def ginput_check_points(df, rectangles, regr):
get_ipython().magic('matplotlib auto')
time.sleep(3)
plt.clf()
plt.subplot(1, 2, 1)
plt.gca().axis('equal')
plt.gca().set_title('triple click to quit')
plot_rectangles(rectangles, names=True)
plot_numbers = sorted(set(df.plot_nr))
for nr in plot_numbers:
d = df[df.plot_nr == nr]
plt.plot(d.x, d.y, '.')
return _ginput_show_info(df, _show_reg_fun(regr))
def test_nrs(df, plot_numbers):
plot_rectangles(rectangles, names=True)
for nr in plot_numbers:
d = df[df.plot_nr == nr]
plt.plot(d.x, d.y, '.')
treatments = experiment.treatments
treatment_names = sr.find_treatment_names(treatments)
#FOR REFERENCE: This is copied from all_e22_experiments.py
#treatments = {int(x[0]): {'mixture': x[1],
# 'rock_type': x[2],
# 'fertilizer': x[3],
# 'experiment': x[4]}
#Plot size to show within python window, unless specified otherwise later on
plt.rcParams['figure.figsize'] = (10, 6)
# Displays a plot of the rectangles, as specified in the experiment file (not for buckets)
plt.cla()
plot_rectangles(rectangles)
# with treatments:
plt.cla()
keys = list(rectangles)
r = [rectangles[k] for k in keys]
tr = ['_'.join(treatments[k].values()) for k in keys]# todo
plot_rectangles(r, tr)
plt.show()
# How to do regressions: The next line makes the "regressor
# object" regr which will be used further below. It contains the
# functions and parameters for doing the regressions. The parameters
# are collected in the dict named options. (Organizing the code this
# way makes it easier to replace the regression function with your own
# functions.)