使用 Python / hvplot 叠加来自两个不同数据源的两个图
Overlay of two plots from two different data sources using Python / hvplot
我想在 hvplot（来源：xarray/NetCDF）上绘制线图（来源：pandas 数据帧）。
xarray 看起来像这样：
dataDIR = 'ceilodata.nc'
DS = xr.open_dataset(dataDIR)
DS = DS.transpose()
print(DS)
<xarray.Dataset>
Dimensions: (range_hr: 32, range: 1024, layer: 3, time: 5760)
Coordinates:
* range_hr (range_hr) float32 0.001 4.995 9.99 ... 144.9 149.9 154.8
* range (range) float32 14.98 29.97 44.96 ... 1.533e+04 1.534e+04
* layer (layer) int32 1 2 3
* time (time) datetime64[ns] 2022-03-18 ... 2022-03-18T23:59:46
Data variables: (12/41)
zenith float32 ...
wavelength float32 ...
scaling float32 ...
range_gate_hr float32 ...
range_gate float32 ...
longitude float32 ...
... ...
cbe (layer, time) int16 ...
beta_raw_hr (range_hr, time) float32 ...
beta_raw (range, time) float32 ...
bcc (time) int8 ...
base (time) float32 ...
average_time (time) int32 ...
Attributes: (12/13)
comment:
software_version: 15.06.1 2.13 1.040 1
title: CHM15k Nimbus
wmo_id: 10865
month: 3
source: CHM160138
... ...
serlom: TUB160038
location: muenchen
year: 2022
device_name: CHM160138
institution: DWD
day: 18
pandas 数据帧源如下所示：
df = pd.read_csv('PTU.csv')
print(df)
Unnamed: 0 PTU
0 2022-03-18 07:38:56 451.839
1 2022-03-18 07:38:57 468.826
2 2022-03-18 07:38:58 469.093
3 2022-03-18 07:38:59 469.356
4 2022-03-18 07:39:00 469.623
... ... ...
6140 2022-03-18 09:21:16 31690.600
6141 2022-03-18 09:21:17 31694.700
6142 2022-03-18 09:21:18 31692.900
6143 2022-03-18 09:21:19 31712.000
6144 2022-03-18 09:21:20 31711.500
[6145 rows x 2 columns]
两者都是时间相关数据集，但具有不同的时间戳和频率。时间是每个数据集中的索引。
我试图将它们与额外导入的全息图一起绘制。虽然每个单独的图都没有问题，但将它们组合在一起似乎并不像我尝试的那样有效：
import hvplot.pandas
import holoviews as hv
# cmap of the xarray:
ceilo = (DS.b_r.hvplot(cmap="viridis_r", width = 850, height = 600, title = 'title', clim = (5, 80))
# line plot of the data frame
p = df.hvplot.line()
# add pressure line plot to pcolormeshplot using * which overlays the line on the plot
ceilo * p
但这以带有以下完整回溯的错误消息结束：
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-10-2b1c6baca339> in <module>
24 p = df.hvplot.line()
25 # add pressure line plot to pcolormeshplot using * which overlays the line on the plot
---> 26 ceilo * df
c:\python38\lib\site-packages\pandas\core\ops\common.py in new_method(self, other)
68 other = item_from_zerodim(other)
69
---> 70 return method(self, other)
71
72 return new_method
c:\python38\lib\site-packages\pandas\core\arraylike.py in __rmul__(self, other)
118 @unpack_zerodim_and_defer("__rmul__")
119 def __rmul__(self, other):
--> 120 return self._arith_method(other, roperator.rmul)
121
122 @unpack_zerodim_and_defer("__truediv__")
c:\python38\lib\site-packages\pandas\core\frame.py in _arith_method(self, other, op)
6936 other = ops.maybe_prepare_scalar_for_op(other, (self.shape[axis],))
6937
-> 6938 self, other = ops.align_method_FRAME(self, other, axis, flex=True, level=None)
6939
6940 new_data = self._dispatch_frame_op(other, op, axis=axis)
c:\python38\lib\site-packages\pandas\core\ops\__init__.py in align_method_FRAME(left, right, axis, flex, level)
275 elif is_list_like(right) and not isinstance(right, (ABCSeries, ABCDataFrame)):
276 # GH 36702. Raise when attempting arithmetic with list of array-like.
--> 277 if any(is_array_like(el) for el in right):
278 raise ValueError(
279 f"Unable to coerce list of {type(right[0])} to Series/DataFrame"
c:\python38\lib\site-packages\holoviews\core\element.py in __iter__(self)
94 def __iter__(self):
95 "Disable iterator interface."
---> 96 raise NotImplementedError('Iteration on Elements is not supported.')
97
98
NotImplementedError: Iteration on Elements is not supported.
不同的时间频率是这里的问题吗？考虑到底层 cmap-(matplotlib)-plot 的正确时间戳和高度，线图应沿 x 轴和 y 轴定向。
为了说明我的目标，这是我的目标图片：
感谢阅读/帮助。
我找到了这个案例的解决方案：
两个数据集时间列的格式必须相同。在我的例子中是：datetime64[ns]（采用 NetCDF xarray）。这就是为什么我将数据帧时间列转换为 datetime64[ns]:
df.Datetime = df.Datetime.astype('datetime64')
我还发现数据是“对象”类型。所以我把它改成了“浮动”：
df.PTU = df.PTU.astype(float) # convert to correct data type
最后一步是选择 hvplot，因为这有助于绘制 xarray 数据
import hvplot.xarray
hvplot.quadmesh
这是我的最终解决方案：
title = ('Ceilo data + '\ndate: '+ str(DS.year) + '-' + str(DS.month) + '-' + str(DS.day))
ceilo = (DS.br.hvplot.quadmesh(cmap="viridis_r", width = 850, height = 600, title = title,
clim = (1000, 10000), # set colorbar limits
cnorm = ('log'), # choose log scale
clabel = ('colorbar title'),
rot = 0 # degree rotation of ticks
)
)
# from: https://justinbois.github.io/bootcamp/2020/lessons/l27_holoviews.html
# take care! may take 2...3 minutes to be ploted:
p = hv.Points(data=df,
kdims=['Datetime', 'PTU'],
).opts(#alpha=0.7,
color='red',
size=1,
ylim=(0, 5000))
# add PTU line plot to quadmesh plot using * which overlays the line on the plot
ceilo * p
我想在 hvplot（来源：xarray/NetCDF）上绘制线图（来源：pandas 数据帧）。
xarray 看起来像这样：
dataDIR = 'ceilodata.nc'
DS = xr.open_dataset(dataDIR)
DS = DS.transpose()
print(DS)
<xarray.Dataset>
Dimensions: (range_hr: 32, range: 1024, layer: 3, time: 5760)
Coordinates:
* range_hr (range_hr) float32 0.001 4.995 9.99 ... 144.9 149.9 154.8
* range (range) float32 14.98 29.97 44.96 ... 1.533e+04 1.534e+04
* layer (layer) int32 1 2 3
* time (time) datetime64[ns] 2022-03-18 ... 2022-03-18T23:59:46
Data variables: (12/41)
zenith float32 ...
wavelength float32 ...
scaling float32 ...
range_gate_hr float32 ...
range_gate float32 ...
longitude float32 ...
... ...
cbe (layer, time) int16 ...
beta_raw_hr (range_hr, time) float32 ...
beta_raw (range, time) float32 ...
bcc (time) int8 ...
base (time) float32 ...
average_time (time) int32 ...
Attributes: (12/13)
comment:
software_version: 15.06.1 2.13 1.040 1
title: CHM15k Nimbus
wmo_id: 10865
month: 3
source: CHM160138
... ...
serlom: TUB160038
location: muenchen
year: 2022
device_name: CHM160138
institution: DWD
day: 18
pandas 数据帧源如下所示：
df = pd.read_csv('PTU.csv')
print(df)
Unnamed: 0 PTU
0 2022-03-18 07:38:56 451.839
1 2022-03-18 07:38:57 468.826
2 2022-03-18 07:38:58 469.093
3 2022-03-18 07:38:59 469.356
4 2022-03-18 07:39:00 469.623
... ... ...
6140 2022-03-18 09:21:16 31690.600
6141 2022-03-18 09:21:17 31694.700
6142 2022-03-18 09:21:18 31692.900
6143 2022-03-18 09:21:19 31712.000
6144 2022-03-18 09:21:20 31711.500
[6145 rows x 2 columns]
两者都是时间相关数据集，但具有不同的时间戳和频率。时间是每个数据集中的索引。
我试图将它们与额外导入的全息图一起绘制。虽然每个单独的图都没有问题，但将它们组合在一起似乎并不像我尝试的那样有效：
import hvplot.pandas
import holoviews as hv
# cmap of the xarray:
ceilo = (DS.b_r.hvplot(cmap="viridis_r", width = 850, height = 600, title = 'title', clim = (5, 80))
# line plot of the data frame
p = df.hvplot.line()
# add pressure line plot to pcolormeshplot using * which overlays the line on the plot
ceilo * p
但这以带有以下完整回溯的错误消息结束：
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-10-2b1c6baca339> in <module>
24 p = df.hvplot.line()
25 # add pressure line plot to pcolormeshplot using * which overlays the line on the plot
---> 26 ceilo * df
c:\python38\lib\site-packages\pandas\core\ops\common.py in new_method(self, other)
68 other = item_from_zerodim(other)
69
---> 70 return method(self, other)
71
72 return new_method
c:\python38\lib\site-packages\pandas\core\arraylike.py in __rmul__(self, other)
118 @unpack_zerodim_and_defer("__rmul__")
119 def __rmul__(self, other):
--> 120 return self._arith_method(other, roperator.rmul)
121
122 @unpack_zerodim_and_defer("__truediv__")
c:\python38\lib\site-packages\pandas\core\frame.py in _arith_method(self, other, op)
6936 other = ops.maybe_prepare_scalar_for_op(other, (self.shape[axis],))
6937
-> 6938 self, other = ops.align_method_FRAME(self, other, axis, flex=True, level=None)
6939
6940 new_data = self._dispatch_frame_op(other, op, axis=axis)
c:\python38\lib\site-packages\pandas\core\ops\__init__.py in align_method_FRAME(left, right, axis, flex, level)
275 elif is_list_like(right) and not isinstance(right, (ABCSeries, ABCDataFrame)):
276 # GH 36702. Raise when attempting arithmetic with list of array-like.
--> 277 if any(is_array_like(el) for el in right):
278 raise ValueError(
279 f"Unable to coerce list of {type(right[0])} to Series/DataFrame"
c:\python38\lib\site-packages\holoviews\core\element.py in __iter__(self)
94 def __iter__(self):
95 "Disable iterator interface."
---> 96 raise NotImplementedError('Iteration on Elements is not supported.')
97
98
NotImplementedError: Iteration on Elements is not supported.
不同的时间频率是这里的问题吗？考虑到底层 cmap-(matplotlib)-plot 的正确时间戳和高度，线图应沿 x 轴和 y 轴定向。
为了说明我的目标，这是我的目标图片：
感谢阅读/帮助。
我找到了这个案例的解决方案：
两个数据集时间列的格式必须相同。在我的例子中是：datetime64[ns]（采用 NetCDF xarray）。这就是为什么我将数据帧时间列转换为 datetime64[ns]:
df.Datetime = df.Datetime.astype('datetime64')
我还发现数据是“对象”类型。所以我把它改成了“浮动”：
df.PTU = df.PTU.astype(float) # convert to correct data type
最后一步是选择 hvplot，因为这有助于绘制 xarray 数据
import hvplot.xarray
hvplot.quadmesh
这是我的最终解决方案：
title = ('Ceilo data + '\ndate: '+ str(DS.year) + '-' + str(DS.month) + '-' + str(DS.day))
ceilo = (DS.br.hvplot.quadmesh(cmap="viridis_r", width = 850, height = 600, title = title,
clim = (1000, 10000), # set colorbar limits
cnorm = ('log'), # choose log scale
clabel = ('colorbar title'),
rot = 0 # degree rotation of ticks
)
)
# from: https://justinbois.github.io/bootcamp/2020/lessons/l27_holoviews.html
# take care! may take 2...3 minutes to be ploted:
p = hv.Points(data=df,
kdims=['Datetime', 'PTU'],
).opts(#alpha=0.7,
color='red',
size=1,
ylim=(0, 5000))
# add PTU line plot to quadmesh plot using * which overlays the line on the plot
ceilo * p