Example: Circuit SystemY#

from opendssdirect import dss

# If you downloaded just the notebook, remember to update the path to the DSS script below

dss('Redirect "../../tests/data/13Bus/IEEE13Nodeckt.dss"')

The function dss.YMatrix.getYsparse() returns the vectors that form the compressed sparse representation of the matrix. This is the preferred usage for large system.

We recommend using scipy.sparse for this usage:

from scipy.sparse import csc_matrix

systemY = csc_matrix(dss.YMatrix.getYsparse())

systemY

<41x41 sparse matrix of type '<class 'numpy.complex128'>'
	with 267 stored elements in Compressed Sparse Column format>

print(systemY)

  (0, 0)	(0.8178822742754986-4.636999108384914j)
  (1, 0)	(-0.13055025449836521+1.4833269062112926j)
  (2, 0)	(-0.13055025449836524+1.4833269062112926j)
  (3, 0)	(-9.283352668986776+74.26682135189421j)
  (4, 0)	(9.283352668986776-74.26682135189421j)
  (0, 1)	(-0.13055025449836521+1.4833269062112926j)
  (1, 1)	(0.8178822742754985-4.636999108384914j)
  (2, 1)	(-0.13055025449836524+1.4833269062112926j)
  (4, 1)	(-9.283352668986776+74.26682135189421j)
  (5, 1)	(9.283352668986776-74.26682135189421j)
  (0, 2)	(-0.13055025449836524+1.4833269062112926j)
  (1, 2)	(-0.13055025449836524+1.4833269062112926j)
  (2, 2)	(0.8178822742754985-4.636999108384913j)
  (3, 2)	(9.283352668986776-74.26682135189421j)
  (5, 2)	(-9.283352668986776+74.26682135189421j)
  (0, 3)	(-9.283352668986776+74.26682135189421j)
  (2, 3)	(9.283352668986776-74.26682135189421j)
  (3, 3)	(1890.6787348910132-5002.165990528294j)
  (6, 3)	(-1369.165023011177+1369.165023011177j)
  (0, 4)	(9.283352668986776-74.26682135189421j)
  (1, 4)	(-9.283352668986776+74.26682135189421j)
  (4, 4)	(1890.6787348910132-5002.165990528294j)
  (7, 4)	(-1393.9089692101743+1393.9089692101743j)
  (1, 5)	(9.283352668986776-74.26682135189421j)
  (2, 5)	(-9.283352668986776+74.26682135189421j)
  :	:
  (35, 36)	(-0.2526096623056667+1.3934005374671847j)
  (36, 36)	(1.7734368716962685-5.901187416512638j)
  (18, 37)	(-3.8523736320183444+1.598948297622844j)
  (19, 37)	(2.407733520011465-0.9993426967242526j)
  (20, 37)	(-7.175068362002635+6.675422271119008j)
  (32, 37)	(1.4446401120068793-0.5996056180345513j)
  (33, 37)	(-4.3050410172015825+4.0052533626714055j)
  (37, 37)	(11.480109379204219-10.680675553822601j)
  (16, 38)	(-10000000+0j)
  (21, 38)	(-2.4286016759142925+3.079395289523383j)
  (22, 38)	(-2.428601675914292+3.079395289523384j)
  (23, 38)	(2.428601675914292-3.079395289523384j)
  (24, 38)	(-11.546745157318384+8.197439395398543j)
  (25, 38)	(2.4286016759142925-3.079395289523383j)
  (38, 38)	(10000011.546745157-8.197432541942769j)
  (15, 39)	(-7.175068362002635+6.675422271119008j)
  (17, 39)	(2.407733520011465-0.9993426967242526j)
  (27, 39)	(-4.291089932048226+1.6378059450141607j)
  (39, 39)	(11.46615829405086-8.313221446001002j)
  (40, 39)	(-2.407733520011465+0.9993426902982676j)
  (15, 40)	(2.407733520011465-0.9993426967242526j)
  (17, 40)	(-7.224938066978001+6.644943442253672j)
  (26, 40)	(-6.5300017720164165+6.619904745555312j)
  (39, 40)	(-2.407733520011465+0.9993426902982676j)
  (40, 40)	(13.754939838994417-13.264848127833126j)

If you have matplotlib installed, you can plot it with spy:

from matplotlib import pyplot as plt
plt.spy(systemY)

<matplotlib.lines.Line2D at 0x7ff3bb91c080>

../_images/9d2bc88057dbb659285d2268fa7077cf50c1c98b69bf85013d830552f512a64e.png

Although not recommended, you can also get the whole dense system Y matrix as below, just like in the official OpenDSS COM implementation. This is kept for compatibility with the official implementation. It is useful to quickly analyze very small circuits without installing extra Python packages.

dss.Circuit.SystemY() # avoid, this is a dense matrix (memory intensive)

[0.8178822742754986,
 -4.636999108384914,
 -0.13055025449836521,
4833269062112926,
 -0.13055025449836524,
4833269062112926,
 -9.283352668986776,
26682135189421,
283352668986776,
 -74.26682135189421,
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4833269062112926,
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283352668986776,
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6787348910132,
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