<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 Using Daymet data from ORNL webservice
Daymet is a a daily gridded weather dataset distributed through the ORNL DAAC
https
Step1:  Which gives us a nice data frame with weather data for the Oak Ridge National Lab
Step2:  Which we can visualize using matplotlib and seaborn
Step3:  And ask questions such as
<ASSISTANT_TASK:>
Python Code:
import sys
sys.path.append(r"..")
import daymetpy
ornl_lat, ornl_long = 35.9313167, -84.3104124
df = daymetpy.daymet_timeseries(lon=ornl_long, lat=ornl_lat, start_year=2012, end_year=2013)
Explanation: Using Daymet data from ORNL webservice
Daymet is a a daily gridded weather dataset distributed through the ORNL DAAC
https://daymet.ornl.gov/
As part of their tools for obtaining data they have created a webservice that allows one to download a timeseries of temperature and precipitation for any location in the US.
Functions in Daymetpy allow for painless use of this service
DaymetPy package developed by Koen Hufkens 
https://bitbucket.org/khufkens/daymetpy
End of explanation
df.head()
Explanation: Which gives us a nice data frame with weather data for the Oak Ridge National Lab
End of explanation
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
fig, (ax1, ax2) = plt.subplots(2, figsize=(18, 10), sharex=True)
rolling3day = pd.rolling_mean(df, 15)
ax1.fill_between(rolling3day.index, rolling3day.tmin, rolling3day.tmax, alpha=0.5, lw=0)
ax1.plot(df.index, df[['tmax', 'tmin']].mean(axis=1), lw=2, alpha=0.5)
ax1.set_title('Daymet temp at ORNL', fontsize=20)
ax1.set_ylabel(u'Temp. (°C)', fontsize=20)
monthlysum = df.resample("M", how='sum')
ax2.bar(monthlysum.index, monthlysum.prcp, width=20,)
ax2.set_title('Daymet precip at ORNL', fontsize=20)
ax2.set_ylabel(u'Precip. (mm)', fontsize=20)
fig.tight_layout()
Explanation: Which we can visualize using matplotlib and seaborn
End of explanation
denver_loc = (-104.9903, 39.7392)
miami_loc = (-80.2089, 25.7753)
denver = daymetpy.daymet_timeseries(lon=denver_loc[0], lat=denver_loc[1], start_year=2012, end_year=2014)
miami = daymetpy.daymet_timeseries(lon=miami_loc[0], lat=miami_loc[1], start_year=2012, end_year=2014)
fig, ax1 = plt.subplots(1, figsize=(18, 10))
rolling3day = pd.rolling_mean(denver, 15)
ax1.fill_between(rolling3day.index, rolling3day.tmin, rolling3day.tmax, 
                 alpha=0.4, lw=0, label='Denver', color=sns.xkcd_palette(['faded green'])[0])
ax1.set_title('Denver vs Miami temps (15 day mean)', fontsize=20)
rolling3day = pd.rolling_mean(miami, 15)
ax1.fill_between(rolling3day.index, rolling3day.tmin, rolling3day.tmax, 
                 alpha=0.4, lw=0, label='Miami', color=sns.xkcd_palette(['dusty purple'])[0])
ax1.set_ylabel(u'Temp. (°C)', fontsize=20)
fig.tight_layout()
plt.legend(fontsize=20)
Explanation: And ask questions such as: "What does the difference in temperature between Denver and Miami look like?"
End of explanation
<END_TASK>