54 lines
2.0 KiB
Python
54 lines
2.0 KiB
Python
import numpy as np
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import scipy.stats as s
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import glob as glob # glob
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import pandas as pd
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filelist = glob.glob("hw3_*.txt")
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filelist.sort()
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def read(file):
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fileobj = open(file, "r")
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outputstr = fileobj.readlines()
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fileobj.close()
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outputarray = np.zeros(len(outputstr))
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for i in np.arange(len(outputstr)):
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outputarray[i] = float(outputstr[i])
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return outputarray
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# parameters = ["mean", "median", "std", "iqr", "skew", "kurtosis"]
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# for i in range(len(filelist)):
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# print(filelist[i])
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# for n, param in enumerate(parameters):
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# np_function = getattr(np, param)
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# result = np_function(read(filelist[n]))
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# print(result)
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for n in range(len(filelist)):
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print(filelist[n])
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mean = np.mean(read(filelist[n]))
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print("mean: " + str(mean))
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median = np.median(read(filelist[n]))
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print("median: " + str(median))
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stddev = np.std(read(filelist[n]))
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print("stddev: " + str(stddev))
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iqr = s.iqr(read(filelist[n]))
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print("iqr: " + str(iqr))
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skew = s.skew(read(filelist[n]))
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print("skew: " + str(skew))
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kurtosis = s.kurtosis(read(filelist[n]))
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print("kurtosis: " + str(kurtosis)+"\n")
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# the mean and median are similar for all files, indicating solid, outlier free data.
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# standard deviation is quite high for everything except wind shear, indicating either \
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# inconsistent readings for everything but wind shear, or more likely, smaller units and \
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# higher rates of change.
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# the difference between shr1's iqr and stddev is larger than that of shr2's (shr2's is \
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# quite close to its stddev), possibility of one minor outlier
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# none of the data is very skewed, the largest (absolute value) being 0.54896, and \
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# all of the data has negative kurtosis, meaning when distibuted, it will have a shallower \
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# peak than the bell curve (e^x^2)
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# the february and may datasets are similar in that their wind shears are similar, though \
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# mays is still larger. they are different in that mays SRH and CAPE are both much higher, \
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# so mays tornadoes are much stronger.
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