speeding up FPI code

fixing issue with OMNI test

pyspedas/mms/fpi/mms_fpi_make_compressionlossbars.py

@@ -2,7 +2,7 @@
 import numpy as np
 from fnmatch import fnmatch
 from pytplot import get_data, store_data, options
-from pyspedas import time_double
+from pyspedas import time_datetime
 
 
 def mms_fpi_make_compressionlossbars(tname, lossy=False):
@@ -58,7 +58,7 @@ def mms_fpi_make_compressionlossbars(tname, lossy=False):
         logging.error('Unable to determine data rate from variable name.')
         return
 
-    data = get_data(tname)
+    data = get_data(tname, dt=True)
     metadata = get_data(tname, metadata=True)
 
     if data is None:
@@ -72,7 +72,7 @@ def mms_fpi_make_compressionlossbars(tname, lossy=False):
         version = file_id.split('_v')[1].split('.')
         if version[0] == '2':
             if version[1] == '1':
-                if data.times[0] < time_double('2016-04-01'):
+                if data.times[0] < time_datetime('2016-04-01'):
                     lossy = 3
                 else:
                     lossy = 1

pyspedas/mms/fpi/mms_fpi_make_errorflagbars.py

@@ -80,7 +80,7 @@ def mms_fpi_make_errorflagbars(tname, level='l2'):
     instrument = tname.split('_')[1].upper()
     data_rate = tname.split('_')[3].capitalize()
 
-    data = get_data(tname)
+    data = get_data(tname, dt=True)
     metadata = get_data(tname, metadata=True)
 
     if metadata is None:

pyspedas/mms/fpi/mms_fpi_split_tensor.py

@@ -16,7 +16,7 @@ def mms_fpi_split_tensor(tensor_variable):
         List of variables created.
     """
 
-    data = get_data(tensor_variable)
+    data = get_data(tensor_variable, dt=True)
 
     if data is None:
         logging.error('Problem returning data from the variable: ' + tensor_variable)

pyspedas/mms/fpi/mms_load_fpi_calc_pad.py

@@ -67,9 +67,9 @@ def mms_load_fpi_calc_pad(probe='1', level='sitl', datatype='', data_rate='', su
 
         pad_avg_name = obsstr+'PitchAngDist_avg'+suffix
 
-        low_en = get_data(pad_vars[0])
-        mid_en = get_data(pad_vars[1])
-        high_en = get_data(pad_vars[2])
+        low_en = get_data(pad_vars[0], dt=True)
+        mid_en = get_data(pad_vars[1], dt=True)
+        high_en = get_data(pad_vars[2], dt=True)
 
         if low_en is None or mid_en is None or high_en is None:
             v3_low_pad = tnames(pad_vars[0].lower()+'_'+data_rate)
@@ -78,9 +78,9 @@ def mms_load_fpi_calc_pad(probe='1', level='sitl', datatype='', data_rate='', su
             if v3_low_pad == [] or v3_mid_pad == [] or v3_high_pad == []:
                 continue
 
-            low_en = get_data(v3_low_pad[0])
-            mid_en = get_data(v3_mid_pad[0])
-            high_en = get_data(v3_high_pad[0])
+            low_en = get_data(v3_low_pad[0], dt=True)
+            mid_en = get_data(v3_mid_pad[0], dt=True)
+            high_en = get_data(v3_high_pad[0], dt=True)
             pad_avg_name = pad_avg_name.lower()
 
         e_pad_sum = low_en.y+mid_en.y+high_en.y

pyspedas/mms/tests/fpi_tests.py

@@ -25,11 +25,11 @@ def test_pad(self):
         pa_dist = mms_pad_fpi(dists_e, trange=trange, mag_data='mms1_fgm_b_gse_brst_l2_bvec')
         pa_dist = mms_pad_fpi(dists, time='2015-10-16/13:06:30', units='eflux', mag_data='mms1_fgm_b_gse_brst_l2_bvec')
         pa_dist = mms_pad_fpi(dists,
-                              subtract_bulk=True,
-                              time='2015-10-16/13:06:30',
-                              units='eflux',
-                              mag_data='mms1_fgm_b_gse_brst_l2_bvec',
-                              vel_data='mms1_dis_bulkv_gse_brst')
+                      subtract_bulk=True,
+                      time='2015-10-16/13:06:30',
+                      units='eflux',
+                      mag_data='mms1_fgm_b_gse_brst_l2_bvec',
+                      vel_data='mms1_dis_bulkv_gse_brst')
 
     def test_split_tensors(self):
         data = pyspedas.mms.fpi(trange=['2015-10-16/13:06', '2015-10-16/13:07'],

pyspedas/omni/tests/tests.py

@@ -1,6 +1,5 @@
 import os
 import unittest
-import pandas as pd
 from pyspedas.utilities.data_exists import data_exists
 import pyspedas
 
@@ -9,8 +8,7 @@ class LoadTestCases(unittest.TestCase):
     def test_utc_timestamp_regression(self):
         varname = 'BX_GSE'
         data_omni = pyspedas.omni.data(trange=['2010-01-01/00:00:00', '2010-01-02/00:00:00'],notplot=True,varformat=varname,time_clip=True)
-        date_time = pd.to_datetime(data_omni[varname]['x'],unit='s')
-        self.assertTrue(str(date_time[0]) == '2010-01-01 00:00:00')
+        self.assertTrue(str(data_omni[varname]['x'][0]) == '2010-01-01 00:00:00')
 
     def test_load_hro2_data(self):
         omni_vars = pyspedas.omni.data()

pyspedas/particles/spd_slice2d/tplot_average.py

@@ -1,7 +1,7 @@
 import logging
 import numpy as np
 from pytplot import get_data
-from pyspedas import time_double
+from pyspedas import time_string
 
 
 def tplot_average(tvar, trange, quiet=False):
@@ -20,7 +20,7 @@ def tplot_average(tvar, trange, quiet=False):
     -------
         Average value of the tplot variable
     """
-    data = get_data(tvar)
+    data = get_data(tvar, dt=True)
 
     if data is None:
         logging.error('Error reading: ' + tvar)
@@ -30,10 +30,10 @@ def tplot_average(tvar, trange, quiet=False):
         logging.error('Error: time range must be two element array.')
         return
 
-    trange = time_double(trange)
+    trange = time_string(trange)
 
-    t0 = np.min(trange)
-    t1 = np.max(trange)
+    t0 = np.datetime64(trange[0])
+    t1 = np.datetime64(trange[-1])
 
     if not quiet:
         logging.info('Averaging ' + tvar)