lib/vis.py
import matplotlib as mpl
import matplotlib.pyplot as plt
plt.switch_backend('Agg')
import numpy as np
from scipy.optimize import curve_fit
from sklearn.metrics import mean_squared_error
import config
import util
import igraph
from random import randint
import math
import matplotlib.pyplot as plt
import os
import lib.in_out.saver as saver
from numpy.random import normal
from scipy.optimize import curve_fit
from scipy import stats
from numpy import genfromtxt
import glob
def plot_data (data, output_directory, output_file_name):
x_data, y_data = (d for d in data)
x = np.array(x_data)
y = np.array(y_data)
plt.figure()
plt.plot(x, y, 'b-', label="Data")
plt.legend()
# plt.show()
saver.check_if_dir_exists(output_directory)
plt.savefig(output_directory + "/" + output_file_name + ".png")
plt.close()
def generate_probability_distribution(data):
"""
Normalises y coordinates, dividing it by sum of all entries of y coordiantes
Args:
data(list of list): list of list representation csv data (with 2 coordinates)
Returns:
x-coordinate (list)
freq (list) normalised-y-coordinates
"""
if data:
topRows = [int(x[1]) for x in data]
total = sum(topRows)
freq = [x/float(total) for x in topRows]
return range(0, len(data)), freq
else:
print "ERROR generate_probability_distribution"
return -1, -1
# FOR CL and RT anaylysis
def exponential_curve_fit_and_plot(data, output_directory, output_file_name):
"""
Fit to an expontial curve and draw the x-y data
Args:
data(list of list): list of list representation csv data (with 2 coordinates)
output_drectory(str): location to save graph
output_file_name(str): name of the image file to be saved
Returns:
a (int) : curve fit variable for the equation a * np.exp(-b * x) + c
b (int) : curve fit variable for the equation a * np.exp(-b * x) + c
c (int) : curve fit variable for the equation a * np.exp(-b * x) + c
mse (int) : Mean Squared error from the fit
"""
x_pdf, y_pdf = generate_probability_distribution(data)
if y_pdf != -1:
x = np.array(x_pdf)
y = np.array(y_pdf)
popt, pcov = curve_fit(util.exponential_curve_func, x, y)
[a, b, c] = popt
mse = mean_squared_error(util.exponential_curve_func(x, *popt), y)
if config.DEBUGGER:
print "CURVE FIT", output_file_name, "|", a, b, c, "MSE =", mse
plt.figure()
plt.plot(x, y, 'b-', label="Data")
plt.plot(x, util.exponential_curve_func(x, *popt), 'r-', label="Fitted Curve")
axes = plt.gca()
axes.set_xlim([0, 20])
axes.set_ylim([0, 1])
plt.legend()
# plt.show()
saver.check_if_dir_exists(output_directory)
plt.savefig(output_directory + "/" + output_file_name + ".png")
plt.close()
return [a, b, c, mse]
# Ignoring Initial Zeros in CRT
def exponential_curve_fit_and_plot_x_shifted(data, output_directory, output_file_name):
"""
Fit to an expontial curve and draw the x-y data
Also ignores the the input untill first non-zero y-coordinate and shifts the graph along
y axes untill that first non-zero entry
Args:
data(list of list): list of list representation csv data (with 2 coordinates)
output_drectory(str): location to save graph
output_file_name(str): name of the image file to be saved
Returns:
a (int) : curve fit variable for the equation a * np.exp(-b * x) + c
b (int) : curve fit variable for the equation a * np.exp(-b * x) + c
c (int) : curve fit variable for the equation a * np.exp(-b * x) + c
first_non_zero_index (int): amount by which the graph is shifted along y axis
mse (int) : Mean Squared error from the fit
"""
x_pdf, y_pdf = generate_probability_distribution(data)
if y_pdf != -1:
first_non_zero_index = -1
if filter(lambda x: x != 0, y_pdf):
first_non_zero_index = y_pdf.index(filter(lambda x: x != 0, y_pdf)[0])
x = np.array(x_pdf[0: -1*first_non_zero_index])
y = np.array(y_pdf[first_non_zero_index:])
popt, pcov = curve_fit(util.exponential_curve_func, x, y)
[a, b, c] = popt
mse = mean_squared_error(util.exponential_curve_func(x, *popt), y)
if config.DEBUGGER:
print "CURVE FIT", output_file_name, "|", a, b, c, "x-shift =", first_non_zero_index, "MSE =", mse
plt.figure()
plt.plot(x, y, 'b-', label="Data")
plt.plot(x, util.exponential_curve_func(x, *popt), 'r-', label="Fitted Curve")
axes = plt.gca()
# axes.set_xlim([0 ,20])
axes.set_ylim([0, 1])
plt.xticks(range(0, 20, 5), xrange(first_non_zero_index, len(x), 5), size='small')
plt.legend()
# plt.show()
saver.check_if_dir_exists(output_directory)
plt.savefig(output_directory + "/" + output_file_name + ".png")
plt.close()
return [a, b, c, mse, first_non_zero_index]
def plot_infomap_igraph(i_graph, membership, output_directory, output_file_name, show_edges=True, aux_data=None):
"""
Plots the informap community generated by igraph
Args:
i_graph(object): igraph graph object
membership(list): membership generated by infomap.community_infomap
output_drectory(str): location to save graph
output_file_name(str): name of the image file to be saved
show_edges(bool): toggle to disable/enable edges during viz
Returns:
null
"""
if membership is not None:
graph_copy = i_graph.copy()
edges = []
edges_colors = []
for edge in i_graph.es():
if membership[edge.tuple[0]] != membership[edge.tuple[1]]:
edges.append(edge)
edges_colors.append("#55555520")
# edges_colors.append("#00000000")
else:
edges_colors.append("#00000099")
graph_copy.delete_edges(edges)
layout = graph_copy.layout("kk")
i_graph.es["color"] = edges_colors
else:
layout = i_graph.layout("kk")
i_graph.es["color"] = "gray"
visual_style = {}
visual_style["vertex_label_dist"] = 0
visual_style["vertex_label_size"] = 18
if show_edges:
visual_style["edge_color"] = i_graph.es["color"]
else:
visual_style["edge_color"] = "#00000000"
visual_style["vertex_size"] = 32
visual_style["layout"] = layout
visual_style["bbox"] = (1024, 768)
visual_style["margin"] = 40
#visual_style["edge_label"] = i_graph.es["weight"]
if i_graph.es:
visual_style["edge_width"] = igraph.rescale(i_graph.es['weight'], out_range=(1, 10))
for vertex in i_graph.vs():
vertex["label"] = vertex.index
if membership is not None:
colors = []
for i in range(0, max(membership)+1):
colors.append('%06X' % randint(0, 0xFFFFFF))
for vertex in i_graph.vs():
vertex["vertex_shape"] = "circle"
vertex["color"] = str('#') + colors[membership[vertex.index]]
# coloring for channels vs users
vertex["vertex_shape"] = "square" if (vertex["id"].isdigit() and int(vertex["id"]) >= 1000000) else "circle"
# vertex["color"] = "red" if (vertex["id"].isdigit() and int(vertex["id"]) >= 1000000) else "#00ff00"
if aux_data:
if aux_data["type"] == "MULTI_CH":
# ['#kubuntu-devel', '#kubuntu', '#ubuntu-devel']
color_nodes = ["#ff0000", "#00ff00", "#0000ff"]
vertex["color"] = color_nodes[np.argmax(aux_data["uc_adj"][aux_data["user_hash"].index(vertex["id"])])]
if not aux_data:
visual_style["vertex_color"] = i_graph.vs["color"]
visual_style["vertex_shape"] = i_graph.vs["vertex_shape"]
saver.check_if_dir_exists(output_directory)
igraph.plot(i_graph, (output_directory + "/" + output_file_name + ".png"), **visual_style)
if config.DEBUGGER:
print "INFOMAPS visualisation for", output_file_name, "completed"
def generate_log_plots(plot_data, output_directory, output_file_name):
"""
Generate log plots for given time frame
Args:
plot_data (list of list): data to be plotted
output_drectory(str): location to save graph
output_file_name(str): name of the image file to be saved
Returns:
slope : The slope of linear fit for the log plot.
r_square :
mean_sqaure_error : Mean sqaure error for best fit.
"""
sum_each_row = []
for row in plot_data[2:]: #ignore degree 0 and text, starting from degree 1
sum_each_row.append(row)
x_axis_log = [math.log(i) for i in xrange(1, len(sum_each_row) + 1)]
y_axis_log = [math.log(i) if i>0 else 0 for i in sum_each_row[0:] ]
slope,intercept,r_square,mean_squared_error = calc_plot_linear_fit(x_axis_log, y_axis_log, output_directory, output_file_name)
return slope,intercept,r_square,mean_squared_error
def calc_plot_linear_fit(x_in, y_in, output_directory, output_file_name):
"""
Calculate and plot linar fit for data
Args:
x_in (list of int): x_axis data
y_in (list of int): y_axis data
output_drectory(str): location to save graph
output_file_name(str): name of the image file to be saved
Returns:
null
"""
# get x and y vectors
if x_in and y_in:
x = np.array(x_in)
y = np.array(y_in)
slope, intercept, r_value, p_value, std_err = stats.linregress(x_in, y_in)
line = [slope*xi+intercept for xi in x_in]
print str(slope)+"\t"+str(intercept)+"\t"+str(r_value**2)+"\t"+str(mean_squared_error(y, line))
saver.check_if_dir_exists(output_directory)
axes = plt.gca()
axes.set_xlim([0, 3])
axes.set_ylim([0, 6])
plt.xlabel("log(degree)")
plt.ylabel("log(no_of_nodes)")
# fit with np.polyfit
m, b = np.polyfit(x, y, 1)
plt.plot(x, y, '-')
plt.plot(x, m*x + b, '-')
plt.legend(['Data', 'Fit'], loc='upper right')
plt.savefig(output_directory+"/" + output_file_name+".png")
plt.close()
return slope,intercept,r_value**2,mean_squared_error(y, line)
else:
print "ERROR calc_plot_linear_fit"
return -1, -1, -1, -1
def matplotlob_csv_heatmap_generator(csv_file, output_directory, output_file_name):
"""
Plots heatmaps for all the csv files in the given directory
Can be used as a script for generating heatmaps, faster alternative to plotly
Args:
in_directory (str): location of input csv files
output_drectory(str): location to save graph
output_file_name(str): name of the image file to be saved
Returns:
null
"""
column_labels = map(str, range(1, 32))
row_labels = map(str, range(1, 49))
data = genfromtxt(csv_file, delimiter=',')
print(data)
fig, ax = plt.subplots(figsize=(10, 10))
heatmap = ax.pcolor(data, cmap=plt.cm.Reds)
cbar = plt.colorbar(heatmap)
def np_arrange_helper(data, disp):
return np.arange(data) + disp
# put the major ticks at the middle of each cell
ax.set_xticks(np_arrange_helper(data.shape[0], 0.5), minor=False)
ax.set_yticks(np_arrange_helper(data.shape[1], 0.5), minor=False)
# want a more natural, table-like display
ax.invert_yaxis()
ax.xaxis.tick_top()
ax.set_xticklabels(row_labels, minor=False)
ax.set_yticklabels(column_labels, minor=False)
plt.savefig(output_directory+"/" + output_file_name+".png")
plt.close()
def box_plot(data, output_directory, output_file_name):
"""
Plots Box Plots
Args:
data (list): data
output_drectory(str): location to save graph
output_file_name(str): name of the image file to be saved
Returns:
null
"""
plt.figure()
plt.boxplot(data)
plt.legend()
saver.check_if_dir_exists(output_directory)
plt.savefig(output_directory + "/" + output_file_name + ".png")
plt.close()