def start():
try:
# Create target Directory
os.mkdir(data_dir)
print("Directory", data_dir, "Created.")
except FileExistsError:
pass
file_path = os.path.join(data_dir, 'data.csv')
if not (os.path.exists(file_path)):
f = open(file_path, 'w')
f.close()
read_file = open(file_path, 'r+')
lines = read_file.readlines()
if (lines.__len__() == 0):
placeholder = 'Taking over the world'
else:
placeholder = get_placeholder_from_line(lines[-1])
read_file.close()
append_file = open(file_path, 'a+')
result = entry(text="What are you working on?", placeholder=placeholder)
now = datetime.datetime.now()
data_to_append = f"{now},{result}\n"
print(f"Appending {data_to_append} to datefile: {file_path}.")
append_file.write(data_to_append)
append_file.close()
del D[col]
###Making a subset based on regions
#User input and application
q = zp.question(
title="Mutation Filtering",
text=
"Do you want to select a subset of regions? Click 'No' in order to analyze all regions!"
)
if q == True:
regs = M["sample_source_location_state"].unique()
regions = [str(a + 1) + " " + b for (a, b) in zip(range(len(regs)), regs)]
selection = zp.entry(
title="Mutation Filtering",
text=
"Please enter the NUMBER of the regions you want to include! e.g. 1,2,4\n\n"
+ "\n".join(regions))
sel = [regs[i] for i in [int(j) - 1 for j in selection.split(",")]]
M = M[M["sample_source_location_state"].isin(sel)]
for col in D.columns:
if not (col.split("_")[0] + "_" + col.split("_")[1]) in M.index:
del D[col]
###Making a subset based on locations
#User input and application
q = zp.question(
title="Mutation Filtering",
text=
"Do you want to select a subset of locations? Click 'No' in order to analyze all locations!"
title="CoMA",
text=
"A tree file is required for the calculation of Faith's phylogenetic diversity!"
)
print("\nProcess terminated!")
print(
"\n________________________________________________________________________________\n"
)
sys.exit(1)
metric = "faith_pd"
label = "Faith's PD"
mname = "faith_pd"
ftype = zp.entry(
title="CoMA",
text=
"Which fileformat do you prefer?\n\neps, jpeg, pdf, png, ps, raw, rgba, svg, svgz, tiff\n"
)
if ftype == None:
print("\nProcess terminated!")
print(
"\n________________________________________________________________________________\n"
)
sys.exit(1)
elif ftype not in [
"eps", "jpeg", "pdf", "png", "ps", "raw", "rgba", "svg", "svgz", "tiff"
]:
zp.error(title="CoMA", text="Invalid Input, process terminated!")
print("\nProcess terminated!")
print(
"\n________________________________________________________________________________\n"
#! /usr/bin/env python
#-*- coding: UTF-8 -*-
from zenipy import entry
from zenipy import message
peso = entry(text='Peso - Use ponto como separador do Peso',
placeholder='Digite O Peso',
title='IMC')
altura = entry(text='Altura - Use ponto como separador da Altura',
placeholder='Digite A Altura',
title='IMC')
imc = float(peso) / (float(altura) * float(altura))
if imc < 17:
message(title='IMC', text="%s " '\nIMC Excessivamente Baixo' % (imc))
elif (17 < imc < 18.49):
message(title='IMC', text="%s " '\nIMC Baixo' % (imc))
elif (18.5 < imc < 24.99):
message(title='IMC', text="%s " '\nIMC Normal' % (imc))
elif (25 < imc < 29.99):
message(title='IMC', text="%s " '\nIMC Sobrepeso' % (imc))
elif (30 < imc < 34.99):
message(title='IMC', text="%s " '\nIMC Obesidade I' % (imc))
elif (35 < imc < 39.99):
message(title='IMC', text="%s " '\nIMC Obesidade II (Severa)' % (imc))
elif imc > 40:
message(title='IMC', text="%s " '\nIMC Obesidade III (Móbida)' % (imc))
metric = "chao1"
label = "Chao1"
mname = "chao1"
if metric == "Faith_PD":
try:
tree_file = sys.argv[4]
except:
zp.error(title="CoMA", text="A tree file is required for the calculation of Faith's phylogenetic diversity!")
print("\nProcess terminated!")
print("\n________________________________________________________________________________\n")
sys.exit(1)
metric = "faith_pd"
label = "Faith's PD"
mname = "faith_pd"
var = zp.entry(title="CoMA", text="Based on which metadata variable do you want to group your samples?\n\nYou can select between the following variables:\n\n" + ", ".join(map_df.columns) + "\n")
ftype = zp.entry(title="CoMA", text="Which fileformat do you prefer?\n\neps, jpeg, pdf, png, ps, raw, rgba, svg, svgz, tiff\n")
if ftype == None:
print("\nProcess terminated!")
print("\n________________________________________________________________________________\n")
sys.exit(1)
elif ftype not in ["eps", "jpeg", "pdf", "png", "ps", "raw", "rgba", "svg", "svgz", "tiff"]:
zp.error(title="CoMA", text="Invalid Input, process terminated!")
print("\nProcess terminated!")
print("\n________________________________________________________________________________\n")
sys.exit(1)
else:
figname = "alphadiversity_" + mname + "_" + var + "." + ftype
if ftype in ["jpeg", "png", "raw", "rgba", "tiff"]:
import zenipy as zp
import sys
import os
import subprocess
infile = sys.argv[1]
outfile = sys.argv[2]
with open(infile) as inf:
inf.readline()
samples = inf.readline().split("\t")[1:-1]
variables = zp.entry(
title="CoMA",
text=
"Please type in the variable names you want to add, seperated with ',' (e.g. season,body_site,year)"
)
with open(outfile, "w") as outf:
outf.write("\t" + "\t".join(variables.split(",")) + "\n")
for sample in samples:
outf.write(sample + len(variables.split(",")) * "\t" + "\n")
zp.message(
title="CoMA",
text=
"Please fill in all missing data in the map file; save then as tab-delimited text file. When you are finished, a quick check will be done to prove your input.\n\nClick now OK to start entering your metadata!",
height=100)
os.system("libreoffice --calc %s" % (outfile))
)
if mapping == True:
try:
map_file = sys.argv[2]
except:
pz.error(title="CoMA",
text="No mapping file provided, process terminated!")
print("\nNo mapping file provided, process terminated!")
print(
"\n________________________________________________________________________________\n"
)
sys.exit(1)
map_DF = pd.read_csv(map_file, delimiter="\t", index_col=0)
var = pz.entry(
title="CoMA",
text=
"Based on which metadata variable do you want to group your samples?\n\nYou can select between the following variables:\n\n"
+ ", ".join(map_DF.columns) + "\n")
if len(map_DF[var].value_counts()) > 3:
pz.error(
title="CoMA",
text=
"You can only create Venn plots for the comparison of 2 or 3 groups! You provided %s groups, process terminated!"
)
print(
"\nYou can only create Venn plots for the comparison of 2 or 3 groups! You provided %s groups, process terminated!"
)
print(
"\n________________________________________________________________________________\n"
)
sys.exit(1)
import os
import pandas as pd
import zenipy as zp
otu_table = sys.argv[1]
map_file = sys.argv[2]
outfile = sys.argv[3]
otu_DF = pd.read_csv(otu_table, delimiter="\t", header=1, index_col=0)
samples = list(otu_DF.columns)[0:-1]
map_DF = pd.read_csv(map_file, delimiter="\t", index_col=0)
var = zp.entry(
title="CoMA",
text=
"Based on which metadata variable do you want to group your samples?\n\nYou can select between the following variables:\n\n"
+ ", ".join(map_DF.columns) + "\n")
DF = pd.concat([otu_DF.T, map_DF], axis=1)
DF = DF.reset_index().groupby(var)
pairs = DF.index.value_counts().index.tolist()
unique = list(set([a for (a, b) in pairs]))
unique_dic = {}
for item in unique:
unique_dic[item] = [b for (a, b) in pairs if item in (a, b)]
#Creation of new dataframe
new_DF = pd.DataFrame()
#!/usr/bin/python3
import os
from zenipy import entry
## Generates a LaTeX png, stores it in the user's clipboard
## read user input (argument) as a latex equation
latex_equation = "$$" + entry(text='Input LaTeX:', placeholder='', title='ClipLaTeX', timeout=None) + "$$"
## skeleton for latex .tex file
before_eq_string = r"\documentclass{article} \usepackage{amsmath} \usepackage{amssymb} \begin{document} \thispagestyle{empty} \setlength{\parindent}{0pt}"
after_eq_string = r"\end{document}"
## set latex_filename parameter
latex_filename = "tempfile"
## create the .tex file from the skeleton
tex_file_contents = before_eq_string + latex_equation + after_eq_string
tex_file = open(latex_filename + ".tex", "w")
tex_file.write(tex_file_contents)
tex_file.close()
## create the pdf (`pdflatex -jobname='filename to write' file.tex`)
# to specify output name: -jobname=STRING flag before the FILE flag at the end
os.system('pdflatex ' + latex_filename + '.tex > /dev/null 2>&1')
## crop the pdf to remove excess whitespace
os.system('pdfcrop -margin 3 ' + latex_filename + '.pdf ' + latex_filename + '.pdf > /dev/null 2>&1')
## create the png from the pdf (`convert -density 3000 file.pdf -quality 90 file.png`)
# (c) Sebastian Hupfauf 20018
#
# Script groups samples/replicates together and sort the newly defined groups
# in alphabetical order.
#
# USAGE: python group.py otu_table_file
import zenipy as pz
import sys
import os
import statistics
import time
method = pz.entry(title="CoMA", text="Please choose the method for grouping:\n\nSum: sum of all reads\nMean: mean of all reads\nMedian: median of all reads\n")
if method == None:
print("\nGrouping process terminated!")
print("\n________________________________________________________________________________\n")
sys.exit(3)
if method not in ["Sum", "Mean", "Median"]:
pz.error(title="CoMA", text="Invalid Input, process terminated!")
print("\nGrouping process terminated!")
print("\n________________________________________________________________________________\n")
sys.exit(3)
input_file = "otu_table.txt"
output_file = "otu_table_out.txt"
with open(input_file) as inf:
inf.readline()
samples = [str(a.strip()) + "\n" for a in inf.readline().strip().split("\t")[1:-1]]
num = [str(b) + "\t" for b in range(1, len(samples) + 1)]
#!/usr/bin/python3 # Author: Chris Moneyron, Purdue University, [email protected] # Professor: Nina Mahmoudian, Purdue University, [email protected] # # Plot position over time for all Crazyflies using xy_pos csv log import pandas as pd import zenipy import sys from matplotlib import pyplot as plt from tkinter.filedialog import Tk, askopenfilename # Ask user for work width and length measurements work_width = zenipy.entry(text="Enter workspace X length (meters):", placeholder="3.5", title="Crazyflie Mission Planning") work_length = zenipy.entry(text="Enter workspace Y length (meters):", placeholder="4", title="Crazyflie Mission Planning") work_width = float(work_width) work_length = float(work_length) # Ask user which scenario to plot (static, mobile preplan, mobile replan) mission_type = zenipy.zlist(title="Crazyflie Mission Planning", text="Select the mission type", columns=["Mission Type"], items=["Static", "MobilePreplan", "MobileReplan"]) mission_type = mission_type[0] if mission_type == "Static": log_location = '/home/naslab/crazyflie_ws/src/NASLab_Crazyflies/crazyflie_scripts/static_cf_actual_pos_logs/' elif mission_type == "MobilePreplan": log_location = '/home/naslab/crazyflie_ws/src/NASLab_Crazyflies/crazyflie_scripts/' \ 'mobile_preplan_cf_actual_pos_logs/'
