zabop

Funding for the Kepler and K2 missions are provided by the NASA Science Mission directorate. EP was supported by the Bolyai János Research Scholarship, LM by the Premium Postdoctoral Research Program of the Hungarian Academy of Sciences. This research received funding from the Hungarian NKFIH grants K-115709, PD-121203, and from the Lendület LP2014-17 and LP2018-7 grants of the Hungarian Academy of Sciences. This research made use of Lightkurve, a Python package for Kepler and TESS data analysis (Lightkurve Collaboration, 2018).

zabop

import plotly.graph_objects as go
import networkx as nx
G = nx.random_geometric_graph(200, 0.125)

edge_x = []
edge_y = []
for edge in G.edges():
    x0, y0 = G.nodes[edge[0]]['pos']
    x1, y1 = G.nodes[edge[1]]['pos']
    edge_x.append(x0)

zabop

#!/usr/bin/env python
# coding: utf-8

# In[129]:


import pandas as pd
import glob
import datetime
import time

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#necessary imports
import pandas as pd
import glob
import datetime
import time
import matplotlib.pyplot as plt
import pathlib
import pathlib
from collections import ChainMap
import os

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const cvs = document.getElementById("myCanvas");
const ctx = cvs.getContext("2d");


cvs.width = 400;
cvs.height = 400;

let frames = 0;

let foodEaten = false;

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label type name period App A1 R21 R31 P21 P31
1 RRab OGLE-BLG-RRLYR-00162 0.546320 0.6121 0.2013 0.5346 0.3341 5.7577 5.4747
1 RRab OGLE-BLG-RRLYR-00172 0.477439 0.6754 0.2262 0.5049 0.3545 5.5847 5.1604
1 RRab OGLE-BLG-RRLYR-00175 0.476680 0.7326 0.2290 0.5257 0.3923 5.5592 5.0775
1 RRab OGLE-BLG-RRLYR-00181 0.510993 0.4663 0.1884 0.4036 0.2535 5.6331 4.8843
1 RRab OGLE-BLG-RRLYR-00183 0.512152 0.6847 0.2136 0.5271 0.3886 5.6474 5.2774
1 RRab OGLE-BLG-RRLYR-00184 0.533320 0.7260 0.2327 0.5098 0.3855 5.6229 5.1438
1 RRab OGLE-BLG-RRLYR-00194 0.662719 0.2479 0.1010 0.3731 0.1906 6.2095 0.1472
1 RRab OGLE-BLG-RRLYR-00195 0.803384 0.2860 0.1178 0.3762 0.1645 0.2110 0.6094
1 RRab OGLE-BLG-RRLYR-00197 0.587164 0.3774 0.1458 0.4317 0.2259 5.9258 5.7170

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import numpy as np
from keras import layers
from keras import models
from keras.datasets import imdb
from keras.utils.np_utils import to_categorical
from sklearn.preprocessing import MultiLabelBinarizer
import matplotlib.pyplot as plt

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(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)