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Author: Joanna Funkhouser
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Description: Jupyter notebook 341fa16/slr_intro_descriptive_stats/slr_attempt_import.ipynb
Compute Environment: Ubuntu 18.04 (Deprecated)
In [4]:
import csv
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f = open("slr_sla_gbl_free_txj1j2_90.csv") csv_f = csv.reader(f) #for row in csv_f: # print (row)
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#data = [[row[0], eval(row[12]), eval(row[26])] for row in csv_f] #print (data)
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import pandas as pd import numpy as np
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#df = pd.read_csv("slr_sla_gbl_free_txj1j2_90.csv", error_bad_lines=False) #df
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data = pd.read_csv('slr_sla_gbl_free_txj1j2_90.csv', skiprows=5, index_col = 0) #data
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data.ix[1993.0123:2015.8995, ['TOPEX/Poseidon']]
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data['TOPEX/Poseidon'].median()
-1.54
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data['TOPEX/Poseidon'].mean()
-1.281545454545453
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data['TOPEX/Poseidon'].std()
11.788058796093676
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data['TOPEX/Poseidon'].max() #6
24.120000000000001
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data['TOPEX/Poseidon'].min() #Level started negative and has risen slowly over the years
-26.77
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data['TOPEX/Poseidon'].count() #4
440
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data.ix[945:947, ['Jason-1']]
Jason-1
945 39.96
946 NaN
947 43.47
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data['Jason-1'].dropna()
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data['Jason-1'].median()
21.3
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data['Jason-1'].mean()
21.87150485436892
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data['Jason-1'].std()
9.17064857558141
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data['Jason-1'].max() #6
43.840000000000003
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data['Jason-1'].min() #slow increase in values
-3.3700000000000001
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data['Jason-1'].count() #4
412
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data['Jason-2'].median()
34.32
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data['Jason-2'].mean()
34.92374531835205
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data['Jason-2'].std()
8.815705537014296
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data['Jason-2'].max() #6
55.890000000000001
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data['Jason-2'].min() #Started around 20, slowly over years increased to 50s
19.969999999999999
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data['Jason-2'].count() #4
267
In [5]:
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1062 entries, 0 to 1061 Data columns (total 4 columns): year 1062 non-null float64 TOPEX/Poseidon 440 non-null float64 Jason-1 412 non-null float64 Jason-2 267 non-null float64 dtypes: float64(4) memory usage: 33.3 KB

1)Purpose of measurements, why millions were spent on them:

TOPEX/Poseidon:

Mapping ocean surface topography, looking at sea level

"TOPEX/Poseidon' radar altimeter provided the first continuous global coverage of the surface topography of the oceans."

"TOPEX/Poseidon provided measurements of the surface height of 95 percent of the ice-free ocean to an accuracy of 3.3 centimeters."

Ocean circulation patterns, and climate can also be better understood by this.

Jason-1:

Continuation of researching ocean topography...So is Jason-2

  1. Methods

Altimeter= "An altimeter or an altitude meter is an instrument used to measure the altitude of an object above a fixed level."

Uses radar to get distance

In [3]:
data.describe()
/projects/anaconda3/lib/python3.5/site-packages/numpy/lib/function_base.py:3834: RuntimeWarning: Invalid value encountered in percentile RuntimeWarning)
TOPEX/Poseidon Jason-1 Jason-2
count 440.000000 412.000000 267.000000
mean -1.281545 21.871505 34.923745
std 11.788059 9.170649 8.815706
min -26.770000 -3.370000 19.970000
25% NaN NaN NaN
50% NaN NaN NaN
75% NaN NaN NaN
max 24.120000 43.840000 55.890000
In [8]:
jason1 = data['Jason-1'].dropna() #jason1
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j1meansum = 0.0; j1count = 0;
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print (j1meansum)
0.0
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j1meansum = 0.0; j1count = 0; for x in jason1: j1meansum += x j1count +=1 print(j1meansum) print(j1count)
9011.06 412
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j1mean = j1meansum / j1count print (j1mean)
21.8715048544
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j1sum = 0.0; for x in jason1: j1sum = float(j1sum) + float((x-j1mean)**2) print (j1sum)
34565.42686699028
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j1std = float(np.sqrt(j1sum/j1count)) j1std
9.159512386196605
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jason1 = data['Jason-1'].dropna() maxlist = -1000 minlist = 1000000 j1count = 0 #orderlist = [] for x in jason1: if (x > maxlist): maxlist = x #orderlist.append(x) if (x < minlist): minlist = x #orderlist.insert(0, x) x += 1 print (maxlist) print (minlist) #print (orderlist)
43.84 -3.37
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orderlist = [] for x in jason1: if (orderlist == []): orderlist.append(x) else: for n in orderlist: if (x >= orderlist[n]): orderlist.insert(n, x) print (orderlist)
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-21-db5cf303f9c7> in <module>() 5 else: 6 for n in orderlist: ----> 7 if (x >= orderlist[n]): 8 orderlist.insert(n, x) 9 print (orderlist) TypeError: list indices must be integers or slices, not numpy.float64
In [15]:
data['Jason-1'][2015.8995]
nan
In [18]:
jason1.head()
year 2003.3300 -3.37 2002.0537 2.42 2002.4340 3.85 2002.4611 5.05 2002.1898 5.32 Name: Jason-1, dtype: float64

orderlist = jason1.sort() print(orderlist)

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orderlist = d
In [83]:
def Jomeanstd(name): "return standard deviation and mean of column 'name'" numbers = data[name].dropna() ##getting rid of naN meansum = 0.0; countlist = 0; sumlist = 0.0; for x in numbers: ## counting how many are in list and adding up all values meansum += x countlist += 1 meanlist = meansum/countlist ##gives the mean of the list #print (name, " mean:", meanlist) for x in numbers: ###adding up the sum of the squares of x_1-mean sumlist = float(sumlist) + float((x-meanlist)**2) stdlist = float(np.sqrt(sumlist/countlist)) print (name, " mean:", meanlist) print (name, " std:", stdlist) return (meanlist, stdlist)
In [86]:
ToPomean, ToPostd = Jomeanstd('TOPEX/Poseidon') J1mean, J1std = Jomeanstd('Jason-1') J2mean, J2std = Jomeanstd('Jason-2')
TOPEX/Poseidon mean: -1.28154545455 TOPEX/Poseidon std: 11.774655654981524 Jason-1 mean: 21.8715048544 Jason-1 std: 9.159512386196605 Jason-2 mean: 34.9237453184 Jason-2 std: 8.799181238443293
In [87]:
J1mean*2
43.743009708737837
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  1. Units are mm and years?
  1. Data is arranged in a csv, with commas being the delimiter separating the values. There is also a rather large header.
  1. To make a clear concise form, put the data into a table, probably using pandas
  1. What is the standard?
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