First scatter plot + python interface.

QuNet-Paper/heatmap.jl

@@ -1,7 +1,9 @@
 using QuNet
+using DataFrames
+using CSV
 
 grid_size = 10
-num_trials = 100
+num_trials = 1000
 num_pairs = 50
 
 net = GridNetwork(grid_size, grid_size)
@@ -10,4 +12,10 @@ net = GridNetwork(grid_size, grid_size)
 #    num_trials::Int64, num_pairs::Int64; max_paths=3, src_layer::Int64=-1,
 #    dst_layer::Int64=-1, edge_perc_rate=0.0)
 coord_list = QuNet.heat_data(net, num_trials, num_pairs, max_paths=4)
-println(coord_list)
+
+# List comprehension
+e = [coord_list[i][1] for i in 1:length(coord_list)]
+f = [coord_list[i][2] for i in 1:length(coord_list)]
+# # Convert cood_list to data_frame and write to csv
+df = DataFrame(Efficiency = e, Fidelity = f)
+CSV.write("data/heatmap.csv", df)

QuNet-Paper/heatmap.py

@@ -1,47 +1,64 @@
-# https://www.python-graph-gallery.com/86-avoid-overlapping-in-scatterplot-with-2d-density
-
 # Libraries
 import numpy as np
+import pandas as pd
 import matplotlib.pyplot as plt
 from scipy.stats import kde
 
-# Create data: 200 points
-data = np.random.multivariate_normal([0, 0], [[1, 0.5], [0.5, 3]], 200)
-x, y = data.T
-
-# Create a figure with 6 plot areas
-fig, axes = plt.subplots(ncols=6, nrows=1, figsize=(21, 5))
-
-# Everything starts with a Scatterplot
-axes[0].set_title('Scatterplot')
-axes[0].plot(x, y, 'ko')
-# As you can see there is a lot of overlapping here!
-
-# Thus we can cut the plotting window in several hexbins
-nbins = 20
-axes[1].set_title('Hexbin')
-axes[1].hexbin(x, y, gridsize=nbins, cmap=plt.cm.BuGn_r)
+df = pd.read_csv("/home/hudson/.julia/dev/QuNet/data/heatmap.csv")
+print(df)
 
-# 2D Histogram
-axes[2].set_title('2D Histogram')
-axes[2].hist2d(x, y, bins=nbins, cmap=plt.cm.BuGn_r)
+# Extract data into x, y
+print(df["Efficiency"])
+e = df["Efficiency"].tolist()
+f = df["Fidelity"].tolist()
 
-# Evaluate a gaussian kde on a regular grid of nbins x nbins over data extents
-k = kde.gaussian_kde(data.T)
-xi, yi = np.mgrid[x.min():x.max():nbins * 1j, y.min():y.max():nbins * 1j]
-zi = k(np.vstack([xi.flatten(), yi.flatten()]))
-
-# plot a density
-axes[3].set_title('Calculate Gaussian KDE')
-axes[3].pcolormesh(xi, yi, zi.reshape(xi.shape), shading='auto', cmap=plt.cm.BuGn_r)
-
-# add shading
-axes[4].set_title('2D Density with shading')
-axes[4].pcolormesh(xi, yi, zi.reshape(xi.shape), shading='gouraud', cmap=plt.cm.BuGn_r)
-
-# contour
-axes[5].set_title('Contour')
-axes[5].pcolormesh(xi, yi, zi.reshape(xi.shape), shading='gouraud', cmap=plt.cm.BuGn_r)
-axes[5].contour(xi, yi, zi.reshape(xi.shape))
+# Create a scatter plot
+fig, axes = plt.subplots()
+axes.set_title("Scatterplot")
+axes.plot(e, f, 'ko')
+axes.set_xlabel("Efficiency")
+axes.set_ylabel("Fidelity")
 plt.show()
 
+
+# # Sample Code to test plot functionality:
+# # https://www.python-graph-gallery.com/86-avoid-overlapping-in-scatterplot-with-2d-density
+# # Create data: 200 points
+# data = np.random.multivariate_normal([0, 0], [[1, 0.5], [0.5, 3]], 200)
+# x, y = data.T
+#
+# # Create a figure with 6 plot areas
+# fig, axes = plt.subplots(ncols=6, nrows=1, figsize=(21, 5))
+#
+# # Everything starts with a Scatterplot
+# axes[0].set_title('Scatterplot')
+# axes[0].plot(x, y, 'ko')
+# # As you can see there is a lot of overlapping here!
+#
+# # Thus we can cut the plotting window in several hexbins
+# nbins = 20
+# axes[1].set_title('Hexbin')
+# axes[1].hexbin(x, y, gridsize=nbins, cmap=plt.cm.BuGn_r)
+#
+# # 2D Histogram
+# axes[2].set_title('2D Histogram')
+# axes[2].hist2d(x, y, bins=nbins, cmap=plt.cm.BuGn_r)
+#
+# # Evaluate a gaussian kde on a regular grid of nbins x nbins over data extents
+# k = kde.gaussian_kde(data.T)
+# xi, yi = np.mgrid[x.min():x.max():nbins * 1j, y.min():y.max():nbins * 1j]
+# zi = k(np.vstack([xi.flatten(), yi.flatten()]))
+#
+# # plot a density
+# axes[3].set_title('Calculate Gaussian KDE')
+# axes[3].pcolormesh(xi, yi, zi.reshape(xi.shape), shading='auto', cmap=plt.cm.BuGn_r)
+#
+# # add shading
+# axes[4].set_title('2D Density with shading')
+# axes[4].pcolormesh(xi, yi, zi.reshape(xi.shape), shading='gouraud', cmap=plt.cm.BuGn_r)
+#
+# # contour
+# axes[5].set_title('Contour')
+# axes[5].pcolormesh(xi, yi, zi.reshape(xi.shape), shading='gouraud', cmap=plt.cm.BuGn_r)
+# axes[5].contour(xi, yi, zi.reshape(xi.shape))
+# plt.show()