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Adds a rank size plot to inequality #518

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Dec 8, 2019
Merged

Adds a rank size plot to inequality #518

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ac97342
added rank size plot to inequality
jstac Dec 6, 2019
64a16f1
fix pep 8 complaints
mmcky Dec 8, 2019
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9 changes: 6 additions & 3 deletions quantecon/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,8 @@
import numba
except:
raise ImportError(
"Cannot import numba from current anaconda distribution. Please run `conda install numba` to install the latest version.")
"Cannot import numba from current anaconda distribution. \
Please run `conda install numba` to install the latest version.")

#-Modules-#
from . import distributions
Expand All @@ -25,7 +26,8 @@
# from .game_theory import <objects-here> #Place Holder if we wish to promote any general objects to the qe namespace.
from .graph_tools import DiGraph, random_tournament_graph
from .gridtools import cartesian, mlinspace, simplex_grid, simplex_index
from .inequality import lorenz_curve, gini_coefficient, shorrocks_index
from .inequality import lorenz_curve, gini_coefficient, shorrocks_index, \
rank_size_plot
from .kalman import Kalman
from .lae import LAE
from .arma import ARMA
Expand All @@ -37,7 +39,8 @@
from .quadsums import var_quadratic_sum, m_quadratic_sum
#->Propose Delete From Top Level
#Promote to keep current examples working
from .markov import MarkovChain, random_markov_chain, random_stochastic_matrix, gth_solve, tauchen, rouwenhorst
from .markov import MarkovChain, random_markov_chain, random_stochastic_matrix, \
gth_solve, tauchen, rouwenhorst
#Imports that Should be Deprecated with markov package
from .markov import mc_compute_stationary, mc_sample_path
#<-
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37 changes: 37 additions & 0 deletions quantecon/inequality.py
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Expand Up @@ -117,3 +117,40 @@ def shorrocks_index(A):
diag_sum = np.diag(A).sum()

return (m - diag_sum) / (m - 1)


def rank_size_plot(data, ax, label=None, c=1.0):
"""
Generate rank-size data corresponding to distribution data.

Examples
--------

> import numpy as np
> import matplotlib.pyplot as plt
> y = np.exp(np.random.randn(1000)) # simulate data
> fig, ax = plt.subplots()
> rank_size_plot(y, ax)
> plt.show()

Parameters
----------

data : array_like
the set of observations
c : int or float
restrict plot to top (c x 100)% of the distribution
ax : axis object
for plotting on, has method ax.loglog
"""
w = - np.sort(- data) # Reverse sort
w = w[:int(len(w) * c)] # extract top c%
rank_data = np.arange(len(w)) + 1
size_data = w
ax.loglog(rank_data, size_data, 'o', markersize=3.0, alpha=0.5, label=label)
if label:
ax.legend()
ax.set_xlabel("log rank")
ax.set_ylabel("log size")