import random 
#consider using a lower speed, even 35,000 is better distribution than 150,000
# along with a greater precision like 4 decimals, this way you may 
# more likely get a lower index
try:
  speed = int(input('enter speed ')) #35000  # per second. 
except:
  speed = 33000
  
day_part = float(round(random.uniform(0,1), 4)) 
num_seconds = float(day_part * 86400) 
distance = speed * num_seconds 
print(f'speed:{speed}\nday_part: {day_part}\nnum_seconds:{num_seconds}\ndistance:{distance}') 
# compute whether a planet has been passed 
past_mercury = distance >= 3604000 
past_venus = distance >= 6724000  
past_earth = distance >= 9296000  
past_mars = distance >= 141600000  
past_jupiter = distance >= 483800000  
past_saturn = distance >= 890800000  
past_uranus = distance >= 1784000000  
past_neptune = distance >= 2793000000  
out_of_bounds = (not past_mercury) or past_neptune

midpoint = (483800000 - 141600000) / 2.
print(f'midpoint:{midpoint}')
relative = distance-midpoint
print(f'relative to midpoint: {relative}')

r_mercury = 360400 - midpoint
r_venus = 6724000 - midpoint
r_earth = 9296000 - midpoint
r_mars = 141600000 - midpoint
# midpoint goes here
r_jupiter = 483800000 - midpoint  
r_saturn = 890800000 - midpoint
r_uranus = 1784000000 - midpoint
r_neptune = 2793000000 - midpoint

rp = [ r_mercury, r_venus, r_earth, r_mars,
    0.0,
     r_jupiter, r_saturn, r_uranus, r_neptune]
print(rp)
'''
'''


# only ONE of these variables should result in a True value 
merc_venus = past_mercury and not past_venus and not out_of_bounds 
venus_earth = past_venus and not past_earth and not out_of_bounds 
earth_mars = past_earth and not past_mars and not out_of_bounds 
mars_jupiter = past_mars and not past_jupiter and not out_of_bounds  
jupiter_saturn = past_jupiter and not past_saturn and not out_of_bounds 
saturn_uranus = past_saturn and not past_uranus and not out_of_bounds 
uranus_neptune = past_uranus and not past_neptune and not out_of_bounds 

# the above logic COULD be refactored into the below logic,
# but keep it as-is so you could see what evaluates.
s = (not past_mercury) * 'Not made it to Mercury yet (between sun & Mercury)' \
or merc_venus * 'between Mercury and Venus' \
or venus_earth * 'between Venus and Earth' \
or earth_mars * 'between Earth and Mars' \
or mars_jupiter * 'between Mars and Jupiter' \
or jupiter_saturn * 'between Jupiter and Saturn' \
or saturn_uranus * 'between Saturn and Uranus'  \
or uranus_neptune * 'between Uranus and Neptune'  \
or past_neptune * 'Past Neptune already!' 
print(s)

first = past_neptune * 'neptune' \
 or past_uranus * 'uranus' \
 or past_saturn * 'saturn' \
 or past_jupiter * 'jupiter' \
 or past_mars * 'mars' \
 or past_earth * 'earth' \
 or past_venus * 'venus' \
 or past_mercury * 'mercury' \
 or 'sun'
last = past_neptune * 'out of bounds' \
 or past_uranus * 'neptune' \
 or past_saturn * 'uranus' \
 or past_jupiter * 'saturn' \
 or past_mars * 'jupiter' \
 or past_earth * 'mars' \
 or past_venus * 'earth' \
 or past_mercury * 'venus' \
 or 'mercury'
print(f'NEW: Between {first} and {last}')


 

Python Online Compiler

Write, Run & Share Python code online using OneCompiler's Python online compiler for free. It's one of the robust, feature-rich online compilers for python language, supporting both the versions which are Python 3 and Python 2.7. Getting started with the OneCompiler's Python editor is easy and fast. The editor shows sample boilerplate code when you choose language as Python or Python2 and start coding.

Taking inputs (stdin)

OneCompiler's python online editor supports stdin and users can give inputs to programs using the STDIN textbox under the I/O tab. Following is a sample python program which takes name as input and print your name with hello.

import sys
name = sys.stdin.readline()
print("Hello "+ name)

About Python

Python is a very popular general-purpose programming language which was created by Guido van Rossum, and released in 1991. It is very popular for web development and you can build almost anything like mobile apps, web apps, tools, data analytics, machine learning etc. It is designed to be simple and easy like english language. It's is highly productive and efficient making it a very popular language.

Tutorial & Syntax help

Loops

1. If-Else:

When ever you want to perform a set of operations based on a condition IF-ELSE is used.

if conditional-expression
    #code
elif conditional-expression
    #code
else:
    #code

Note:

Indentation is very important in Python, make sure the indentation is followed correctly

2. For:

For loop is used to iterate over arrays(list, tuple, set, dictionary) or strings.

Example:

mylist=("Iphone","Pixel","Samsung")
for i in mylist:
    print(i)

3. While:

While is also used to iterate a set of statements based on a condition. Usually while is preferred when number of iterations are not known in advance.

while condition  
    #code 

Collections

There are four types of collections in Python.

1. List:

List is a collection which is ordered and can be changed. Lists are specified in square brackets.

Example:

mylist=["iPhone","Pixel","Samsung"]
print(mylist)

2. Tuple:

Tuple is a collection which is ordered and can not be changed. Tuples are specified in round brackets.

Example:

myTuple=("iPhone","Pixel","Samsung")
print(myTuple)

Below throws an error if you assign another value to tuple again.

myTuple=("iPhone","Pixel","Samsung")
print(myTuple)
myTuple[1]="onePlus"
print(myTuple)

3. Set:

Set is a collection which is unordered and unindexed. Sets are specified in curly brackets.

Example:

myset = {"iPhone","Pixel","Samsung"}
print(myset)

4. Dictionary:

Dictionary is a collection of key value pairs which is unordered, can be changed, and indexed. They are written in curly brackets with key - value pairs.

Example:

mydict = {
    "brand" :"iPhone",
    "model": "iPhone 11"
}
print(mydict)

Supported Libraries

Following are the libraries supported by OneCompiler's Python compiler

NameDescription
NumPyNumPy python library helps users to work on arrays with ease
SciPySciPy is a scientific computation library which depends on NumPy for convenient and fast N-dimensional array manipulation
SKLearn/Scikit-learnScikit-learn or Scikit-learn is the most useful library for machine learning in Python
PandasPandas is the most efficient Python library for data manipulation and analysis
DOcplexDOcplex is IBM Decision Optimization CPLEX Modeling for Python, is a library composed of Mathematical Programming Modeling and Constraint Programming Modeling