Figure 4: Boxplot

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

from scipy.interpolate import InterpolatedUnivariateSpline

import smpsite as smp

%matplotlib inline

ERROR 1: PROJ: proj_create_from_database: Open of /srv/conda/envs/notebook/share/proj failed

df = pd.read_csv("../../outputs/fig4_5000sim_total.csv")
df

	Unnamed: 0	plong	plat	total_samples	samples_per_sites	S2_vgp	error_angle	S2_vgp_real	n_tot	N	n0	kappa_within_site	site_lat	site_long	outlier_rate	secular_method	kappa_secular	ignore_outliers	hash
0	0	231.233958	89.078084	100.0	2	258.092561	0.921916	191.7229	100	50	2	50	30.0	0.0	0.0	G	NaN	True	-8335591600388957984
1	1	63.804499	89.231872	100.0	2	181.571799	0.768128	191.7229	100	50	2	50	30.0	0.0	0.0	G	NaN	True	-8335591600388957984
2	2	291.665245	87.059161	100.0	2	179.071413	2.940839	191.7229	100	50	2	50	30.0	0.0	0.0	G	NaN	True	-8335591600388957984
3	3	330.248972	87.105972	100.0	2	163.079929	2.894028	191.7229	100	50	2	50	30.0	0.0	0.0	G	NaN	True	-8335591600388957984
4	4	38.224125	87.075199	100.0	2	218.421320	2.924801	191.7229	100	50	2	50	30.0	0.0	0.0	G	NaN	True	-8335591600388957984
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
524995	4995	148.732544	84.169662	96.0	6	-155.962415	5.830338	191.7229	102	17	6	50	30.0	0.0	0.4	G	NaN	vandamme	2995613283241899664
524996	4996	57.944083	88.316125	84.0	6	-296.623262	1.683875	191.7229	102	17	6	50	30.0	0.0	0.4	G	NaN	vandamme	2995613283241899664
524997	4997	332.897508	82.935519	96.0	6	205.145417	7.064481	191.7229	102	17	6	50	30.0	0.0	0.4	G	NaN	vandamme	2995613283241899664
524998	4998	289.433482	82.479833	96.0	6	-27.928311	7.520167	191.7229	102	17	6	50	30.0	0.0	0.4	G	NaN	vandamme	2995613283241899664
524999	4999	315.637990	86.584133	90.0	6	58.631713	3.415867	191.7229	102	17	6	50	30.0	0.0	0.4	G	NaN	vandamme	2995613283241899664

525000 rows × 19 columns

fig, ax = plt.subplots(figsize=(14,6))

outlier_rate = .10

df_ = df[df.outlier_rate == outlier_rate]

def rename_outlier_method(x):
    if x == "vandamme": 
        return "Vandamme"
    elif x == "False":
        return "No detection"
    elif x == "True":
        return "Perfect detection"
    else: 
        raise ValueError()

df_["Sampling Strategy"] = df_.apply(lambda row : rename_outlier_method(row.ignore_outliers), axis=1) 
        
my_pal = {"Vandamme": "#e84118", 
          "No detection": "#ecf0f1",
          "Perfect detection": "#0097e6"}

hue_order = ["Vandamme", "Perfect detection", "No detection"]

# create grouped boxplot 
sns.boxplot(data = df_,
            x = 'samples_per_sites',
            y = 'error_angle',
            hue = 'Sampling Strategy',
            hue_order = hue_order, 
            palette=my_pal,
            fliersize=0.4, 
            boxprops=dict(alpha=.9),
            width=0.8, 
            #orient = "h",
            ax=ax)

plt.legend(loc='upper left', fontsize=16)
ax.set_ylim(0,10)

plt.xlabel("Samples per site ($n_0$)", fontsize=20)
plt.ylabel("Angular error (degrees)", fontsize=20)
plt.yticks(fontsize=18)
plt.xticks([0,1,2,3,4,5,6,7], fontsize=18)

ax.spines[['right', 'top']].set_visible(False)

#plt.savefig("Figure4_{}.pdf".format(outlier_rate), format="pdf", bbox_inches='tight')
#plt.savefig("Figure4_{}.png".format(outlier_rate), format="png", bbox_inches='tight')

/tmp/ipykernel_3098/4131771631.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_["Sampling Strategy"] = df_.apply(lambda row : rename_outlier_method(row.ignore_outliers), axis=1)

VGP scatter

fig, ax = plt.subplots(figsize=(14,6))

df["error_S_vgp"] = (df.S2_vgp ** .5 - df.S2_vgp_real ** .5) / df.S2_vgp_real ** .5 * 100

outlier_rate = .4

df_ = df[df.outlier_rate == outlier_rate]

def rename_outlier_method(x):
    if x == "vandamme": 
        return "Vandamme"
    elif x == "False":
        return "No detection"
    elif x == "True":
        return "Perfect detection"
    else: 
        raise ValueError()

df_["Sampling Strategy"] = df_.apply(lambda row : rename_outlier_method(row.ignore_outliers), axis=1) 
        
my_pal = {"Vandamme": "#e84118", 
          "No detection": "#ecf0f1",
          "Perfect detection": "#0097e6"}

hue_order = ["Vandamme", "Perfect detection", "No detection"]

plt.axhline(y=0, color = 'black', ls="--", lw=1)

# create grouped boxplot 
sns.boxplot(data = df_,
            x = 'samples_per_sites',
            y = 'error_S_vgp',
            hue = 'Sampling Strategy',
            hue_order = hue_order, 
            palette=my_pal,
            fliersize=0.4, 
            boxprops=dict(alpha=.9),
            width=0.8, 
            #orient = "h",
            ax=ax)

plt.legend(loc='upper right', fontsize=16)
ax.set_ylim(-1,1)

plt.xlabel("Samples per site ($n_0$)", fontsize=20)
plt.ylabel("Percentual error for $S_b$", fontsize=20)
plt.yticks(fontsize=18)
plt.xticks([0,1,2,3,4,5,6,7], fontsize=18)
plt.yticks([-100, -75, -50, -25, 0, 25, 50, 75, 100], [ "-100%", "-75%", "-50%","-25%","0%","25%","50%","75%", "100%"])


ax.spines[['right', 'top']].set_visible(False)

plt.savefig("Figure5_{}.pdf".format(outlier_rate), format="pdf", bbox_inches='tight')
plt.savefig("Figure5_{}.png".format(outlier_rate), format="png", bbox_inches='tight')

/tmp/ipykernel_3098/2634743452.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_["Sampling Strategy"] = df_.apply(lambda row : rename_outlier_method(row.ignore_outliers), axis=1)