Loading [MathJax]/extensions/Safe.js

import math
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator as MNL
from matplotlib.colors import ListedColormap as LC
import pandas as pd
from pandas.api.types import CategoricalDtype
import seaborn as sns

#Get all pairs of numbers such that:
#1) Both the product and sum are at most 31.
#2) At least one of the product and sum are at most 12. 
def get_date_pairs() :
    texts = ["01", "02", "03", "04", "05", "06",
             "07", "08", "09", "10", "11", "12"]
    data = []
    for a in texts[0:6]:
        ai = int(a)
        for b in texts[ai-1:12]:
            bi = int(b)
            pi = ai*bi
            si = ai+bi
            
            if ai == bi:
                #One possibility for day/month.
                k1 = 1
            else :
                #Two possibilities for day/month.
                k1 = 2            

            if (pi <= 31 and si <= 31):
                if (pi <= 12 or si <= 12):

                    #One possibility for day/month.
                    k2 = 1  
                    
                    if (pi <= 12 and si <= 12):
                        #Two possibilities for day/month.
                        k2 = 2
                        
                        if pi == si:
                            #Unless they are both the same, pi = si = 4.
                            k2 = 1
                    
                    p = str(pi)
                    if pi < 10 :
                        p = "0" + str(pi)
                    s = str(si)
                    if si < 10 :
                        s = "0" + str(si)

                    
                    label = s + "/" + p
                    if pi < si :
                        label = p + "/" + s
                        
                    datum = (b, a, b + "/" + a + " -->\n" + label, k1*k2)
                    data.append(datum)
    return(data)



#Returns a dataframe of Number String A, Number String B, Label, and Group.
def get_data_df() :

    #Get brood pairs.
    data = get_date_pairs()
    #Initiate lists.
    num1 = []
    num2 = []
    label = []
    group = []
    
    for d in data :
        num1.append(d[0])
        num2.append(d[1])
        label.append(d[2])
        group.append(d[3])
    
    df = pd.DataFrame(data = {"A": num1,
                              "B": num2,
                              "Label": label,
                              "Group": group})
    return(df)

sns.set()
fig = plt.figure(figsize = (15, 9))
ax = fig.add_subplot()

cmap = LC(["#7AD151", "#2A788E", "#440154"])

df = get_data_df()
ns = ["1 Possibility",
      "2 Possibilities",
      "4 Possibilities"]
df['Group'] = df['Group'].astype(CategoricalDtype(categories = [1,2,4],
                                                  ordered = True))
scatter = plt.scatter(df["A"],
          df["B"],
          c = df['Group'].cat.codes,
          cmap = cmap,
          s = 0.01,
          alpha = 1 )

for i in range(len(df)) :
    
    x = int(df["A"][i])-1
    y = int(df["B"][i])-1
    c = ["",
         "#7AD151",
         "#2A788E",
         "",
         "#440154"][df["Group"][i]]
    
    fill = plt.fill((x-0.45, x+0.45, x+0.45, x-0.45, x-0.45),
             (y+0.15, y+0.15, y-0.15, y-0.15, y+0.15),
             c = c,
             ec = 'k',
             alpha = 0.6)
    
    plt.text(x,
             y,
             df["Label"][i],
             ha = 'center',
             va = 'center',
             size = 13,
             weight='bold')

#Title setup.
ax.set_title('Pairs of Dates', fontsize = 24)

#X-axis setup.
ax.set_xlabel('Number', fontsize = 22)

#Y-axis setup.
ax.set_ylabel('Number', fontsize = 22)

ax.tick_params(axis = 'both', which = 'major', labelsize = 18)

plt.legend(title = "  Pair of Dates Possibilities",
           title_fontsize = 20,
           handles = scatter.legend_elements()[0],
           labels = ns,
           ncol = 1,
           loc = "upper right",
           fontsize = 18);

fig.savefig("2023.01.20 Express.png")

df

	A	B	Label	Group
0	01	01	01/01 -->\n01/02	2
1	02	01	02/01 -->\n02/03	4
2	03	01	03/01 -->\n03/04	4
3	04	01	04/01 -->\n04/05	4
4	05	01	05/01 -->\n05/06	4
5	06	01	06/01 -->\n06/07	4
6	07	01	07/01 -->\n07/08	4
7	08	01	08/01 -->\n08/09	4
8	09	01	09/01 -->\n09/10	4
9	10	01	10/01 -->\n10/11	4
10	11	01	11/01 -->\n11/12	4
11	12	01	12/01 -->\n12/13	2
12	02	02	02/02 -->\n04/04	1
13	03	02	03/02 -->\n05/06	4
14	04	02	04/02 -->\n06/08	4
15	05	02	05/02 -->\n07/10	4
16	06	02	06/02 -->\n08/12	4
17	07	02	07/02 -->\n09/14	2
18	08	02	08/02 -->\n10/16	2
19	09	02	09/02 -->\n11/18	2
20	10	02	10/02 -->\n12/20	2
21	03	03	03/03 -->\n06/09	2
22	04	03	04/03 -->\n07/12	4
23	05	03	05/03 -->\n08/15	2
24	06	03	06/03 -->\n09/18	2
25	07	03	07/03 -->\n10/21	2
26	08	03	08/03 -->\n11/24	2
27	09	03	09/03 -->\n12/27	2
28	04	04	04/04 -->\n08/16	1
29	05	04	05/04 -->\n09/20	2
30	06	04	06/04 -->\n10/24	2
31	07	04	07/04 -->\n11/28	2
32	05	05	05/05 -->\n10/25	1
33	06	05	06/05 -->\n11/30	2

df['Group'] = pd.to_numeric(df['Group'])
df['Group'].sum()

95

Rohan Lewis

2023.01.23