def main():
plt.plotfile('prices.txt',
delimiter=' ',
cols=(0, 1, 2),
names=('t', 'untraded', 'correlated'),
marker='o')
plt.show()
def plotter(fname, fdelimiter, foutput, fargument, fdir):
"""
This function parse the data in csv file and then calls different
plotting functions"
"""
try:
fopen = open(fname,'r')
csvreader = csv.reader(fopen, delimiter = ",")
fields = csvreader.next()
except:
print "Can not open input csv file",fname
fopen.close
data = np.loadtxt(fname, delimiter = fdelimiter, unpack = True, skiprows = 1)
mpl.plotfile(fname, cols = range(len(fields)/2), delimiter = fdelimiter, subplots = True, newfig = True)
print "creating a example figure with ", len(fields)/2-1, "subplots"
allfig = fdir + os.path.sep + "All_fig" + "." + foutput
mpl.savefig(allfig)
mpl.clf()
i = 0;
while i < len(fields):
if fargument != i:
mpl.plot(data[fargument], data[i])
fieldname=fdir +os.path.sep + fields[fargument] +"_" + fields[i] + "." + foutput
mpl.xlabel(fields[fargument])
mpl.ylabel(fields[i])
mpl.title(fields[fargument] + " vs " + fields[i])
#Issues with transparent option when png is opted need fine tuning.
#Check Howto section of matplotlib website
#mpl.savefig(fieldname, transparent = True)
mpl.grid(True)
mpl.savefig(fieldname)
print "Saving", fieldname
mpl.clf()
i=i+1
def delta_od_L():
pyplot.plotfile(dane_dir + 'delta_od_L.txt', delimiter=' ', names=['a', 'b'], cols=(0, 1), marker='.', color=niebieski, ls='')
pyplot.plot((1, 5), (0.4, 0.4), 'k-')
pyplot.text(1, 0.5, 'wartosc bulk')
pyplot.xlabel('L [nm]')
pyplot.ylabel(u'\u0394 [eV]')
pyplot.savefig(wykresy_dir + 'delta_od_L.png')
def Tc_od_L():
pyplot.plotfile(dane_dir + 'Tc_od_L.txt', delimiter=' ', names=['a', 'b'], cols=(0, 1), marker='.', color=niebieski, ls='')
pyplot.plot((1, 5), (4, 4), 'k-')
pyplot.text(1, 4, 'wartosc bulk')
pyplot.xlabel('L [nm]')
pyplot.ylabel(r'$T_c$ [K]')
pyplot.savefig(wykresy_dir + 'Tc_od_L.png')
def delta_od_T():
pliki = glob(dane_dir + 'delta_od_T*.txt')
for nazwa in pliki:
pyplot.plotfile(nazwa, delimiter=' ', names=['a', 'b'], cols=(0, 1), marker='.', color=niebieski, ls='')
pyplot.xlabel('T [K]')
pyplot.ylabel(u'\u03bc [eV]') # czy mev?
pyplot.savefig(nazwa.replace('dane', 'wykresy').replace('txt', 'png'))
def queue_length():
plt.clf()
plt.plotfile(fname=DATA_ROOT_DIR + "queue_length.data",
cols=(0, 1),
skiprows=0,
delimiter=" ",
newfig=False,
label="Items")
plt.title("Queue Length")
plt.xlabel("Time (milliseconds)")
plt.ylabel("Queue Length")
plt.legend()
plt.grid()
plt.savefig(PLOT_SAVE_LOCATION + "queue_length")
def mem(serviceName):
plt.clf()
plt.plotfile(fname=DATA_ROOT_DIR + serviceName + "/" + "mem.data",
cols=(0, 1),
skiprows=0,
delimiter=" ",
newfig=False,
label="Memory")
plt.title("Memory")
plt.xlabel("Time (seconds)")
plt.ylabel("Memory utilization (%)")
plt.legend()
plt.grid()
plt.savefig(PLOT_SAVE_LOCATION + serviceName + "/" + "mem")
def cpu(serviceName):
plt.clf()
plt.plotfile(fname=DATA_ROOT_DIR + serviceName + "/" + "cpu0.data",
cols=(0, 1),
skiprows=0,
delimiter=" ",
newfig=False,
label="CPU 0")
plt.plotfile(fname=DATA_ROOT_DIR + serviceName + "/" + "cpu1.data",
cols=(0, 1),
skiprows=0,
delimiter=" ",
newfig=False,
label="CPU 1")
plt.plotfile(fname=DATA_ROOT_DIR + serviceName + "/" + "cpu2.data",
cols=(0, 1),
skiprows=0,
delimiter=" ",
newfig=False,
label="CPU 2")
plt.plotfile(fname=DATA_ROOT_DIR + serviceName + "/" + "cpu3.data",
cols=(0, 1),
skiprows=0,
delimiter=" ",
newfig=False,
label="CPU 3")
plt.title("CPU")
plt.xlabel("Time (seconds)")
plt.ylabel("CPU utilization (%)")
plt.legend()
plt.grid()
plt.savefig(PLOT_SAVE_LOCATION + serviceName + "/" + "cpu")
def point_in_time_distribution():
plt.clf()
plt.plotfile(fname=DATA_ROOT_DIR + "rt_pit.data",
cols=(0, 1),
skiprows=0,
delimiter=" ",
newfig=False,
label="PIT Response Time")
plt.title("Point in Time Distribution")
plt.xlabel("Time (milliseconds)")
plt.ylabel("PIT Response Time (milliseconds)")
plt.legend()
plt.grid()
plt.savefig(PLOT_SAVE_LOCATION + "point_in_time_distribution")
def requests_per_sec():
plt.clf()
plt.plotfile(fname=DATA_ROOT_DIR + "requests_per_sec.data",
cols=(0, 1),
skiprows=0,
delimiter=" ",
newfig=False,
label="Requests")
plt.title("Requests per Second")
plt.xlabel("Time (seconds)")
plt.ylabel("# Requests")
plt.legend()
plt.grid()
plt.savefig(PLOT_SAVE_LOCATION + "reqs_per_sec")
def stockstats_figures(self):
for line in stockstats_files:
print line
try:
faname = cbook.get_sample_data(line, asfileobj=False)
faname = faname[71:]
plt.style.use("Solarize_Light2")
plt.plotfile(faname, ('date', '30', '70', 'rsi_12'),
subplots=False)
plt.savefig(line + '_rsi.png')
# plt.plotfile(faname, ('date','macdh','macd','macds'), plotfuncs={'macdh': 'bar'}, subplots=False)
# plt.savefig(line+'_macd.png')
except:
pass
def plot_by_cpu(serviceName, i):
plt.clf()
plt.plotfile(fname=DATA_ROOT_DIR + serviceName + "/" + "cpu" + str(i) +
".data",
cols=(0, 1),
skiprows=0,
delimiter=" ",
newfig=False,
label=str("CPU " + str(i)))
plt.title("CPU " + str(i) + " Plot")
plt.xlabel("Time (seconds)")
plt.ylabel("CPU utilization (%)")
plt.legend()
plt.grid()
plt.savefig(PLOT_SAVE_LOCATION + serviceName + "/" + "cpu-" + str(i))
def response_time_distribution():
plt.clf()
plt.plotfile(fname=DATA_ROOT_DIR + "rt_dist.data",
cols=(0, 1),
skiprows=0,
delimiter=" ",
newfig=False,
label="# Requests")
plt.title("Response Time Distribution")
plt.xlabel("Response Time (milliseconds)")
plt.ylabel("# Requests")
plt.xlim((-30, 3000))
plt.yscale(value='log')
plt.legend()
plt.grid()
plt.savefig(PLOT_SAVE_LOCATION + "response_time_distribution")
def plotgraph(filepath, imagefolder):
p1 = filepath.split('/')
p2 = p1[-1]
iname = p2.strip('.txt')
plt.plotfile(filepath,
delimiter=',',
cols=(0, 1),
names=('YYYY-MM', 'Total Hits'),
marker='o')
new_graph_name = iname + str(time.time()) + ".png"
for filename in os.listdir(imagefolder):
if filename.startswith(iname): # not to remove other images
os.remove(imagefolder + filename)
plt.savefig(imagefolder + new_graph_name)
imagename = imagefolder + new_graph_name
return imagename
def plot(self, fromCsvFile, col1, col1Name, col2, col2Name):
""" Plots out the csvFile. Takes 2 columns and their names. """
plt.plotfile(fromCsvFile, delimiter=self.delimiter, cols=(col1, col2), skiprows=1,
names=(col1Name, col2Name), linestyle='None', marker='.')
y = []
x = []
with open(fromCsvFile, 'rb') as csvFile:
dat = csv.reader(csvFile, delimiter=self.delimiter)
for row in dat:
y.append(int(row[col2]))
x.append(int(row[col1]))
m, b = np.polyfit(x, y, 1)
plt.plot(x, np.array(x) * m + b, color='red')
def OnPlot(self, event):
cursor= self.conn.execute("SELECT FILE_NAME FROM MOLECULE where MOL_NUMBER==?", (self.plot_list[0],))
files = cursor.fetchall()
#print files[0][0]
tf = open(files[0][0],'r+')
d = tf.readlines()
tf.seek(0)
for line in d:
s=re.search(r'[a-zA-Z]',line)
if s:
tf.write('#'+line)
else:
tf.write(line)
tf.truncate()
tf.close()
plt.plotfile(str(files[0][0]), delimiter=' ',comments = '#', cols=(0, 1),
names=('Raman Shift ($\mathregular{Cm^{-1}}$)', 'Intensity (arb. units)'), )
plt.title('Raman Spectra of {}'.format(files[0][0]))
plt.show()
def main(argv):
inputfile = ''
outputfile = ''
try:
opts, args = getopt.getopt(argv,"hi:o:",["ifile=","ofile="])
except getopt.GetoptError:
print ('test.py -i <inputfile> -o <outputfile>')
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print ('test.py -i <inputfile> -o <outputfile>')
sys.exit()
elif opt in ("-i", "--ifile"):
inputfile = arg
elif opt in ("-o", "--ofile"):
outputfile = arg
plt.plotfile('reinas-tiempo.txt', delimiter=' ', cols=(0, 1),
names=('reinas', 'tiempo'), marker='o')
plt.savefig(outputfile)
def plot():
#reading data from two recent files
list_of_files = glob.glob('/Users/anuja/Desktop/testing/signals/*')
recent_data = sorted(list_of_files, key=os.path.getmtime)
#signal 1
try:
#reading data from file and ploting
plt.plotfile(recent_data[-1],('time','dis','vel','acc'), delimiter=' ', marker='None',newfig=True, subplots=True)
except IOError:
print("Error reading file.")
sys.exit(1)
actual_time = strftime("%Y-%m-%d %H-%M-%S", gmtime())
save_path = '/Users/anuja/Desktop/testing/plots'
file_name = os.path.join(save_path, 'signal_1' + str(actual_time)+".png")
pl.savefig(file_name, bbox_inches='tight')
plt.show()
def disk(serviceName):
plt.clf()
plt.plotfile(fname=DATA_ROOT_DIR + serviceName + "/" + "diskread.data",
cols=(0, 1),
skiprows=0,
delimiter=" ",
newfig=False,
label="Read")
plt.plotfile(fname=DATA_ROOT_DIR + serviceName + "/" + "diskwrite.data",
cols=(0, 1),
skiprows=0,
delimiter=" ",
newfig=False,
label="Write")
plt.title("Disk")
plt.xlabel("Time (seconds)")
plt.ylabel("Disk I/O (in kB)")
plt.legend()
plt.grid()
plt.savefig(PLOT_SAVE_LOCATION + serviceName + "/" + "disk")
def main(argv):
inputfile = ''
outputfile = ''
try:
opts, args = getopt.getopt(argv, "hi:o:", ["ifile=", "ofile="])
except getopt.GetoptError:
print('test.py -i <inputfile> -o <outputfile>')
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print('test.py -i <inputfile> -o <outputfile>')
sys.exit()
elif opt in ("-i", "--ifile"):
inputfile = arg
elif opt in ("-o", "--ofile"):
outputfile = arg
plt.plotfile('reinas-tiempo.txt',
delimiter=' ',
cols=(0, 1),
names=('reinas', 'tiempo'),
marker='o')
plt.savefig(outputfile)
def main():
""" dispatcher """
xdata = []
ydata = []
with open (incvs, 'rb') as fin, open (tcsv, 'wb') as touf:
print >>touf, "%s,%s,%s,%s" % ("dt", "cont", "cpu", "virtmem")
for row in fin:
r = row.split()
print >>touf, "%s,%s,%s,%s" % (r[0] + ' ' + r[1], r[2][4:], r[3], r[4])
print "%s,%s,%s,%s" % (r[0] + ' ' + r[1], r[2][4:], r[3], r[4])
plt.plotfile (tcsv, cols = ("cont", "cpu"))
plt.xlabel(u"число контейнеров")
# plt.xlabel("number of containers")
plt.ylabel(u"использование ЦПУ")
# plt.ylabel("CPU usage")
plt.ylim(0, 100)
plt.grid()
plt.xticks(rotation=45)
# plt.xticks(rotation='vertical')
plt.savefig ("docker-cpu.png", bbox_inches='tight')
plt.plotfile (tcsv, cols = ("cont", "virtmem"))
# plt.xlabel("number of containers")
plt.xlabel(u"число контейнеров")
# plt.ylabel("virtual memory")
plt.ylabel(u"виртуальная память")
plt.ylim(0, 100)
plt.grid()
plt.xticks(rotation=45)
# plt.xticks(rotation='vertical')
plt.savefig ("docker-virtmem.png", bbox_inches='tight')
#~ plt.show()
return 0
import glob import os import numpy as np import array import datetime import time import pandas as pd import sklearn.cluster import numpy as np import matplotlib.pyplot as plt import Node import re import csv import matplotlib.cbook as cbook file = os.getcwd() + 'topics.hist.csv' fname = cbook.get_sample_data(file) plt.plotfile(fname, cols=(0, 1), delimiter=',')
def updatecanvas(self):
incsv='test.csv'
utcsv='create.csv'
##################################################
print "starting"
rownr = 5
head = False
headlist = False
columns = False
with open('test.csv') as inf:
for line in inf:
parts = line.split(",")
partlen = len(parts)
#print "partlen: ",partlen, " parts: ", parts
if not headlist:
headlist = parts
elif not columns:
columns = parts
print "headlist: ", headlist
print "columns : ", partlen
new_cvs_file_list = []
#new_cvs_file_list.append("empty")
colum_with_values = []
colum_with_no_values = []
replace_append_data = ''
for colum in range(partlen):
print "COLUM NAME: ", headlist[colum]
check_empty_data = True
countlines = 0
with open(incsv) as inf:
for line in inf:
parts = line.split(",") # split line into parts
if len(parts) > 1: # if at least 2 parts/columns
#print "NEW_CVS_FILE_LIST: ", new_cvs_file_list, " fethcning value: ", colum
if not head:
head = parts[colum]
else:
print parts[colum] # print column 2
if parts[colum] == headlist[colum]:
print "header TOP"
elif parts[colum] != '"0"':
print "parts have value: ", parts[colum]
check_empty_data = False
countlines +=1
if not check_empty_data:
print "DATA in: ", headlist[colum]
colum_with_values.append(headlist[colum])
else:
print "EMPTY IN: ", headlist[colum]
colum_with_no_values.append(headlist[colum])
print "These columes have legit valuse: ", colum_with_values
for colum in range(partlen):
print "COLUM NAME: ", headlist[colum]
check_empty_data = True
countlines = 0
with open(incsv) as inf:
for line in inf:
parts = line.split(",") # split line into parts
if len(parts) > 1: # if at least 2 parts/columns
if colum == 0:
new_cvs_file_list.append(parts[colum])
#print "NEW_CVS_FILE_LIST: ", new_cvs_file_list, " fethcning value: ", colum
#elif headlist[colum] != parts[colum] and headlist[colum] in colum_with_values:
elif headlist[colum] in colum_with_values:
if colum != 0:
# reading new file structure
read_copy_of_row = new_cvs_file_list[countlines]
new_cvs_file_list[countlines] = read_copy_of_row + ',' + parts[colum]
countlines +=1
print headlist[colum]," -> ", parts[colum]
myfile = open(utcsv,'w')
for row in new_cvs_file_list:
print row
myfile.write(row + '\n')
myfile.close()
fname = open(utcsv)
plt.plotfile(fname, (colum_with_values), subplots=False)
plt.xlabel(r'$date$')
plt.ylabel(r'$levels$')
# #plt.show()
plt.savefig('data/test.png')
#
# print "column with missing valuse: ", colum_with_no_values
##################################################
self.image.source = "data/test.png"
def simple_plot(csv_file, columns, headers):
plt.clf()
plt.close()
plt.plotfile(csv_file, columns, names=headers, newfig=True)
plt.show()
# When working with dates, additionally call
# `pandas.plotting.register_matplotlib_converters` and use the ``parse_dates``
# argument of `pandas.read_csv`::
pd.plotting.register_matplotlib_converters()
with cbook.get_sample_data('msft.csv') as file:
msft = pd.read_csv(file, parse_dates=['Date'])
###############################################################################
# Use indices
# -----------
# Deprecated:
plt.plotfile(fname, (0, 5, 6))
# Use instead:
msft.plot(0, [5, 6], subplots=True)
###############################################################################
# Use names
# ---------
# Deprecated:
plt.plotfile(fname, ('date', 'volume', 'adj_close'))
# Use instead:
msft.plot("Date", ["Volume", "Adj. Close*"], subplots=True)
###############################################################################
import numpy as np
import matplotlib.pyplot as plt
import sys
import os
args = sys.argv[1:]
filenames = [f for f in os.listdir('./widths') if f.endswith(".out")]
for filename in filenames:
try:
plt.plotfile('./widths/' + filename,
delimiter=' ',
cols=(0, 1),
names=('y from resonance', 'winding number'),
marker='o')
"""plt.show()"""
plt.savefig("./plots/" + filename.replace(".out", ".png"))
print("Making plots for", filename)
plt.close()
except ValueError:
print("There was nothing in", filename)
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np
#workUnits=pd.read_csv('C:\Users\lokesh_chandra\PycharmProjects\Streaming\work_units.csv',header=None,names=['date_trunc','count'],index_col=0)
# #print(workUnits)
# #plot1=workUnits[1:3]
#
# print (workUnits)
#workUnits.set_index(workUnits['date_trunc'],inplace=True)
#workUnits.plot()
#plt.show()
#plt.plot(workUnits)
# read_csv = pd.read_csv('C:/Users/lokesh_chandra/PycharmProjects/Streaming/work_units.csv')
# read_csv['date_trunc'] = pd.to_datetime(read_csv['date_trunc'])
# #print(read_csv['count'])
# plt.plot(read_csv["date_trunc"], read_csv["count"])
#
# plt.show()
# #plt.clf()
#workUnits=pd.read_csv('C:\Users\lokesh_chandra\PycharmProjects\Streaming\work_units.csv',parse_dates=[1],infer_datetime_format=True,keep_date_col=True)
#workUnits= pd.DatetimeIndex(workUnits['Date'],format='%Y-%m-%d')
#plt.plot_date(x='Date', y='count', fmt="r-")
plt.plotfile('work_units.csv', (0, 1))
plt.title("Workunits completed in a day")
plt.xlabel("Dates")
plt.ylabel("No. of Workunits")
#plt.grid(True)
plt.show()
if colum != 0:
# reading new file structure
read_copy_of_row = new_cvs_file_list[countlines]
new_cvs_file_list[countlines] = read_copy_of_row + ',' + parts[colum]
countlines +=1
print headlist[colum]," -> ", parts[colum]
myfile = open(utcsv,'w')
for row in new_cvs_file_list:
print row
myfile.write(row + '\n')
myfile.close()
fname = open(utcsv)
plt.plotfile(fname, (colum_with_values), subplots=False)
plt.xlabel(r'$date$')
plt.ylabel(r'$levels$')
plt.show()
plt.savefig('data_graph.png')
print "column with missing valuse: ", colum_with_no_values
plotdata()
import sys import matplotlib.pyplot as plt for argument in range(1,len(sys.argv),2): plt.plotfile(sys.argv[argument],(0,int(sys.argv[argument+1]))) plt.show()
import sys
from matplotlib.ticker import FuncFormatter
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.cbook as cbook
filename = sys.argv[1]
data = cbook.get_sample_data(filename, asfileobj=False)
plt.plotfile(data, ('time', 'rss', 'storage_used', 'je_allocated', 'je_resident', 'reclaim_pending', 'queue_size'), subplots=False, delimiter=',')
plt.xlabel(r'seconds')
plt.ylabel(r'memory_used')
yaxis = plt.twiny()
yaxis.yaxis.set_major_formatter(FuncFormatter(lambda y,_: '%1.f' % (y/(1024*1024))))
plt.plotfile(data, ('time', 'mainrecord', 'docrecords'), subplots=False, delimiter=',')
plt.show()
#!/usr/bin/env python
#import numpy as np
import matplotlib.pyplot as plot
plot.plotfile('sw_open.txt',delimiter=' ', cols=(0, 1), names=('col1', 'col2'), marker='o')
plot.show()
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.cbook as cbook
fname = cbook.get_sample_data(
'/home/akshaymshet/processed_data/events/bishop.csv', asfileobj=False)
# test 1; use ints
plt.plotfile(fname, (10, 12))
# test 2; use names
plt.plotfile(fname, ('weekday', 'pixel'))
# test 3; use semilogy for volume
#plt.plotfile(fname, ('weekday', 'pixel'),plotfuncs={'volume': 'semilogy'})
# test 4; use semilogy for volume
#plt.plotfile(fname, (10, 12), plotfuncs={5: 'semilogy'})
# test 5; single subplot
#plt.plotfile(fname, ('weekday', 'pixel'), subplots=False)
# test 6; labeling, if no names in csv-file
#plt.plotfile(fname2, cols=(0, 1, 2), delimiter=' ', names=['$x$', '$f(x)=x^2$', '$f(x)=x^3$'])
# test 7; more than one file per figure--illustrated here with a single file
#plt.plotfile(fname2, cols=(0, 1), delimiter=' ')
#plt.plotfile(fname2, cols=(0, 2), newfig=False,delimiter=' ') # use current figure
#plt.xlabel(r'$x$')
#plt.ylabel(r'$f(x) = x^2, x^3$')
print analysis
if (x - 1) % 50000 == 0:
chainsSSR = np.load(os.getcwd() + '/data/chainsSSRDual_' + str(x) + y +
'.npy')
else:
chainsSSR = np.load(os.getcwd() + '/data/chainsSSRDual_' +
str(x - x % 50000 + 1) + y + '.npy')
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
for chain in chains:
chain = chain[chain[:, 0] >= 0]
ax.plot(chain[:, 0], chain[:, 1], chain[:, 2])
plt.savefig(os.getcwd() + '/data/3dplot_' + str(x) + y + ".pdf")
plt.plotfile(os.getcwd() + '/data/energyDual_' + str(x) + y)
plt.savefig(os.getcwd() + '/data/energyDual_' + str(x) + y + ".pdf")
plt.close()
binomial = []
for k in xrange(0, 6):
m = 5
binomial += [an.choose(m, k) * (0.5**k) * (0.5**(m - k))]
saved = np.save(os.getcwd() + '/data/savedSSRDual_' + str(x) + y, analysis)
spectra = open(os.getcwd() + '/data/savedSpectraDual_' + str(x), 'a')
spectra.write(str(analysis) + "\n")
spectra.flush()
spectra.close()
for saw in chains:
# if saw[len(saw)-1, 0] == -1 or saw[len(saw)-1, 1] == -1:
# saw[::2, 0] += 0.5
# plt.plot(saw[0:int(len(saw)*.333)+1, 0], saw[0:int(len(saw)*.333)+1, 1])
# else:
saw = saw[saw[:,0] >= 0]
#saw[::2, 0] += 0.5
plt.plot(saw[:, 0], saw[:, 1])
plt.show()
profile = lattice.sum(axis=0)
plt.plot(profile[0:400])
plt.show()
plt.scatter(points[:,0],points[:,1])
# plt.show()
plt.plotfile('EnergiesHex42b')
plt.show()
binomial = []
for k in xrange(0,4):
n =3
binomial += [choose(n,k)*(0.6**k)*(0.4**(n-k))]
spectra = open('Saved_spectraHex40b','a')
spectra.write(str(analysis)+"\n")
ssr = SSR(analysis, binomial)
SSR = open('Saved_SSRhex40b', 'a')
SSR.write('+1\t' + str(ssr) + '\t' + '250000\n')
SSR.close()
import sys
import matplotlib.pyplot as plt
for argument in range(1, len(sys.argv), 2):
print(0,
int(sys.argv[argument + 1]) * 2 + 1,
int(sys.argv[argument + 1]) * 2 + 2)
plt.plotfile(sys.argv[argument], (0, int(sys.argv[argument + 1]) * 2 + 1,
int(sys.argv[argument + 1]) * 2 + 2))
plt.show()
import matplotlib.pyplot as plt
plt.plotfile('datos1.csv', cols=(0,2), delimiter=',')
plt.plotfile('datos1.csv', cols=(0,3), delimiter=',', newfig=False)
plt.xlabel('x')
plt.ylabel('Derivada')
plt.savefig('datos1')
plt.show()
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.cbook as cbook
fname = cbook.get_sample_data('/home/akshaymshet/processed_data/events/bishop.csv', asfileobj=False)
# test 1; use ints
plt.plotfile(fname, (10, 12))
# test 2; use names
plt.plotfile(fname, ('weekday', 'pixel'))
# test 3; use semilogy for volume
#plt.plotfile(fname, ('weekday', 'pixel'),plotfuncs={'volume': 'semilogy'})
# test 4; use semilogy for volume
#plt.plotfile(fname, (10, 12), plotfuncs={5: 'semilogy'})
# test 5; single subplot
#plt.plotfile(fname, ('weekday', 'pixel'), subplots=False)
# test 6; labeling, if no names in csv-file
#plt.plotfile(fname2, cols=(0, 1, 2), delimiter=' ', names=['$x$', '$f(x)=x^2$', '$f(x)=x^3$'])
# test 7; more than one file per figure--illustrated here with a single file
#plt.plotfile(fname2, cols=(0, 1), delimiter=' ')
#plt.plotfile(fname2, cols=(0, 2), newfig=False,delimiter=' ') # use current figure
#plt.xlabel(r'$x$')
#plt.ylabel(r'$f(x) = x^2, x^3$')
parser.add_argument('-o',
dest='output',
type=str,
default='',
help='output file name')
args = parser.parse_args()
names = tuple(args.columns.split(','))
cols = tuple([int(x) for x in names])
for infile in args.datfile:
label = "%s%s" % (os.path.basename(infile), cols)
plt.plotfile(infile,
cols=cols,
names=names,
delimiter=args.delimiter,
marker=args.marker,
linestyle=args.linestyle,
label=label)
if args.xlabel:
plt.xlabel(args.xlabel)
if args.ylabel:
plt.ylabel(args.ylabel)
if args.xlog:
plt.xscale('log')
if args.ylog:
plt.yscale('log')
if args.legend:
plt.legend(loc='best')
if args.output:
plt.savefig(args.output)
parser.add_argument('--delm', dest='delimiter', type=str, default=' ', help='delimiter')
parser.add_argument('-l', '--legend', dest='legend', action='store_true', help='legend')
parser.add_argument('-m', '--marker', dest='marker', type=str, default='o', help='delimiter')
parser.add_argument('--ls', dest='linestyle', type=str, default='-', help='linestyle')
parser.add_argument('-c', '--columns', dest='columns', type=str, default='0,1', help='column index')
parser.add_argument('-f', '--format', dest='format', type=str, default='', help='output file format')
parser.add_argument('-o', dest='output', type=str, default='', help='output file name')
args = parser.parse_args()
names = tuple(args.columns.split(','))
cols = tuple([int(x) for x in names])
for infile in args.datfile:
label = "%s%s" % (os.path.basename(infile), cols)
plt.plotfile(infile,
cols=cols, names=names,
delimiter=args.delimiter, marker=args.marker, linestyle=args.linestyle,
label=label)
if args.xlabel:
plt.xlabel(args.xlabel)
if args.ylabel:
plt.ylabel(args.ylabel)
if args.xlog:
plt.xscale('log')
if args.ylog:
plt.yscale('log')
if args.legend:
plt.legend(loc='best')
if args.output:
plt.savefig(args.output)
print("file %s was written" % args.output)
else:
#!/usr/bin/python
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 14, 'font.family': 'sans'})
fig, ax = plt.subplots()
ax = plt.plotfile('./time.dat', cols=(0, 1), delimiter=' ')
#plt.plotfile(*np.loadtxt('time.dat', unpack=True))
#ax.plot(t, s, lw=2, color="red", ls = "-", alpha = 0.5,
# marker = "o", markersize = 8.0,
# )
# #animated = True, aa = True, clip_on = True)
fig.savefig('fig.pdf')
#!/usr/bin/env python
import os
import sys
import matplotlib.pyplot as plt
print "Enter the number of grid points"
n=raw_input()
print "Enter the name of the Figure"
Fig=raw_input()
#plt.plotfile('Solar_VV.dat', cols=(1,2), label='Mercury', delimiter=' ')
#plt.plotfile('Solar_VV.dat', cols=(5,6),label='Venus', newfig=False, delimiter=' ')
#plt.plotfile('Solar_VV.dat', cols=(9,10),label='Earth', newfig=False, delimiter=' ')
#plt.plotfile('Solar_VV.dat', cols=(13,14),label='Mars', newfig=False, delimiter=' ')
plt.plotfile('Solar_VV.dat', cols=(17,18),label='Jupiter', newfig=False, delimiter=' ')
plt.plotfile('Solar_VV.dat', cols=(21,22),label='Saturn', newfig=False, delimiter=' ')
plt.plotfile('Solar_VV.dat', cols=(25,26),label='Uranus', newfig=False, delimiter=' ')
plt.plotfile('Solar_VV.dat', cols=(29,30),label='Neptune', newfig=False, delimiter=' ')
plt.plotfile('Solar_VV.dat', cols=(33,34),label='Pluto', newfig=False, delimiter=' ')
plt.title('y vs x '+n)
plt.xlabel('x(au)')
plt.ylabel('y(au)')
plt.legend()
plt.grid()
plt.savefig(Fig)
plt.show()
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.cbook as cbook
fname = cbook.get_sample_data('msft.csv', asfileobj=False)
fname2 = cbook.get_sample_data('data_x_x2_x3.csv', asfileobj=False)
# test 1; use ints
plt.plotfile(fname, (0, 5, 6))
# test 2; use names
plt.plotfile(fname, ('date', 'volume', 'adj_close'))
# test 3; use semilogy for volume
plt.plotfile(fname, ('date', 'volume', 'adj_close'),
plotfuncs={'volume': 'semilogy'})
# test 4; use semilogy for volume
plt.plotfile(fname, (0, 5, 6), plotfuncs={5: 'semilogy'})
# test 5; single subplot
plt.plotfile(fname, ('date', 'open', 'high', 'low', 'close'), subplots=False)
# test 6; labeling, if no names in csv-file
plt.plotfile(fname2, cols=(0, 1, 2), delimiter=' ',
names=['$x$', '$f(x)=x^2$', '$f(x)=x^3$'])
# test 7; more than one file per figure--illustrated here with a single file
plt.plotfile(fname2, cols=(0, 1), delimiter=' ')
plt.plotfile(fname2, cols=(0, 2), newfig=False,
import matplotlib.pyplot as plt
import numpy as np
import csv
import sys
file = 'Data2.csv'
fname = open(file, 'rt')
plt.plotfile(fname, ('angle', 'raw', 'median', 'harmonic', 'arithmetic'),
subplots=False)
plt.show()
import matplotlib.pyplot as plt
import numpy as np
from csv import reader
data = np.genfromtxt('sample_data.csv',delimiter=',',names=['t','a','b','c','d'])
# test 1; use ints
plt.plot(data['t'])
plt.show()
'''
# test 2; use names
plt.plotfile(fname, ('date', 'volume', 'adj_close'))
# test 3; use semilogy for volume
plt.plotfile(fname, ('date', 'volume', 'adj_close'),
plotfuncs={'volume': 'semilogy'})
# test 4; use semilogy for volume
plt.plotfile(fname, (0, 5, 6), plotfuncs={5: 'semilogy'})
# test 5; single subplot
plt.plotfile(fname, ('date', 'open', 'high', 'low', 'close'), subplots=False)
# test 6; labeling, if no names in csv-file
plt.plotfile(fname2, cols=(0, 1, 2), delimiter=' ',
names=['$x$', '$f(x)=x^2$', '$f(x)=x^3$'])
# test 7; more than one file per figure--illustrated here with a single file
def plotfile(*args, **kwargs):
r"""starkplot wrapper for plotfile"""
return _pyplot.plotfile(*args, **kwargs)
def testPlot():
plt.plotfile(fname, ('date', 'volume', 'adj_close'))
plt.plot([1, 0, 0, 4])
plt.ylabel('some numbers')
plt.show()
import matplotlib.pyplot as plt
plt.plotfile('600_1%_532e_Mo F1_80sec.asc', delimiter=' ', cols=(0, 1),
names=('col1', 'col2'), )
plt.show()
#!/usr/bin/env python
import os
import sys
import matplotlib.pyplot as plt
with open(sys.argv[1],'r') as File, open(sys.argv[2],'r') as File2:
print "Enter the number of grid points"
n=raw_input()
print "Enter the name of the Figure"
Fig=raw_input()
plt.plotfile(File, cols=(0,3), label='RK4', delimiter=' ')
plt.plotfile(File2, cols=(0,3),label='Verlet', newfig=False, delimiter=' ')
plt.title('Energy vs time '+n)
plt.xlabel('time(yr)')
plt.ylabel(r'$ E_{tot} (au/yr)^2$')
plt.legend()
plt.grid()
plt.savefig(Fig)
plt.show()
import matplotlib.pyplot as plt
import numpy as np
debug_file = open("../debug.csv","r")
#headers = debug_file.readline()
#labels = headers.split()
#print labels
#for label in labels:
plt.plotfile(debug_file, (0, 1, 2),subplots=False,newfig=False)
plt.show()
# add some text for labels, title and axes ticks
ax.set_ylabel('---------------------------Scores----------------------------')
ax.set_title('Scores by group and gender')
ax.set_xticks(ind + width)
ax.set_yticklabels(('Ahh', '--G1--', 'G2', 'G3', 'G4', 'G5', 'G5',
'G5', 'G5'), rotation=90)
ax.legend((rects1[0], rects2[0]), ('Men', 'Women'))
def autolabel(rects):
# attach some text labels
for rect in rects:
height = rect.get_height()
ax.text(rect.get_x() + rect.get_width() / 2., 1.05 * height, '%d' %
int(height), ha='center', va='bottom')
autolabel(rects1)
autolabel(rects2)
fname = cbook.get_sample_data('msft.csv', asfileobj=False)
plt.plotfile(fname, ('date', 'open', 'high', 'low', 'close'), subplots=False)
#plt.xlabel(r'$x$')
#plt.ylabel(r'$f(x) = x^2, x^3$')
plt.draw()
#fig1.set_size_inches(18.5, 10.5, forward = True)
plt.savefig("test.png")
plt.show()
#!/usr/bin/env python
from argparse import ArgumentParser
import matplotlib.pyplot as plt
if __name__ == '__main__' :
parser = ArgumentParser(description="Plot a CSV file")
parser.add_argument("datafile", help="The CSV file")
# Require at least one column name
parser.add_argument("columns", nargs="+",
help="Names of columns to plot")
parser.add_argument("--save", help="Save the plot as ...")
parser.add_argument("--no-show", action="store_true",
help="Don't show the plot")
args = parser.parse_args()
plt.plotfile(args.datafile, args.columns)
if args.save:
plt.savefig(args.save)
if not args.no_show:
plt.show()
import matplotlib.pyplot as plt
import numpy as np
#fname = open('msft.csv.org')
#fname = open('test.csv')
fname = open('create.csv')
#plt.plotfile(fname, ('date','emotion','headache','hipleft','hipright','shoulderleft','shoulderright','spinallow','spinalmiddle','spinalneck'), subplots=False)
plt.plotfile(fname, ('date','emotion','headache','hipleft','hipright','shoulderleft','spinallow','spinalneck'), subplots=False)
#Date,emotion,headache,hipleft,hipright,shoulderleft,spinallow,spinalneck
#plt.figtext("hello")
plt.xlabel(r'$date$')
plt.ylabel(r'$levels$')
plt.show()
plt.savefig('data_graph.png')
#!/usr/bin/env python
import os
import sys
import matplotlib.pyplot as plt
print "Enter the number of grid points"
n=raw_input()
print "Enter the name of the Figure"
Fig=raw_input()
plt.plotfile('Ea_Ju_VVE3.dat', cols=(1,2), label='Earth', delimiter=' ')
plt.plotfile('Ea_Ju_VVE3.dat', cols=(5,6),label='Jupiter', newfig=False, delimiter=' ')
plt.title('y vs x '+n)
plt.xlabel('x(au)')
plt.ylabel('y(au)')
plt.legend()
plt.axis([-10,10,-10,10])
plt.grid()
plt.savefig(Fig)
plt.show()
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('white')
import warnings
warnings.filterwarnings("ignore")
num_eq = 3
it_max = 100 # set to it_max run
fname = "coordinates.dat" # data file name
# plot each coordinate (angular momentum) vs time
for i in range(num_eq):
# note that i,j correspond to q_i, and not the column number in .dat file
plt.plotfile(fname, delimiter=' ', cols=(0, i + 1), c='black')
plt.title(
'$q_{%d}$ as a function of time for torque-free motion (it_max = %d)' %
(i, it_max))
plt.xlabel('Time')
plt.ylabel('$q_{%d}$' % i)
plt.savefig('q_%d_vs_t_%d.pdf' %
(i, it_max)) # plots are saved under q_i_vs_t.pdf
plt.show(block=True)
# plot kinetic energy vs time
plt.plotfile(fname, delimiter=' ', cols=(0, 4), c='black')
plt.title(
'Kinetic Energy as a function of time for torque-free motion (it_max = %d)'
% it_max)
plt.xlabel("Time")
# test 1; use ints
#plt.plotfile(fname, (0, 5, 6))
# test 2; use names
#plt.plotfile(fname, ('date', 'volume', 'adj_close'))
# test 3; use semilogy for volume
#plt.plotfile(fname, ('date', 'volume', 'adj_close'),
# plotfuncs={'volume': 'semilogy'})
# test 4; use semilogy for volume
#plt.plotfile(fname, (0, 5, 6), plotfuncs={5: 'semilogy'})
# test 5; single subplot
plt.plotfile(fname, ('date', 'pain', 'high', 'low', 'close'), subplots=True)
# test 6; labeling, if no names in csv-file
#plt.plotfile(fname2, cols=(0, 1, 2), delimiter=' ',
# names=['$x$', '$f(x)=x^2$', '$f(x)=x^3$'])
# test 7; more than one file per figure--illustrated here with a single file
#plt.plotfile(fname2, cols=(0, 1), delimiter=' ')
#plt.plotfile(fname2, cols=(0, 2), newfig=False,
# delimiter=' ') # use current figure
plt.xlabel(r'$x$')
plt.ylabel(r'$f(x) = x^2, x^3$')
# test 8; use bar for volume
#plt.plotfile(fname, (0, 5, 6), plotfuncs={5: 'bar'})
#!/usr/bin/python
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 14, 'font.family': 'sans'})
fig, ax = plt.subplots()
ax = plt.plotfile('./time.dat', cols=(0, 1), delimiter = ' ')
#plt.plotfile(*np.loadtxt('time.dat', unpack=True))
#ax.plot(t, s, lw=2, color="red", ls = "-", alpha = 0.5,
# marker = "o", markersize = 8.0,
# )
# #animated = True, aa = True, clip_on = True)
fig.savefig('fig.pdf')
an.acceptance_rate(i+1,count)
an.chains_to_xyz(chains, 'ShortDual_'+str(n)+alph, lattice)
analysis = an.sep_analysis(chains)
analysis = tuple(x/float(sum(analysis)) for x in analysis)
print analysis
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
for chain in chains:
chain = chain[chain[:,0] >= 0]
ax.plot(chain[:,0],chain[:,1],chain[:,2])
#plt.show()
plt.plotfile('EnergiesDual_'+str(n)+alph)
plt.savefig("energyDual_" +str(n)+alph+".pdf")
plt.close()
binomial = []
for k in xrange(0,6):
m =5
binomial += [an.choose(m,k)*(0.5**k)*(0.5**(m-k))]
saved = np.save('Safe_SSRDual'+str(n)+alph, analysis)
#spectra = open(r'C:\Users\Maggie\Documents\GitHub\scf-mc\Saved_spectra3D_'+str(n), 'w')
#with spectra:
spectra = open('Saved_spectraDual_'+str(n), 'a')
spectra.write(str(analysis) +"\n")
spectra.flush()
__author__ = 'Brian'
import csv
import matplotlib.pyplot as plt
import numpy as np
plt.plotfile('C:\Users\Brian\Desktop\Brian\Universitetet\Kandidat\Master Thesis\WeLoveGREEN-ENERGY\DATASET_FOR_GREEN_ENERGY_PLOTTING\wind_vs_prices.csv', delimiter=';', cols=(0, 1),
names=('Wind Speed', 'Electricity Price'), linestyle='None', marker='.')
y=[]
x=[]
with open('C:\Users\Brian\Desktop\Brian\Universitetet\Kandidat\Master Thesis\WeLoveGREEN-ENERGY\DATASET_FOR_GREEN_ENERGY_PLOTTING\wind_vs_prices.csv', 'rb') as csvfile:
dat = csv.reader(csvfile, delimiter=';')
for row in dat:
y.append(int(row[1]))
x.append(int(row[0]))
m,b = np.polyfit(x,y,1)
plt.plot(x, np.array(x) * m +b, color='red')
plt.show()
import matplotlib.pyplot as plt
plt.plotfile("graph_ten_topics.txt", ("features", "accuracy"))
plt.title("Simple Plot")
plt.show()
