replace freq string H and S with lower case string

Author: kaklise

documentation/composite_signal.rst

@@ -36,7 +36,7 @@ the PerformanceMonitoring object.
     >>> import numpy as np
     >>> import pecos
     >>> pm = pecos.monitoring.PerformanceMonitoring()
-    >>> index = pd.date_range('1/1/2017', periods=24, freq='H')
+    >>> index = pd.date_range('1/1/2017', periods=24, freq='h')
     >>> data = {'A': np.arange(24)}
     >>> df = pd.DataFrame(data, index=index)
     >>> pm.add_dataframe(df)

documentation/metrics.rst

@@ -31,7 +31,7 @@ To compute QCI,
     >>> import numpy as np
     >>> import pecos
     >>> pm = pecos.monitoring.PerformanceMonitoring()
-    >>> index = pd.date_range('1/1/2017', periods=24, freq='H')
+    >>> index = pd.date_range('1/1/2017', periods=24, freq='h')
     >>> data = {'A': np.arange(24)}
     >>> df = pd.DataFrame(data, index=index)
     >>> pm.add_dataframe(df)

documentation/timefilter.rst

@@ -16,7 +16,7 @@ The following example defines a time filter between 3 AM and 9 PM,
     >>> import numpy as np
     >>> import pecos
     >>> pm = pecos.monitoring.PerformanceMonitoring()
-    >>> index = pd.date_range('1/1/2017', periods=24, freq='H')
+    >>> index = pd.date_range('1/1/2017', periods=24, freq='h')
     >>> data = {'A': np.arange(24)}
     >>> df = pd.DataFrame(data, index=index)
     >>> pm.add_dataframe(df)

examples/dashboard/dashboard_example_1.py

@@ -40,7 +40,7 @@
 
         # Populate the object with a dataframe and translation dictionary
         # In this example, fake data is generated using a sin wave function
-        index = pd.date_range(analysis_date, periods=24, freq='H')
+        index = pd.date_range(analysis_date, periods=24, freq='h')
         data=np.sin(np.random.rand(3,1)*np.arange(0,24,1))
         df = pd.DataFrame(data=data.transpose(), index=index, columns=['A', 'B', 'C'])
         pm.add_dataframe(df)

pecos/tests/test_graphics.py

@@ -67,7 +67,7 @@ def test_plot_timeseries1(self):
             os.remove(filename)
 
         periods = 5
-        index = pd.date_range('1/1/2016', periods=periods, freq='H')
+        index = pd.date_range('1/1/2016', periods=periods, freq='h')
         data = np.array([[1,2,3], [4,5,6], [7,8,9], [10,11,12], [13,14,15]])
         df = pd.DataFrame(data=data, index=index, columns=['A', 'B', 'C'])
 
@@ -84,7 +84,7 @@ def test_plot_timeseries2(self):
             os.remove(filename)
 
         periods = 5
-        index = pd.date_range('1/1/2016', periods=periods, freq='H')
+        index = pd.date_range('1/1/2016', periods=periods, freq='h')
         data = np.array([[1,2,3], [4,5,6], [7,8,9], [10,11,12], [13,14,15]])
         df = pd.DataFrame(data=data, index=index, columns=['A', 'B', 'C'])
         tfilter = pd.Series(data = (df.index < index[3]), index = df.index)
@@ -147,7 +147,7 @@ def test_plot_doy_heatmap1(self):
             os.remove(filename)
 
         periods = 5*24 # 5 days
-        index = pd.date_range('3/1/2016', periods=periods, freq='H')
+        index = pd.date_range('3/1/2016', periods=periods, freq='h')
         data = np.random.rand(periods)
         df = pd.DataFrame(data=data, index=index, columns=['A'])
 
@@ -164,7 +164,7 @@ def test_plot_doy_heatmap2(self):
             os.remove(filename)
 
         periods = 365*12
-        index = pd.date_range('1/1/2016', periods=periods, freq='2H')
+        index = pd.date_range('1/1/2016', periods=periods, freq='2h')
         data = np.random.rand(periods)
         df = pd.DataFrame(data=data, index=index, columns=['A'])
         overlay = pd.DataFrame(index=[1,100,200,300,365], 
@@ -191,7 +191,7 @@ def test_plot_test_results2(self):
         filename_root = abspath(join(testdir, 'plot_test_results2'))
         pm = pecos.monitoring.PerformanceMonitoring()
         periods = 5
-        index = pd.date_range('1/1/2016', periods=periods, freq='H')
+        index = pd.date_range('1/1/2016', periods=periods, freq='h')
         data = np.array([[1,2,3], [4,5,6], [7,8,9], [10,11,12], [13,14,15]])
         df = pd.DataFrame(data=data, index=index, columns=['A', 'B', 'C'])
         tfilter = pd.Series(data = (df.index < index[3]), index = df.index)

pecos/tests/test_io.py

@@ -54,7 +54,7 @@ def test_write_test_results1(self):
 
         pm = pecos.monitoring.PerformanceMonitoring()
         periods = 5
-        index = pd.date_range('1/1/2016', periods=periods, freq='H')
+        index = pd.date_range('1/1/2016', periods=periods, freq='h')
         data = np.array([[1,2,3], [4,5,6], [7,8,9], [10,11,12], [13,14,15]])
         df = pd.DataFrame(data=data, index=index, columns=['A', 'B', 'C'])
         tfilter = pd.Series(data = (df.index < index[3]), index = df.index)
@@ -95,7 +95,7 @@ def test_write_monitoring_report2(self):# with test results and graphics (encode
 
         pm = pecos.monitoring.PerformanceMonitoring()
         periods = 5
-        index = pd.date_range('1/1/2016', periods=periods, freq='H')
+        index = pd.date_range('1/1/2016', periods=periods, freq='h')
         data = np.array([[1,2,3], [4,5,6], [7,8,9], [10,11,12], [13,14,15]])
         df = pd.DataFrame(data=data, index=index, columns=['A', 'B', 'C'])
         tfilter = pd.Series(data = (df.index < index[3]), index = df.index)

pecos/tests/test_metrics.py

@@ -13,7 +13,7 @@
 class TestMetrics(unittest.TestCase):
 
     def test_pd_far(self):
-        index = pd.date_range('1/1/2016', periods=4, freq='H')
+        index = pd.date_range('1/1/2016', periods=4, freq='h')
 
         actual = np.array([[True,  False, False], 
                            [False, False, True], 
@@ -40,7 +40,7 @@ def test_pd_far(self):
 
     def test_integral(self):
         periods = 5
-        index = pd.date_range('1/1/2016', periods=periods, freq='H')
+        index = pd.date_range('1/1/2016', periods=periods, freq='h')
         data = np.array([[1,2,3], [4,4,3], [7,6,np.nan], [4,8,4], [1,8.5,6]])
         df = pd.DataFrame(data=data, index=index, columns=['A', 'B', 'C'])
 
@@ -63,7 +63,7 @@ def test_integral(self):
 
     def test_derivative(self):
         periods = 5
-        index = pd.date_range('1/1/2016', periods=periods, freq='H')
+        index = pd.date_range('1/1/2016', periods=periods, freq='h')
         data = np.array([[1,2,3], [4,4,3], [7,6,np.nan], [4,8,4], [1,8.5,6]])
         df = pd.DataFrame(data=data, index=index, columns=['A', 'B', 'C'])
 
@@ -96,7 +96,7 @@ def test_derivative(self):
     def test_qci_no_test_results(self):
         periods = 5
         np.random.seed(100)
-        index = pd.date_range('1/1/2016', periods=periods, freq='H')
+        index = pd.date_range('1/1/2016', periods=periods, freq='h')
         data=np.sin(np.random.rand(3,1)*np.arange(0,periods,1))
         df = pd.DataFrame(data=data.transpose(), index=index, columns=['A', 'B', 'C'])
         trans = dict(zip(df.columns, [[col] for col in df.columns]))
@@ -116,7 +116,7 @@ def test_qci_no_test_results(self):
     def test_qci_with_test_results(self):
         periods = 5
         np.random.seed(100)
-        index = pd.date_range('1/1/2016', periods=periods, freq='H')
+        index = pd.date_range('1/1/2016', periods=periods, freq='h')
         data=np.sin(np.random.rand(3,1)*np.arange(0,periods,1))
         df = pd.DataFrame(data=data.transpose(), index=index, columns=['A', 'B', 'C'])
         trans = dict(zip(df.columns, [[col] for col in df.columns]))
@@ -158,7 +158,7 @@ def test_qci_with_test_results(self):
     def test_rmse(self):
 
         periods = 5
-        index = pd.date_range('1/1/2016', periods=periods, freq='H')
+        index = pd.date_range('1/1/2016', periods=periods, freq='h')
         x1 = pd.DataFrame(data=np.array([4, 4, 4.5, 2.7, 6]), index=index, columns=['Power'])
         x2 = pd.DataFrame(data=np.array([5,10,4.5,3,4]), index=index, columns=['Power'])
 

pecos/tests/test_monitoring.py

@@ -377,7 +377,7 @@ class Test_check_delta(unittest.TestCase):
 
     @classmethod
     def setUp(self):
-        index = pd.date_range('1/1/2017', periods=24, freq='H')
+        index = pd.date_range('1/1/2017', periods=24, freq='h')
         data = {'A': [0.5,-0.3,0.2,0,0.5,-0.45,0.35,-0.4,0.5,1.5,0.5,-0.5,0.5,-0.5,5,6,10,10.5,10,10.3,10,10.8,10,9.9], 
                 'B': [0,1,2.2,3,3.8,5,6,7.1,8,9,10,5,-2,1,0,0.5,0,5,3,9.5,8.2,7,np.nan,5]}
         df = pd.DataFrame(data, index=index)
@@ -522,7 +522,7 @@ class Test_check_outlier(unittest.TestCase):
 
     @classmethod
     def setUp(self):
-        index = pd.date_range('1/1/2017', periods=24, freq='H')
+        index = pd.date_range('1/1/2017', periods=24, freq='h')
         data = {'A': [112,114,113,132,134,127,150,120,117,112,107,99,140,
                       98,88,98,106,110,107,79,102,115,np.nan,91]}
         df = pd.DataFrame(data, index=index)
@@ -570,7 +570,7 @@ class Test_check_custom(unittest.TestCase):
     def setUp(self):
         N = 1000
         np.random.seed(92837)
-        index = pd.date_range('1/1/2020', periods=N, freq='S')
+        index = pd.date_range('1/1/2020', periods=N, freq='s')
         data = {'A': np.random.normal(size=N),'B': np.random.normal(size=N)}
         df = pd.DataFrame(data, index=index)
 

pecos/tests/test_pv.py

@@ -15,7 +15,7 @@ class TestPV(unittest.TestCase):
     def test_insolation(self):
         # same test as metrics.time_integral
         periods = 5
-        index = pd.date_range('1/1/2016', periods=periods, freq='H')
+        index = pd.date_range('1/1/2016', periods=periods, freq='h')
         data = np.array([[1,2,3], [4,5,6], [7,8,9], [10,11,12], [13,14,15]])
         df = pd.DataFrame(data=data, index=index, columns=['A', 'B', 'C'])
 
@@ -28,7 +28,7 @@ def test_insolation(self):
     def test_energy(self):
         # same test as metrics.time_integral
         periods = 5
-        index = pd.date_range('1/1/2016', periods=periods, freq='H')
+        index = pd.date_range('1/1/2016', periods=periods, freq='h')
         data = np.array([[1,2,3], [4,5,6], [7,8,9], [10,11,12], [13,14,15]])
         df = pd.DataFrame(data=data, index=index, columns=['A', 'B', 'C'])
 
@@ -41,7 +41,7 @@ def test_energy(self):
     def test_performance_ratio(self):
 
         periods = 5
-        index = pd.date_range('1/1/2016', periods=periods, freq='H')
+        index = pd.date_range('1/1/2016', periods=periods, freq='h')
         E = pd.Series(data=np.array([4, 4, 4.5, 2.7, 6]), index=index)
         POA = pd.Series(data=np.array([1000,1250,1250,900,1500]), index=index)
 
@@ -52,7 +52,7 @@ def test_performance_ratio(self):
     def test_normalized_current(self):
 
         periods = 5
-        index = pd.date_range('1/1/2016', periods=periods, freq='H')
+        index = pd.date_range('1/1/2016', periods=periods, freq='h')
         I = pd.Series(data=np.array([4, 4, 4.5, 2.7, 6]), index=index)
         POA = pd.Series(data=np.array([1000,1250,1250,900,1500]), index=index)
 
@@ -63,7 +63,7 @@ def test_normalized_current(self):
     def test_normalized_efficiency(self):
 
         periods = 5
-        index = pd.date_range('1/1/2016', periods=periods, freq='H')
+        index = pd.date_range('1/1/2016', periods=periods, freq='h')
         P = pd.Series(data=np.array([4, 4, 4.5, 2.7, 6]), index=index)
         POA = pd.Series(data=np.array([1000,1250,1250,900,1500]), index=index)
 
@@ -74,7 +74,7 @@ def test_normalized_efficiency(self):
     def test_performance_index(self):
 
         periods = 5
-        index = pd.date_range('1/1/2016', periods=periods, freq='H')
+        index = pd.date_range('1/1/2016', periods=periods, freq='h')
         P = pd.Series(data=np.array([4, 4, 4.5, 2.7, 6]), index=index)
         P_expected = pd.Series(data=np.array([5,10,4.5,3,4]), index=index)
 
@@ -85,7 +85,7 @@ def test_performance_index(self):
     def test_energy_yield(self):
 
         periods = 5
-        index = pd.date_range('1/1/2016', periods=periods, freq='H')
+        index = pd.date_range('1/1/2016', periods=periods, freq='h')
         E = pd.Series(data=np.array([4, 4, 4.5, 2.7, 6]), index=index)
 
         EY = pecos.pv.energy_yield(E, 10)
@@ -95,7 +95,7 @@ def test_energy_yield(self):
     def test_clearness_index(self):
 
         periods = 5
-        index = pd.date_range('1/1/2016', periods=periods, freq='H')
+        index = pd.date_range('1/1/2016', periods=periods, freq='h')
         DNI = pd.Series(data=np.array([4, 4, 4.5, 2.7, 6]), index=index)
         ExI = pd.Series(data=np.array([5,10,4.5,3,4]), index=index)
 

pecos/tests/test_utils.py

@@ -144,7 +144,7 @@ class TestEvaluateString(unittest.TestCase):
 
     @classmethod
     def setUp(self):
-        index = pd.date_range('1/1/2020', periods=72, freq='H')
+        index = pd.date_range('1/1/2020', periods=72, freq='h')
         data = {'A': np.random.rand(72),
                 'B': np.random.rand(72)}
         self.df = pd.DataFrame(data, index=index)