Merge branch 'master' into issue135_forecastUncertainty_2

Author: dhblum

.github/workflows/build_test_containers.yml

@@ -0,0 +1,93 @@
+name: Build, test and publish containers
+
+on:
+  pull_request:
+    branches:
+      -  '*'
+  push:
+    branches:
+      -  '*'
+    tags:
+      - '*'
+
+jobs:
+
+  simulation-tests:
+    name: Build containers and run boptest simulation tests
+    runs-on: ubuntu-22.04
+    timeout-minutes: 240
+    env:
+      COMPOSE_PROJECT_NAME: boptest_service
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v2
+
+      - name: Install Python
+        uses: actions/setup-python@v2
+        with:
+          python-version: "3.9"
+
+      - name: Build and run stack
+        run: |
+          cd service
+          docker compose build
+          docker compose up -d web worker
+
+      - name: dump docker logs
+        uses: jwalton/gh-docker-logs@v1
+
+      - name: Upload test cases to minio
+        run: |
+          # The provision script will wait for the web service to be available,
+          # so there is no need for an external wait-for-it script
+          cd service
+          docker compose run --no-deps provision
+          curl http://localhost/testcases
+
+      - name: Run example
+        run: |
+          python --version
+          pip install requests matplotlib numpy pandas
+          PYTHONPATH=$GITHUB_WORKSPACE
+          echo "PYTHONPATH=$PYTHONPATH" >> $GITHUB_ENV
+          # Storing PYTHONPATH above doesn't work for python so setting it below at run 
+          PYTHONPATH=$PWD python examples/python/testcase1.py 
+
+      - name: Run tests
+        run: |
+           cd service
+           docker compose run --no-deps test
+
+      - name: Dump docker logs on failure
+        if: failure()
+        uses: jwalton/gh-docker-logs@v1
+
+      - name: Log in to the GitHub container registry
+        uses: docker/login-action@f054a8b539a109f9f41c372932f1ae047eff08c9
+        with:
+          registry: ghcr.io
+          username: ${{ secrets.GH_USERNAME }}
+          password: ${{ secrets.GH_REGISTRY }}
+        
+      - name: Publish docker images to GitHub Registry
+        if: |
+          github.ref == 'refs/heads/develop' ||
+          contains(github.ref, 'refs/tags') 
+        shell: bash
+        run: service/ci/publish_to_github.sh
+
+#------------------  Push to docker hub (disabled) -------------------------------------
+#
+#      - name: Log in to Docker Hub
+#        uses: docker/login-action@f054a8b539a109f9f41c372932f1ae047eff08c9
+#        with:
+#          username: ${{ secrets.DOCKER_USER }}
+#          password: ${{ secrets.DOCKER_PASS }}
+#
+#      - name: Publish docker images to Docker Hub
+#        if: |
+#          github.ref == 'refs/heads/develop' ||
+#          github.ref == 'refs/heads/experimental' ||
+#          contains(github.ref, 'refs/tags') 
+#        shell: bash
+#        run: service/ci/publish_to_docker.sh

bacnet/create_ttl.py

@@ -18,7 +18,7 @@
 baseurl = 'http://127.0.0.1:80'
 
 # Instatiate test case
-test_case_name = 'bestest_air'
+test_case_name = 'multizone_office_simple_hydronic'
 testid = requests.post("{0}/testcases/{1}/select".format(baseurl, test_case_name)).json()["testid"]
 
 # Write the file prefix

data/data_manager.py

@@ -9,11 +9,10 @@
 
 '''
 
-import matplotlib.pyplot as plt
+
 import pandas as pd
 import numpy as np
 import zipfile
-from scipy import interpolate
 import warnings
 import os
 import json
@@ -264,7 +263,7 @@ def save_data_and_jsons(self, fmu_path):
         self.z_fmu.close()
 
     def get_data(self, horizon=24*3600, interval=None, index=None,
-                 variables=None, category=None, plot=False):
+                 variables=None, category=None):
         '''Retrieve test case data from the fmu. The data
         is stored within the csv files that are
         located in the resources folder of the test case fmu.
@@ -293,9 +292,7 @@ def get_data(self, horizon=24*3600, interval=None, index=None,
             The possible options are specified at categories.json.
             This argument cannot be used together with the `variables`
             argument.
-        plot : Boolean, default is False
-            True if desired to plot the retrieved data
-
+            
         Returns
         -------
         data: dict
@@ -372,16 +369,6 @@ def get_data(self, horizon=24*3600, interval=None, index=None,
         # Add starting year back to index desired by user
         data_slice_reindexed.index = data_slice_reindexed.index + year_start
 
-        if plot:
-            if category is None:
-                to_plot = data_slice_reindexed.keys()
-            else:
-                to_plot = self.categories[category]
-            for var in to_plot:
-                data_slice_reindexed[var].plot()
-                plt.legend()
-                plt.show()
-
         # Reset the index to keep the 'time' column in the data
         # Transform data frame to dictionary
         return data_slice_reindexed.reset_index().to_dict('list')
@@ -465,17 +452,16 @@ def load_data_and_jsons(self):
                         for category in self.categories:
                             # Use linear interpolation for continuous variables
                             if any(col.startswith(key) for key in self.categories['weather']):
-                                g = interpolate.interp1d(df['time'],df[col],
-                                    kind='linear')
-                                self.case.data.loc[:,col] = \
-                                    g(self.case.data.index)
+                                
+                                self.case.data.loc[:,col] = np.interp(self.case.data.index,\
+                                                                     df['time'],df[col])
                             # Use forward fill for discrete variables
                             elif any(col.startswith(key) for key in self.categories[category]):
-                                g = interpolate.interp1d(df['time'],df[col],
-                                    kind='zero')
-                                self.case.data.loc[:,col] = \
-                                    g(self.case.data.index)
-            else:
+
+                                self.case.data.loc[:,col] = self.interp0(self.case.data.index,\
+                                                                     df['time'].values,df[col].values)
+
+            else:                        
                 warnings.warn('The following file does not have '\
                 'time column and therefore no data is going to '\
                 'be used from this file as test case data.', Warning)
@@ -549,14 +535,50 @@ def interpolate_data(self,df,index):
         for key in df.keys():
             # Use linear interpolation for continuous variables
             if key in self.categories['weather']:
-                f = interpolate.interp1d(self.case.data.index,
-                    self.case.data[key], kind='linear')
+                df.loc[:,key] = np.interp(index,self.case.data.index,
+                self.case.data[key])
             # Use forward fill for discrete variables
             else:
-                f = interpolate.interp1d(self.case.data.index,
-                    self.case.data[key], kind='zero')
-            df.loc[:,key] = f(index)
+                df.loc[:,key] = self.interp0(index,self.case.data.index.values,
+                self.case.data[key].values)
         return df
+    
+    def interp0(self,x, xp, yp):
+        """ Zeroth order hold interpolation w/ same
+        (base)   signature  as numpy.interp.
+        Parameters
+        ----------
+        x : np.array
+            The x-coordinates at which to evaluate the interpolated values.
+            
+        xp : np.array
+            The x-coordinates of the data points, must be increasing.
+        
+        yp : np.array
+            The y-coordinates of the data points, same length as xp.
+            
+        Returns
+        -------
+        y : np.array
+            The interpolated values, same length as x.
+        """
+    
+        def func(x0,k):
+            if x0 <= xp[0]:
+                return yp[0], k
+            if x0 >= xp[-1]:
+                return yp[-1], k
+           
+            while x0 >= xp[k]:
+                k += 1
+            return yp[k-1], k
+        k = 0
+        y = list()
+        for x0 in x:           
+            y0,k = func(x0,k)
+            y.append(y0)
+        return np.array(y)
+
 
 if __name__ == "__main__":
     import sys

forecast/forecaster.py

@@ -40,8 +40,9 @@ def __init__(self, testcase, forecast_uncertainty_params_path='forecast/forecast
         # Load forecast uncertainty parameters
         self.uncertainty_params = self.load_uncertainty_params(forecast_uncertainty_params_path)
 
-    def get_forecast(self, point_names, horizon=24 * 3600, interval=3600,
-                     wea_tem_dry_bul=None, wea_sol_glo_hor=None, seed=None):
+    def get_forecast(self, point_names, horizon=24*3600, interval=3600,
+                     wea_tem_dry_bul=None, wea_sol_glo_hor=None, seed=None,
+                     category=None):
         '''
         Retrieves forecast data for specified points over a given horizon and interval.
 
@@ -50,9 +51,11 @@ def get_forecast(self, point_names, horizon=24 * 3600, interval=3600,
         point_names : list of str
             List of data point names for which the forecast is to be retrieved.
         horizon : int, optional
-            Forecast horizon in seconds (default is 86400 seconds, i.e., one day).
+            Forecast horizon in seconds.
+            Default is 86400 seconds (24 hours).
         interval : int, optional
-            Time interval between forecast points in seconds (default is 3600 seconds, i.e., one hour).
+            Time interval between forecast points in seconds.
+            Default is 3600 seconds (one hour).
         wea_tem_dry_bul : str, optional
             Uncertainty level for outside air dry bulb temperature.  'low', 'medium', or 'high'
             If None, defaults to no forecast error.
@@ -65,6 +68,12 @@ def get_forecast(self, point_names, horizon=24 * 3600, interval=3600,
             Seed for the random number generator to ensure reproducibility of the stochastic forecast error.
             If None, no seed is used.
             Default is None.
+        category : string, optional
+            Type of data to retrieve from the test case.
+            If None it will return all available test case
+            data without filtering it by any category.
+            Possible options are 'weather', 'prices',
+            'emissions', 'occupancy', internalGains, 'setpoints'.
 
         Returns
         -------
@@ -74,13 +83,9 @@ def get_forecast(self, point_names, horizon=24 * 3600, interval=3600,
         '''
 
         # Set uncertainty parameters to 0 if no forecast uncertainty
-        temperature_params = {
-            "F0": 0, "K0": 0, "F": 0, "K": 0, "mu": 0
-        }
+        temperature_params = {"F0": 0, "K0": 0, "F": 0, "K": 0, "mu": 0}
 
-        solar_params = {
-            "ag0": 0, "bg0": 0, "phi": 0, "ag": 0, "bg": 0
-        }
+        solar_params = {"ag0": 0, "bg0": 0, "phi": 0, "ag": 0, "bg": 0}
 
         if wea_tem_dry_bul is not None:
             temperature_params.update(self.uncertainty_params['temperature'][wea_tem_dry_bul])
@@ -91,7 +96,8 @@ def get_forecast(self, point_names, horizon=24 * 3600, interval=3600,
         # Get the forecast
         forecast = self.case.data_manager.get_data(variables=point_names,
                                                    horizon=horizon,
-                                                   interval=interval)
+                                                   interval=interval,
+                                                   category=category)
 
         # Add any outside dry bulb temperature error
         if 'TDryBul' in point_names and any(temperature_params.values()):

kpis/kpi_calculator.py

@@ -11,7 +11,6 @@
 
 import numpy as np
 import pandas as pd
-from scipy.integrate import trapz
 from collections import OrderedDict
 
 class KPI_Calculator(object):
@@ -252,9 +251,9 @@ def get_thermal_discomfort(self):
                 dT_upper = data - UpperSetp
                 dT_upper[dT_upper<0]=0
                 self.tdis_dict[signal[:-1]+'dTlower_y'] += \
-                    trapz(dT_lower,self._get_data_from_last_index('time',self.i_last_tdis))/3600.
+                    np.trapezoid(dT_lower,self._get_data_from_last_index('time',self.i_last_tdis))/3600.
                 self.tdis_dict[signal[:-1]+'dTupper_y'] += \
-                    trapz(dT_upper,self._get_data_from_last_index('time',self.i_last_tdis))/3600.
+                    np.trapezoid(dT_upper,self._get_data_from_last_index('time',self.i_last_tdis))/3600.
                 self.tdis_tot = self.tdis_tot + \
                     self.tdis_dict[signal[:-1]+'dTlower_y']/len(self.sources_tdis) + \
                     self.tdis_dict[signal[:-1]+'dTupper_y']/len(self.sources_tdis) # Normalize total by number of sources
@@ -299,7 +298,7 @@ def get_iaq_discomfort(self):
                 dI_upper = data - UpperSetp
                 dI_upper[dI_upper<0]=0
                 self.idis_dict[signal[:-1]+'dIupper_y'] += \
-                    trapz(dI_upper, self._get_data_from_last_index('time',self.i_last_idis))/3600.
+                    np.trapezoid(dI_upper, self._get_data_from_last_index('time',self.i_last_idis))/3600.
                 self.idis_tot = self.idis_tot + \
                           self.idis_dict[signal[:-1]+'dIupper_y']/len(self.sources_idis) # Normalize total by number of sources
 
@@ -334,7 +333,7 @@ def get_energy(self):
                 for signal in self.case.kpi_json[source]:
                     pow_data = np.array(self._get_data_from_last_index(signal,self.i_last_ener))
                     self.ener_dict[signal] += \
-                        trapz(pow_data,
+                        np.trapezoid(pow_data,
                               self._get_data_from_last_index('time',self.i_last_ener))*2.77778e-7 # Convert to kWh
                     self.ener_dict_by_source[source+'_'+signal] += \
                         self.ener_dict[signal]
@@ -542,7 +541,7 @@ def get_cost(self, scenario='Constant'):
             for signal in self.case.kpi_json[source]:
                 pow_data = np.array(self._get_data_from_last_index(signal,self.i_last_cost))
                 self.cost_dict[signal] += \
-                    trapz(np.multiply(source_price_data,pow_data),
+                    np.trapezoid(np.multiply(source_price_data,pow_data),
                           self._get_data_from_last_index('time',self.i_last_cost))*factor
                 self.cost_dict_by_source[source+'_'+signal] += \
                     self.cost_dict[signal]
@@ -586,7 +585,7 @@ def get_emissions(self):
                 for signal in self.case.kpi_json[source]:
                     pow_data = np.array(self._get_data_from_last_index(signal,self.i_last_emis))
                     self.emis_dict[signal] += \
-                        trapz(np.multiply(source_emissions_data,pow_data),
+                        np.trapezoid(np.multiply(source_emissions_data,pow_data),
                               self._get_data_from_last_index('time',self.i_last_emis))*2.77778e-7 # Convert to kWh
                     self.emis_dict_by_source[source+'_'+signal] += \
                         self.emis_dict[signal]