Ukuran hex dalam hexagon matplotlib berdasarkan kepadatan poin terdekat

Saya punya kode berikut yang menghasilkan gambar berikut

import numpy as np
np.random.seed(3)
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame()
df['X'] = list(np.random.randint(100, size=100)) + list(np.random.randint(30, size=100))
df['Y'] = list(np.random.randint(100, size=100)) + list(np.random.randint(30, size=100))

df['Bin'] = df.apply(lambda row: .1 if row['X'] < 30 and row['Y'] < 30 else .9, axis=1)

fig, ax = plt.subplots(figsize=(10,10))
plt.scatter(df['X'], df['Y'])

Saya membuat grafik data menggunakan hexbins, seperti yang tercantum di bawah ini

from matplotlib import cm

fig, ax = plt.subplots(figsize=(10,10))
hexbin = ax.hexbin(df['X'], df['Y'], C=df['Bin'], gridsize=20, cmap= cm.get_cmap('RdYlBu_r'),edgecolors='black')
plt.show()

Saya ingin mengubah ukuran segi enam berdasarkan kepadatan titik yang diplot di area yang dicakup segi enam. Misalnya, segi enam di kiri bawah (di mana titik-titiknya kompak) akan lebih besar dari segi enam di tempat lain (di mana titik-titiknya jarang). Apakah ada cara untuk melakukan ini?

Sunting: Saya mencoba solusi ini , tetapi saya tidak tahu bagaimana cara mewarnai heksa berdasarkan df ['Bin'], atau bagaimana mengatur ukuran hex min dan max.

from matplotlib.collections import PatchCollection
from matplotlib.path import Path
from matplotlib.patches import PathPatch
fig, ax = plt.subplots(figsize=(10,10))
hexbin = ax.hexbin(df['X'], df['Y'], C=df['Bins'], gridsize=20, cmap= cm.get_cmap('RdYlBu_r'),edgecolors='black')
def sized_hexbin(ax,hc):
    offsets = hc.get_offsets()
    orgpath = hc.get_paths()[0]
    verts = orgpath.vertices
    values = hc.get_array()
    ma = values.max()
    patches = []
    for offset,val in zip(offsets,values):
        v1 = verts*val/ma+offset
        path = Path(v1, orgpath.codes)
        patch = PathPatch(path)
        patches.append(patch)

    pc = PatchCollection(patches, cmap=cm.get_cmap('RdYlBu_r'), edgecolors='black')
    pc.set_array(values)
    ax.add_collection(pc)
    hc.remove()

sized_hexbin(ax,hexbin)
plt.show()

Anda mungkin ingin menghabiskan waktu dalam memahami pemetaan warna.

    import numpy as np
    np.random.seed(3)
    import pandas as pd
    import matplotlib.pyplot as plt
    from matplotlib.collections import PatchCollection
    from matplotlib.path import Path
    from matplotlib.patches import PathPatch
    df = pd.DataFrame()
    df['X'] = list(np.random.randint(100, size=100)) + list(np.random.randint(30, size=100))
    df['Y'] = list(np.random.randint(100, size=100)) + list(np.random.randint(30, size=100))

    df['Bin'] = df.apply(lambda row: .1 if row['X'] < 30 and row['Y'] < 30 else .9, axis=1)

    #fig, ((ax1, ax2)) = plt.subplots(1, 2, sharex=True, sharey=True)
    ax1 = plt.scatter(df['X'], df['Y'])

    fig,ax2 = plt.subplots(figsize=(10,10))
    hexbin = ax2.hexbin(df['X'], df['Y'], C=df['Bin'], gridsize=20,edgecolors='black',cmap= 'RdBu', reduce_C_function=np.bincount) #**

    def sized_hexbin(ax,hc):
        offsets = hc.get_offsets()
        orgpath = hc.get_paths()[0]
        verts = orgpath.vertices
        values = hc.get_array()
        ma = values.max()
        patches = []
        for offset,val in zip(offsets,values):
            v1 = verts*val/ma + offset
            path = Path(v1, orgpath.codes)
            patch = PathPatch(path)
            patches.append(patch)

        pc = PatchCollection(patches, cmap= 'RdBu', edgecolors='black')
        pc.set_array(values)

        ax.add_collection(pc)

        hc.remove()

    sized_hexbin(ax2,hexbin)
    cb = plt.colorbar(hexbin, ax=ax2)

    plt.show()

To plot the chart based on df['bins'] values - 

Need to change the reduce_C_function in #** marked line -

    hexbin = ax2.hexbin(df['X'], df['Y'], C=df['Bin'], gridsize=20,edgecolors='black',cmap= 'RdBu', reduce_C_function=np.sum)

[![enter image description here][2]][2]


  [1]: https://i.stack.imgur.com/kv0U4.png
  [2]: https://i.stack.imgur.com/mb0gD.png

# Another variation of the chart :

# Where size is based on count of points in the bins and color is based on values of the df['bin']./ Also added if condition to control minimum hexbin size.


import numpy as np
np.random.seed(3)
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.collections import PatchCollection
from matplotlib.path import Path
from matplotlib.patches import PathPatch
from functools import partial

mycmp = 'coolwarm'

df = pd.DataFrame()
df['X'] = list(np.random.randint(100, size=100)) + list(np.random.randint(30, size=100))
df['Y'] = list(np.random.randint(100, size=100)) + list(np.random.randint(30, size=100))

df['Bin'] = df.apply(lambda row: .1 if row['X'] < 30 and row['Y'] < 30 else .9, axis=1)

#fig, ((ax1, ax2)) = plt.subplots(1, 2, sharex=True, sharey=True)
ax1 = plt.scatter(df['X'], df['Y'])


fig,ax2 = plt.subplots(figsize=(10,10))
hexbin = ax2.hexbin(df['X'], df['Y'], C=df['Bin'], gridsize=15,edgecolors='black',cmap= newcmp , reduce_C_function=np.bincount)
hexbin2 = ax2.hexbin(df['X'], df['Y'], C=df['Bin'], gridsize=15,edgecolors='black',cmap= newcmp , reduce_C_function=np.mean)

def sized_hexbin(ax,hc,hc2):
    offsets = hc.get_offsets()
    orgpath = hc.get_paths()[0]
    verts = orgpath.vertices
    values1 = hc.get_array()
    values2 = hc2.get_array()
    ma = values1.max()
    patches = []

    for offset,val in zip(offsets,values1):
        # Adding condition for minimum size 
        if (val/ma) < 0.2:
            val_t = 0.2
        else:
            val_t = val/ma
        v1 =  verts*val_t + offset
        path = Path(v1, orgpath.codes)
        print(path)
        patch = PathPatch(path)
        patches.append(patch)

    pc = PatchCollection(patches, cmap=  newcmp)  #edgecolors='black'
    pc.set_array(values2)

    ax.add_collection(pc)
    hc.remove()
    hc2.remove()


sized_hexbin(ax2,hexbin,hexbin2)
cb = plt.colorbar(hexbin2, ax=ax2)

plt.xlim((-5, 100))
plt.ylim((-5, 100))

plt.show()