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data_visualisation.py 1.48 KiB
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import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns 
from cycler import cycler
import json
import dataframe_image as dfi

#make the plots a bit less ugly

CB91_Blue = '#2CBDFE'
CB91_Green = '#47DBCD'
CB91_Pink = '#F3A0F2'
CB91_Purple = '#9D2EC5'
CB91_Violet = '#661D98'
CB91_Amber = '#F5B14C'

color_list = [CB91_Pink, CB91_Blue, CB91_Green, CB91_Amber,
              CB91_Purple, CB91_Violet]
plt.rcParams['axes.prop_cycle'] = plt.cycler(color=color_list)

#some colour palette playing around

cm = sns.cubehelix_palette(start=.5, rot=-.75, as_cmap=True)
cm1 = sns.cubehelix_palette(start=.5, rot=-.5, as_cmap=True)
cm2 = sns.cubehelix_palette(as_cmap=True)

#read data

data_overview = pd.DataFrame(pd.read_csv("data_overview/data_overview.csv"))

# pairplot initial features -- kinda useless in our case, but hey

"""
data_pairplot = sns.pairplot(ceramics_motives)

data_pairplot.savefig(r"project\pictures_general\data_pairplot.png")

"""

data_overview_styled = data_overview.style.background_gradient(cmap=cm)

dfi.export(data_overview_styled, "data_overview/data_overview.png", table_conversion = "matplotlib")

singular_and_averaged_over_fanfics = pd.DataFrame(pd.read_csv("data_overview/singular_and_then_averaged_over_grishaverse_fanfic_metrics.csv"))
saaof = singular_and_averaged_over_fanfics.style.background_gradient(cmap=cm)
dfi.export(saaof, "data_overview/internal_fanfic_metrics.png", table_conversion = "matplotlib")