// SOLUTIONS
The file datafile_1.csv contains a three-dimensional dataset and associated uncertainty in the data. Read the data file into numpy arrays and visualize it using two new types of plots:
Hints:
skiprows, comments, and unpack do?ax.view_init(elev=ANGLE1,azim=ANGLE2), where ANGLE1 and ANGLE2 are in degrees.
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# put your code here, and add additional cells as necessary.
%matplotlib inline
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
alldata = np.loadtxt('datafile_1.csv',comments='#',unpack=True,delimiter=',')
xval = alldata[0]
xerr = alldata[1]
yval = alldata[2]
yerr = alldata[3]
zval = alldata[4]
zerr = alldata[5]
plt.errorbar(xval,yval,xerr=xerr,yerr=yerr,fmt='ro',ecolor='b')
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plt.errorbar(xval,zval,xerr=xerr,yerr=zerr,fmt='ro',ecolor='b')
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plt.errorbar(yval,zval,xerr=yerr,yerr=zerr,fmt='ro',ecolor='b')
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fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot(xval, yval, zval,'bo')
ax.view_init(elev=30., azim=20)
Now, we're going to experiment with data exploration. You have two data files to examine:
GLB.Ts.csv, which contains mean global air temperature from 1880 through the present day (retrieved from the NASA GISS surface temperature website, "Global-mean monthly, seasonal, and annual means, 1880-present"). Each row in the data file contains the year, monthly global average, yearly global average, and seasonal global average. See this file for clues as to what the columns mean.bintanja2008.txt, which is a reconstruction of the global surface temperature, deep-sea temperature, ice volume, and relative sea level for the last 3 million years. This data comes from the National Oceanic and Atmospheric Administration's National Climatic Data Center website, and can be found here.Some important notes:
GLB.Ts.csv, the temperature units are in 0.01 degrees Celsius difference from the reference period 1950-1980 - in other words, the number 40 corresponds to a difference of +0.4 degrees C compared to the average temperature between 1950 and 1980. (This means you'll have to renormalize your values by a factor of 100.)bintanja2008.txt, column 9, "Global sea level relative to present," is in confusing units - more positive values actually correspond to lower sea levels than less positive values. You may want to multiply column 9 by -1 in order to get more sensible values.There are many possible ways to examine this data. First, read both data files into numpy arrays - it's fine to load them into a single combined multi-dimensional array if you want, or split the data into multiple arrays. We'll then try a few things:
GLB.Ts.csv (say, the average temperature smoothed over 10 years), how might you characterize the yearly variability? Try plotting the smoothed value along with the raw data and show how they differ.bintanja2008.txt - try plotting multiple variables as a function of time together using the pyplot subplot functionality (and some more complicated subplot examples for further help). Do they seem to be related in some way? (Hint: plot surface temperature, deep sea temperature, ice volume, and sea level, and zoom in from 3 Myr to ~100,000 years)bintanja2008.txt versus each other (i.e., surface temperature vs. ice volume or sea level) - do you see correlations?
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# put your code here, and add additional cells as necessary.
# column 0 is years, column 13 is yearly average
global_temp = np.loadtxt('GLB.Ts.csv',skiprows=1,unpack=True,delimiter=',')
# plot the monthly data so we can get some sense of range.
for i in range(1,13):
plt.plot(global_temp[0],global_temp[i]/100.0,'r-')
# plot yearly data over this
plt.plot(global_temp[0],global_temp[13]/100.0,'b.')
# this is going to confuse people; encourage them to google it.
# http://docs.scipy.org/doc/numpy-1.10.1/reference/generated/numpy.nanmin.html
print("min, max, mean: {:3f} {:3f} {:3f}".format(np.nanmin(global_temp[13])/100.0,
np.nanmax(global_temp[13])/100.0,
np.nanmean(global_temp[13])/100.0))
smoothed = np.zeros_like(global_temp[13])
for i in range(global_temp[13].size):
start = i - 10
end = i + 10
if start < 0:
start = 0
if end > global_temp[13].size:
end = global_temp[13].size
smoothed[i] = global_temp[13][start:end].sum() / global_temp[13][start:end].size
plt.plot(global_temp[0],smoothed/100.0,'g-',linewidth=3)
# calculate standard deviation around the smoothed value
print(np.nanstd(global_temp[13]-smoothed)/100.0)
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global_temp_3myr = np.loadtxt('bintanja2008.txt',skiprows=110,unpack=True)
tbegin = 100
tend = 0
plt.subplot(4, 1, 1)
plt.plot(global_temp_3myr[0],global_temp_3myr[4],'k-')
plt.title('Data over 3 Myr')
plt.ylabel('Sfc Temperature [K]')
plt.xlabel('time (kyr)')
plt.xlim(tbegin,0)
plt.subplot(4, 1, 2)
plt.plot(global_temp_3myr[0],global_temp_3myr[3],'r-')
plt.ylabel('deep sea temperature [K]')
plt.xlabel('time (kyr)')
plt.xlim(tbegin,0)
plt.subplot(4, 1, 3)
plt.plot(global_temp_3myr[0],global_temp_3myr[7],'g-')
plt.ylabel('ice volume [m]')
plt.xlabel('time (kyr)')
plt.xlim(tbegin,0)
plt.subplot(4, 1, 4)
plt.plot(global_temp_3myr[0],-global_temp_3myr[8],'b-')
plt.ylabel('sea level [m]')
plt.xlabel('time (kyr)')
plt.xlim(tbegin,0)
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plt.plot(global_temp_3myr[7],-global_temp_3myr[8],'b.')
plt.xlabel('ice thickness [m]')
plt.ylabel('sea level [m]')
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plt.plot(global_temp_3myr[4],-global_temp_3myr[8],'r.')
plt.xlabel('temperature [K]')
plt.ylabel('sea level [m]')
In the cell below, describe some of the conclusions that you've drawn from the data you have just explored!
// put your thoughts here.
// put your answers here!