Examples: Replace deprecated "np.random.seed()" with "np.random.Generator()" (NPY002 warning by ruff)

examples/gallery/histograms/histogram.py

@@ -12,12 +12,12 @@
 import numpy as np
 import pygmt
 
-np.random.seed(100)
-
 # Generate random elevation data from a normal distribution
+rng = np.random.default_rng(seed=100)
 mean = 100  # mean of distribution
 stddev = 25  # standard deviation of distribution
-data = mean + stddev * np.random.randn(521)
+data = rng.normal(mean, stddev, 521)
+
 
 fig = pygmt.Figure()
 

examples/gallery/histograms/scatter_and_histograms.py

@@ -13,15 +13,16 @@
 import numpy as np
 import pygmt
 
-np.random.seed(19680801)
-
 # Generate random data from a standard normal distribution centered on 0
-x = np.random.randn(1000)
-y = np.random.randn(1000)
+# with a standard deviation of 1
+rng = np.random.default_rng(seed=19680801)
+x = rng.normal(0, 1, 1000)
+y = rng.normal(0, 1, 1000)
 
 # Get axis limits
 xymax = max(np.max(np.abs(x)), np.max(np.abs(y)))
 
+
 fig = pygmt.Figure()
 fig.basemap(
     region=[-xymax - 0.5, xymax + 0.5, -xymax - 0.5, xymax + 0.5],

examples/gallery/symbols/points.py

@@ -11,10 +11,11 @@
 import pygmt
 
 # Generate a random set of points to plot
-np.random.seed(42)
+rng = np.random.default_rng(seed=42)
 region = [150, 240, -10, 60]
-x = np.random.uniform(region[0], region[1], 100)
-y = np.random.uniform(region[2], region[3], 100)
+x = rng.uniform(region[0], region[1], 100)
+y = rng.uniform(region[2], region[3], 100)
+
 
 fig = pygmt.Figure()
 # Create a 15 cm x 15 cm basemap with a Cartesian projection (X) using the

examples/gallery/symbols/scatter.py

@@ -13,18 +13,20 @@
 import numpy as np
 import pygmt
 
-np.random.seed(19680801)
+rng = np.random.default_rng(seed=19680801)
 n = 200  # number of random data points
 
 fig = pygmt.Figure()
 fig.basemap(
-    region=[-0.1, 1.1, -0.1, 1.1],
+    region=[-1, 1, -1, 1],
     projection="X10c/10c",
-    frame=["xa0.2fg", "ya0.2fg", "WSrt"],
+    frame=["xa0.5fg", "ya0.5fg", "WSrt"],
 )
 for fill in ["gray73", "darkorange", "slateblue"]:
-    x, y = np.random.rand(2, n)  # random X and Y data in [0,1]
-    size = np.random.rand(n) * 0.5  # random size [0,0.5], in cm
+    x = rng.normal(0, 0.5, n)  # random x data
+    y = rng.normal(0, 0.5, n)  # random y data
+    size = rng.normal(0, 0.5, n) * 0.5  # random size, in cm
+
     # plot data points as circles (style="c"), with different sizes
     fig.plot(
         x=x,

examples/tutorials/advanced/cartesian_histograms.py

@@ -22,16 +22,16 @@
 # %%
 # Generate random data from a normal distribution:
 
-np.random.seed(100)
+rng = np.random.default_rng(seed=100)
 
 # Mean of distribution
 mean = 100
 # Standard deviation of distribution
 stddev = 20
 
 # Create two data sets
-data01 = np.random.normal(mean, stddev, 42)
-data02 = np.random.normal(mean, stddev * 2, 42)
+data01 = rng.normal(mean, stddev, 42)
+data02 = rng.normal(mean, stddev * 2, 42)
 
 
 # %%