Lazy High Charts: change colors of a pie chart [rails 3] - ruby-on-rails-3

I'm working in a rails 3 project, I want to change the pie chart colors that I generate with the LazyHighChart gem and I don;t know how to do that
This is my method controller
def set_pie_chart(data)
fixed_data = []
data.each_pair do |key, value|
fixed_data << [key.name, value]
end
#color = data.keys.map {|e| "#" + e.colour } # e.colour is like '333333'
#chart = LazyHighCharts::HighChart.new('pie') do |c|
c.chart({:defaultSeriesType=>"pie" , :margin=> [0, 0, 0, 0]})
series = {
type: 'pie',
name: 'total expenses',
data: fixed_data,
colors: ['green','red'] # intent
}
c.series(series)
c.colors = ['red','blue','black'] # intent
c.options[:colors] = ['green','blue','yellow'] # intent
c.options['colors'] = ['red','blue','yellow'] # intent
c.options[:title][:text] = nil
c.plot_options(:pie=>{
cursor: "pointer",
center: ['50%','37%'],
color: 'red', #intent
dataLabels: { enabled: false }
})
end
end
this method doesn't leave any error, what is the correct way or its not possible with this gem?
or what other good alternative gems could I use for my project?

I had the same problem and I solved it by putting the color options in my view. This is what I did:
#my_view_helper.rb
def chart_colors
html =
"[{linearGradient: [0, 0, 0, 200], stops: [[0, '#e28b02'],[1, '#f1bc70']]},
{linearGradient: [0, 0, 0, 200], stops: [[0, '#a5ba57'],[1, '#a8bb51']]},
{linearGradient: [0, 0, 0, 200], stops: [[0, '#1e93d6'],[1, '#35aff6']]},
{linearGradient: [0, 0, 0, 200], stops: [[0, '#c8cf99'],[1, '#cdcfa9']]},
{linearGradient: [0, 0, 0, 200], stops: [[0, '#709ab1'],[1, '#a7c5d0']]},
{linearGradient: [0, 0, 0, 200], stops: [[0, '#c76f4e'],[1, '#fba98e']]},
{linearGradient: [0, 0, 0, 200], stops: [[0, '#95d6e3'],[1, '#bbe8ed']]}]"
html
end
#my_view.html.haml
#chart
= high_chart("pie_1", #chart) do |c|
= "options.colors = #{chart_colors}".html_safe

#chart = LazyHighCharts::HighChart.new('pie') do |c|
c.colors(["red","green","blue"]);
end

Related

Why is this simple Deck.GL OrbitView not interactive?

I think I'm doing something very basic wrong, or I'm misunderstanding something very fundamental, but I just can't make this respond to mouse events – it's just frozen in its initial view (Code Pen sample):
const {Deck, OrbitView, SimpleMeshLayer, COORDINATE_SYSTEM} = deck;
const {CubeGeometry} = luma;
const view = {
target: [0, 0, 0],
zoom: 0,
rotationOrbit: 145,
rotationX: 65,
minRotationX: -90,
maxRotationX: 90,
minZoom: -10,
maxZoom: 10
}
deck = new Deck({
views: new OrbitView({
orbitAxis: "Y"
}),
layers: [
new SimpleMeshLayer({
initialViewState: view,
controller: true,
data: [
{
position: [-25, 0, 0],
color: [255, 0, 0]
},
{
position: [25, 0, 0],
color: [0, 255, 0]
}
],
coordinateSystem: COORDINATE_SYSTEM.CARTESIAN,
mesh: new CubeGeometry(),
getPosition: d => d.position,
getColor: d => d.color,
getScale: [20, 20, 20]
})
]
});
Fixed it. I was confused about which properties go where. I left the buggy code here, and fixed it in the Pen, if you want to compare before and after.

CoreML: Failed in 2nd reshape after missing custom layer info

I our very big Tensorflow (2.x) model there is a part where we use InceptionResNetV2 and create multi-pooled model from it:
model_base = InceptionResNetV2(weights = 'imagenet',
include_top = False,
input_shape = input_shape)
ImgResizer = Lambda(lambda x: tf.image.resize(x, pool_size, method='area'),
name='feature_resizer')
feature_layers = [l for l in model_base.layers if 'mixed' in l.name]
feature_layers = [feature_layers[i] for i in indexes]
pools = [ImgResizer(l.output) for l in feature_layers]
conc_pools = Concatenate(name='conc_pools', axis=3)(pools)
model = Model(inputs = model_base.input, outputs = conc_pools)
Here we use Lambda function to resize 4D tensor ([batch, height, width, channels]) to a new 4D tensor with pool_size (5,5), input_shape=(None, None, 3) and indexes=list(range(43)). Unfortunately, this operation (ResizeArea) is not supported in the current version of coremltools (5.0) and I have to do a custom operation wrapper and then implement it on a swift.
#register_op(doc_str='Custom ResizeArea Layer', is_custom_op=True)
class custom_resize_area(Operation):
input_spec = InputSpec(
x = TensorInputType(),
s = ScalarOrTensorInputType()
)
bindings = { 'class_name' : 'CustomResizeArea',
'input_order' : ['x', 's'],
'parameters' : [],
'description' : "Resize area custom layer"
}
def __init__(self, **kwargs):
super(custom_resize_area, self).__init__(**kwargs)
def type_inference(self):
x_type = self.x.dtype
x_shape = self.x.shape
s = list(self.s.val)
ret_shape = list(x_shape)
ret_shape[1] = s[0]
ret_shape[2] = s[1]
#print(x_shape, ret_shape)
return types.tensor(x_type, ret_shape)
# Override ResizeArea op with override=True flag
#register_tf_op(tf_alias=['ResizeArea'], override=True)
def CustomResizeArea(context, node):
#input: "model_2/mixed_5b/concat"
#input: "model_2/lambda/resize/size"
x = context[node.inputs[0]]
s = context[node.inputs[1]]
x = mb.custom_resize_area(x=x, s=s, name=node.name)
context.add(node.name, x)
The conversion was successful and now I started to implement this custom layer on swift. Here is a part from my code:
#objc(CustomResizeArea) class CustomResizeArea: NSObject, MLCustomLayer {
required init(parameters: [String : Any]) throws {
super.init()
}
func setWeightData(_ weights: [Data]) throws {}
func outputShapes(forInputShapes inputShapes: [[NSNumber]]) throws
-> [[NSNumber]] {
print(#function, inputShapes)
let outputShape = inputShapes[0]
// [ sequence, batch, channel, height, width ] - why?
if outputShape.count == 5 {
print(outputShape)
return [outputShape]
}
print([outputShape[0], 5, 5, outputShape[3]])
return [[outputShape[0], 5, 5, outputShape[3]]]
}
...
}
I am publishing only outputShapes function since the CoreML gives me an error before evaluate function will actually be executed. Here the last logs (with some output shapes parameters inside):
outputShapes(forInputShapes:) [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]
[0, 0, 0, 0, 0]
outputShapes(forInputShapes:) [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]
[0, 0, 0, 0, 0]
.....
outputShapes(forInputShapes:) [[1, 0, 0, 448], [2]]
[1, 5, 5, 448]
outputShapes(forInputShapes:) [[1, 0, 0, 448], [2]]
[1, 5, 5, 448]
outputShapes(forInputShapes:) [[1, 0, 0, 448], [2]]
[1, 5, 5, 448]
2021-11-06 10:15:55.085694+0100 snafu[11263:4717917] [espresso] [Espresso::handle_ex_plan] exception=Failed in 2nd reshape after missing custom layer info.
2021-11-06 10:15:55.086108+0100 snafu[11263:4717917] [coreml] Error in adding network -1.
2021-11-06 10:15:55.086477+0100 snafu[11263:4717917] [coreml] MLModelAsset: load failed with error Error Domain=com.apple.CoreML Code=0 "Error in declaring network." UserInfo={NSLocalizedDescription=Error in declaring network.}
I have no idea why I get [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]] as an inout, and why are there zeros in, for example, [[1, 0, 0, 448], 2]. Actually my model should support any image size on input, that's why? I think I have tried everything...
P.S.:
I also tried to replace area reize method with bilinear in the python code (Lambda) because there is a BilinearResize converter in coremltools. But I can not convert it, because I get the following error:
File "/home/alex/anaconda3/envs/tfgpu/lib/python3.8/site-packages/coremltools/converters/mil/mil/builder.py", line 75, in _add_const
raise ValueError("Cannot add const {}".format(val))
ValueError: Cannot add const 5.0001/is57
I suppose that 5.0001 here is my pool_size, but I don't understand why is it incorrect.

Clustering Group of Products using K-means

Use case: cluster group of cloth products using column look_id
Sample of data :
The initial step was to change categorical data to numerical using LabelEncoder
Pseudo Code :
Group LookIDs in dataFrame
Data Preprocessing Change df to Numpy Array
Cluster using K-means
I have started by transform GroupBy df to Numpy Array but the result was not convenient trailing zero
Sample
[[['Hackett', 'Shorts', 'Pink', 'Light', 'SS17'], ['Diesel', 'T-Shirt', 'Navy', 'Dark', 'AW17'], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]], [['Hackett', 'Blazer', 'Green', 'Medium', 'AW16'], ['Reiss', 'Formal Shirts', 'Blue', 'Medium', 'AW17'], ['Van Gils', 'Lace up', 'Black', 'Dark', 'SS17']
DataFrame to Numpy Code Sample :
g = m.groupby('look_id').cumcount()
L = (m.set_index(['look_id',g])
.unstack(fill_value=0)
.stack().groupby(level=0)
.apply(lambda x: x.values.tolist())
.tolist())
How I can represent this correctly and do clustering

Rendering a cube in Vulkan vs OpenGL

I wrote a simple OpenGL program which merely renders a cube from an angle. It's as simple as you can get: vertex buffer only (no index buffer), a vertex shader which only multiplies the vertices by an MVP matrix from a uniform buffer, and a static fragment shader which just returns red. More recently, I have tried writing this same program in Vulkan, but I have run into some issues.
I started by following the Intel API without secrets tutorial to setup a simple 2d texture rendering program, but when I took the leap into 3d, I started having issues. In order to debug this, I simplified the program to match my older OpenGL program (removed texturing and some other extra stuff I did in Vulkan), and even went as far as to use the exact same vertex and MVP data. However, I just can't get the cube to render correctly in Vulkan.
I am aware that OpenGL coordinates do not map directly to Vulkan coordinates, as the Y coordinate is flipped, but if anything that should just flip the image upside down, and I already tried switching the Y values in the MVP. I feel like there is some other detail I am missing here with coordinates, but I just can't figure it out searching around and looking at guides about converting OpenGL code bases to Vulkan.
I'm including the data I am uploading to the shaders, and some of the core code from the Vulkan code base. The Vulkan code is in D, so it's similar to C++, but a little different. With the library I'm using for wrapping Vulkan (erupted), the device level functions are loaded into a device dispatch (access as device.dispatch in the code), and when they are called on the dispatch without the vk prefix, the device and command buffer (which is assigned to the dispatch in code) arguments of the function are auto populated.
Vertex Data:
[ [1, 1, 1, 1],
[1, 1, -1, 1],
[-1, 1, -1, 1],
[1, 1, 1, 1],
[-1, 1, -1, 1],
[-1, 1, 1, 1],
[1, 1, 1, 1],
[1, -1, 1, 1],
[1, -1, -1, 1],
[1, 1, 1, 1],
[1, -1, -1, 1],
[1, 1, -1, 1],
[1, 1, -1, 1],
[1, -1, -1, 1],
[-1, -1, -1, 1],
[1, 1, -1, 1],
[-1, -1, -1, 1],
[-1, 1, -1, 1],
[-1, 1, -1, 1],
[-1, -1, -1, 1],
[-1, -1, 1, 1],
[-1, 1, -1, 1],
[-1, -1, 1, 1],
[-1, 1, 1, 1],
[-1, 1, 1, 1],
[-1, -1, 1, 1],
[1, -1, 1, 1],
[-1, 1, 1, 1],
[1, -1, 1, 1],
[1, 1, 1, 1],
[1, -1, 1, 1],
[1, -1, -1, 1],
[-1, -1, -1, 1],
[1, -1, 1, 1],
[-1, -1, -1, 1],
[-1, -1, 1, 1] ]
MVP:
[ [-1.0864, -0.993682, -0.687368, -0.685994],
[0, 2.07017, 0.515526, -0.514496],
[-1.44853, 0.745262, 0.515526, 0.514496],
[-8.04095e-16, 0, 5.64243, 5.83095] ]
Graphics Pipeline Setup:
VkPipelineShaderStageCreateInfo[] shader_stage_infos = [
{
stage: VK_SHADER_STAGE_VERTEX_BIT,
_module: vertex_shader,
pName: "main"
},
{
stage: VK_SHADER_STAGE_FRAGMENT_BIT,
_module: fragment_shader,
pName: "main"
}
];
VkVertexInputBindingDescription[] vertex_binding_descriptions = [
{
binding: 0,
stride: VertexData.sizeof,
inputRate: VK_VERTEX_INPUT_RATE_VERTEX
}
];
VkVertexInputAttributeDescription[] vertex_attribute_descriptions = [
{
location: 0,
binding: vertex_binding_descriptions[0].binding,
format: VK_FORMAT_R32G32B32A32_SFLOAT,
offset: VertexData.x.offsetof
},
{
location: 1,
binding: vertex_binding_descriptions[0].binding,
format: VK_FORMAT_R32G32_SFLOAT,
offset: VertexData.u.offsetof
}
];
VkPipelineVertexInputStateCreateInfo vertex_input_state_info = {
vertexBindingDescriptionCount: vertex_binding_descriptions.length.to!uint,
pVertexBindingDescriptions: vertex_binding_descriptions.ptr,
vertexAttributeDescriptionCount: vertex_attribute_descriptions.length.to!uint,
pVertexAttributeDescriptions: vertex_attribute_descriptions.ptr
};
VkPipelineInputAssemblyStateCreateInfo input_assembly_state_info = {
topology: VK_PRIMITIVE_TOPOLOGY_TRIANGLE_LIST,
primitiveRestartEnable: VK_FALSE
};
VkPipelineViewportStateCreateInfo viewport_state_info = {
viewportCount: 1,
pViewports: null,
scissorCount: 1,
pScissors: null
};
VkPipelineRasterizationStateCreateInfo rasterization_state_info = {
depthBiasClamp: 0.0,
polygonMode: VK_POLYGON_MODE_FILL,
cullMode: VK_CULL_MODE_FRONT_AND_BACK,
frontFace: VK_FRONT_FACE_COUNTER_CLOCKWISE,
lineWidth: 1
};
VkPipelineMultisampleStateCreateInfo multisample_state_info = {
rasterizationSamples: VK_SAMPLE_COUNT_1_BIT,
minSampleShading: 1
};
VkPipelineColorBlendAttachmentState[] color_blend_attachment_states = [
{
blendEnable: VK_FALSE,
srcColorBlendFactor: VK_BLEND_FACTOR_ONE,
dstColorBlendFactor: VK_BLEND_FACTOR_ZERO,
colorBlendOp: VK_BLEND_OP_ADD,
srcAlphaBlendFactor: VK_BLEND_FACTOR_ONE,
dstAlphaBlendFactor: VK_BLEND_FACTOR_ZERO,
alphaBlendOp: VK_BLEND_OP_ADD,
colorWriteMask:
VK_COLOR_COMPONENT_R_BIT |
VK_COLOR_COMPONENT_G_BIT |
VK_COLOR_COMPONENT_B_BIT |
VK_COLOR_COMPONENT_A_BIT
}
];
VkPipelineColorBlendStateCreateInfo color_blend_state_info = {
logicOpEnable: VK_FALSE,
logicOp: VK_LOGIC_OP_COPY,
attachmentCount: color_blend_attachment_states.length.to!uint,
pAttachments: color_blend_attachment_states.ptr,
blendConstants: [ 0, 0, 0, 0 ]
};
VkDynamicState[] dynamic_states = [
VK_DYNAMIC_STATE_VIEWPORT,
VK_DYNAMIC_STATE_SCISSOR
];
VkPipelineDynamicStateCreateInfo dynamic_state_info = {
dynamicStateCount: dynamic_states.length.to!uint,
pDynamicStates: dynamic_states.ptr
};
VkGraphicsPipelineCreateInfo pipeline_info = {
stageCount: shader_stage_infos.length.to!uint,
pStages: shader_stage_infos.ptr,
pVertexInputState: &vertex_input_state_info,
pInputAssemblyState: &input_assembly_state_info,
pTessellationState: null,
pViewportState: &viewport_state_info,
pRasterizationState: &rasterization_state_info,
pMultisampleState: &multisample_state_info,
pDepthStencilState: null,
pColorBlendState: &color_blend_state_info,
pDynamicState: &dynamic_state_info,
layout: pipeline_layout,
renderPass: render_pass,
subpass: 0,
basePipelineHandle: VK_NULL_HANDLE,
basePipelineIndex: -1
};
VkPipeline[1] pipelines;
checkVk(device.dispatch.CreateGraphicsPipelines(VK_NULL_HANDLE, 1, [pipeline_info].ptr, pipelines.ptr));
pipeline = pipelines[0];
Drawing:
if(device.dispatch.WaitForFences(1, [fence].ptr, VK_FALSE, 1000000000) != VK_SUCCESS)
throw new StringException("timed out waiting for fence");
device.dispatch.ResetFences(1, [fence].ptr);
uint image_index;
switch(device.dispatch.AcquireNextImageKHR(swapchain.swapchain, uint64_t.max, image_available_semaphore, VK_NULL_HANDLE, &image_index)) {
case VK_SUCCESS:
case VK_SUBOPTIMAL_KHR:
break;
case VK_ERROR_OUT_OF_DATE_KHR:
on_window_size_changed();
break;
default:
throw new StringException("unhandled vk result on swapchain image acquisition");
}
if(framebuffer != VK_NULL_HANDLE) device.dispatch.DestroyFramebuffer(framebuffer);
VkFramebufferCreateInfo framebuffer_info = {
renderPass: swapchain.render_pass,
attachmentCount: 1,
pAttachments: [swapchain.image_resources[image_index].image_view].ptr,
width: swapchain.extent.width,
height: swapchain.extent.height,
layers: 1
};
checkVk(device.dispatch.CreateFramebuffer(&framebuffer_info, &framebuffer));
VkCommandBufferBeginInfo cmd_begin_info = { flags: VK_COMMAND_BUFFER_USAGE_ONE_TIME_SUBMIT_BIT };
VkImageSubresourceRange image_subresource_range = {
aspectMask: VK_IMAGE_ASPECT_COLOR_BIT,
baseMipLevel: 0,
levelCount: 1,
baseArrayLayer: 0,
layerCount: 1,
};
VkImageMemoryBarrier barrier_from_present_to_draw = {
srcAccessMask: VK_ACCESS_MEMORY_READ_BIT,
dstAccessMask: VK_ACCESS_MEMORY_READ_BIT,
oldLayout: VK_IMAGE_LAYOUT_PRESENT_SRC_KHR,
newLayout: VK_IMAGE_LAYOUT_PRESENT_SRC_KHR,
srcQueueFamilyIndex: device.present_queue.family_index,
dstQueueFamilyIndex: device.graphics_queue.family_index,
image: swapchain.image_resources[image_index].image,
subresourceRange: image_subresource_range
};
VkImageMemoryBarrier barrier_from_draw_to_present = {
srcAccessMask: VK_ACCESS_MEMORY_READ_BIT,
dstAccessMask: VK_ACCESS_MEMORY_READ_BIT,
oldLayout: VK_IMAGE_LAYOUT_PRESENT_SRC_KHR,
newLayout: VK_IMAGE_LAYOUT_PRESENT_SRC_KHR,
srcQueueFamilyIndex: device.graphics_queue.family_index,
dstQueueFamilyIndex: device.present_queue.family_index,
image: swapchain.image_resources[image_index].image,
subresourceRange: image_subresource_range
};
VkViewport viewport = {
x: 0,
y: 0,
width: swapchain.extent.width,
height: swapchain.extent.height,
minDepth: 0,
maxDepth: 1
};
VkRect2D scissor = {
offset: {
x: 0,
y: 0
},
extent: swapchain.extent
};
VkClearValue[] clear_values = [
{ color: { [ 1.0, 0.8, 0.4, 0.0 ] } }
];
VkRenderPassBeginInfo render_pass_begin_info = {
renderPass: swapchain.render_pass,
framebuffer: framebuffer,
renderArea: {
offset: {
x: 0,
y: 0
},
extent: swapchain.extent
},
clearValueCount: clear_values.length.to!uint,
pClearValues: clear_values.ptr
};
device.dispatch.commandBuffer = command_buffer;
device.dispatch.BeginCommandBuffer(&cmd_begin_info);
if(device.graphics_queue.handle != device.present_queue.handle)
device.dispatch.CmdPipelineBarrier(
VK_PIPELINE_STAGE_COLOR_ATTACHMENT_OUTPUT_BIT,
VK_PIPELINE_STAGE_COLOR_ATTACHMENT_OUTPUT_BIT,
0, 0, null, 0, null, 1,
&barrier_from_present_to_draw
);
device.dispatch.CmdBeginRenderPass(&render_pass_begin_info, VK_SUBPASS_CONTENTS_INLINE);
device.dispatch.CmdBindPipeline(VK_PIPELINE_BIND_POINT_GRAPHICS, swapchain.pipeline);
device.dispatch.CmdSetViewport(0, 1, &viewport);
device.dispatch.CmdSetScissor(0, 1, &scissor);
const(ulong) vertex_buffer_offset = 0;
device.dispatch.CmdBindVertexBuffers(0, 1, &vertex_buffer, &vertex_buffer_offset);
device.dispatch.CmdBindDescriptorSets(VK_PIPELINE_BIND_POINT_GRAPHICS, pipeline_layout, 0, 1, &descriptor_set, 0, null);
device.dispatch.CmdDraw(draw_count, 1, 0, 0);
device.dispatch.CmdEndRenderPass();
if(device.graphics_queue.handle != device.present_queue.handle)
device.dispatch.CmdPipelineBarrier(
VK_PIPELINE_STAGE_COLOR_ATTACHMENT_OUTPUT_BIT,
VK_PIPELINE_STAGE_BOTTOM_OF_PIPE_BIT,
0, 0, null, 0, null, 1,
&barrier_from_draw_to_present
);
checkVk(device.dispatch.EndCommandBuffer());
device.dispatch.commandBuffer = VK_NULL_HANDLE;
VkSubmitInfo submit_info = {
waitSemaphoreCount: 1,
pWaitSemaphores: [image_available_semaphore].ptr,
pWaitDstStageMask: castFrom!(VkPipelineStageFlagBits*).to!(const(uint)*)([VK_PIPELINE_STAGE_TRANSFER_BIT].ptr),
commandBufferCount: 1,
pCommandBuffers: [command_buffer].ptr,
signalSemaphoreCount: 1,
pSignalSemaphores: [rendering_finished_semaphore].ptr
};
checkVk(device.dispatch.vkQueueSubmit(device.graphics_queue.handle, 1, [submit_info].ptr, fence));
VkPresentInfoKHR present_info = {
waitSemaphoreCount: 1,
pWaitSemaphores: [rendering_finished_semaphore].ptr,
swapchainCount: 1,
pSwapchains: [swapchain.swapchain].ptr,
pImageIndices: [image_index].ptr
};
switch(device.dispatch.vkQueuePresentKHR(device.present_queue.handle, &present_info)) {
case VK_SUCCESS:
break;
case VK_ERROR_OUT_OF_DATE_KHR:
case VK_SUBOPTIMAL_KHR:
on_window_size_changed();
break;
default:
throw new StringException("unhandled vk result on presentation");
}
(I can't embed the images because my rep is too low, sorry)
Program Outputs:
OpenGL draws the cube as expected
OpenGL Output
Vulkan does not render anything except for the clear color.
UPDATE:
After fixing the cull mode by changing it to VK_CULL_MODE_NONE, this is the result I get:
Output after cull mode fix
VK_CULL_MODE_FRONT_AND_BACK
I think this is your problem :)
After cull mode fix, seems that your problem in your vertex data layout. Vulkan expects (accordingly to your layout binding) something like
struct Vertex {
vec4 x;
vec2 u;
};
Vertex VertexData[] = {...};
because you set VK_VERTEX_INPUT_RATE_VERTEX in your vertex_binding_descriptions.inputRate field.
And it seems that in your case you should set VK_VERTEX_INPUT_RATE_INSTANCE instead to work with buffers after each other.
Fix: Have seen your new comment, it looks like i misunderstood your vertices layout, so it won't help.

how to create 0-1 matrix from probability matrix

(In any language) For a research project, I am stuck on how to convert a matrix P of probability values to a matrix A such that A_ij = 1 with probability P_ij and 0 otherwise? I have looked through various random number generator documentations, but have been unable to figure out how to do this.
If I understand correctly:
In [11]: p = np.random.uniform(size=(5,5))
In [12]: p
Out[12]:
array([[ 0.45481883, 0.21242567, 0.3124863 , 0.00485797, 0.31970718],
[ 0.91995847, 0.29907277, 0.59154085, 0.85847147, 0.13227595],
[ 0.91914631, 0.5495813 , 0.58648856, 0.08037582, 0.23005148],
[ 0.12464628, 0.70657028, 0.75975869, 0.77632964, 0.24587041],
[ 0.69259133, 0.183515 , 0.65500547, 0.19526148, 0.26975325]])
In [13]: a = (p.round(1)==0.7).astype(np.int8)
In [14]: a
Out[14]:
array([[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 1, 0, 0, 0],
[1, 0, 1, 0, 0]], dtype=int8)