From 92eb5fc1a611b8f44c138d1198fe3e901956ed66 Mon Sep 17 00:00:00 2001 From: Matthias Cuntz Date: Sun, 16 Jun 2024 19:21:42 +0200 Subject: [PATCH] Removed all warning with new pandas and numpy versions --- CHANGELOG.rst | 3 + docs/html/.buildinfo | 2 +- docs/html/_modules/hesseflux/gapfill.html | 112 ++-- docs/html/_modules/hesseflux/madspikes.html | 62 ++- docs/html/_modules/hesseflux/nee2gpp.html | 515 +++++++++++------- docs/html/_modules/hesseflux/ustarfilter.html | 89 +-- docs/html/_modules/index.html | 30 +- docs/html/_sources/contents.rst.txt | 2 + docs/html/_sources/userguide.rst.txt | 387 +++++++------ docs/html/_static/alabaster.css | 23 +- docs/html/_static/basic.css | 26 +- docs/html/_static/doctools.js | 2 +- docs/html/_static/documentation_options.js | 5 +- docs/html/_static/language_data.js | 4 +- docs/html/_static/pygments.css | 1 + docs/html/_static/searchtools.js | 191 ++++--- docs/html/_static/sphinx_highlight.js | 16 +- docs/html/api.html | 38 +- docs/html/authors.html | 36 +- docs/html/changelog.html | 36 +- docs/html/contents.html | 36 +- docs/html/gapfill.html | 62 +-- docs/html/genindex.html | 30 +- docs/html/index.html | 54 +- docs/html/madspikes.html | 57 +- docs/html/nee2gpp.html | 115 ++-- docs/html/objects.inv | Bin 514 -> 543 bytes docs/html/py-modindex.html | 30 +- docs/html/search.html | 36 +- docs/html/searchindex.js | 2 +- docs/html/userguide.html | 441 ++++++++------- docs/html/ustarfilter.html | 70 +-- docs/source/conf.py | 4 +- docs/source/contents.rst | 2 + docs/source/userguide.rst | 387 +++++++------ example/hesseflux_example.cfg | 2 +- example/postproc_europe-fluxdata.py | 17 +- src/hesseflux/gapfill.py | 77 +-- src/hesseflux/madspikes.py | 27 +- src/hesseflux/nee2gpp.py | 482 +++++++++------- src/hesseflux/ustarfilter.py | 54 +- 41 files changed, 1998 insertions(+), 1567 deletions(-) diff --git a/CHANGELOG.rst b/CHANGELOG.rst index fab04ed..ac9b9f2 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -4,6 +4,9 @@ Changelog All notable changes after its initial release in May 2020 (v2.0) are documented in this file. +v5.1 (??? 2024) + * Removed all warning with new pandas and numpy versions. + v5.0 (Jan 2023) * Dropped support for Python 3.6 because cannot test it anymore. * Add timecolumns and ftimeformat to config and post-processing file in diff --git a/docs/html/.buildinfo b/docs/html/.buildinfo index ed2c7d4..de007f8 100644 --- a/docs/html/.buildinfo +++ b/docs/html/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: a229c28f73769ec9b4c577ab465ca4f4 +config: 52be7eb0a3df7d9001d8779717d404cb tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/html/_modules/hesseflux/gapfill.html b/docs/html/_modules/hesseflux/gapfill.html index e7700b6..2023604 100644 --- a/docs/html/_modules/hesseflux/gapfill.html +++ b/docs/html/_modules/hesseflux/gapfill.html @@ -1,24 +1,24 @@ - - + - hesseflux.gapfill — hesseflux 5.1.dev0 documentation - - - - - - + hesseflux.gapfill — hesseflux 5.1.dev2 documentation + + + + + + + + - @@ -74,7 +74,9 @@

Source code for hesseflux.gapfill

 __all__ = ['gapfill']
 
 
-
[docs]def gapfill(dfin, flag=None, date=None, timeformat='%Y-%m-%d %H:%M:%S',
+

+[docs]
+def gapfill(dfin, flag=None, date=None, timeformat='%Y-%m-%d %H:%M:%S',
             colhead=None,
             sw_dev=50., ta_dev=2.5, vpd_dev=5.,
             longgap=60, fullday=False, undef=-9999, ddof=1,
@@ -353,10 +355,10 @@ 
Source code for hesseflux.gapfill
             break
     astr = 'Global radiation with name SW or starting with SW_'
     astr = astr + ' must be in input.'
-    assert sw_id,  astr
+    assert sw_id, astr
     astr = 'Air temperature with name TA or starting with TA_'
     astr = astr + ' must be in input.'
-    assert ta_id,  astr
+    assert ta_id, astr
     astr = 'Vapour pressure deficit with name VPD or starting'
     astr = astr + ' with VPD_ must be in input.'
     assert vpd_id, astr
@@ -421,11 +423,11 @@ 
Source code for hesseflux.gapfill
         if firstvalid > nn:
             if verbose > 1:
                 print('    Large margin at beginning: ', firstvalid)
-            largegap[0:(firstvalid-nn)] = True
-        if lastvalid < (ndata-nn):
+            largegap[0:(firstvalid - nn)] = True
+        if lastvalid < (ndata - nn):
             if verbose > 1:
-                print('    Large margin at end: ', lastvalid-nn)
-            largegap[(lastvalid+nn):] = True
+                print('    Large margin at end: ', lastvalid - nn)
+            largegap[(lastvalid + nn):] = True
 
         # Large gaps
 
@@ -439,12 +441,12 @@ 
Source code for hesseflux.gapfill
                     index += [i]
                     count  = 1
             if i > 0:
-                if (dflag[i] != 0) and (dflag[i-1] == 0):
+                if (dflag[i] != 0) and (dflag[i - 1] == 0):
                     index += [i]
                     count  = 1
                 elif dflag[i] != 0:
                     count += 1
-                elif (dflag[i] == 0) and (dflag[i-1] != 0):
+                elif (dflag[i] == 0) and (dflag[i - 1] != 0):
                     length += [count]
                     count = 0
                 else:
@@ -456,17 +458,18 @@ 
Source code for hesseflux.gapfill
         for i in range(len(index)):
             if length[i] > nn:
                 if verbose > 1:
-                    print('    Large gap: ', index[i], ':', index[i]+length[i])
-                largegap[index[i]:index[i]+length[i]] = True
+                    print('    Large gap: ', index[i], ':',
+                          index[i] + length[i])
+                largegap[index[i]:index[i] + length[i]] = True
 
         # set or unset rest of days in large gaps
         if fullday:
-            for i in range(ndata-1):
+            for i in range(ndata - 1):
                 # end of large margin
-                if largegap[i] and not largegap[i+1]:
+                if largegap[i] and not largegap[i + 1]:
                     largegap[np.where(day == day[i])[0]] = False
                 # beginning of large margin
-                elif not largegap[i] and largegap[i+1]:
+                elif not largegap[i] and largegap[i + 1]:
                     largegap[np.where(day == day[i])[0]] = False
                 else:
                     continue
@@ -499,14 +502,14 @@ 
Source code for hesseflux.gapfill
                 # search for values around the met-conditions
                 # in a window of time
                 # (one week in the first iteration and odd weeks in the next)
-                j1  = j - np.arange(1, week+1, dtype=int) + 1
+                j1  = j - np.arange(1, week + 1, dtype=int) + 1
                 j2  = j + np.arange(1, week, dtype=int)
                 jj  = np.append(j1, j2)
-                win = np.unique(np.sort(np.clip(jj, 0, ndata-1)))
+                win = np.unique(np.sort(np.clip(jj, 0, ndata - 1)))
                 # get boolean array where meteo-conditions are in a given width
-                conditions = ( (np.abs(sw[win]-sw[j])   < sw_devmax) &
-                               (np.abs(ta[win]-ta[j])   < ta_dev) &
-                               (np.abs(vpd[win]-vpd[j]) < vpd_dev) &
+                conditions = ( (np.abs(sw[win] - sw[j])   < sw_devmax) &
+                               (np.abs(ta[win] - ta[j])   < ta_dev) &
+                               (np.abs(vpd[win] - vpd[j]) < vpd_dev) &
                                total_flg[win] )
                 num4avg = np.sum(conditions)
                 # we need at least two samples with similar conditions
@@ -523,10 +526,10 @@ 
Source code for hesseflux.gapfill
                         dflag_f[j] = 1
                     continue
                 else:  # --> extend time window to two weeks
-                    j1  = j - np.arange(1, 2*week+1, dtype=int) + 1
-                    j2  = j + np.arange(1, 2*week, dtype=int)
+                    j1  = j - np.arange(1, 2 * week + 1, dtype=int) + 1
+                    j2  = j + np.arange(1, 2 * week, dtype=int)
                     jj  = np.append(j1, j2)
-                    win = np.unique(np.sort(np.clip(jj, 0, ndata-1)))
+                    win = np.unique(np.sort(np.clip(jj, 0, ndata - 1)))
                     conditions = ( (np.abs(sw[win]  - sw[j])  < sw_devmax) &
                                    (np.abs(ta[win]  - ta[j])  < ta_dev) &
                                    (np.abs(vpd[win] - vpd[j]) < vpd_dev) &
@@ -554,12 +557,12 @@ 
Source code for hesseflux.gapfill
 
             # Method 2: just global radiation available
             if sw_flg[j] == 0:
-                j1  = j - np.arange(1, week+1, dtype=int) + 1
+                j1  = j - np.arange(1, week + 1, dtype=int) + 1
                 j2  = j + np.arange(1, week, dtype=int)
                 jj  = np.append(j1, j2)
-                win = np.unique(np.sort(np.clip(jj, 0, ndata-1)))
+                win = np.unique(np.sort(np.clip(jj, 0, ndata - 1)))
                 # get boolean array where meteo-conditions are in a given width
-                conditions = ( (np.abs(sw[win]-sw[j]) < sw_devmax) &
+                conditions = ( (np.abs(sw[win] - sw[j]) < sw_devmax) &
                                total_flg[win] )
                 num4avg = np.sum(conditions)
                 # we need at least two samples with similar conditions
@@ -578,12 +581,12 @@ 
Source code for hesseflux.gapfill
             # Method 3: same hour
             enough = False
             for i in range(2):
-                t_win = (nperday * (2*i+1))//2
-                j1  = j - np.arange(1, t_win+1, dtype=int) + 1
+                t_win = (nperday * (2 * i + 1)) // 2
+                j1  = j - np.arange(1, t_win + 1, dtype=int) + 1
                 j2  = j + np.arange(1, t_win, dtype=int)
                 jj  = np.append(j1, j2)
-                win = np.unique(np.sort(np.clip(jj, 0, ndata-1)))
-                conditions = ( (np.abs(hour[win]-hour[j]) < 1.1)
+                win = np.unique(np.sort(np.clip(jj, 0, ndata - 1)))
+                conditions = ( (np.abs(hour[win] - hour[j]) < 1.1)
                                & (dflag[win] == 0) )
                 num4avg = np.sum(conditions)
                 if num4avg >= 2:
@@ -607,10 +610,10 @@ 
Source code for hesseflux.gapfill
             # Method 4: same as 1 but for 3-12 weeks
             if meteo_flg[j]:
                 for multi in range(3, 12):
-                    j1  = j - np.arange(1, multi*week+1, dtype=int) + 1
-                    j2  = j + np.arange(1, multi*week, dtype=int)
+                    j1  = j - np.arange(1, multi * week + 1, dtype=int) + 1
+                    j2  = j + np.arange(1, multi * week, dtype=int)
                     jj  = np.append(j1, j2)
-                    win = np.unique(np.sort(np.clip(jj, 0, ndata-1)))
+                    win = np.unique(np.sort(np.clip(jj, 0, ndata - 1)))
                     conditions = ( (np.abs(sw[win]  - sw[j])  < sw_devmax) &
                                    (np.abs(ta[win]  - ta[j])  < ta_dev) &
                                    (np.abs(vpd[win] - vpd[j]) < vpd_dev) &
@@ -640,10 +643,10 @@ 
Source code for hesseflux.gapfill
             # Method 5: same as 2 but for 2-12 weeks
             if sw_flg[j] == 0:
                 for multi in range(2, 12):
-                    j1  = j - np.arange(1, multi*week+1, dtype=int) + 1
-                    j2  = j + np.arange(1, multi*week, dtype=int)
+                    j1  = j - np.arange(1, multi * week + 1, dtype=int) + 1
+                    j2  = j + np.arange(1, multi * week, dtype=int)
                     jj  = np.append(j1, j2)
-                    win = np.unique(np.sort(np.clip(jj, 0, ndata-1)))
+                    win = np.unique(np.sort(np.clip(jj, 0, ndata - 1)))
                     # get boolean array where meteo-conditions are
                     # in a given width
                     conditions = ( (np.abs(sw[win] - sw[j]) < sw_devmax) &
@@ -669,12 +672,12 @@ 
Source code for hesseflux.gapfill
 
             # Method 6: same as 3 but for 3-120 days
             for i in range(3, 120):
-                t_win = nperday * (2*i+1)/2
-                j1  = j - np.arange(1, t_win+1, dtype=int) + 1
+                t_win = nperday * (2 * i + 1) / 2
+                j1  = j - np.arange(1, t_win + 1, dtype=int) + 1
                 j2  = j + np.arange(1, t_win, dtype=int)
                 jj  = np.append(j1, j2)
-                win = np.unique(np.sort(np.clip(jj, 0, ndata-1)))
-                conditions = ( (np.abs(hour[win]-hour[j]) < 1.1)
+                win = np.unique(np.sort(np.clip(jj, 0, ndata - 1)))
+                conditions = ( (np.abs(hour[win] - hour[j]) < 1.1)
                                & (dflag[win] == 0) )
                 num4avg = np.sum(conditions)
                 if num4avg >= 2:
@@ -716,6 +719,7 @@ 
Source code for hesseflux.gapfill
         return dfout, ffout

 
 
+
 if __name__ == '__main__':
     import doctest
     doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)
@@ -823,7 +827,7 @@ 
Related Topics

   
 
 

-
+
 
         

       

       

     

     

-      ©2009-2023, Matthias Cuntz.
+      ©2009-2024, Matthias Cuntz.
       
       |
-      Powered by Sphinx 6.1.3
-      & Alabaster 0.7.13
+      Powered by Sphinx 7.3.7
+      & Alabaster 0.7.16
       
     

 
diff --git a/docs/html/_modules/hesseflux/madspikes.html b/docs/html/_modules/hesseflux/madspikes.html
index 0522cf1..e688fc3 100644
--- a/docs/html/_modules/hesseflux/madspikes.html
+++ b/docs/html/_modules/hesseflux/madspikes.html
@@ -1,24 +1,24 @@
-
 
 
-
+
   
     
     
-    hesseflux.madspikes — hesseflux 5.1.dev0 documentation
-    
-    
-    
-    
-    
-    
+    hesseflux.madspikes — hesseflux 5.1.dev2 documentation
+    
+    
+    
+    
+    
+    
     
     
    
   
   
+
+  
   
-  
 
   
   
@@ -59,6 +59,7 @@ 
Source code for hesseflux.madspikes
     * Removed iteration, Apr 2020, Matthias Cuntz
     * Using numpy docstring format, May 2020, Matthias Cuntz
     * Improved flake8 and numpy docstring, Oct 2021, Matthias Cuntz
+    * Removed np.float and np.bool, Jun 2024, Matthias Cuntz
 
 """
 from __future__ import division, absolute_import, print_function
@@ -70,9 +71,11 @@ 
Source code for hesseflux.madspikes
 __all__ = ['madspikes']
 
 
-
[docs]def madspikes(dfin, flag=None, isday=None,
+

+[docs]
+def madspikes(dfin, flag=None, isday=None,
               colhead=None, undef=-9999,
-              nscan=15*48, nfill=1*48,
+              nscan=15 * 48, nfill=1 * 48,
               z=7, deriv=2, swthr=10.,
               plot=False):
     """
@@ -146,7 +149,7 @@ 
Source code for hesseflux.madspikes
             estr = ('Length of colhead must be number of columns in input'
                     'array. len(colhead)=' + str(len(colhead)) +
                     ' shape(input)=(' + str(dfin.shape[0]) + ',' +
-                    str(dfin.shape[1])+').')
+                    str(dfin.shape[1]) + ').')
             raise ValueError(estr)
     else:
         isnumpy = False
@@ -206,15 +209,15 @@ 
Source code for hesseflux.madspikes
 
     # parameters
     nrow, ncol = df.shape
-    half_scan_win = nscan//2
-    half_fill_win = nfill//2
+    half_scan_win = nscan // 2
+    half_fill_win = nfill // 2
 
     # calculate dusk and dawn times and separate in day and night
-    isdawn = np.zeros(nrow, dtype=np.bool)
-    isdusk = np.zeros(nrow, dtype=np.bool)
-    dis    = isday.astype(int) - np.roll(isday,-1).astype(int)
+    isdawn = np.zeros(nrow, dtype=bool)
+    isdusk = np.zeros(nrow, dtype=bool)
+    dis    = isday.astype(int) - np.roll(isday, -1).astype(int)
     isdawn[:-1]    = np.where(dis[:-1] == -1, True, False)
-    isdusk[:-1]    = np.where(dis[:-1] ==  1, True, False)
+    isdusk[:-1]    = np.where(dis[:-1] == 1, True, False)
     isddday        = isdawn
     tmp            = np.roll(isdusk, 1)
     isddday[1:]   += tmp[1:]  # start and end of day
@@ -246,23 +249,25 @@ 
Source code for hesseflux.madspikes
             np.nan)
 
         # iterate over fill window
-        for j in range(half_fill_win, nrow-1, 2*half_fill_win):
+        for j in range(half_fill_win, nrow - 1, 2 * half_fill_win):
             j1 = max(j - half_scan_win - 1, 0)
             j2 = min(j + half_scan_win + 1, nrow)
             fill_start = max(j - half_fill_win, 1)
-            fill_end   = min(j + half_fill_win, nrow-1)
+            fill_end   = min(j + half_fill_win, nrow - 1)
 
             dd = data_day[j1:j2].to_numpy()
             day_flag = mad(np.ma.masked_array(data=dd, mask=np.isnan(dd)),
                            z=z, deriv=deriv)
             ff.iloc[fill_start:fill_end, cols.index(hcol)] += (
-                np.where(day_flag[fill_start-j1-1:fill_end-j1-1], 2, 0))
+                np.where(day_flag[fill_start - j1 - 1:fill_end - j1 - 1],
+                         2, 0))
 
             nn = data_night[j1:j2]
             night_flag = mad(np.ma.masked_array(data=nn, mask=np.isnan(nn)),
                              z=z, deriv=deriv)
             ff.iloc[fill_start:fill_end, cols.index(hcol)] += (
-                np.where(night_flag[fill_start-j1-1:fill_end-j1-1], 2, 0))
+                np.where(night_flag[fill_start - j1 - 1:fill_end - j1 - 1],
+                         2, 0))
 
         if plot:
             fig = plt.figure(1)
@@ -288,6 +293,7 @@ 
Source code for hesseflux.madspikes
         return ff

 
 
+
 if __name__ == '__main__':
     import doctest
     doctest.testmod()
@@ -339,7 +345,7 @@ 
Related Topics

   
 
 

-
+
 
         

       

       

     

     

-      ©2009-2023, Matthias Cuntz.
+      ©2009-2024, Matthias Cuntz.
       
       |
-      Powered by Sphinx 6.1.3
-      & Alabaster 0.7.13
+      Powered by Sphinx 7.3.7
+      & Alabaster 0.7.16
       
     

 
diff --git a/docs/html/_modules/hesseflux/nee2gpp.html b/docs/html/_modules/hesseflux/nee2gpp.html
index adfe081..b910adf 100644
--- a/docs/html/_modules/hesseflux/nee2gpp.html
+++ b/docs/html/_modules/hesseflux/nee2gpp.html
@@ -1,24 +1,24 @@
-
 
 
-
+
   
     
     
-    hesseflux.nee2gpp — hesseflux 5.1.dev0 documentation
-    
-    
-    
-    
-    
-    
+    hesseflux.nee2gpp — hesseflux 5.1.dev2 documentation
+    
+    
+    
+    
+    
+    
     
     
    
   
   
+
+  
   
-  
 
   
   
@@ -49,13 +49,15 @@ 
Source code for hesseflux.nee2gpp
 * Set default undef to NaN, Mar 2012, Arndt Piayda
 * Add wrapper nee2gpp for individual routines, Nov 2012, Matthias Cuntz
 * Ported to Python 3, Feb 2013, Matthias Cuntz
-* Use generel cost function cost_abs from functions module, May 2013, Matthias Cuntz
+* Use generel cost function cost_abs from functions module,
+  May 2013, Matthias Cuntz
 * Use fmin_tnc to allow params < 0, Aug 2014, Arndt Piayda
 * Keyword nogppnight, Aug 2014, Arndt Piayda
 * Add wrapper nee2gpp for individual routines, Nov 2012, Matthias Cuntz
 * Add wrapper nee2gpp for individual routines, Nov 2012, Matthias Cuntz
 * Input can be pandas Dataframe or numpy array(s), Apr 2020, Matthias Cuntz
 * Using numpy docstring format, May 2020, Matthias Cuntz
+* Removed np.float and np.int, Jun 2024, Matthias Cuntz
 
 .. moduleauthor:: Matthias Cuntz, Arndt Piayda
 
@@ -79,8 +81,11 @@ 
Source code for hesseflux.nee2gpp
 
 
 # ----------------------------------------------------------------------
-
[docs]def nee2gpp(dfin, flag=None, isday=None, date=None, timeformat='%Y-%m-%d %H:%M:%S', colhead=None,
-            undef=-9999, method='reichstein', nogppnight=False, swthr=10.):
+

+[docs]
+def nee2gpp(dfin, flag=None, isday=None, date=None,
+            timeformat='%Y-%m-%d %H:%M:%S', colhead=None, undef=-9999,
+            method='reichstein', nogppnight=False, swthr=10.):
     """
     Calculate photosynthesis (GPP) and ecosystem respiration (RECO)
     from Eddy covariance CO2 flux data.
@@ -104,26 +109,29 @@ 
Source code for hesseflux.nee2gpp
         incoming shortwave radiation and air vapour pressure deficit.
 
         `dfin` can be a pandas.Dataframe with the columns
-        'FC' or 'NEE' (or starting with 'FC\_' or 'NEE\_') for observed CO2 flux [umol(CO2) m-2 s-1]
-        'TA'    (or starting with 'TA\_') for air temperature [K]
+        'FC' or 'NEE' (or starting with 'FC\\_' or 'NEE\\_') for observed
+        CO2 flux [umol(CO2) m-2 s-1]
+        'TA'    (or starting with 'TA\\_') for air temperature [K]
 
         `method='lasslop'` or `method='day'` needs also
-        'SW_IN' (or starting with 'SW_IN') for incoming short-wave radiation [W m-2]
+        'SW_IN' (or starting with 'SW_IN') for incoming short-wave
+        radiation [W m-2]
         'VPD'   (or starting with 'VPD') for air vapour deficit [Pa]
         The index is taken as date variable.
 
         `dfin` can also me a numpy array with the same columns. In this case
         `colhead`, `date`, and possibly `dateformat` must be given.
     flag : pandas.Dataframe or numpy.array, optional
-        flag Dataframe or array has the same shape as `dfin`. Non-zero values in
-        `flag` will be treated as missing values in `dfin`.
+        flag Dataframe or array has the same shape as `dfin`.
+        Non-zero values in `flag` will be treated as missing values in `dfin`.
 
         `flag` must follow the same rules as `dfin` if pandas.Dataframe.
 
-        If `flag` is numpy array, `df.columns.values` will be used as column heads
-        and the index of `dfin` will be copied to `flag`.
+        If `flag` is numpy array, `df.columns.values` will be used as column
+        heads and the index of `dfin` will be copied to `flag`.
     isday : array_like of bool, optional
-        True when it is day, False when night. Must have the same length as `dfin.shape[0]`.
+        True when it is day, False when night. Must have the same length
+        as `dfin.shape[0]`.
 
         If `isday` is not given, `dfin` must have a column with head 'SW_IN' or
         starting with 'SW_IN'. `isday` will then be `dfin['SW_IN'] > swthr`.
@@ -136,22 +144,29 @@ 
Source code for hesseflux.nee2gpp
         See strftime documentation of Python's datetime module:
         https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior
     colhed : array_like of str, optional
-        column names if `dfin` is numpy array. See `dfin` for mandatory column names.
+        column names if `dfin` is numpy array. See `dfin` for mandatory
+        column names.
     undef : float, optional
-        values having `undef` value are treated as missing values in `dfin` (default: -9999)
+        values having `undef` value are treated as missing values in `dfin`
+        (default: -9999)
     method : str, optional
         method to use for partitioning. Possible values are:
 
-        'global' or 'falge':     fit of Reco vs. temperature to all nighttime data
+        'global' or 'falge':     fit of Reco vs. temperature to all nighttime
+                                 data
 
-        'local' of 'reichstein': several fits over the season of Reco vs. temperature
-                                 as in Reichstein et al. (2005) (default)
+        'local' of 'reichstein': several fits over the season of Reco vs.
+                                 temperature as in Reichstein et al. (2005)
+                                 (default)
 
-        'day' or 'lasslop':      method of Lasslop et al. (2010) fitting a light-response curve
+        'day' or 'lasslop':      method of Lasslop et al. (2010) fitting a
+                                 light-response curve
     nogppnight : float, optional
-        GPP will be set to zero at night. RECO will then equal NEE at night (default: False)
+        GPP will be set to zero at night. RECO will then equal NEE at night
+        (default: False)
     swthr : float, optional
-        Threshold to determine daytime from incoming shortwave radiation if `isday` not given (default: 10).
+        Threshold to determine daytime from incoming shortwave radiation
+        if `isday` not given (default: 10).
 
     Returns
     -------
@@ -162,22 +177,25 @@ 
Source code for hesseflux.nee2gpp
 
     Notes
     -----
-    Negative respiration possible at night if GPP is forced to 0 with `nogppnight=True`.
+    Negative respiration possible at night if GPP is forced to 0 with
+    `nogppnight=True`.
 
     References
     ----------
     .. [1] Falge et al. (2001)
-       Gap filling strategies for defensible annual sums of net ecosystem exchange,
+       Gap filling strategies for defensible annual sums of
+       net ecosystem exchange,
        Acricultural and Forest Meteorology 107, 43-69
 
     .. [2] Reichstein et al. (2005)
-       On the separation of net ecosystem exchange into assimilation and ecosystem
-       respiration: review and improved algorithm,
+       On the separation of net ecosystem exchange into assimilation
+       and ecosystem respiration: review and improved algorithm,
        Global Change Biology 11, 1424-1439
 
     .. [3] Lasslop et al. (2010)
-       Separation of net ecosystem exchange into assimilation and respiration using
-       a light response curve approach: critical issues and global evaluation,
+       Separation of net ecosystem exchange into assimilation and respiration
+       using a light response curve approach: critical issues and global
+       evaluation,
        Global Change Biology 16, 187-208
 
     Examples
@@ -206,7 +224,8 @@ 
Source code for hesseflux.nee2gpp
     >>> # flag
     >>> flag = np.where(dfin == undef, 2, 0)
     >>> # partition
-    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead, undef=undef, method='local')
+    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead,
+    ...                     undef=undef, method='local')
     >>> print(GPP[1120:1128])
     [-9.99900000e+03 -9.99900000e+03 -9.99900000e+03  4.40606871e+00
       8.31942152e+00  1.06242542e+01  8.49245664e+00  1.12381973e+01]
@@ -214,27 +233,32 @@ 
Source code for hesseflux.nee2gpp
     [1.68311981 1.81012431 1.9874173  2.17108871 2.38759152 2.64372415
      2.90076664 3.18592735]
 
-    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead, undef=undef, method='local')
+    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead,
+    ...                     undef=undef, method='local')
     >>> print(GPP[1120:1128])
     [-9.99900000e+03 -9.99900000e+03 -9.99900000e+03  4.40606871e+00
       8.31942152e+00  1.06242542e+01  8.49245664e+00  1.12381973e+01]
 
-    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead, undef=undef, method='global')
+    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead,
+    ...                     undef=undef, method='global')
     >>> print(GPP[1120:1128])
     [-9.99900000e+03 -9.99900000e+03 -9.99900000e+03  4.33166157e+00
       8.18228013e+00  1.04092252e+01  8.19395317e+00  1.08427448e+01]
 
-    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead, undef=undef, method='Reichstein')
+    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead,
+    ...                     undef=undef, method='Reichstein')
     >>> print(GPP[1120:1128])
     [-9.99900000e+03 -9.99900000e+03 -9.99900000e+03  4.40606871e+00
       8.31942152e+00  1.06242542e+01  8.49245664e+00  1.12381973e+01]
 
-    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead, undef=undef, method='reichstein')
+    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead,
+    ...                     undef=undef, method='reichstein')
     >>> print(GPP[1120:1128])
     [-9.99900000e+03 -9.99900000e+03 -9.99900000e+03  4.40606871e+00
       8.31942152e+00  1.06242542e+01  8.49245664e+00  1.12381973e+01]
 
-    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead, undef=undef, method='day')
+    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead,
+    ...                     undef=undef, method='day')
     >>> print(GPP[1120:1128])
     [-9.99900000e+03 -9.99900000e+03 -9.99900000e+03  2.78457540e+00
       6.63212545e+00  8.88902165e+00  6.74243873e+00  9.51364527e+00]
@@ -245,56 +269,66 @@ 
Source code for hesseflux.nee2gpp
     History
     -------
     Written  Matthias Cuntz, Mar 2012
-    Modified Arndt Piayda,   Mar 2012 - undef=np.nan
-             Matthias Cuntz, Nov 2012 - wrapper for individual routines nee2gpp_reichstein etc.
-             Matthias Cuntz, Feb 2013 - ported to Python 3
-             Matthias Cuntz, May 2013 - replaced cost functions by generel cost function cost_abs if possible
-             Arndt Piayda,   Aug 2014 - replaced fmin with fmin_tnc to permit params<0,
-                                        permit gpp<0 at any time if nogppnight=True 
+    Modified Arndt Piayda,   Mar 2012
+                 - undef=np.nan
+             Matthias Cuntz, Nov 2012
+                 - wrapper for individual routines nee2gpp_reichstein etc.
+             Matthias Cuntz, Feb 2013
+                 - ported to Python 3
+             Matthias Cuntz, May 2013
+                 - replaced cost functions by generel cost function cost_abs
+                   if possible
+             Arndt Piayda,   Aug 2014
+                 - replaced fmin with fmin_tnc to permit params<0,
+                   permit gpp<0 at any time if nogppnight=True
+             Matthias Cuntz, Jun 2024
+                 - removed np.int and np.float
+
     """
     # Check input
     # numpy or panda
     if isinstance(dfin, (np.ndarray, np.ma.MaskedArray)):
         isnumpy = True
-        istrans = False
-        assert colhead is not None, 'colhead must be given if input is numpy.ndarray.'
+        assert colhead is not None, (
+            'colhead must be given if input is numpy.ndarray.')
         if dfin.shape[0] == len(colhead):
-            istrans = True
             df = pd.DataFrame(dfin.T, columns=colhead)
         elif dfin.shape[1] == len(colhead):
             df = pd.DataFrame(dfin, columns=colhead)
         else:
-            raise ValueError('Length of colhead must be number of columns in input array. len(colhead)='+str(len(colhead))+' shape(input)=('+str(dfin.shape[0])+','+str(dfin.shape[1])+').')
+            raise ValueError('Length of colhead must be number of columns'
+                             ' in input array. len(colhead)=' +
+                             str(len(colhead)) + ' shape(input)=(' +
+                             str(dfin.shape[0]) + ',' +
+                             str(dfin.shape[1]) + ').')
         assert date is not None, 'Date must be given if input is numpy arrary.'
         df['Datetime'] = pd.to_datetime(date, format=timeformat)
         df.set_index('Datetime', drop=True, inplace=True)
     else:
         isnumpy = False
-        istrans = False
-        assert isinstance(dfin, pd.core.frame.DataFrame), 'Input must be either numpy.ndarray or pandas.DataFrame.'
+        assert isinstance(dfin, pd.core.frame.DataFrame), (
+            'Input must be either numpy.ndarray or pandas.DataFrame.')
         df = dfin.copy(deep=True)
 
     # Incoming flags
     if flag is not None:
         if isinstance(flag, (np.ndarray, np.ma.MaskedArray)):
-            fisnumpy = True
-            fistrans = False
             if flag.shape[0] == len(df):
                 ff = pd.DataFrame(flag, columns=df.columns.values)
             elif flag.shape[1] == len(df):
-                fistrans = True
                 ff = pd.DataFrame(flag.T, columns=df.columns.values)
             else:
-                raise ValueError('flag must have same shape as data array. data: ({:d},{:d}); flag: ({:d},{:d})'.format(dfin.shape[0], dfin.shape[1], flag.shape[0], flag.shape[1]))
+                raise ValueError(
+                    'flag must have same shape as data array.'
+                    ' data: ({:d},{:d}); flag: ({:d},{:d})'.format(
+                        dfin.shape[0], dfin.shape[1], flag.shape[0],
+                        flag.shape[1]))
             ff = ff.set_index(df.index)
         else:
-            fisnumpy = False
-            fistrans = False
-            assert isinstance(flag, pd.core.frame.DataFrame), 'Flag must be either numpy.ndarray or pandas.DataFrame.'
+            assert isinstance(flag, pd.core.frame.DataFrame), (
+                'Flag must be either numpy.ndarray or pandas.DataFrame.')
             ff = flag.copy(deep=True)
     else:
-        fisnumpy = isnumpy
-        fistrans = istrans
         # flags: 0: good; 1: input flagged; 2: output flagged
         ff              = df.copy(deep=True).astype(int)
         ff[:]           = 0
@@ -308,9 +342,11 @@ 
Source code for hesseflux.nee2gpp
             if cc.startswith('SW_IN'):
                 sw_id = cc
                 break
-        assert sw_id, 'Global radiation with name SW or starting with SW_ must be in input if isday not given.'
-        isday = df[sw_id] > swthr # Papale et al. (Biogeosciences, 2006): 20; REddyProc: 10
-    if isinstance(isday, (pd.core.series.Series,pd.core.frame.DataFrame)):
+        assert sw_id, ('Global radiation with name SW or starting with'
+                       ' SW_ must be in input if isday not given.')
+        # Papale et al. (Biogeosciences, 2006): 20; REddyProc: 10
+        isday = df[sw_id] > swthr
+    if isinstance(isday, (pd.core.series.Series, pd.core.frame.DataFrame)):
         isday = isday.to_numpy()
     isday[isday == undef] = np.nan
     ff[np.isnan(isday)]   = 1
@@ -320,10 +356,12 @@ 
Source code for hesseflux.nee2gpp
         dfout = _nee2gpp_falge(df, ff, isday, undef=undef)
     # Local relationship = Reichstein et al. (2005)
     elif ((method.lower() == 'local') | (method.lower() == 'reichstein')):
-        dfout = _nee2gpp_reichstein(df, ff, isday, undef=undef, nogppnight=nogppnight)
+        dfout = _nee2gpp_reichstein(df, ff, isday, undef=undef,
+                                    nogppnight=nogppnight)
     # Lasslop et al. (2010) daytime method
     elif ((method.lower() == 'day') | (method.lower() == 'lasslop')):
-        dfout = _nee2gpp_lasslop(df, ff, isday, undef=undef, nogppnight=nogppnight)
+        dfout = _nee2gpp_lasslop(df, ff, isday, undef=undef,
+                                 nogppnight=nogppnight)
     # Include new methods here
     else:
         raise ValueError('Error nee2gpp: method not implemented yet.')
@@ -334,6 +372,7 @@ 
Source code for hesseflux.nee2gpp
         return dfout

 
 
+
 # ----------------------------------------------------------------------
 def _nee2gpp_falge(df, ff, isday, undef=-9999):
     """
@@ -347,8 +386,9 @@ 
Source code for hesseflux.nee2gpp
         time series of CO2 fluxes and air temperature.
 
         pandas.Dataframe with the columns
-        'FC' or 'NEE' (or starting with 'FC\_' or 'NEE\_') for observed CO2 flux [umol(CO2) m-2 s-1]
-        'TA'    (or starting with 'TA\_') for air temperature [K]
+        'FC' or 'NEE' (or starting with 'FC\\_' or 'NEE\\_') for observed
+        CO2 flux [umol(CO2) m-2 s-1]
+        'TA'    (or starting with 'TA\\_') for air temperature [K]
         The index is taken as date variable.
     ff : pandas.Dataframe
         flag Dataframe or array has the same shape as `df`. Non-zero values in
@@ -356,9 +396,11 @@ 
Source code for hesseflux.nee2gpp
 
         `ff` must follow the same rules as `df`.
     isday : array_like of bool
-        True when it is day, False when night. Must have the same length as `df.shape[0].`
+        True when it is day, False when night. Must have the same length
+        as `df.shape[0].`
     undef : float, optional
-        values having `undef` value are treated as missing values in `df` (default: -9999)
+        values having `undef` value are treated as missing values in `df`
+        (default: -9999)
 
     Returns
     -------
@@ -369,7 +411,8 @@ 
Source code for hesseflux.nee2gpp
     References
     ----------
     .. [1] Falge et al. (2001)
-       Gap filling strategies for defensible annual sums of net ecosystem exchange,
+       Gap filling strategies for defensible annual sums of
+       net ecosystem exchange,
        Acricultural and Forest Meteorology 107, 43-69
 
     Examples
@@ -384,7 +427,8 @@ 
Source code for hesseflux.nee2gpp
     >>> head  = fread(ifile, skip=2, header=True)
     >>> head1 = head[0]
     >>> # date
-    >>> jdate = date2dec(dy=dat[0,:], mo=dat[1,:], yr=dat[2,:], hr=dat[3,:], mi=dat[4,:])
+    >>> jdate = date2dec(dy=dat[0,:], mo=dat[1,:], yr=dat[2,:],
+    ...                  hr=dat[3,:], mi=dat[4,:])
     >>> adate = dec2date(jdate, eng=True)
     >>> # colhead
     >>> idx   = []
@@ -398,7 +442,8 @@ 
Source code for hesseflux.nee2gpp
     >>> # flag
     >>> flag = np.where(dfin == undef, 2, 0)
     >>> # partition
-    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead, undef=undef, method='global')
+    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead,
+    ...                     undef=undef, method='global')
     >>> print(GPP[1120:1128])
     [-9.99900000e+03 -9.99900000e+03 -9.99900000e+03  4.33166157e+00
       8.18228013e+00  1.04092252e+01  8.19395317e+00  1.08427448e+01]
@@ -409,11 +454,14 @@ 
Source code for hesseflux.nee2gpp
     Modified Arndt Piayda,   Mar 2012 - undef=np.nan
              Matthias Cuntz, Nov 2012 - individual routine
              Matthias Cuntz, Feb 2013 - ported to Python 3
+             Matthias Cuntz, Jun 2024 - removed np.int and np.float
+
     """
     # Variables
     fc_id = ''
     for cc in df.columns:
-        if cc.startswith('FC_') or (cc == 'FC') or cc.startswith('NEE_') or (cc == 'NEE'):
+        if ( cc.startswith('FC_') or (cc == 'FC') or cc.startswith('NEE_') or
+             (cc == 'NEE') ):
             fc_id = cc
             break
     ta_id = ''
@@ -421,12 +469,15 @@ 
Source code for hesseflux.nee2gpp
         if cc.startswith('TA_') or (cc == 'TA'):
             ta_id = cc
             break
-    assert fc_id, 'Carbon net flux with name FC or NEE or starting with FC_ or NEE_ must be in input.'
-    assert ta_id, 'Air temperature with name TA or starting with TA_ must be in input.'
+    assert fc_id, ('Carbon net flux with name FC or NEE or starting with FC_'
+                   ' or NEE_ must be in input.')
+    assert ta_id, ('Air temperature with name TA or starting with TA_ must'
+                   ' be in input.')
 
     nee    = np.ma.array(df[fc_id], mask=(ff[fc_id] > 0))
     t      = np.ma.array(df[ta_id], mask=(ff[ta_id] > 0))
-    misday = np.ma.array(isday, mask=((~np.isfinite(isday)) | (isday == undef)))
+    misday = np.ma.array(isday, mask=((~np.isfinite(isday)) |
+                                      (isday == undef)))
 
     # Partition - Global relationship as in Falge et al. (2001)
 
@@ -439,16 +490,16 @@ 
Source code for hesseflux.nee2gpp
     p        = opt.fmin(cost_abs, [2., 200.],
                         args=(lloyd_fix_p, tt, net), disp=False)
 
-    Reco     = np.ones(ndata)*undef
+    Reco     = np.ones(ndata) * undef
     ii       = np.where(~t.mask)[0]
     Reco[ii] = lloyd_fix(t[ii], p[0], p[1])
 
     # GPP
-    GPP     = np.ones(ndata)*undef
+    GPP     = np.ones(ndata) * undef
     ii      = np.where(~(t.mask | nee.mask))[0]
     GPP[ii] = Reco[ii] - nee[ii]
 
-    dfout = pd.DataFrame({'GPP':GPP, 'RECO':Reco}, index=df.index)
+    dfout = pd.DataFrame({'GPP': GPP, 'RECO': Reco}, index=df.index)
 
     return dfout
 
@@ -467,8 +518,9 @@ 
Source code for hesseflux.nee2gpp
         time series of CO2 fluxes and air temperature.
 
         pandas.Dataframe with the columns
-        'FC' or 'NEE' (or starting with 'FC\_' or 'NEE\_') for observed CO2 flux [umol(CO2) m-2 s-1]
-        'TA'    (or starting with 'TA\_') for air temperature [K]
+        'FC' or 'NEE' (or starting with 'FC\\_' or 'NEE\\_') for
+        observed CO2 flux [umol(CO2) m-2 s-1]
+        'TA'    (or starting with 'TA\\_') for air temperature [K]
         The index is taken as date variable.
     ff : pandas.Dataframe
         flag Dataframe or array has the same shape as `df`. Non-zero values in
@@ -476,11 +528,14 @@ 
Source code for hesseflux.nee2gpp
 
         `ff` must follow the same rules as `df`.
     isday : array_like of bool
-        True when it is day, False when night. Must have the same length as `df.shape[0].`
+        True when it is day, False when night. Must have the same length
+        as `df.shape[0].`
     undef : float, optional
-        values having `undef` value are treated as missing values in `df` (default: -9999)
+        values having `undef` value are treated as missing values in `df`
+        (default: -9999)
     nogppnight : float, optional
-        GPP will be set to zero at night. RECO will then equal NEE at night (default: False)
+        GPP will be set to zero at night. RECO will then equal NEE at night
+        (default: False)
 
     Returns
     -------
@@ -491,8 +546,8 @@ 
Source code for hesseflux.nee2gpp
     References
     ----------
     .. [2] Reichstein et al. (2005)
-       On the separation of net ecosystem exchange into assimilation and ecosystem
-       respiration: review and improved algorithm,
+       On the separation of net ecosystem exchange into assimilation and
+       ecosystem respiration: review and improved algorithm,
        Global Change Biology 11, 1424-1439
 
     Examples
@@ -507,7 +562,8 @@ 
Source code for hesseflux.nee2gpp
     >>> head  = fread(ifile, skip=2, header=True)
     >>> head1 = head[0]
     >>> # date
-    >>> jdate = date2dec(dy=dat[0,:], mo=dat[1,:], yr=dat[2,:], hr=dat[3,:], mi=dat[4,:])
+    >>> jdate = date2dec(dy=dat[0,:], mo=dat[1,:], yr=dat[2,:], hr=dat[3,:],
+    ...                  mi=dat[4,:])
     >>> adate = dec2date(jdate, eng=True)
     >>> # colhead
     >>> idx   = []
@@ -521,7 +577,8 @@ 
Source code for hesseflux.nee2gpp
     >>> # flag
     >>> flag = np.where(dfin == undef, 2, 0)
     >>> # partition
-    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead, undef=undef, method='local')
+    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead,
+    ...                     undef=undef, method='local')
     >>> print(GPP[1120:1128])
     [-9.99900000e+03 -9.99900000e+03 -9.99900000e+03  4.40606871e+00
       8.31942152e+00  1.06242542e+01  8.49245664e+00  1.12381973e+01]
@@ -529,17 +586,20 @@ 
Source code for hesseflux.nee2gpp
     [1.68311981 1.81012431 1.9874173  2.17108871 2.38759152 2.64372415
      2.90076664 3.18592735]
 
-    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead, undef=undef, method='local')
+    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead,
+    ...                     undef=undef, method='local')
     >>> print(GPP[1120:1128])
     [-9.99900000e+03 -9.99900000e+03 -9.99900000e+03  4.40606871e+00
       8.31942152e+00  1.06242542e+01  8.49245664e+00  1.12381973e+01]
 
-    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead, undef=undef, method='Reichstein')
+    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead,
+    ...                     undef=undef, method='Reichstein')
     >>> print(GPP[1120:1128])
     [-9.99900000e+03 -9.99900000e+03 -9.99900000e+03  4.40606871e+00
       8.31942152e+00  1.06242542e+01  8.49245664e+00  1.12381973e+01]
 
-    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead, undef=undef, method='reichstein')
+    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead,
+    ...                     undef=undef, method='reichstein')
     >>> print(GPP[1120:1128])
     [-9.99900000e+03 -9.99900000e+03 -9.99900000e+03  4.40606871e+00
       8.31942152e+00  1.06242542e+01  8.49245664e+00  1.12381973e+01]
@@ -550,11 +610,14 @@ 
Source code for hesseflux.nee2gpp
     Modified Arndt Piayda,   Mar 2012 - undef=np.nan
              Matthias Cuntz, Nov 2012 - individual routine
              Matthias Cuntz, Feb 2013 - ported to Python 3
+             Matthias Cuntz, Jun 2024 - removed np.int and np.float
+
     """
     # Variables
     fc_id = ''
     for cc in df.columns:
-        if cc.startswith('FC_') or (cc == 'FC') or cc.startswith('NEE_') or (cc == 'NEE'):
+        if ( cc.startswith('FC_') or (cc == 'FC') or cc.startswith('NEE_') or
+             (cc == 'NEE') ):
             fc_id = cc
             break
     ta_id = ''
@@ -562,118 +625,133 @@ 
Source code for hesseflux.nee2gpp
         if cc.startswith('TA_') or (cc == 'TA'):
             ta_id = cc
             break
-    assert fc_id, 'Carbon net flux with name FC or NEE or starting with FC_ or NEE_ must be in input.'
-    assert ta_id, 'Air temperature with name TA or starting with TA_ must be in input.'
+    assert fc_id, ('Carbon net flux with name FC or NEE or starting with FC_'
+                   ' or NEE_ must be in input.')
+    assert ta_id, ('Air temperature with name TA or starting with TA_ must'
+                   ' be in input.')
 
     nee    = np.ma.array(df[fc_id], mask=(ff[fc_id] > 0))
     t      = np.ma.array(df[ta_id], mask=(ff[ta_id] > 0))
-    misday = np.ma.array(isday, mask=((~np.isfinite(isday)) | (isday == undef)))
+    misday = np.ma.array(isday, mask=((~np.isfinite(isday)) |
+                                      (isday == undef)))
     dates  = df.index.to_julian_date()
 
     # Partition - Local relationship = Reichstein et al. (2005)
 
     ndata = nee.size
-    GPP   = np.ones(ndata)*undef
-    Reco  = np.ones(ndata)*undef
-    dfout = pd.DataFrame({'GPP':GPP, 'RECO':Reco}, index=df.index)
+    GPP   = np.ones(ndata) * undef
+    Reco  = np.ones(ndata) * undef
+    dfout = pd.DataFrame({'GPP': GPP, 'RECO': Reco}, index=df.index)
 
     # Select valid nighttime
     mask  = misday | nee.mask | t.mask | misday.mask
     ii    = np.where(~mask)[0]
-    if (ii.size==0):
-        # raise ValueError('Error _nee2gpp_reichstein: no valid nighttime data.')
+    if (ii.size == 0):
+        # raise ValueError('Error _nee2gpp_reichstein:'
+        #                  ' no valid nighttime data.')
         print('Warning _nee2gpp_reichstein: no valid nighttime data.')
         return dfout
     jul  = dates[ii]
     tt   = np.ma.compressed(t[ii])
     net  = np.ma.compressed(nee[ii])
     # 1. each 5 days, in 15 day period, fit if range of T > 5
-    locp = [] # local param
-    locs = [] # local err
-    dmin = np.floor(np.amin(jul)).astype(np.int) # be aware that julian days starts at noon, i.e. 1.0 is 12h
-    dmax = np.ceil(np.amax(jul)).astype(np.int)  # so the search will be from noon to noon and thus includes all nights
-    for i in range(dmin,dmax,5):
-        iii  = np.where((jul>=i) & (jul<(i+14)))[0]
+    locp = []  # local param
+    locs = []  # local err
+    # be aware that julian days starts at noon, i.e. 1.0 is 12h
+    # so the search will be from noon to noon and thus includes all nights
+    dmin = np.floor(np.amin(jul)).astype(int)
+    dmax = np.ceil(np.amax(jul)).astype(int)
+    for i in range(dmin, dmax, 5):
+        iii  = np.where((jul >= i) & (jul < (i + 14)))[0]
         niii = iii.size
         if niii > 6:
             tt1  = tt[iii]
             net1 = net[iii]
-            mm   = ~mad(net1, z=4.5) # make fit more robust by removing outliers
+            # make fit more robust by removing outliers
+            mm   = ~mad(net1, z=4.5)
             if (np.ptp(tt[iii]) >= 5.) & (np.sum(mm) > 6):
                 p, temp1, temp2 = opt.fmin_tnc(cost_lloyd_fix, [2., 200.],
-                                               bounds=[[0.,None], [0.,None]],
+                                               bounds=[[0., None], [0., None]],
                                                args=(tt1[mm], net1[mm]),
                                                approx_grad=True, disp=False)
                 try:
-                    p1, c = opt.curve_fit(lloyd_fix, tt1[mm], net1[mm], p0=p, maxfev=10000) # params, covariance
-                    if np.all(np.isfinite(c)): # possible return of curvefit: c=inf
+                    # params, covariance
+                    p1, c = opt.curve_fit(lloyd_fix, tt1[mm], net1[mm],
+                                          p0=p, maxfev=10000)
+                    # possible return of curvefit: c=inf
+                    if np.all(np.isfinite(c)):
                         s = np.sqrt(np.diag(c))
                     else:
-                        s = 10.*np.abs(p)
+                        s = 10. * np.abs(p)
                 except:
-                    s = 10.*np.abs(p)
+                    s = 10. * np.abs(p)
                 locp += [p]
                 locs += [s]
                 # if ((s[1]/p[1])<0.5) & (p[1] > 0.): pdb.set_trace()
     if len(locp) == 0:
-        # raise ValueError('Error _nee2gpp_reichstein: No local relationship found.')
+        # raise ValueError('Error _nee2gpp_reichstein:'
+        #                  ' No local relationship found.')
         print('Warning _nee2gpp_reichstein: No local relationship found.')
         return dfout
-    locp   = np.squeeze(np.array(locp).astype(np.float))
-    locs   = np.squeeze(np.array(locs).astype(np.float))
+    locp   = np.squeeze(np.array(locp).astype(float))
+    locs   = np.squeeze(np.array(locs).astype(float))
     # 2. E0 = avg of best 3
     # Reichstein et al. (2005), p. 1430, 1st paragraph.
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
-        iii  = np.where((locp[:,1] > 0.) & (locp[:,1] < 450.) & (np.abs(locs[:,1]/locp[:,1]) < 0.5))[0]
+        iii  = np.where((locp[:, 1] > 0.) & (locp[:, 1] < 450.) &
+                        (np.abs(locs[:, 1] / locp[:, 1]) < 0.5))[0]
     niii = iii.size
-    if niii==0:
-        # raise ValueError('Error _nee2gpp_reichstein: No good local relationship found.')
+    if niii == 0:
+        # raise ValueError('Error _nee2gpp_reichstein:'
+        #                  ' No good local relationship found.')
         # loosen the criteria: take the best three estimates anyway
-        iii   = np.where((locp[:,1] > 0.))[0]
+        iii   = np.where((locp[:, 1] > 0.))[0]
         niii = iii.size
-        if niii<1:
+        if niii < 1:
             # raise ValueError('Error _nee2gpp_reichstein: No E0>0 found.')
             print('Warning _nee2gpp_reichstein: No E0>0 found.')
             return dfout
-        lp    = locp[iii,:]
-        ls    = locs[iii,:]
-        iis   = np.argsort(ls[:,1])
-        bestp = np.mean(lp[iis[0:np.minimum(3,niii)],:],axis=0)
-        bests = np.mean(ls[iis[0:np.minimum(3,niii)],:],axis=0)
-    elif niii==1:
-        bestp = np.squeeze(locp[iii,:])
-        bests = np.squeeze(locs[iii,:])
-    elif niii==2:
-        bestp = np.mean(locp[iii,:],axis=0)
-        bests = np.mean(locs[iii,:],axis=0)
+        lp    = locp[iii, :]
+        ls    = locs[iii, :]
+        iis   = np.argsort(ls[:, 1])
+        bestp = np.mean(lp[iis[0:np.minimum(3, niii)], :], axis=0)
+        bests = np.mean(ls[iis[0:np.minimum(3, niii)], :], axis=0)
+    elif niii == 1:
+        bestp = np.squeeze(locp[iii, :])
+        bests = np.squeeze(locs[iii, :])
+    elif niii == 2:
+        bestp = np.mean(locp[iii, :], axis=0)
+        bests = np.mean(locs[iii, :], axis=0)
         # ls    = locs[iii,:]
         # iis   = np.argsort(ls[:,1])
     else:
-        lp    = locp[iii,:]
-        ls    = locs[iii,:]
-        iis   = np.argsort(ls[:,1])
-        bestp = np.mean(lp[iis[0:3],:],axis=0)
-        bests = np.mean(ls[iis[0:3],:],axis=0)
+        lp    = locp[iii, :]
+        ls    = locs[iii, :]
+        iis   = np.argsort(ls[:, 1])
+        bestp = np.mean(lp[iis[0:3], :], axis=0)
+        bests = np.mean(ls[iis[0:3], :], axis=0)
 
     # 3. Refit Rref with fixed E0, each 4 days
-    refp  = [] # Rref param
-    refii = [] # mean index of data points
+    refp  = []  # Rref param
+    refii = []  # mean index of data points
     E0    = bestp[1]
     et    = lloyd_fix(tt, 1., E0)
-    for i in range(dmin,dmax,4):
-        iii  = np.where((jul>=i) & (jul<(i+4)))[0]
+    for i in range(dmin, dmax, 4):
+        iii  = np.where((jul >= i) & (jul < (i + 4)))[0]
         niii = iii.size
         if niii > 3:
             # Calc directly minisation of (nee-p*et)**2
             p, temp1, temp2 = opt.fmin_tnc(cost_abs, [2.],
-                                           bounds=[[0.,None]],
-                                           args=(lloyd_only_rref_p, et[iii], net[iii]),
+                                           bounds=[[0., None]],
+                                           args=(lloyd_only_rref_p, et[iii],
+                                                 net[iii]),
                                            approx_grad=True, disp=False)
             refp  += [p]
-            refii += [np.int((iii[0]+iii[-1])//2)]
+            refii += [int((iii[0] + iii[-1]) // 2)]
     if len(refp) == 0:
-        # raise ValueError('Error _nee2gpp_reichstein: No ref relationship found.')
+        # raise ValueError('Error _nee2gpp_reichstein:'
+        #                  ' No ref relationship found.')
         print('Warning _nee2gpp_reichstein: No ref relationship found.')
         return dfout
     refp  = np.squeeze(np.array(refp))
@@ -683,28 +761,28 @@ 
Source code for hesseflux.nee2gpp
     Rref = np.interp(dates, jul[refii], refp)
 
     # 5. Calc Reco
-    Reco     = np.ones(ndata)*undef
+    Reco     = np.ones(ndata) * undef
     ii       = np.where(~t.mask)[0]
     Reco[ii] = lloyd_fix(t[ii], Rref[ii], E0)
 
     # 6. Calc GPP
-    GPP     = np.ones(ndata)*undef
+    GPP     = np.ones(ndata) * undef
     ii      = np.where(~(t.mask | nee.mask))[0]
     GPP[ii] = Reco[ii] - nee[ii]
 
     # 7. Set GPP=0 at night, if wanted
     if nogppnight:
-        mask = misday | nee.mask | t.mask | misday.mask # night
+        mask = misday | nee.mask | t.mask | misday.mask  # night
         ii   = np.where(~mask)[0]
         Reco[ii] = nee[ii]
         GPP[ii]  = 0.
         # and prohibit negative gpp at any time
-        mask = nee.mask | t.mask | (GPP>0.)
+        mask = nee.mask | t.mask | (GPP > 0.)
         ii   = np.where(~mask)[0]
         Reco[ii] -= GPP[ii]
         GPP[ii]  = 0.
 
-    dfout = pd.DataFrame({'GPP':GPP, 'RECO':Reco}, index=df.index)
+    dfout = pd.DataFrame({'GPP': GPP, 'RECO': Reco}, index=df.index)
 
     return dfout
 
@@ -723,9 +801,11 @@ 
Source code for hesseflux.nee2gpp
         incoming shortwave radiation, and air vapour pressure deficit.
 
         `df` can be a pandas.Dataframe with the columns
-        'FC' or 'NEE' (or starting with 'FC\_' or 'NEE\_') for observed CO2 flux [umol(CO2) m-2 s-1]
-        'TA'    (or starting with 'TA\_') for air temperature [K]
-        'SW_IN' (or starting with 'SW_IN') for incoming short-wave radiation [W m-2]
+        'FC' or 'NEE' (or starting with 'FC\\_' or 'NEE\\_') for observed
+        CO2 flux [umol(CO2) m-2 s-1]
+        'TA'    (or starting with 'TA\\_') for air temperature [K]
+        'SW_IN' (or starting with 'SW_IN') for incoming short-wave
+        radiation [W m-2]
         'VPD'   (or starting with 'VPD') for air vapour deficit [Pa]
         The index is taken as date variable.
     ff : pandas.Dataframe or numpy.array, optional
@@ -734,11 +814,14 @@ 
Source code for hesseflux.nee2gpp
 
         `ff` must follow the same rules as `df` if pandas.Dataframe.
     isday : array_like of bool, optional
-        True when it is day, False when night. Must have the same length as `df.shape[0]`.
+        True when it is day, False when night. Must have the same length
+        as `df.shape[0]`.
     undef : float, optional
-        values having `undef` value are treated as missing values in `df` (default: -9999)
+        values having `undef` value are treated as missing values in `df`
+        (default: -9999)
     nogppnight : float, optional
-        GPP will be set to zero at night. RECO will then equal NEE at night (default: False)
+        GPP will be set to zero at night. RECO will then equal NEE at night
+        (default: False)
 
     Returns
     -------
@@ -749,8 +832,9 @@ 
Source code for hesseflux.nee2gpp
     References
     ----------
     .. [3] Lasslop et al. (2010)
-       Separation of net ecosystem exchange into assimilation and respiration using
-       a light response curve approach: critical issues and global evaluation,
+       Separation of net ecosystem exchange into assimilation and respiration
+       using a light response curve approach: critical issues and global
+       evaluation,
        Global Change Biology 16, 187-208
 
     Examples
@@ -765,7 +849,8 @@ 
Source code for hesseflux.nee2gpp
     >>> head  = fread(ifile, skip=2, header=True)
     >>> head1 = head[0]
     >>> # date
-    >>> jdate = date2dec(dy=dat[0,:], mo=dat[1,:], yr=dat[2,:], hr=dat[3,:], mi=dat[4,:])
+    >>> jdate = date2dec(dy=dat[0,:], mo=dat[1,:], yr=dat[2,:], hr=dat[3,:],
+                         mi=dat[4,:])
     >>> adate = dec2date(jdate, eng=True)
     >>> # colhead
     >>> idx   = []
@@ -779,7 +864,8 @@ 
Source code for hesseflux.nee2gpp
     >>> # flag
     >>> flag = np.where(dfin == undef, 2, 0)
     >>> # partition
-    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead, undef=undef, method='day')
+    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead,
+                            undef=undef, method='day')
     >>> print(GPP[1120:1128])
     [-9.99900000e+03 -9.99900000e+03 -9.99900000e+03  2.78457540e+00
       6.63212545e+00  8.88902165e+00  6.74243873e+00  9.51364527e+00]
@@ -793,11 +879,14 @@ 
Source code for hesseflux.nee2gpp
     Modified Arndt Piayda,   Mar 2012 - undef=np.nan
              Matthias Cuntz, Nov 2012 - individual routine
              Matthias Cuntz, Feb 2013 - ported to Python 3
+             Matthias Cuntz, Jun 2024 - removed np.int and np.float
+
     """
     # Variables
     fc_id = ''
     for cc in df.columns:
-        if cc.startswith('FC_') or (cc == 'FC') or cc.startswith('NEE_') or (cc == 'NEE'):
+        if ( cc.startswith('FC_') or (cc == 'FC') or cc.startswith('NEE_') or
+             (cc == 'NEE') ):
             fc_id = cc
             break
     ta_id = ''
@@ -815,24 +904,29 @@ 
Source code for hesseflux.nee2gpp
         if cc.startswith('VPD_') or (cc == 'VPD'):
             vpd_id = cc
             break
-    assert fc_id,  'Carbon net flux with name FC or NEE or starting with FC_ or NEE_ must be in input.'
-    assert ta_id,  'Air temperature with name TA or starting with TA_ must be in input.'
-    assert sw_id,  'Global radiation with name SW or starting with SW_ must be in input.'
-    assert vpd_id, 'Vapour pressure deficit with name VPD or starting with VPD_ must be in input.'
-
-    nee    = np.ma.array(df[fc_id],  mask=(ff[fc_id] > 0))
-    t      = np.ma.array(df[ta_id],  mask=(ff[ta_id] > 0))
-    sw     = np.ma.array(df[sw_id],  mask=(ff[sw_id] > 0))
+    assert fc_id, ('Carbon net flux with name FC or NEE or starting with FC_'
+                   ' or NEE_ must be in input.')
+    assert ta_id, ('Air temperature with name TA or starting with TA_ must'
+                   ' be in input.')
+    assert sw_id, ('Global radiation with name SW or starting with SW_ must'
+                   ' be in input.')
+    assert vpd_id, ('Vapour pressure deficit with name VPD or starting with'
+                    ' VPD_ must be in input.')
+
+    nee    = np.ma.array(df[fc_id], mask=(ff[fc_id] > 0))
+    t      = np.ma.array(df[ta_id], mask=(ff[ta_id] > 0))
+    sw     = np.ma.array(df[sw_id], mask=(ff[sw_id] > 0))
     vpd    = np.ma.array(df[vpd_id], mask=(ff[vpd_id] > 0))
-    misday = np.ma.array(isday, mask=((~np.isfinite(isday)) | (isday == undef)))
+    misday = np.ma.array(isday, mask=((~np.isfinite(isday)) |
+                                      (isday == undef)))
     dates  = df.index.to_julian_date()
 
     # Partition - Lasslop et al. (2010) method
 
     ndata = nee.size
-    GPP   = np.ones(ndata)*undef
-    Reco  = np.ones(ndata)*undef
-    dfout = pd.DataFrame({'GPP':GPP, 'RECO':Reco}, index=df.index)
+    GPP   = np.ones(ndata) * undef
+    Reco  = np.ones(ndata) * undef
+    dfout = pd.DataFrame({'GPP': GPP, 'RECO': Reco}, index=df.index)
 
     do_lgpp = False
     mask  = nee.mask | t.mask | misday.mask | sw.mask | vpd.mask
@@ -855,7 +949,8 @@ 
Source code for hesseflux.nee2gpp
     aalpha = 0.01
     qnet   = np.sort(dnet)
     nqnet  = qnet.size
-    abeta0 = np.abs(qnet[np.floor(0.97*nqnet).astype(np.int)]-qnet[np.ceil(0.03*nqnet).astype(np.int)])
+    abeta0 = np.abs(qnet[np.floor(0.97 * nqnet).astype(int)] -
+                    qnet[np.ceil(0.03 * nqnet).astype(int)])
     ak     = 0.
     # out
     lE0    = []
@@ -865,18 +960,19 @@ 
Source code for hesseflux.nee2gpp
         lk     = []
     lRref  = []
     lii    = []
-    dmin = np.floor(np.amin(dates)).astype(np.int)
-    dmax = np.ceil(np.amax(dates)).astype(np.int)
+    dmin = np.floor(np.amin(dates)).astype(int)
+    dmax = np.ceil(np.amax(dates)).astype(int)
     zaehl = -1
-    for i in range(dmin,dmax,2):
+    for i in range(dmin, dmax, 2):
         good = True
         # 1. Estimate E0 from nighttime data
-        iii  = np.squeeze(np.where((njul>=i) & (njul<(i+12))))
+        iii  = np.squeeze(np.where((njul >= i) & (njul < (i + 12))))
         niii = iii.size
         if niii > 3:
             p, temp1, temp2 = opt.fmin_tnc(cost_abs, [aRref, 100.],
-                                           bounds=[[0.,None], [0.,None]],
-                                           args=(lloyd_fix_p, ntt[iii], nnet[iii]),
+                                           bounds=[[0., None], [0., None]],
+                                           args=(lloyd_fix_p, ntt[iii],
+                                                 nnet[iii]),
                                            approx_grad=True, disp=False)
             E0 = np.maximum(p[1], 50.)
         else:
@@ -887,7 +983,7 @@ 
Source code for hesseflux.nee2gpp
                 good = False
                 continue
         # 2. Estimate alpha, k, beta0, Rref from daytime data
-        iii  = np.squeeze(np.where((djul>=i) & (djul<(i+4))))
+        iii  = np.squeeze(np.where((djul >= i) & (djul < (i + 4))))
         niii = iii.size
         if niii > 3:
             et     = lloyd_fix(dtt[iii], 1., E0)
@@ -896,34 +992,37 @@ 
Source code for hesseflux.nee2gpp
             ibeta0 = abeta0
             ik     = ak
             iRref  = aRref
-            bounds = [[None,None], [None,None], [None,None], [None,None]]
+            bounds = [[None, None], [None, None], [None, None], [None, None]]
             while again:
                 again = False
-                p, nfeval, rc  = opt.fmin_tnc(cost_lasslop, [ialpha, ibeta0, ik, iRref],
+                p, nfeval, rc  = opt.fmin_tnc(cost_lasslop,
+                                              [ialpha, ibeta0, ik, iRref],
                                               bounds=bounds,
-                                              args=(dsw[iii], et, dvpd[iii], dnet[iii]),
+                                              args=(dsw[iii], et, dvpd[iii],
+                                                    dnet[iii]),
                                               approx_grad=True, disp=False)
-                # if parameters beyond some bounds, set params and redo the optim or skip
-                if ((p[0] < 0.) | (p[0] > 0.22)): # alpha
+                # if parameters beyond some bounds, set params and redo
+                # the optim or skip
+                if ((p[0] < 0.) | (p[0] > 0.22)):  # alpha
                     again = True
                     if zaehl >= 0:
-                        bounds[0] = [lalpha[zaehl],lalpha[zaehl]]
+                        bounds[0] = [lalpha[zaehl], lalpha[zaehl]]
                         ialpha    = lalpha[zaehl]
                     else:
-                        bounds[0] = [0.,0.]
+                        bounds[0] = [0., 0.]
                         ialpha    = 0.
-                if p[1] < 0.:                     # beta0
-                    bounds[1] = [0.,0.]
+                if p[1] < 0.:                      # beta0
+                    bounds[1] = [0., 0.]
                     ibeta0    = 0.
                     again = True
                 if p[1] > 250.:
                     good = False
                     continue
-                if p[2] < 0.:                     # k
-                    bounds[2] = [0.,0.]
+                if p[2] < 0.:                      # k
+                    bounds[2] = [0., 0.]
                     ik        = 0.
                     again = True
-                if p[3] < 0:                      # Rref
+                if p[3] < 0:                       # Rref
                     good = False
                     continue
             if good:
@@ -932,7 +1031,7 @@ 
Source code for hesseflux.nee2gpp
                     lbeta0 = lbeta0 + [p[1]]
                     lk     = lk     + [p[2]]
                 lRref  = lRref  + [p[3]]
-                lii    = lii    + [np.int((iii[0]+iii[-1])/2)]
+                lii    = lii    + [int((iii[0] + iii[-1]) / 2)]
             else:
                 continue
         else:
@@ -940,7 +1039,8 @@ 
Source code for hesseflux.nee2gpp
         lE0    = lE0 + [E0]
         zaehl += 1
     if len(lE0) == 0:
-        # raise ValueError('Error _nee2gpp_lasslop: No day relationship found.')
+        # raise ValueError('Error _nee2gpp_lasslop:'
+        #                  ' No day relationship found.')
         print('Warning _nee2gpp_lasslop: No day relationship found.')
         return dfout
     lE0 = np.squeeze(np.array(lE0))
@@ -956,7 +1056,7 @@ 
Source code for hesseflux.nee2gpp
     Rref = np.interp(dates, djul[lii], lRref)
 
     # 4. Calc Reco
-    Reco     = np.ones(ndata)*undef
+    Reco     = np.ones(ndata) * undef
     ii       = np.squeeze(np.where(~t.mask))
     Reco[ii] = lloyd_fix(t[ii], Rref[ii], E0[ii])
 
@@ -969,26 +1069,27 @@ 
Source code for hesseflux.nee2gpp
         lmask    = t.mask | misday.mask | sw.mask | vpd.mask
         ii       = np.squeeze(np.where(~lmask))
         lgpp     = np.zeros(ndata)
-        lgpp[ii] = lasslop(sw[ii], et[ii], vpd[ii], alpha[ii], beta0[ii], k[ii], Rref[ii]) - Reco[ii]
+        lgpp[ii] = lasslop(sw[ii], et[ii], vpd[ii], alpha[ii],
+                           beta0[ii], k[ii], Rref[ii]) - Reco[ii]
 
     # 6. GPP
-    GPP     = np.ones(ndata)*undef
+    GPP     = np.ones(ndata) * undef
     ii      = np.squeeze(np.where(~(t.mask | nee.mask)))
     GPP[ii] = Reco[ii] - nee[ii]
 
     # 7. Set GPP=0 at night, if wanted
     if nogppnight:
-        mask = misday | nee.mask | t.mask | misday.mask # night
+        mask = misday | nee.mask | t.mask | misday.mask  # night
         ii   = np.where(~mask)[0]
         Reco[ii] = nee[ii]
         GPP[ii]  = 0.
         # and prohibit negative gpp at any time
-        mask = nee.mask | t.mask | (GPP>0.)
+        mask = nee.mask | t.mask | (GPP > 0.)
         ii   = np.where(~mask)[0]
         Reco[ii] -= GPP[ii]
         GPP[ii]   = 0.
 
-    dfout = pd.DataFrame({'GPP':GPP, 'RECO':Reco}, index=df.index)
+    dfout = pd.DataFrame({'GPP': GPP, 'RECO': Reco}, index=df.index)
 
     return dfout
 
@@ -1103,7 +1204,7 @@ 
Related Topics

   
 
 

-
+
 
         

       

       

     

     

-      ©2009-2023, Matthias Cuntz.
+      ©2009-2024, Matthias Cuntz.
       
       |
-      Powered by Sphinx 6.1.3
-      & Alabaster 0.7.13
+      Powered by Sphinx 7.3.7
+      & Alabaster 0.7.16
       
     

 
diff --git a/docs/html/_modules/hesseflux/ustarfilter.html b/docs/html/_modules/hesseflux/ustarfilter.html
index a0705a6..182c5bb 100644
--- a/docs/html/_modules/hesseflux/ustarfilter.html
+++ b/docs/html/_modules/hesseflux/ustarfilter.html
@@ -1,24 +1,24 @@
-
 
 
-
+
   
     
     
-    hesseflux.ustarfilter — hesseflux 5.1.dev0 documentation
-    
-    
-    
-    
-    
-    
+    hesseflux.ustarfilter — hesseflux 5.1.dev2 documentation
+    
+    
+    
+    
+    
+    
     
     
    
   
   
+
+  
   
-  
 
   
   
@@ -67,6 +67,8 @@ 
Source code for hesseflux.ustarfilter
       only ustar data when NEE and Ta are valid, Jan 2023, Matthias Cuntz
     * Use 90% of ustar if no threshold found also for seasonout,
       Jan 2023, Matthias Cuntz
+    * Removed np.float and np.bool, Jun 2024, Matthias Cuntz
+    * do not register pandas platting backend, Jun 2024, Matthias Cuntz
 
 """
 import numpy as np
@@ -76,7 +78,9 @@ 
Source code for hesseflux.ustarfilter
 __all__ = ['ustarfilter']
 
 
-
[docs]def ustarfilter(dfin, flag=None, isday=None, date=None,
+

+[docs]
+def ustarfilter(dfin, flag=None, isday=None, date=None,
                 timeformat='%Y-%m-%d %H:%M:%S', colhead=None, ustarmin=0.01,
                 nboot=1, undef=-9999, plot=False, seasonout=False, nmon=3,
                 ntaclasses=7, corrcheck=0.5, nustarclasses=20,
@@ -189,7 +193,7 @@ 
Source code for hesseflux.ustarfilter
             estr = ('Length of colhead must be number of columns in input'
                     ' array. len(colhead)=' + str(len(colhead)) +
                     ' shape(input)=(' + str(dfin.shape[0]) + ',' +
-                    str(dfin.shape[1])+').')
+                    str(dfin.shape[1]) + ').')
             raise ValueError(estr)
         assert date is not None, 'Date must be given if input is numpy arrary.'
         df['Datetime'] = pd.to_datetime(date, format=timeformat)
@@ -313,10 +317,10 @@ 
Source code for hesseflux.ustarfilter
     yrmin  = df.index.min().year
     nyears = yrmax - yrmin + 1
     ndays  = (df.index.max() - df.index.min()).days + 1
-    assert ndays//nyears > 360, 'Full years must be given.'
+    assert ndays // nyears > 360, 'Full years must be given.'
 
     # calculate thresholds
-    nperiod = 12//nmon  # number of nmon periods per year
+    nperiod = 12 // nmon  # number of nmon periods per year
     if seasonout:
         bustars = np.ones((nboot, nyears, nperiod)) * undef
     else:
@@ -349,8 +353,8 @@ 
Source code for hesseflux.ustarfilter
                 flag_p   = ( (~isday_b) &
                              (ff_b[fc_id] == 0) & (ff_b[ustar_id] == 0) &
                              (ff_b[ta_id] == 0) &
-                             (df_b.index.month > p*nmon) &
-                             (df_b.index.month <= (p+1)*nmon) )
+                             (df_b.index.month > p * nmon) &
+                             (df_b.index.month <= (p + 1) * nmon) )
                 fc_p    = df_b.loc[flag_p, fc_id]
                 ustar_p = df_b.loc[flag_p, ustar_id]
                 ta_p    = df_b.loc[flag_p, ta_id]
@@ -361,11 +365,11 @@ 
Source code for hesseflux.ustarfilter
                     continue
                 ta_q = np.quantile(
                     ta_p,
-                    np.arange(ntaclasses + 1, dtype=np.float) /
-                    np.float(ntaclasses))
+                    np.arange(ntaclasses + 1, dtype=float) /
+                    float(ntaclasses))
                 ta_q[0] -= 0.1  # 1st include min
                 for t in range(ntaclasses):
-                    iita    = (ta_p > ta_q[t]) & (ta_p <= ta_q[t+1])
+                    iita    = (ta_p > ta_q[t]) & (ta_p <= ta_q[t + 1])
                     fc_t    = fc_p[iita]
                     ustar_t = ustar_p[iita]
                     ta_t    = ta_p[iita]
@@ -378,17 +382,17 @@ 
Source code for hesseflux.ustarfilter
                     # ustar classes
                     ustar_q = np.quantile(
                         ustar_t,
-                        np.arange(nustarclasses + 1, dtype=np.float) /
-                        np.float(nustarclasses))
+                        np.arange(nustarclasses + 1, dtype=float) /
+                        float(nustarclasses))
                     ustar_q[0] -= 0.01  # 1st include min
-                    for u in range(nustarclasses-1):
+                    for u in range(nustarclasses - 1):
                         iiustar = ((ustar_t > ustar_q[u]) &
-                                   (ustar_t <= ustar_q[u+1]))
+                                   (ustar_t <= ustar_q[u + 1]))
                         fc_u    = fc_t[iiustar]
-                        fc_a    = fc_t[ustar_t > ustar_q[u+1]]
+                        fc_a    = fc_t[ustar_t > ustar_q[u + 1]]
 
-                        if abs(fc_u.mean()) >= abs(plateaucrit*fc_a.mean()):
-                            custars.append(ustar_q[u+1])
+                        if abs(fc_u.mean()) >= abs(plateaucrit * fc_a.mean()):
+                            custars.append(ustar_q[u + 1])
                             break
 
                 # median of thresholds of all temperature classes =
@@ -423,7 +427,8 @@ 
Source code for hesseflux.ustarfilter
                 else:
                     flag_b = ( (~isday_b) &
                                (ff_b[ustar_id] == 0) )
-                    bustars[b, y] = np.quantile(df_b.loc[flag_b, ustar_id], 0.9)
+                    bustars[b, y] = np.quantile(df_b.loc[flag_b, ustar_id],
+                                                0.9)
 
     # set minimum ustar threshold
     bustars = np.maximum(bustars, ustarmin)
@@ -434,14 +439,14 @@ 
Source code for hesseflux.ustarfilter
     # flag out with original DatetimeIndex
     off    = ustar_in.astype(int)
     off[:] = 0
-    ii     = np.zeros(len(off), dtype=np.bool)
+    ii     = np.zeros(len(off), dtype=bool)
     if seasonout:
         for y in range(nyears):
             yy = yrmin + y
             for p in range(nperiod):
                 iiyr = ( (df.index.year == yy) &   # df DatetimeIndex
-                         (df.index.month > p*nmon) &
-                         (df.index.month <= (p+1)*nmon) )
+                         (df.index.month > p * nmon) &
+                         (df.index.month <= (p + 1) * nmon) )
                 ii[iiyr] = ustar_in[iiyr] < oustars[1, y, p]
     else:
         for y in range(nyears):
@@ -451,11 +456,14 @@ 
Source code for hesseflux.ustarfilter
     off[ii] = 2  # original DatetimeIndex
 
     if plot:
+        import matplotlib as mpl
+        mpl.use('PDF')  # set directly after import matplotlib
+        from matplotlib.backends.backend_pdf import PdfPages
         import matplotlib.pyplot as plt
-        import matplotlib.backends.backend_pdf as pdf
-        pd.plotting.register_matplotlib_converters()
+        # import matplotlib.backends.backend_pdf as PdfPages
+        # pd.plotting.register_matplotlib_converters()
 
-        pp = pdf.PdfPages('ustarfilter.pdf')
+        pp = PdfPages('ustarfilter.pdf')
         if seasonout:
             for y in range(nyears):
                 yy = yrmin + y
@@ -470,8 +478,8 @@ 
Source code for hesseflux.ustarfilter
                     flag_p = ( (~isday_f) &
                                (ff_f[fc_id] == 0) & (ff_f[ustar_id] == 0) &
                                (ff_f[ta_id] == 0) &
-                               (df_f.index.month > p*nmon) &
-                               (df_f.index.month <= (p+1)*nmon) )
+                               (df_f.index.month > p * nmon) &
+                               (df_f.index.month <= (p + 1) * nmon) )
                     fc_p    = df_f.loc[flag_p, fc_id]
                     ustar_p = df_f.loc[flag_p, ustar_id]
 
@@ -522,6 +530,7 @@ 
Source code for hesseflux.ustarfilter
         return oustars, off

 
 
+
 if __name__ == '__main__':
     import doctest
     doctest.testmod()
@@ -573,7 +582,7 @@ 
Related Topics

   
 
 

-
+
 
         

       

       

     

     

-      ©2009-2023, Matthias Cuntz.
+      ©2009-2024, Matthias Cuntz.
       
       |
-      Powered by Sphinx 6.1.3
-      & Alabaster 0.7.13
+      Powered by Sphinx 7.3.7
+      & Alabaster 0.7.16
       
     

 
diff --git a/docs/html/_modules/index.html b/docs/html/_modules/index.html
index 37ef691..89c1611 100644
--- a/docs/html/_modules/index.html
+++ b/docs/html/_modules/index.html
@@ -1,24 +1,24 @@
-
 
 
-
+
   
     
     
-    Overview: module code — hesseflux 5.1.dev0 documentation
-    
-    
-    
-    
-    
-    
+    Overview: module code — hesseflux 5.1.dev2 documentation
+    
+    
+    
+    
+    
+    
     
     
    
   
   
+
+  
   
-  
 
   
   
@@ -81,7 +81,7 @@ 
Related Topics

   
 
 

-
+
 
         

       

       

     

     

-      ©2009-2023, Matthias Cuntz.
+      ©2009-2024, Matthias Cuntz.
       
       |
-      Powered by Sphinx 6.1.3
-      & Alabaster 0.7.13
+      Powered by Sphinx 7.3.7
+      & Alabaster 0.7.16
       
     

 
diff --git a/docs/html/_sources/contents.rst.txt b/docs/html/_sources/contents.rst.txt
index 6a7737a..cd4301c 100644
--- a/docs/html/_sources/contents.rst.txt
+++ b/docs/html/_sources/contents.rst.txt
@@ -11,3 +11,5 @@ Contents
    api
    changelog
    authors
+..
+   reddyproc
diff --git a/docs/html/_sources/userguide.rst.txt b/docs/html/_sources/userguide.rst.txt
index d328c0f..1033a16 100644
--- a/docs/html/_sources/userguide.rst.txt
+++ b/docs/html/_sources/userguide.rst.txt
@@ -2,34 +2,37 @@
 User Guide
 ----------
 
-``hesseflux`` collects functions used for processing Eddy covariance data of the
-ICOS_ ecosystem site FR-Hes_.
+``hesseflux`` collects functions used for processing Eddy covariance
+data of the ICOS_ ecosystem site FR-Hes_.
 
-The post-processing functionality for Eddy flux data is similar to the R-package
-REddyProc_ and includes basically the steps described in `Papale et al.
-(Biogeosciences, 2006)`_ plus some extensions such as the daytime method of flux
-partitioning (`Lasslop et al., Global Change Biology 2010`_) and the estimation
-of uncertainties on the fluxes as in `Lasslop et al. (Biogeosci, 2008)`_.
+The post-processing functionality for Eddy flux data is similar to the
+R-package REddyProc_ and includes basically the steps described in
+`Papale et al.  (Biogeosciences, 2006)`_ plus some extensions such as
+the daytime method of flux partitioning (`Lasslop et al., Global
+Change Biology 2010`_) and the estimation of uncertainties on the
+fluxes as in `Lasslop et al. (Biogeosci, 2008)`_.
 
-Only the post-processing steps are described here. We are happy to discuss any
-processing or post-processing directly. Contact us at mc (at) macu (dot) de.
+Only the post-processing steps are described here. We are happy to
+discuss any processing or post-processing directly. Contact us at mc
+(at) macu (dot) de.
 
 
 europe-fluxdata.eu file format
 ==============================
 
-The first processing steps at the ICOS ecosystem site FR-Hes (not shown) brings
-the data in a format that can be submitted to the database
-`europe-fluxdata.eu`_. The database predates ICOS and is somewhat a precursor of
-the ICOS data processing.
+The first processing steps at the ICOS ecosystem site FR-Hes (not
+shown) brings the data in a format that can be submitted to the
+database `europe-fluxdata.eu`_. The database predates ICOS and is
+somewhat a precursor of the ICOS data processing.
 
-The file format of `europe-fluxdata.eu` is hence very similar to the ICOS
-format. The only known difference to us is the unit of atmospheric pressure,
-which is in hPa in `europe-fluxdata.eu`_ and in kPa in `ICOS ecosystems`_. The
-file format has notably one header line with variable names. There are no units
-in the file. ``hesseflux`` provides a little helper script
-`europe-fluxdata_units.py` in the `bin` directory that adds a second header line
-with units. The script can be run on the output as:
+The file format of `europe-fluxdata.eu` is hence very similar to the
+ICOS format. The only known difference to us is the unit of
+atmospheric pressure, which is in hPa in `europe-fluxdata.eu`_ and in
+kPa in `ICOS ecosystems`_. The file format has notably one header line
+with variable names. There are no units in the file. ``hesseflux``
+provides a little helper script `europe-fluxdata_units.py` in the
+`bin` directory that adds a second header line with units. The script
+can be run on the output as:
 
 .. code-block:: bash
 
@@ -39,20 +42,21 @@ with units. The script can be run on the output as:
 Post-processing Eddy covariance data
 ====================================
 
-The script `postproc_europe-fluxdata.py` in the `example` directory provides a
-template for post-processing data that is in the `europe-fluxdata.eu`_ file
-format. It basically makes all steps described in `Papale et al.
-(Biogeosciences, 2006)`_. The script is governed by a configuration file in
-Python's standard :mod:`configparser` format. The example configuration file
-`hesseflux_example.cfg` in the `example` directory is highly commented and
-should be (almost) self-explanatory. The script is called like:
+The script `postproc_europe-fluxdata.py` in the `example` directory
+provides a template for post-processing data that is in the
+`europe-fluxdata.eu`_ file format. It basically makes all steps
+described in `Papale et al.  (Biogeosciences, 2006)`_. The script is
+governed by a configuration file in Python's standard
+:mod:`configparser` format. The example configuration file
+`hesseflux_example.cfg` in the `example` directory is highly commented
+and should be (almost) self-explanatory. The script is called like:
 
 .. code-block:: bash
 		
    python postproc_europe-fluxdata.py hesseflux_example.cfg
 
-This script should be taken as a template for one's own post-processing but
-includes most standard post-processing steps.
+This script should be taken as a template for one's own
+post-processing but includes most standard post-processing steps.
 
 Here we describe the main parts of the post-processing script.
 
@@ -60,8 +64,8 @@ Here we describe the main parts of the post-processing script.
 Reading the configuration file
 ------------------------------
 
-The script `postproc_europe-fluxdata.py` starts by reading the configuration
-file `hesseflux_example.cfg`:
+The script `postproc_europe-fluxdata.py` starts by reading the
+configuration file `hesseflux_example.cfg`:
 
 .. code-block:: python
 
@@ -74,8 +78,9 @@ file `hesseflux_example.cfg`:
    config.read(configfile)
 
 It then analyses the configuration options. The first section in the
-configuration file are the options controlling which steps shall be performed by
-the script. The section in the `hesseflux_example.cfg` looks like:
+configuration file are the options controlling which steps shall be
+performed by the script. The section in the `hesseflux_example.cfg`
+looks like:
 
 .. code-block:: python
 
@@ -107,15 +112,17 @@ And the code in `postproc_europe-fluxdata.py` is:
    fill      = config['POSTSWITCH'].getboolean('fill',      True)
    fluxerr   = config['POSTSWITCH'].getboolean('fluxerr',   True)
 
-All options are boolean and set to `True` by default if they are not given in
-the configuration file. All post-processing steps except uncertainty estimation
-of flux data would be performed in the given example.
+All options are boolean and set to `True` by default if they are not
+given in the configuration file. All post-processing steps except
+uncertainty estimation of flux data would be performed in the given
+example.
 
 
 Read the data
 -------------
 
-The script would then read in the data. The section in the configuration file is:
+The script would then read in the data. The section in the
+configuration file is:
 
 .. code-block:: python
 
@@ -159,9 +166,10 @@ The analysis of the options in `postproc_europe-fluxdata.py` is:
 
 Note that strings are given without quotes in the configuration file.
 
-`inputfile` can be a single filename or a comma-separated list of filenames. If
-it is missing or empty, the script will try to open a GUI, where one can choose
-input files. The data will be appended if several input files are given.
+`inputfile` can be a single filename or a comma-separated list of
+filenames. If it is missing or empty, the script will try to open a
+GUI, where one can choose input files. The data will be appended if
+several input files are given.
 
 The (first) input file is read as:
 
@@ -169,36 +177,37 @@ The (first) input file is read as:
 
    import pandas as pd
 
-   parser = lambda date: pd.to_datetime(date, format=timeformat)
    infile = inputfile[0]
    df     = pd.read_csv(infile, sep, skiprows=skiprows, parse_dates=[0],
-                        date_parser=parser, index_col=0, header=0)
-
-:mod:`pandas` will use the first column as index (`index_col=0`), assuming that
-these are dates (`parse_dates=[0]`) in the format `timeformat`, where columns
-are separated by `sep`. The defaults follow the `europe-fluxdata.eu`_ format but
-similar formats may be used, and script and/or configuration file can be adapted
-easily. Only variable names (still) have to follow `europe-fluxdata.eu`_,
-`ICOS`_ or `Ameriflux`_ format at the moment. If the input file has a second
+                        date_format=timeformat, index_col=0, header=0)
+
+:mod:`pandas` will use the first column as index (`index_col=0`),
+assuming that these are dates (`parse_dates=[0]`) in the format
+`timeformat`, where columns are separated by `sep`. The defaults
+follow the `europe-fluxdata.eu`_ format but similar formats may be
+used, and script and/or configuration file can be adapted easily. Only
+variable names (still) have to follow `europe-fluxdata.eu`_, `ICOS`_
+or `Ameriflux`_ format at the moment. If the input file has a second
 header line with units, one can skip it giving `skiprows=[1]` (not
 `skiprows=1`).
 
-All input files are supposed to be in the same format if `inputfile` is a
-comma-separated list of filenames, and they will be read with the same command
-above. The :mod:`pandas` dataframes (`df`) will simply be appended.
+All input files are supposed to be in the same format if `inputfile`
+is a comma-separated list of filenames, and they will be read with the
+same command above. The :mod:`pandas` dataframes (`df`) will simply be
+appended.
 
 
 The flag dataframe
 ------------------
 
-All Not-a-Number (NaN) values will be set to `undef` and will be ignored in the
-following.
+All Not-a-Number (NaN) values will be set to `undef` and will be
+ignored in the following.
 
-This happens via a second dataframe (`dff`), having the same columns and index
-as the input dataframe `df`, representing quality flags. All cells that have a
-value other than `0` in the flag dataframe `dff` will be ignored in the
-dataframe `df`. This means all cells of `df` with `undef` will be set to `2` in
-`dff` immediately:
+This happens via a second dataframe (`dff`), having the same columns
+and index as the input dataframe `df`, representing quality flags. All
+cells that have a value other than `0` in the flag dataframe `dff`
+will be ignored in the dataframe `df`. This means all cells of `df`
+with `undef` will be set to `2` in `dff` immediately:
 
 .. code-block:: python
 
@@ -214,12 +223,13 @@ dataframe `df`. This means all cells of `df` with `undef` will be set to `2` in
 Day / night
 -----------
 
-Most post-processing routines differentiate between daytime and nighttime data.
-`Papale et al. (Biogeosciences, 2006)`_ use a threshold of 20 W m\ :sup:`-2` of
-global radiation to distinguish between day and night. `REddyProc`_ uses
-incoming shortwave radiation greater than 10 W m\ :sup:`2` as daytime. The
-shortwave radiation threshold `swthr` (same name as in ReddyProc) can be used to
-define the appropriate threshold. The default is 10 W m\ :sup:`2`. The column
+Most post-processing routines differentiate between daytime and
+nighttime data.  `Papale et al. (Biogeosciences, 2006)`_ use a
+threshold of 20 W m\ :sup:`-2` of global radiation to distinguish
+between day and night. `REddyProc`_ uses incoming shortwave radiation
+greater than 10 W m\ :sup:`2` as daytime. The shortwave radiation
+threshold `swthr` (same name as in ReddyProc) can be used to define
+the appropriate threshold. The default is 10 W m\ :sup:`2`. The column
 `SW_IN_1_1_1` has to exist in the input data.
 
 .. code-block:: python
@@ -231,8 +241,8 @@ define the appropriate threshold. The default is 10 W m\ :sup:`2`. The column
 Data check
 ----------
 
-`postproc_europe-fluxdata.py` checks the units of air temperature (i.e. the
-first column starting with `TA_`).
+`postproc_europe-fluxdata.py` checks the units of air temperature
+(i.e. the first column starting with `TA_`).
 
 .. code-block:: python
 
@@ -245,15 +255,17 @@ first column starting with `TA_`).
         tkelvin = 0.
     df.loc[dff[hout[0]]==0, hout[0]] += tkelvin
 
-:func:`_findfirststart(starts, names)` is a helper function that finds the first
-occurrence in `names` that starts with the string `starts`. This helper function
-is used for the moment until ``hesseflux`` has the functionality that the user
-can give individual variable names.
+:func:`_findfirststart(starts, names)` is a helper function that finds
+the first occurrence in `names` that starts with the string
+`starts`. This helper function is used for the moment until
+``hesseflux`` has the functionality that the user can give individual
+variable names.
 
-The script calculates air vapour pressure deficit `VPD_PI_1_1_1` from air
-temperature and relative humidity (i.e. the first column starting with `RH_`) if
-not given in input data using the function :func:`esat` of `pyjams`_ for
-saturation vapour pressure:
+The script calculates air vapour pressure deficit `VPD_PI_1_1_1` from
+air temperature and relative humidity (i.e. the first column starting
+with `RH_`) if not given in input data using the function
+:func:`esat` of `pyjams`_ for saturation vapour
+pressure:
 
 .. code-block:: python
 
@@ -303,8 +315,8 @@ It further assures that VPD is in Pa for further calculations.
        vpdpa = 1.                  # Pa
    df.loc[dff[hout[0]] == 0, hout[0]] *= vpdpa
 
-And finally determines the time intervals of the input data `dtsec` (s) and the
-number of time steps per day `ntday`.
+And finally determines the time intervals of the input data
+`dtsec` (s) and the number of time steps per day `ntday`.
 
 .. code-block:: python
 
@@ -316,10 +328,11 @@ number of time steps per day `ntday`.
 Spike / outlier flagging
 ------------------------
 
-If `outlier=True` is set in the configuration file, spikes will be detected with
-the method given in `Papale et al. (Biogeosciences, 2006)`_. A median absolute
-deviation (MAD) filter will be used on the second derivatives of the time series
-in two-week chunks. The section in `hesseflux_example.cfg` looks like:
+If `outlier=True` is set in the configuration file, spikes will be
+detected with the method given in `Papale et al. (Biogeosciences,
+2006)`_. A median absolute deviation (MAD) filter will be used on the
+second derivatives of the time series in two-week chunks. The section
+in `hesseflux_example.cfg` looks like:
 
 .. code-block:: python
 
@@ -338,29 +351,31 @@ in two-week chunks. The section in `hesseflux_example.cfg` looks like:
    # int
    deriv = 2
 
-`nfill` is the number of days that are treated at once. `nfill=1` means that the
-time series will be stepped through day by day. `nscan` are the days to be
-considered when calculating the mean absolute deviations. `nscan=15` means that
-7 days before the fill day, the fill day itself, and 7 days after the fill day
-will be used for the robust statistic. However, only spikes detected within the
-inner `nfill` days will be flagged in the `nscan` days. Spikes will be detected
-if they deviate more than `z` mean absolute deviations from the median.
-
-For example, `nfill=3`, `nscan=15`, and `z=7` means that the time series will be
-treated in steps of 3 days. Each 3 days, MAD statistics will be calculated using
-15 days around the middle of the 3 days. Then all values within the 3 days that
-deviate more 7 mean absolute deviations from the median of the 15 days will be
-flagged.
-
-`deriv=2` applies the MAD filter to the second derivatives. A spike has
-normally a strong curvature and hence a large second derivative. `deriv=1` is
-currently not implemented. `deriv=0` applies the filter to the raw time series.
-This might be useful to find outliers in smooth time series such as soil
-moisture. `deriv=0` is also used on the 20 Hz Eddy raw data in the quality and
-uncertainty strategy of `Mauder et al. (Agric Forest Meteo, 2013)`_.
-
-The default values, if options are not given in the configuration file, are
-`nscan=15`, `nfill=1`, `z=7`, and `deriv=2`.
+`nfill` is the number of days that are treated at once. `nfill=1`
+means that the time series will be stepped through day by day. `nscan`
+are the days to be considered when calculating the mean absolute
+deviations. `nscan=15` means that 7 days before the fill day, the fill
+day itself, and 7 days after the fill day will be used for the robust
+statistic. However, only spikes detected within the inner `nfill` days
+will be flagged in the `nscan` days. Spikes will be detected if they
+deviate more than `z` mean absolute deviations from the median.
+
+For example, `nfill=3`, `nscan=15`, and `z=7` means that the time
+series will be treated in steps of 3 days. Each 3 days, MAD statistics
+will be calculated using 15 days around the middle of the 3 days. Then
+all values within the 3 days that deviate more 7 mean absolute
+deviations from the median of the 15 days will be flagged.
+
+`deriv=2` applies the MAD filter to the second derivatives. A spike
+has normally a strong curvature and hence a large second
+derivative. `deriv=1` is currently not implemented. `deriv=0` applies
+the filter to the raw time series.  This might be useful to find
+outliers in smooth time series such as soil moisture. `deriv=0` is
+also used on the 20 Hz Eddy raw data in the quality and uncertainty
+strategy of `Mauder et al. (Agric Forest Meteo, 2013)`_.
+
+The default values, if options are not given in the configuration
+file, are `nscan=15`, `nfill=1`, `z=7`, and `deriv=2`.
 
 `postproc_europe-fluxdata.py` calls the spike detection like this:
 
@@ -378,17 +393,17 @@ The default values, if options are not given in the configuration file, are
    for ii, hh in enumerate(hout):
        dff.loc[sflag[hh] == 2, hh] = 3
 
-The function :func:`madspikes` returns flag columns for the input variables
-where spiked data is flagged as 2. The scripts sets the corresponding columns in
-the flag dataframe `dff` to 3 (3 is used just to keep track where the flag was
-set).
+The function :func:`~hesseflux.madspikes.madspikes` returns flag
+columns for the input variables where spiked data is flagged as 2. The
+scripts sets the corresponding columns in the flag dataframe `dff` to
+3 (3 is used just to keep track where the flag was set).
 
 
 u* filtering
 ------------
 
-If `ustar=True` is set in the configuration file, a u*-filter will be applied
-following `Papale et al. (Biogeosciences, 2006)`_.
+If `ustar=True` is set in the configuration file, a u*-filter will be
+applied following `Papale et al. (Biogeosciences, 2006)`_.
 
 The section in `hesseflux_example.cfg` looks like:
 
@@ -416,24 +431,27 @@ The section in `hesseflux_example.cfg` looks like:
    # bool
    applyustarflag = True
 
-A minimum threshold `ustarmin` is defined under which data is flagged by
-default. `Papale et al. (Biogeosciences, 2006)`_ suggest 0.1 for forests and
-0.01 for other land cover types. `postproc_europe-fluxdata.py` sets 0.01 as its
-default value. Uncertainty of the u* threshold is calculated via bootstrapping
-in Papale et al. `nboot` gives the number of bootstrapping for the uncertainty
-estimate of the u* threshold. The algorithm divides the input data in 6
-temperature classes and 20 u* classes within each temperature class per season.
-It then determines the threshold for each season as the average u* of the u*
-class where the average CO2 flux is less than `plateaucrit` times the average of
-all CO2 fluxes with u* greater than the u* class. `Papale et al.
-(Biogeosciences, 2006)`_ took 6 temperature classes and `plateaucrit=0.99`,
-while `REddyProc`_ takes 7 temperature classes and `plateaucrit=0.95`, which are
-also the defaults in ``hesseflux``. `Papale et al. (Biogeosciences, 2006)`_ also
-used the maximum of the four seasonal u* thresholds as the threshold applied to
-all the year. If `seasonout=True`, the seasonal u* thresholds will be applied
-instead of the maximum of four seasonal u* thresholds. One can also set
-`applyustarflag=False` to just calculate the u* thresholds without applying them
-to experiment with different parameter values.
+A minimum threshold `ustarmin` is defined under which data is flagged
+by default. `Papale et al. (Biogeosciences, 2006)`_ suggest 0.1 for
+forests and 0.01 for other land cover
+types. `postproc_europe-fluxdata.py` sets 0.01 as its default
+value. Uncertainty of the u* threshold is calculated via bootstrapping
+in Papale et al. `nboot` gives the number of bootstrapping for the
+uncertainty estimate of the u* threshold. The algorithm divides the
+input data in 6 temperature classes and 20 u* classes within each
+temperature class per season.  It then determines the threshold for
+each season as the average u* of the u* class where the average CO2
+flux is less than `plateaucrit` times the average of all CO2 fluxes
+with u* greater than the u* class. `Papale et al.  (Biogeosciences,
+2006)`_ took 6 temperature classes and `plateaucrit=0.99`, while
+`REddyProc`_ takes 7 temperature classes and `plateaucrit=0.95`, which
+are also the defaults in ``hesseflux``. `Papale et
+al. (Biogeosciences, 2006)`_ also used the maximum of the four
+seasonal u* thresholds as the threshold applied to all the year. If
+`seasonout=True`, the seasonal u* thresholds will be applied instead
+of the maximum of four seasonal u* thresholds. One can also set
+`applyustarflag=False` to just calculate the u* thresholds without
+applying them to experiment with different parameter values.
 
 The u*-filtering is then performed as:
 
@@ -469,16 +487,17 @@ The u*-filtering is then performed as:
        for ii, hh in enumerate(hout):
            dff.loc[flag == 2, hh] = 5
 
-The function :func:`ustarfilter` returns the u* 5, 50 and 95 percentiles of the
-bootstrapped u* thresholds as well as flag columns, which is 0 except where u*
-is smaller than the median u*-threshold. The scripts sets the columns of the
-Eddy fluxes in the flag dataframe `dff` to 5 (5 to keep track where the flag was
-set).
+The function :func:`~hesseflux.ustarfilter.ustarfilter` returns the u*
+5, 50 and 95 percentiles of the bootstrapped u* thresholds as well as
+flag columns, which is 0 except where u* is smaller than the median
+u*-threshold. The scripts sets the columns of the Eddy fluxes in the
+flag dataframe `dff` to 5 (5 to keep track where the flag was set).
 
-One might not want to do u* filtering, but use for example Integral Turbulence
-Characteristics (ITC) that were calculated, for example, with
-`EddyPro`_\ :sup:`(R)`. These should be set right at the start after reading the input
-data into the dataframe `df` and producing the flag dataframe `dff` like:
+One might not want to do u* filtering, but use for example Integral
+Turbulence Characteristics (ITC) that were calculated, for example,
+with `EddyPro`_\ :sup:`(R)`. These should be set right at the start
+after reading the input data into the dataframe `df` and producing the
+flag dataframe `dff` like:
 
 .. code-block:: python
 
@@ -488,12 +507,13 @@ data into the dataframe `df` and producing the flag dataframe `dff` like:
 Partitioning of Net Ecosystem Exchange
 --------------------------------------
 
-If `partition=True` is set in the configuration file, two estimates of Gross
-Primary Productivity (GPP) and Ecosystem Respiration (RECO) are calculated:
-firstly with the method of `Reichstein et al. (Glob Change Biolo, 2005)`_ using
-nighttime data only, and secondly with the method of `Lasslop et al. (Glob
-Change Biolo, 2010)`_ using a light-response curve on 'daytime' data. The
-configuration `hesseflux_example.cfg` gives only one option in this section:
+If `partition=True` is set in the configuration file, two estimates of
+Gross Primary Productivity (GPP) and Ecosystem Respiration (RECO) are
+calculated: firstly with the method of `Reichstein et al. (Glob Change
+Biolo, 2005)`_ using nighttime data only, and secondly with the method
+of `Lasslop et al. (Glob Change Biolo, 2010)`_ using a light-response
+curve on 'daytime' data. The configuration `hesseflux_example.cfg`
+gives only one option in this section:
 
 .. code-block:: python
 
@@ -503,11 +523,11 @@ configuration `hesseflux_example.cfg` gives only one option in this section:
    # bool
    nogppnight = False
 
-Many people find it unaesthetic that the 'daytime' method gives negative GPP at
-night. We esteem this the correct behaviour, reflecting the uncertainty in the
-gross flux estimates. However, one can set `nogppnight=True` to set GPP=0 at
-night and RECO=NEE in this case, the latter having then all variability of the
-net fluxes.
+Many people find it unaesthetic that the 'daytime' method gives
+negative GPP at night. We esteem this the correct behaviour,
+reflecting the uncertainty in the gross flux estimates. However, one
+can set `nogppnight=True` to set GPP=0 at night and RECO=NEE in this
+case, the latter having then all variability of the net fluxes.
 
 The partitioning is calculated as:
 
@@ -550,10 +570,11 @@ The partitioning is calculated as:
 Gap-filling / Imputation
 ------------------------
 
-Marginal Distribution Sampling (MDS) of `Reichstein et al. (Glob Change Biolo,
-2005)`_ is implemented as imputation or so-called gap-filling algorithm. The
-algorithm looks for similar conditions in the vicinity of a missing data point,
-if option `fill=True`. The configuration file is:
+Marginal Distribution Sampling (MDS) of `Reichstein et al. (Glob
+Change Biolo, 2005)`_ is implemented as imputation or so-called
+gap-filling algorithm. The algorithm looks for similar conditions in
+the vicinity of a missing data point, if option `fill=True`. The
+configuration file is:
 
 .. code-block:: python
 
@@ -571,12 +592,13 @@ if option `fill=True`. The configuration file is:
    # avoid extrapolation in gaps longer than longgap days
    longgap = 60
 
-If a flux data point is missing, times with incoming shortwave radiation in the
-range of `sw_dev` around the actual shortwave radiation will be looked for, as
-well as air temperatures within `ta_dev` and air vapour pressure deficit within
-`vpd_dev`. The mean of flux values at the similar conditions is then taken as
-fill value. The function does not fill long gaps longer than `longgap` days. A
-good summary is given in Fig. A1 of `Reichstein et al. (Glob Change Biolo,
+If a flux data point is missing, times with incoming shortwave
+radiation in the range of `sw_dev` around the actual shortwave
+radiation will be looked for, as well as air temperatures within
+`ta_dev` and air vapour pressure deficit within `vpd_dev`. The mean of
+flux values at the similar conditions is then taken as fill value. The
+function does not fill long gaps longer than `longgap` days. A good
+summary is given in Fig. A1 of `Reichstein et al. (Glob Change Biolo,
 2005)`_.
 
 The script invokes MDS as:
@@ -613,22 +635,25 @@ The script invokes MDS as:
    df = pd.concat([df,  df_f],  axis=1)
    dff = pd.concat([dff, dff_f], axis=1)
 
-The function :func:`gapfill` returns the filled columns `df_f` as well as flag
-columns `dff_f` indicating fill quality. Fill quality A-C of `Reichstein et al.
-(Glob Change Biolo, 2005)`_ are translated to quality flags 1-3.
+The function :func:`~hesseflux.gapfill.gapfill` returns the filled
+columns `df_f` as well as flag columns `dff_f` indicating fill
+quality. Fill quality A-C of `Reichstein et al.  (Glob Change Biolo,
+2005)`_ are translated to quality flags 1-3.
 
 
 Uncertainty estimates of flux data
 ----------------------------------
 
 `Lasslop et al. (Biogeosci, 2008)`_ presented an algorithm to estimate
-uncertainties of Eddy covariance fluxes using Marginal Distribution Sampling
-(MDS). The gap-filling function :func:`gapfill` can be used for uncertainty
-estimation giving the keyword `err=True`. The same thresholds as for gap-filling
-are used.
+uncertainties of Eddy covariance fluxes using Marginal Distribution
+Sampling (MDS). The gap-filling function
+:func:`~hesseflux.gapfill.gapfill` can be used for uncertainty
+estimation giving the keyword `err=True`. The same thresholds as for
+gap-filling are used.
 
-The script `postproc_europe-fluxdata.py` uses the function :func:`gapfill` to
-calculate flux uncertainties like:
+The script `postproc_europe-fluxdata.py` uses the function
+:func:`~hesseflux.gapfill.gapfill` to calculate flux uncertainties
+like:
 
 .. code-block:: python
 
@@ -667,17 +692,17 @@ calculate flux uncertainties like:
    for cc in range(len(colin)):
        dff[colout[cc]] = dff[colin[cc]]
 
-We recommend, however, to calculate flux uncertainties with the Eddy covariance
-raw data as described in `Mauder et al. (Agric Forest Meteo, 2013)`_. This is
-for example implemented in the processing softwares `EddyPro`_\ :sup:`(R)` or
-`TK3`_.
+We recommend, however, to calculate flux uncertainties with the Eddy
+covariance raw data as described in `Mauder et al. (Agric Forest
+Meteo, 2013)`_. This is for example implemented in the processing
+softwares `EddyPro`_\ :sup:`(R)` or `TK3`_.
 
 
 Writing the output file
 -----------------------
 
 The dataframe is written to the output file with :mod:`pandas`
-:func:`pandas.Dataframe.to_csv`:
+:func:`to_csv` method:
 
 .. code-block:: python
 
@@ -702,13 +727,15 @@ The configuration for output is:
    # bool
    outflagcols = False
 
-If `outputfile` is missing or empty, the script will try to open a GUI, where
-one can choose an output directory and the filename will then be name of the
-configuration file with the suffix '.csv'. If `outundef=True` then all values in
-`df` with a flag value in `dff` greater than zero will be set to `undef`. The
-script can also add flag columns, prefixed with `flag_`, for each column in
-`df`, if `outflagcols=True`. The script will always output the columns with the
-flags for fill quality if gap-filling was performed: option `fill=True`.
+If `outputfile` is missing or empty, the script will try to open a
+GUI, where one can choose an output directory and the filename will
+then be name of the configuration file with the suffix '.csv'. If
+`outundef=True` then all values in `df` with a flag value in `dff`
+greater than zero will be set to `undef`. The script can also add flag
+columns, prefixed with `flag_`, for each column in `df`, if
+`outflagcols=True`. The script will always output the columns with the
+flags for fill quality if gap-filling was performed: option
+`fill=True`.
 
 The whole code to write the output file is:
 
@@ -724,7 +751,7 @@ The whole code to write the output file is:
    if outundef:
        for cc in df.columns:
            if cc.split('_')[-4] != 'f':  # exclude gap-filled columns
-               df[cc].where(dff[cc] == 0, other=undef, inplace=True)
+               df[cc] = df[cc].where(dff[cc] == 0, other=undef)
    if outflagcols:
        def _add_flag(c):
            return 'flag_' + c
diff --git a/docs/html/_static/alabaster.css b/docs/html/_static/alabaster.css
index 517d0b2..e3174bf 100644
--- a/docs/html/_static/alabaster.css
+++ b/docs/html/_static/alabaster.css
@@ -69,6 +69,11 @@ div.relations {
 }
 
 
+div.sphinxsidebar {
+    max-height: 100%;
+    overflow-y: auto;
+}
+
 div.sphinxsidebar a {
     color: #444;
     text-decoration: none;
@@ -155,6 +160,14 @@ div.sphinxsidebar input {
     font-size: 1em;
 }
 
+div.sphinxsidebar #searchbox input[type="text"] {
+    width: 160px;
+}
+
+div.sphinxsidebar .search > div {
+    display: table-cell;
+}
+
 div.sphinxsidebar hr {
     border: none;
     height: 1px;
@@ -638,15 +651,7 @@ a:hover tt, a:hover code {
     display: none!important;
 }
 
-/* Make nested-list/multi-paragraph items look better in Releases changelog
- * pages. Without this, docutils' magical list fuckery causes inconsistent
- * formatting between different release sub-lists.
- */
-div#changelog > div.section > ul > li > p:only-child {
-    margin-bottom: 0;
-}
-
-/* Hide fugly table cell borders in ..bibliography:: directive output */
+/* Hide ugly table cell borders in ..bibliography:: directive output */
 table.docutils.citation, table.docutils.citation td, table.docutils.citation th {
   border: none;
   /* Below needed in some edge cases; if not applied, bottom shadows appear */
diff --git a/docs/html/_static/basic.css b/docs/html/_static/basic.css
index 7577acb..e5179b7 100644
--- a/docs/html/_static/basic.css
+++ b/docs/html/_static/basic.css
@@ -4,7 +4,7 @@
  *
  * Sphinx stylesheet -- basic theme.
  *
- * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -222,7 +222,7 @@ table.modindextable td {
 /* -- general body styles --------------------------------------------------- */
 
 div.body {
-    min-width: 360px;
+    min-width: inherit;
     max-width: 800px;
 }
 
@@ -237,6 +237,10 @@ a.headerlink {
     visibility: hidden;
 }
 
+a:visited {
+    color: #551A8B;
+}
+
 h1:hover > a.headerlink,
 h2:hover > a.headerlink,
 h3:hover > a.headerlink,
@@ -670,6 +674,16 @@ dd {
     margin-left: 30px;
 }
 
+.sig dd {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
+.sig dl {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
 dl > dd:last-child,
 dl > dd:last-child > :last-child {
     margin-bottom: 0;
@@ -738,6 +752,14 @@ abbr, acronym {
     cursor: help;
 }
 
+.translated {
+    background-color: rgba(207, 255, 207, 0.2)
+}
+
+.untranslated {
+    background-color: rgba(255, 207, 207, 0.2)
+}
+
 /* -- code displays --------------------------------------------------------- */
 
 pre {
diff --git a/docs/html/_static/doctools.js b/docs/html/_static/doctools.js
index d06a71d..4d67807 100644
--- a/docs/html/_static/doctools.js
+++ b/docs/html/_static/doctools.js
@@ -4,7 +4,7 @@
  *
  * Base JavaScript utilities for all Sphinx HTML documentation.
  *
- * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
diff --git a/docs/html/_static/documentation_options.js b/docs/html/_static/documentation_options.js
index 09a35b7..5aed7fa 100644
--- a/docs/html/_static/documentation_options.js
+++ b/docs/html/_static/documentation_options.js
@@ -1,6 +1,5 @@
-var DOCUMENTATION_OPTIONS = {
-    URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
-    VERSION: '5.1.dev0',
+const DOCUMENTATION_OPTIONS = {
+    VERSION: '5.1.dev2',
     LANGUAGE: 'en',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',
diff --git a/docs/html/_static/language_data.js b/docs/html/_static/language_data.js
index 250f566..367b8ed 100644
--- a/docs/html/_static/language_data.js
+++ b/docs/html/_static/language_data.js
@@ -5,7 +5,7 @@
  * This script contains the language-specific data used by searchtools.js,
  * namely the list of stopwords, stemmer, scorer and splitter.
  *
- * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -13,7 +13,7 @@
 var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
 
 
-/* Non-minified version is copied as a separate JS file, is available */
+/* Non-minified version is copied as a separate JS file, if available */
 
 /**
  * Porter Stemmer
diff --git a/docs/html/_static/pygments.css b/docs/html/_static/pygments.css
index 691aeb8..0d49244 100644
--- a/docs/html/_static/pygments.css
+++ b/docs/html/_static/pygments.css
@@ -17,6 +17,7 @@ span.linenos.special { color: #000000; background-color: #ffffc0; padding-left:
 .highlight .cs { color: #408090; background-color: #fff0f0 } /* Comment.Special */
 .highlight .gd { color: #A00000 } /* Generic.Deleted */
 .highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */
 .highlight .gr { color: #FF0000 } /* Generic.Error */
 .highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
 .highlight .gi { color: #00A000 } /* Generic.Inserted */
diff --git a/docs/html/_static/searchtools.js b/docs/html/_static/searchtools.js
index 97d56a7..92da3f8 100644
--- a/docs/html/_static/searchtools.js
+++ b/docs/html/_static/searchtools.js
@@ -4,7 +4,7 @@
  *
  * Sphinx JavaScript utilities for the full-text search.
  *
- * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -57,12 +57,12 @@ const _removeChildren = (element) => {
 const _escapeRegExp = (string) =>
   string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
 
-const _displayItem = (item, searchTerms) => {
+const _displayItem = (item, searchTerms, highlightTerms) => {
   const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
-  const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT;
   const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
   const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
   const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+  const contentRoot = document.documentElement.dataset.content_root;
 
   const [docName, title, anchor, descr, score, _filename] = item;
 
@@ -75,28 +75,35 @@ const _displayItem = (item, searchTerms) => {
     if (dirname.match(/\/index\/$/))
       dirname = dirname.substring(0, dirname.length - 6);
     else if (dirname === "index/") dirname = "";
-    requestUrl = docUrlRoot + dirname;
+    requestUrl = contentRoot + dirname;
     linkUrl = requestUrl;
   } else {
     // normal html builders
-    requestUrl = docUrlRoot + docName + docFileSuffix;
+    requestUrl = contentRoot + docName + docFileSuffix;
     linkUrl = docName + docLinkSuffix;
   }
   let linkEl = listItem.appendChild(document.createElement("a"));
   linkEl.href = linkUrl + anchor;
   linkEl.dataset.score = score;
   linkEl.innerHTML = title;
-  if (descr)
+  if (descr) {
     listItem.appendChild(document.createElement("span")).innerHTML =
       " (" + descr + ")";
+    // highlight search terms in the description
+    if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+      highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+  }
   else if (showSearchSummary)
     fetch(requestUrl)
       .then((responseData) => responseData.text())
       .then((data) => {
         if (data)
           listItem.appendChild(
-            Search.makeSearchSummary(data, searchTerms)
+            Search.makeSearchSummary(data, searchTerms, anchor)
           );
+        // highlight search terms in the summary
+        if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+          highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
       });
   Search.output.appendChild(listItem);
 };
@@ -109,26 +116,43 @@ const _finishSearch = (resultCount) => {
     );
   else
     Search.status.innerText = _(
-      `Search finished, found ${resultCount} page(s) matching the search query.`
-    );
+      "Search finished, found ${resultCount} page(s) matching the search query."
+    ).replace('${resultCount}', resultCount);
 };
 const _displayNextItem = (
   results,
   resultCount,
-  searchTerms
+  searchTerms,
+  highlightTerms,
 ) => {
   // results left, load the summary and display it
   // this is intended to be dynamic (don't sub resultsCount)
   if (results.length) {
-    _displayItem(results.pop(), searchTerms);
+    _displayItem(results.pop(), searchTerms, highlightTerms);
     setTimeout(
-      () => _displayNextItem(results, resultCount, searchTerms),
+      () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
       5
     );
   }
   // search finished, update title and status message
   else _finishSearch(resultCount);
 };
+// Helper function used by query() to order search results.
+// Each input is an array of [docname, title, anchor, descr, score, filename].
+// Order the results by score (in opposite order of appearance, since the
+// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically.
+const _orderResultsByScoreThenName = (a, b) => {
+  const leftScore = a[4];
+  const rightScore = b[4];
+  if (leftScore === rightScore) {
+    // same score: sort alphabetically
+    const leftTitle = a[1].toLowerCase();
+    const rightTitle = b[1].toLowerCase();
+    if (leftTitle === rightTitle) return 0;
+    return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+  }
+  return leftScore > rightScore ? 1 : -1;
+};
 
 /**
  * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
@@ -152,13 +176,26 @@ const Search = {
   _queued_query: null,
   _pulse_status: -1,
 
-  htmlToText: (htmlString) => {
+  htmlToText: (htmlString, anchor) => {
     const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
-    htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() });
+    for (const removalQuery of [".headerlinks", "script", "style"]) {
+      htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
+    }
+    if (anchor) {
+      const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`);
+      if (anchorContent) return anchorContent.textContent;
+
+      console.warn(
+        `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.`
+      );
+    }
+
+    // if anchor not specified or not found, fall back to main content
     const docContent = htmlElement.querySelector('[role="main"]');
-    if (docContent !== undefined) return docContent.textContent;
+    if (docContent) return docContent.textContent;
+
     console.warn(
-      "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template."
+      "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template."
     );
     return "";
   },
@@ -231,16 +268,7 @@ const Search = {
     else Search.deferQuery(query);
   },
 
-  /**
-   * execute search (requires search index to be loaded)
-   */
-  query: (query) => {
-    const filenames = Search._index.filenames;
-    const docNames = Search._index.docnames;
-    const titles = Search._index.titles;
-    const allTitles = Search._index.alltitles;
-    const indexEntries = Search._index.indexentries;
-
+  _parseQuery: (query) => {
     // stem the search terms and add them to the correct list
     const stemmer = new Stemmer();
     const searchTerms = new Set();
@@ -276,16 +304,32 @@ const Search = {
     // console.info("required: ", [...searchTerms]);
     // console.info("excluded: ", [...excludedTerms]);
 
-    // array of [docname, title, anchor, descr, score, filename]
-    let results = [];
+    return [query, searchTerms, excludedTerms, highlightTerms, objectTerms];
+  },
+
+  /**
+   * execute search (requires search index to be loaded)
+   */
+  _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+    const allTitles = Search._index.alltitles;
+    const indexEntries = Search._index.indexentries;
+
+    // Collect multiple result groups to be sorted separately and then ordered.
+    // Each is an array of [docname, title, anchor, descr, score, filename].
+    const normalResults = [];
+    const nonMainIndexResults = [];
+
     _removeChildren(document.getElementById("search-progress"));
 
-    const queryLower = query.toLowerCase();
+    const queryLower = query.toLowerCase().trim();
     for (const [title, foundTitles] of Object.entries(allTitles)) {
-      if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) {
+      if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
         for (const [file, id] of foundTitles) {
           let score = Math.round(100 * queryLower.length / title.length)
-          results.push([
+          normalResults.push([
             docNames[file],
             titles[file] !== title ? `${titles[file]} > ${title}` : title,
             id !== null ? "#" + id : "",
@@ -300,46 +344,47 @@ const Search = {
     // search for explicit entries in index directives
     for (const [entry, foundEntries] of Object.entries(indexEntries)) {
       if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
-        for (const [file, id] of foundEntries) {
-          let score = Math.round(100 * queryLower.length / entry.length)
-          results.push([
+        for (const [file, id, isMain] of foundEntries) {
+          const score = Math.round(100 * queryLower.length / entry.length);
+          const result = [
             docNames[file],
             titles[file],
             id ? "#" + id : "",
             null,
             score,
             filenames[file],
-          ]);
+          ];
+          if (isMain) {
+            normalResults.push(result);
+          } else {
+            nonMainIndexResults.push(result);
+          }
         }
       }
     }
 
     // lookup as object
     objectTerms.forEach((term) =>
-      results.push(...Search.performObjectSearch(term, objectTerms))
+      normalResults.push(...Search.performObjectSearch(term, objectTerms))
     );
 
     // lookup as search terms in fulltext
-    results.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+    normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms));
 
     // let the scorer override scores with a custom scoring function
-    if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item)));
-
-    // now sort the results by score (in opposite order of appearance, since the
-    // display function below uses pop() to retrieve items) and then
-    // alphabetically
-    results.sort((a, b) => {
-      const leftScore = a[4];
-      const rightScore = b[4];
-      if (leftScore === rightScore) {
-        // same score: sort alphabetically
-        const leftTitle = a[1].toLowerCase();
-        const rightTitle = b[1].toLowerCase();
-        if (leftTitle === rightTitle) return 0;
-        return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
-      }
-      return leftScore > rightScore ? 1 : -1;
-    });
+    if (Scorer.score) {
+      normalResults.forEach((item) => (item[4] = Scorer.score(item)));
+      nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item)));
+    }
+
+    // Sort each group of results by score and then alphabetically by name.
+    normalResults.sort(_orderResultsByScoreThenName);
+    nonMainIndexResults.sort(_orderResultsByScoreThenName);
+
+    // Combine the result groups in (reverse) order.
+    // Non-main index entries are typically arbitrary cross-references,
+    // so display them after other results.
+    let results = [...nonMainIndexResults, ...normalResults];
 
     // remove duplicate search results
     // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
@@ -353,14 +398,19 @@ const Search = {
       return acc;
     }, []);
 
-    results = results.reverse();
+    return results.reverse();
+  },
+
+  query: (query) => {
+    const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query);
+    const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms);
 
     // for debugging
     //Search.lastresults = results.slice();  // a copy
     // console.info("search results:", Search.lastresults);
 
     // print the results
-    _displayNextItem(results, results.length, searchTerms);
+    _displayNextItem(results, results.length, searchTerms, highlightTerms);
   },
 
   /**
@@ -458,14 +508,18 @@ const Search = {
       // add support for partial matches
       if (word.length > 2) {
         const escapedWord = _escapeRegExp(word);
-        Object.keys(terms).forEach((term) => {
-          if (term.match(escapedWord) && !terms[word])
-            arr.push({ files: terms[term], score: Scorer.partialTerm });
-        });
-        Object.keys(titleTerms).forEach((term) => {
-          if (term.match(escapedWord) && !titleTerms[word])
-            arr.push({ files: titleTerms[word], score: Scorer.partialTitle });
-        });
+        if (!terms.hasOwnProperty(word)) {
+          Object.keys(terms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: terms[term], score: Scorer.partialTerm });
+          });
+        }
+        if (!titleTerms.hasOwnProperty(word)) {
+          Object.keys(titleTerms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: titleTerms[term], score: Scorer.partialTitle });
+          });
+        }
       }
 
       // no match but word was a required one
@@ -488,9 +542,8 @@ const Search = {
 
       // create the mapping
       files.forEach((file) => {
-        if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1)
-          fileMap.get(file).push(word);
-        else fileMap.set(file, [word]);
+        if (!fileMap.has(file)) fileMap.set(file, [word]);
+        else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word);
       });
     });
 
@@ -541,8 +594,8 @@ const Search = {
    * search summary for a given text. keywords is a list
    * of stemmed words.
    */
-  makeSearchSummary: (htmlText, keywords) => {
-    const text = Search.htmlToText(htmlText);
+  makeSearchSummary: (htmlText, keywords, anchor) => {
+    const text = Search.htmlToText(htmlText, anchor);
     if (text === "") return null;
 
     const textLower = text.toLowerCase();
diff --git a/docs/html/_static/sphinx_highlight.js b/docs/html/_static/sphinx_highlight.js
index aae669d..8a96c69 100644
--- a/docs/html/_static/sphinx_highlight.js
+++ b/docs/html/_static/sphinx_highlight.js
@@ -29,14 +29,19 @@ const _highlight = (node, addItems, text, className) => {
       }
 
       span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+      const rest = document.createTextNode(val.substr(pos + text.length));
       parent.insertBefore(
         span,
         parent.insertBefore(
-          document.createTextNode(val.substr(pos + text.length)),
+          rest,
           node.nextSibling
         )
       );
       node.nodeValue = val.substr(0, pos);
+      /* There may be more occurrences of search term in this node. So call this
+       * function recursively on the remaining fragment.
+       */
+      _highlight(rest, addItems, text, className);
 
       if (isInSVG) {
         const rect = document.createElementNS(
@@ -140,5 +145,10 @@ const SphinxHighlight = {
   },
 };
 
-_ready(SphinxHighlight.highlightSearchWords);
-_ready(SphinxHighlight.initEscapeListener);
+_ready(() => {
+  /* Do not call highlightSearchWords() when we are on the search page.
+   * It will highlight words from the *previous* search query.
+   */
+  if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
+  SphinxHighlight.initEscapeListener();
+});
diff --git a/docs/html/api.html b/docs/html/api.html
index ee404fc..41f0d59 100644
--- a/docs/html/api.html
+++ b/docs/html/api.html
@@ -1,18 +1,17 @@
-
 
 
-
+
   
     
-    
-
-    API Reference — hesseflux 5.1.dev0 documentation
-    
-    
-    
-    
-    
-    
+    
+
+    API Reference — hesseflux 5.1.dev2 documentation
+    
+    
+    
+    
+    
+    
     
     
     
@@ -20,8 +19,9 @@
    
   
   
+
+  
   
-  
 
   
   
@@ -34,7 +34,7 @@
           

             
   

-
API Reference¶

+
API Reference¶

 
hesseflux provides functions used in the processing and
 post-processing of the Eddy covariance flux data

 
It was developed for the ICOS ecosystem site FR-Hes.

@@ -52,7 +52,7 @@
 
 
 

-
Subpackages¶

+
Subpackages¶

 
@@ -174,7 +174,7 @@ 
Related Topics

-
+

-      ©2009-2023, Matthias Cuntz.
+      ©2009-2024, Matthias Cuntz.
       
       |
-      Powered by Sphinx 6.1.3
-      & Alabaster 0.7.13
+      Powered by Sphinx 7.3.7
+      & Alabaster 0.7.16
       
       |
       
 
-
+
   
     
-    
-
-    Authors — hesseflux 5.1.dev0 documentation
-    
-    
-    
-    
-    
-    
+    
+
+    Authors — hesseflux 5.1.dev2 documentation
+    
+    
+    
+    
+    
+    
     
     
     
    
   
   
+
+  
   
-  
 
   
   
@@ -33,7 +33,7 @@
           

             
   

-
Authors¶

+
Authors¶

 
Created May 2020 by Matthias Cuntz.

 
It is distributed under the MIT License, see LICENSE file.

 
Copyright (c) 2009-2022 Matthias Cuntz

@@ -98,7 +98,7 @@ 
Related Topics

   
 
 

-
+
 
         

-      ©2009-2023, Matthias Cuntz.
+      ©2009-2024, Matthias Cuntz.
       
       |
-      Powered by Sphinx 6.1.3
-      & Alabaster 0.7.13
+      Powered by Sphinx 7.3.7
+      & Alabaster 0.7.16
       
       |
       
 
-
+
   
     
-    
-
-    Changelog — hesseflux 5.1.dev0 documentation
-    
-    
-    
-    
-    
-    
+    
+
+    Changelog — hesseflux 5.1.dev2 documentation
+    
+    
+    
+    
+    
+    
     
     
     
@@ -20,8 +19,9 @@
    
   
   
+
+  
   
-  
 
   
   
@@ -34,7 +34,7 @@
           

             
   

-
Changelog¶

+
Changelog¶

 
All notable changes after its initial release in May 2020 (v2.0) are documented
 in this file.

 

@@ -157,7 +157,7 @@ 
Related Topics

   
 
 

-
+
 
         

-      ©2009-2023, Matthias Cuntz.
+      ©2009-2024, Matthias Cuntz.
       
       |
-      Powered by Sphinx 6.1.3
-      & Alabaster 0.7.13
+      Powered by Sphinx 7.3.7
+      & Alabaster 0.7.16
       
       |
       
 
-
+
   
     
-    
-
-    Contents — hesseflux 5.1.dev0 documentation
-    
-    
-    
-    
-    
-    
+    
+
+    Contents — hesseflux 5.1.dev2 documentation
+    
+    
+    
+    
+    
+    
     
     
     
    
   
   
+
+  
   
-  
 
   
   
@@ -33,7 +33,7 @@
           

             
   

-
Contents¶

+
Contents¶

 

 
    
 
  • hesseflux
      
@@ -137,7 +137,7 @@ 
      Related Topics
      
   
    • 
 

 

-
+
 
         

       

-      ©2009-2023, Matthias Cuntz.
+      ©2009-2024, Matthias Cuntz.
       
       |
-      Powered by Sphinx 6.1.3
-      & Alabaster 0.7.13
+      Powered by Sphinx 7.3.7
+      & Alabaster 0.7.16
       
       |
       
 
-
+
   
     
-    
+    
 
-    gapfill — hesseflux 5.1.dev0 documentation
-    
-    
-    
-    
-    
-    
+    gapfill — hesseflux 5.1.dev2 documentation
+    
+    
+    
+    
+    
+    
     
     
     
@@ -20,8 +19,9 @@
    
   
   
+
+  
   
-  
 
   
   
@@ -34,7 +34,7 @@
           

             
   

-
gapfill¶

+
gapfill¶

 
Fill gaps of flux data from Eddy covariance measurements or estimate flux
 uncertainties

 
This module was written by Matthias Cuntz while at Department of
@@ -76,7 +76,7 @@
 
 

 

-gapfill(dfin, flag=None, date=None, timeformat='%Y-%m-%d %H:%M:%S', colhead=None, sw_dev=50.0, ta_dev=2.5, vpd_dev=5.0, longgap=60, fullday=False, undef=-9999, ddof=1, err=False, errmean=False, verbose=0)[source]¶

+gapfill(dfin, flag=None, date=None, timeformat='%Y-%m-%d %H:%M:%S', colhead=None, sw_dev=50.0, ta_dev=2.5, vpd_dev=5.0, longgap=60, fullday=False, undef=-9999, ddof=1, err=False, errmean=False, verbose=0)[source]¶
 
Fill gaps of flux data from Eddy covariance measurements
 or estimate flux uncertainties

 
Fills gaps in flux data from Eddy covariance measurements with
@@ -113,33 +113,33 @@
 heads and the index of dfin will be copied to flag.

 
  • date (array_like of string, optional) – 1D-array_like of calendar dates in format given in timeformat.
 date must be given if dfin is numpy array.
    • 
-
  • timeformat (str, optional) – Format of dates in date, if given (default: ‘%Y-%m-%d %H:%M:%S’).
+
  • timeformat (str, optional) – Format of dates in date, if given (default: ‘%Y-%m-%d %H:%M:%S’).
 See strftime documentation of Python’s datetime module:
 https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior
    • 
-
  • colhed (array_like of str, optional) – column names if dfin is numpy array. See dfin for mandatory
+
  • colhed (array_like of str, optional) – column names if dfin is numpy array. See dfin for mandatory
 column names.
    • 
-
  • sw_dev (float, optional) – threshold for maximum deviation of global radiation (default: 50)
    • 
-
  • ta_dev (float, optional) – threshold for maximum deviation of air Temperature (default: 2.5)
    • 
-
  • vpd_dev (float, optional) – threshold for maximum deviation of vpd (default: 5)
    • 
-
  • longgap (int, optional) – avoid extrapolation into a gap longer than longgap days (default: 60)
    • 
-
  • fullday (bool, optional) – True: move beginning of large gap to start of next day and move end of
+
  • sw_dev (float, optional) – threshold for maximum deviation of global radiation (default: 50)
    • 
+
  • ta_dev (float, optional) – threshold for maximum deviation of air Temperature (default: 2.5)
    • 
+
  • vpd_dev (float, optional) – threshold for maximum deviation of vpd (default: 5)
    • 
+
  • longgap (int, optional) – avoid extrapolation into a gap longer than longgap days (default: 60)
    • 
+
  • fullday (bool, optional) – True: move beginning of large gap to start of next day and move end of
 large gap to end of last day (default: False)
    • 
-
  • undef (float, optional) – values having undef value are treated as missing values in dfin
+
  • undef (float, optional) – values having undef value are treated as missing values in dfin
 (default: -9999).
 np.nan is not allowed as undef (not working).
    • 
-
  • ddof (int, optional) – Delta Degrees of Freedom. The divisor used in calculation of standard
+
  • ddof (int, optional) – Delta Degrees of Freedom. The divisor used in calculation of standard
 deviation for error estimates (err=True) is N-ddof, where N
 represents the number of elements (default: 1).
    • 
-
  • err (bool, optional) – True: fill every data point with standard deviation instead of mean,
+
  • err (bool, optional) – True: fill every data point with standard deviation instead of mean,
 i.e. used for error generation as in Lasslop et al. (Biogeosci 2008)
 (default: False)
    • 
-
  • errmean (bool, optional) – True: also return mean value of values for error estimates
+
  • errmean (bool, optional) – True: also return mean value of values for error estimates
 if err == True (default: False)
    • 
-
  • shape (bool or tuple, optional) – 
    True: output have the same shape as input data if dfin is
+
  • shape (bool or tuple, optional) – 
    True: output have the same shape as input data if dfin is
 numpy array; if a tuple is given, then this tuple is used to reshape.
    
 
    False: outputs are 1D arrays if dfin is numpy array (default: False).
    
 
    • 
-
  • verbose (int, optional) – Verbosity level 0-3 (default: 0). 0 is no output; 3 is very verbose.
    • 
+
  • verbose (int, optional) – Verbosity level 0-3 (default: 0). 0 is no output; 3 is very verbose.
    • 
 
 
    
 
    Returns:
    
@@ -313,7 +313,7 @@ 
    Related Topics
    
   
 
 
    
-
+
 
         
    
-      ©2009-2023, Matthias Cuntz.
+      ©2009-2024, Matthias Cuntz.
       
       |
-      Powered by Sphinx 6.1.3
-      & Alabaster 0.7.13
+      Powered by Sphinx 7.3.7
+      & Alabaster 0.7.16
       
       |
       
 
-
+
   
     
     
-    Index — hesseflux 5.1.dev0 documentation
-    
-    
-    
-    
-    
-    
+    Index — hesseflux 5.1.dev2 documentation
+    
+    
+    
+    
+    
+    
     
     
    
   
   
+
+  
   
-  
 
   
   
@@ -177,7 +177,7 @@ 
    Related Topics
    
   
 
 
    
-
+
 
         
       
       
    
     
     
    
-      ©2009-2023, Matthias Cuntz.
+      ©2009-2024, Matthias Cuntz.
       
       |
-      Powered by Sphinx 6.1.3
-      & Alabaster 0.7.13
+      Powered by Sphinx 7.3.7
+      & Alabaster 0.7.16
       
     
    
 
diff --git a/docs/html/index.html b/docs/html/index.html
index a2ec4a4..76e14c6 100644
--- a/docs/html/index.html
+++ b/docs/html/index.html
@@ -1,18 +1,17 @@
-
 
 
-
+
-    
-
-    hesseflux — hesseflux 5.1.dev0 documentation
-    
-    
-    
-    
-    
-    
+    
+
+    hesseflux — hesseflux 5.1.dev2 documentation
+    
+    
+    
+    
+    
+    
@@ -20,8 +19,9 @@
    
   
   
+
+  
   
-  
   
@@ -34,15 +34,17 @@
           
    
             
   
    
-
    hesseflux¶
    
+
    hesseflux¶
    
 
    hesseflux provides functions used in the processing and post-processing of
 the Eddy covariance flux data of the ICOS ecosystem site FR-Hes.
    
 Zenodo DOI
 PyPI version
-License
-Build Status
+License
+
+Build Status
+
 
    
-
    About hesseflux¶
    
+
    About hesseflux¶
    
 
    hesseflux collects functions used for processing Eddy covariance data of the
 ICOS ecosystem site FR-Hes.
    
 
    The post-processing functionality for Eddy flux data is similar to the R-package
@@ -51,16 +53,16 @@ 
    About hessefluxLasslop et al., Global Change Biology 2010).
    
 
    
 
    
-
    Documentation¶
    
+
    Documentation¶
    
 
    The complete documentation for hesseflux is available at:
    
 
    
 
    
 
    
 
    
-
    Quick usage guide¶
    
+
    Quick usage guide¶
    
 
    
-
    Post-processing Eddy covariance data that is in europe-fluxdata.eu format¶
    
+
    Post-processing Eddy covariance data that is in europe-fluxdata.eu format¶
    
 
    An example script that makes all the steps described in Papale et al.
 (Biogeosciences, 2006) is given in the example directory. It is simply called:
    
 
    python postproc_europe-fluxdata.py hesseflux_example.cfg
@@ -73,7 +75,7 @@ 
    Post-processing Eddy covariance data that is in europe-fluxdata.eu format
 
    
 
    
-
    Installation¶
    
+
    Installation¶
    
 
    The easiest way to install is via pip:
    
 
    pip install hesseflux
 
    
@@ -89,7 +91,7 @@ 
    Installation
-
    License¶
    
+
    License¶
    
 
    hesseflux is distributed under the MIT License. See the LICENSE file for
 details.
    
 
    Copyright (c) 2009-2022 Matthias Cuntz
    
@@ -155,7 +157,7 @@ 
    Related Topics
    
   
 
 
    
-
+
 
         
    
-      ©2009-2023, Matthias Cuntz.
+      ©2009-2024, Matthias Cuntz.
       
       |
-      Powered by Sphinx 6.1.3
-      & Alabaster 0.7.13
+      Powered by Sphinx 7.3.7
+      & Alabaster 0.7.16
       
       |
       
 
-
+
   
     
-    
-
-    madspikes — hesseflux 5.1.dev0 documentation
-    
-    
-    
-    
-    
-    
+    
+
+    madspikes — hesseflux 5.1.dev2 documentation
+    
+    
+    
+    
+    
+    
     
     
     
@@ -20,8 +19,9 @@
    
   
   
+
+  
   
-  
 
   
   
@@ -34,7 +34,7 @@
           
    
             
   
    
-
    madspikes¶
    
+
    madspikes¶
    
 
    Spike detection using a moving median absolute difference filter
    
 
    This module was original written by Tino Rau and Matthias Cuntz, and
 maintained by Arndt Piayda while at Department of Computational
@@ -66,12 +66,13 @@
 
  • Removed iteration, Apr 2020, Matthias Cuntz
    • 
 
  • Using numpy docstring format, May 2020, Matthias Cuntz
    • 
 
  • Improved flake8 and numpy docstring, Oct 2021, Matthias Cuntz
    • 
+
  • Removed np.float and np.bool, Jun 2024, Matthias Cuntz
    • 
 
 
 
 
    
 
    
-madspikes(dfin, flag=None, isday=None, colhead=None, undef=-9999, nscan=720, nfill=48, z=7, deriv=2, swthr=10.0, plot=False)[source]¶
    
+madspikes(dfin, flag=None, isday=None, colhead=None, undef=-9999, nscan=720, nfill=48, z=7, deriv=2, swthr=10.0, plot=False)[source]¶
 
    Spike detection using a moving median absolute difference filter
    
 
    Used with Eddy vovariance data in Papale et al. (Biogeosciences, 2006).
    
 
    
@@ -85,28 +86,28 @@
 flag will be treated as missing values in dfin.
 If flag is numpy array, df.columns.values will be used as column
 heads.
    
-
  • isday (array_like of bool, optional) – True when it is day, False when night. Must have the same length as
+
  • isday (array_like of bool, optional) – True when it is day, False when night. Must have the same length as
 dfin.shape[0]. If isday is not given, dfin must have a column
 with head ‘SW_IN’ or starting with ‘SW_IN’. isday will then be
 dfin[‘SW_IN’] > swthr.
    • 
-
  • colhed (array_like of str, optional) – column names if dfin is numpy array.
    • 
-
  • undef (float, optional) – values having undef value are treated as missing values in dfin
+
  • colhed (array_like of str, optional) – column names if dfin is numpy array.
    • 
+
  • undef (float, optional) – values having undef value are treated as missing values in dfin
 (default: -9999). np.nan as undef is not allowed (not working).
    • 
-
  • nscan (int, optional) – size of moving window to calculate mad in time steps (default: 15*48)
    • 
-
  • nfill (int, optional) – step size of moving window to calculate mad in time steps
+
  • nscan (int, optional) – size of moving window to calculate mad in time steps (default: 15*48)
    • 
+
  • nfill (int, optional) – step size of moving window to calculate mad in time steps
 (default: 1*48). MAD will be calculated in nscan time window.
 Resulting mask will be applied only in nfill window in the middle
 of the nscan window. Then nscan window will be moved by nfill
 time steps.
    • 
-
  • z (float, optional) – Input is allowed to deviate maximum z standard deviations from the
+
  • z (float, optional) – Input is allowed to deviate maximum z standard deviations from the
 median (default: 7)
    • 
-
  • deriv (int, optional) – 
    0: Act on raw input.
    
+
  • deriv (int, optional) – 
    0: Act on raw input.
    
 
    1: Use first derivatives.
    
 
    2: Use 2nd derivatives (default).
    
 
    • 
-
  • swthr (float, optional) – Threshold to determine daytime from incoming shortwave radiation if
+
  • swthr (float, optional) – Threshold to determine daytime from incoming shortwave radiation if
 isday not given (default: 10).
    • 
-
  • plot (bool, optional) – True: data and spikes are plotted into madspikes.pdf (default: False).
    • 
+
  • plot (bool, optional) – True: data and spikes are plotted into madspikes.pdf (default: False).
    • 
 
 
    
 
    Returns:
    
@@ -179,7 +180,7 @@ 
    Related Topics
    
   
 
 
    
-
+
 
         
    
-      ©2009-2023, Matthias Cuntz.
+      ©2009-2024, Matthias Cuntz.
       
       |
-      Powered by Sphinx 6.1.3
-      & Alabaster 0.7.13
+      Powered by Sphinx 7.3.7
+      & Alabaster 0.7.16
       
       |
       
 
-
+
   
     
-    
+    
 
-    nee2gpp — hesseflux 5.1.dev0 documentation
-    
-    
-    
-    
-    
-    
+    nee2gpp — hesseflux 5.1.dev2 documentation
+    
+    
+    
+    
+    
+    
     
     
     
@@ -20,8 +19,9 @@
    
   
   
+
+  
   
-  
 
   
   
@@ -34,7 +34,7 @@
           
    
             
   
    
-
    nee2gpp¶
    
+
    nee2gpp¶
    
 
    
 
    nee2gppEstimates photosynthesis (GPP) and ecosystem respiration (RECO)
    from Eddy covariance CO2 flux data.
    
 
    
@@ -51,13 +51,15 @@
 
  • Set default undef to NaN, Mar 2012, Arndt Piayda
    • 
 
  • Add wrapper nee2gpp for individual routines, Nov 2012, Matthias Cuntz
    • 
 
  • Ported to Python 3, Feb 2013, Matthias Cuntz
    • 
-
  • Use generel cost function cost_abs from functions module, May 2013, Matthias Cuntz
    • 
+
  • Use generel cost function cost_abs from functions module,
+May 2013, Matthias Cuntz
    • 
 
  • Use fmin_tnc to allow params < 0, Aug 2014, Arndt Piayda
    • 
 
  • Keyword nogppnight, Aug 2014, Arndt Piayda
    • 
 
  • Add wrapper nee2gpp for individual routines, Nov 2012, Matthias Cuntz
    • 
 
  • Add wrapper nee2gpp for individual routines, Nov 2012, Matthias Cuntz
    • 
 
  • Input can be pandas Dataframe or numpy array(s), Apr 2020, Matthias Cuntz
    • 
 
  • Using numpy docstring format, May 2020, Matthias Cuntz
    • 
+
  • Removed np.float and np.int, Jun 2024, Matthias Cuntz
    • 
 
 
    The following functions are provided
    
 
    
 
 
    gapfill(dfin[, flag, date, timeformat, ...])
    		
   
 
 
 
         
       
       
     
     
       
       
     
     
       
       
     
     
       
     
     
       
       
     
     
   
     
     
     
     
 
   
       
       
     
     
       
       
     
     
    
@@ -69,7 +71,7 @@
 
    
 
    
 
    
-nee2gpp(dfin, flag=None, isday=None, date=None, timeformat='%Y-%m-%d %H:%M:%S', colhead=None, undef=-9999, method='reichstein', nogppnight=False, swthr=10.0)[source]¶
    
+nee2gpp(dfin, flag=None, isday=None, date=None, timeformat='%Y-%m-%d %H:%M:%S', colhead=None, undef=-9999, method='reichstein', nogppnight=False, swthr=10.0)[source]¶
 
    Calculate photosynthesis (GPP) and ecosystem respiration (RECO)
 from Eddy covariance CO2 flux data.
    
 
    It uses either
    
@@ -89,43 +91,53 @@
 
  • dfin (pandas.Dataframe or numpy.array) – 
    time series of CO2 fluxes and air temperature, and possibly
 incoming shortwave radiation and air vapour pressure deficit.
    
 
    dfin can be a pandas.Dataframe with the columns
-‘FC’ or ‘NEE’ (or starting with ‘FC_’ or ‘NEE_’) for observed CO2 flux [umol(CO2) m-2 s-1]
+‘FC’ or ‘NEE’ (or starting with ‘FC_’ or ‘NEE_’) for observed
+CO2 flux [umol(CO2) m-2 s-1]
 ‘TA’    (or starting with ‘TA_’) for air temperature [K]
    
 
    method=’lasslop’ or method=’day’ needs also
-‘SW_IN’ (or starting with ‘SW_IN’) for incoming short-wave radiation [W m-2]
+‘SW_IN’ (or starting with ‘SW_IN’) for incoming short-wave
+radiation [W m-2]
 ‘VPD’   (or starting with ‘VPD’) for air vapour deficit [Pa]
 The index is taken as date variable.
    
 
    dfin can also me a numpy array with the same columns. In this case
 colhead, date, and possibly dateformat must be given.
    
 
    • 
-
  • flag (pandas.Dataframe or numpy.array, optional) – 
    flag Dataframe or array has the same shape as dfin. Non-zero values in
-flag will be treated as missing values in dfin.
    
+
  • flag (pandas.Dataframe or numpy.array, optional) – 
    flag Dataframe or array has the same shape as dfin.
+Non-zero values in flag will be treated as missing values in dfin.
    
 
    flag must follow the same rules as dfin if pandas.Dataframe.
    
-
    If flag is numpy array, df.columns.values will be used as column heads
-and the index of dfin will be copied to flag.
    
+
    If flag is numpy array, df.columns.values will be used as column
+heads and the index of dfin will be copied to flag.
    
 
    • 
-
  • isday (array_like of bool, optional) – 
    True when it is day, False when night. Must have the same length as dfin.shape[0].
    
+
  • isday (array_like of bool, optional) – 
    True when it is day, False when night. Must have the same length
+as dfin.shape[0].
    
 
    If isday is not given, dfin must have a column with head ‘SW_IN’ or
 starting with ‘SW_IN’. isday will then be dfin[‘SW_IN’] > swthr.
    
 
    • 
 
  • date (array_like of string, optional) – 
    1D-array_like of calendar dates in format given in timeformat.
    
 
    date must be given if dfin is numpy array.
    
 
    • 
-
  • timeformat (str, optional) – Format of dates in date, if given (default: ‘%Y-%m-%d %H:%M:%S’).
+
  • timeformat (str, optional) – Format of dates in date, if given (default: ‘%Y-%m-%d %H:%M:%S’).
 See strftime documentation of Python’s datetime module:
 https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior
    • 
-
  • colhed (array_like of str, optional) – column names if dfin is numpy array. See dfin for mandatory column names.
    • 
-
  • undef (float, optional) – values having undef value are treated as missing values in dfin (default: -9999)
    • 
-
  • method (str, optional) – 
    method to use for partitioning. Possible values are:
    
-
    ’global’ or ‘falge’:     fit of Reco vs. temperature to all nighttime data
    
+
  • colhed (array_like of str, optional) – column names if dfin is numpy array. See dfin for mandatory
+column names.
    • 
+
  • undef (float, optional) – values having undef value are treated as missing values in dfin
+(default: -9999)
    • 
+
  • method (str, optional) – 
    method to use for partitioning. Possible values are:
    
 
    
-
    ’local’ of ‘reichstein’: several fits over the season of Reco vs. temperature
    as in Reichstein et al. (2005) (default)
    
+
    ’global’ or ‘falge’:     fit of Reco vs. temperature to all nighttime
    data
    
+
    
+
    ’local’ of ‘reichstein’: several fits over the season of Reco vs.
    temperature as in Reichstein et al. (2005)
+(default)
    
+
    
+
    ’day’ or ‘lasslop’:      method of Lasslop et al. (2010) fitting a
    light-response curve
    
 
    
 
    
-
    ’day’ or ‘lasslop’:      method of Lasslop et al. (2010) fitting a light-response curve
    
 
    • 
-
  • nogppnight (float, optional) – GPP will be set to zero at night. RECO will then equal NEE at night (default: False)
    • 
-
  • swthr (float, optional) – Threshold to determine daytime from incoming shortwave radiation if isday not given (default: 10).
    • 
+
  • nogppnight (float, optional) – GPP will be set to zero at night. RECO will then equal NEE at night
+(default: False)
    • 
+
  • swthr (float, optional) – Threshold to determine daytime from incoming shortwave radiation
+if isday not given (default: 10).
    • 
 
 
    
 
    Returns:
    
@@ -139,28 +151,31 @@
 
    
 
    
 
    Notes
    
-
    Negative respiration possible at night if GPP is forced to 0 with nogppnight=True.
    
+
    Negative respiration possible at night if GPP is forced to 0 with
+nogppnight=True.
    
 
    
 
    References
    
 
    
 
    
 [1]
 
    Falge et al. (2001)
-Gap filling strategies for defensible annual sums of net ecosystem exchange,
+Gap filling strategies for defensible annual sums of
+net ecosystem exchange,
 Acricultural and Forest Meteorology 107, 43-69
    
 
    
 
    
 [2]
 
    Reichstein et al. (2005)
-On the separation of net ecosystem exchange into assimilation and ecosystem
-respiration: review and improved algorithm,
+On the separation of net ecosystem exchange into assimilation
+and ecosystem respiration: review and improved algorithm,
 Global Change Biology 11, 1424-1439
    
 
    
 
    
 [3]
 
    Lasslop et al. (2010)
-Separation of net ecosystem exchange into assimilation and respiration using
-a light response curve approach: critical issues and global evaluation,
+Separation of net ecosystem exchange into assimilation and respiration
+using a light response curve approach: critical issues and global
+evaluation,
 Global Change Biology 16, 187-208
    
 
    
 
    
@@ -189,7 +204,8 @@
 >>> # flag
 >>> flag = np.where(dfin == undef, 2, 0)
 >>> # partition
->>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead, undef=undef, method='local')
+>>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead,
+...                     undef=undef, method='local')
 >>> print(GPP[1120:1128])
 [-9.99900000e+03 -9.99900000e+03 -9.99900000e+03  4.40606871e+00
   8.31942152e+00  1.06242542e+01  8.49245664e+00  1.12381973e+01]
@@ -198,31 +214,36 @@
  2.90076664 3.18592735]
 
    
 
    
-
    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead, undef=undef, method='local')
+
    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead,
+...                     undef=undef, method='local')
 >>> print(GPP[1120:1128])
 [-9.99900000e+03 -9.99900000e+03 -9.99900000e+03  4.40606871e+00
   8.31942152e+00  1.06242542e+01  8.49245664e+00  1.12381973e+01]
 
    
 
    
-
    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead, undef=undef, method='global')
+
    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead,
+...                     undef=undef, method='global')
 >>> print(GPP[1120:1128])
 [-9.99900000e+03 -9.99900000e+03 -9.99900000e+03  4.33166157e+00
   8.18228013e+00  1.04092252e+01  8.19395317e+00  1.08427448e+01]
 
    
 
    
-
    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead, undef=undef, method='Reichstein')
+
    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead,
+...                     undef=undef, method='Reichstein')
 >>> print(GPP[1120:1128])
 [-9.99900000e+03 -9.99900000e+03 -9.99900000e+03  4.40606871e+00
   8.31942152e+00  1.06242542e+01  8.49245664e+00  1.12381973e+01]
 
    
 
    
-
    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead, undef=undef, method='reichstein')
+
    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead,
+...                     undef=undef, method='reichstein')
 >>> print(GPP[1120:1128])
 [-9.99900000e+03 -9.99900000e+03 -9.99900000e+03  4.40606871e+00
   8.31942152e+00  1.06242542e+01  8.49245664e+00  1.12381973e+01]
 
    
 
    
-
    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead, undef=undef, method='day')
+
    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead,
+...                     undef=undef, method='day')
 >>> print(GPP[1120:1128])
 [-9.99900000e+03 -9.99900000e+03 -9.99900000e+03  2.78457540e+00
   6.63212545e+00  8.88902165e+00  6.74243873e+00  9.51364527e+00]
@@ -294,7 +315,7 @@ 
    Related Topics
    
   
 
 
    
-
+
 
         
    
       
    
       
    
     
    
     
    
-      ©2009-2023, Matthias Cuntz.
+      ©2009-2024, Matthias Cuntz.
       
       |
-      Powered by Sphinx 6.1.3
-      & Alabaster 0.7.13
+      Powered by Sphinx 7.3.7
+      & Alabaster 0.7.16
       
       |
       4w1fK+uL;*69MNEG;gb?ZhDJtRtOuYFKiDTK0Xx^TU94EUiYQ-%ce>0QV
zn+lwRqUn#q47;{deFNf7NK>yW-!?ZvNpD4(RFPbj(iC;mFvp}_cTsoI>)h9!7HjV3
z##Sn4>ThtYa8@r=E)2^cwI?aut^qcsF|2mdE|f-sxJe=Nb7Swkw0M6A9xSUQ`h{9b
zog|U^v#c=$Z(BI8cB;!ox}GmzJZF1xfC4P&9A>JKU(iqvGfv3fSDke%Ge}czSTJr$
zCFGbqv3VtR2~Ahh)L9ISCU@iAGjDTxsC-6ns_^b(SfF1F%W{LyNLG;Rd&J4ah`@m~qf2D~cG
z^X>LAhlk<`Ko(mxv4pdKfzz9R(f8MAkg5d^EiaGAa@-b_8jQu`AjdL`aT;19ZSC-b
ze`;Te!z*rQxScqVHm`V5(1P}Rrsq<3J;zE(h+W-;)exD*j@<2s@LH8b#^8o%}z
HwPg!hWVXbY

delta 383
zcmV-_0f7FW1cC&RL;*07MNEIUiUaBasRHo;CfPU*ZZg%6nE8Z6a5K>!y>EIHGpjh271lD%TB)t*M_H
zdtO`J{eUHfS-ny@Gb97mE>fsn0c?cUq&8|-N)v(HsF3)nakP#be+YjL45>u=m0CY-xgI$u899}nyU8Ze+L$du&YATj4N7UbyK!B~8%KLQW7?cC$$flDP)8nd01B^&Kz5YN2s7}>zqU}iy
z)M5Hr%vel9M84Z%o?dfA($V#;ek}fI)#oyO2;h5|tI(f@6y@Rx^{L2zBlvjsSEXpa
z**;hB)Vu)jX6sEP;o>b&dh;*(@fHod?twz{<~=eWw*_vhmZCumfAlkaKXL)ZXu*iF
ds{tQj76bp~m+Wm?7x-lo1AUf$zrRIN^s0RGzF7bO

diff --git a/docs/html/py-modindex.html b/docs/html/py-modindex.html
index a691043..442c009 100644
--- a/docs/html/py-modindex.html
+++ b/docs/html/py-modindex.html
@@ -1,25 +1,25 @@
-
 
 
-
+
   
     
     
-    Python Module Index — hesseflux 5.1.dev0 documentation
-    
-    
-    
-    
-    
-    
+    Python Module Index — hesseflux 5.1.dev2 documentation
+    
+    
+    
+    
+    
+    
     
     
 
    
   
   
+
+  
   
-  
 
 
 
@@ -117,7 +117,7 @@ 
    Related Topics
    
   
 
 
    
-
+
 
         
    
       
    
       
    
     
    
     
    
-      ©2009-2023, Matthias Cuntz.
+      ©2009-2024, Matthias Cuntz.
       
       |
-      Powered by Sphinx 6.1.3
-      & Alabaster 0.7.13
+      Powered by Sphinx 7.3.7
+      & Alabaster 0.7.16
       
     
    
 
diff --git a/docs/html/search.html b/docs/html/search.html
index b4ad941..f4e7a46 100644
--- a/docs/html/search.html
+++ b/docs/html/search.html
@@ -1,29 +1,30 @@
-
 
 
-
+
   
     
     
-    Search — hesseflux 5.1.dev0 documentation
-    
-    
-    
+    Search — hesseflux 5.1.dev2 documentation
+    
+    
+    
     
-    
-    
-    
+    
+    
+    
     
     
     
     
-  
-  
+    
+    
+    
    
   
   
+
+  
   
-  
 
 
   
@@ -61,10 +62,7 @@ 
    Search
    
   
   
   
-  
-  
    
-  
-  
    
+  
    
   
 
           
    
@@ -116,11 +114,11 @@ 
    Related Topics
    
       
    
     
    
     
    
-      ©2009-2023, Matthias Cuntz.
+      ©2009-2024, Matthias Cuntz.
       
       |
-      Powered by Sphinx 6.1.3
-      & Alabaster 0.7.13
+      Powered by Sphinx 7.3.7
+      & Alabaster 0.7.16
       
     
    
 
diff --git a/docs/html/searchindex.js b/docs/html/searchindex.js
index 453802f..101ccca 100644
--- a/docs/html/searchindex.js
+++ b/docs/html/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["api", "authors", "changelog", "contents", "gapfill", "index", "madspikes", "nee2gpp", "userguide", "ustarfilter"], "filenames": ["api.rst", "authors.rst", "changelog.rst", "contents.rst", "gapfill.rst", "index.rst", "madspikes.rst", "nee2gpp.rst", "userguide.rst", "ustarfilter.rst"], "titles": ["API Reference", "Authors", "Changelog", "Contents", "gapfill", "hesseflux", "madspikes", "nee2gpp", "User Guide", "ustarfilter"], "terms": {"hesseflux": [0, 3, 8], "provid": [0, 4, 5, 6, 7, 8, 9], "function": [0, 2, 4, 5, 6, 7, 8, 9], "us": [0, 2, 4, 5, 6, 7, 8, 9], "process": [0, 2, 3], "post": [0, 2, 3], "eddi": [0, 3, 4, 6, 7, 9], "covari": [0, 3, 4, 7, 9], "flux": [0, 2, 3, 4, 5, 7, 9], "data": [0, 2, 3, 4, 6, 7, 9], "It": [0, 1, 5, 7, 8], "wa": [0, 2, 4, 6, 7, 8, 9], "develop": 0, "ico": [0, 5, 8], "ecosystem": [0, 3, 4, 5, 7], "site": [0, 5, 8], "fr": [0, 5, 8], "he": [0, 5, 8], "The": [0, 2, 3, 4, 5, 6, 7, 9], "i": [0, 1, 3, 4, 6, 7, 8, 9], "similar": [0, 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9], "rather": 2, "rau": [1, 6, 10], "raw": [6, 8, 9], "rd": 8, "rdata": 8, "rdylbu_r": 8, "read": [2, 3, 8], "read_csv": [8, 9], "read_r": 8, "readthedoc": [0, 2], "realis": 8, "recherch": [4, 6, 7, 10], "reco": [7, 8, 9], "reco_1_1_1": [8, 9], "reco_1_1_2": [8, 9], "reco_f_1_1_1": [8, 9], "reco_f_1_1_2": [8, 9], "reco_pi_1_1_1": [8, 9], "reco_pi_1_1_2": [8, 9], "reco_pi_f_1_1_1": [8, 9], "reco_pi_f_1_1_2": [8, 9], "recommend": [8, 9], "reddyproc": [0, 5, 9], "reduct": 10, "refactor": 0, "refer": [3, 7, 8, 9], "refin": 2, "reflect": [8, 9], "regist": 10, "reichstein": [4, 7, 8, 9], "rel": 9, "releas": [2, 7], "remov": [0, 2, 6, 7, 8, 10], "renam": [4, 8, 9], "replac": 8, "repositori": 2, "repres": [4, 9], "reproduc": 8, "requir": [0, 2, 5], "research": [4, 6, 7, 10], "reset": 8, "reshap": 4, "respir": [4, 7, 8, 9], "respons": [7, 8, 9], "restructur": 2, "result": [6, 8], "return": [0, 2, 4, 6, 7, 8, 9, 10], "review": [4, 7], "rg": [4, 7, 8], "rh": [8, 9], "rh_": 9, "rh_1_1_1": 8, 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"suggest": 9, "sum": [7, 8], "summari": [8, 9], "support": [0, 2], "suppos": 9, "sustar_detail": 8, "sw_dev": [4, 8, 9], "sw_in": [4, 6, 7, 8, 9, 10], "sw_in_1_1_1": [8, 9], "switch": 9, "swthr": [6, 7, 8, 9, 10], "sy": 9, "t": 8, "ta": [2, 4, 7, 8, 9, 10], "ta_": [4, 7, 8, 9, 10], "ta_1_1_1": 8, "ta_dev": [4, 8, 9], "ta_id": 9, "tab": 8, "taclass": 8, "tair": [4, 7, 8], "take": [8, 9, 10], "taken": [4, 7, 9, 10], "temperatur": [4, 7, 8, 9, 10], "templat": 9, "temporarili": 8, "test": 2, "test_gapfil": 4, "test_nee2gpp": 7, "text": [2, 8], "tha": 8, "tha_1998_nee_ori": 8, "than": [2, 4, 8, 9, 10], "tharandt": [4, 8], "thei": 9, "them": 9, "thi": [2, 4, 6, 7, 8, 9, 10], "thirdli": 8, "three": 8, "threshold": [2, 4, 6, 7, 9, 10], "through": [5, 9], "thuenen": 1, "tight": 8, "time": [4, 6, 7, 8, 9, 10], "timecolumn": 2, "timeformat": [4, 7, 8, 9, 10], "timestamp": 8, "tino": [1, 6, 10], "tk": 9, "tk3": 9, "tkelvin": 9, "to_csv": [8, 9], "to_datetim": 8, "to_numpi": 9, "toml": [0, 2], "took": 9, "tool": [2, 4, 9], "track": 9, "trail": 2, "translat": [8, 9], "transpos": [4, 7], "travisci": 2, "treat": [4, 6, 7, 9, 10], "true": [4, 6, 7, 8, 9, 10], "try": [8, 9], "ts_1_1_1": 8, "tsoil": 8, "tupl": [4, 10], "turbul": 9, "twine": 2, "two": [7, 8, 9], "txt": [8, 9], "type": [4, 6, 7, 9, 10], "typic": 8, "u": [2, 3, 8], "ufz": [1, 4, 6, 7, 10], "umol": [7, 10], "unaesthet": [8, 9], "uncertainti": [3, 4], "undef": [2, 4, 6, 7, 8, 9, 10], "under": [1, 5, 7, 9], "unflag": 8, "unit": [8, 9], "until": 9, "updat": [0, 2], "us": [0, 2, 4, 5, 6, 7, 8, 9, 10], "usag": 3, "useabl": 0, "user": [3, 5], "ustar": [2, 9, 10], "ustar_1_1_1": 8, "ustar_test_1_1_1": 9, "ustaraggr": 8, "ustarclass": 8, "ustarfilt": [0, 2, 3, 8, 9], "ustarmin": [8, 9, 10], "uwaterloo": 1, "v": 7, "v1": [0, 2], "v2": [0, 2], "v3": [0, 2], "v4": [0, 2], "v5": [0, 2], "valid": [2, 10], "valu": [0, 2, 4, 6, 7, 8, 9, 10], "valueerror": 9, "vapour": [4, 7, 8, 9], "vari": 8, "variabl": [4, 7, 8, 9, 10], "veloc": [8, 10], "verbos": [4, 8, 9], "veri": [4, 9], "via": [5, 9], "vicin": [4, 8, 9], "vignett": 8, "visualis": 8, "vovari": 6, "vpd": [4, 7, 8, 9], "vpd_1_1_1": 8, "vpd_dev": [4, 8, 9], "vpd_id": 9, "vpd_pi_1_1_1": 9, "vpdpa": 9, "w": [4, 7, 9], "wa": [0, 2, 4, 6, 7, 8, 9, 10], "wai": 5, "want": 9, "wave": [4, 7], "we": [8, 9], "week": 9, "well": [4, 8, 9, 10], "welltestpi": 5, "were": [8, 9], "wheel": 2, "when": [2, 6, 7, 9, 10], "where": [4, 6, 7, 8, 9, 10], "which": [8, 9, 10], "while": [4, 6, 7, 9, 10], "whitespac": 2, "whole": [8, 9], "window": [2, 4, 6, 9], "winter": 8, "within": [8, 9], "without": 9, "work": [4, 6, 8, 10], "workflow": 8, "would": [8, 9], "wrapper": 7, "write": 3, "written": [0, 4, 6, 7, 8, 9, 10], "x": 8, "xticklabel": 8, "y": [4, 7, 8, 9, 10], "y0": 4, "ydh": 8, "year": [2, 4, 8, 9, 10], "you": 8, "yr": [4, 7], "yticklabel": 8, "z": [6, 9], "zero": [4, 6, 7, 8, 9, 10], "\ufb02ux": 4}, "titles": ["API Reference", "Authors", "Changelog", "Contents", "gapfill", 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"user": 9, "ustar": 8, "ustarfilt": 10, "write": [8, 9]}})
\ No newline at end of file
diff --git a/docs/html/userguide.html b/docs/html/userguide.html
index d07d62d..63bcba1 100644
--- a/docs/html/userguide.html
+++ b/docs/html/userguide.html
@@ -1,18 +1,17 @@
-
 
 
-
+
   
     
-    
+    
 
-    User Guide — hesseflux 5.1.dev0 documentation
-    
-    
-    
-    
-    
-    
+    User Guide — hesseflux 5.1.dev2 documentation
+    
+    
+    
+    
+    
+    
     
     
     
@@ -20,8 +19,9 @@
    
   
   
+
+  
   
-  
 
   
   
@@ -34,52 +34,56 @@
           
    
             
   
    
-
    User Guide¶
    
-
    hesseflux collects functions used for processing Eddy covariance data of the
-ICOS ecosystem site FR-Hes.
    
-
    The post-processing functionality for Eddy flux data is similar to the R-package
-REddyProc and includes basically the steps described in Papale et al.
-(Biogeosciences, 2006) plus some extensions such as the daytime method of flux
-partitioning (Lasslop et al., Global Change Biology 2010) and the estimation
-of uncertainties on the fluxes as in Lasslop et al. (Biogeosci, 2008).
    
-
    Only the post-processing steps are described here. We are happy to discuss any
-processing or post-processing directly. Contact us at mc (at) macu (dot) de.
    
+
    User Guide¶
    
+
    hesseflux collects functions used for processing Eddy covariance
+data of the ICOS ecosystem site FR-Hes.
    
+
    The post-processing functionality for Eddy flux data is similar to the
+R-package REddyProc and includes basically the steps described in
+Papale et al.  (Biogeosciences, 2006) plus some extensions such as
+the daytime method of flux partitioning (Lasslop et al., Global
+Change Biology 2010) and the estimation of uncertainties on the
+fluxes as in Lasslop et al. (Biogeosci, 2008).
    
+
    Only the post-processing steps are described here. We are happy to
+discuss any processing or post-processing directly. Contact us at mc
+(at) macu (dot) de.
    
 
    
-
    europe-fluxdata.eu file format¶
    
-
    The first processing steps at the ICOS ecosystem site FR-Hes (not shown) brings
-the data in a format that can be submitted to the database
-europe-fluxdata.eu. The database predates ICOS and is somewhat a precursor of
-the ICOS data processing.
    
-
    The file format of europe-fluxdata.eu is hence very similar to the ICOS
-format. The only known difference to us is the unit of atmospheric pressure,
-which is in hPa in europe-fluxdata.eu and in kPa in ICOS ecosystems. The
-file format has notably one header line with variable names. There are no units
-in the file. hesseflux provides a little helper script
-europe-fluxdata_units.py in the bin directory that adds a second header line
-with units. The script can be run on the output as:
    
+
    europe-fluxdata.eu file format¶
    
+
    The first processing steps at the ICOS ecosystem site FR-Hes (not
+shown) brings the data in a format that can be submitted to the
+database europe-fluxdata.eu. The database predates ICOS and is
+somewhat a precursor of the ICOS data processing.
    
+
    The file format of europe-fluxdata.eu is hence very similar to the
+ICOS format. The only known difference to us is the unit of
+atmospheric pressure, which is in hPa in europe-fluxdata.eu and in
+kPa in ICOS ecosystems. The file format has notably one header line
+with variable names. There are no units in the file. hesseflux
+provides a little helper script europe-fluxdata_units.py in the
+bin directory that adds a second header line with units. The script
+can be run on the output as:
    
 
    python europe-fluxdata_units.py output_file_of_postproc_europe-fluxdata.csv
 
    
 
    
 
    
 
    
-
    Post-processing Eddy covariance data¶
    
-
    The script postproc_europe-fluxdata.py in the example directory provides a
-template for post-processing data that is in the europe-fluxdata.eu file
-format. It basically makes all steps described in Papale et al.
-(Biogeosciences, 2006). The script is governed by a configuration file in
-Python’s standard configparser format. The example configuration file
-hesseflux_example.cfg in the example directory is highly commented and
-should be (almost) self-explanatory. The script is called like:
    
+
    Post-processing Eddy covariance data¶
    
+
    The script postproc_europe-fluxdata.py in the example directory
+provides a template for post-processing data that is in the
+europe-fluxdata.eu file format. It basically makes all steps
+described in Papale et al.  (Biogeosciences, 2006). The script is
+governed by a configuration file in Python’s standard
+configparser format. The example configuration file
+hesseflux_example.cfg in the example directory is highly commented
+and should be (almost) self-explanatory. The script is called like:
    
 
    python postproc_europe-fluxdata.py hesseflux_example.cfg
 
    
 
    
-
    This script should be taken as a template for one’s own post-processing but
-includes most standard post-processing steps.
    
+
    This script should be taken as a template for one’s own
+post-processing but includes most standard post-processing steps.
    
 
    Here we describe the main parts of the post-processing script.
    
 
    
-
    Reading the configuration file¶
    
-
    The script postproc_europe-fluxdata.py starts by reading the configuration
-file hesseflux_example.cfg:
    
+
    Reading the configuration file¶
    
+
    The script postproc_europe-fluxdata.py starts by reading the
+configuration file hesseflux_example.cfg:
    
 
    import sys
 import configparser
 
@@ -90,8 +94,9 @@ 
    Reading the configuration file
    [POSTSWITCH]
 # spike detection (Papale et al., Biogeoci 2006)
 # bool
@@ -119,13 +124,15 @@ 
    Reading the configuration filefluxerr   = config['POSTSWITCH'].getboolean('fluxerr',   True)
 
    
 
    
-
    All options are boolean and set to True by default if they are not given in
-the configuration file. All post-processing steps except uncertainty estimation
-of flux data would be performed in the given example.
    
+
    All options are boolean and set to True by default if they are not
+given in the configuration file. All post-processing steps except
+uncertainty estimation of flux data would be performed in the given
+example.
    
 
    
 
    
-
    Read the data¶
    
-
    The script would then read in the data. The section in the configuration file is:
    
+
    Read the data¶
    
+
    The script would then read in the data. The section in the
+configuration file is:
    
 
    [POSTIO]
 # can be comma separated list or single file
 # str
@@ -164,39 +171,41 @@ 
    Read the data
    import pandas as pd
 
-parser = lambda date: pd.to_datetime(date, format=timeformat)
 infile = inputfile[0]
 df     = pd.read_csv(infile, sep, skiprows=skiprows, parse_dates=[0],
-                     date_parser=parser, index_col=0, header=0)
+                     date_format=timeformat, index_col=0, header=0)
 
    
 
    
-
    pandas will use the first column as index (index_col=0), assuming that
-these are dates (parse_dates=[0]) in the format timeformat, where columns
-are separated by sep. The defaults follow the europe-fluxdata.eu format but
-similar formats may be used, and script and/or configuration file can be adapted
-easily. Only variable names (still) have to follow europe-fluxdata.eu,
-ICOS or Ameriflux format at the moment. If the input file has a second
+
    pandas will use the first column as index (index_col=0),
+assuming that these are dates (parse_dates=[0]) in the format
+timeformat, where columns are separated by sep. The defaults
+follow the europe-fluxdata.eu format but similar formats may be
+used, and script and/or configuration file can be adapted easily. Only
+variable names (still) have to follow europe-fluxdata.eu, ICOS
+or Ameriflux format at the moment. If the input file has a second
 header line with units, one can skip it giving skiprows=[1] (not
 skiprows=1).
    
-
    All input files are supposed to be in the same format if inputfile is a
-comma-separated list of filenames, and they will be read with the same command
-above. The pandas dataframes (df) will simply be appended.
    
+
    All input files are supposed to be in the same format if inputfile
+is a comma-separated list of filenames, and they will be read with the
+same command above. The pandas dataframes (df) will simply be
+appended.
    
 
    
 
    
-
    The flag dataframe¶
    
-
    All Not-a-Number (NaN) values will be set to undef and will be ignored in the
-following.
    
-
    This happens via a second dataframe (dff), having the same columns and index
-as the input dataframe df, representing quality flags. All cells that have a
-value other than 0 in the flag dataframe dff will be ignored in the
-dataframe df. This means all cells of df with undef will be set to 2 in
-dff immediately:
    
+
    The flag dataframe¶
    
+
    All Not-a-Number (NaN) values will be set to undef and will be
+ignored in the following.
    
+
    This happens via a second dataframe (dff), having the same columns
+and index as the input dataframe df, representing quality flags. All
+cells that have a value other than 0 in the flag dataframe dff
+will be ignored in the dataframe df. This means all cells of df
+with undef will be set to 2 in dff immediately:
    
 
    # NaN -> undef
 df.fillna(undef, inplace=True)
 
@@ -208,13 +217,14 @@ 
    The flag dataframe
-
    Day / night¶
    
-
    Most post-processing routines differentiate between daytime and nighttime data.
-Papale et al. (Biogeosciences, 2006) use a threshold of 20 W m-2 of
-global radiation to distinguish between day and night. REddyProc uses
-incoming shortwave radiation greater than 10 W m2 as daytime. The
-shortwave radiation threshold swthr (same name as in ReddyProc) can be used to
-define the appropriate threshold. The default is 10 W m2. The column
+
    Day / night¶
    
+
    Most post-processing routines differentiate between daytime and
+nighttime data.  Papale et al. (Biogeosciences, 2006) use a
+threshold of 20 W m-2 of global radiation to distinguish
+between day and night. REddyProc uses incoming shortwave radiation
+greater than 10 W m2 as daytime. The shortwave radiation
+threshold swthr (same name as in ReddyProc) can be used to define
+the appropriate threshold. The default is 10 W m2. The column
 SW_IN_1_1_1 has to exist in the input data.
    
 
    # day / night
 isday = df['SW_IN_1_1_1'] > swthr
@@ -222,9 +232,9 @@ 
    Day / night
-
    Data check¶
    
-
    postproc_europe-fluxdata.py checks the units of air temperature (i.e. the
-first column starting with TA_).
    
+
    Data check¶
    
+
    postproc_europe-fluxdata.py checks the units of air temperature
+(i.e. the first column starting with TA_).
    
 
    # Check Ta in Kelvin
 hta = ['TA_']
 hout = _findfirststart(hta, df.columns)
@@ -235,14 +245,16 @@ 
    Data checkdf.loc[dff[hout[0]]==0, hout[0]] += tkelvin
 
    
 
    
-
    _findfirststart(starts, names)() is a helper function that finds the first
-occurrence in names that starts with the string starts. This helper function
-is used for the moment until hesseflux has the functionality that the user
-can give individual variable names.
    
-
    The script calculates air vapour pressure deficit VPD_PI_1_1_1 from air
-temperature and relative humidity (i.e. the first column starting with RH_) if
-not given in input data using the function esat() of pyjams for
-saturation vapour pressure:
    
+
    _findfirststart(starts, names)() is a helper function that finds
+the first occurrence in names that starts with the string
+starts. This helper function is used for the moment until
+hesseflux has the functionality that the user can give individual
+variable names.
    
+
    The script calculates air vapour pressure deficit VPD_PI_1_1_1 from
+air temperature and relative humidity (i.e. the first column starting
+with RH_) if not given in input data using the function
+esat() of pyjams for saturation vapour
+pressure:
    
 
    import numpy as np
 import pyjams as pj
 
@@ -288,8 +300,8 @@ 
    Data checkdf.loc[dff[hout[0]] == 0, hout[0]] *= vpdpa
 
    
 
    
-
    And finally determines the time intervals of the input data dtsec (s) and the
-number of time steps per day ntday.
    
+
    And finally determines the time intervals of the input data
+dtsec (s) and the number of time steps per day ntday.
    
 
    # time stepping
 dsec = (df.index[1] - df.index[0]).seconds
 ntday = np.rint(86400 / dsec).astype(int)
@@ -297,11 +309,12 @@ 
    Data check
-
    Spike / outlier flagging¶
    
-
    If outlier=True is set in the configuration file, spikes will be detected with
-the method given in Papale et al. (Biogeosciences, 2006). A median absolute
-deviation (MAD) filter will be used on the second derivatives of the time series
-in two-week chunks. The section in hesseflux_example.cfg looks like:
    
+
    Spike / outlier flagging¶
    
+
    If outlier=True is set in the configuration file, spikes will be
+detected with the method given in Papale et al. (Biogeosciences,
+2006). A median absolute deviation (MAD) filter will be used on the
+second derivatives of the time series in two-week chunks. The section
+in hesseflux_example.cfg looks like:
    
 
    [POSTMAD]
 # spike / outlier detection, see help(hesseflux.madspikes)
 # scan window in days for spike detection
@@ -318,26 +331,28 @@ 
    Spike / outlier flaggingderiv = 2
 
    
 
    
-
    nfill is the number of days that are treated at once. nfill=1 means that the
-time series will be stepped through day by day. nscan are the days to be
-considered when calculating the mean absolute deviations. nscan=15 means that
-7 days before the fill day, the fill day itself, and 7 days after the fill day
-will be used for the robust statistic. However, only spikes detected within the
-inner nfill days will be flagged in the nscan days. Spikes will be detected
-if they deviate more than z mean absolute deviations from the median.
    
-
    For example, nfill=3, nscan=15, and z=7 means that the time series will be
-treated in steps of 3 days. Each 3 days, MAD statistics will be calculated using
-15 days around the middle of the 3 days. Then all values within the 3 days that
-deviate more 7 mean absolute deviations from the median of the 15 days will be
-flagged.
    
-
    deriv=2 applies the MAD filter to the second derivatives. A spike has
-normally a strong curvature and hence a large second derivative. deriv=1 is
-currently not implemented. deriv=0 applies the filter to the raw time series.
-This might be useful to find outliers in smooth time series such as soil
-moisture. deriv=0 is also used on the 20 Hz Eddy raw data in the quality and
-uncertainty strategy of Mauder et al. (Agric Forest Meteo, 2013).
    
-
    The default values, if options are not given in the configuration file, are
-nscan=15, nfill=1, z=7, and deriv=2.
    
+
    nfill is the number of days that are treated at once. nfill=1
+means that the time series will be stepped through day by day. nscan
+are the days to be considered when calculating the mean absolute
+deviations. nscan=15 means that 7 days before the fill day, the fill
+day itself, and 7 days after the fill day will be used for the robust
+statistic. However, only spikes detected within the inner nfill days
+will be flagged in the nscan days. Spikes will be detected if they
+deviate more than z mean absolute deviations from the median.
    
+
    For example, nfill=3, nscan=15, and z=7 means that the time
+series will be treated in steps of 3 days. Each 3 days, MAD statistics
+will be calculated using 15 days around the middle of the 3 days. Then
+all values within the 3 days that deviate more 7 mean absolute
+deviations from the median of the 15 days will be flagged.
    
+
    deriv=2 applies the MAD filter to the second derivatives. A spike
+has normally a strong curvature and hence a large second
+derivative. deriv=1 is currently not implemented. deriv=0 applies
+the filter to the raw time series.  This might be useful to find
+outliers in smooth time series such as soil moisture. deriv=0 is
+also used on the 20 Hz Eddy raw data in the quality and uncertainty
+strategy of Mauder et al. (Agric Forest Meteo, 2013).
    
+
    The default values, if options are not given in the configuration
+file, are nscan=15, nfill=1, z=7, and deriv=2.
    
 
    postproc_europe-fluxdata.py calls the spike detection like this:
    
 
    # assume *_PI variables after raw variables, e.g. LE before LE_PI,
 # if available
@@ -352,15 +367,15 @@ 
    Spike / outlier flaggingdff.loc[sflag[hh] == 2, hh] = 3
 
    
 
    
-
    The function madspikes() returns flag columns for the input variables
-where spiked data is flagged as 2. The scripts sets the corresponding columns in
-the flag dataframe dff to 3 (3 is used just to keep track where the flag was
-set).
    
+
    The function madspikes() returns flag
+columns for the input variables where spiked data is flagged as 2. The
+scripts sets the corresponding columns in the flag dataframe dff to
+3 (3 is used just to keep track where the flag was set).
    
 
    
 
    
-
    u* filtering¶
    
-
    If ustar=True is set in the configuration file, a u*-filter will be applied
-following Papale et al. (Biogeosciences, 2006).
    
+
    u* filtering¶
    
+
    If ustar=True is set in the configuration file, a u*-filter will be
+applied following Papale et al. (Biogeosciences, 2006).
    
 
    The section in hesseflux_example.cfg looks like:
    
 
    [POSTUSTAR]
 # ustar filtering, see help(hesseflux.ustarfilter)
@@ -385,24 +400,27 @@ 
    u* filteringapplyustarflag = True
 
    
 
    
-
    A minimum threshold ustarmin is defined under which data is flagged by
-default. Papale et al. (Biogeosciences, 2006) suggest 0.1 for forests and
-0.01 for other land cover types. postproc_europe-fluxdata.py sets 0.01 as its
-default value. Uncertainty of the u* threshold is calculated via bootstrapping
-in Papale et al. nboot gives the number of bootstrapping for the uncertainty
-estimate of the u* threshold. The algorithm divides the input data in 6
-temperature classes and 20 u* classes within each temperature class per season.
-It then determines the threshold for each season as the average u* of the u*
-class where the average CO2 flux is less than plateaucrit times the average of
-all CO2 fluxes with u* greater than the u* class. Papale et al.
-(Biogeosciences, 2006) took 6 temperature classes and plateaucrit=0.99,
-while REddyProc takes 7 temperature classes and plateaucrit=0.95, which are
-also the defaults in hesseflux. Papale et al. (Biogeosciences, 2006) also
-used the maximum of the four seasonal u* thresholds as the threshold applied to
-all the year. If seasonout=True, the seasonal u* thresholds will be applied
-instead of the maximum of four seasonal u* thresholds. One can also set
-applyustarflag=False to just calculate the u* thresholds without applying them
-to experiment with different parameter values.
    
+
    A minimum threshold ustarmin is defined under which data is flagged
+by default. Papale et al. (Biogeosciences, 2006) suggest 0.1 for
+forests and 0.01 for other land cover
+types. postproc_europe-fluxdata.py sets 0.01 as its default
+value. Uncertainty of the u* threshold is calculated via bootstrapping
+in Papale et al. nboot gives the number of bootstrapping for the
+uncertainty estimate of the u* threshold. The algorithm divides the
+input data in 6 temperature classes and 20 u* classes within each
+temperature class per season.  It then determines the threshold for
+each season as the average u* of the u* class where the average CO2
+flux is less than plateaucrit times the average of all CO2 fluxes
+with u* greater than the u* class. Papale et al.  (Biogeosciences,
+2006) took 6 temperature classes and plateaucrit=0.99, while
+REddyProc takes 7 temperature classes and plateaucrit=0.95, which
+are also the defaults in hesseflux. Papale et
+al. (Biogeosciences, 2006) also used the maximum of the four
+seasonal u* thresholds as the threshold applied to all the year. If
+seasonout=True, the seasonal u* thresholds will be applied instead
+of the maximum of four seasonal u* thresholds. One can also set
+applyustarflag=False to just calculate the u* thresholds without
+applying them to experiment with different parameter values.
    
 
    The u*-filtering is then performed as:
    
 
    hfilt = ['NEE', 'USTAR', 'TA_']
 hout = _findfirststart(hfilt, df.columns)
@@ -435,27 +453,29 @@ 
    u* filteringdff.loc[flag == 2, hh] = 5
 
    
 
    
-
    The function ustarfilter() returns the u* 5, 50 and 95 percentiles of the
-bootstrapped u* thresholds as well as flag columns, which is 0 except where u*
-is smaller than the median u*-threshold. The scripts sets the columns of the
-Eddy fluxes in the flag dataframe dff to 5 (5 to keep track where the flag was
-set).
    
-
    One might not want to do u* filtering, but use for example Integral Turbulence
-Characteristics (ITC) that were calculated, for example, with
-EddyPro(R). These should be set right at the start after reading the input
-data into the dataframe df and producing the flag dataframe dff like:
    
+
    The function ustarfilter() returns the u*
+5, 50 and 95 percentiles of the bootstrapped u* thresholds as well as
+flag columns, which is 0 except where u* is smaller than the median
+u*-threshold. The scripts sets the columns of the Eddy fluxes in the
+flag dataframe dff to 5 (5 to keep track where the flag was set).
    
+
    One might not want to do u* filtering, but use for example Integral
+Turbulence Characteristics (ITC) that were calculated, for example,
+with EddyPro(R). These should be set right at the start
+after reading the input data into the dataframe df and producing the
+flag dataframe dff like:
    
 
    dff.loc[df['FC_SSITC_TEST_1_1_1']>0, 'FC_1_1_1'] = 2
 
    
 
    
 
    
 
    
-
    Partitioning of Net Ecosystem Exchange¶
    
-
    If partition=True is set in the configuration file, two estimates of Gross
-Primary Productivity (GPP) and Ecosystem Respiration (RECO) are calculated:
-firstly with the method of Reichstein et al. (Glob Change Biolo, 2005) using
-nighttime data only, and secondly with the method of Lasslop et al. (Glob
-Change Biolo, 2010) using a light-response curve on ‘daytime’ data. The
-configuration hesseflux_example.cfg gives only one option in this section:
    
+
    Partitioning of Net Ecosystem Exchange¶
    
+
    If partition=True is set in the configuration file, two estimates of
+Gross Primary Productivity (GPP) and Ecosystem Respiration (RECO) are
+calculated: firstly with the method of Reichstein et al. (Glob Change
+Biolo, 2005) using nighttime data only, and secondly with the method
+of Lasslop et al. (Glob Change Biolo, 2010) using a light-response
+curve on ‘daytime’ data. The configuration hesseflux_example.cfg
+gives only one option in this section:
    
 
    [POSTPARTITION]
 # partitioning, see help(hesseflux.nee2gpp)
 # if True, set GPP=0 at night
@@ -463,11 +483,11 @@ 
    Partitioning of Net Ecosystem Exchangenogppnight = False
 
    
 
    
-
    Many people find it unaesthetic that the ‘daytime’ method gives negative GPP at
-night. We esteem this the correct behaviour, reflecting the uncertainty in the
-gross flux estimates. However, one can set nogppnight=True to set GPP=0 at
-night and RECO=NEE in this case, the latter having then all variability of the
-net fluxes.
    
+
    Many people find it unaesthetic that the ‘daytime’ method gives
+negative GPP at night. We esteem this the correct behaviour,
+reflecting the uncertainty in the gross flux estimates. However, one
+can set nogppnight=True to set GPP=0 at night and RECO=NEE in this
+case, the latter having then all variability of the net fluxes.
    
 
    The partitioning is calculated as:
    
 
    hpart = ['NEE', 'SW_IN', 'TA_', 'VPD']
 hout = _findfirststart(hpart, df.columns)
@@ -505,11 +525,12 @@ 
    Partitioning of Net Ecosystem Exchange
-
    Gap-filling / Imputation¶
    
-
    Marginal Distribution Sampling (MDS) of Reichstein et al. (Glob Change Biolo,
-2005) is implemented as imputation or so-called gap-filling algorithm. The
-algorithm looks for similar conditions in the vicinity of a missing data point,
-if option fill=True. The configuration file is:
    
+
    Gap-filling / Imputation¶
    
+
    Marginal Distribution Sampling (MDS) of Reichstein et al. (Glob
+Change Biolo, 2005) is implemented as imputation or so-called
+gap-filling algorithm. The algorithm looks for similar conditions in
+the vicinity of a missing data point, if option fill=True. The
+configuration file is:
    
 
    [POSTGAP]
 # gap-filling with MDS, see help(hesseflux.gapfill)
 # max deviation of SW_IN
@@ -525,12 +546,13 @@ 
    Gap-filling / Imputationlonggap = 60
 
    
 
    
-
    If a flux data point is missing, times with incoming shortwave radiation in the
-range of sw_dev around the actual shortwave radiation will be looked for, as
-well as air temperatures within ta_dev and air vapour pressure deficit within
-vpd_dev. The mean of flux values at the similar conditions is then taken as
-fill value. The function does not fill long gaps longer than longgap days. A
-good summary is given in Fig. A1 of Reichstein et al. (Glob Change Biolo,
+
    If a flux data point is missing, times with incoming shortwave
+radiation in the range of sw_dev around the actual shortwave
+radiation will be looked for, as well as air temperatures within
+ta_dev and air vapour pressure deficit within vpd_dev. The mean of
+flux values at the similar conditions is then taken as fill value. The
+function does not fill long gaps longer than longgap days. A good
+summary is given in Fig. A1 of Reichstein et al. (Glob Change Biolo,
 2005).
    
 
    The script invokes MDS as:
    
 
    hfill = ['SW_IN', 'TA_', 'VPD']
@@ -564,19 +586,22 @@ 
    Gap-filling / Imputationdff = pd.concat([dff, dff_f], axis=1)
 
    
 
    
-
    The function gapfill() returns the filled columns df_f as well as flag
-columns dff_f indicating fill quality. Fill quality A-C of Reichstein et al.
-(Glob Change Biolo, 2005) are translated to quality flags 1-3.
    
+
    The function gapfill() returns the filled
+columns df_f as well as flag columns dff_f indicating fill
+quality. Fill quality A-C of Reichstein et al.  (Glob Change Biolo,
+2005) are translated to quality flags 1-3.
    
 
    
 
    
-
    Uncertainty estimates of flux data¶
    
+
    Uncertainty estimates of flux data¶
    
 
    Lasslop et al. (Biogeosci, 2008) presented an algorithm to estimate
-uncertainties of Eddy covariance fluxes using Marginal Distribution Sampling
-(MDS). The gap-filling function gapfill() can be used for uncertainty
-estimation giving the keyword err=True. The same thresholds as for gap-filling
-are used.
    
-
    The script postproc_europe-fluxdata.py uses the function gapfill() to
-calculate flux uncertainties like:
    
+uncertainties of Eddy covariance fluxes using Marginal Distribution
+Sampling (MDS). The gap-filling function
+gapfill() can be used for uncertainty
+estimation giving the keyword err=True. The same thresholds as for
+gap-filling are used.
    
+
    The script postproc_europe-fluxdata.py uses the function
+gapfill() to calculate flux uncertainties
+like:
    
 
    hfill = ['SW_IN', 'TA_', 'VPD']
 hout = _findfirststart(hfill, df.columns)
 assert len(hout) == 3, 'Could not find SW_IN, TA or VPD in input file.'
@@ -613,15 +638,15 @@ 
    Uncertainty estimates of flux datadff[colout[cc]] = dff[colin[cc]]
 
    
 
    
-
    We recommend, however, to calculate flux uncertainties with the Eddy covariance
-raw data as described in Mauder et al. (Agric Forest Meteo, 2013). This is
-for example implemented in the processing softwares EddyPro(R) or
-TK3.
    
+
    We recommend, however, to calculate flux uncertainties with the Eddy
+covariance raw data as described in Mauder et al. (Agric Forest
+Meteo, 2013). This is for example implemented in the processing
+softwares EddyPro(R) or TK3.
    
 
    
 
    
-
    Writing the output file¶
    
-
    The dataframe is written to the output file with pandas
-pandas.Dataframe.to_csv():
    
+
    Writing the output file¶
    
+
    The dataframe is written to the output file with pandas
+to_csv() method:
    
 
    df.to_csv(outputfile, sep=sep, na_rep=str(undef), index=True,
           date_format=timeformat)
 
    
@@ -641,13 +666,15 @@ 
    Writing the output fileoutflagcols = False
 
    
 
    
-
    If outputfile is missing or empty, the script will try to open a GUI, where
-one can choose an output directory and the filename will then be name of the
-configuration file with the suffix ‘.csv’. If outundef=True then all values in
-df with a flag value in dff greater than zero will be set to undef. The
-script can also add flag columns, prefixed with flag_, for each column in
-df, if outflagcols=True. The script will always output the columns with the
-flags for fill quality if gap-filling was performed: option fill=True.
    
+
    If outputfile is missing or empty, the script will try to open a
+GUI, where one can choose an output directory and the filename will
+then be name of the configuration file with the suffix ‘.csv’. If
+outundef=True then all values in df with a flag value in dff
+greater than zero will be set to undef. The script can also add flag
+columns, prefixed with flag_, for each column in df, if
+outflagcols=True. The script will always output the columns with the
+flags for fill quality if gap-filling was performed: option
+fill=True.
    
 
    The whole code to write the output file is:
    
 
    # Back to original units
 hta = ['TA_']
@@ -659,7 +686,7 @@ 
    Writing the output fileif outundef:
     for cc in df.columns:
         if cc.split('_')[-4] != 'f':  # exclude gap-filled columns
-            df[cc].where(dff[cc] == 0, other=undef, inplace=True)
+            df[cc] = df[cc].where(dff[cc] == 0, other=undef)
 if outflagcols:
     def _add_flag(c):
         return 'flag_' + c
@@ -753,7 +780,7 @@ 
    Related Topics
    
   
 
 
    
-
+
 
         
    
       
    
       
    
     
    
     
    
-      ©2009-2023, Matthias Cuntz.
+      ©2009-2024, Matthias Cuntz.
       
       |
-      Powered by Sphinx 6.1.3
-      & Alabaster 0.7.13
+      Powered by Sphinx 7.3.7
+      & Alabaster 0.7.16
       
       |
       
 
-
+
   
     
-    
-
-    ustarfilter — hesseflux 5.1.dev0 documentation
-    
-    
-    
-    
-    
-    
+    
+
+    ustarfilter — hesseflux 5.1.dev2 documentation
+    
+    
+    
+    
+    
+    
     
     
     
@@ -20,8 +19,9 @@
    
   
   
+
+  
   
-  
 
   
   
@@ -34,7 +34,7 @@
           
    
             
   
    
-
    ustarfilter¶
    
+
    ustarfilter¶
    
 
    Filter Eddy Covariance data with friction velocity
    
 
    This module was original written by Tino Rau and Matthias Cuntz, and
 maintained by Arndt Piayda while at Department of Computational
@@ -74,12 +74,14 @@
 only ustar data when NEE and Ta are valid, Jan 2023, Matthias Cuntz
    
 
  • Use 90% of ustar if no threshold found also for seasonout,
 Jan 2023, Matthias Cuntz
    • 
+
  • Removed np.float and np.bool, Jun 2024, Matthias Cuntz
    • 
+
  • do not register pandas platting backend, Jun 2024, Matthias Cuntz
    • 
 
 
 
 
    
 
    
-ustarfilter(dfin, flag=None, isday=None, date=None, timeformat='%Y-%m-%d %H:%M:%S', colhead=None, ustarmin=0.01, nboot=1, undef=-9999, plot=False, seasonout=False, nmon=3, ntaclasses=7, corrcheck=0.5, nustarclasses=20, plateaucrit=0.95, swthr=10.0)[source]¶
    
+ustarfilter(dfin, flag=None, isday=None, date=None, timeformat='%Y-%m-%d %H:%M:%S', colhead=None, ustarmin=0.01, nboot=1, undef=-9999, plot=False, seasonout=False, nmon=3, ntaclasses=7, corrcheck=0.5, nustarclasses=20, plateaucrit=0.95, swthr=10.0)[source]¶
 
    Filter Eddy Covariance data with friction velocity
    
 
    Flag Eddy Covariance data using a threshold of friction velocity (ustar)
 below which ustar correlates with a reduction in CO2 flux. The algorithm
@@ -104,36 +106,36 @@
 flag must follow the same rules as dfin if pandas.Dataframe.
 If flag is numpy array, df.columns.values will be used as column
 heads and the index of dfin will be copied to flag.
    
-
  • isday (array_like of bool, optional) – True when it is day, False when night. Must have the same length as
+
  • isday (array_like of bool, optional) – True when it is day, False when night. Must have the same length as
 dfin.shape[0].
 If isday is not given, dfin must have a column with head ‘SW_IN’ or
 starting with ‘SW_IN’. isday will then be dfin[‘SW_IN’] > swthr.
    • 
 
  • date (array_like of string, optional) – 1D-array_like of calendar dates in format given in timeformat.
 date must be given if dfin is numpy array.
    • 
-
  • timeformat (str, optional) – Format of dates in date, if given (default: ‘%Y-%m-%d %H:%M:%S’).
+
  • timeformat (str, optional) – Format of dates in date, if given (default: ‘%Y-%m-%d %H:%M:%S’).
 See strftime documentation of Python’s datetime module:
 https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior
    • 
-
  • colhead (array_like of str, optional) – column names if dfin is numpy array. See dfin for mandatory column
+
  • colhead (array_like of str, optional) – column names if dfin is numpy array. See dfin for mandatory column
 names.
    • 
-
  • ustarmin (float, optional) – minimum ustar threshold (default: 0.01).
+
  • ustarmin (float, optional) – minimum ustar threshold (default: 0.01).
 Papale et al. (Biogeosciences, 2006) take 0.1 for forest and 0.01
 otherwise.
    • 
-
  • nboot (int, optional) – number of boot straps for estimate of confidence interval of ustar
+
  • nboot (int, optional) – number of boot straps for estimate of confidence interval of ustar
 threshold (default: 1)
    • 
-
  • undef (float, optional) – values having undef value are treated as missing values in dfin
+
  • undef (float, optional) – values having undef value are treated as missing values in dfin
 (default: -9999)
    • 
-
  • plot (bool, optional) – True: data and ustar thresholds are plotted into ustarfilter.pdf
+
  • plot (bool, optional) – True: data and ustar thresholds are plotted into ustarfilter.pdf
 (default: False)
    • 
-
  • seasonout (bool, optional) – True: return ustar thresholds for each season (default: False)
    • 
-
  • nmon (int, optional) – Number of months to combine for a season (default: 3).
    • 
-
  • ntaclasses (int, optional) – Number of temperature classes per nmon months (default: 7).
    • 
-
  • corrcheck (float, optional) – Skip temperature class if absolute of correlation coefficient between
+
  • seasonout (bool, optional) – True: return ustar thresholds for each season (default: False)
    • 
+
  • nmon (int, optional) – Number of months to combine for a season (default: 3).
    • 
+
  • ntaclasses (int, optional) – Number of temperature classes per nmon months (default: 7).
    • 
+
  • corrcheck (float, optional) – Skip temperature class if absolute of correlation coefficient between
 air temperature and ustar is greater equal corrcheck (default: 0.5).
    • 
-
  • nustarclasses (int, optional) – Number of ustar classes per temperature class (default: 20).
    • 
-
  • plateaucrit (float, optional) – The ustar threshold is the smallest ustar class that has an average CO2
+
  • nustarclasses (int, optional) – Number of ustar classes per temperature class (default: 20).
    • 
+
  • plateaucrit (float, optional) – The ustar threshold is the smallest ustar class that has an average CO2
 flux, which is higher than plateaucrit times the mean CO2 flux of all
 ustar classes above this class (default: 0.95).
    • 
-
  • swthr (float, optional) – Threshold to determine daytime from incoming shortwave radiation if
+
  • swthr (float, optional) – Threshold to determine daytime from incoming shortwave radiation if
 isday not given (default: 10).
    • 
 
 
    
@@ -146,7 +148,7 @@
 
    
 
 
    Return type:
    
-
    tuple of numpy arrays, pandas.Dataframe or numpy array
    
+
    tuple of numpy arrays, pandas.Dataframe or numpy array
    
 
    
 
    
 
    
@@ -216,7 +218,7 @@ 
    Related Topics
    
   
 
 
    
-
+
 
         
    
       
    
       
    
     
    
     
    
-      ©2009-2023, Matthias Cuntz.
+      ©2009-2024, Matthias Cuntz.
       
       |
-      Powered by Sphinx 6.1.3
-      & Alabaster 0.7.13
+      Powered by Sphinx 7.3.7
+      & Alabaster 0.7.16
       
       |
        1:
         for infile in inputfile[1:]:
             df1 = pd.read_csv(infile, sep=sep, skiprows=skiprows,
-                              parse_dates=[0], date_parser=parser, index_col=0,
-                              header=0)
+                              parse_dates=[0], date_format=timeformat,
+                              index_col=0, header=0)
             tindex = df1.index.to_numpy()
             dtstep = (0.5 - ftimestep) * (tindex[1] - tindex[0])
             tindex += dtstep
@@ -489,7 +490,7 @@ def _add_f(c):
             else:
                 icc = -1
             if ccs[icc] != 'f':  # exclude gap-filled columns
-                df[cc].where(dff[cc] == 0, other=undef, inplace=True)
+                df[cc] = df[cc].where(dff[cc] == 0, other=undef)
     if outflagcols:
         print('   Add flag columns.')
 
diff --git a/src/hesseflux/gapfill.py b/src/hesseflux/gapfill.py
index 0d8734d..ea397b9 100644
--- a/src/hesseflux/gapfill.py
+++ b/src/hesseflux/gapfill.py
@@ -320,10 +320,10 @@ def gapfill(dfin, flag=None, date=None, timeformat='%Y-%m-%d %H:%M:%S',
             break
     astr = 'Global radiation with name SW or starting with SW_'
     astr = astr + ' must be in input.'
-    assert sw_id,  astr
+    assert sw_id, astr
     astr = 'Air temperature with name TA or starting with TA_'
     astr = astr + ' must be in input.'
-    assert ta_id,  astr
+    assert ta_id, astr
     astr = 'Vapour pressure deficit with name VPD or starting'
     astr = astr + ' with VPD_ must be in input.'
     assert vpd_id, astr
@@ -388,11 +388,11 @@ def gapfill(dfin, flag=None, date=None, timeformat='%Y-%m-%d %H:%M:%S',
         if firstvalid > nn:
             if verbose > 1:
                 print('    Large margin at beginning: ', firstvalid)
-            largegap[0:(firstvalid-nn)] = True
-        if lastvalid < (ndata-nn):
+            largegap[0:(firstvalid - nn)] = True
+        if lastvalid < (ndata - nn):
             if verbose > 1:
-                print('    Large margin at end: ', lastvalid-nn)
-            largegap[(lastvalid+nn):] = True
+                print('    Large margin at end: ', lastvalid - nn)
+            largegap[(lastvalid + nn):] = True
 
         # Large gaps
 
@@ -406,12 +406,12 @@ def gapfill(dfin, flag=None, date=None, timeformat='%Y-%m-%d %H:%M:%S',
                     index += [i]
                     count  = 1
             if i > 0:
-                if (dflag[i] != 0) and (dflag[i-1] == 0):
+                if (dflag[i] != 0) and (dflag[i - 1] == 0):
                     index += [i]
                     count  = 1
                 elif dflag[i] != 0:
                     count += 1
-                elif (dflag[i] == 0) and (dflag[i-1] != 0):
+                elif (dflag[i] == 0) and (dflag[i - 1] != 0):
                     length += [count]
                     count = 0
                 else:
@@ -423,17 +423,18 @@ def gapfill(dfin, flag=None, date=None, timeformat='%Y-%m-%d %H:%M:%S',
         for i in range(len(index)):
             if length[i] > nn:
                 if verbose > 1:
-                    print('    Large gap: ', index[i], ':', index[i]+length[i])
-                largegap[index[i]:index[i]+length[i]] = True
+                    print('    Large gap: ', index[i], ':',
+                          index[i] + length[i])
+                largegap[index[i]:index[i] + length[i]] = True
 
         # set or unset rest of days in large gaps
         if fullday:
-            for i in range(ndata-1):
+            for i in range(ndata - 1):
                 # end of large margin
-                if largegap[i] and not largegap[i+1]:
+                if largegap[i] and not largegap[i + 1]:
                     largegap[np.where(day == day[i])[0]] = False
                 # beginning of large margin
-                elif not largegap[i] and largegap[i+1]:
+                elif not largegap[i] and largegap[i + 1]:
                     largegap[np.where(day == day[i])[0]] = False
                 else:
                     continue
@@ -466,14 +467,14 @@ def gapfill(dfin, flag=None, date=None, timeformat='%Y-%m-%d %H:%M:%S',
                 # search for values around the met-conditions
                 # in a window of time
                 # (one week in the first iteration and odd weeks in the next)
-                j1  = j - np.arange(1, week+1, dtype=int) + 1
+                j1  = j - np.arange(1, week + 1, dtype=int) + 1
                 j2  = j + np.arange(1, week, dtype=int)
                 jj  = np.append(j1, j2)
-                win = np.unique(np.sort(np.clip(jj, 0, ndata-1)))
+                win = np.unique(np.sort(np.clip(jj, 0, ndata - 1)))
                 # get boolean array where meteo-conditions are in a given width
-                conditions = ( (np.abs(sw[win]-sw[j])   < sw_devmax) &
-                               (np.abs(ta[win]-ta[j])   < ta_dev) &
-                               (np.abs(vpd[win]-vpd[j]) < vpd_dev) &
+                conditions = ( (np.abs(sw[win] - sw[j])   < sw_devmax) &
+                               (np.abs(ta[win] - ta[j])   < ta_dev) &
+                               (np.abs(vpd[win] - vpd[j]) < vpd_dev) &
                                total_flg[win] )
                 num4avg = np.sum(conditions)
                 # we need at least two samples with similar conditions
@@ -490,10 +491,10 @@ def gapfill(dfin, flag=None, date=None, timeformat='%Y-%m-%d %H:%M:%S',
                         dflag_f[j] = 1
                     continue
                 else:  # --> extend time window to two weeks
-                    j1  = j - np.arange(1, 2*week+1, dtype=int) + 1
-                    j2  = j + np.arange(1, 2*week, dtype=int)
+                    j1  = j - np.arange(1, 2 * week + 1, dtype=int) + 1
+                    j2  = j + np.arange(1, 2 * week, dtype=int)
                     jj  = np.append(j1, j2)
-                    win = np.unique(np.sort(np.clip(jj, 0, ndata-1)))
+                    win = np.unique(np.sort(np.clip(jj, 0, ndata - 1)))
                     conditions = ( (np.abs(sw[win]  - sw[j])  < sw_devmax) &
                                    (np.abs(ta[win]  - ta[j])  < ta_dev) &
                                    (np.abs(vpd[win] - vpd[j]) < vpd_dev) &
@@ -521,12 +522,12 @@ def gapfill(dfin, flag=None, date=None, timeformat='%Y-%m-%d %H:%M:%S',
 
             # Method 2: just global radiation available
             if sw_flg[j] == 0:
-                j1  = j - np.arange(1, week+1, dtype=int) + 1
+                j1  = j - np.arange(1, week + 1, dtype=int) + 1
                 j2  = j + np.arange(1, week, dtype=int)
                 jj  = np.append(j1, j2)
-                win = np.unique(np.sort(np.clip(jj, 0, ndata-1)))
+                win = np.unique(np.sort(np.clip(jj, 0, ndata - 1)))
                 # get boolean array where meteo-conditions are in a given width
-                conditions = ( (np.abs(sw[win]-sw[j]) < sw_devmax) &
+                conditions = ( (np.abs(sw[win] - sw[j]) < sw_devmax) &
                                total_flg[win] )
                 num4avg = np.sum(conditions)
                 # we need at least two samples with similar conditions
@@ -545,12 +546,12 @@ def gapfill(dfin, flag=None, date=None, timeformat='%Y-%m-%d %H:%M:%S',
             # Method 3: same hour
             enough = False
             for i in range(2):
-                t_win = (nperday * (2*i+1))//2
-                j1  = j - np.arange(1, t_win+1, dtype=int) + 1
+                t_win = (nperday * (2 * i + 1)) // 2
+                j1  = j - np.arange(1, t_win + 1, dtype=int) + 1
                 j2  = j + np.arange(1, t_win, dtype=int)
                 jj  = np.append(j1, j2)
-                win = np.unique(np.sort(np.clip(jj, 0, ndata-1)))
-                conditions = ( (np.abs(hour[win]-hour[j]) < 1.1)
+                win = np.unique(np.sort(np.clip(jj, 0, ndata - 1)))
+                conditions = ( (np.abs(hour[win] - hour[j]) < 1.1)
                                & (dflag[win] == 0) )
                 num4avg = np.sum(conditions)
                 if num4avg >= 2:
@@ -574,10 +575,10 @@ def gapfill(dfin, flag=None, date=None, timeformat='%Y-%m-%d %H:%M:%S',
             # Method 4: same as 1 but for 3-12 weeks
             if meteo_flg[j]:
                 for multi in range(3, 12):
-                    j1  = j - np.arange(1, multi*week+1, dtype=int) + 1
-                    j2  = j + np.arange(1, multi*week, dtype=int)
+                    j1  = j - np.arange(1, multi * week + 1, dtype=int) + 1
+                    j2  = j + np.arange(1, multi * week, dtype=int)
                     jj  = np.append(j1, j2)
-                    win = np.unique(np.sort(np.clip(jj, 0, ndata-1)))
+                    win = np.unique(np.sort(np.clip(jj, 0, ndata - 1)))
                     conditions = ( (np.abs(sw[win]  - sw[j])  < sw_devmax) &
                                    (np.abs(ta[win]  - ta[j])  < ta_dev) &
                                    (np.abs(vpd[win] - vpd[j]) < vpd_dev) &
@@ -607,10 +608,10 @@ def gapfill(dfin, flag=None, date=None, timeformat='%Y-%m-%d %H:%M:%S',
             # Method 5: same as 2 but for 2-12 weeks
             if sw_flg[j] == 0:
                 for multi in range(2, 12):
-                    j1  = j - np.arange(1, multi*week+1, dtype=int) + 1
-                    j2  = j + np.arange(1, multi*week, dtype=int)
+                    j1  = j - np.arange(1, multi * week + 1, dtype=int) + 1
+                    j2  = j + np.arange(1, multi * week, dtype=int)
                     jj  = np.append(j1, j2)
-                    win = np.unique(np.sort(np.clip(jj, 0, ndata-1)))
+                    win = np.unique(np.sort(np.clip(jj, 0, ndata - 1)))
                     # get boolean array where meteo-conditions are
                     # in a given width
                     conditions = ( (np.abs(sw[win] - sw[j]) < sw_devmax) &
@@ -636,12 +637,12 @@ def gapfill(dfin, flag=None, date=None, timeformat='%Y-%m-%d %H:%M:%S',
 
             # Method 6: same as 3 but for 3-120 days
             for i in range(3, 120):
-                t_win = nperday * (2*i+1)/2
-                j1  = j - np.arange(1, t_win+1, dtype=int) + 1
+                t_win = nperday * (2 * i + 1) / 2
+                j1  = j - np.arange(1, t_win + 1, dtype=int) + 1
                 j2  = j + np.arange(1, t_win, dtype=int)
                 jj  = np.append(j1, j2)
-                win = np.unique(np.sort(np.clip(jj, 0, ndata-1)))
-                conditions = ( (np.abs(hour[win]-hour[j]) < 1.1)
+                win = np.unique(np.sort(np.clip(jj, 0, ndata - 1)))
+                conditions = ( (np.abs(hour[win] - hour[j]) < 1.1)
                                & (dflag[win] == 0) )
                 num4avg = np.sum(conditions)
                 if num4avg >= 2:
diff --git a/src/hesseflux/madspikes.py b/src/hesseflux/madspikes.py
index fdfdced..bcdba9f 100644
--- a/src/hesseflux/madspikes.py
+++ b/src/hesseflux/madspikes.py
@@ -26,6 +26,7 @@
     * Removed iteration, Apr 2020, Matthias Cuntz
     * Using numpy docstring format, May 2020, Matthias Cuntz
     * Improved flake8 and numpy docstring, Oct 2021, Matthias Cuntz
+    * Removed np.float and np.bool, Jun 2024, Matthias Cuntz
 
 """
 from __future__ import division, absolute_import, print_function
@@ -39,7 +40,7 @@
 
 def madspikes(dfin, flag=None, isday=None,
               colhead=None, undef=-9999,
-              nscan=15*48, nfill=1*48,
+              nscan=15 * 48, nfill=1 * 48,
               z=7, deriv=2, swthr=10.,
               plot=False):
     """
@@ -113,7 +114,7 @@ def madspikes(dfin, flag=None, isday=None,
             estr = ('Length of colhead must be number of columns in input'
                     'array. len(colhead)=' + str(len(colhead)) +
                     ' shape(input)=(' + str(dfin.shape[0]) + ',' +
-                    str(dfin.shape[1])+').')
+                    str(dfin.shape[1]) + ').')
             raise ValueError(estr)
     else:
         isnumpy = False
@@ -173,15 +174,15 @@ def madspikes(dfin, flag=None, isday=None,
 
     # parameters
     nrow, ncol = df.shape
-    half_scan_win = nscan//2
-    half_fill_win = nfill//2
+    half_scan_win = nscan // 2
+    half_fill_win = nfill // 2
 
     # calculate dusk and dawn times and separate in day and night
-    isdawn = np.zeros(nrow, dtype=np.bool)
-    isdusk = np.zeros(nrow, dtype=np.bool)
-    dis    = isday.astype(int) - np.roll(isday,-1).astype(int)
+    isdawn = np.zeros(nrow, dtype=bool)
+    isdusk = np.zeros(nrow, dtype=bool)
+    dis    = isday.astype(int) - np.roll(isday, -1).astype(int)
     isdawn[:-1]    = np.where(dis[:-1] == -1, True, False)
-    isdusk[:-1]    = np.where(dis[:-1] ==  1, True, False)
+    isdusk[:-1]    = np.where(dis[:-1] == 1, True, False)
     isddday        = isdawn
     tmp            = np.roll(isdusk, 1)
     isddday[1:]   += tmp[1:]  # start and end of day
@@ -213,23 +214,25 @@ def madspikes(dfin, flag=None, isday=None,
             np.nan)
 
         # iterate over fill window
-        for j in range(half_fill_win, nrow-1, 2*half_fill_win):
+        for j in range(half_fill_win, nrow - 1, 2 * half_fill_win):
             j1 = max(j - half_scan_win - 1, 0)
             j2 = min(j + half_scan_win + 1, nrow)
             fill_start = max(j - half_fill_win, 1)
-            fill_end   = min(j + half_fill_win, nrow-1)
+            fill_end   = min(j + half_fill_win, nrow - 1)
 
             dd = data_day[j1:j2].to_numpy()
             day_flag = mad(np.ma.masked_array(data=dd, mask=np.isnan(dd)),
                            z=z, deriv=deriv)
             ff.iloc[fill_start:fill_end, cols.index(hcol)] += (
-                np.where(day_flag[fill_start-j1-1:fill_end-j1-1], 2, 0))
+                np.where(day_flag[fill_start - j1 - 1:fill_end - j1 - 1],
+                         2, 0))
 
             nn = data_night[j1:j2]
             night_flag = mad(np.ma.masked_array(data=nn, mask=np.isnan(nn)),
                              z=z, deriv=deriv)
             ff.iloc[fill_start:fill_end, cols.index(hcol)] += (
-                np.where(night_flag[fill_start-j1-1:fill_end-j1-1], 2, 0))
+                np.where(night_flag[fill_start - j1 - 1:fill_end - j1 - 1],
+                         2, 0))
 
         if plot:
             fig = plt.figure(1)
diff --git a/src/hesseflux/nee2gpp.py b/src/hesseflux/nee2gpp.py
index a01d506..f1b594c 100644
--- a/src/hesseflux/nee2gpp.py
+++ b/src/hesseflux/nee2gpp.py
@@ -16,13 +16,15 @@
 * Set default undef to NaN, Mar 2012, Arndt Piayda
 * Add wrapper nee2gpp for individual routines, Nov 2012, Matthias Cuntz
 * Ported to Python 3, Feb 2013, Matthias Cuntz
-* Use generel cost function cost_abs from functions module, May 2013, Matthias Cuntz
+* Use generel cost function cost_abs from functions module,
+  May 2013, Matthias Cuntz
 * Use fmin_tnc to allow params < 0, Aug 2014, Arndt Piayda
 * Keyword nogppnight, Aug 2014, Arndt Piayda
 * Add wrapper nee2gpp for individual routines, Nov 2012, Matthias Cuntz
 * Add wrapper nee2gpp for individual routines, Nov 2012, Matthias Cuntz
 * Input can be pandas Dataframe or numpy array(s), Apr 2020, Matthias Cuntz
 * Using numpy docstring format, May 2020, Matthias Cuntz
+* Removed np.float and np.int, Jun 2024, Matthias Cuntz
 
 .. moduleauthor:: Matthias Cuntz, Arndt Piayda
 
@@ -46,8 +48,9 @@
 
 
 # ----------------------------------------------------------------------
-def nee2gpp(dfin, flag=None, isday=None, date=None, timeformat='%Y-%m-%d %H:%M:%S', colhead=None,
-            undef=-9999, method='reichstein', nogppnight=False, swthr=10.):
+def nee2gpp(dfin, flag=None, isday=None, date=None,
+            timeformat='%Y-%m-%d %H:%M:%S', colhead=None, undef=-9999,
+            method='reichstein', nogppnight=False, swthr=10.):
     """
     Calculate photosynthesis (GPP) and ecosystem respiration (RECO)
     from Eddy covariance CO2 flux data.
@@ -71,26 +74,29 @@ def nee2gpp(dfin, flag=None, isday=None, date=None, timeformat='%Y-%m-%d %H:%M:%
         incoming shortwave radiation and air vapour pressure deficit.
 
         `dfin` can be a pandas.Dataframe with the columns
-        'FC' or 'NEE' (or starting with 'FC\_' or 'NEE\_') for observed CO2 flux [umol(CO2) m-2 s-1]
-        'TA'    (or starting with 'TA\_') for air temperature [K]
+        'FC' or 'NEE' (or starting with 'FC\\_' or 'NEE\\_') for observed
+        CO2 flux [umol(CO2) m-2 s-1]
+        'TA'    (or starting with 'TA\\_') for air temperature [K]
 
         `method='lasslop'` or `method='day'` needs also
-        'SW_IN' (or starting with 'SW_IN') for incoming short-wave radiation [W m-2]
+        'SW_IN' (or starting with 'SW_IN') for incoming short-wave
+        radiation [W m-2]
         'VPD'   (or starting with 'VPD') for air vapour deficit [Pa]
         The index is taken as date variable.
 
         `dfin` can also me a numpy array with the same columns. In this case
         `colhead`, `date`, and possibly `dateformat` must be given.
     flag : pandas.Dataframe or numpy.array, optional
-        flag Dataframe or array has the same shape as `dfin`. Non-zero values in
-        `flag` will be treated as missing values in `dfin`.
+        flag Dataframe or array has the same shape as `dfin`.
+        Non-zero values in `flag` will be treated as missing values in `dfin`.
 
         `flag` must follow the same rules as `dfin` if pandas.Dataframe.
 
-        If `flag` is numpy array, `df.columns.values` will be used as column heads
-        and the index of `dfin` will be copied to `flag`.
+        If `flag` is numpy array, `df.columns.values` will be used as column
+        heads and the index of `dfin` will be copied to `flag`.
     isday : array_like of bool, optional
-        True when it is day, False when night. Must have the same length as `dfin.shape[0]`.
+        True when it is day, False when night. Must have the same length
+        as `dfin.shape[0]`.
 
         If `isday` is not given, `dfin` must have a column with head 'SW_IN' or
         starting with 'SW_IN'. `isday` will then be `dfin['SW_IN'] > swthr`.
@@ -103,22 +109,29 @@ def nee2gpp(dfin, flag=None, isday=None, date=None, timeformat='%Y-%m-%d %H:%M:%
         See strftime documentation of Python's datetime module:
         https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior
     colhed : array_like of str, optional
-        column names if `dfin` is numpy array. See `dfin` for mandatory column names.
+        column names if `dfin` is numpy array. See `dfin` for mandatory
+        column names.
     undef : float, optional
-        values having `undef` value are treated as missing values in `dfin` (default: -9999)
+        values having `undef` value are treated as missing values in `dfin`
+        (default: -9999)
     method : str, optional
         method to use for partitioning. Possible values are:
 
-        'global' or 'falge':     fit of Reco vs. temperature to all nighttime data
+        'global' or 'falge':     fit of Reco vs. temperature to all nighttime
+                                 data
 
-        'local' of 'reichstein': several fits over the season of Reco vs. temperature
-                                 as in Reichstein et al. (2005) (default)
+        'local' of 'reichstein': several fits over the season of Reco vs.
+                                 temperature as in Reichstein et al. (2005)
+                                 (default)
 
-        'day' or 'lasslop':      method of Lasslop et al. (2010) fitting a light-response curve
+        'day' or 'lasslop':      method of Lasslop et al. (2010) fitting a
+                                 light-response curve
     nogppnight : float, optional
-        GPP will be set to zero at night. RECO will then equal NEE at night (default: False)
+        GPP will be set to zero at night. RECO will then equal NEE at night
+        (default: False)
     swthr : float, optional
-        Threshold to determine daytime from incoming shortwave radiation if `isday` not given (default: 10).
+        Threshold to determine daytime from incoming shortwave radiation
+        if `isday` not given (default: 10).
 
     Returns
     -------
@@ -129,22 +142,25 @@ def nee2gpp(dfin, flag=None, isday=None, date=None, timeformat='%Y-%m-%d %H:%M:%
 
     Notes
     -----
-    Negative respiration possible at night if GPP is forced to 0 with `nogppnight=True`.
+    Negative respiration possible at night if GPP is forced to 0 with
+    `nogppnight=True`.
 
     References
     ----------
     .. [1] Falge et al. (2001)
-       Gap filling strategies for defensible annual sums of net ecosystem exchange,
+       Gap filling strategies for defensible annual sums of
+       net ecosystem exchange,
        Acricultural and Forest Meteorology 107, 43-69
 
     .. [2] Reichstein et al. (2005)
-       On the separation of net ecosystem exchange into assimilation and ecosystem
-       respiration: review and improved algorithm,
+       On the separation of net ecosystem exchange into assimilation
+       and ecosystem respiration: review and improved algorithm,
        Global Change Biology 11, 1424-1439
 
     .. [3] Lasslop et al. (2010)
-       Separation of net ecosystem exchange into assimilation and respiration using
-       a light response curve approach: critical issues and global evaluation,
+       Separation of net ecosystem exchange into assimilation and respiration
+       using a light response curve approach: critical issues and global
+       evaluation,
        Global Change Biology 16, 187-208
 
     Examples
@@ -173,7 +189,8 @@ def nee2gpp(dfin, flag=None, isday=None, date=None, timeformat='%Y-%m-%d %H:%M:%
     >>> # flag
     >>> flag = np.where(dfin == undef, 2, 0)
     >>> # partition
-    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead, undef=undef, method='local')
+    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead,
+    ...                     undef=undef, method='local')
     >>> print(GPP[1120:1128])
     [-9.99900000e+03 -9.99900000e+03 -9.99900000e+03  4.40606871e+00
       8.31942152e+00  1.06242542e+01  8.49245664e+00  1.12381973e+01]
@@ -181,27 +198,32 @@ def nee2gpp(dfin, flag=None, isday=None, date=None, timeformat='%Y-%m-%d %H:%M:%
     [1.68311981 1.81012431 1.9874173  2.17108871 2.38759152 2.64372415
      2.90076664 3.18592735]
 
-    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead, undef=undef, method='local')
+    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead,
+    ...                     undef=undef, method='local')
     >>> print(GPP[1120:1128])
     [-9.99900000e+03 -9.99900000e+03 -9.99900000e+03  4.40606871e+00
       8.31942152e+00  1.06242542e+01  8.49245664e+00  1.12381973e+01]
 
-    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead, undef=undef, method='global')
+    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead,
+    ...                     undef=undef, method='global')
     >>> print(GPP[1120:1128])
     [-9.99900000e+03 -9.99900000e+03 -9.99900000e+03  4.33166157e+00
       8.18228013e+00  1.04092252e+01  8.19395317e+00  1.08427448e+01]
 
-    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead, undef=undef, method='Reichstein')
+    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead,
+    ...                     undef=undef, method='Reichstein')
     >>> print(GPP[1120:1128])
     [-9.99900000e+03 -9.99900000e+03 -9.99900000e+03  4.40606871e+00
       8.31942152e+00  1.06242542e+01  8.49245664e+00  1.12381973e+01]
 
-    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead, undef=undef, method='reichstein')
+    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead,
+    ...                     undef=undef, method='reichstein')
     >>> print(GPP[1120:1128])
     [-9.99900000e+03 -9.99900000e+03 -9.99900000e+03  4.40606871e+00
       8.31942152e+00  1.06242542e+01  8.49245664e+00  1.12381973e+01]
 
-    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead, undef=undef, method='day')
+    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead,
+    ...                     undef=undef, method='day')
     >>> print(GPP[1120:1128])
     [-9.99900000e+03 -9.99900000e+03 -9.99900000e+03  2.78457540e+00
       6.63212545e+00  8.88902165e+00  6.74243873e+00  9.51364527e+00]
@@ -212,56 +234,66 @@ def nee2gpp(dfin, flag=None, isday=None, date=None, timeformat='%Y-%m-%d %H:%M:%
     History
     -------
     Written  Matthias Cuntz, Mar 2012
-    Modified Arndt Piayda,   Mar 2012 - undef=np.nan
-             Matthias Cuntz, Nov 2012 - wrapper for individual routines nee2gpp_reichstein etc.
-             Matthias Cuntz, Feb 2013 - ported to Python 3
-             Matthias Cuntz, May 2013 - replaced cost functions by generel cost function cost_abs if possible
-             Arndt Piayda,   Aug 2014 - replaced fmin with fmin_tnc to permit params<0,
-                                        permit gpp<0 at any time if nogppnight=True 
+    Modified Arndt Piayda,   Mar 2012
+                 - undef=np.nan
+             Matthias Cuntz, Nov 2012
+                 - wrapper for individual routines nee2gpp_reichstein etc.
+             Matthias Cuntz, Feb 2013
+                 - ported to Python 3
+             Matthias Cuntz, May 2013
+                 - replaced cost functions by generel cost function cost_abs
+                   if possible
+             Arndt Piayda,   Aug 2014
+                 - replaced fmin with fmin_tnc to permit params<0,
+                   permit gpp<0 at any time if nogppnight=True
+             Matthias Cuntz, Jun 2024
+                 - removed np.int and np.float
+
     """
     # Check input
     # numpy or panda
     if isinstance(dfin, (np.ndarray, np.ma.MaskedArray)):
         isnumpy = True
-        istrans = False
-        assert colhead is not None, 'colhead must be given if input is numpy.ndarray.'
+        assert colhead is not None, (
+            'colhead must be given if input is numpy.ndarray.')
         if dfin.shape[0] == len(colhead):
-            istrans = True
             df = pd.DataFrame(dfin.T, columns=colhead)
         elif dfin.shape[1] == len(colhead):
             df = pd.DataFrame(dfin, columns=colhead)
         else:
-            raise ValueError('Length of colhead must be number of columns in input array. len(colhead)='+str(len(colhead))+' shape(input)=('+str(dfin.shape[0])+','+str(dfin.shape[1])+').')
+            raise ValueError('Length of colhead must be number of columns'
+                             ' in input array. len(colhead)=' +
+                             str(len(colhead)) + ' shape(input)=(' +
+                             str(dfin.shape[0]) + ',' +
+                             str(dfin.shape[1]) + ').')
         assert date is not None, 'Date must be given if input is numpy arrary.'
         df['Datetime'] = pd.to_datetime(date, format=timeformat)
         df.set_index('Datetime', drop=True, inplace=True)
     else:
         isnumpy = False
-        istrans = False
-        assert isinstance(dfin, pd.core.frame.DataFrame), 'Input must be either numpy.ndarray or pandas.DataFrame.'
+        assert isinstance(dfin, pd.core.frame.DataFrame), (
+            'Input must be either numpy.ndarray or pandas.DataFrame.')
         df = dfin.copy(deep=True)
 
     # Incoming flags
     if flag is not None:
         if isinstance(flag, (np.ndarray, np.ma.MaskedArray)):
-            fisnumpy = True
-            fistrans = False
             if flag.shape[0] == len(df):
                 ff = pd.DataFrame(flag, columns=df.columns.values)
             elif flag.shape[1] == len(df):
-                fistrans = True
                 ff = pd.DataFrame(flag.T, columns=df.columns.values)
             else:
-                raise ValueError('flag must have same shape as data array. data: ({:d},{:d}); flag: ({:d},{:d})'.format(dfin.shape[0], dfin.shape[1], flag.shape[0], flag.shape[1]))
+                raise ValueError(
+                    'flag must have same shape as data array.'
+                    ' data: ({:d},{:d}); flag: ({:d},{:d})'.format(
+                        dfin.shape[0], dfin.shape[1], flag.shape[0],
+                        flag.shape[1]))
             ff = ff.set_index(df.index)
         else:
-            fisnumpy = False
-            fistrans = False
-            assert isinstance(flag, pd.core.frame.DataFrame), 'Flag must be either numpy.ndarray or pandas.DataFrame.'
+            assert isinstance(flag, pd.core.frame.DataFrame), (
+                'Flag must be either numpy.ndarray or pandas.DataFrame.')
             ff = flag.copy(deep=True)
     else:
-        fisnumpy = isnumpy
-        fistrans = istrans
         # flags: 0: good; 1: input flagged; 2: output flagged
         ff              = df.copy(deep=True).astype(int)
         ff[:]           = 0
@@ -275,9 +307,11 @@ def nee2gpp(dfin, flag=None, isday=None, date=None, timeformat='%Y-%m-%d %H:%M:%
             if cc.startswith('SW_IN'):
                 sw_id = cc
                 break
-        assert sw_id, 'Global radiation with name SW or starting with SW_ must be in input if isday not given.'
-        isday = df[sw_id] > swthr # Papale et al. (Biogeosciences, 2006): 20; REddyProc: 10
-    if isinstance(isday, (pd.core.series.Series,pd.core.frame.DataFrame)):
+        assert sw_id, ('Global radiation with name SW or starting with'
+                       ' SW_ must be in input if isday not given.')
+        # Papale et al. (Biogeosciences, 2006): 20; REddyProc: 10
+        isday = df[sw_id] > swthr
+    if isinstance(isday, (pd.core.series.Series, pd.core.frame.DataFrame)):
         isday = isday.to_numpy()
     isday[isday == undef] = np.nan
     ff[np.isnan(isday)]   = 1
@@ -287,10 +321,12 @@ def nee2gpp(dfin, flag=None, isday=None, date=None, timeformat='%Y-%m-%d %H:%M:%
         dfout = _nee2gpp_falge(df, ff, isday, undef=undef)
     # Local relationship = Reichstein et al. (2005)
     elif ((method.lower() == 'local') | (method.lower() == 'reichstein')):
-        dfout = _nee2gpp_reichstein(df, ff, isday, undef=undef, nogppnight=nogppnight)
+        dfout = _nee2gpp_reichstein(df, ff, isday, undef=undef,
+                                    nogppnight=nogppnight)
     # Lasslop et al. (2010) daytime method
     elif ((method.lower() == 'day') | (method.lower() == 'lasslop')):
-        dfout = _nee2gpp_lasslop(df, ff, isday, undef=undef, nogppnight=nogppnight)
+        dfout = _nee2gpp_lasslop(df, ff, isday, undef=undef,
+                                 nogppnight=nogppnight)
     # Include new methods here
     else:
         raise ValueError('Error nee2gpp: method not implemented yet.')
@@ -314,8 +350,9 @@ def _nee2gpp_falge(df, ff, isday, undef=-9999):
         time series of CO2 fluxes and air temperature.
 
         pandas.Dataframe with the columns
-        'FC' or 'NEE' (or starting with 'FC\_' or 'NEE\_') for observed CO2 flux [umol(CO2) m-2 s-1]
-        'TA'    (or starting with 'TA\_') for air temperature [K]
+        'FC' or 'NEE' (or starting with 'FC\\_' or 'NEE\\_') for observed
+        CO2 flux [umol(CO2) m-2 s-1]
+        'TA'    (or starting with 'TA\\_') for air temperature [K]
         The index is taken as date variable.
     ff : pandas.Dataframe
         flag Dataframe or array has the same shape as `df`. Non-zero values in
@@ -323,9 +360,11 @@ def _nee2gpp_falge(df, ff, isday, undef=-9999):
 
         `ff` must follow the same rules as `df`.
     isday : array_like of bool
-        True when it is day, False when night. Must have the same length as `df.shape[0].`
+        True when it is day, False when night. Must have the same length
+        as `df.shape[0].`
     undef : float, optional
-        values having `undef` value are treated as missing values in `df` (default: -9999)
+        values having `undef` value are treated as missing values in `df`
+        (default: -9999)
 
     Returns
     -------
@@ -336,7 +375,8 @@ def _nee2gpp_falge(df, ff, isday, undef=-9999):
     References
     ----------
     .. [1] Falge et al. (2001)
-       Gap filling strategies for defensible annual sums of net ecosystem exchange,
+       Gap filling strategies for defensible annual sums of
+       net ecosystem exchange,
        Acricultural and Forest Meteorology 107, 43-69
 
     Examples
@@ -351,7 +391,8 @@ def _nee2gpp_falge(df, ff, isday, undef=-9999):
     >>> head  = fread(ifile, skip=2, header=True)
     >>> head1 = head[0]
     >>> # date
-    >>> jdate = date2dec(dy=dat[0,:], mo=dat[1,:], yr=dat[2,:], hr=dat[3,:], mi=dat[4,:])
+    >>> jdate = date2dec(dy=dat[0,:], mo=dat[1,:], yr=dat[2,:],
+    ...                  hr=dat[3,:], mi=dat[4,:])
     >>> adate = dec2date(jdate, eng=True)
     >>> # colhead
     >>> idx   = []
@@ -365,7 +406,8 @@ def _nee2gpp_falge(df, ff, isday, undef=-9999):
     >>> # flag
     >>> flag = np.where(dfin == undef, 2, 0)
     >>> # partition
-    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead, undef=undef, method='global')
+    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead,
+    ...                     undef=undef, method='global')
     >>> print(GPP[1120:1128])
     [-9.99900000e+03 -9.99900000e+03 -9.99900000e+03  4.33166157e+00
       8.18228013e+00  1.04092252e+01  8.19395317e+00  1.08427448e+01]
@@ -376,11 +418,14 @@ def _nee2gpp_falge(df, ff, isday, undef=-9999):
     Modified Arndt Piayda,   Mar 2012 - undef=np.nan
              Matthias Cuntz, Nov 2012 - individual routine
              Matthias Cuntz, Feb 2013 - ported to Python 3
+             Matthias Cuntz, Jun 2024 - removed np.int and np.float
+
     """
     # Variables
     fc_id = ''
     for cc in df.columns:
-        if cc.startswith('FC_') or (cc == 'FC') or cc.startswith('NEE_') or (cc == 'NEE'):
+        if ( cc.startswith('FC_') or (cc == 'FC') or cc.startswith('NEE_') or
+             (cc == 'NEE') ):
             fc_id = cc
             break
     ta_id = ''
@@ -388,12 +433,15 @@ def _nee2gpp_falge(df, ff, isday, undef=-9999):
         if cc.startswith('TA_') or (cc == 'TA'):
             ta_id = cc
             break
-    assert fc_id, 'Carbon net flux with name FC or NEE or starting with FC_ or NEE_ must be in input.'
-    assert ta_id, 'Air temperature with name TA or starting with TA_ must be in input.'
+    assert fc_id, ('Carbon net flux with name FC or NEE or starting with FC_'
+                   ' or NEE_ must be in input.')
+    assert ta_id, ('Air temperature with name TA or starting with TA_ must'
+                   ' be in input.')
 
     nee    = np.ma.array(df[fc_id], mask=(ff[fc_id] > 0))
     t      = np.ma.array(df[ta_id], mask=(ff[ta_id] > 0))
-    misday = np.ma.array(isday, mask=((~np.isfinite(isday)) | (isday == undef)))
+    misday = np.ma.array(isday, mask=((~np.isfinite(isday)) |
+                                      (isday == undef)))
 
     # Partition - Global relationship as in Falge et al. (2001)
 
@@ -406,16 +454,16 @@ def _nee2gpp_falge(df, ff, isday, undef=-9999):
     p        = opt.fmin(cost_abs, [2., 200.],
                         args=(lloyd_fix_p, tt, net), disp=False)
 
-    Reco     = np.ones(ndata)*undef
+    Reco     = np.ones(ndata) * undef
     ii       = np.where(~t.mask)[0]
     Reco[ii] = lloyd_fix(t[ii], p[0], p[1])
 
     # GPP
-    GPP     = np.ones(ndata)*undef
+    GPP     = np.ones(ndata) * undef
     ii      = np.where(~(t.mask | nee.mask))[0]
     GPP[ii] = Reco[ii] - nee[ii]
 
-    dfout = pd.DataFrame({'GPP':GPP, 'RECO':Reco}, index=df.index)
+    dfout = pd.DataFrame({'GPP': GPP, 'RECO': Reco}, index=df.index)
 
     return dfout
 
@@ -434,8 +482,9 @@ def _nee2gpp_reichstein(df, ff, isday, undef=-9999, nogppnight=False):
         time series of CO2 fluxes and air temperature.
 
         pandas.Dataframe with the columns
-        'FC' or 'NEE' (or starting with 'FC\_' or 'NEE\_') for observed CO2 flux [umol(CO2) m-2 s-1]
-        'TA'    (or starting with 'TA\_') for air temperature [K]
+        'FC' or 'NEE' (or starting with 'FC\\_' or 'NEE\\_') for
+        observed CO2 flux [umol(CO2) m-2 s-1]
+        'TA'    (or starting with 'TA\\_') for air temperature [K]
         The index is taken as date variable.
     ff : pandas.Dataframe
         flag Dataframe or array has the same shape as `df`. Non-zero values in
@@ -443,11 +492,14 @@ def _nee2gpp_reichstein(df, ff, isday, undef=-9999, nogppnight=False):
 
         `ff` must follow the same rules as `df`.
     isday : array_like of bool
-        True when it is day, False when night. Must have the same length as `df.shape[0].`
+        True when it is day, False when night. Must have the same length
+        as `df.shape[0].`
     undef : float, optional
-        values having `undef` value are treated as missing values in `df` (default: -9999)
+        values having `undef` value are treated as missing values in `df`
+        (default: -9999)
     nogppnight : float, optional
-        GPP will be set to zero at night. RECO will then equal NEE at night (default: False)
+        GPP will be set to zero at night. RECO will then equal NEE at night
+        (default: False)
 
     Returns
     -------
@@ -458,8 +510,8 @@ def _nee2gpp_reichstein(df, ff, isday, undef=-9999, nogppnight=False):
     References
     ----------
     .. [2] Reichstein et al. (2005)
-       On the separation of net ecosystem exchange into assimilation and ecosystem
-       respiration: review and improved algorithm,
+       On the separation of net ecosystem exchange into assimilation and
+       ecosystem respiration: review and improved algorithm,
        Global Change Biology 11, 1424-1439
 
     Examples
@@ -474,7 +526,8 @@ def _nee2gpp_reichstein(df, ff, isday, undef=-9999, nogppnight=False):
     >>> head  = fread(ifile, skip=2, header=True)
     >>> head1 = head[0]
     >>> # date
-    >>> jdate = date2dec(dy=dat[0,:], mo=dat[1,:], yr=dat[2,:], hr=dat[3,:], mi=dat[4,:])
+    >>> jdate = date2dec(dy=dat[0,:], mo=dat[1,:], yr=dat[2,:], hr=dat[3,:],
+    ...                  mi=dat[4,:])
     >>> adate = dec2date(jdate, eng=True)
     >>> # colhead
     >>> idx   = []
@@ -488,7 +541,8 @@ def _nee2gpp_reichstein(df, ff, isday, undef=-9999, nogppnight=False):
     >>> # flag
     >>> flag = np.where(dfin == undef, 2, 0)
     >>> # partition
-    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead, undef=undef, method='local')
+    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead,
+    ...                     undef=undef, method='local')
     >>> print(GPP[1120:1128])
     [-9.99900000e+03 -9.99900000e+03 -9.99900000e+03  4.40606871e+00
       8.31942152e+00  1.06242542e+01  8.49245664e+00  1.12381973e+01]
@@ -496,17 +550,20 @@ def _nee2gpp_reichstein(df, ff, isday, undef=-9999, nogppnight=False):
     [1.68311981 1.81012431 1.9874173  2.17108871 2.38759152 2.64372415
      2.90076664 3.18592735]
 
-    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead, undef=undef, method='local')
+    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead,
+    ...                     undef=undef, method='local')
     >>> print(GPP[1120:1128])
     [-9.99900000e+03 -9.99900000e+03 -9.99900000e+03  4.40606871e+00
       8.31942152e+00  1.06242542e+01  8.49245664e+00  1.12381973e+01]
 
-    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead, undef=undef, method='Reichstein')
+    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead,
+    ...                     undef=undef, method='Reichstein')
     >>> print(GPP[1120:1128])
     [-9.99900000e+03 -9.99900000e+03 -9.99900000e+03  4.40606871e+00
       8.31942152e+00  1.06242542e+01  8.49245664e+00  1.12381973e+01]
 
-    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead, undef=undef, method='reichstein')
+    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead,
+    ...                     undef=undef, method='reichstein')
     >>> print(GPP[1120:1128])
     [-9.99900000e+03 -9.99900000e+03 -9.99900000e+03  4.40606871e+00
       8.31942152e+00  1.06242542e+01  8.49245664e+00  1.12381973e+01]
@@ -517,11 +574,14 @@ def _nee2gpp_reichstein(df, ff, isday, undef=-9999, nogppnight=False):
     Modified Arndt Piayda,   Mar 2012 - undef=np.nan
              Matthias Cuntz, Nov 2012 - individual routine
              Matthias Cuntz, Feb 2013 - ported to Python 3
+             Matthias Cuntz, Jun 2024 - removed np.int and np.float
+
     """
     # Variables
     fc_id = ''
     for cc in df.columns:
-        if cc.startswith('FC_') or (cc == 'FC') or cc.startswith('NEE_') or (cc == 'NEE'):
+        if ( cc.startswith('FC_') or (cc == 'FC') or cc.startswith('NEE_') or
+             (cc == 'NEE') ):
             fc_id = cc
             break
     ta_id = ''
@@ -529,118 +589,133 @@ def _nee2gpp_reichstein(df, ff, isday, undef=-9999, nogppnight=False):
         if cc.startswith('TA_') or (cc == 'TA'):
             ta_id = cc
             break
-    assert fc_id, 'Carbon net flux with name FC or NEE or starting with FC_ or NEE_ must be in input.'
-    assert ta_id, 'Air temperature with name TA or starting with TA_ must be in input.'
+    assert fc_id, ('Carbon net flux with name FC or NEE or starting with FC_'
+                   ' or NEE_ must be in input.')
+    assert ta_id, ('Air temperature with name TA or starting with TA_ must'
+                   ' be in input.')
 
     nee    = np.ma.array(df[fc_id], mask=(ff[fc_id] > 0))
     t      = np.ma.array(df[ta_id], mask=(ff[ta_id] > 0))
-    misday = np.ma.array(isday, mask=((~np.isfinite(isday)) | (isday == undef)))
+    misday = np.ma.array(isday, mask=((~np.isfinite(isday)) |
+                                      (isday == undef)))
     dates  = df.index.to_julian_date()
 
     # Partition - Local relationship = Reichstein et al. (2005)
 
     ndata = nee.size
-    GPP   = np.ones(ndata)*undef
-    Reco  = np.ones(ndata)*undef
-    dfout = pd.DataFrame({'GPP':GPP, 'RECO':Reco}, index=df.index)
+    GPP   = np.ones(ndata) * undef
+    Reco  = np.ones(ndata) * undef
+    dfout = pd.DataFrame({'GPP': GPP, 'RECO': Reco}, index=df.index)
 
     # Select valid nighttime
     mask  = misday | nee.mask | t.mask | misday.mask
     ii    = np.where(~mask)[0]
-    if (ii.size==0):
-        # raise ValueError('Error _nee2gpp_reichstein: no valid nighttime data.')
+    if (ii.size == 0):
+        # raise ValueError('Error _nee2gpp_reichstein:'
+        #                  ' no valid nighttime data.')
         print('Warning _nee2gpp_reichstein: no valid nighttime data.')
         return dfout
     jul  = dates[ii]
     tt   = np.ma.compressed(t[ii])
     net  = np.ma.compressed(nee[ii])
     # 1. each 5 days, in 15 day period, fit if range of T > 5
-    locp = [] # local param
-    locs = [] # local err
-    dmin = np.floor(np.amin(jul)).astype(np.int) # be aware that julian days starts at noon, i.e. 1.0 is 12h
-    dmax = np.ceil(np.amax(jul)).astype(np.int)  # so the search will be from noon to noon and thus includes all nights
-    for i in range(dmin,dmax,5):
-        iii  = np.where((jul>=i) & (jul<(i+14)))[0]
+    locp = []  # local param
+    locs = []  # local err
+    # be aware that julian days starts at noon, i.e. 1.0 is 12h
+    # so the search will be from noon to noon and thus includes all nights
+    dmin = np.floor(np.amin(jul)).astype(int)
+    dmax = np.ceil(np.amax(jul)).astype(int)
+    for i in range(dmin, dmax, 5):
+        iii  = np.where((jul >= i) & (jul < (i + 14)))[0]
         niii = iii.size
         if niii > 6:
             tt1  = tt[iii]
             net1 = net[iii]
-            mm   = ~mad(net1, z=4.5) # make fit more robust by removing outliers
+            # make fit more robust by removing outliers
+            mm   = ~mad(net1, z=4.5)
             if (np.ptp(tt[iii]) >= 5.) & (np.sum(mm) > 6):
                 p, temp1, temp2 = opt.fmin_tnc(cost_lloyd_fix, [2., 200.],
-                                               bounds=[[0.,None], [0.,None]],
+                                               bounds=[[0., None], [0., None]],
                                                args=(tt1[mm], net1[mm]),
                                                approx_grad=True, disp=False)
                 try:
-                    p1, c = opt.curve_fit(lloyd_fix, tt1[mm], net1[mm], p0=p, maxfev=10000) # params, covariance
-                    if np.all(np.isfinite(c)): # possible return of curvefit: c=inf
+                    # params, covariance
+                    p1, c = opt.curve_fit(lloyd_fix, tt1[mm], net1[mm],
+                                          p0=p, maxfev=10000)
+                    # possible return of curvefit: c=inf
+                    if np.all(np.isfinite(c)):
                         s = np.sqrt(np.diag(c))
                     else:
-                        s = 10.*np.abs(p)
+                        s = 10. * np.abs(p)
                 except:
-                    s = 10.*np.abs(p)
+                    s = 10. * np.abs(p)
                 locp += [p]
                 locs += [s]
                 # if ((s[1]/p[1])<0.5) & (p[1] > 0.): pdb.set_trace()
     if len(locp) == 0:
-        # raise ValueError('Error _nee2gpp_reichstein: No local relationship found.')
+        # raise ValueError('Error _nee2gpp_reichstein:'
+        #                  ' No local relationship found.')
         print('Warning _nee2gpp_reichstein: No local relationship found.')
         return dfout
-    locp   = np.squeeze(np.array(locp).astype(np.float))
-    locs   = np.squeeze(np.array(locs).astype(np.float))
+    locp   = np.squeeze(np.array(locp).astype(float))
+    locs   = np.squeeze(np.array(locs).astype(float))
     # 2. E0 = avg of best 3
     # Reichstein et al. (2005), p. 1430, 1st paragraph.
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
-        iii  = np.where((locp[:,1] > 0.) & (locp[:,1] < 450.) & (np.abs(locs[:,1]/locp[:,1]) < 0.5))[0]
+        iii  = np.where((locp[:, 1] > 0.) & (locp[:, 1] < 450.) &
+                        (np.abs(locs[:, 1] / locp[:, 1]) < 0.5))[0]
     niii = iii.size
-    if niii==0:
-        # raise ValueError('Error _nee2gpp_reichstein: No good local relationship found.')
+    if niii == 0:
+        # raise ValueError('Error _nee2gpp_reichstein:'
+        #                  ' No good local relationship found.')
         # loosen the criteria: take the best three estimates anyway
-        iii   = np.where((locp[:,1] > 0.))[0]
+        iii   = np.where((locp[:, 1] > 0.))[0]
         niii = iii.size
-        if niii<1:
+        if niii < 1:
             # raise ValueError('Error _nee2gpp_reichstein: No E0>0 found.')
             print('Warning _nee2gpp_reichstein: No E0>0 found.')
             return dfout
-        lp    = locp[iii,:]
-        ls    = locs[iii,:]
-        iis   = np.argsort(ls[:,1])
-        bestp = np.mean(lp[iis[0:np.minimum(3,niii)],:],axis=0)
-        bests = np.mean(ls[iis[0:np.minimum(3,niii)],:],axis=0)
-    elif niii==1:
-        bestp = np.squeeze(locp[iii,:])
-        bests = np.squeeze(locs[iii,:])
-    elif niii==2:
-        bestp = np.mean(locp[iii,:],axis=0)
-        bests = np.mean(locs[iii,:],axis=0)
+        lp    = locp[iii, :]
+        ls    = locs[iii, :]
+        iis   = np.argsort(ls[:, 1])
+        bestp = np.mean(lp[iis[0:np.minimum(3, niii)], :], axis=0)
+        bests = np.mean(ls[iis[0:np.minimum(3, niii)], :], axis=0)
+    elif niii == 1:
+        bestp = np.squeeze(locp[iii, :])
+        bests = np.squeeze(locs[iii, :])
+    elif niii == 2:
+        bestp = np.mean(locp[iii, :], axis=0)
+        bests = np.mean(locs[iii, :], axis=0)
         # ls    = locs[iii,:]
         # iis   = np.argsort(ls[:,1])
     else:
-        lp    = locp[iii,:]
-        ls    = locs[iii,:]
-        iis   = np.argsort(ls[:,1])
-        bestp = np.mean(lp[iis[0:3],:],axis=0)
-        bests = np.mean(ls[iis[0:3],:],axis=0)
+        lp    = locp[iii, :]
+        ls    = locs[iii, :]
+        iis   = np.argsort(ls[:, 1])
+        bestp = np.mean(lp[iis[0:3], :], axis=0)
+        bests = np.mean(ls[iis[0:3], :], axis=0)
 
     # 3. Refit Rref with fixed E0, each 4 days
-    refp  = [] # Rref param
-    refii = [] # mean index of data points
+    refp  = []  # Rref param
+    refii = []  # mean index of data points
     E0    = bestp[1]
     et    = lloyd_fix(tt, 1., E0)
-    for i in range(dmin,dmax,4):
-        iii  = np.where((jul>=i) & (jul<(i+4)))[0]
+    for i in range(dmin, dmax, 4):
+        iii  = np.where((jul >= i) & (jul < (i + 4)))[0]
         niii = iii.size
         if niii > 3:
             # Calc directly minisation of (nee-p*et)**2
             p, temp1, temp2 = opt.fmin_tnc(cost_abs, [2.],
-                                           bounds=[[0.,None]],
-                                           args=(lloyd_only_rref_p, et[iii], net[iii]),
+                                           bounds=[[0., None]],
+                                           args=(lloyd_only_rref_p, et[iii],
+                                                 net[iii]),
                                            approx_grad=True, disp=False)
             refp  += [p]
-            refii += [np.int((iii[0]+iii[-1])//2)]
+            refii += [int((iii[0] + iii[-1]) // 2)]
     if len(refp) == 0:
-        # raise ValueError('Error _nee2gpp_reichstein: No ref relationship found.')
+        # raise ValueError('Error _nee2gpp_reichstein:'
+        #                  ' No ref relationship found.')
         print('Warning _nee2gpp_reichstein: No ref relationship found.')
         return dfout
     refp  = np.squeeze(np.array(refp))
@@ -650,28 +725,28 @@ def _nee2gpp_reichstein(df, ff, isday, undef=-9999, nogppnight=False):
     Rref = np.interp(dates, jul[refii], refp)
 
     # 5. Calc Reco
-    Reco     = np.ones(ndata)*undef
+    Reco     = np.ones(ndata) * undef
     ii       = np.where(~t.mask)[0]
     Reco[ii] = lloyd_fix(t[ii], Rref[ii], E0)
 
     # 6. Calc GPP
-    GPP     = np.ones(ndata)*undef
+    GPP     = np.ones(ndata) * undef
     ii      = np.where(~(t.mask | nee.mask))[0]
     GPP[ii] = Reco[ii] - nee[ii]
 
     # 7. Set GPP=0 at night, if wanted
     if nogppnight:
-        mask = misday | nee.mask | t.mask | misday.mask # night
+        mask = misday | nee.mask | t.mask | misday.mask  # night
         ii   = np.where(~mask)[0]
         Reco[ii] = nee[ii]
         GPP[ii]  = 0.
         # and prohibit negative gpp at any time
-        mask = nee.mask | t.mask | (GPP>0.)
+        mask = nee.mask | t.mask | (GPP > 0.)
         ii   = np.where(~mask)[0]
         Reco[ii] -= GPP[ii]
         GPP[ii]  = 0.
 
-    dfout = pd.DataFrame({'GPP':GPP, 'RECO':Reco}, index=df.index)
+    dfout = pd.DataFrame({'GPP': GPP, 'RECO': Reco}, index=df.index)
 
     return dfout
 
@@ -690,9 +765,11 @@ def _nee2gpp_lasslop(df, ff, isday, undef=-9999, nogppnight=False):
         incoming shortwave radiation, and air vapour pressure deficit.
 
         `df` can be a pandas.Dataframe with the columns
-        'FC' or 'NEE' (or starting with 'FC\_' or 'NEE\_') for observed CO2 flux [umol(CO2) m-2 s-1]
-        'TA'    (or starting with 'TA\_') for air temperature [K]
-        'SW_IN' (or starting with 'SW_IN') for incoming short-wave radiation [W m-2]
+        'FC' or 'NEE' (or starting with 'FC\\_' or 'NEE\\_') for observed
+        CO2 flux [umol(CO2) m-2 s-1]
+        'TA'    (or starting with 'TA\\_') for air temperature [K]
+        'SW_IN' (or starting with 'SW_IN') for incoming short-wave
+        radiation [W m-2]
         'VPD'   (or starting with 'VPD') for air vapour deficit [Pa]
         The index is taken as date variable.
     ff : pandas.Dataframe or numpy.array, optional
@@ -701,11 +778,14 @@ def _nee2gpp_lasslop(df, ff, isday, undef=-9999, nogppnight=False):
 
         `ff` must follow the same rules as `df` if pandas.Dataframe.
     isday : array_like of bool, optional
-        True when it is day, False when night. Must have the same length as `df.shape[0]`.
+        True when it is day, False when night. Must have the same length
+        as `df.shape[0]`.
     undef : float, optional
-        values having `undef` value are treated as missing values in `df` (default: -9999)
+        values having `undef` value are treated as missing values in `df`
+        (default: -9999)
     nogppnight : float, optional
-        GPP will be set to zero at night. RECO will then equal NEE at night (default: False)
+        GPP will be set to zero at night. RECO will then equal NEE at night
+        (default: False)
 
     Returns
     -------
@@ -716,8 +796,9 @@ def _nee2gpp_lasslop(df, ff, isday, undef=-9999, nogppnight=False):
     References
     ----------
     .. [3] Lasslop et al. (2010)
-       Separation of net ecosystem exchange into assimilation and respiration using
-       a light response curve approach: critical issues and global evaluation,
+       Separation of net ecosystem exchange into assimilation and respiration
+       using a light response curve approach: critical issues and global
+       evaluation,
        Global Change Biology 16, 187-208
 
     Examples
@@ -732,7 +813,8 @@ def _nee2gpp_lasslop(df, ff, isday, undef=-9999, nogppnight=False):
     >>> head  = fread(ifile, skip=2, header=True)
     >>> head1 = head[0]
     >>> # date
-    >>> jdate = date2dec(dy=dat[0,:], mo=dat[1,:], yr=dat[2,:], hr=dat[3,:], mi=dat[4,:])
+    >>> jdate = date2dec(dy=dat[0,:], mo=dat[1,:], yr=dat[2,:], hr=dat[3,:],
+                         mi=dat[4,:])
     >>> adate = dec2date(jdate, eng=True)
     >>> # colhead
     >>> idx   = []
@@ -746,7 +828,8 @@ def _nee2gpp_lasslop(df, ff, isday, undef=-9999, nogppnight=False):
     >>> # flag
     >>> flag = np.where(dfin == undef, 2, 0)
     >>> # partition
-    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead, undef=undef, method='day')
+    >>> GPP, Reco = nee2gpp(dfin, flag=flag, date=adate, colhead=colhead,
+                            undef=undef, method='day')
     >>> print(GPP[1120:1128])
     [-9.99900000e+03 -9.99900000e+03 -9.99900000e+03  2.78457540e+00
       6.63212545e+00  8.88902165e+00  6.74243873e+00  9.51364527e+00]
@@ -760,11 +843,14 @@ def _nee2gpp_lasslop(df, ff, isday, undef=-9999, nogppnight=False):
     Modified Arndt Piayda,   Mar 2012 - undef=np.nan
              Matthias Cuntz, Nov 2012 - individual routine
              Matthias Cuntz, Feb 2013 - ported to Python 3
+             Matthias Cuntz, Jun 2024 - removed np.int and np.float
+
     """
     # Variables
     fc_id = ''
     for cc in df.columns:
-        if cc.startswith('FC_') or (cc == 'FC') or cc.startswith('NEE_') or (cc == 'NEE'):
+        if ( cc.startswith('FC_') or (cc == 'FC') or cc.startswith('NEE_') or
+             (cc == 'NEE') ):
             fc_id = cc
             break
     ta_id = ''
@@ -782,24 +868,29 @@ def _nee2gpp_lasslop(df, ff, isday, undef=-9999, nogppnight=False):
         if cc.startswith('VPD_') or (cc == 'VPD'):
             vpd_id = cc
             break
-    assert fc_id,  'Carbon net flux with name FC or NEE or starting with FC_ or NEE_ must be in input.'
-    assert ta_id,  'Air temperature with name TA or starting with TA_ must be in input.'
-    assert sw_id,  'Global radiation with name SW or starting with SW_ must be in input.'
-    assert vpd_id, 'Vapour pressure deficit with name VPD or starting with VPD_ must be in input.'
-
-    nee    = np.ma.array(df[fc_id],  mask=(ff[fc_id] > 0))
-    t      = np.ma.array(df[ta_id],  mask=(ff[ta_id] > 0))
-    sw     = np.ma.array(df[sw_id],  mask=(ff[sw_id] > 0))
+    assert fc_id, ('Carbon net flux with name FC or NEE or starting with FC_'
+                   ' or NEE_ must be in input.')
+    assert ta_id, ('Air temperature with name TA or starting with TA_ must'
+                   ' be in input.')
+    assert sw_id, ('Global radiation with name SW or starting with SW_ must'
+                   ' be in input.')
+    assert vpd_id, ('Vapour pressure deficit with name VPD or starting with'
+                    ' VPD_ must be in input.')
+
+    nee    = np.ma.array(df[fc_id], mask=(ff[fc_id] > 0))
+    t      = np.ma.array(df[ta_id], mask=(ff[ta_id] > 0))
+    sw     = np.ma.array(df[sw_id], mask=(ff[sw_id] > 0))
     vpd    = np.ma.array(df[vpd_id], mask=(ff[vpd_id] > 0))
-    misday = np.ma.array(isday, mask=((~np.isfinite(isday)) | (isday == undef)))
+    misday = np.ma.array(isday, mask=((~np.isfinite(isday)) |
+                                      (isday == undef)))
     dates  = df.index.to_julian_date()
 
     # Partition - Lasslop et al. (2010) method
 
     ndata = nee.size
-    GPP   = np.ones(ndata)*undef
-    Reco  = np.ones(ndata)*undef
-    dfout = pd.DataFrame({'GPP':GPP, 'RECO':Reco}, index=df.index)
+    GPP   = np.ones(ndata) * undef
+    Reco  = np.ones(ndata) * undef
+    dfout = pd.DataFrame({'GPP': GPP, 'RECO': Reco}, index=df.index)
 
     do_lgpp = False
     mask  = nee.mask | t.mask | misday.mask | sw.mask | vpd.mask
@@ -822,7 +913,8 @@ def _nee2gpp_lasslop(df, ff, isday, undef=-9999, nogppnight=False):
     aalpha = 0.01
     qnet   = np.sort(dnet)
     nqnet  = qnet.size
-    abeta0 = np.abs(qnet[np.floor(0.97*nqnet).astype(np.int)]-qnet[np.ceil(0.03*nqnet).astype(np.int)])
+    abeta0 = np.abs(qnet[np.floor(0.97 * nqnet).astype(int)] -
+                    qnet[np.ceil(0.03 * nqnet).astype(int)])
     ak     = 0.
     # out
     lE0    = []
@@ -832,18 +924,19 @@ def _nee2gpp_lasslop(df, ff, isday, undef=-9999, nogppnight=False):
         lk     = []
     lRref  = []
     lii    = []
-    dmin = np.floor(np.amin(dates)).astype(np.int)
-    dmax = np.ceil(np.amax(dates)).astype(np.int)
+    dmin = np.floor(np.amin(dates)).astype(int)
+    dmax = np.ceil(np.amax(dates)).astype(int)
     zaehl = -1
-    for i in range(dmin,dmax,2):
+    for i in range(dmin, dmax, 2):
         good = True
         # 1. Estimate E0 from nighttime data
-        iii  = np.squeeze(np.where((njul>=i) & (njul<(i+12))))
+        iii  = np.squeeze(np.where((njul >= i) & (njul < (i + 12))))
         niii = iii.size
         if niii > 3:
             p, temp1, temp2 = opt.fmin_tnc(cost_abs, [aRref, 100.],
-                                           bounds=[[0.,None], [0.,None]],
-                                           args=(lloyd_fix_p, ntt[iii], nnet[iii]),
+                                           bounds=[[0., None], [0., None]],
+                                           args=(lloyd_fix_p, ntt[iii],
+                                                 nnet[iii]),
                                            approx_grad=True, disp=False)
             E0 = np.maximum(p[1], 50.)
         else:
@@ -854,7 +947,7 @@ def _nee2gpp_lasslop(df, ff, isday, undef=-9999, nogppnight=False):
                 good = False
                 continue
         # 2. Estimate alpha, k, beta0, Rref from daytime data
-        iii  = np.squeeze(np.where((djul>=i) & (djul<(i+4))))
+        iii  = np.squeeze(np.where((djul >= i) & (djul < (i + 4))))
         niii = iii.size
         if niii > 3:
             et     = lloyd_fix(dtt[iii], 1., E0)
@@ -863,34 +956,37 @@ def _nee2gpp_lasslop(df, ff, isday, undef=-9999, nogppnight=False):
             ibeta0 = abeta0
             ik     = ak
             iRref  = aRref
-            bounds = [[None,None], [None,None], [None,None], [None,None]]
+            bounds = [[None, None], [None, None], [None, None], [None, None]]
             while again:
                 again = False
-                p, nfeval, rc  = opt.fmin_tnc(cost_lasslop, [ialpha, ibeta0, ik, iRref],
+                p, nfeval, rc  = opt.fmin_tnc(cost_lasslop,
+                                              [ialpha, ibeta0, ik, iRref],
                                               bounds=bounds,
-                                              args=(dsw[iii], et, dvpd[iii], dnet[iii]),
+                                              args=(dsw[iii], et, dvpd[iii],
+                                                    dnet[iii]),
                                               approx_grad=True, disp=False)
-                # if parameters beyond some bounds, set params and redo the optim or skip
-                if ((p[0] < 0.) | (p[0] > 0.22)): # alpha
+                # if parameters beyond some bounds, set params and redo
+                # the optim or skip
+                if ((p[0] < 0.) | (p[0] > 0.22)):  # alpha
                     again = True
                     if zaehl >= 0:
-                        bounds[0] = [lalpha[zaehl],lalpha[zaehl]]
+                        bounds[0] = [lalpha[zaehl], lalpha[zaehl]]
                         ialpha    = lalpha[zaehl]
                     else:
-                        bounds[0] = [0.,0.]
+                        bounds[0] = [0., 0.]
                         ialpha    = 0.
-                if p[1] < 0.:                     # beta0
-                    bounds[1] = [0.,0.]
+                if p[1] < 0.:                      # beta0
+                    bounds[1] = [0., 0.]
                     ibeta0    = 0.
                     again = True
                 if p[1] > 250.:
                     good = False
                     continue
-                if p[2] < 0.:                     # k
-                    bounds[2] = [0.,0.]
+                if p[2] < 0.:                      # k
+                    bounds[2] = [0., 0.]
                     ik        = 0.
                     again = True
-                if p[3] < 0:                      # Rref
+                if p[3] < 0:                       # Rref
                     good = False
                     continue
             if good:
@@ -899,7 +995,7 @@ def _nee2gpp_lasslop(df, ff, isday, undef=-9999, nogppnight=False):
                     lbeta0 = lbeta0 + [p[1]]
                     lk     = lk     + [p[2]]
                 lRref  = lRref  + [p[3]]
-                lii    = lii    + [np.int((iii[0]+iii[-1])/2)]
+                lii    = lii    + [int((iii[0] + iii[-1]) / 2)]
             else:
                 continue
         else:
@@ -907,7 +1003,8 @@ def _nee2gpp_lasslop(df, ff, isday, undef=-9999, nogppnight=False):
         lE0    = lE0 + [E0]
         zaehl += 1
     if len(lE0) == 0:
-        # raise ValueError('Error _nee2gpp_lasslop: No day relationship found.')
+        # raise ValueError('Error _nee2gpp_lasslop:'
+        #                  ' No day relationship found.')
         print('Warning _nee2gpp_lasslop: No day relationship found.')
         return dfout
     lE0 = np.squeeze(np.array(lE0))
@@ -923,7 +1020,7 @@ def _nee2gpp_lasslop(df, ff, isday, undef=-9999, nogppnight=False):
     Rref = np.interp(dates, djul[lii], lRref)
 
     # 4. Calc Reco
-    Reco     = np.ones(ndata)*undef
+    Reco     = np.ones(ndata) * undef
     ii       = np.squeeze(np.where(~t.mask))
     Reco[ii] = lloyd_fix(t[ii], Rref[ii], E0[ii])
 
@@ -936,26 +1033,27 @@ def _nee2gpp_lasslop(df, ff, isday, undef=-9999, nogppnight=False):
         lmask    = t.mask | misday.mask | sw.mask | vpd.mask
         ii       = np.squeeze(np.where(~lmask))
         lgpp     = np.zeros(ndata)
-        lgpp[ii] = lasslop(sw[ii], et[ii], vpd[ii], alpha[ii], beta0[ii], k[ii], Rref[ii]) - Reco[ii]
+        lgpp[ii] = lasslop(sw[ii], et[ii], vpd[ii], alpha[ii],
+                           beta0[ii], k[ii], Rref[ii]) - Reco[ii]
 
     # 6. GPP
-    GPP     = np.ones(ndata)*undef
+    GPP     = np.ones(ndata) * undef
     ii      = np.squeeze(np.where(~(t.mask | nee.mask)))
     GPP[ii] = Reco[ii] - nee[ii]
 
     # 7. Set GPP=0 at night, if wanted
     if nogppnight:
-        mask = misday | nee.mask | t.mask | misday.mask # night
+        mask = misday | nee.mask | t.mask | misday.mask  # night
         ii   = np.where(~mask)[0]
         Reco[ii] = nee[ii]
         GPP[ii]  = 0.
         # and prohibit negative gpp at any time
-        mask = nee.mask | t.mask | (GPP>0.)
+        mask = nee.mask | t.mask | (GPP > 0.)
         ii   = np.where(~mask)[0]
         Reco[ii] -= GPP[ii]
         GPP[ii]   = 0.
 
-    dfout = pd.DataFrame({'GPP':GPP, 'RECO':Reco}, index=df.index)
+    dfout = pd.DataFrame({'GPP': GPP, 'RECO': Reco}, index=df.index)
 
     return dfout
 
diff --git a/src/hesseflux/ustarfilter.py b/src/hesseflux/ustarfilter.py
index 61fe68f..7ef2c6f 100644
--- a/src/hesseflux/ustarfilter.py
+++ b/src/hesseflux/ustarfilter.py
@@ -34,6 +34,8 @@
       only ustar data when NEE and Ta are valid, Jan 2023, Matthias Cuntz
     * Use 90% of ustar if no threshold found also for seasonout,
       Jan 2023, Matthias Cuntz
+    * Removed np.float and np.bool, Jun 2024, Matthias Cuntz
+    * do not register pandas platting backend, Jun 2024, Matthias Cuntz
 
 """
 import numpy as np
@@ -156,7 +158,7 @@ def ustarfilter(dfin, flag=None, isday=None, date=None,
             estr = ('Length of colhead must be number of columns in input'
                     ' array. len(colhead)=' + str(len(colhead)) +
                     ' shape(input)=(' + str(dfin.shape[0]) + ',' +
-                    str(dfin.shape[1])+').')
+                    str(dfin.shape[1]) + ').')
             raise ValueError(estr)
         assert date is not None, 'Date must be given if input is numpy arrary.'
         df['Datetime'] = pd.to_datetime(date, format=timeformat)
@@ -280,10 +282,10 @@ def ustarfilter(dfin, flag=None, isday=None, date=None,
     yrmin  = df.index.min().year
     nyears = yrmax - yrmin + 1
     ndays  = (df.index.max() - df.index.min()).days + 1
-    assert ndays//nyears > 360, 'Full years must be given.'
+    assert ndays // nyears > 360, 'Full years must be given.'
 
     # calculate thresholds
-    nperiod = 12//nmon  # number of nmon periods per year
+    nperiod = 12 // nmon  # number of nmon periods per year
     if seasonout:
         bustars = np.ones((nboot, nyears, nperiod)) * undef
     else:
@@ -316,8 +318,8 @@ def ustarfilter(dfin, flag=None, isday=None, date=None,
                 flag_p   = ( (~isday_b) &
                              (ff_b[fc_id] == 0) & (ff_b[ustar_id] == 0) &
                              (ff_b[ta_id] == 0) &
-                             (df_b.index.month > p*nmon) &
-                             (df_b.index.month <= (p+1)*nmon) )
+                             (df_b.index.month > p * nmon) &
+                             (df_b.index.month <= (p + 1) * nmon) )
                 fc_p    = df_b.loc[flag_p, fc_id]
                 ustar_p = df_b.loc[flag_p, ustar_id]
                 ta_p    = df_b.loc[flag_p, ta_id]
@@ -328,11 +330,11 @@ def ustarfilter(dfin, flag=None, isday=None, date=None,
                     continue
                 ta_q = np.quantile(
                     ta_p,
-                    np.arange(ntaclasses + 1, dtype=np.float) /
-                    np.float(ntaclasses))
+                    np.arange(ntaclasses + 1, dtype=float) /
+                    float(ntaclasses))
                 ta_q[0] -= 0.1  # 1st include min
                 for t in range(ntaclasses):
-                    iita    = (ta_p > ta_q[t]) & (ta_p <= ta_q[t+1])
+                    iita    = (ta_p > ta_q[t]) & (ta_p <= ta_q[t + 1])
                     fc_t    = fc_p[iita]
                     ustar_t = ustar_p[iita]
                     ta_t    = ta_p[iita]
@@ -345,17 +347,17 @@ def ustarfilter(dfin, flag=None, isday=None, date=None,
                     # ustar classes
                     ustar_q = np.quantile(
                         ustar_t,
-                        np.arange(nustarclasses + 1, dtype=np.float) /
-                        np.float(nustarclasses))
+                        np.arange(nustarclasses + 1, dtype=float) /
+                        float(nustarclasses))
                     ustar_q[0] -= 0.01  # 1st include min
-                    for u in range(nustarclasses-1):
+                    for u in range(nustarclasses - 1):
                         iiustar = ((ustar_t > ustar_q[u]) &
-                                   (ustar_t <= ustar_q[u+1]))
+                                   (ustar_t <= ustar_q[u + 1]))
                         fc_u    = fc_t[iiustar]
-                        fc_a    = fc_t[ustar_t > ustar_q[u+1]]
+                        fc_a    = fc_t[ustar_t > ustar_q[u + 1]]
 
-                        if abs(fc_u.mean()) >= abs(plateaucrit*fc_a.mean()):
-                            custars.append(ustar_q[u+1])
+                        if abs(fc_u.mean()) >= abs(plateaucrit * fc_a.mean()):
+                            custars.append(ustar_q[u + 1])
                             break
 
                 # median of thresholds of all temperature classes =
@@ -390,7 +392,8 @@ def ustarfilter(dfin, flag=None, isday=None, date=None,
                 else:
                     flag_b = ( (~isday_b) &
                                (ff_b[ustar_id] == 0) )
-                    bustars[b, y] = np.quantile(df_b.loc[flag_b, ustar_id], 0.9)
+                    bustars[b, y] = np.quantile(df_b.loc[flag_b, ustar_id],
+                                                0.9)
 
     # set minimum ustar threshold
     bustars = np.maximum(bustars, ustarmin)
@@ -401,14 +404,14 @@ def ustarfilter(dfin, flag=None, isday=None, date=None,
     # flag out with original DatetimeIndex
     off    = ustar_in.astype(int)
     off[:] = 0
-    ii     = np.zeros(len(off), dtype=np.bool)
+    ii     = np.zeros(len(off), dtype=bool)
     if seasonout:
         for y in range(nyears):
             yy = yrmin + y
             for p in range(nperiod):
                 iiyr = ( (df.index.year == yy) &   # df DatetimeIndex
-                         (df.index.month > p*nmon) &
-                         (df.index.month <= (p+1)*nmon) )
+                         (df.index.month > p * nmon) &
+                         (df.index.month <= (p + 1) * nmon) )
                 ii[iiyr] = ustar_in[iiyr] < oustars[1, y, p]
     else:
         for y in range(nyears):
@@ -418,11 +421,14 @@ def ustarfilter(dfin, flag=None, isday=None, date=None,
     off[ii] = 2  # original DatetimeIndex
 
     if plot:
+        import matplotlib as mpl
+        mpl.use('PDF')  # set directly after import matplotlib
+        from matplotlib.backends.backend_pdf import PdfPages
         import matplotlib.pyplot as plt
-        import matplotlib.backends.backend_pdf as pdf
-        pd.plotting.register_matplotlib_converters()
+        # import matplotlib.backends.backend_pdf as PdfPages
+        # pd.plotting.register_matplotlib_converters()
 
-        pp = pdf.PdfPages('ustarfilter.pdf')
+        pp = PdfPages('ustarfilter.pdf')
         if seasonout:
             for y in range(nyears):
                 yy = yrmin + y
@@ -437,8 +443,8 @@ def ustarfilter(dfin, flag=None, isday=None, date=None,
                     flag_p = ( (~isday_f) &
                                (ff_f[fc_id] == 0) & (ff_f[ustar_id] == 0) &
                                (ff_f[ta_id] == 0) &
-                               (df_f.index.month > p*nmon) &
-                               (df_f.index.month <= (p+1)*nmon) )
+                               (df_f.index.month > p * nmon) &
+                               (df_f.index.month <= (p + 1) * nmon) )
                     fc_p    = df_f.loc[flag_p, fc_id]
                     ustar_p = df_f.loc[flag_p, ustar_id]