set random seed for less build churn

.github/pull_request_template.md

@@ -1,5 +1,6 @@
 Doc upgrade checklist:
 
+- [ ] random seed is set if using random data
 - [ ] file has been moved from `unconverted/x/y.md` to `x/y.md`
 - [ ] old boilerplate at top and bottom of file has been removed
 - [ ] Every example is independently runnable and is optimized for short line count

python/2D-Histogram.md

@@ -42,6 +42,7 @@ jupyter:
 import plotly.graph_objects as go
 
 import numpy as np
+np.random.seed(1)
 
 x = np.random.randn(500)
 y = np.random.randn(500)+1

python/2d-histogram-contour.md

@@ -42,6 +42,7 @@ jupyter:
 import plotly.graph_objects as go
 
 import numpy as np
+np.random.seed(1)
 
 x = np.random.uniform(-1, 1, size=500)
 y = np.random.uniform(-1, 1, size=500)

python/3d-axes.md

@@ -48,6 +48,7 @@ For creating 3D charts, see [this page](https://plot.ly/python/3d-charts/).
 ```python
 import plotly.graph_objects as go
 import numpy as np
+np.random.seed(1)
 
 N = 70
 

python/annotated_heatmap.md

@@ -110,11 +110,12 @@ fig.show()
 ```python
 import plotly.figure_factory as ff
 import numpy as np
+np.random.seed(1)
 
 z = np.random.randn(20, 20)
 z_text = np.around(z, decimals=2) # Only show rounded value (full value on hover)
 
-fig = ff.create_annotated_heatmap(z, annotation_text=z_text, colorscale='Greys', 
+fig = ff.create_annotated_heatmap(z, annotation_text=z_text, colorscale='Greys',
                                   hoverinfo='z')
 
 # Make text size smaller
@@ -183,7 +184,7 @@ z = [[.8, .0, .0, .0, .0, .0, .0, .0, .0, .0, .0, .0, .0, .0, .0, .0, .0, 1.],
 # Display element name and atomic mass on hover
 hover=[]
 for x in range(len(symbol)):
-    hover.append([i + '<br>' + 'Atomic Mass: ' + str(j) 
+    hover.append([i + '<br>' + 'Atomic Mass: ' + str(j)
                       for i, j in zip(element[x], atomic_mass[x])])
 
 # Invert Matrices
@@ -208,4 +209,4 @@ For more info on Plotly heatmaps, see: https://plot.ly/python/reference/#heatmap
 
 ```python
 help(ff.create_annotated_heatmap)
-```
+```

python/box-plots.md

@@ -97,6 +97,7 @@ When data are not available as tidy dataframes, it is also possible to use the m
 ```python
 import plotly.graph_objects as go
 import numpy as np
+np.random.seed(1)
 
 y0 = np.random.randn(50) - 1
 y1 = np.random.randn(50) + 1

python/click-events.md

@@ -43,6 +43,7 @@ jupyter:
 import plotly.graph_objects as go
 
 import numpy as np
+np.random.seed(1)
 
 x = np.random.rand(100)
 y = np.random.rand(100)

python/compare-webgl-svg.md

@@ -48,6 +48,7 @@ for increased speed, improved interactivity, and the ability to plot even more d
 import plotly.graph_objects as go
 
 import numpy as np
+np.random.seed(1)
 
 N = 75000
 
@@ -103,4 +104,4 @@ fig.show()
 
 For more information see <br>
 `Scattergl()` : https://plot.ly/python/reference/#scattergl <br>
-`Scatter()` : https://plot.ly/python/reference/#scatter
+`Scatter()` : https://plot.ly/python/reference/#scatter

python/custom-buttons.md

@@ -258,6 +258,7 @@ import plotly.graph_objects as go
 
 # Generate dataset
 import numpy as np
+np.random.seed(1)
 
 x0 = np.random.normal(2, 0.4, 400)
 y0 = np.random.normal(2, 0.4, 400)

python/dendrogram.md

@@ -46,6 +46,7 @@ Dendrogram plots are commonly used in computational biology to show the clusteri
 ```python
 import plotly.figure_factory as ff
 import numpy as np
+np.random.seed(1)
 
 X = np.random.rand(15, 12) # 15 samples, with 12 dimensions each
 fig = ff.create_dendrogram(X)
@@ -183,4 +184,4 @@ fig.show()
 
 ```python
 help(ff.create_dendrogram)
-```
+```

python/distplot.md

@@ -71,6 +71,7 @@ A histogram, a kde plot and a rug plot are displayed.
 ```python
 import plotly.figure_factory as ff
 import numpy as np
+np.random.seed(1)
 
 x = np.random.randn(1000)
 hist_data = [x]

python/dropdowns.md

@@ -256,6 +256,7 @@ import plotly.graph_objects as go
 
 # Generate dataset
 import numpy as np
+np.random.seed(1)
 
 x0 = np.random.normal(2, 0.4, 400)
 y0 = np.random.normal(2, 0.4, 400)

python/heatmaps.md

@@ -123,13 +123,14 @@ fig.show()
 import plotly.graph_objects as go
 import datetime
 import numpy as np
+np.random.seed(1)
 
 programmers = ['Alex','Nicole','Sara','Etienne','Chelsea','Jody','Marianne']
 
 base = datetime.datetime.today()
 dates = base - np.arange(180) * datetime.timedelta(days=1)
 z = np.random.poisson(size=(len(programmers), len(dates)))
-    
+
 fig = go.Figure(data=go.Heatmap(
         z=z,
         x=dates,
@@ -159,4 +160,4 @@ IFrame(src= "https://dash-simple-apps.plotly.host/dash-heatmapplot/code", width=
 ```
 
 #### Reference
-See https://plot.ly/python/reference/#heatmap for more information and chart attribute options!
+See https://plot.ly/python/reference/#heatmap for more information and chart attribute options!

python/histograms.md

@@ -138,6 +138,7 @@ When data are not available as tidy dataframes, it is also possible to use the m
 import plotly.graph_objects as go
 
 import numpy as np
+np.random.seed(1)
 
 x = np.random.randn(500)
 

python/images.md

@@ -149,6 +149,7 @@ fig.show()
 import plotly.graph_objects as go
 
 import numpy as np
+np.random.seed(1)
 from scipy.signal import savgol_filter
 
 # Simulate spectroscopy data

python/line-and-scatter.md

@@ -99,6 +99,7 @@ import plotly.graph_objects as go
 
 # Create random data with numpy
 import numpy as np
+np.random.seed(1)
 
 N = 100
 random_x = np.linspace(0, 1, N)

python/line-charts.md

@@ -97,6 +97,7 @@ import plotly.graph_objects as go
 
 # Create random data with numpy
 import numpy as np
+np.random.seed(1)
 
 N = 100
 random_x = np.linspace(0, 1, N)

python/marker-style.md

@@ -62,6 +62,7 @@ import plotly.graph_objects as go
 
 # Generate example data
 import numpy as np
+np.random.seed(1)
 
 x = np.random.uniform(low=3, high=6, size=(500,))
 y = np.random.uniform(low=3, high=6, size=(500,))
@@ -313,4 +314,4 @@ fig.show()
 ```
 
 ### Reference
-See https://plot.ly/python/reference/ for more information and chart attribute options!
+See https://plot.ly/python/reference/ for more information and chart attribute options!

python/orca-management.md

@@ -53,6 +53,7 @@ Now let's create a simple scatter plot with 100 random points of variying color
 import plotly.graph_objects as go
 
 import numpy as np
+np.random.seed(1)
 
 # Generate scatter plot data
 N = 100
@@ -209,4 +210,4 @@ In addition to the `executable` property, the `plotly.io.orca.config` object can
 
 
 ### Saving Configuration Settings
-Configuration options can optionally be saved to the `~/.plotly/` directory by calling the `plotly.io.config.save()` method.  Saved setting will be automatically loaded at the start of future sessions.
+Configuration options can optionally be saved to the `~/.plotly/` directory by calling the `plotly.io.config.save()` method.  Saved setting will be automatically loaded at the start of future sessions.

python/random-walk.md

@@ -47,6 +47,7 @@ The jitter in the data points along the x and y axes are meant to illuminate whe
 ```python
 import plotly.graph_objects as go
 import numpy as np
+np.random.seed(1)
 
 l = 100
 steps = np.random.choice([-1, 1], size=l) + 0.05 * np.random.randn(l) # l steps
@@ -112,7 +113,7 @@ steps = np.random.choice([-1, 1], size=(N, l)) + 0.05 * np.random.standard_norma
 position = np.cumsum(steps, axis=1) # integrate all positions by summing steps values along time axis
 
 fig = go.Figure(data=go.Histogram(x=position[:, -1])) # positions at final time step
-fig.show()                                                                        
+fig.show()
 ```
 
 ```python
@@ -134,7 +135,7 @@ fig.update_xaxes(title_text='$t$')
 fig.update_yaxes(title_text='$l$', col=1)
 fig.update_yaxes(title_text='$l^2$', col=2)
 fig.update_layout(showlegend=False)
-fig.show()                                                                        
+fig.show()
 ```
 
 #### Advanced Tip

python/shapes.md

@@ -462,6 +462,7 @@ fig.show()
 import plotly.graph_objects as go
 
 import numpy as np
+np.random.seed(1)
 
 # Generate data
 x0 = np.random.normal(2, 0.45, 300)
@@ -706,4 +707,4 @@ fig.show()
 ```
 
 ### Reference
-See https://plot.ly/python/reference/#layout-shapes for more information and chart attribute options!
+See https://plot.ly/python/reference/#layout-shapes for more information and chart attribute options!

python/smoothing.md

@@ -65,6 +65,8 @@ import scipy
 
 from scipy import signal
 
+np.random.seed(1)
+
 x = np.linspace(0, 10, 100)
 y = np.sin(x)
 noise = 2 * np.random.random(len(x)) - 1 # uniformly distributed between -1 and 1
@@ -93,8 +95,8 @@ fig.add_trace(go.Scatter(
 
 fig.add_trace(go.Scatter(
     x=x,
-    y=signal.savgol_filter(y, 
-                           53, # window size used for filtering 
+    y=signal.savgol_filter(y,
+                           53, # window size used for filtering
                            3), # order of fitted polynomial
     mode='markers',
     marker=dict(

python/static-image-export.md

@@ -76,6 +76,7 @@ Now let's create a simple scatter plot with 100 random points of variying color
 ```python
 import plotly.graph_objects as go
 import numpy as np
+np.random.seed(1)
 
 N = 100
 x = np.random.rand(N)

python/table.md

@@ -121,7 +121,7 @@ fig = go.Figure(data=[go.Table(
 fig.show()
 ```
 
-#### Alternating Row Colors 
+#### Alternating Row Colors
 
 ```python
 import plotly.graph_objects as go
@@ -190,6 +190,7 @@ fig.show()
 import plotly.graph_objects as go
 from plotly.colors import n_colors
 import numpy as np
+np.random.seed(1)
 
 colors = n_colors('rgb(255, 200, 200)', 'rgb(200, 0, 0)', 9, colortype='rgb')
 a = np.random.randint(low=0, high=9, size=10)
@@ -229,4 +230,4 @@ IFrame(src= "https://dash-simple-apps.plotly.host/dash-tableplot/code", width="1
 ```
 
 #### Reference
-For more information on tables and table attributes see: https://plot.ly/python/reference/#table.
+For more information on tables and table attributes see: https://plot.ly/python/reference/#table.

python/violin.md

@@ -244,6 +244,7 @@ A ridgeline plot ([previously known as Joy Plot](https://serialmentor.com/blog/2
 import plotly.graph_objects as go
 from plotly.colors import n_colors
 import numpy as np
+np.random.seed(1)
 
 # 12 sets of normal distributed random data, with increasing mean and standard deviation
 data = (np.linspace(1, 2, 12)[:, np.newaxis] * np.random.randn(12, 200) +

python/webgl-vs-svg.md

@@ -51,6 +51,7 @@ import plotly.express as px
 
 import pandas as pd
 import numpy as np
+np.random.seed(1)
 
 N = 100000