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import numpy as np
from pyunicorn.timeseries import RecurrencePlot, RecurrenceNetwork, CrossRecurrencePlot, JointRecurrencePlot


def logistic_map(x0, r, T):
    """
    Returns a time series of length T using the logistic map
    x_(n+1) = r*x_n(1-x_n) at parameter r and using the initial condition x0.

    INPUT: x0 - Initial condition, 0 <= x0 <= 1

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VšĮ Šviesos kūgiai
LT717044060007931577
paskirtis: 
Parama Ukrainos medikams

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import pandas as pd
import numpy as np
taxa = pd.read_csv("Raw_NOW_data/now_export_locsp_2022-04-27.csv", delimiter="\t", na_values = ["\\N", "n", "N"], \
                   dtype={'MAX_AGE': 'float64', 'MIN_AGE': 'float64', 'LIDNUM': 'int'})
taxa = taxa.fillna(-999)
print(taxa.shape)


NA_g = taxa['GENUS']==-999

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# -------------------- 2E1.
# . Which of the expressions below correspond to the statement: the probability of rain on Monday?
#Pr(rain)
#Pr(rain|Monday)
#Pr(Monday|rain)
#Pr(rain, Monday)/ Pr(Monday)
#2) Pr(rain|Monday)

# ------------------------------ 2E2.
#Which of the following statements corresponds to the expression: Pr(Monday|rain)? 

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for file in *.PDF; do
    pdftk $file cat 1-endsouth output $file"_rot.pdf"
done

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from __future__ import division
import math
import matplotlib.pyplot as plt
import numpy as np

width = 200
length = 300
n_lines = 6
min_dist = 10
min_separation = 50

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int duty = 0;
int steps = 256; //no. of light levels
int sunrisespeed = 20;//no. of pulses, change to ~5400 to have 30min-long sunrise
int minutesUntilSunrise = 0; //change to time until sunrise in minutes
int i;
int j;
int pulsepin = 9;
unsigned long currentMillis = 0;
int previousMillis = 0;

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\usepackage{minted}
\begin{document}

\subsection*{Implementation}
\begin{minted}{python}
for i in range(m):
	diff = self.dataset - points[:, i, newaxis]
	tdiff = dot(self.inv_cov, diff)
	energy = sum(diff * tdiff, axis=0) / 2.0
	result[i] = sum(exp(-energy), axis=0)

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from __future__ import division
#A decently-looking table in A&A long table format (http://www.aanda.org/author-information/latex-issues/latex-examples#large_tables)
#all the data columns are provided as lists, selected from a database. I cut that part out for clarity.

ofile = open('texts/datatable.tex', 'w')

ofile.write('\documentclass[onecolumn, 11pt]{aa}')
ofile.write('\usepackage{array}\n')
ofile.write('\pagestyle{empty}\n')
ofile.write('\setlength\extrarowheight{4pt}\n')

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# A minimal working example of fitting your model to data using SciPy. 
# I rewrote the Cookbook example (http://wiki.scipy.org/Cookbook/FittingData) for a simpler case
# and simpler syntax. 

from __future__ import division
from pylab import *
from scipy import *
import numpy as np
from scipy import optimize