Extract Conductor Coordinates

Use the Output parser to extract conductor coordinates and forces

Download Input files

• dipole_2d.data

• dipole_2d.post.xml

Setting up

Standard Loading of input, Parsing and Initializing RoxiePlotOutputs object

import pandas as pd

from roxieapi.output.parser import RoxieOutputParser

parser = RoxieOutputParser("../input_files/dipole_2d.post.xml")

Extract Geometry

geom = parser.opt[1].coilGeometries

columns = ["cond", "block", "layer", "x1", "y1", "x2", "y2", "x3", "y3", "x4", "y4"]
data = [
    (cond.nr, cond.block_id, cond.layer_id, *cond.geometry.flatten())
    for cond in geom.values()
]

df_geom = pd.DataFrame(data, columns=columns)

df_geom

	cond	block	layer	x1	y1	x2	y2	x3	y3	x4	y4
0	1	0 1 Name: block_nr, dtype: int64	0 1 Name: layer_nr, dtype: int64	44.038746	1.612252	44.049389	0.250293	59.149850	0.250293	59.137363	1.848244
1	2	0 1 Name: block_nr, dtype: int64	0 1 Name: layer_nr, dtype: int64	43.980533	3.233498	44.012460	1.871873	59.111077	2.107865	59.073618	3.705426
2	3	0 1 Name: block_nr, dtype: int64	0 1 Name: layer_nr, dtype: int64	43.863107	4.852578	43.916309	3.491617	59.009394	3.963545	58.946973	5.560326
3	4	0 1 Name: block_nr, dtype: int64	0 1 Name: layer_nr, dtype: int64	43.685891	6.468005	43.760356	5.108043	58.844222	5.815790	58.756854	7.411400
4	5	0 1 Name: block_nr, dtype: int64	0 1 Name: layer_nr, dtype: int64	43.448846	8.078300	43.544557	6.719667	58.615520	7.663062	58.503225	9.257112
...	...	...	...	...	...	...	...	...	...	...	...
155	156	21 22 Name: block_nr, dtype: int64	21 2 Name: layer_nr, dtype: int64	-7.870124	27.096695	-9.500046	26.499157	-14.851506	40.620019	-12.913627	41.330457
156	157	22 23 Name: block_nr, dtype: int64	22 2 Name: layer_nr, dtype: int64	-9.727495	-26.422087	-11.344054	-25.789289	-17.000947	-39.790591	-15.078956	-40.542950
157	158	22 23 Name: block_nr, dtype: int64	22 2 Name: layer_nr, dtype: int64	-7.870124	-27.096695	-9.500046	-26.499157	-14.851506	-40.620019	-12.913627	-41.330457
158	159	23 24 Name: block_nr, dtype: int64	23 2 Name: layer_nr, dtype: int64	9.727495	-26.422087	11.344054	-25.789289	17.000947	-39.790591	15.078956	-40.542950
159	160	23 24 Name: block_nr, dtype: int64	23 2 Name: layer_nr, dtype: int64	7.870124	-27.096695	9.500046	-26.499157	14.851506	-40.620019	12.913627	-41.330457

160 rows × 11 columns

Merge Geometry with forces

df_forces = parser.opt[1].step[1].conductorForces

df_all = df_geom.merge(df_forces, on="cond")

df_all

	cond	block	layer	x1	y1	x2	y2	x3	y3	x4	y4	fx	fy	ftr	fpa	fra	faz
0	1	0 1 Name: block_nr, dtype: int64	0 1 Name: layer_nr, dtype: int64	44.038746	1.612252	44.049389	0.250293	59.149850	0.250293	59.137363	1.848244	-15999.283	-1145.3686	-1020.3099	-16007.745	-16018.317	-838.13061
1	2	0 1 Name: block_nr, dtype: int64	0 1 Name: layer_nr, dtype: int64	43.980533	3.233498	44.012460	1.871873	59.111077	2.107865	59.073618	3.705426	-15839.633	-4551.7958	-4179.2469	-15941.979	-16058.183	-3707.77230
2	3	0 1 Name: block_nr, dtype: int64	0 1 Name: layer_nr, dtype: int64	43.863107	4.852578	43.916309	3.491617	59.009394	3.963545	58.946973	5.560326	-15436.232	-7940.0025	-7330.9707	-15734.604	-16065.342	-6574.62320
3	4	0 1 Name: block_nr, dtype: int64	0 1 Name: layer_nr, dtype: int64	43.685891	6.468005	43.760356	5.108043	58.844222	5.815790	58.756854	7.411400	-14783.503	-11329.2430	-10504.0300	-15380.802	-16037.026	-9471.93290
4	5	0 1 Name: block_nr, dtype: int64	0 1 Name: layer_nr, dtype: int64	43.448846	8.078300	43.544557	6.719667	58.615520	7.663062	58.503225	9.257112	-13883.643	-14732.9170	-13720.8680	-14884.628	-15981.269	-12426.32000
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
155	156	21 22 Name: block_nr, dtype: int64	21 2 Name: layer_nr, dtype: int64	-7.870124	27.096695	-9.500046	26.499157	-14.851506	40.620019	-12.913627	41.330457	-78154.596	-14997.0900	78541.0080	12820.440	10462.645	78889.71200
156	157	22 23 Name: block_nr, dtype: int64	22 2 Name: layer_nr, dtype: int64	-9.727495	-26.422087	-11.344054	-25.789289	-17.000947	-39.790591	-15.078956	-40.542950	-71466.124	14505.0560	-71836.3880	12543.403	13136.504	-71730.29900
157	158	22 23 Name: block_nr, dtype: int64	22 2 Name: layer_nr, dtype: int64	-7.870124	-27.096695	-9.500046	-26.499157	-14.851506	-40.620019	-12.913627	-41.330457	-78154.596	14997.0900	-78541.0080	12820.440	10462.645	-78889.71200
158	159	23 24 Name: block_nr, dtype: int64	23 2 Name: layer_nr, dtype: int64	9.727495	-26.422087	11.344054	-25.789289	17.000947	-39.790591	15.078956	-40.542950	71466.124	14505.0560	71836.3880	12543.403	13136.504	71730.29900
159	160	23 24 Name: block_nr, dtype: int64	23 2 Name: layer_nr, dtype: int64	7.870124	-27.096695	9.500046	-26.499157	14.851506	-40.620019	12.913627	-41.330457	78154.596	14997.0900	78541.0080	12820.440	10462.645	78889.71200

160 rows × 17 columns