Plot heatmapsnumpy

3 files changed, 30 insertions, 40 deletions

diff --git a/.arcconfig b/.arcconfig
deleted file mode 120000
index b947811..0000000
--- a/.arcconfig
+++ /dev/null
@@ -1 +0,0 @@
-../dill/.arcconfig
\ No newline at end of file
diff --git a/data b/data
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/data
diff --git a/diffs.py b/diffs.py
index 8eb0088..3ca1f3a 100755
--- a/diffs.py
+++ b/diffs.py
@@ -3,15 +3,16 @@ from datetime import datetime
 from phabricator import Phabricator
 import numpy as np
 import matplotlib.pyplot as plt
-import scipy.stats as st
-from sklearn.datasets.samples_generator import make_blobs
+import matplotlib.cm as cm
+import matplotlib.dates as mdates
+from scipy.ndimage.filters import gaussian_filter
 
 start = datetime(year=2020, month=7, day=1)
 
 
 def format_stamp(stamp):
     dt = datetime.utcfromtimestamp(stamp)
-    date = (dt - start).days
+    date = mdates.date2num(dt)
     time = dt.hour + dt.minute / 60.0
     return [date, time]
 
@@ -25,43 +26,33 @@ for d in diff:
     modified = format_stamp(int(d["dateModified"]))
     dates += [created, modified]
 
-# Extract x and y
-x = [i for i, j in dates]
-y = [j for i, j in dates]
-# Define the borders
-xmin = 0
-xmax = 60
-ymin = 0
-ymax = 24
-print(xmin, xmax, ymin, ymax)
-# Create meshgrid
-xx, yy = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
-
-positions = np.vstack([xx.ravel(), yy.ravel()])
-values = np.vstack([x, y])
-kernel = st.gaussian_kde(values)
-f = np.reshape(kernel(positions).T, xx.shape)
-
-print(f)
-
-fig = plt.figure(figsize=(13, 7))
-ax = plt.axes(projection='3d')
-surf = ax.plot_surface(xx, yy, f, rstride=1, cstride=1, cmap='coolwarm', edgecolor='none')
-ax.set_xlabel('x')
-ax.set_ylabel('y')
-ax.set_zlabel('PDF')
-ax.set_title('Surface plot of Gaussian 2D KDE')
-fig.colorbar(surf, shrink=0.5, aspect=5) # add color bar indicating the PDF
-ax.view_init(90, -90)
 
-plt.show()
 
-#h =plt.hist2d(x, y)
-#plt.colorbar(h[3])
-#plt.show()
+def myplot(x, y, s, bins=1000):
+    heatmap, xedges, yedges = np.histogram2d(x, y, bins=bins)
+    heatmap = gaussian_filter(heatmap, sigma=s)
+
+    extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
+    return heatmap.T, extent
+
 
+fig, axs = plt.subplots(2, 2)
 
-#plt.scatter(X[:, 0], X[:, 1], s=50, c = truth)
-#plt.title(f"Example of a mixture of {n_components} distributions")
-#plt.xlabel("x")
-#plt.ylabel("y");
+
+sigmas = [0, 16, 32, 48]
+x = [i for i, j in dates]
+y = [j for i, j in dates]
+daymonthFmt = mdates.DateFormatter('%d %B')
+
+for ax, s in zip(axs.flatten(), sigmas):
+    ax.xaxis.set_major_formatter(daymonthFmt)
+    _ = plt.xticks(rotation=90)
+    if s == 0:
+        ax.plot(x, y, 'k.', markersize=5)
+        ax.set_title("Scatter plot")
+    else:
+        img, extent = myplot(x, y, s)
+        ax.imshow(img, extent=extent, origin='lower', cmap=cm.jet)
+        ax.set_title("Smoothing with  $\sigma$ = %d" % s)
+
+plt.show()