3. Cool Plots

import matplotlib.pyplot as plt
import numpy as np


def koch_snowflake(order, scale=10):
    """
    Return two lists x, y of point coordinates of the Koch snowflake.

    Parameters
    ----------
    order : int
        The recursion depth.
    scale : float
        The extent of the snowflake (edge length of the base triangle).
    """
    def _koch_snowflake_complex(order):
        if order == 0:
            # initial triangle
            angles = np.array([0, 120, 240]) + 90
            return scale / np.sqrt(3) * np.exp(np.deg2rad(angles) * 1j)
        else:
            ZR = 0.5 - 0.5j * np.sqrt(3) / 3

            p1 = _koch_snowflake_complex(order - 1)  # start points
            p2 = np.roll(p1, shift=-1)  # end points
            dp = p2 - p1  # connection vectors

            new_points = np.empty(len(p1) * 4, dtype=np.complex128)
            new_points[::4] = p1
            new_points[1::4] = p1 + dp / 3
            new_points[2::4] = p1 + dp * ZR
            new_points[3::4] = p1 + dp / 3 * 2
            return new_points

    points = _koch_snowflake_complex(order)
    x, y = points.real, points.imag
    return x, y

x, y = koch_snowflake(order=5)

plt.figure(figsize=(8, 8))
plt.axis('equal')
plt.fill(x, y)
plt.show()

from sklearn.ensemble import HistGradientBoostingRegressor
import numpy as np
import matplotlib.pyplot as plt

# Simple regression function for X * cos(X)
rng = np.random.RandomState(42)
X_1d = np.linspace(0, 10, num=2000)
X = X_1d.reshape(-1, 1)
y = X_1d * np.cos(X_1d) + rng.normal(scale=X_1d / 3)

quantiles = [0.95, 0.5, 0.05]
parameters = dict(loss="quantile", max_bins=32, max_iter=50)
hist_quantiles = {
    f"quantile={quantile:.2f}": HistGradientBoostingRegressor(
        **parameters, quantile=quantile
    ).fit(X, y)
    for quantile in quantiles
}

fig, ax = plt.subplots()
ax.plot(X_1d, y, "o", alpha=0.5, markersize=1)
for quantile, hist in hist_quantiles.items():
    ax.plot(X_1d, hist.predict(X), label=quantile)
_ = ax.legend(loc="lower left")

plt.show()

# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause

import matplotlib.pyplot as plt
import numpy as np

from sklearn import datasets
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import RBF
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.svm import SVC

iris = datasets.load_iris()

# we only take the first two features for visualization
X = iris.data[:, 0:2]
y = iris.target

n_features = X.shape[1]

C = 10
kernel = 1.0 * RBF([1.0, 1.0])  # for GPC

# Create different classifiers.
classifiers = {
    "L1 logistic": LogisticRegression(
        C=C, penalty="l1", solver="saga", multi_class="multinomial", max_iter=10000
    ),
    "L2 logistic (Multinomial)": LogisticRegression(
        C=C, penalty="l2", solver="saga", multi_class="multinomial", max_iter=10000
    ),
    "L2 logistic (OvR)": LogisticRegression(
        C=C, penalty="l2", solver="saga", multi_class="ovr", max_iter=10000
    ),
    "Linear SVC": SVC(kernel="linear", C=C, probability=True, random_state=0),
    "GPC": GaussianProcessClassifier(kernel),
}

n_classifiers = len(classifiers)

plt.figure(figsize=(3 * 2, n_classifiers * 2))
plt.subplots_adjust(bottom=0.2, top=0.95)

xx = np.linspace(3, 9, 100)
yy = np.linspace(1, 5, 100).T
xx, yy = np.meshgrid(xx, yy)
Xfull = np.c_[xx.ravel(), yy.ravel()]

for index, (name, classifier) in enumerate(classifiers.items()):
    classifier.fit(X, y)

    y_pred = classifier.predict(X)
    accuracy = accuracy_score(y, y_pred)
    print("Accuracy (train) for %s: %0.1f%% " % (name, accuracy * 100))

    # View probabilities:
    probas = classifier.predict_proba(Xfull)
    n_classes = np.unique(y_pred).size
    for k in range(n_classes):
        plt.subplot(n_classifiers, n_classes, index * n_classes + k + 1)
        plt.title("Class %d" % k)
        if k == 0:
            plt.ylabel(name)
        imshow_handle = plt.imshow(
            probas[:, k].reshape((100, 100)), extent=(3, 9, 1, 5), origin="lower"
        )
        plt.xticks(())
        plt.yticks(())
        idx = y_pred == k
        if idx.any():
            plt.scatter(X[idx, 0], X[idx, 1], marker="o", c="w", edgecolor="k")

ax = plt.axes([0.15, 0.04, 0.7, 0.05])
plt.title("Probability")
plt.colorbar(imshow_handle, cax=ax, orientation="horizontal")

plt.show()

### Generate data
from skimage.data import coins

orig_coins = coins()

import numpy as np
from scipy.ndimage import gaussian_filter
from skimage.transform import rescale

smoothened_coins = gaussian_filter(orig_coins, sigma=2)
rescaled_coins = rescale(
    smoothened_coins,
    0.2,
    mode="reflect",
    anti_aliasing=False,
)
X = np.reshape(rescaled_coins, (-1, 1))

# Define structure of the data
from sklearn.feature_extraction.image import grid_to_graph
connectivity = grid_to_graph(*rescaled_coins.shape)

# Compute clustering
import time as time
from sklearn.cluster import AgglomerativeClustering

print("Compute structured hierarchical clustering...")
st = time.time()
n_clusters = 27  # number of regions
ward = AgglomerativeClustering(
    n_clusters=n_clusters, linkage="ward", connectivity=connectivity
)
ward.fit(X)
label = np.reshape(ward.labels_, rescaled_coins.shape)
print(f"Elapsed time: {time.time() - st:.3f}s")
print(f"Number of pixels: {label.size}")
print(f"Number of clusters: {np.unique(label).size}")

# plotting
import matplotlib.pyplot as plt
plt.figure(figsize=(5, 5))
plt.imshow(rescaled_coins, cmap=plt.cm.gray)
for l in range(n_clusters):
    plt.contour(
        label == l,
        colors=[
            plt.cm.nipy_spectral(l / float(n_clusters)),
        ],
    )
plt.axis("off")
plt.show()

import matplotlib.pyplot as plt
import numpy as np

# Fixing random state for reproducibility
np.random.seed(19680801)

N = 100
r0 = 0.6
x = 0.9 * np.random.rand(N)
y = 0.9 * np.random.rand(N)
area = (20 * np.random.rand(N))**2  # 0 to 10 point radii
c = np.sqrt(area)
r = np.sqrt(x ** 2 + y ** 2)
area1 = np.ma.masked_where(r < r0, area)
area2 = np.ma.masked_where(r >= r0, area)
plt.scatter(x, y, s=area1, marker='^', c=c)
plt.scatter(x, y, s=area2, marker='o', c=c)
# Show the boundary between the regions:
theta = np.arange(0, np.pi / 2, 0.01)
plt.plot(r0 * np.cos(theta), r0 * np.sin(theta))
plt.show()

import matplotlib.cm as cm
import matplotlib.pyplot as plt
from matplotlib.patches import Circle, PathPatch
from matplotlib.path import Path
from matplotlib.transforms import Affine2D
import numpy as np

# Fixing random state for reproducibility
np.random.seed(19680801)


r = np.random.rand(50)
t = np.random.rand(50) * np.pi * 2.0
x = r * np.cos(t)
y = r * np.sin(t)

fig, ax = plt.subplots(figsize=(6, 6))
circle = Circle((0, 0), 1, facecolor='none',
                edgecolor=(0, 0.8, 0.8), linewidth=3, alpha=0.5)
ax.add_patch(circle)

im = plt.imshow(np.random.random((100, 100)),
                origin='lower', cmap=cm.winter,
                interpolation='spline36',
                extent=([-1, 1, -1, 1]))
im.set_clip_path(circle)

plt.plot(x, y, 'o', color=(0.9, 0.9, 1.0), alpha=0.8)

# Dolphin from OpenClipart library by Andy Fitzsimon
#   <cc:License rdf:about="http://web.resource.org/cc/PublicDomain">
#     <cc:permits rdf:resource="http://web.resource.org/cc/Reproduction"/>
#     <cc:permits rdf:resource="http://web.resource.org/cc/Distribution"/>
#     <cc:permits rdf:resource="http://web.resource.org/cc/DerivativeWorks"/>
#   </cc:License>

# Data to draw the dolphin shapes.
dolphin = """
M -0.59739425,160.18173 C -0.62740401,160.18885 -0.57867129,160.11183
-0.57867129,160.11183 C -0.57867129,160.11183 -0.5438361,159.89315
-0.39514638,159.81496 C -0.24645668,159.73678 -0.18316813,159.71981
-0.18316813,159.71981 C -0.18316813,159.71981 -0.10322971,159.58124
-0.057804323,159.58725 C -0.029723983,159.58913 -0.061841603,159.60356
-0.071265813,159.62815 C -0.080250183,159.65325 -0.082918513,159.70554
-0.061841203,159.71248 C -0.040763903,159.7194 -0.0066711426,159.71091
0.077336307,159.73612 C 0.16879567,159.76377 0.28380306,159.86448
0.31516668,159.91533 C 0.3465303,159.96618 0.5011127,160.1771
0.5011127,160.1771 C 0.63668998,160.19238 0.67763022,160.31259
0.66556395,160.32668 C 0.65339985,160.34212 0.66350443,160.33642
0.64907098,160.33088 C 0.63463742,160.32533 0.61309688,160.297
0.5789627,160.29339 C 0.54348657,160.28968 0.52329693,160.27674
0.50728856,160.27737 C 0.49060916,160.27795 0.48965803,160.31565
0.46114204,160.33673 C 0.43329696,160.35786 0.4570711,160.39871
0.43309565,160.40685 C 0.4105108,160.41442 0.39416631,160.33027
0.3954995,160.2935 C 0.39683269,160.25672 0.43807996,160.21522
0.44567915,160.19734 C 0.45327833,160.17946 0.27946869,159.9424
-0.061852613,159.99845 C -0.083965233,160.0427 -0.26176109,160.06683
-0.26176109,160.06683 C -0.30127962,160.07028 -0.21167141,160.09731
-0.24649368,160.1011 C -0.32642366,160.11569 -0.34521187,160.06895
-0.40622293,160.0819 C -0.467234,160.09485 -0.56738444,160.17461
-0.59739425,160.18173
"""

vertices = []
codes = []
parts = dolphin.split()
i = 0
code_map = {
    'M': Path.MOVETO,
    'C': Path.CURVE4,
    'L': Path.LINETO,
}

while i < len(parts):
    path_code = code_map[parts[i]]
    npoints = Path.NUM_VERTICES_FOR_CODE[path_code]
    codes.extend([path_code] * npoints)
    vertices.extend([[*map(float, y.split(','))]
                     for y in parts[i + 1:][:npoints]])
    i += npoints + 1
vertices = np.array(vertices)
vertices[:, 1] -= 160

dolphin_path = Path(vertices, codes)
dolphin_patch = PathPatch(dolphin_path, 
                        facecolor=(0.6, 0.6, 0.6),
                        edgecolor=(0.0, 0.0, 0.0))
ax.add_patch(dolphin_patch)

vertices = Affine2D().rotate_deg(60).transform(vertices)
dolphin_path2 = Path(vertices, codes)
dolphin_patch2 = PathPatch(dolphin_path2, 
                        facecolor=(0.5, 0.5, 0.5),
                        edgecolor=(0.0, 0.0, 0.0))
ax.add_patch(dolphin_patch2)

plt.show()

import statsmodels.api as sm
import pandas as pd
import matplotlib.pyplot as plt

data_loader = sm.datasets.sunspots.load_pandas()
df = data_loader.data

df
df.head()
df.tail()
#Take the modulo of each value with 1 
#to get the fractional part
fractional_nums = df['SUNACTIVITY'].apply(lambda x: x % 1) 
fractional_nums[fractional_nums > 0].head()
print(df['SUNACTIVITY'].describe())

plt.xlabel('Year')
plt.ylabel('Sun Activity')

myX = list(df["YEAR"])
myY = list(df["SUNACTIVITY"])

plt.plot(myX, myY)
plt.show()