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ModPlot.py
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ModPlot.py
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#!/usr/bin/env python
from traits.etsconfig.api import ETSConfig
ETSConfig.toolkit = 'qt'
import traits.api as ta
import traitsui.api as tua
import PlotData as jpl
import QuantityLists as ql
import SetsOfFits as sff
import pandas as pad
from copy import deepcopy
from collections import OrderedDict
from BootStrapping import BootStrap
from MiscFuns import convert_to_hex,op_dict,op_dict_std
import pickle as pickle
import os
import matplotlib.pyplot as pl
import numpy as np
import time
# jpl.tpad,tsize,xsize,ysize = 20,40,60,60
# legcol,legsize,legalpha = 1,25,0.7
# linewidth,dotsize,errorbarlen = 2,10,6
# def_tick_size = 30
# def_grid = True
# jpl.linewidth,dotsize,errorbarlen = 4,20,12
# def_tick_size = 55
# jpl.def_grid = False
def_arrow_styles = ['-','->','-[','|-|','-|>','<-','<->','<|-','<|-|>','fancy','simple','wedge']
data_plot = None
backup_plot = []
hold_file = './prev_file.py3p'
x_funs = OrderedDict()
x_funs['None'] = None
x_funs['square'] = np.square
x_funs['sqrt'] = np.sqrt
x_funs['log'] = np.log
x_funs['exp'] = np.exp
def check_index_and_None(this_data,this_index,this_key,def_val = False):
if this_index not in this_data.index or this_data.loc[this_index,this_key] is None:
return def_val
else:
return this_data.loc[this_index,this_key]
def fmt_Range(this_list):
this_list = list(OrderedDict.fromkeys(this_list))
# return map(get_val,this_list)
return this_list
class DataFrame( ta.HasTraits ):
Load = ta.Button()
Apply = ta.Button()
Duplicate = ta.Button()
Wipe = ta.Button()
Undo_Wipe = ta.Button()
Apply_Move = ta.Button()
Create_Arrow = ta.Button()
Combine = ta.Button()
resample = ta.Str('1000')
transpose_table = ta.Bool(False)
chi_min = ta.Float(0)
chi_max = ta.Float(2)
fix_min = ta.Int(0)
cut_max = ta.Int(100)
Hist_Comb_Min = ta.Button()
fix_max = ta.Int(100)
cut_min = ta.Int(0)
Hist_Comb_Max = ta.Button()
this_aindex = ta.Int(0)
animate_dim = ta.Int(0)
Animate = ta.Button()
Dump_Fit_Table = ta.Button()
fit_file_name = ta.File()
fmt_latex = ta.Bool(True)
x_range_min = ta.Int(0)
x_range_max = ta.Int(-1)
x_increment = ta.Int(1)
x_scale = ta.Float(1)
x_function = ta.Enum(list(x_funs.keys()))
shift = ta.Float(1.0)
shift_overlap = ta.Float(0.005)
do_band = ta.Bool(False)
band_index = ta.Int(-1)
show_band_eval = ta.Bool(True)
arrow_x = ta.Float(0.0)
arrow_y = ta.Float(0.0)
arrow_x_text = ta.Float(1.0)
arrow_y_text = ta.Float(1.0)
arrow_text_size = ta.Float(30)
arrow_color = ta.Str('black')
arrow_style = ta.Enum(def_arrow_styles)
key_list = ta.List([''])
this_key = ta.Enum(values='key_list')
this_key_2 = ta.Enum(values='key_list')
plot_this = ta.Bool(True)
label = ta.Str('test')
Physical_Units = ta.Bool(True)
plot_err = ta.Enum(['No','relative','absolute'])
supress_legend = ta.Bool(False)
color = ta.Color(None)
symbol = ta.Str('o')
Median = ta.Bool(False)
scale_y = ta.Float(1.0)
shift_y = ta.Float(0)
fit_dim_pick = ta.Int(1)
ShowEval = ta.List(ta.Float,[0])
extrap_fade = ta.Float(1.5)
Hairline_plot = ta.Bool(False)
line_alpha_scale = ta.Float(20)
line_alpha_abs = ta.Float(1)
Force_Param_Name = ta.Str('')
Force_Param_Units = ta.Str('')
fit_custom_xrange = ta.Bool(False)
fit_x_min = ta.Str('FromDataMin')
fit_x_max = ta.Str('ToDataMax')
suppress_key_index = ta.Bool(False)
dim_list = ta.List([''])
pick_dimension = ta.Enum(values='dim_list')
test_pick_dim = ta.Bool(False)
dim_1_list = ta.List([''])
dim_2_list = ta.List([''])
dim_3_list = ta.List([''])
dim_4_list = ta.List([''])
dim_5_list = ta.List([''])
dim_6_list = ta.List([''])
dim_7_list = ta.List([''])
dim_8_list = ta.List([''])
dim_1 = ta.Enum([1,2,3,4])
dim_1 = ta.Enum(values = 'dim_1_list')
dim_2 = ta.Enum(values = 'dim_2_list')
dim_3 = ta.Enum(values = 'dim_3_list')
dim_4 = ta.Enum(values = 'dim_4_list')
dim_5 = ta.Enum(values = 'dim_5_list')
dim_6 = ta.Enum(values = 'dim_6_list')
dim_7 = ta.Enum(values = 'dim_7_list')
dim_8 = ta.Enum(values = 'dim_8_list')
test_dim_1 = ta.Bool(False)
test_dim_2 = ta.Bool(False)
test_dim_3 = ta.Bool(False)
test_dim_4 = ta.Bool(False)
test_dim_5 = ta.Bool(False)
test_dim_6 = ta.Bool(False)
test_dim_7 = ta.Bool(False)
test_dim_8 = ta.Bool(False)
vary_len = 0
move_to_index = ta.Int(0)
combine_operation = ta.Enum(list(op_dict.keys()))
new_label = ta.Str('')
view = tua.View(
tua.Group(
tua.Item('this_key'),
tua.Item('plot_this'),
tua.Item('label'),
tua.Item('Physical_Units'),
tua.Item('plot_err'),
tua.Item('color',style='custom'),
tua.Item('symbol'),
tua.Item('Median'),
tua.Item('scale_y'),
tua.Item('shift_y'),
tua.Item('supress_legend'),
tua.Item('suppress_key_index'),
tua.Item('pick_dimension',visible_when='test_pick_dim'),
tua.Item('dim_1',visible_when='test_dim_1',name='pie'),
tua.Item('dim_2',visible_when='test_dim_2'),
tua.Item('dim_3',visible_when='test_dim_3'),
tua.Item('dim_4',visible_when='test_dim_4'),
tua.Item('dim_5',visible_when='test_dim_5'),
tua.Item('dim_6',visible_when='test_dim_6'),
tua.Item('dim_7',visible_when='test_dim_7'),
tua.Item('dim_8',visible_when='test_dim_8'),
# tua.Item('Load', show_label=False),
tua.Item('Apply', show_label=False),
tua.Item('Duplicate', show_label=False),
tua.Item('Wipe', show_label=False),
tua.Item('Undo_Wipe', show_label=False),
tua.Item('move_to_index'),
tua.Item('Apply_Move', show_label=False),
label='Main'
),tua.Group(
tua.Item('this_key'),
tua.Item('plot_this'),
tua.Item('x_scale'),
tua.Item('x_function'),
tua.Item('shift'),
tua.Item('shift_overlap'),
tua.Item('x_range_min'),
tua.Item('x_range_max'),
tua.Item('x_increment'),
tua.Item('do_band'),
tua.Item('band_index',enabled_when='do_band'),
tua.Item('show_band_eval',enabled_when='do_band'),
tua.Item('Apply', show_label=False),
tua.Item('Duplicate', show_label=False),
tua.Item('Wipe', show_label=False),
tua.Item('Undo_Wipe', show_label=False),
tua.Item('move_to_index'),
tua.Item('Apply_Move', show_label=False),
tua.Item('animate_dim'),
tua.Item('Animate', show_label=False),
label='x_axis_modify'
),tua.Group(
tua.Item('this_key'),
tua.Item('plot_this'),
tua.Item('extrap_fade'),
tua.Item('Hairline_plot'),
tua.Item('line_alpha_scale',visible_when='Hairline_plot'),
tua.Item('line_alpha_abs',visible_when='Hairline_plot'),
tua.Item('fit_custom_xrange'),
tua.Item('fit_x_min',enabled_when='fit_custom_xrange'),
tua.Item('fit_x_max',enabled_when='fit_custom_xrange'),
tua.Item('ShowEval'),
tua.Item('fit_dim_pick'),
tua.Item('Force_Param_Name'),
tua.Item('Force_Param_Units'),
tua.Item('Apply', show_label=False),
tua.Item('Duplicate', show_label=False),
tua.Item('Wipe', show_label=False),
tua.Item('Undo_Wipe', show_label=False),
label='Fitting_stuff'
),tua.Group(
tua.Item('this_key'),
tua.Item('plot_this'),
tua.Item('arrow_x'),
tua.Item('arrow_y'),
tua.Item('arrow_x_text'),
tua.Item('arrow_y_text'),
tua.Item('arrow_text_size'),
tua.Item('arrow_color'),
tua.Item('arrow_style'),
tua.Item('supress_legend'),
tua.Item('label'),
tua.Item('Apply', show_label=False),
tua.Item('Duplicate', show_label=False),
tua.Item('Wipe', show_label=False),
tua.Item('Undo_Wipe', show_label=False),
tua.Item('Create_Arrow',show_label=False),
tua.Item('fit_file_name'),
tua.Item('transpose_table'),
tua.Item('Dump_Fit_Table', show_label=False),
label='Arrow'
),tua.Group(
tua.Item('this_key'),
tua.Item('this_key_2'),
tua.Item('combine_operation'),
tua.Item('new_label'),
tua.Item('Combine', show_label=False),
tua.Item('fit_file_name'),
tua.Item('fmt_latex'),
tua.Item('resample'),
tua.Item('chi_min'),
tua.Item('chi_max'),
tua.Item('fix_min'),
tua.Item('cut_max'),
tua.Item('Hist_Comb_Min', show_label=False),
tua.Item('fix_max'),
tua.Item('cut_min'),
tua.Item('Hist_Comb_Max', show_label=False),
tua.Item('fit_file_name'),
tua.Item('transpose_table'),
tua.Item('Dump_Fit_Table', show_label=False),
label='Combine'
),
buttons=['OK'],
resizable=True
)
def _Hist_Comb_Max_fired(self):
global data_plot
this_data = data_plot.plot_data.loc[:,self.this_key]
if 'histogram_vary' == this_data['type']:
if 'boot_data' in this_data and hasattr(this_data['boot_data'],'Comb_Max_fitr'):
app_data = deepcopy(this_data)
app_data['type'] = 'histogram_vary'
if self.resample.lower() == 'false':
app_data['boot_data'] = app_data['boot_data'].Comb_Max_fitr(self.fix_max,cut_min=self.cut_min,
chi_min=self.chi_min,chi_max=self.chi_max)
else:
app_data['boot_data'] = app_data['boot_data'].Comb_Max_fitr(self.fix_max,cut_min=self.cut_min,
resample=int(self.resample),
chi_min=self.chi_min,chi_max=self.chi_max)
app_data['label'] = app_data['label']+'_fixmax='+str(self.fix_max)+'_cutmin='+str(self.cut_min)
app_data['key_select'] = app_data['boot_data'].index[0]
data_plot.AppendData(app_data)
self.key_list = list(data_plot.plot_data.keys())
self._Load_fired()
self._Apply_fired()
def _Hist_Comb_Min_fired(self):
global data_plot
this_data = data_plot.plot_data.loc[:,self.this_key]
if 'histogram_vary' == this_data['type']:
if 'boot_data' in this_data and hasattr(this_data['boot_data'],'Comb_Min_fitr'):
app_data = deepcopy(this_data)
app_data['type'] = 'histogram_vary'
if self.resample.lower() == 'false':
app_data['boot_data'] = app_data['boot_data'].Comb_Min_fitr(self.fix_min,cut_max=self.cut_max,
chi_min=self.chi_min,chi_max=self.chi_max)
else:
app_data['boot_data'] = app_data['boot_data'].Comb_Min_fitr(self.fix_min,cut_max=self.cut_max,
resample=int(self.resample),
chi_min=self.chi_min,chi_max=self.chi_max)
app_data['label'] = app_data['label']+'_fixmin='+str(self.fix_min)+'_cutmax='+str(self.fit_max)
app_data['key_select'] = app_data['boot_data'].index[0]
data_plot.AppendData(app_data)
self.key_list = list(data_plot.plot_data.keys())
self._Load_fired()
self._Apply_fired()
def _Dump_Fit_Table_fired(self):
global data_plot
this_data = data_plot.plot_data.loc[:,self.this_key]
if 'type' in this_data and ('fit_vary' == this_data['type'] or 'histogram_vary' == this_data['type']):
if 'fit_class' in this_data and hasattr(this_data['fit_class'],'Get_Formatted_Table'):
this_type = 'fit_class'
do_GFT = True
elif 'boot_data' in this_data:
this_type = 'boot_data'
do_GFT = hasattr(this_data['boot_data'],'Get_Formatted_Table')
else:
do_GFT = False
if do_GFT:
this_xml = this_data[this_type].Get_Formatted_Table(fmt_latex=self.fmt_latex)
elif isinstance(this_data[this_type].iloc[0],BootStrap):
this_xml = this_data[this_type].apply(lambda x : x.MakeValAndErr(Dec=2,latex=True))
else:
this_xml = this_data[this_type]
if self.transpose_table:
this_xml = this_xml.transpose()
with open(self.fit_file_name,'w') as f:
f.write(this_xml.to_latex(escape=False))
with open(self.fit_file_name+'.py3p','wb') as f:
if hasattr(this_data[this_type],'RemoveFuns'): this_data[this_type].RemoveFuns()
pickle.dump(this_data[this_type],f)
if hasattr(this_data[this_type],'GetFuns'): this_data[this_type].GetFuns()
else:
print('Type of data is not fit vary')
def _Combine_fired(self):
first_data = deepcopy(data_plot.plot_data.loc[:,self.this_key])
second_data = deepcopy(data_plot.plot_data.loc[:,self.this_key_2])
if first_data['type'] != second_data['type']:
raise TypeError('both data types must be equal!!')
if 'plot' in first_data['type'] or 'error_bar' in first_data['type']:
if isinstance(first_data['y_data'],(list,tuple)):
first_data['y_data'] = np.array(first_data['y_data'])
if isinstance(second_data['y_data'],(list,tuple)):
second_data['y_data'] = np.array(second_data['y_data'])
if first_data['scale'] != 1:
first_data['y_data'] = first_data['y_data']*first_data['scale']
if second_data['scale'] != 1:
second_data['y_data'] = second_data['y_data']*second_data['scale']
first_data['y_data'] = op_dict[self.combine_operation](first_data['y_data'], second_data['y_data'])
if 'yerr_data' in first_data and 'yerr_data' in second_data:
if isinstance(first_data['yerr_data'],(list,tuple)):
first_data['yerr_data'] = np.array(first_data['yerr_data'])
if isinstance(second_data['yerr_data'],(list,tuple)):
second_data['yerr_data'] = np.array(second_data['yerr_data'])
if first_data['scale'] != 1:
first_data['yerr_data'] = first_data['y_data']*first_data['scale']
if second_data['scale'] != 1:
second_data['yerr_data'] = second_data['y_data']*second_data['scale']
first_data['yerr_data'] = op_dict_std[self.combine_operation](first_data['yerr_data'],
second_data['yerr_data'],
first_data['y_data'],
second_data['y_data'])
else:
print(first_data['type'])
print(second_data['type'])
raise TypeError('data must be plot or error_bar')
first_data['scale'] = 1.0
first_data['label'] = self.new_label
first_data.name = self.new_label
data_plot.AppendData(first_data)
self.key_list = list(data_plot.plot_data.keys())
self._Load_fired()
self._Apply_fired()
def _Create_Arrow_fired(self):
global data_plot
app_data = deepcopy(data_plot.plot_data.loc[:,self.this_key])
app_data['type'] = 'arrow'
app_data['x_data'] = [self.arrow_x,self.arrow_x_text]
app_data['y_data'] = [self.arrow_y,self.arrow_y_text]
app_data['label'] = self.label
app_data['supress_legend'] = self.supress_legend
app_data['arrow_text_size'] = self.arrow_text_size
app_data['arrow_color'] = self.arrow_color
app_data['arrow_style'] = self.arrow_style
data_plot.AppendData(app_data)
self.key_list = list(data_plot.plot_data.keys())
self._Load_fired()
self._Apply_fired()
def _Apply_Move_fired(self):
global data_plot
this_data = deepcopy(data_plot.plot_data)
index_list = np.array(this_data.columns)
if self.move_to_index >= len(data_plot.plot_data.index):
print('move_to_index is not in range of 0 to ',len(data_plot.plot_data.index)-1)
return
from_index = list(index_list).index(self.this_key)
if from_index > self.move_to_index:
index_list = np.insert(index_list,self.move_to_index,self.this_key)
index_list = np.delete(index_list,from_index+1)
elif from_index < self.move_to_index:
index_list = np.insert(index_list,self.move_to_index+1,self.this_key)
index_list = np.delete(index_list,from_index)
this_data = this_data[index_list]
data_plot.ImportData(this_data)
data_plot.PrintData()
data_plot.PlotAll()
self._Load_fired()
def _Undo_Wipe_fired(self):
global data_plot,backup_plot
if len(backup_plot) == 0:
print('Error, no backup present')
data_plot.plot_data = backup_plot[-1]
del backup_plot[-1]
self.key_list = list(data_plot.plot_data.keys())
self._Load_fired()
self._Apply_fired()
def _Wipe_fired(self):
global data_plot,backup_plot
# if self.this_key[-2] == '_':
backup_plot.append(deepcopy(data_plot.plot_data))
data_plot.plot_data = data_plot.plot_data.drop(self.this_key,axis=1)
print('removed' , self.this_key)
self.key_list = list(data_plot.plot_data.keys())
self._Load_fired()
self._Apply_fired()
# else:
# print 'Cannot remove original data.'
def _Duplicate_fired(self):
global data_plot
app_data = deepcopy(data_plot.plot_data.loc[:,self.this_key])
data_plot.AppendData(app_data)
self.key_list = list(data_plot.plot_data.keys())
self._Load_fired()
self._Apply_fired()
def __init__(self):
global data_plot
self.key_list = list(data_plot.plot_data.keys())
self.this_key = self.key_list[0]
self.this_key_2 = self.key_list[0]
self._Load_fired()
if self.test_pick_dim:
self.pick_dimension = self.dim_list[data_plot.plot_data.loc['key_select',self.this_key].index(slice(None))]
self._Apply_fired()
# def _label_fired(self):
# self._Apply_fired()
# def _color_fired(self):
# self._Apply_fired()
# def _shift_fired(self):
# self._Apply_fired()
# def _symbol_fired(self):
# if len(self.symbol)==1:
# self._Apply_fired()
#
# def _scale_y_fired(self):
# self._Apply_fired()
def _this_key_fired(self):
self._Load_fired()
def _this_key_2_fired(self):
self._Load_fired()
def _pick_dimension_fired(self):
self._Load_fired()
# def _dim_1_fired(self):
# self._Load_fired()
# def _dim_2_fired(self):
# self._Load_fired()
# def _dim_3_fired(self):
# self._Load_fired()
# def _dim_4_fired(self):
# self._Load_fired()
# def _dim_5_fired(self):
# self._Load_fired()
# def _dim_6_fired(self):
# self._Load_fired()
# def _dim_7_fired(self):
# self._Load_fired()
# def _dim_8_fired(self):
# self._Load_fired()
def _Load_fired(self):
global data_plot
self.test_pick_dim = False
for ic in range(1,8):
setattr(self,'test_dim_'+str(ic),False)
if data_plot is None:
raise EnvironmentError('plot data has not been loaded')
# self.key_list = data_plot.plot_data.keys()
self.plot_this = bool(check_index_and_None(data_plot.plot_data,'plot_this',self.this_key,def_val=False))
if 'arrow' in data_plot.plot_data.loc['type',self.this_key]:
if 'x_data' in data_plot.plot_data.index:
self.arrow_x,self.arrow_x_text = data_plot.plot_data.loc['x_data',self.this_key][:2]
else:
self.arrow_x,self.arrow_x_text = 0,1
if 'y_data' in data_plot.plot_data.index:
self.arrow_y,self.arrow_y_text = data_plot.plot_data.loc['y_data',self.this_key][:2]
else:
self.arrow_y,self.arrow_y_text = 0,1
self.arrow_text_size = check_index_and_None(data_plot.plot_data,'arrow_text_size',self.this_key,def_val = 30)
self.arrow_color = check_index_and_None(data_plot.plot_data,'arrow_color',self.this_key,def_val = 'black')
self.arrow_style = check_index_and_None(data_plot.plot_data,'arrow_style',self.this_key,def_val = 'fancy')
self.label = check_index_and_None(data_plot.plot_data,'label',self.this_key,def_val = 'None')
self.Physical_Units = check_index_and_None(data_plot.plot_data,'phys_axies',self.this_key,def_val = True)
self.extrap_fade = check_index_and_None(data_plot.plot_data,'extrap_fade',self.this_key,def_val = 1.5)
self.Hairline_plot = check_index_and_None(data_plot.plot_data,'hairline',self.this_key,def_val = True)
if 'hair_alpha' in data_plot.plot_data.index:
if isinstance(data_plot.plot_data.loc['hair_alpha',self.this_key],(tuple,list,np.ndarray)):
self.line_alpha_scale = data_plot.plot_data.loc['hair_alpha',self.this_key][0]
self.line_alpha_abs = data_plot.plot_data.loc['hair_alpha',self.this_key][1]
else:
self.line_alpha_scale = data_plot.plot_data.loc['hair_alpha',self.this_key]
self.line_alpha_abs = 1
else:
self.line_alpha_scale = 20
self.line_alpha_abs = 1
if 'xdatarange' in data_plot.plot_data.index:
if data_plot.plot_data.loc['xdatarange',self.this_key] == 'Data':
self.fit_custom_xrange = False
else:
self.fit_custom_xrange = True
try:
self.fit_x_min,self.fit_x_max = data_plot.plot_data.loc['xdatarange',self.this_key]
except Exception as err:
pass
else:
self.fit_custom_xrange = False
if 'plot_err' in data_plot.plot_data.index:
for itype in ['No','relative','absolute']:
if data_plot.plot_data.loc['plot_err',self.this_key].lower() in itype.lower():
self.plot_err = itype
break
else:
self.plot_err = 'No'
else:
self.plot_err = 'No'
if 'color' in data_plot.plot_data.index:
if isinstance(data_plot.plot_data.loc['color',self.this_key],(list,tuple)):
self.color = convert_to_hex(data_plot.plot_data.loc['color',self.this_key])
else:
try:
self.color = data_plot.plot_data.loc['color',self.this_key]
except Exception as err:
pass
else:
self.color = 'blue'
self.shift = float(check_index_and_None(data_plot.plot_data,'shift',self.this_key,def_val = 0.0))
self.supress_legend = bool(check_index_and_None(data_plot.plot_data,'supress_legend',self.this_key,def_val = False))
xrmin_hold = check_index_and_None(data_plot.plot_data,'x_range_min',self.this_key,def_val = 0)
if not isinstance(xrmin_hold,bool):
self.x_range_min = int(xrmin_hold)
xrmax_hold = check_index_and_None(data_plot.plot_data,'x_range_max',self.this_key,def_val = -1)
if not isinstance(xrmax_hold,bool):
self.x_range_max = int(xrmax_hold)
if self.x_range_max == 0:
self.x_range_max = -1
xrinc_hold = check_index_and_None(data_plot.plot_data,'x_increment',self.this_key,def_val = 1)
if not isinstance(xrmax_hold,bool):
self.x_increment = int(xrinc_hold)
if self.x_increment == 0:
self.x_increment = 1
this_band = check_index_and_None(data_plot.plot_data,'do_band',self.this_key,def_val = False)
if isinstance(this_band,int) and not isinstance(this_band,bool):
self.do_band = True
self.band_index = this_band
self.show_band_eval = bool(check_index_and_None(data_plot.plot_data,'show_band_eval',self.this_key,def_val = False))
else:
self.do_band = False
self.show_band_eval = False
self.symbol = str(check_index_and_None(data_plot.plot_data,'symbol',self.this_key,def_val = 'None'))
self.Median = bool(check_index_and_None(data_plot.plot_data,'Median',self.this_key,def_val = False))
self.suppress_key_index = bool(check_index_and_None(data_plot.plot_data,'suppress_key_index',self.this_key,def_val = False))
hold_xfun = check_index_and_None(data_plot.plot_data,'x_fun',self.this_key,def_val = 'None')
if hasattr(hold_xfun,'__name__') and not isinstance(hold_xfun.__name__,bool):
self.x_function = hold_xfun.__name__
else:
self.x_function = 'None'
self.x_scale = float(check_index_and_None(data_plot.plot_data,'x_scale',self.this_key,def_val = 1.0))
self.scale_y = float(check_index_and_None(data_plot.plot_data,'scale',self.this_key,def_val = 1.0))
self.shift_y = float(check_index_and_None(data_plot.plot_data,'y_shift',self.this_key,def_val = 0.0))
# if len(self.ShowEval) == 0:
# pass
hold_ShowEval = check_index_and_None(data_plot.plot_data,'ShowEval',self.this_key,def_val = [])
if isinstance(hold_ShowEval,(list,tuple,np.ndarray)):
self.ShowEval = list(hold_ShowEval)
else:
self.ShowEval = []
self.fit_dim_pick = int(check_index_and_None(data_plot.plot_data,'xaxis',self.this_key,def_val = 0))
self.Force_Param_Name = str(check_index_and_None(data_plot.plot_data,'FPName',self.this_key,def_val = ''))
self.Force_Param_Units = str(check_index_and_None(data_plot.plot_data,'FPUnits',self.this_key,def_val = ''))
if '_vary' in data_plot.plot_data.loc['type',self.this_key]:
this_series = data_plot.plot_data.loc[:,self.this_key]
update_dim = isinstance(this_series['key_select'],tuple) and not jpl.Py2Read
if 'fit' in this_series['type']:
if 'fit_class' in this_series:
if isinstance(this_series['fit_class'],sff.SetOfFitFuns):
this_fit = this_series['fit_class'].Fit_Stats_fmt['Fit']
else:
this_fit = this_series['fit_class']
if isinstance(this_fit.index,pad.MultiIndex):
self.test_pick_dim = False
self.vary_len = len(this_fit.index.names)
for icd,(idim,ikey) in enumerate(zip(this_fit.index.names,this_series['key_select'])):
setattr(self,'test_dim_'+str(icd+1),True)
this_list = list(this_fit.index.get_level_values(idim))
setattr(self,'dim_'+str(icd+1)+'_list',fmt_Range(this_list))
if update_dim and not isinstance(ikey,slice):
setattr(self,'dim_'+str(icd+1),str(ikey))
else:
self.vary_len = 1
self.test_pick_dim = False
self.test_dim_1 = True
self.dim_1_list = fmt_Range(list(this_fit.index))
elif 'boot_data' in list(this_series.keys()) and this_series['boot_data'] is not None \
and not isinstance(this_series['boot_data'],bool):
if isinstance(this_series['boot_data'],sff.SetOfFitFuns):
this_boot = this_series['boot_data'].Get_Extrapolation(fmted=True)
else:
this_boot = this_series['boot_data']
if isinstance(this_boot.index,pad.MultiIndex):
self.test_pick_dim = False
self.vary_len = len(this_boot.index.names)
for icd,(idim,ikey) in enumerate(zip(this_boot.index.names,this_series['key_select'])):
setattr(self,'test_dim_'+str(icd+1),True)
this_list = list(this_boot.index.get_level_values(idim))
setattr(self,'dim_'+str(icd+1)+'_list',fmt_Range(this_list))
if update_dim and not isinstance(ikey,slice):
setattr(self,'dim_'+str(icd+1),str(ikey))
else:
self.vary_len = 1
self.test_pick_dim = False
self.test_dim_1 = True
self.dim_1_list = fmt_Range(list(this_boot.index))
elif 'y_data' in list(this_series.keys()) and isinstance(this_series['y_data'],pad.Series):
if isinstance(this_series['y_data'].index,pad.MultiIndex):
if 'line' in data_plot.plot_data.loc['type',self.this_key]:
self.test_pick_dim = False
self.vary_len = len(this_series['y_data'].index.names)
for icd,idim in enumerate(this_series['y_data'].index.names):
setattr(self,'test_dim_'+str(icd+1),True)
this_list = list(this_series['y_data'].index.get_level_values(idim))
setattr(self,'dim_'+str(icd+1)+'_list',fmt_Range(this_list))
else:
self.test_pick_dim = True
self.dim_list = this_series['y_data'].index.names
self.vary_len = len(this_series['y_data'].index.names)
for icd,(idim,ikey) in enumerate(zip(this_series['y_data'].index.names,this_series['key_select'])):
if idim == str(self.pick_dimension):
setattr(self,'test_dim_'+str(icd+1),False)
else:
setattr(self,'test_dim_'+str(icd+1),True)
this_list = list(this_series['y_data'].index.get_level_values(idim))
setattr(self,'dim_'+str(icd+1)+'_list',fmt_Range(this_list))
if update_dim and not isinstance(ikey,slice):
setattr(self,'dim_'+str(icd+1),str(ikey))
else:
self.test_pick_dim = False
self.test_dim_1 = True
this_list = list(this_series['y_data'].index)
self.dim_1_list = fmt_Range(this_list)
if 'x_data' in list(this_series.keys()) and isinstance(this_series['x_data'],pad.Series):
if isinstance(this_series['x_data'].index,pad.MultiIndex):
self.test_pick_dim = True
self.dim_list = this_series['x_data'].index.names
self.vary_len = len(this_series['x_data'].index.names)
for icd,(idim,ikey) in enumerate(zip(this_series['x_data'].index.names,this_series['key_select'])):
if idim == str(self.pick_dimension):
setattr(self,'test_dim_'+str(icd+1),False)
else:
setattr(self,'test_dim_'+str(icd+1),True)
this_list = list(this_series['x_data'].index.get_level_values(idim))
setattr(self,'dim_'+str(icd+1)+'_list',fmt_Range(this_list))
if update_dim and not isinstance(ikey,slice):
setattr(self,'dim_'+str(icd+1),str(ikey))
else:
self.test_pick_dim = False
self.test_dim_1 = True
self.dim_1_list = fmt_Range(list(this_series['x_data'].index))
self.fit_file_name = data_plot.plot_info['save_file'].replace('.pdf',self.pick_dimension+'.tex')
def _Animate_fired(self):
self._Apply_fired()
self.this_aindex = data_plot.Animate(this_key=self.this_key,a_index=self.animate_dim,multi_index=self.this_aindex)
if self.this_aindex is -1:
self.this_aindex = 0
def _Apply_fired(self):
# global customleg,customleglist
# global wtitle,wxlab,wrlab
# global legloc
global data_plot
this_data = deepcopy(data_plot.plot_data)
this_data.loc['plot_this',self.this_key] = self.plot_this
if self.label != 'None':
this_data.loc['label',self.this_key] = self.label
if self.Physical_Units != 'None':
this_data.loc['phys_axies',self.this_key] = self.Physical_Units
if self.extrap_fade != 'None' and self.extrap_fade != 0:
this_data.loc['extrap_fade',self.this_key] = self.extrap_fade
else:
this_data.loc['extrap_fade',self.this_key] = 1.5
if self.Hairline_plot != 'None':
this_data.loc['hairline',self.this_key] = self.Hairline_plot
alpha_hold = []
if self.line_alpha_scale != 'None':
if self.line_alpha_scale < 0:
print('Warning, no negative line_alpha_scale! breaks alpha scaling... assuming abs')
alpha_hold.append(abs(self.line_alpha_scale))
else:
if isinstance(this_data.loc['hair_alpha',self.this_key],(tuple,list,np.ndarray)):
alpha_hold.append(this_data.loc['hair_alpha',self.this_key][0])
else:
alpha_hold.append(this_data.loc['hair_alpha',self.this_key])
if self.fit_custom_xrange:
this_data.loc['xdatarange',self.this_key] = [self.fit_x_min,self.fit_x_max]
else:
this_data.loc['xdatarange',self.this_key] = 'Data'
if self.line_alpha_abs != 'None':
if 0 < self.line_alpha_abs > 1:
print('Warning, line_alpha_abs is between 0 and 1! breaks alpha scaling... assuming 1')
alpha_hold.append(1.0)
else:
alpha_hold.append(self.line_alpha_abs)
else:
if isinstance(this_data.loc['hair_alpha',self.this_key],(tuple,list,np.ndarray)):
alpha_hold.append(this_data.loc['hair_alpha',self.this_key][1])
this_data.loc['hair_alpha',self.this_key] = alpha_hold
this_data.loc['plot_err',self.this_key] = self.plot_err
if self.color != 'None':
this_data.loc['color',self.this_key] = self.color
if self.supress_legend != 'None':
this_data.loc['supress_legend',self.this_key] = self.supress_legend
if self.shift_overlap != 'None':
this_data.loc['shift_overlap',self.this_key] = self.shift_overlap
if self.shift != 'None':
this_data.loc['shift',self.this_key] = self.shift
this_data.loc['x_range_min',self.this_key] = self.x_range_min
this_data.loc['x_range_max',self.this_key] = self.x_range_max
this_data.loc['x_increment',self.this_key] = self.x_increment
if self.do_band:
this_data.loc['do_band',self.this_key] = self.band_index
this_data.loc['show_band_eval',self.this_key] = self.show_band_eval
else:
this_data.loc['do_band',self.this_key] = False
this_data.loc['show_band_eval',self.this_key] = False
if self.symbol != 'None':
this_data.loc['symbol',self.this_key] = self.symbol
if self.Median != 'None':
this_data.loc['Median',self.this_key] = self.Median
if self.scale_y != 'None':
this_data.loc['scale',self.this_key] = self.scale_y
if self.shift_y != 'None':
this_data.loc['y_shift',self.this_key] = self.shift_y
if self.suppress_key_index != 'None':
this_data.loc['suppress_key_index',self.this_key] = self.suppress_key_index
if len(list(self.ShowEval)) > 0:
this_data.loc['ShowEval',self.this_key] = list(self.ShowEval)
else:
this_data.loc['ShowEval',self.this_key] = None
this_data.loc['xaxis',self.this_key] = int(self.fit_dim_pick)
if self.Force_Param_Name != 'None':
this_data.loc['FPName',self.this_key] = self.Force_Param_Name
if self.Force_Param_Units != 'None':
this_data.loc['FPUnits',self.this_key] = self.Force_Param_Units
if self.Force_Param_Units != 'None':
this_data.loc['FPUnits',self.this_key] = self.Force_Param_Units
if 'arrow' in this_data.loc['type',self.this_key]:
if self.arrow_x != 'None' and self.arrow_x_text != 'None':
this_data.loc['x_data',self.this_key] = [self.arrow_x,self.arrow_x_text]
if self.arrow_y != 'None' and self.arrow_y_text != 'None':
this_data.loc['y_data',self.this_key] = [self.arrow_y,self.arrow_y_text]
if self.arrow_text_size != 'None':
this_data.loc['arrow_text_size',self.this_key] = self.arrow_text_size
if self.arrow_color != 'None':
this_data.loc['arrow_color',self.this_key] = self.arrow_color
if self.arrow_style != 'None':
this_data.loc['arrow_style',self.this_key] = self.arrow_style
this_data.loc['x_fun',self.this_key] = x_funs[self.x_function]
this_data.loc['x_scale',self.this_key] = self.x_scale
if '_vary' in data_plot.plot_data.loc['type',self.this_key]:
if self.test_pick_dim:
this_key = []
for ic in range(1,self.vary_len+1):
if getattr(self,'test_dim_'+str(ic)):
this_key.append(str(getattr(self,'dim_'+str(ic))))
else:
this_key.append(slice(None))
this_key = tuple(this_key)
else:
this_key = []
for ic in range(1,self.vary_len+1):
if getattr(self,'test_dim_'+str(ic)):
this_key.append(str(getattr(self,'dim_'+str(ic))))
if len(this_key) == 0:
this_key = slice(None)
this_data.loc['key_select',self.this_key] = this_key
data_plot.ImportData(this_data)
print(data_plot)
data_plot.PlotAll()
self._Load_fired()
class InfoFrame( ta.HasTraits ):
save_file = ta.File('')
legend_location = ta.Str('')
legend_location = ta.Enum(ql.LegLocList)
legend_number_of_columns = ta.Int(jpl.legcol)
legend_font_size = ta.Int(jpl.legsize)
legend_alpha = ta.Float(jpl.legalpha)
title = ta.Str('')
x_label = ta.Str('')
y_label = ta.Str('')
title_pad = ta.Int(jpl.tpad)
title_size = ta.Int(jpl.tsize)
x_label_size = ta.Int(jpl.xsize)
y_label_size = ta.Int(jpl.ysize)
grid = ta.Bool(jpl.def_grid)
dot_size = ta.Int(jpl.dotsize)
line_width = ta.Int(jpl.linewidth)
error_cap_len = ta.Int(jpl.errorbarlen)
x_axis_min = ta.Str('None')
x_axis_max = ta.Str('None')
y_axis_min = ta.Str('None')
y_axis_max = ta.Str('None')
y_axis_max = ta.Str('None')
custom_y_labels = ta.Bool(False)
flip_vert = ta.Bool(False)
mirror_vert = ta.Bool(False)
y_axis_labels = ta.List([''])
y_scale = ta.Enum(['None',"linear", "log", "symlog", "logit"])
x_scale = ta.Enum(['None',"linear", "log", "symlog", "logit"])
x_zero_line = ta.Bool(False)
x_zero_line_val = ta.Float(0)
y_zero_line = ta.Bool(False)
y_zero_line_val = ta.Float(0)
Load = ta.Button()
Apply = ta.Button()
Reset_Axies = ta.Button()
tick_size = ta.Int(jpl.def_tick_size)
xTick_min = ta.Float(0)
xTick_max = ta.Float(0)
xTick_inc = ta.Float(0)
yTick_min = ta.Float(0)
yTick_max = ta.Float(0)
yTick_inc = ta.Float(0)
view = tua.View(
tua.Group(
tua.Item('save_file'),
tua.Item('legend_location'),
tua.Item('legend_number_of_columns'),
tua.Item('legend_font_size'),
tua.Item('legend_alpha'),
tua.Item('title'),
tua.Item('x_label'),
tua.Item('y_label'),
tua.Item('title_pad'),
tua.Item('title_size'),
tua.Item('x_label_size'),
tua.Item('y_label_size'),
tua.Item('grid'),
tua.Item('x_zero_line'),
tua.Item('x_zero_line_val'),
tua.Item('y_zero_line'),
tua.Item('y_zero_line_val'),
tua.Item('dot_size'),
tua.Item('line_width'),
tua.Item('error_cap_len'),
tua.Item('x_axis_min'),
tua.Item('x_axis_max'),
tua.Item('x_scale'),
tua.Item('y_axis_min'),
tua.Item('y_axis_max'),
tua.Item('y_scale'),
tua.Item('Apply', show_label=False),
tua.Item('Reset_Axies', show_label=False),
label='Main'
),tua.Group(
tua.Item('tick_size'),
tua.Item('xTick_min'),
tua.Item('xTick_max'),
tua.Item('xTick_inc'),
tua.Item('yTick_min'),
tua.Item('yTick_max'),
tua.Item('yTick_inc'),
tua.Item('custom_y_labels'),
tua.Item('flip_vert',enabled_when='custom_y_labels'),
tua.Item('mirror_vert',enabled_when='custom_y_labels'),
tua.Item('y_axis_labels',enabled_when='custom_y_labels'),
tua.Item('Apply', show_label=False),
tua.Item('Reset_Axies', show_label=False),
label='Ticks'
),
buttons=['OK'],
resizable=True
)
def __init__(self):
global data_plot
self._Load_fired()
self.save_file = data_plot.plot_info['save_file']
self._Apply_fired()
def _Load_fired(self):
if data_plot is None:
raise EnvironmentError('plot data has not been loaded')
if data_plot.plot_info['leg_ncol'] == 'best':
self.legend_number_of_columns = 1
else:
self.legend_number_of_columns = int(data_plot.plot_info['leg_ncol'])
self.legend_font_size = int(data_plot.plot_info['leg_fontsize'])
self.legend_alpha = float(data_plot.plot_info['leg_alpha'])
if 'xTick_min' in data_plot.plot_info:
self.xTick_min = data_plot.plot_info['xTick_min']
else:
self.xTick_max = 0
if 'xTick_max' in data_plot.plot_info:
self.xTick_max = data_plot.plot_info['xTick_max']
else:
self.xTick_inc = 0
if 'xTick_inc' in data_plot.plot_info:
self.xTick_inc = data_plot.plot_info['xTick_inc']
else:
self.xTick_inc = 0
if 'yTick_min' in data_plot.plot_info:
self.yTick_min = data_plot.plot_info['yTick_min']
else:
self.yTick_min = 0
if 'yTick_max' in data_plot.plot_info:
self.yTick_max = data_plot.plot_info['yTick_max']
else:
self.yTick_max = 0
if 'yTick_inc' in data_plot.plot_info:
self.yTick_inc = data_plot.plot_info['yTick_inc']