55 lines
1.7 KiB
Python
55 lines
1.7 KiB
Python
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import scipy.optimize
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import numpy as np
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from defusedxml import ElementTree
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from collections import deque
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import matplotlib.pyplot as plt
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from scipy.optimize import curve_fit
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fit_f1 = lambda x, K, alpha: K * np.exp(alpha * x)
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fit_f2 = lambda x, a, b: a*x + b
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data = {"Germany": deque()
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, "France": deque()
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, "Italy": deque()
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# , "United States": deque()
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# , "Angola": deque()
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# , "China": deque()
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}
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with open("data/API_NY.GDP.MKTP.KN_DS2_en_xml_v2_10230884.xml") as fin:
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tree = ElementTree.parse(fin)
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for record in tree.getroot().find("data").findall("record"):
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this_data = {field.get("name"): field.text for field in record.findall("field")}
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if(this_data["Country or Area"] in data):
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if(this_data["Value"] != None):
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data[this_data["Country or Area"]].append((this_data["Year"], this_data["Value"]))
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class Data(object):
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def __init__(self, raw_data):
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self.x = np.array([int(k) for k, v in raw_data])
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self.y = np.array([float(v) for k, v in raw_data])
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plots = deque()
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for country, values in data.items():
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values = Data(values)
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popt1, pcov = curve_fit(fit_f1, values.x, values.y
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, p0=[values.y[0], 1])
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popt2, pcov = curve_fit(fit_f2, values.x, values.y
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, p0=[values.y[0], (values.y[-1] - values.y[0])/(values.x[-1] - values.x[0])])
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f1 = lambda x: fit_f1(x, popt1[0], popt1[1])
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f2 = lambda x: fit_f2(x, popt2[0], popt2[1])
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p1, = plt.plot(values.x, values.y, label="{}: real".format(country))
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p2, = plt.plot(values.x, f1(values.x), label="%s: exponential fit, K=%.3e, $\\alpha$=%.3e" % (country, *popt1))
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p3, = plt.plot(values.x, f2(values.x), label="%s: linear fit, a=%.3e, b=%.3e" % (country, *popt2))
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plots.extend([p1, p2, p3])
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plt.legend(handles=list(plots))
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plt.show()
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