Grid missing in single subplot - matplotlib

Question

I'm using identical code to create each subplot in the following figure and I cannot figure out why one of the subplots is missing its grid lines.
Strangely, I can add grid lines after the fact:
In [18]: ax2b
Out[18]: <AxesSubplot:title={'center':'12:10-12:16 (after 13:00 PST Oct 26th)'}>
In [19]: ax2b.xaxis.grid(which='major'); fig
Out[19]:
But that's exactly how my script works so I don't understand why the subplot at (2,2) is somehow ignored.
I've done my very best to rule out typos or errors. Here's the script for plot generation (data not included sorry):
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import ticker
run5 = combine_run5_data()
pdtpst = '2022-10-26T13:00'
colname = 'Licor_CO2'
cycle1a = run5[:pdtpst].between_time('05:00', '05:35').groupby(lambda idx: idx.dayofyear)
cycle2a = run5[:pdtpst].between_time('11:00', '11:35').groupby(lambda idx: idx.dayofyear)
cycle3a = run5[:pdtpst].between_time('17:00', '17:35').groupby(lambda idx: idx.dayofyear)
cycle4a = run5[:pdtpst].between_time('23:00', '23:35').groupby(lambda idx: idx.dayofyear)
cycle1b = run5[pdtpst:].between_time('06:00', '06:35').groupby(lambda idx: idx.dayofyear)
cycle2b = run5[pdtpst:].between_time('12:00', '12:35').groupby(lambda idx: idx.dayofyear)
cycle3b = run5[pdtpst:].between_time('18:00', '18:35').groupby(lambda idx: idx.dayofyear)
cycle4b = run5[pdtpst:].between_time('00:00', '00:35').groupby(lambda idx: idx.dayofyear)
nrows, ncols = 4, 2
fig = plt.figure(figsize=(6*ncols,3*nrows), constrained_layout=True)
fig.suptitle('Run 5 drain cycle flagging (pink=active, blue=recovery)')
fig.supylabel('LI-COR carbon dioxide data, normalized to 0-1')
fig.supxlabel('Pacific Standard Time (UTC-0800)')
xmajor = 5 # minutes
xminor = 1 # minutes
ax1a = fig.add_subplot(nrows, ncols, 1)
ax1b = fig.add_subplot(nrows, ncols, 2)
ax2a = fig.add_subplot(nrows, ncols, 3)
ax2b = fig.add_subplot(nrows, ncols, 4)
ax3a = fig.add_subplot(nrows, ncols, 5)
ax3b = fig.add_subplot(nrows, ncols, 6)
ax4a = fig.add_subplot(nrows, ncols, 7)
ax4b = fig.add_subplot(nrows, ncols, 8)
for doy, grp in cycle1a:
grp.index -= grp.index.floor('D')
data = grp[colname] / max(grp[colname])
ax1a.plot(data.index, data)
ax1a.set_title('5:10-5:16 (before 13:00 PST Oct 26th)')
ax1a.set_xlim(min(data.index).value, max(data.index).value)
ax1a.yaxis.grid(which='major')
ax1a.xaxis.grid(which='both')
ax1a.xaxis.set_major_locator(ticker.MultipleLocator(1e9*60*xmajor))
ax1a.xaxis.set_minor_locator(ticker.MultipleLocator(1e9*60*xminor))
ax1a.xaxis.set_major_formatter(lambda x,p: pd.Timestamp(x).strftime('%H:%M'))
ax1a.axvspan(pd.Timedelta('05:10:00').value, # HINT requires hh:mm:ss format
pd.Timedelta('05:16:00').value,
alpha=0.5,
color='pink')
ax1a.axvspan(pd.Timedelta('05:16:00').value,
pd.Timedelta('05:22:00').value,
alpha=0.8,
color='skyblue')
for doy, grp in cycle1b:
grp.index -= grp.index.floor('D')
data = grp[colname] / max(grp[colname])
ax1b.plot(data.index, data)
ax1b.set_title('6:10-6:16 (after 13:00 PST Oct 26th)')
ax1b.set_xlim(min(data.index).value, max(data.index).value)
ax1b.yaxis.grid(which='major')
ax1b.xaxis.grid(which='both')
ax1b.xaxis.set_major_locator(ticker.MultipleLocator(1e9*60*xmajor))
ax1b.xaxis.set_minor_locator(ticker.MultipleLocator(1e9*60*xminor))
ax1b.xaxis.set_major_formatter(lambda x,p: pd.Timestamp(x).strftime('%H:%M'))
ax1b.axvspan(pd.Timedelta('06:10:00').value, # HINT requires hh:mm:ss format
pd.Timedelta('06:16:00').value,
alpha=0.5,
color='pink')
ax1b.axvspan(pd.Timedelta('06:16:00').value,
pd.Timedelta('06:22:00').value,
alpha=0.8,
color='skyblue')
for doy, grp in cycle2a:
grp.index -= grp.index.floor('D')
data = grp[colname] / max(grp[colname])
ax2a.plot(data.index, data)
ax2a.set_title('11:10-11:16 (before 13:00 PST Oct 26th)')
ax2a.set_xlim(min(data.index).value, max(data.index).value)
ax2a.yaxis.grid(which='major')
ax2a.xaxis.grid(which='both')
ax2a.xaxis.set_major_locator(ticker.MultipleLocator(1e9*60*xmajor))
ax2a.xaxis.set_minor_locator(ticker.MultipleLocator(1e9*60*xminor))
ax2a.xaxis.set_major_formatter(lambda x,p: pd.Timestamp(x).strftime('%H:%M'))
ax2a.axvspan(pd.Timedelta('11:10:00').value, # HINT requires hh:mm:ss format
pd.Timedelta('11:16:00').value,
alpha=0.5,
color='pink')
ax2a.axvspan(pd.Timedelta('11:16:00').value,
pd.Timedelta('11:22:00').value,
alpha=0.8,
color='skyblue')
#### XXXX this one doesn't plot correctly ###############
for doy, grp in cycle2b:
grp.index -= grp.index.floor('D')
data = grp[colname] / max(grp[colname])
ax2b.plot(data.index, data)
ax2b.set_title('12:10-12:16 (after 13:00 PST Oct 26th)')
ax2b.set_xlim(min(data.index).value, max(data.index).value)
ax2b.yaxis.grid(which='major')
ax2b.xaxis.grid(which='both')
ax2b.xaxis.set_major_locator(ticker.MultipleLocator(1e9*60*xmajor))
ax2b.xaxis.set_minor_locator(ticker.MultipleLocator(1e9*60*xminor))
ax2b.xaxis.set_major_formatter(lambda x,p: pd.Timestamp(x).strftime('%H:%M'))
ax2b.axvspan(pd.Timedelta('12:10:00').value, # HINT requires hh:mm:ss format
pd.Timedelta('12:16:00').value,
alpha=0.5,
color='pink')
ax2b.axvspan(pd.Timedelta('12:16:00').value,
pd.Timedelta('12:22:00').value,
alpha=0.8,
color='skyblue')
##########################################################
for doy, grp in cycle3a:
grp.index -= grp.index.floor('D')
data = grp[colname] / max(grp[colname])
ax3a.plot(data.index, data)
ax3a.set_title('17:10-17:16 (before 13:00 PST Oct 26th)')
ax3a.set_xlim(min(data.index).value, max(data.index).value)
ax3a.yaxis.grid(which='major')
ax3a.xaxis.grid(which='both')
ax3a.xaxis.set_major_locator(ticker.MultipleLocator(1e9*60*xmajor))
ax3a.xaxis.set_minor_locator(ticker.MultipleLocator(1e9*60*xminor))
ax3a.xaxis.set_major_formatter(lambda x,p: pd.Timestamp(x).strftime('%H:%M'))
ax3a.axvspan(pd.Timedelta('17:10:00').value, # HINT requires hh:mm:ss format
pd.Timedelta('17:16:00').value,
alpha=0.5,
color='pink')
ax3a.axvspan(pd.Timedelta('17:16:00').value,
pd.Timedelta('17:22:00').value,
alpha=0.8,
color='skyblue')
for doy, grp in cycle3b:
grp.index -= grp.index.floor('D')
data = grp[colname] / max(grp[colname])
ax3b.plot(data.index, data)
ax3b.set_title('18:10-18:16 (after 13:00 PST Oct 26th)')
ax3b.set_xlim(min(data.index).value, max(data.index).value)
ax3b.yaxis.grid(which='major')
ax3b.xaxis.grid(which='both')
ax3b.xaxis.set_major_locator(ticker.MultipleLocator(1e9*60*xmajor))
ax3b.xaxis.set_minor_locator(ticker.MultipleLocator(1e9*60*xminor))
ax3b.xaxis.set_major_formatter(lambda x,p: pd.Timestamp(x).strftime('%H:%M'))
ax3b.axvspan(pd.Timedelta('18:10:00').value, # HINT requires hh:mm:ss format
pd.Timedelta('18:16:00').value,
alpha=0.5,
color='pink')
ax3b.axvspan(pd.Timedelta('18:16:00').value,
pd.Timedelta('18:22:00').value,
alpha=0.8,
color='skyblue')
for doy, grp in cycle4a:
grp.index -= grp.index.floor('D')
data = grp[colname] / max(grp[colname])
ax4a.plot(data.index, data)
ax4a.set_title('23:10-23:16 (before 13:00 PST Oct 26th)')
ax4a.set_xlim(min(data.index).value, max(data.index).value)
ax4a.yaxis.grid(which='major')
ax4a.xaxis.grid(which='both')
ax4a.xaxis.set_major_locator(ticker.MultipleLocator(1e9*60*xmajor))
ax4a.xaxis.set_minor_locator(ticker.MultipleLocator(1e9*60*xminor))
ax4a.xaxis.set_major_formatter(lambda x,p: pd.Timestamp(x).strftime('%H:%M'))
ax4a.axvspan(pd.Timedelta('23:10:00').value, # HINT requires hh:mm:ss format
pd.Timedelta('23:16:00').value,
alpha=0.5,
color='pink')
ax4a.axvspan(pd.Timedelta('23:16:00').value,
pd.Timedelta('23:22:00').value,
alpha=0.8,
color='skyblue')
for doy, grp in cycle4b:
grp.index -= grp.index.floor('D')
data = grp[colname] / max(grp[colname])
ax4b.plot(data.index, data)
ax4b.set_title('00:10-0:16 (after 13:00 PST Oct 26th)')
ax4b.set_xlim(min(data.index).value, max(data.index).value)
ax4b.yaxis.grid(which='major')
ax4b.xaxis.grid(which='both')
ax4b.xaxis.set_major_locator(ticker.MultipleLocator(1e9*60*xmajor))
ax4b.xaxis.set_minor_locator(ticker.MultipleLocator(1e9*60*xminor))
ax4b.xaxis.set_major_formatter(lambda x,p: pd.Timestamp(x).strftime('%H:%M'))
ax4b.axvspan(pd.Timedelta('00:10:00').value, # HINT requires hh:mm:ss format
pd.Timedelta('00:16:00').value,
alpha=0.5,
color='pink')
ax4b.axvspan(pd.Timedelta('00:16:00').value,
pd.Timedelta('00:22:00').value,
alpha=0.8,
color='skyblue')
I'm constrained by environment unfortunately and still use older versions (e.g. matplotlib 3.4.1, pandas 1.2.4, numpy 1.20.2). Perhaps there was an issue that got resolved and I should be updating?
In [25]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.9.final.0
python-bits : 64
OS : Windows
OS-release : 7
Version : 6.1.7601
machine : AMD64
processor : Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : English_United States.1252
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 23.0
setuptools : 56.0.0
Cython : 0.29.23
pytest : 6.2.3
hypothesis : 6.9.2
sphinx : 3.5.4
blosc : 1.10.2
feather : 0.4.1
xlsxwriter : 1.3.9
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: 0.9.0
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.7
fastparquet : 0.5.0
gcsfs : None
matplotlib : 3.4.1
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.4.8
tables : 3.6.1
tabulate : None
xarray : 0.17.0
xlrd : None
xlwt : None
numba : 0.53.1

Answer 1

Oops- there's no clear reason why it only affects subplot (2,2) but something about repeatedly calling axis formatting functions causes this to occur.
Moving formatting calls outside the for..loop resolved the problem:
for doy, grp in cycle2b:
grp.index -= grp.index.floor('D')
data = grp[colname] / max(grp[colname])
ax2b.plot(data.index, data)
ax2b.set_title('12:10-12:16 (after 13:00 PST Oct 26th)')
ax2b.set_xlim(min(data.index).value, max(data.index).value)
ax2b.yaxis.grid(which='major')
ax2b.xaxis.grid(which='both')
ax2b.xaxis.set_major_locator(ticker.MultipleLocator(1e9*60*xmajor))
ax2b.xaxis.set_minor_locator(ticker.MultipleLocator(1e9*60*xminor))
ax2b.xaxis.set_major_formatter(lambda x,p: pd.Timestamp(x).strftime('%H:%M'))
ax2b.axvspan(pd.Timedelta('12:10:00').value, # HINT requires hh:mm:ss format
pd.Timedelta('12:16:00').value,
alpha=0.5,
color='pink')
ax2b.axvspan(pd.Timedelta('12:16:00').value,
pd.Timedelta('12:22:00').value,
alpha=0.8,
color='skyblue')