def plot_gaze_data(sample_df_filt,
fig_path,
subj_id,
session_n,
reward_code,
id_str=None,
fig_name='test',
display_resolution=(1280, 1024)):
from jupyterthemes import jtplot
plt.rcParams['pdf.fonttype'] = 42
plt.rcParams['font.family'] = 'Calibri'
jtplot.style(context='poster', fscale=2, spines=False, theme='grade3')
sns.set_context('poster', font_scale=2)
sns.set_color_codes("muted")
max_gaze_x, max_gaze_y = display_resolution
fig_name = ('gaze_map' + '_sub-' + str(subj_id) + '_sess-' +
str(session_n) + '_cond-' + str(reward_code))
if id_str:
fig_name = fig_name + '_' + id_str
fig = plt.figure()
plt.plot(sample_df.gaze_x, sample_df.gaze_y, '.')
plt.xlim([0, max_gaze_x])
plt.ylim(
[max_gaze_y, 0]
) # ensure that the screen coordinate system is accurate represented (origin at upper left)
center_x_dist = display_resolution[
0] / 5 # as specified in experimental code
center_x, center_y = (max_gaze_x / 2, max_gaze_y / 2)
target_y = center_y + 15 # as specified in experimental code
left_target_x, left_target_y = center_x - center_x_dist, target_y
right_target_x, right_target_y = center_x + center_x_dist, target_y
marker_size = 3000
plt.scatter(center_x, center_y, color='gray', marker='+', s=marker_size)
plt.scatter(left_target_x,
left_target_y,
color='purple',
marker='d',
s=marker_size)
plt.scatter(right_target_x,
right_target_y,
color='purple',
marker='d',
s=marker_size)
if fig_name:
plt.savefig(os.path.join(fig_path, fig_name + '.png'))
return fig, fig_name
def gruvbox_style(pallete="higher_contrast"):
"""Set the current plotting style to gruvbox with higher contrast
pallete
"""
palletes = {
"higher_contrast": [
'#3572C6', '#83a83b', '#c44e52', '#d5c4a1', '#8172b2', '#b57614',
'#8ec07c', '#ff711a', '#d3869b', '#6C7A89', '#77BEDB', '#4168B7',
'#27ae60', '#e74c3c', '#ff914d', '#bc89e0', '#3498db', '#fabd2f',
'#fb4934', '#b16286', '#83a598', '#fe8019', '#b8bb26', '#a89984'
],
"bright": [
'#77BEDB', '#fb4934', '#8ec07c', '#ff914d', '#bc89e0', '#fabd2f',
'#fbf1c7', '#3498db'
],
"paired": [
'#77BEDB', '#4168B7', '#8ec07c', '#b8bb26', '#d3869b', '#fb4934',
'#bdae93', '#ebdbb2'
]
}
new_cycler = cycler.cycler("color", palletes[pallete])
jtplot.style(theme='gruvboxd', context='notebook', figsize=style.figsize)
mpl.rcParams["axes.prop_cycle"] = new_cycler
mpl.rcParams["axes.axisbelow"] = True
style.minor_grid_color = "#32302f"
style.current_style = "gruvbox"
def visualize(x,y, subj_id, session_n, reward_code,
stimulus_onset=500, trial_end=2000, interval_end=4000,
id_str=None, estimator=np.mean):
""" Visualize the trial-averaged task-evoked pupillary response. """
from jupyterthemes import jtplot
plt.rcParams['pdf.fonttype'] = 42
plt.rcParams['font.family'] = 'Calibri'
fig_name = ('tepr' + '_sub-' + str(subj_id) + '_sess-' +
str(session_n) + '_cond-' + str(reward_code))
if id_str:
fig_name = fig_name + '_' + id_str
jtplot.style(context='poster', fscale=2, spines=False, theme='grade3')
sns.set_context('poster', font_scale=2)
sns.set_color_codes("muted")
fig = plt.figure()
sns.lineplot(x, y,
estimator=estimator)
plt.axvline(x=stimulus_onset, linestyle='dashed', color='k')
# plt.axvline(x=trial_end, linestyle='dashed', color='k')
plt.xlabel('time from stimulus onset (ms)'); plt.ylabel('pupil diameter (a.u.)')
# plt.title(fig_name)
plt.xticks(np.arange(0,
interval_end+stimulus_onset, stimulus_onset), np.arange(-1*stimulus_onset,
interval_end, stimulus_onset))
plt.xlim([0, trial_end])
# plt.ylim([3000, 10000])
return fig, fig_name
def plot_evoked_response_map(ordered_samples_df,
ordered_message_df,
fig_name,
fig_path,
trial_end_sample_idx=1500):
"""call signature:
_, samples_pivot = plot_evoked_response_map(sample_df, message_df, fig_name='trial_ordered_evoked_responses')
_= plot_evoked_response_map(rt_ordered_samples_df, rt_ordered_msg_df, fig_name='RT_ordered_evoked_responses')
_= plot_evoked_response_map(random_ordered_samples_df, random_ordered_msg_df, fig_name='random_ordered_evoked_responses')
"""
n_trials = len(ordered_samples_df.trial_epoch.unique())
jtplot.style('grade3',
context='poster',
fscale=1.4,
spines=False,
gridlines='--')
ordered_samples_df = ordered_samples_df.loc[
ordered_samples_df.trial_sample < trial_end_sample_idx]
samples_sparse = ordered_samples_df[[
'trial_sample', 'trial_epoch', 'z_pupil_diameter'
]]
samples_sparse['reset_trial_epoch_idx'] = np.repeat(
np.arange(0, n_trials), trial_end_sample_idx)
# hack to get pivot to respect the stated order of the trial epochs
# otherwise, will sort the index ...
samples_pivot = samples_sparse.pivot(index='reset_trial_epoch_idx',
columns='trial_sample',
values='z_pupil_diameter')
plt.ioff()
plt.figure(1)
fig, ax = plt.subplots(figsize=(10, 10))
sns.heatmap(samples_pivot,
fmt="g",
cmap='viridis',
cbar_kws={'label': 'pupil diameter'},
robust=True,
vmin=0,
vmax=2)
ax.scatter(x=ordered_message_df.trial_response_time,
y=range(n_trials),
marker='.',
color='white',
s=30)
plt.title(fig_name)
plt.ylabel('trial')
plt.savefig(os.path.join(fig_path, fig_name + '.png'))
plt.close()
return fig_name, samples_pivot
def plot_config(theme='grade3',
context='poster',
fscale=1.4,
spines=False,
gridlines='--'):
"""Configure plots."""
jtplot.style(theme=theme,
context=context,
fscale=fscale,
spines=spines,
gridlines=gridlines)
return None
def init_mpl():
import matplotlib as mpl
from jupyterthemes import jtplot
jtplot.style(theme='monokai', context='notebook', ticks=True, grid=True)
colors = mpl.rcParams['axes.prop_cycle'].by_key()['color']
i = 6
colors[i] = hex(int(colors[i].replace('#', '0x'), 16) ^ 0xFFFFFF).replace('0x', '#')
mpl.rcParams['axes.prop_cycle'] = mpl.cycler(color=colors)
mpl.rcParams['path.simplify'] = True
mpl.rcParams['path.simplify_threshold'] = 1
mpl.rcParams['agg.path.chunksize'] = 100000
def _create_figure(self):
# the following import is here in order to avoid a circular import error
from spb.defaults import cfg
use_jupyterthemes = cfg["matplotlib"]["use_jupyterthemes"]
mpl_jupytertheme = cfg["matplotlib"]["jupytertheme"]
if (self._get_mode() == 0) and use_jupyterthemes:
# set matplotlib style to match the used Jupyter theme
try:
from jupyterthemes import jtplot
jtplot.style(mpl_jupytertheme)
except:
pass
is_3Dvector = any([s.is_3Dvector for s in self.series])
aspect = self.aspect
if aspect != "auto":
if aspect == "equal" and is_3Dvector:
# vector_plot uses an aspect="equal" by default. In that case
# we would get:
# NotImplementedError: Axes3D currently only supports the aspect
# argument 'auto'. You passed in 1.0.
# This fixes it
aspect = "auto"
elif aspect == "equal":
aspect = 1.0
else:
aspect = float(aspect[1]) / aspect[0]
if self._kwargs.get("fig", None) is not None:
# We assume we are generating a PlotGrid object, hence the figure
# and the axes are provided by the user.
self._fig = self._kwargs.pop("fig", None)
self.ax = self._kwargs.pop("ax", None)
else:
self._fig = plt.figure(figsize=self.size)
is_3D = [s.is_3D for s in self.series]
if any(is_3D) and (not all(is_3D)):
raise ValueError(
"The matplotlib backend can not mix 2D and 3D.")
kwargs = dict(aspect=aspect)
if all(is_3D):
kwargs["projection"] = "3d"
self.ax = self._fig.add_subplot(1, 1, 1, **kwargs)
def plot_workers(l, centr_time, centr_acc):
t = []
a = []
try:
for i in l:
name1 = "np_arrays/total_time" + str(i) + ".npy"
name2 = "np_arrays/total_acc" + str(i) + ".npy"
t.append(np.load(name1))
a.append(np.load(name2))
t1 = [i[0] for i in t]
t2 = [i[1] for i in t]
a1 = [i[0] for i in a]
a2 = [i[1] for i in a]
jtplot.style(theme='grade3')
figure(figsize=(15, 15))
plt.subplot(221)
plt.plot(l, t1, label='1st pass', marker='x', markersize=4)
plt.plot(l, t2, label='2nd pass', marker='x', markersize=4)
plt.axhline(y=centr_time[0], linestyle='-', label='centr 1')
plt.axhline(y=centr_time[1], linestyle='-', label='centr 2')
plt.xlabel("Number of workers")
plt.ylabel("Time (s)")
title = 'Total time for 1 and 2 passes on the dataset to number of workers'
plt.title(title)
plt.legend()
plt.subplot(222)
plt.plot(l, a1, label='1st pass', marker='x', markersize=4)
plt.plot(l, a2, label='2nd pass', marker='x', markersize=4)
plt.axhline(y=centr_acc[0], linestyle='-', label='centr 1')
plt.axhline(y=centr_acc[1], linestyle='-', label='centr 2')
plt.xlabel("Number of workers")
plt.ylabel("Accuracy")
title = 'Total accuracy for 1 and 2 passes on the dataset to number of workers'
plt.title(title)
plt.legend()
plt.savefig('B_Plots/workers')
plt.show()
except:
print("Something went wrong")
return
def anim_plot(data, lim=None, delta=1e-3):
from jupyterthemes import jtplot
from time import sleep
if lim is None:
data_min, data_max = data.min(0), data.max(0)
lim = ((data_min[0], data_max[0]), (data_min[1], data_max[1]))
%matplotlib notebook
plt.ion()
fig, ax = plt.subplots()
for i in tqdm_notebook(range(len(data))):
ax.scatter(data[:i, 0], data[:i, 1], marker='.')
if lim[0] is not None: ax.set_xlim(lim[0][0], lim[0][1])
if lim[1] is not None: ax.set_ylim(lim[1][0], lim[1][1])
fig.set_size_inches(10, 8)
fig.canvas.draw()
sleep(delta)
ax.clear()
%matplotlib inline
jtplot.style(context='notebook', fscale=2, figsize=(15, 10))
import matplotlib.pyplot as plt
from jupyterthemes import jtplot
jtplot.style(theme='onedork')
def linear_regression(X_train, y_train, X_test, y_test, y_prediction):
plt.plot(X_train, y_train, '.', color='r', label='Train Data')
plt.plot(X_test, y_test, '.', color='r', label='Test Data')
plt.plot(X_test, y_prediction, color='m', label='Prediction')
plt.legend()
plt.show()
return 0
import itertools
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
import pandas as pd
import seaborn as sns
import scipy.sparse as sparse
from ipywidgets import *
from jupyterthemes import jtplot
jtplot.style(theme='grade3', figsize=(16, 10))
################### COLOUR MAPS ######################
from matplotlib.colors import LinearSegmentedColormap
optum_cmap = LinearSegmentedColormap.from_list(
'optum', ["#E87722", "#A32A2E", "#422C88", "#078576", "#627D32"])
optum_cmap_simple = LinearSegmentedColormap.from_list(
'optum', ["#E87722", "#078576"]) # for gradients
########################################################
def make_sequences(df, elements_field="new_elements", exits_field="new_exits"):
"""
Generates sequences of item-exit_code and yields them
"""
for _, row in df.iterrows():
try:
elements = np.array(row[elements_field].split("|")).astype(int)
def butter_bandpass(lowcut, highcut, nyq_rate, order=6):
normal_lowcut = lowcut / nyq_rate
normal_highcut = highcut / nyq_rate
Wn = [normal_lowcut, normal_highcut]
b, a = butter(order, Wn, btype='bandpass', analog=False)
return b, a
#%%
# Plot freq response of filters
import matplotlib.pyplot as plt
from jupyterthemes import jtplot
jtplot.style('chesterish')
# Filter inputs
samp_rate = 44100
nyq_rate = 0.5 * samp_rate
lowcut = 10000
highcut = 8000
b_lowcut = 3000
b_highcut = 10000
lfilt_order = 6
hfilt_order = 6
bfilt_order = 6
# Get filter coefficients
b_low, a_low = butter_lowpass(lowcut, nyq_rate, order=lfilt_order)
b_high, a_high = butter_highpass(highcut, nyq_rate, hfilt_order)
# 기본 패키지 불러오기
import numpy as np
import pandas as pd
import warnings
import mglearn
import matplotlib.pyplot as plt
from jupyterthemes import jtplot
%matplotlib inline
# jtplot style 설정
jtplot.style(theme='gruvboxd', grid=False)
# matplotlib 한글 폰트 설정
plt.rcParams['font.family'] = 'NanumGothic'
# -부호 깨지지 않게 하기
plt.rcParams['axes.unicode_minus'] = False
# 경고 무시
warnings.filterwarnings("ignore")
from tqdm.auto import tqdm
from datetime import datetime
from pytz import timezone, utc
KST = datetime.now(timezone('Asia/Seoul'))
fmt = "%Y_%m_%d_%H_%M_%S"
now_time = KST.strftime(fmt)
print(now_time)
physical_devices_list = tf.config.list_physical_devices('GPU')
physical_devices = physical_devices_list[:4]
tf.config.set_visible_devices(physical_devices, 'GPU')
print(physical_devices)
tf.config.set_soft_device_placement(True)
for idx, device in enumerate(physical_devices):
tf.config.experimental.set_memory_growth(device, True)
from jupyterthemes import jtplot
jtplot.style(theme='grade3')
import logging
logger = logging.getLogger()
logger.setLevel(logging.CRITICAL)
logging.disable(sys.maxsize)
from cal_score import scoring
from pathlib import Path
from collections import Counter
import seaborn as sns
import json
from sklearn.utils import class_weight
# In[2]:
np_load_old = np.load
np.load = lambda *a, **k: np_load_old(*a, allow_pickle=True, **k)
from sys import platform
import os
import numpy as np
from jupyterthemes import jtplot
import matplotlib.pyplot as plt
plt.rcParams['pdf.fonttype'] = 42
plt.rcParams['font.family'] = 'DejaVu Sans'
import seaborn as sns
import pandas as pd
pd.options.mode.chained_assignment = None # default='warn'
import glob
import scipy.stats as stats
jtplot.style('grade3',
context='poster',
fscale=1.4,
spines=False,
gridlines='--')
def rt_order_df(samples_df, messages_df):
rt_ordered_msg_df = messages_df.sort_values(
by='trial_response_time', ascending=True).reset_index(drop=True)
rt_ordered_trials = rt_ordered_msg_df.trial.values
rt_ordered_samples_df_temp = pd.DataFrame()
rt_ordered_samples_df_temp = [
rt_ordered_samples_df_temp.append(
tqdm.pandas()
# constants
ML20MPATH = "../data/ml-20m/"
MODELSPATH = "../models/"
DATAPATH = "../data/streamlit/"
SHOW_TOPN_MOVIES = (
200 # recommend me a movie. show only top ... movies, higher values lead to slow ux
)
# disable it if you get an error
from jupyterthemes import jtplot
jtplot.style(theme="grade3")
def render_header():
st.write(
"""
<p align="center">
<img src="https://raw.githubusercontent.com/awarebayes/RecNN/master/res/logo%20big.png">
</p>
<p align="center">
<iframe src="https://ghbtns.com/github-btn.html?user=awarebayes&repo=recnn&type=star&count=true&size=large" frameborder="0" scrolling="0" width="160px" height="30px"></iframe>
<iframe src="https://ghbtns.com/github-btn.html?user=awarebayes&repo=recnn&type=fork&count=true&size=large" frameborder="0" scrolling="0" width="158px" height="30px"></iframe>
<iframe src="https://ghbtns.com/github-btn.html?user=awarebayes&type=follow&count=true&size=large" frameborder="0" scrolling="0" width="220px" height="30px"></iframe>
</p>
# coding: utf-8
# # 你的第一个神经网络
#
# 在此项目中，你将构建你的第一个神经网络，并用该网络预测每日自行车租客人数。我们提供了一些代码，但是需要你来实现神经网络（大部分内容）。提交此项目后，欢迎进一步探索该数据和模型。
# In[1]:
get_ipython().magic('matplotlib inline')
get_ipython().magic("config InlineBackend.figure_format = 'retina'")
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from jupyterthemes import jtplot
jtplot.style(ticks=True)
# ## 加载和准备数据
#
# 构建神经网络的关键一步是正确地准备数据。不同尺度级别的变量使网络难以高效地掌握正确的权重。我们在下方已经提供了加载和准备数据的代码。你很快将进一步学习这些代码！
# In[2]:
data_path = 'Bike-Sharing-Dataset/hour.csv'
rides = pd.read_csv(data_path)
# In[3]:
from matplotlib.colors import LinearSegmentedColormap
import matplotlib.pylab as plt
import matplotlib
from sklearn.manifold import TSNE
import pandas as pd
import numpy as np
import datetime
import copy
import time
from robusta import utils, metrics
import seaborn as sns
from jupyterthemes import jtplot
jtplot.style('gruvboxd')
matplotlib.use('nbagg')
__all__ = ['plot_fold', 'print_fold']
def plot_fold(cv):
if not cv.plot:
return
scores = cv.get_results('score')
k_folds = len(scores)
n_folds = cv.n_folds
if k_folds == 1:
import pandas as pd
import numpy as np
from os import environ as env
# Pandas options
pd.options.display.max_columns = 30
# Visualization
import matplotlib.pyplot as plt
import seaborn as sns
try:
from jupyterthemes import jtplot
jtplot.style(theme = 'oceans16')
except ImportError:
plt.style.use('bmh')
# If in ipython, load autoreload extension & run plots inline
if 'ipython' in globals():
print('\nWelcome to IPython!')
ipython.magic('load_ext autoreload')
ipython.magic('autoreload 2')
ipython.magic('matplotlib inline')
print('Usual libraries have been loaded.')
import torch, fastai, sys, os
from fastai.vision import *
from fastai.vision.data import SegmentationProcessor
import ants
from ants.core.ants_image import ANTsImage
from jupyterthemes import jtplot
sys.path.insert(0, './exp')
jtplot.style(theme='gruvboxd')
# Set a root directory
path = Path('/home/ubuntu/MultiCampus/MICCAI_BraTS_2019_Data_Training')
def is_mod(fn: str, mod: str) -> bool:
"Check if file path contains a specified name of modality used for MRI"
import re
r = re.compile('.*' + mod, re.IGNORECASE)
return True if r.match(fn) else False
def is_mods(fn: str, mods: Collection[str]) -> bool:
"Check if file path contains specified names of modality used for MRI"
import re
return any([is_mod(fn, mod) for mod in mods])
def _path_to_same_str(p_fn):
"path -> str, but same on nt+posix, for alpha-sort only"
s_fn = str(p_fn)
s_fn = s_fn.replace('\\', '.')
s_fn = s_fn.replace('/', '.')
"""
These is the standard setup for the notebooks.
"""
%matplotlib inline
%load_ext autoreload
%autoreload 2
from jupyterthemes import jtplot
jtplot.style(theme='onedork', context='notebook', ticks=True, grid=False)
import pandas as pd
pd.options.display.max_rows = 999
pd.options.display.max_columns = 999
pd.set_option("display.max_columns", None)
import numpy as np
import os
import matplotlib.pyplot as plt
#plt.style.use('paper')
#import data
import copy
from rolldecay.bis_system import BisSystem
from rolldecay import database
from mdldb.run
from sklearn.pipeline import Pipeline
from rolldecayestimators.transformers import CutTransformer, LowpassFilterDerivatorTransformer, ScaleFactorTransformer, OffsetTransformer
from rolldecayestimators.direct_estimator_cubic import EstimatorQuadraticB, EstimatorCubic
from rolldecayestimators.ikeda_estimator import IkedaQuadraticEstimator
import rolldecayestimators.equations as equations
#!/usr/bin/env python
# coding: utf-8
# In[ ]:
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
try:
from jupyterthemes import jtplot
jtplot.style()
except:
pass
# In[ ]:
ip = pd.read_csv('../../../data/cleandata/Info pluviometricas/Merged Data/merged.csv',
sep = ';',
dtype = {'Local_0': object, 'Local_1':object,
'Local_2':object, 'Local_3':object})
print(list(ip.columns))
ip.head()
# #### Umidade Relativa
#!/usr/bin/env python
# coding: utf-8
# In[7]:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from IPython.display import IFrame
from jupyterthemes import jtplot
# In[10]:
jtplot.style(theme='monokai')
# In[2]:
plt.rc('font', size=14)
plt.rc('figure', figsize=(8, 6))
plt.rc('text', usetex=True)
# lo que me pregunto es qué tan mal estaba el voumen. efectivamente los cambios de parametro de red son demasiado pequeños y estan pasando cosas raras.
# Notar que hay cambios de pendiente para a = 1.005
# In[3]:
datavol = pd.read_csv('datavol.dat', sep=' ')
# In[4]:
datavol
import numpy as np
import seaborn as sns
from matplotlib import pyplot as plt
import xgboost as xgb
from datetime import datetime
from sklearn.metrics import classification_report, accuracy_score, roc_curve, auc, roc_auc_score, plot_roc_curve
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score, cross_validate
from sklearn.model_selection import GridSearchCV
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, classification_report
from jupyterthemes import jtplot
jtplot.style(theme='monokai', context='notebook', ticks=True, grid=False)
def plot_roc(testy, lr_probs):
# calculate scores
ns_probs = [0 for _ in range(len(testy))]
ns_auc = roc_auc_score(testy, ns_probs)
lr_auc = roc_auc_score(testy, lr_probs)
# summarize scores
print('No Skill: ROC AUC=%.3f' % (ns_auc))
print('Logistic: ROC AUC=%.3f' % (lr_auc))
# calculate roc curves
ns_fpr, ns_tpr, _ = roc_curve(testy, ns_probs)
lr_fpr, lr_tpr, _ = roc_curve(testy, lr_probs)
# plot the roc curve for the model
plt.plot(ns_fpr, ns_tpr, linestyle='--', label='No Skill')
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from jupyterthemes import jtplot
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.metrics import mean_squared_error
jtplot.style(theme="monokai")
data = pd.read_csv("data/reg_demo_data.csv")
data.head()
data.drop(list(data.columns)[0], axis=1, inplace=True)
data.head()
print(list(data.columns))
X = np.array(data['x']).reshape(-1, 1)
Y = np.array(data['y']).reshape(-1, 1)
X_train, X_test, Y_train, Y_test = train_test_split(X,
Y,
test_size=0.2,
random_state=5)
# Linear Regression Model
model = LinearRegression()
model.fit(X_train, Y_train)
y_train_pred = model.predict(X_train)
# Regression Assessment
curve_df = pd.DataFrame()
curve_df['curve 1'] = df[df['Curves'].str.startswith("curve 1")]['Values'].reset_index(drop=True)
curve_df['curve 2'] = df[df['Curves'].str.startswith("curve 2")]['Values'].reset_index(drop=True)
curve_df['curve 3'] = df[df['Curves'].str.startswith("curve 3")]['Values'].reset_index(drop=True)
return curve_df
processed_df = process(df)
processed_df.head()
# Plot
from jupyterthemes import jtplot
jtplot.style(theme="monokai", grid=False)
x = np.arange(-1000, 1000)
fig, ((ax1, ax2, ax3)) = plt.subplots(1,3, figsize=(30,15))
ax1.scatter(x, processed_df['curve 1'])
ax1.set_xlabel("Values", size=20, color="silver")
ax1.set_ylabel("Curve 1 Function", size=16, color="silver")
ax2.scatter(x, processed_df['curve 2'])
ax2.set_xlabel("Values", size=20, color="silver")
ax2.set_ylabel("Curve 2 Function", size=20, color="silver")
ax3.scatter(x, processed_df['curve 3'])
ax3.set_xlabel("Values", size=20, color="silver")
ax3.set_ylabel("Curve 3 Function", size=20, color="silver")
plt.suptitle("Scatter Plots from Curve Data Frame", size=30, color="gold")
fig.tight_layout(rect=[0, 0.03, 1, 0.95])
# ***** LEGEND *****
# * States = {no lights blinking, left light blinking, right light blinking, both lights blinking}
# * State Keys(respectively) = {nlb, llb, rlb, blb}
# * Switches(appended to reciever vert to express input value) = {left-blinker, right-blinker, panic-button}
# * Switch Keys = {L-B, R-B, P-B}
#### Jupyter notebook cell start ####
%matplotlib inline
from jupyterthemes import jtplot
jtplot.style(context='talk', fscale=1, spines=False, gridlines='--')
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import networkx as nx
df = pd.DataFrame({'from':
['start (nlb)', 'start (nlb)', 'start (nlb)', '<- (L-B) -> llb', '(P-B) -> blb' ],
'to': ['<- (L-B) -> llb', '(R-B) -> rlb ->', '(P-B) -> blb', '(R-B) -> rlb ->', 'start (nlb)']})
G=nx.from_pandas_edgelist(df, 'to', 'from')
nx.draw(G, with_labels=True, pos=nx.shell_layout(G), nodecolor='r', edge_color='b')
plt.show()
#### Jupyter notebook cell end ####
import pandas as pd
import GPy
from jupyterthemes import jtplot
import numpy as np
import pylab as pb
import matplotlib.pyplot as plt
jtplot.style(theme='default')
# funcion para plotear multi-task GP (Ricardo Andrade-Pacheco)
def plot_2outputs(modelo, xlim):
fig = pb.figure(figsize=(12, 8))
# Output 1
ax1 = fig.add_subplot(211)
ax1.set_xlim(xlim)
ax1.set_title('Output 1')
modelo.plot(plot_limits=xlim,
fixed_inputs=[(1, 0)],
which_data_rows=slice(0, 100),
ax=ax1)
# Output 2
ax2 = fig.add_subplot(212)
ax2.set_xlim(xlim)
ax2.set_title('Output 2')
modelo.plot(plot_limits=xlim,
fixed_inputs=[(1, 1)],
which_data_rows=slice(100, 200),
ax=ax2)
# format_version: '1.2'
# jupytext_version: 1.2.3
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# %%
import matplotlib.pyplot as plt
import numpy as np
import torch
import pandas as pd
import pickle
from jupyterthemes import jtplot
jtplot.style('oceans16')
# %% [markdown]
# # Position Change to the Nearest Car between Frames
# %%
t1 = torch.load(
'../traffic-data/state-action-cost/data_i80_v0/trajectories-0400-0415/all_data.pth'
)
t2 = torch.load(
'../traffic-data/state-action-cost/data_i80_v0/trajectories-0500-0515/all_data.pth'
)
t3 = torch.load(
'../traffic-data/state-action-cost/data_i80_v0/trajectories-0515-0530/all_data.pth'
)
t_states_full = t1['states'] + t2['states'] + t3['states']
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
import numpy as np
from jupyterthemes import jtplot
jtplot.style(ticks=True, grid=True)
class MatrixClassifier:
def __init__(self):
0 == 0
def analyze(self, M):
self.isSquare = M.shape[0] == M.shape[1]
if self.isSquare:
self.det = np.linalg.det(M)
self.columnLengths = np.diag(M.T @ M)
self.areAxesOrderPreserved = True if self.det > 0 else False
self.stretch = np.abs(self.det)
self.isStretchOne = np.abs(self.stretch - 1) < 0.00001
self.areAllColumnsUnitLength = np.all(
np.abs(self.columnLengths - 1) < 0.00001)
return { "isSquare":self.isSquare, \
"isStretchOne":self.isStretchOne, \
"areAxesOrderPreserved": self.areAxesOrderPreserved,
