zmoon
commented
1 day ago Hi @DCAIRMAN thanks for this report. I just tried your AQS add-data case and was not able to reproduce.
I did this:
import monetio as mio
import pandas as pd
dates = pd.date_range(start='2004-01-01', end='2004-12-31')
df = mio.aqs.add_data(dates, daily=True, param=['VOC','OZONE'], download=False)'event_type' is okay:
>>> df.event_type.value_counts()
None 1505837
Included 21200
Excluded 4
Name: event_type, dtype: int64Note that df here is in the wide format already so long_to_wide(df) is not needed (and would fail). Sorry that the examples are not super up-to-date.
Did you install the current version monetio from conda-forge? If so, could you try running again? Sometimes this "Metadata mismatch found in from_delayed" error occurs when you try to access the data while changes are being made on the server (though I don't know what they would be changing in 2004 data).
I'm new to the Monetio package, so I'm working through the practices examples on the ARL website. When I run this code (see below), I get this error message:
import numpy as np import pandas as pd import monetio as mio ##observations for MONET import matplotlib.pyplot as plt import seaborn as sns ##using this for better color palettes import cartopy.crs as ccrs ##map projections import cartopy.feature as cfeature ##poltical and geographic features
sns.set_style("ticks") ##plot configuration sns.set_context("talk") ##plot configure for size of text
dates = pd.date_range(start='2004-01-01',end='2004-12-31') df = mio.aqs.add_data(dates,daily=True,param=['VOC','OZONE'], download=True)
from numpy import sort
for i in sort(df.columns): print(i)
from monet.util import tools
new = tools.long_to_wide(df) new.head()
new.dropna(subset=["ISOPRENE"]).siteid.unique().shape
for i in sort(new.columns): print(i) new.describe()
new.index = new.time new["OZONE_ppb"] = new.OZONE * 1000 print(new.OZONE_ppb.mean())
f, ax = plt.subplots(figsize=(10, 4)) new.ISOPRENE.resample("D").mean().plot(ax=ax) plt.show()
ValueError: Metadata mismatch found in
from_delayed.Partition type:
pandas.core.frame.DataFrame+--------------+--------+----------+ | Column | Found | Expected | +--------------+--------+----------+ | 'event_type' | object | float64 | +--------------+--------+----------+Any insights