Closed as not planned
Description
Bug report
Gap in time series data causes MPL to jump to a very small datetime that doesnt exist in the dataset.
If I don't manually set the axis, it tries to autofit.
ax1.set_xlim( [ min(datana.date.dt.to_pydatetime()) , max(datana.date.dt.to_pydatetime()) ])
Autofitting looks like this:
Note that the minimum x datetime in the dataset is in 2019! Not sure where the 1680ish time comes from. I've manually checked what actually gets passed into mpl, a numpy object array of datetime objects, does not have a time value below 2019.
Abbreviated code:
import datetime
import time
import matplotlib
matplotlib.use("agg")
import matplotlib.pyplot as plt
import pandas as pd
fig1, ax1 = plt.subplots( figsize=(15,5) )
data = pd.read_csv("temps.dat",header=None, names=['date','temp'])
data.temp = data.temp/1000
data.date = pd.to_datetime(data.date)
data.date = data.date.dt.ceil(freq='s') #strip microseconds
data['roll'] = data['temp'].rolling(window=722 ).mean()
data['roll2'] = data['temp'].rolling(window=14220 ).mean()
datana = data.dropna(subset=['roll'])
datana2 = data.dropna(subset=['roll2'])
ax1.cla()
ax1.plot(datana2.date.dt.to_pydatetime() ,datana2.roll2, lw=3, alpha=.8)
ax1.plot(datana.date.dt.to_pydatetime() ,datana.roll, lw=.7)
ax1.set_xlim( [ min(datana.date.dt.to_pydatetime()) , max(datana.date.dt.to_pydatetime()) ])
fig1.autofmt_xdate()
fig1.savefig("fig1.png",format='png' ,bbox_inches='tight', dpi=500)
temps.zip
Matplotlib version
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Operating system: debian (raspbian)
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Matplotlib version: 3.1.2 , in a venv, installed by pip
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Matplotlib backend (
print(matplotlib.get_backend())): agg -
Python version: 3.7
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Other libraries: pandas, Pillow