View difference between Paste ID: hJFR7HbA and cfpqUmef

 #!/usr/bin/env python2
 # -*- coding: utf-8 -*-
 from __future__ import division
 import numpy as np
 import pandas as pd       # pip install pandas
 import rosbag_pandas      # pip install rosbag_pandas
 
 # Fast Introduction to pandas:
 # http://www.gregreda.com/2013/10/26/intro-to-pandas-data-structures/
 #
 # tl;dr: pandas extends numpy array with named columns and a time index
 
 
 # Example usage:
 # dataframe = rosbag_pandas.bag_to_dataframe('2014-08-20-17-37-47.bag')
-# ridf=reindex_df(dataframe, dataframe.uC_time__data)
+# ridf=reindex_dataframe(dataframe, dataframe.uC_time__data)
 # print ridf
 # plt.plot(ridf.index, ridf.sensors_imu_data_raw__angular_velocity_z)
 
 
 
 def reindex_dataframe(dataframe, reference_series, idx_start_from_zero=True):
     '''Reindex the pandas dataframe to only contain the timestamps from reference_series.
     The closest values (time-wise) for each column are used.
     idx_start_from_zero specifies whether the reindexed timestamps shall start from zero.
     Only columns with float64 values are include in the reindexed dataframe.
     '''
     if type(reference_series) is str:
         reference_series = dataframe[reference_series]
 
     idx = notnull_keys_sec(reference_series)
     n = idx.shape[0]
     data = dict()
     for col in dataframe.columns:
         col = df.columns[i]
         data[col] = np.empty(shape=(n))
         series = df[col]
         series = series[series.notnull()]
         values = series.values
         keys = datetime_index_to_sec(series.keys())
         values = get_closest_values(idx,keys,values)
         data[col] = values
 
     if idx_start_from_zero:
         idx -= idx[0]
 
     reindexed = pd.DataFrame(data,idx)
 
     return reindexed
 
 
 def datetime_index_to_sec(dti):
     '''Converts pandas DatetimeIndex into seconds.
     This may only work for DatetimeIndex from rosbag_pandas. Untested on other.'''
     return dti.map(lambda x:x.value/1e9)
 
 def notnull_values(series):
     '''Returns all not null values in the panda Series'''
     return series[series.notnull()].values
 
 def notnull_keys(series):
     '''Returns all not null keys in the panda Series'''
     return series[series.notnull()].keys()
 
-        notnull_values(series))
+
     '''Returns all not null keys in the panda Series as seconds'''
     return datetime_index_to_sec(notnull_keys(series))
 
 def get_closest_values(desired_timestamps, timestamps, data):
     '''Returns the values in data whose timestamps are closest to the desired_timestamps.
     Assumes sorted desired_timestamps and uniform dt.'''
     assert len(desired_timestamps.shape)==1
     assert len(timestamps.shape)==1
     assert len(data.shape)==1
     assert timestamps.shape[0] == data.shape[0]
 
     n = timestamps.shape[0]
     dt = np.diff(timestamps).mean()
     start = timestamps[0]
 
     values = np.empty_like(desired_timestamps)
     idx = (desired_timestamps-start)/dt
     idx = np.round(idx)
     idx[idx<0]=0
     idx[idx>n-1]=n-1
     idx=idx.astype('int')
     values = data[idx]
 
     return values
 
 def get_closest_values_from_df(reference_series, series):
     '''Returns the values in series whose timestamps are closest to the timestamps in the reference_series.
     Assumes uniform dt in reference_series.'''
-    return reindexed
+
         notnull_keys_sec(reference_series),
         notnull_keys_sec(series),
         notnull_values(series))