rayheberer

builtin_data <- data()$results[, "Item"]
builtin_df_names <- builtin_data %>% 
  purrr::set_names() %>% 
  purrr::map(purrr::possibly(get, otherwise = NULL)) %>% 
  purrr::keep(is.data.frame) %>% 
  purrr::map(names) %>% 
  purrr::compact()

rayheberer

colors <- c("red", "green", "yellow")
vehicles <- c("bicycle", "car", "submarine", "airplane")
colored_vehicles <- purrr::cross2(colors, vehicles) %>% 
  purrr::map_chr(~paste(.[[1]], .[[2]]))

rayheberer

colors <- c("red", "green", "yellow")
vehicles <- c("bicycle", "car", "submarine", "airplane")
colored_vehicles <- colors %>% 
  purrr::map(function(color) {
    purrr::map_chr(vehicles, ~paste(color, .))
  }) %>% 
  purrr::flatten() %>% 
  unlist()

rayheberer

animals <- c("lion", "tiger", "rhinoceros")
animal_nchars <- animals %>% 
  purrr::set_names() %>% 
  purrr::map(nchar)

rayheberer

Blood of Eden is made up of humans that left earth before the resurrection. Their "eden" is the earth itself, and they seek "revenge for the 10 billion"

The 10 billion are the humans who were left on Earth when the resurrection - presumably following some extinction event - occurred.

John orchestrated the extinction in order to ascend to godhood. By killing all life in the solar system, he created the 9 resurrection beasts.

Alecto is the resurrection beast of Earth, which John made into his cavalier and performed perfect Lyctorhood with.

So John is basically a super-lyctor powered by the soul of a planet, rather than of another human.

rayheberer

import plotly.graph_objects as go
import plotly.express as px


def make_barpolar(vals, labels=None, colors=None, layout_options = None, **fig_kwargs):
    # infer slice angles
    num_slices = len(vals)
    theta = [(i + 1.5) * 360 / num_slices for i in range(num_slices)]
    width = [360 / num_slices for _ in range(num_slices)]
    

rayheberer

import tensorflow as tf
from tf.keras.regularizers import l2

def build_nn(input_shape, 
             hidden_units=(20, 20), 
             output_size=1, 
             output_activation=None,
             hidden_activation=tf.nn.relu, 
             dropout=0.0, 
             regularization=0.0):

rayheberer

import tensorflow as tf

def build_nn(input_shape, hidden_units=(20, 20), output_size=1, output_activation=None):
    inputs = tf.keras.Input(shape=input_shape)
    
    hidden = inputs
    for layer_units in hidden_units:
        hidden = tf.keras.layers.Dense(layer_units, activation=tf.nn.relu)(hidden)
        
    outputs = tf.keras.layers.Dense(output_size, activation=output_activation)(hidden)

rayheberer

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d

def plot_surface(domain, fn, grid_samples=100, title=None, **plot_kwargs):
    x = np.linspace(domain[0][0], domain[0][1], grid_samples)
    y = np.linspace(domain[1][0], domain[1][1], grid_samples)
    
    X, Y = np.meshgrid(x, y)
    

rayheberer

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d

def plot_surface(domain, fn, title=None, grid_samples=100):
    x = np.linspace(domain[0][0], domain[0][1], grid_samples)
    y = np.linspace(domain[1][0], domain[1][1], grid_samples)
    
    X, Y = np.meshgrid(x, y)