[英]How to interpolate latitude/longitude and heading in Pandas
Description: I have a Pandas dataframe formed by three columns: latitude [-90;90], longitude [-180;180] and direction [0;360].描述:我有一个由三列组成的 Pandas 数据框:纬度 [-90;90]、经度 [-180;180] 和方向 [0;360]。 All columns are in degrees.
所有列都以度为单位。 The index is instead formed by date + time like so:
索引由日期 + 时间组成,如下所示:
df = pd.DataFrame({'lat':[87,90,85,10,-40,-85,-89,-40],
'lon':[-150,-178,176,100,10,1,-20,-100],
'dir':[180,200,356,4,20,1,351,20]},
index = pd.to_datetime(['2019-06-17 08:29:07','2019-06-17 08:29:11', '2019-06-17 08:29:16', '2019-06-17 08:29:25', '2019-06-17 08:29:33', '2019-06-17 08:29:40', '2019-06-17 08:29:48', '2019-06-17 08:29:57']))
This is what it looks like:这是它的样子:
lat lon dir
2019-06-17 08:29:07 87 -150 180
2019-06-17 08:29:11 90 -178 200
2019-06-17 08:29:16 85 176 356
2019-06-17 08:29:25 10 100 4
2019-06-17 08:29:33 -40 10 20
2019-06-17 08:29:40 -85 1 1
2019-06-17 08:29:48 -89 -20 351
2019-06-17 08:29:57 -40 -100 20
GOAL: My goal is to add the missing datetimes between the indexes and perform an interpolation (ex linear) between the missing coordinates and angles.目标:我的目标是在索引之间添加缺失的日期时间,并在缺失的坐标和角度之间执行插值(前线性)。 I was able to add the missing dates like so:
我能够像这样添加缺少的日期:
idx = pd.to_datetime(pd.date_range(df.index[0], df.index[-1], freq='s').strftime('%Y-%m-%d %H:%M:%S'))
df = df.reindex(idx, fill_value='NaN')
lat lon dir
2019-06-17 08:29:07 87 -150 180
2019-06-17 08:29:08 NaN NaN NaN
2019-06-17 08:29:09 NaN NaN NaN
2019-06-17 08:29:10 NaN NaN NaN
2019-06-17 08:29:11 90 -178 200
2019-06-17 08:29:12 NaN NaN NaN
2019-06-17 08:29:13 NaN NaN NaN
................... ... ... ...
2019-06-17 08:29:55 NaN NaN NaN
2019-06-17 08:29:56 NaN NaN NaN
2019-06-17 08:29:57 -40 -100 20
In order to achieve my goal I tried to use the pandas function pandas.Series.interpolate
without success because it does not take into account the angle "jumps" between -180;180 for the longitude and the "jump" between 360 and 0 for the direction.为了实现我的目标,我尝试使用熊猫函数
pandas.Series.interpolate
没有成功,因为它没有考虑到经度在 -180;180 之间的“跳跃”角度和 360 和 0 之间的“跳跃”角度方向。
QUESTION: Could you please provide a smart and elengant way to achieve such interpolation so that it takes into account those jumps between the limits of their range?问题:您能否提供一种智能而优雅的方式来实现这种插值,以便考虑到它们范围限制之间的跳跃?
Note: here there is an example just to be more clear (interpolation between -176 and 176): -176,-177,-178,-179,-180/180,179,178,177,176?注意:这里有一个例子更清楚(-176 和 176 之间的插值):-176,-177,-178,-179,-180/180,179,178,177,176?
Here there is the answer to my question:这是我的问题的答案:
df['dir'] = np.rad2deg(np.unwrap(np.deg2rad(df['dir'])))
df['lat'] = np.rad2deg(np.unwrap(np.deg2rad(df['lat'])))
df['lon'] = np.rad2deg(np.unwrap(np.deg2rad(df['lon'])))
df = df.reindex(idx, fill_value=np.nan)
df.reset_index(drop=False, inplace=True)
df = df.interpolate()#pd.merge(left=pd.DataFrame({'index':idx}), right=df, on='index', how='left').interpolate()
df[['lat','lon','dir']] %= 360
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