def plot2d(data, variables, filename):
# default vales
logscale_x = False
logscale_y = False
logable = True
grid_bool = False
axistop_bool = False
axisright_bool = False
axisbottom_bool = True
axisleft_bool = True
legend_outside = True
colors = {}
for i in range(0, len(variables)):
colors[variables[i]] = ['w', 'b', 'g', 'r', 'c', 'm', 'y', 'k'][i]
# intern parameters
plot_range = 1. / 8 # means of two data series differ by this factor,
# the scalng is defined ill
# prepare data --->
# check for appropriate format of input
if (isinstance(variables, list)):
for i in variables:
if (not isinstance(i, str)):
print('second argument must be a list of str')
return None
else:
print('second argument must be a list')
if (not isinstance(filename, str)):
print('third argument must be of type str')
return None
if (not isinstance(data, list)):
print('first argument must be of type list')
return None
for j in data[0]:
if (not isinstance(j, float) and not isinstance(j, int)):
print('data must be passed as a list of int or float')
return None
for i in data[1:]:
for j in i:
if (not isinstance(j, float) and not isinstance(j, int)):
print('data must be passed as list of int or float')
return None
elif j <= 0 and logable:
print('logscale impossible')
logable = False # to avoid using logscale
#get number of rows and colums of data
ncols = len(data)
nrows = len(data[0])
#check wether all rows have the same size and create df
df = {} # initialize empty dictionary similar to dataframe
for i in range(0, ncols):
if not len(data[i]) == nrows:
print('missing values in data set')
return None
else:
df[variables[i]] = data[i]
# <--- prepare data
# ---> analyse data
# get lower and upper bounds of each variable
bounds = {}
for i in variables:
[bounds['lower', i], bounds['upper', i]] = [min(df[i]), max(df[i])]
# check whether magnitudes are approximately equal and use logscale if not
means = [0] * (ncols - 1)
for i in range(1, ncols): # compute means of the columns
means[i - 1] = np.mean(df[variables[i]])
if min(means) / max(
means
) < plot_range and logable: # if variables have different magnitudes set
print('ill scaling - use logscale') # logscale
logscale_y = True
for i in range(
1, ncols
): # if one variable changes magnitude drastically set logscale
if ((max(df[variables[i]]) - min(df[variables[i]])) != 0):
temp = means[i - 1] / (max(df[variables[i]]) -
min(df[variables[i]]))
if temp < plot_range and logable and not logscale_y:
print('very ill scaling - use logscale')
logscale_y = True
# <--- analyse data
# plot --->
fig = plt.figure(1)
ax = Subplot(fig, 111)
fig.add_subplot(ax)
for i in variables[1:]:
ax.scatter(df[variables[0]],
df[i],
label='$' + i + '$',
color=colors[i])
# font
# labels and legend
ax.legend(loc='best')
plt.xlabel(variables[0], **hfont)
if legend_outside:
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
# logscale
if logscale_y:
plt.yscale('log')
if logscale_x:
plt.xscale('log')
# grid
plt.grid(grid_bool)
# visible axis
ax.axis["right"].set_visible(axisright_bool)
ax.axis["top"].set_visible(axistop_bool)
ax.axis["bottom"].set_visible(axisbottom_bool)
ax.axis["left"].set_visible(axisleft_bool)
# <--- plot
# save plot --->
pdfdirectory = filename + '.pdf'
pgfdirectory = filename + '.pgf'
fig.savefig(pdfdirectory)
plt.savefig(pgfdirectory)
# <--- save plot
print('run successful')
return None
def plotwitherrors(time, timeunit, means, meansunit, std):
# ---> basic settings
title = input('Title: ')
y_name = input('Observable name: ')
if title == '' or y_name == '':
title = 'test'
y_name = 'test'
axistop_bool = False
axisright_bool = False
axisbottom_bool = True
axisleft_bool = True
# <--- basic settings
# ---> analyse data
# <--- analyse data
# plot --->
fig = plt.figure()
ax = Subplot(fig, 111)
fig.add_subplot(ax)
ax.errorbar(time,
means,
yerr=std,
marker='o',
markersize=2,
c='black',
linestyle='none',
ecolor='black',
elinewidth=1,
capthick=1,
capsize=3)
ax.plot(time,
means,
color='black',
alpha=0.5,
linewidth=1.0,
linestyle='--')
#legend and labels
ax.legend(loc='best')
plt.xlabel('time in ' + timeunit, **hfont)
plt.ylabel(y_name + ' in ' + meansunit, **hfont)
# visible axis
ax.axis["right"].set_visible(axisright_bool)
ax.axis["top"].set_visible(axistop_bool)
ax.axis["bottom"].set_visible(axisbottom_bool)
ax.axis["left"].set_visible(axisleft_bool)
# <--- plot
# save plot --->
pdfdirectory = '../../figures/' + title + '.pdf'
pgfdirectory = '../../figures/' + title + '.pgf'
fig.savefig(pdfdirectory)
plt.savefig(pgfdirectory)
# <--- save plot
print('test run successful')
return None
