I changed it back to how it was before the code that was

causing errors was commented out.

test_preparation.py

@@ -2,15 +2,16 @@
 test_preparation.py
 unit test for the functions fft_powerspectrum, fft_mag, inv_fft, calc_freq
 """
+from datetime import datetime, timedelta
 import numpy as np
 import pandas as pd
-from preparation import window,unwindow
-
-#from datetime import datetime, timedelta
-#from preparation import fft_powerspectrum, fft_mag, get_timeseries
-
+from preparation import fft_powerspectrum, fft_mag, inv_fft, calc_freq
+import pytest
 
+from preparation import fft_powerspectrum, fft_mag, get_timeseries
+from preparation import window,unwindow
 #from thid import plot_rets
+
 """
 t = np.linspace(0, 2, 2000, endpoint=False)
 f1, f2 = 50, 120
@@ -21,7 +22,7 @@
 print(inv_fft(data))
 print(data)
 plot_rets(freq,fft(data))
-
+"""
 
 t = np.linspace(0, 2, 2000, endpoint=False)
 f1, f2 = 50, 120
@@ -35,55 +36,56 @@ def test_fft(data):
     magnitudes = fft(data)
 from preparation import fft_powerspectrum, fft_mag, inv_fft, calc_freq
 import pytest
-
 @pytest.fixture
-
-
 def data():
     trange = pd.date_range(datetime.now(), datetime.now() + timedelta(days=9), freq='d')
     return pd.Series([1, 2, 3, 2, 1, 2, 3, 2, 1, 2], index=trange)
-    
 def test_fft_powerspectrum(data):
-
-    test the powerspectrums length and type of export
+    """test the powerspectrums length and type of export"""
     powrspec = fft_powerspectrum(data)
     assert len(powrspec) == len(data)/2
     assert isinstance(powrspec, np.ndarray)
-
 def test_fft_mag(data):
-
-    test the fft_mag length and type of expor
+    """test the fft_mag length and type of export"""
     magnitudes = fft_mag(data)
     assert len(magnitudes) == len(data)
     assert isinstance(magnitudes, np.ndarray)
-
 def test_inv_fft(data):
-
-    tests inv_fft length export aswell as testing to see if
-    the fit matches the data
-
+    """tests inv_fft length export aswell as testing to see if
+    the fit matches the data"""
     invdata = inv_fft(fft_mag(data))
     assert len(invdata) == len(data)
     assert np.allclose(invdata, data.values, atol=1e-4)
+def test_get_timeseries_date_column_length():
+    '''
+    This function tests that the length of co2_series (output of get_timeseries) 
+    has the same length of the original file.
+    '''
+    co2_series = get_timeseries('/mauna-loa-data/flask_monthly.json')
+
+    #Check that the length of the Series matches the original data length
+    original_data = pd.read_json('/mauna-loa-data/flask_monthly.json')
+    original_length = len(original_data)  # Count of rows in the JSON data
+    assert len(co2_series) == original_length, (
+        "Length of the datetime index should match the length of the original Month column."
+    )
+
+matr = fft_powerspectrum(data)
 
+print(matr)
 
+#freq = calc_freq(data)
+# plot_rets(freq,fft(data))
+
+#print(np.isclose(0, 0.0001, atol=0.001))
 trange = pd.date_range(datetime.now(), datetime.now()+timedelta(days=9),freq='d')
 data1 = pd.Series([1,2,3,4,5,6,7,8,9,10],index=trange)
-
 print(len(data1))
-
 print(data1.index[0].timestamp())
-
-
 print(data1.iloc[2] * 1)
 
-def test_calc_freq(data):
 
-    calcs the length of the export of calc_freq
 
-    freq = calc_freq(data, 'seconds')
-    assert len(freq) == len(data)
-"""
 
 
 def test_window():
@@ -98,7 +100,6 @@ def test_window():
     end=75
     func=[np.sin(i) for i in x]
     data=pd.Series(func,index=x)
-
     #the test data is windowed and the 
     #endpoints are checked.
     windows=window(data, start,end)
@@ -111,7 +112,6 @@ def test_window():
     ydata=windows["Welch Window"].values
     assert ydata[0]==0
     assert ydata[len(ydata)-1]==0
-
 def test_unwindow():
     """
     test_unwindow ensures that the unwindow function
@@ -145,4 +145,8 @@ def test_unwindow():
         assert round(i,3)==round(new.values[count],3)
         count+=1
 
+def test_calc_freq(data):
+    """calcs the length of the export of calc_freq"""
+    freq = calc_freq(data, 'seconds')
+    assert len(freq) == len(data)