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Diffstat (limited to 'python/atms-310/homework/hw3/homework3.py')
| -rwxr-xr-x | python/atms-310/homework/hw3/homework3.py | 53 |
1 files changed, 53 insertions, 0 deletions
diff --git a/python/atms-310/homework/hw3/homework3.py b/python/atms-310/homework/hw3/homework3.py new file mode 100755 index 0000000..31db6de --- /dev/null +++ b/python/atms-310/homework/hw3/homework3.py @@ -0,0 +1,53 @@ +import numpy as np +import scipy.stats as s +import glob as glob # glob +import pandas as pd + +filelist = glob.glob("hw3_*.txt") +filelist.sort() + +def read(file): + fileobj = open(file, "r") + outputstr = fileobj.readlines() + fileobj.close() + outputarray = np.zeros(len(outputstr)) + for i in np.arange(len(outputstr)): + outputarray[i] = float(outputstr[i]) + return outputarray + +# parameters = ["mean", "median", "std", "iqr", "skew", "kurtosis"] + +# for i in range(len(filelist)): +# print(filelist[i]) +# for n, param in enumerate(parameters): +# np_function = getattr(np, param) +# result = np_function(read(filelist[n])) +# print(result) + +for n in range(len(filelist)): + print(filelist[n]) + mean = np.mean(read(filelist[n])) + print("mean: " + str(mean)) + median = np.median(read(filelist[n])) + print("median: " + str(median)) + stddev = np.std(read(filelist[n])) + print("stddev: " + str(stddev)) + iqr = s.iqr(read(filelist[n])) + print("iqr: " + str(iqr)) + skew = s.skew(read(filelist[n])) + print("skew: " + str(skew)) + kurtosis = s.kurtosis(read(filelist[n])) + print("kurtosis: " + str(kurtosis)+"\n") + +# the mean and median are similar for all files, indicating solid, outlier free data. +# standard deviation is quite high for everything except wind shear, indicating either \ +# inconsistent readings for everything but wind shear, or more likely, smaller units and \ +# higher rates of change. +# the difference between shr1's iqr and stddev is larger than that of shr2's (shr2's is \ +# quite close to its stddev), possibility of one minor outlier +# none of the data is very skewed, the largest (absolute value) being 0.54896, and \ +# all of the data has negative kurtosis, meaning when distibuted, it will have a shallower \ +# peak than the bell curve (e^x^2) +# the february and may datasets are similar in that their wind shears are similar, though \ +# mays is still larger. they are different in that mays SRH and CAPE are both much higher, \ +# so mays tornadoes are much stronger. |
