now the pvts function is interoperable with earth engine

forestools/plot.py

@@ -4,7 +4,7 @@
 import matplotlib.pyplot as plt
 from matplotlib.ticker import MaxNLocator
 import numpy as np
-import pandas
+import pandas as pd
 
 def plot(x, title = None, xlabel = None, ylabel = None, **kwargs):
 
@@ -42,7 +42,7 @@ def plot(x, title = None, xlabel = None, ylabel = None, **kwargs):
  p = x['Monitoring_period']['end']
  cons = 1/3
 
- elif isinstance(ts, (pandas.core.series.Series)):
+ elif isinstance(ts, (pd.core.series.Series)):
  value = x['Breakpoint']['value']
  pos = x['Monitoring_period']['end']
  post = ts.index.get_loc(pos)
@@ -71,7 +71,7 @@ def plot(x, title = None, xlabel = None, ylabel = None, **kwargs):
  # xlabel
  if xlabel is None:
  xlabel = "Index"
- if isinstance(x['Ts'], (pandas.core.series.Series)):
+ if isinstance(x['Ts'], (pd.core.series.Series)):
  xlabel = 'Time'
  xlabel = xlabel
 

forestools/pvts.py

@@ -1,8 +1,8 @@
 #!/usr/bin/env python
 # coding: utf-8
 # %%
-import numpy
-import pandas
+import numpy as np
+import pandas as pd
 
 def pvts(x, startm, endm, threshold = 5):
  '''
@@ -12,7 +12,8 @@ def pvts(x, startm, endm, threshold = 5):
  
  Parameters:
  
- x: Can be numpy.ndarray with 1d or 2d without NaN's or pandas.core.series.Series.
+ x: Can be numpy.ndarray with 1d or 2d without NaN's, pandas.core.series.Series or 
+ pandas.core.indexes.datetimes.DatetimeIndex.
  
  startm: The start of the monitoring time.
  
@@ -25,28 +26,51 @@ def pvts(x, startm, endm, threshold = 5):
  
  '''
 
- if any(numpy.isnan(x)):
- raise Exception('The object cannot contain NaN: {}'.format(x))
+ if isinstance (x, (np.ndarray)):
 
- if isinstance (x, (numpy.ndarray)):
+ if any(np.isnan(x)):
+ raise Exception('The object cannot contain NaN: {}'.format(x))
 
  if x.ndim == 1:
 
- mean_pvts = numpy.mean(x[0:(startm-1)])
- std_pvts = numpy.std(x[0:(startm-1)])
+ mean_pvts = np.mean(x[0:(startm-1)])
+ std_pvts = np.std(x[0:(startm-1)])
  li = mean_pvts - threshold*std_pvts
  value = x[endm]
 
  else:
  raise Exception('2d ndarray not supported')
 
- elif isinstance (x, (pandas.core.series.Series)):
+ elif isinstance (x, (pd.core.series.Series)):
+
+ if any(x.isnull().values):
+ raise Exception('The object cannot contain NaN: {}'.format(x))
+
+ startm_n = x.index.get_loc(startm)
+ endm_n = x.index.get_loc(endm)
+
+ mean_pvts = np.mean(x[0:(startm_n -1)])
+ std_pvts = np.std(x[0:(startm_n -1)])
+ li = mean_pvts - threshold*std_pvts
+ value = x[endm_n]
+
+ elif isinstance (x, (pd.core.frame.DataFrame)):
+
+ if any(x.isnull().values):
+ raise Exception('The object cannot contain NaN: {}'.format(x))
+
+ # whether the time series comes from google earth engine
+ if isinstance (x.index, (pd.core.indexes.datetimes.DatetimeIndex)):
+ #x = x.squeeze()
+ x.index = x.index.date
+ x.index = pd.to_datetime(x.index)
+ x = pd.Series(x.iloc[:, 0], index = x.index)
 
  startm_n = x.index.get_loc(startm)
  endm_n = x.index.get_loc(endm)
 
- mean_pvts = numpy.mean(x[0:(startm_n-1)])
- std_pvts = numpy.std(x[0:(startm_n-1)])
+ mean_pvts = np.mean(x[0:(startm_n -1)])
+ std_pvts = np.std(x[0:(startm_n -1)])
  li = mean_pvts - threshold*std_pvts
  value = x[endm_n]
 
@@ -63,7 +87,7 @@ def pvts(x, startm, endm, threshold = 5):
  else:
  output = {'Ts': x,
  'Monitoring_period': {'start': startm, 'end': endm},
- 'Breakpoint' : {'Year_index': numpy.nan, 'value': numpy.nan},
+ 'Breakpoint' : {'Year_index': np.nan, 'value': np.nan},
  'Threshold' : {'Threshold': threshold, 'Lower_limit': li}} 
  return output