RobertTalbert

def flatten_list(lst): 
  flattened = []
  for item in lst:
    if isinstance(item, list):
      flattened.extend(flatten_list(item))
    else:
      flattened.append(item)
  return flattened

RobertTalbert

[3, 13, 23, 43, 53, 73, 83, 103, 113, 163, 173, 193, 223, 233, 263, 283, 293, 
313, 353, 373, 383, 433, 443, 463, 503, 523, 563, 593, 613, 643, 653, 673, 683, 
733, 743, 773, 823, 853, 863, 883, 953, 983, 1013, 1033, 1063, 1093, 1103, 1123, 
1153, 1163, 1193, 1223, 1283, 1303, 1373, 1423, 1433, 1453, 1483, 1493, 1523, 
1553, 1583, 1613, 1663, 1733, 1783, 1823, 1823, 1873, 1913, 1933, 1973, 1993, 
2003, 2053, 2083, 2113, 2143, 2153, 2203, 2213, 2273, 2293, 2333, 2383, 2393, 
2423, 2473, 2503, 2543, 2593, 2633, 2663, 2683, 2693, 2713, 2753, 2803, 2833, 
2843, 2903, 2953, 2963]

RobertTalbert

# Load libraries

import networkx as nx
import matplotlib.pyplot as plt

# Generate the graph
## The second parameter is a probability that two distinct vertices are adjacent
## Raise the probability for more connections

g = nx.gnp_random_graph(8, 0.5)

RobertTalbert

# List of trails in North Ottawa Dunes park given as tuples. 
# Vertex 100 = Trailhead in Coast Guard Park
# Vertex 200 = Trailhead at North Beach Park
# Vertex 300 = Trail endpoint at Hoffmaster State Park 

[(100,1,.30), (1,24,.16), (1,2,.20), (2,19,.11), (19,25,.07), 
(19,20,.16), (20,200, .16), (2,3,.49), (3,23,.11), (3,4,.34), 
(4,23,.34), (4,18,.07), (4,21,.22), (5,21,.09), (5,6,.16), 
(5,15,.23), (6,7,.34), (6,16,.07), (7,12,.12), (7,8,.15), (8,9,.21), 
(8,13,.25), (9,10,.06), (10,11,.21), (10,12,.51), (11,300,.12), 

RobertTalbert

# Generate a random tree
random_tree = nx.random_tree(n)

# Draw the tree
nx.draw(random_tree, with_labels=True, node_color="lightblue", font_weight="bold")
plt.show()

RobertTalbert

# Python code for MTH 325 F24 Homework 5

## 1. Iterating through a list

vertex_list = [(2,3), (2,4), (4,5), (1,3)]
for vertex in vertex_list: 
  print(vertex[1])
  
## 2. A dictionary

RobertTalbert

# Libraries

library(dplyr)
library(tidyverse)
library(ggplot2)

# Load data
colnames <- c("time", "email", "name", "section", 
              "challenge", "support", 
              "competence", "autonomy", "relatedness",

RobertTalbert

Yearly note template

Based on the official GTD yearly review template and this checklist of GTD review items.

Areas of focus

• Review [[Areas of Focus]]
• Do any of these need changing?
• Do I need to add any?
• Do I need to combine any?

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aliases

(insert tags here)

Definition

[!tldr] Definition Contents

RobertTalbert

# Use basic logic commands to "fake" set operations. 
# Here's union: 

U = [1,2,3,4,5,6,7,8,9,10]
A = [1,2,3,4,5,6]
B = [2,4,6]

# A union B
[x for x in U if ((x in A) or (x in B))]