Fix TDS.init() and TDS._csv_step()

andes/routines/tds.py

@@ -237,8 +237,15 @@ def init(self):
 
         # discard initialized values and use that from CSV if provided
         if self.data_csv is not None:
-            system.dae.x[:] = self.data_csv[0, 1:system.dae.n + 1]
-            system.dae.y[:] = self.data_csv[0, system.dae.n + 1:system.dae.n + system.dae.m + 1]
+            if system.Output.n < 1:
+                system.dae.x[:] = self.data_csv[self.k_csv, 1:system.dae.n + 1]
+                system.dae.y[:] = self.data_csv[self.k_csv, system.dae.n + 1:system.dae.n + system.dae.m + 1]
+            else:
+                xyidx = system.Output.xidx + [yidx+system.dae.n for yidx in system.Output.yidx]
+                _xy = np.zeros(system.dae.n + system.dae.m)
+                _xy[xyidx] = self.data_csv[self.k_csv, 1:]
+                system.dae.x[:] = _xy[:system.dae.n]
+                system.dae.y[:] = _xy[system.dae.n:]
             system.vars_to_models()
 
         # connect to data streaming server
@@ -537,12 +544,23 @@ def itm_step(self):
     def _csv_step(self):
         """
         Fetch data for the next step from ``data_csv``.
+
+        When `Output` exists, the target variables `x` and `y` are filled with the data,
+        while the remaining variables are set to zero.
         """
 
         system = self.system
         if self.data_csv is not None:
-            system.dae.x[:] = self.data_csv[self.k_csv, 1:system.dae.n + 1]
-            system.dae.y[:] = self.data_csv[self.k_csv, system.dae.n + 1:system.dae.n + system.dae.m + 1]
+            if system.Output.n < 1:
+                system.dae.x[:] = self.data_csv[self.k_csv, 1:system.dae.n + 1]
+                system.dae.y[:] = self.data_csv[self.k_csv, system.dae.n + 1:system.dae.n + system.dae.m + 1]
+            else:
+                xyidx = system.Output.xidx + [yidx+system.dae.n for yidx in system.Output.yidx]
+                _xy = np.zeros(system.dae.n + system.dae.m)
+                _xy[xyidx] = self.data_csv[self.k_csv, 1:]
+                system.dae.x[:] = _xy[:system.dae.n]
+                system.dae.y[:] = _xy[system.dae.n:]
+
             system.vars_to_models()
 
         self.converged = True
@@ -926,8 +944,14 @@ def _load_csv(self, csv_file):
         if data.shape[0] < 2:
             logger.warning("CSV data does not contain more than one time step.")
         if data.shape[1] < (self.system.dae.m + self.system.dae.n):
-            logger.warning("CSV data contains fewer variables than required.")
-            logger.warning("Check if the CSV data file is generated from the test case.")
+            if self.system.Output.n < 1:
+                logger.warning("CSV data contains fewer variables than required.")
+                logger.warning("Check if the CSV data file is generated from the test case.")
+            else:
+                logger.info("Output selection detected.")
+                if data.shape[1] - 1 < (len(self.system.Output.xidx + self.system.Output.yidx)):
+                    logger.warning("CSV data contains fewer variables than required.")
+                    logger.warning("Check if the CSV data file is generated with selected output.")
 
         # set start and end times from data
         self.config.t0 = data[0, 0]

docs/source/release-notes.rst

@@ -14,6 +14,8 @@ v1.9.3 (2024-04-XX)
 - In symbolic processor, most variables are assumed to be real, except some
   services that are specified as complex. This will allow generating simplified
   expressions.
+- Adjust `BusFreq.Tw.default` to 0.1.
+- Fix `TDS.init()` and `TDS._csv_step()` to fit loading from CSV when `Output` exists.
 
 v1.9.2 (2024-03-25)
 -------------------

tests/test_output.py

@@ -1,6 +1,7 @@
 """
 Test output selection.
 """
+import os
 
 import numpy as np
 import unittest
@@ -73,3 +74,56 @@ def test_output_xyname(self):
         np.testing.assert_equal(
             ss.dae.ts.get_data(ss.TG2.pout, a=None).shape[1],
             0)
+
+    def test_from_csv(self):
+        """
+        Test from_csv when loading selected output from csv file.
+        """
+        case = andes.get_case("5bus/pjm5bus.json")
+        ss = andes.load(case,
+                        no_output=True,
+                        setup=False,
+                        default_config=True)
+
+        ss.add("Output", {"model": "Bus", "varname": "v"})
+
+        ss.setup()
+
+        ss.PFlow.run()
+        ss.TDS.config.tf = 0.1
+        ss.TDS.run()
+        ss.TDS.load_plotter()
+
+        ss.TDS.plt.export_csv("pjm5bus_selec_out.csv")
+
+        # Test assign CSV in TDS.run()
+        ss2 = andes.load(case,
+                         no_output=True,
+                         setup=False,
+                         default_config=True)
+
+        ss2.add("Output", {"model": "Bus", "varname": "v"})
+
+        ss2.setup()
+
+        ss2.PFlow.run()
+        ss2.TDS.run(from_csv="pjm5bus_selec_out.csv")
+
+        self.assertTrue(ss2.TDS.converged)
+
+        # Test assign CSV in andes.load()
+        ss3 = andes.load(case,
+                         no_output=True,
+                         setup=False,
+                         default_config=True,
+                         from_csv="pjm5bus_selec_out.csv")
+        ss3.add("Output", {"model": "Bus", "varname": "v"})
+
+        ss3.setup()
+
+        ss3.PFlow.run()
+        ss3.TDS.run()
+
+        self.assertTrue(ss3.TDS.converged)
+
+        os.remove("pjm5bus_selec_out.csv")