 +2939,6 @@ { "cell_type": "code", "execution_count": 12, -<<<<<<< HEAD "id": "b7e0d48e-2e25-4993-8bee-c2adbeaa403e", "metadata": {}, "outputs": [], @@ -4183,18 +2955,15 @@ { "cell_type": "code", "execution_count": 13, -======= ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "id": "5fed4c8d-fceb-4d1a-b8d5-f23da51a3903", "metadata": {}, "outputs": [ { -<<<<<<< HEAD "name": "stdout", "output_type": "stream", "text": [ "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", - "\t/tmp/ipykernel_6420/2943747606.py:3: RuntimeWarning: divide by zero encountered in log\n", + "\t/tmp/ipykernel_9640/2943747606.py:3: RuntimeWarning: divide by zero encountered in log\n", " return np.log(x)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n" ] @@ -4202,28 +2971,19 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, -======= - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "metadata": {}, "output_type": "execute_result" }, { -<<<<<<< HEAD "name": "stdout", "output_type": "stream", "text": [ "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", - "\t/tmp/ipykernel_6420/2943747606.py:3: RuntimeWarning: divide by zero encountered in log\n", + "\t/tmp/ipykernel_9640/2943747606.py:3: RuntimeWarning: divide by zero encountered in log\n", " return np.log(x)\n", " (\u001b[1mwarnings.py\u001b[0m:109)\n" ] @@ -4231,10 +2991,6 @@ { "data": { "image/png": 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", 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", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "text/plain": [ "
" ] @@ -4244,7 +3000,6 @@ } ], "source": [ -<<<<<<< HEAD "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", @@ -4256,9 +3011,6 @@ "\n", "ax.legend(loc = \"upper right\")\n", "\n" -======= - "abundance.plot(x = 'v_middle', xlabel = \"$v_{middle}$ in km/s\", ylabel = \"Fractional Abundance\", title = \"Abundace vs velocity\").legend(loc = 'upper right')" ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a ] }, { @@ -4292,11 +3044,7 @@ }, { "cell_type": "code", -<<<<<<< HEAD "execution_count": 14, -======= - "execution_count": 13, ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "id": "418fd4a8-3f59-4933-a06e-da17404b1371", "metadata": {}, "outputs": [ @@ -4335,106 +3083,61 @@ " \n", " \n", " 10\n", -<<<<<<< HEAD " 2.610913e+15\n", " 1148.228361\n", -======= - " 2.612094e+15\n", - " 1147.709110\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " 0.000000e+00\n", " 2\n", " 3551\n", " 5343\n", -<<<<<<< HEAD " 1.736483e+15\n", -======= - " 1.736280e+15\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " 14\n", " 1402\n", " \n", " \n", " 11\n", -<<<<<<< HEAD " 2.623633e+15\n", " 1142.661643\n", -======= - " 2.624666e+15\n", - " 1142.211688\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " 0.000000e+00\n", " 2\n", " 3551\n", " 5343\n", -<<<<<<< HEAD " 1.736483e+15\n", -======= - " 1.736280e+15\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " 14\n", " 1402\n", " \n", " \n", " 12\n", -<<<<<<< HEAD " 2.635277e+15\n", " 1137.612783\n", -======= - " 2.636174e+15\n", - " 1137.225677\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " 0.000000e+00\n", " 2\n", " 3551\n", " 5343\n", -<<<<<<< HEAD " 1.736483e+15\n", -======= - " 1.736280e+15\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " 14\n", " 1402\n", " \n", " \n", " 13\n", -<<<<<<< HEAD " 2.652415e+15\n", " 1130.262087\n", -======= - " 2.653109e+15\n", - " 1129.966483\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " 0.000000e+00\n", " 2\n", " 3551\n", " 5343\n", -<<<<<<< HEAD " 1.736483e+15\n", -======= - " 1.736280e+15\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " 14\n", " 1402\n", " \n", " \n", " 14\n", -<<<<<<< HEAD " 2.666043e+15\n", " 1124.484557\n", -======= - " 2.666574e+15\n", - " 1124.260873\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " 0.000000e+00\n", " 2\n", " 3551\n", " 5343\n", -<<<<<<< HEAD " 1.736483e+15\n", -======= - " 1.736280e+15\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " 14\n", " 1402\n", " \n", @@ -4451,7 +3154,6 @@ " ...\n", " \n", " \n", -<<<<<<< HEAD " 2652735\n", " 1.086349e+15\n", " 2759.632337\n", @@ -4460,21 +3162,10 @@ " 7697\n", " 7697\n", " 1.109094e+15\n", -======= - " 2660385\n", - " 1.081308e+15\n", - " 2772.499444\n", - " 4.970635e-07\n", - " 2\n", - " 7697\n", - " 7672\n", - " 1.108046e+15\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " 12\n", " 1201\n", " \n", " \n", -<<<<<<< HEAD " 2652736\n", " 1.092227e+15\n", " 2744.782074\n", @@ -4483,21 +3174,10 @@ " 7697\n", " 7697\n", " 1.109094e+15\n", -======= - " 2660386\n", - " 1.093554e+15\n", - " 2741.449794\n", - " 5.658597e-07\n", - " 2\n", - " 7697\n", - " 7672\n", - " 1.108046e+15\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " 12\n", " 1201\n", " \n", " \n", -<<<<<<< HEAD " 2652737\n", " 1.099582e+15\n", " 2726.422568\n", @@ -4506,21 +3186,10 @@ " 7697\n", " 7697\n", " 1.109094e+15\n", -======= - " 2660387\n", - " 1.098036e+15\n", - " 2730.260631\n", - " 5.847772e-07\n", - " 2\n", - " 7697\n", - " 7672\n", - " 1.108046e+15\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " 12\n", " 1201\n", " \n", " \n", -<<<<<<< HEAD " 2652738\n", " 1.109661e+15\n", " 2701.657769\n", @@ -4529,21 +3198,10 @@ " 7697\n", " 7697\n", " 1.109094e+15\n", -======= - " 2660388\n", - " 1.104022e+15\n", - " 2715.457636\n", - " 6.066446e-07\n", - " 2\n", - " 7697\n", - " 7672\n", - " 1.108046e+15\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " 12\n", " 1201\n", " \n", " \n", -<<<<<<< HEAD " 2652739\n", " 1.115506e+15\n", " 2687.501944\n", @@ -4552,31 +3210,16 @@ " 7697\n", " 7697\n", " 1.109094e+15\n", -======= - " 2660389\n", - " 1.112540e+15\n", - " 2694.667837\n", - " 6.325031e-07\n", - " 2\n", - " 7697\n", - " 7672\n", - " 1.108046e+15\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " 12\n", " 1201\n", " \n", " \n", "\n", -<<<<<<< HEAD "

1071430 rows × 9 columns

\n", -======= - "

1079320 rows × 9 columns

\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "" ], "text/plain": [ " nus lambdas energies last_interaction_type \\\n", -<<<<<<< HEAD "10 2.610913e+15 1148.228361 0.000000e+00 2 \n", "11 2.623633e+15 1142.661643 0.000000e+00 2 \n", "12 2.635277e+15 1137.612783 0.000000e+00 2 \n", @@ -4588,19 +3231,6 @@ "2652737 1.099582e+15 2726.422568 6.837456e-07 2 \n", "2652738 1.109661e+15 2701.657769 7.107076e-07 2 \n", "2652739 1.115506e+15 2687.501944 7.237699e-07 2 \n", -======= - "10 2.612094e+15 1147.709110 0.000000e+00 2 \n", - "11 2.624666e+15 1142.211688 0.000000e+00 2 \n", - "12 2.636174e+15 1137.225677 0.000000e+00 2 \n", - "13 2.653109e+15 1129.966483 0.000000e+00 2 \n", - "14 2.666574e+15 1124.260873 0.000000e+00 2 \n", - "... ... ... ... ... \n", - "2660385 1.081308e+15 2772.499444 4.970635e-07 2 \n", - "2660386 1.093554e+15 2741.449794 5.658597e-07 2 \n", - "2660387 1.098036e+15 2730.260631 5.847772e-07 2 \n", - "2660388 1.104022e+15 2715.457636 6.066446e-07 2 \n", - "2660389 1.112540e+15 2694.667837 6.325031e-07 2 \n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "\n", " last_line_interaction_out_id last_line_interaction_in_id \\\n", "10 3551 5343 \n", @@ -4609,7 +3239,6 @@ "13 3551 5343 \n", "14 3551 5343 \n", "... ... ... \n", -<<<<<<< HEAD "2652735 7697 7697 \n", "2652736 7697 7697 \n", "2652737 7697 7697 \n", @@ -4628,26 +3257,6 @@ "2652737 1.109094e+15 12 \n", "2652738 1.109094e+15 12 \n", "2652739 1.109094e+15 12 \n", -======= - "2660385 7697 7672 \n", - "2660386 7697 7672 \n", - "2660387 7697 7672 \n", - "2660388 7697 7672 \n", - "2660389 7697 7672 \n", - "\n", - " last_line_interaction_in_nu last_line_interaction_atom \\\n", - "10 1.736280e+15 14 \n", - "11 1.736280e+15 14 \n", - "12 1.736280e+15 14 \n", - "13 1.736280e+15 14 \n", - "14 1.736280e+15 14 \n", - "... ... ... \n", - "2660385 1.108046e+15 12 \n", - "2660386 1.108046e+15 12 \n", - "2660387 1.108046e+15 12 \n", - "2660388 1.108046e+15 12 \n", - "2660389 1.108046e+15 12 \n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "\n", " last_line_interaction_species \n", "10 1402 \n", @@ -4656,7 +3265,6 @@ "13 1402 \n", "14 1402 \n", "... ... \n", -<<<<<<< HEAD "2652735 1201 \n", "2652736 1201 \n", "2652737 1201 \n", @@ -4667,18 +3275,6 @@ ] }, "execution_count": 14, -======= - "2660385 1201 \n", - "2660386 1201 \n", - "2660387 1201 \n", - "2660388 1201 \n", - "2660389 1201 \n", - "\n", - "[1079320 rows x 9 columns]" - ] - }, - "execution_count": 13, ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "metadata": {}, "output_type": "execute_result" } @@ -4698,11 +3294,7 @@ }, { "cell_type": "code", -<<<<<<< HEAD "execution_count": 15, -======= - "execution_count": 14, ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "id": "f31be200-6849-4177-91c7-d59f90da046a", "metadata": {}, "outputs": [ @@ -4737,141 +3329,85 @@ " 0\n", " O I\n", " (8, 800)\n", -<<<<<<< HEAD " 9560\n", -======= - " 9330\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " \n", " \n", " 1\n", " O II\n", " (8, 801)\n", -<<<<<<< HEAD " 2200\n", -======= - " 1920\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " \n", " \n", " 2\n", " O III\n", " (8, 802)\n", -<<<<<<< HEAD " 27640\n", -======= - " 27420\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " \n", " \n", " 3\n", " Mg II\n", " (12, 1201)\n", -<<<<<<< HEAD " 75800\n", -======= - " 73280\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " \n", " \n", " 4\n", " Si II\n", " (14, 1401)\n", -<<<<<<< HEAD " 241460\n", -======= - " 242340\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " \n", " \n", " 5\n", " Si III\n", " (14, 1402)\n", -<<<<<<< HEAD " 407050\n", -======= - " 415620\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " \n", " \n", " 6\n", " Si IV\n", " (14, 1403)\n", -<<<<<<< HEAD " 17110\n", -======= - " 17150\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " \n", " \n", " 7\n", " S I\n", " (16, 1600)\n", -<<<<<<< HEAD " 80\n", -======= - " 50\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " \n", " \n", " 8\n", " S II\n", " (16, 1601)\n", -<<<<<<< HEAD " 165550\n", -======= - " 165050\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " \n", " \n", " 9\n", " S III\n", " (16, 1602)\n", -<<<<<<< HEAD " 51000\n", -======= - " 50950\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " \n", " \n", " 10\n", " S IV\n", " (16, 1603)\n", -<<<<<<< HEAD " 2780\n", -======= - " 2980\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " \n", " \n", " 11\n", " Ar I\n", " (18, 1800)\n", -<<<<<<< HEAD " 480\n", -======= - " 470\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " \n", " \n", " 12\n", " Ar II\n", " (18, 1801)\n", -<<<<<<< HEAD " 30180\n", -======= - " 31250\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " \n", " \n", " 13\n", " Ar III\n", " (18, 1802)\n", -<<<<<<< HEAD " 2880\n", -======= - " 2790\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " \n", " \n", " 14\n", @@ -4883,11 +3419,7 @@ " 15\n", " Ca II\n", " (20, 2001)\n", -<<<<<<< HEAD " 37650\n", -======= - " 38710\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a " \n", " \n", "\n", @@ -4895,7 +3427,6 @@ ], "text/plain": [ " symbol species count\n", -<<<<<<< HEAD "0 O I (8, 800) 9560\n", "1 O II (8, 801) 2200\n", "2 O III (8, 802) 27640\n", @@ -4915,27 +3446,6 @@ ] }, "execution_count": 15, -======= - "0 O I (8, 800) 9330\n", - "1 O II (8, 801) 1920\n", - "2 O III (8, 802) 27420\n", - "3 Mg II (12, 1201) 73280\n", - "4 Si II (14, 1401) 242340\n", - "5 Si III (14, 1402) 415620\n", - "6 Si IV (14, 1403) 17150\n", - "7 S I (16, 1600) 50\n", - "8 S II (16, 1601) 165050\n", - "9 S III (16, 1602) 50950\n", - "10 S IV (16, 1603) 2980\n", - "11 Ar I (18, 1800) 470\n", - "12 Ar II (18, 1801) 31250\n", - "13 Ar III (18, 1802) 2790\n", - "14 Ar IV (18, 1803) 10\n", - "15 Ca II (20, 2001) 38710" - ] - }, - "execution_count": 14, ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "metadata": {}, "output_type": "execute_result" } @@ -4968,11 +3478,7 @@ }, { "cell_type": "code", -<<<<<<< HEAD "execution_count": 16, -======= - "execution_count": 15, ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "id": "837d9f0e-e01a-4e91-95e4-d1a822042d79", "metadata": {}, "outputs": [ @@ -4982,21 +3488,13 @@ "" ] }, -<<<<<<< HEAD "execution_count": 16, -======= - "execution_count": 15, ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "metadata": {}, "output_type": "execute_result" }, { "data": { -<<<<<<< HEAD "image/png": 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", -======= - "image/png": 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", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "text/plain": [ "
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", - "text/html": [ - "
" ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a ] }, "metadata": {}, @@ -7347,11 +3610,7 @@ } ], "source": [ -<<<<<<< HEAD "fig = px.bar(line_interaction_count_df, x='symbol', y='count', log_y = True)\n", -======= - "fig = px.bar(line_interaction_count_df, x='symbol', y='count')\n", ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a "fig.show()" ] }, @@ -7362,7 +3621,6 @@ "source": [ "## Thanks for giving your time. Please suggest any impovements or any mistakes I made." ] -<<<<<<< HEAD }, { "cell_type": "code", @@ -7371,8 +3629,6 @@ "metadata": {}, "outputs": [], "source": [] -======= ->>>>>>> 3603518d5384180c6ef8edbc2921a13ba34a021a } ], "metadata": {