This page was generated from /home/docs/checkouts/readthedocs.org/user_builds/blm/checkouts/latest/docs/tutorials/tutorial-3-Plotting albedo values in one plot.ipynb. Interactive online version: Slideshow:

4.3. Plotting albedo values in one plot

Here you can learn how to plot the albedo of two different days in one plot

First, some packages:

# These packages are necessary later on. Load all the packages in one place for consistency
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pathlib import Path
import datetime

Let’s load the data and calculate albedos for the all data

#The path of the directory where all AMF data are
path_dir = Path.cwd()/'data'/'1'
name_of_site = 'CA-Obs_clean.csv.gz'
path_data = path_dir/name_of_site
path_data.resolve()
df_data = pd.read_csv(path_data, index_col='time',parse_dates=['time'])
ser_alb=df_data['SWOUT']/df_data['SWIN']

Let’s plot the albedo of one day

date1 = '2001 10 27'
ser_alb[ser_alb.between(0,1)&(df_data['SWIN']>5)].loc[date1].plot(marker='o')

<matplotlib.axes._subplots.AxesSubplot at 0x114ed4748>

And for another day

date2 = '2001 11 21'
ser_alb[ser_alb.between(0,1)&(df_data['SWIN']>5)].loc[date2].plot(marker='o')

<matplotlib.axes._subplots.AxesSubplot at 0x113672dd8>

Let’s first try to put the albedo of both days in one plot using previous methods

date1 = '2001 10 21'
ser_alb[ser_alb.between(0,1)&(df_data['SWIN']>5)].loc[date1].plot(marker='o')
date2 = '2001 11 21'
ser_alb[ser_alb.between(0,1)&(df_data['SWIN']>5)].loc[date2].plot(marker='o')

<matplotlib.axes._subplots.AxesSubplot at 0x11394f438>

You see that since the days are different, we do not get a good plot.

In order to fix this, we need to find the time of the day each data point is associated with. For example, for date1, we can find it with using the time level of the index as Dataframe.index.time :

ser_alb[ser_alb.between(0,1)&(df_data['SWIN']>5)].loc[date1].index.time

array([datetime.time(8, 30), datetime.time(9, 0), datetime.time(9, 30),
       datetime.time(10, 0), datetime.time(10, 30), datetime.time(11, 0),
       datetime.time(11, 30), datetime.time(12, 0), datetime.time(12, 30),
       datetime.time(13, 0), datetime.time(13, 30), datetime.time(14, 0),
       datetime.time(14, 30), datetime.time(15, 0), datetime.time(15, 30),
       datetime.time(16, 0), datetime.time(16, 30), datetime.time(17, 0),
       datetime.time(17, 30)], dtype=object)

Now let’s get this for both dates

X1=ser_alb[ser_alb.between(0,1)&(df_data['SWIN']>5)].loc[date1].index.time
X2=ser_alb[ser_alb.between(0,1)&(df_data['SWIN']>5)].loc[date2].index.time
Y1=ser_alb[ser_alb.between(0,1)&(df_data['SWIN']>5)].loc[date1]
Y2=ser_alb[ser_alb.between(0,1)&(df_data['SWIN']>5)].loc[date2]

Let’s try to plot them then in one figure

plt.plot(X1,Y1,'r',label='date 1')
plt.plot(X2,Y2,'b',label='date 2')
plt.legend()
plt.ylabel('albedo')

Text(0, 0.5, 'albedo')

Much better! but still the xticks are a little bit messy. We can use the follwoing to fix this issue

plt.plot(X1,Y1,'r',label='date 1')
plt.plot(X2,Y2,'b',label='date 2')
plt.legend()
plt.ylabel('albedo')
plt.xticks([datetime.time(x, 0) for x in [8,12,16,20]])

([<matplotlib.axis.XTick at 0x11430ba58>,
  <matplotlib.axis.XTick at 0x114383198>,
  <matplotlib.axis.XTick at 0x11440a7f0>,
  <matplotlib.axis.XTick at 0x114415630>],
 <a list of 4 Text xticklabel objects>)