3. Architecture analysis at module scale

3.1. Module Requierement

3.1.1. import standard python modules

[1]:

import pandas as pd
import matplotlib.pyplot as plt

3.1.2. Import strawberry modules

[2]:

import openalea.strawberry #import openalea.strawberry module
from openalea.strawberry.import_mtgfile import import_mtgfile
from openalea.strawberry.import_mtgfile import plant_number_by_varieties
from openalea.strawberry.analysis import extract_at_module_scale
from openalea.strawberry.analysis import occurence_module_order_along_time, pointwisemean_plot, crowntype_distribution

[3]:

# config
pd.set_option('display.multi_sparse', False)

3.2. Import and read mtg files

  • Import and read mtg file for each varieties

[4]:

Gariguette = import_mtgfile(filename= "Gariguette")
Capriss = import_mtgfile(filename = "Capriss")
Darselect = import_mtgfile(filename = "Darselect")
Cir107 = import_mtgfile(filename = "Cir107")
Ciflorette = import_mtgfile(filename = "Ciflorette")
Clery = import_mtgfile(filename="Clery")

  • Import and read mtg for the varities Gariguette and Capriss in the single big mtg file

[5]:

All_varieties = import_mtgfile(filename=["Gariguette","Capriss","Darselect","Cir107","Ciflorette", "Clery"])
plant_number_by_varieties(All_varieties)

Darselect : 54 plants
Capriss : 54 plants
Ciflorette : 54 plants
Cir107 : 54 plants
Gariguette : 54 plants
Clery : 54 plants

3.3. Extraction of data at module scale

To study plant development at module scale you must extract data at module scale.
For this you use variable.extract_at_module_scale function

  • Example with Gariguette

[6]:

Gariguette_extraction_at_module_scale = extract_at_module_scale(Gariguette)
Gariguette_extraction_at_module_scale
Gariguette_extraction_at_module_scale.sort_values(["date","plant","order"],ascending=[True,True,True])[:10]

[6]:
Genotype date modality plant order nb_visible_leaves nb_foliar_primordia nb_total_leaves nb_open_flowers nb_aborted_flowers ... nb_vegetative_bud nb_initiated_bud nb_floral_bud nb_stolons type_of_crown crown_status complete_module stage vid plant_vid
0 Gariguette 2014/12/10 A 1 0 8 4 12 0 0 ... 1 3 7 1 1 3 False H 2 1
1 Gariguette 2014/12/10 A 2 0 8 4 12 0 0 ... 4 3 4 1 1 3 False H 74 73
2 Gariguette 2014/12/10 A 3 0 11 3 14 0 0 ... 3 1 8 2 1 3 False H 147 146
3 Gariguette 2014/12/10 A 4 0 8 3 11 0 0 ... 6 0 5 0 1 3 False H 264 263
4 Gariguette 2014/12/10 A 5 0 6 4 10 0 0 ... 5 2 2 1 1 3 False H 327 326
5 Gariguette 2014/12/10 A 6 0 7 4 11 0 0 ... 3 2 5 1 1 3 False H 383 382
6 Gariguette 2014/12/10 A 7 0 7 4 11 0 0 ... 7 1 2 1 1 3 False H 445 444
7 Gariguette 2014/12/10 A 8 0 6 3 9 0 0 ... 1 3 4 1 1 3 False H 505 504
8 Gariguette 2014/12/10 A 9 0 7 3 10 0 0 ... 4 0 5 1 1 3 False H 562 561
9 Gariguette 2015/01/08 A 1 0 11 8 19 0 0 ... 3 2 10 1 1 3 False H 636 635

10 rows × 21 columns

  • Example with Capriss

[7]:

Capriss_extraction_at_module_scale = extract_at_module_scale(Capriss)
Capriss_extraction_at_module_scale
Capriss_extraction_at_module_scale.sort_values(["date","plant","order"],ascending=[True,True,True])[:10]

[7]:
Genotype date modality plant order nb_visible_leaves nb_foliar_primordia nb_total_leaves nb_open_flowers nb_aborted_flowers ... nb_vegetative_bud nb_initiated_bud nb_floral_bud nb_stolons type_of_crown crown_status complete_module stage vid plant_vid
0 Capriss 2014/12/10 A 1 0 11 3 14 0 0 ... 1 1 9 2 1 3 False H 2 1
1 Capriss 2014/12/10 A 2 0 8 3 11 0 0 ... 5 0 3 2 1 3 False H 121 120
2 Capriss 2014/12/10 A 2 1 2 3 5 0 0 ... 5 0 0 0 3 3 False G 123 120
3 Capriss 2014/12/10 A 3 0 6 2 8 0 0 ... 3 1 3 1 1 3 False H 213 212
4 Capriss 2014/12/10 A 4 0 8 2 10 0 0 ... 3 2 3 2 1 3 False H 267 266
5 Capriss 2014/12/10 A 5 0 8 2 10 0 0 ... 0 2 7 1 1 3 False H 345 344
6 Capriss 2014/12/10 A 6 0 9 2 11 0 0 ... 2 2 6 1 1 3 False H 440 439
7 Capriss 2014/12/10 A 7 0 8 2 10 0 0 ... 4 0 4 1 1 3 False H 528 527
8 Capriss 2014/12/10 A 8 0 6 2 8 0 0 ... 3 1 3 1 1 3 False H 608 607
9 Capriss 2014/12/10 A 9 0 8 2 10 0 0 ... 1 1 6 1 1 3 False H 673 672

10 rows × 21 columns

  • Darselect

[8]:

Darselect_extraction_at_module_scale = extract_at_module_scale(Darselect)
Darselect_extraction_at_module_scale[:10]

[8]:
Genotype date modality plant order nb_visible_leaves nb_foliar_primordia nb_total_leaves nb_open_flowers nb_aborted_flowers ... nb_vegetative_bud nb_initiated_bud nb_floral_bud nb_stolons type_of_crown crown_status complete_module stage vid plant_vid
0 Darselect 2014/12/10 A 1 0 5 0 5 0 0 ... 1 0 0 2 1 4 True 65 2 1
1 Darselect 2014/12/10 A 1 1 2 2 4 0 0 ... 2 0 2 0 2 3 False G 17 1
2 Darselect 2014/12/10 A 2 0 5 0 5 0 0 ... 2 0 0 2 1 4 True 58 37 36
3 Darselect 2014/12/10 A 2 1 1 3 4 0 0 ... 3 0 1 0 2 3 False G 57 36
4 Darselect 2014/12/10 A 3 0 4 0 4 0 0 ... 1 0 2 0 1 4 True cut 81 80
5 Darselect 2014/12/10 A 3 1 2 1 3 0 0 ... 2 0 1 0 2 3 False H 109 80
6 Darselect 2014/12/10 A 4 0 7 3 10 0 0 ... 6 0 3 1 1 3 False H 133 132
7 Darselect 2014/12/10 A 5 0 4 0 4 0 0 ... 1 0 1 0 1 4 True Coupé 194 193
8 Darselect 2014/12/10 A 5 1 2 2 4 0 0 ... 3 0 1 0 2 3 False H 208 193
9 Darselect 2014/12/10 A 6 0 4 0 4 0 0 ... 3 0 0 0 1 4 True cut 228 227

10 rows × 21 columns

  • Cir107

[9]:

Cir107_extraction_at_module_scale = extract_at_module_scale(Cir107)
Cir107_extraction_at_module_scale[:10]

[9]:
Genotype date modality plant order nb_visible_leaves nb_foliar_primordia nb_total_leaves nb_open_flowers nb_aborted_flowers ... nb_vegetative_bud nb_initiated_bud nb_floral_bud nb_stolons type_of_crown crown_status complete_module stage vid plant_vid
0 Cir107 2014/12/10 A 1 0 8 2 10 0 0 ... 2 0 6 2 1 3 False H 2 1
1 Cir107 2014/12/10 A 2 0 9 2 11 0 0 ... 0 0 7 3 1 3 False H 72 71
2 Cir107 2014/12/10 A 2 1 2 1 3 0 0 ... 1 0 2 0 3 3 False H 77 71
3 Cir107 2014/12/10 A 3 0 7 2 9 0 0 ... 0 0 7 1 1 3 False H 165 164
4 Cir107 2014/12/10 A 3 1 2 2 4 0 0 ... 1 0 1 0 3 3 False H 167 164
5 Cir107 2014/12/10 A 4 0 11 1 12 0 0 ... 0 0 9 3 1 4 True I 246 245
6 Cir107 2014/12/10 A 5 0 11 1 12 0 0 ... 1 0 7 3 1 3 False H 356 355
7 Cir107 2014/12/10 A 6 0 11 3 14 0 0 ... 0 0 8 5 1 3 False H 438 437
8 Cir107 2014/12/10 A 6 1 2 2 4 0 0 ... 3 0 1 0 3 3 False H 449 437
9 Cir107 2014/12/10 A 7 0 9 3 12 0 0 ... 0 0 9 2 1 3 False H 549 548

10 rows × 21 columns

  • Ciflorette

[10]:

Ciflorette_extraction_at_module_scale = extract_at_module_scale(Ciflorette)
Ciflorette_extraction_at_module_scale[:10]

[10]:
Genotype date modality plant order nb_visible_leaves nb_foliar_primordia nb_total_leaves nb_open_flowers nb_aborted_flowers ... nb_vegetative_bud nb_initiated_bud nb_floral_bud nb_stolons type_of_crown crown_status complete_module stage vid plant_vid
0 Ciflorette 2014/12/04 A 1 0 4 0 4 0 0 ... 0 0 0 3 1 4 True I 2 1
1 Ciflorette 2014/12/04 A 1 1 3 2 5 0 0 ... 3 1 1 0 2 3 False H 13 1
2 Ciflorette 2014/12/04 A 2 0 7 3 10 0 0 ... 1 1 5 2 1 3 False H 44 43
3 Ciflorette 2014/12/04 A 2 1 1 2 3 0 0 ... 1 1 0 0 3 3 False G 61 43
4 Ciflorette 2014/12/04 A 3 0 3 0 3 0 0 ... 0 0 2 0 1 4 True Cut 121 120
5 Ciflorette 2014/12/04 A 3 1 2 3 5 0 0 ... 2 2 1 0 2 3 False H 142 120
6 Ciflorette 2014/12/04 A 4 0 4 4 8 0 0 ... 1 2 5 0 1 3 False H 173 172
7 Ciflorette 2014/12/04 A 5 0 6 3 9 0 0 ... 0 2 5 2 1 3 False H 236 235
8 Ciflorette 2014/12/04 A 6 0 8 4 12 0 0 ... 4 1 4 2 1 3 False G 310 309
9 Ciflorette 2014/12/04 A 7 0 8 3 11 0 0 ... 2 1 6 1 1 3 False H 375 374

10 rows × 21 columns

  • Clery

[11]:

Clery_extraction_at_module_scale = extract_at_module_scale(Clery)
Clery_extraction_at_module_scale[:10]

[11]:
Genotype date modality plant order nb_visible_leaves nb_foliar_primordia nb_total_leaves nb_open_flowers nb_aborted_flowers ... nb_vegetative_bud nb_initiated_bud nb_floral_bud nb_stolons type_of_crown crown_status complete_module stage vid plant_vid
0 Clery 2014/12/10 A 1 0 11 2 13 0 0 ... 1 0 8 3 1 3 False H 2 1
1 Clery 2014/12/10 A 2 0 11 3 14 0 0 ... 5 2 4 3 1 3 False H 102 101
2 Clery 2014/12/10 A 3 0 8 2 10 0 0 ... 3 0 5 2 1 3 False H 189 188
3 Clery 2014/12/10 A 4 0 4 0 4 0 0 ... 0 0 0 3 1 4 True cut 255 254
4 Clery 2014/12/10 A 4 1 2 2 4 0 0 ... 0 0 4 0 2 3 False H 266 254
5 Clery 2014/12/10 A 5 0 7 2 9 0 0 ... 3 0 5 1 1 3 False H 296 295
6 Clery 2014/12/10 A 6 0 9 2 11 0 0 ... 2 0 6 3 1 3 False H 384 383
7 Clery 2014/12/10 A 7 0 8 3 11 0 0 ... 3 1 5 2 1 3 False H 451 450
8 Clery 2014/12/10 A 8 0 5 0 5 0 0 ... 1 0 1 2 1 4 True cut 537 536
9 Clery 2014/12/10 A 8 1 2 3 5 0 0 ... 4 0 1 0 2 3 False H 568 536

10 rows × 21 columns

  • Exemple of big mtg (All varieties) extraction at module scale

[12]:

All_varieties_extraction_at_module_scale = extract_at_module_scale(All_varieties)
All_varieties_extraction_at_module_scale[:10]

[12]:
Genotype date modality plant order nb_visible_leaves nb_foliar_primordia nb_total_leaves nb_open_flowers nb_aborted_flowers ... nb_vegetative_bud nb_initiated_bud nb_floral_bud nb_stolons type_of_crown crown_status complete_module stage vid plant_vid
0 Clery 2014/12/10 A 1 0 11 2 13 0 0 ... 1 0 8 3 1 3 False H 2 1
1 Clery 2014/12/10 A 2 0 11 3 14 0 0 ... 5 2 4 3 1 3 False H 102 101
2 Clery 2014/12/10 A 3 0 8 2 10 0 0 ... 3 0 5 2 1 3 False H 189 188
3 Clery 2014/12/10 A 4 0 4 0 4 0 0 ... 0 0 0 3 1 4 True cut 255 254
4 Clery 2014/12/10 A 4 1 2 2 4 0 0 ... 0 0 4 0 2 3 False H 266 254
5 Clery 2014/12/10 A 5 0 7 2 9 0 0 ... 3 0 5 1 1 3 False H 296 295
6 Clery 2014/12/10 A 6 0 9 2 11 0 0 ... 2 0 6 3 1 3 False H 384 383
7 Clery 2014/12/10 A 7 0 8 3 11 0 0 ... 3 1 5 2 1 3 False H 451 450
8 Clery 2014/12/10 A 8 0 5 0 5 0 0 ... 1 0 1 2 1 4 True cut 537 536
9 Clery 2014/12/10 A 8 1 2 3 5 0 0 ... 4 0 1 0 2 3 False H 568 536

10 rows × 21 columns

3.4. Exploratory data analysis at module scale

Exploratory analysis analysis at module scale consisted in comparing the frequency distribution of five main variables:
1. Occurence of successive module orders along time 2. Number of leaves by module orders 3. Number of flowers by module orders 4. Number of stolons by module orders 5. Type of crown (extension crown vs branch crown) by module orders 6. Complete vs incomplete modules by modules orders

3.4.1. Occurence of the successive module orders along time

  • For Gariguette varieties

[13]:

Gariguette_frequency = occurence_module_order_along_time(data= Gariguette_extraction_at_module_scale,frequency_type= "cdf")
Gariguette_frequency

[13]:
date 2014/12/10 2015/01/08 2015/02/12 2015/03/06 2015/04/02 2015/05/19
order
0 1.0 0.5625 0.272727 0.236842 0.136364 0.113924
1 1.0 1.0000 1.000000 0.684211 0.530303 0.367089
2 1.0 1.0000 1.000000 0.973684 0.954545 0.658228
3 1.0 1.0000 1.000000 1.000000 1.000000 0.873418
4 1.0 1.0000 1.000000 1.000000 1.000000 0.987342
5 1.0 1.0000 1.000000 1.000000 1.000000 1.000000 
[14]:

Gariguette_frequency.plot.line(marker = "o")
plt.axhline(y = 0.9, color = "r",linestyle="--") # threshold at 0.9 quartile,
plt.axhline(y = 0.75, color = "b",linestyle="--") # threshold at 0.75 quartile
plt.title("Occurence of succesive module order along time for Gariguette")
plt.ylabel("cdf")

[14]:

Text(0, 0.5, 'cdf')
../_images/user_3.Architecture_analysis_at_module_scale_33_1.png

  • For Capriss varieties

[15]:

Capriss_frequency = occurence_module_order_along_time(data= Capriss_extraction_at_module_scale,frequency_type= "cdf")
Capriss_frequency

[15]:
date 2014/12/10 2015/01/07 2015/02/15 2015/03/02 2015/04/03 2015/05/27
order
0 0.818182 0.264706 0.166667 0.138462 0.098901 0.071429
1 1.000000 0.970588 0.870370 0.738462 0.582418 0.412698
2 1.000000 1.000000 1.000000 1.000000 0.989011 0.730159
3 1.000000 1.000000 1.000000 1.000000 1.000000 0.968254
4 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 
[16]:

Capriss_frequency.plot.line(marker = "p")
plt.axhline(y = 0.9, color = "r",linestyle="--") # threshold at 0.9 quartile,
plt.axhline(y = 0.75, color = "b",linestyle="--") # threshold at 0.75 quartile
plt.title("Occurence of successive module order for Capriss")
plt.ylabel("cdf")

[16]:

Text(0, 0.5, 'cdf')
../_images/user_3.Architecture_analysis_at_module_scale_36_1.png

  • ** For Darselect variety**

[17]:

Darselect_frequency = occurence_module_order_along_time(data= Darselect_extraction_at_module_scale,frequency_type= "cdf")
Darselect_frequency

[17]:
date 2014/12/10 2015/01/09 2015/02/11 2015/03/06 2015/04/03 2015/05/20
order
0 0.6 0.45 0.310345 0.236842 0.136364 0.115385
1 1.0 1.00 0.896552 0.631579 0.439394 0.346154
2 1.0 1.00 1.000000 0.894737 0.742424 0.653846
3 1.0 1.00 1.000000 1.000000 1.000000 0.884615
4 1.0 1.00 1.000000 1.000000 1.000000 0.987179
5 1.0 1.00 1.000000 1.000000 1.000000 1.000000 
[18]:

Darselect_frequency.plot.line(marker = "o")
plt.axhline(y = 0.9, color = "r",linestyle="--") # threshold at 0.9 quartile,
plt.axhline(y = 0.75, color = "b",linestyle="--") # threshold at 0.75 quartile
plt.title("Occurence of succesive module order along time for Darselect")
plt.ylabel("cdf")

[18]:

Text(0, 0.5, 'cdf')
../_images/user_3.Architecture_analysis_at_module_scale_39_1.png

  • ** For Cir107 Variety**

[19]:

Cir107_frequency = occurence_module_order_along_time(data= Cir107_extraction_at_module_scale,frequency_type= "cdf")
Cir107_frequency

[19]:
date 2014/12/10 2015/01/08 2015/02/11 2015/03/04 2015/04/02 2015/05/20
order
0 0.642857 0.310345 0.157895 0.123288 0.116883 0.076923
1 1.000000 0.896552 0.736842 0.575342 0.519481 0.376068
2 1.000000 1.000000 0.982456 0.958904 0.909091 0.675214
3 1.000000 1.000000 1.000000 1.000000 1.000000 0.931624
4 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 
[20]:

Cir107_frequency.plot.line(marker = "o")
plt.axhline(y = 0.9, color = "r",linestyle="--") # threshold at 0.9 quartile,
plt.axhline(y = 0.75, color = "b",linestyle="--") # threshold at 0.75 quartile
plt.title("Occurence of succesive module order along time for Cir107")
plt.ylabel("cdf")

[20]:

Text(0, 0.5, 'cdf')
../_images/user_3.Architecture_analysis_at_module_scale_42_1.png

  • **For Ciflorette variety **

[21]:

Ciflorette_frequency = occurence_module_order_along_time(data= Ciflorette_extraction_at_module_scale,frequency_type= "cdf")
Ciflorette_frequency

[21]:
date 2014/12/04 2015/01/07 2015/02/13 2015/03/02 2015/03/30 2015/05/27
order
0 0.692308 0.642857 0.195652 0.18 0.132353 0.084112
1 1.000000 1.000000 0.847826 0.68 0.529412 0.308411
2 1.000000 1.000000 1.000000 0.98 0.955882 0.560748
3 1.000000 1.000000 1.000000 1.00 1.000000 0.813084
4 1.000000 1.000000 1.000000 1.00 1.000000 0.971963
5 1.000000 1.000000 1.000000 1.00 1.000000 1.000000 
[22]:

Ciflorette_frequency.plot.line(marker = "o")
plt.axhline(y = 0.9, color = "r",linestyle="--") # threshold at 0.9 quartile,
plt.axhline(y = 0.75, color = "b",linestyle="--") # threshold at 0.75 quartile
plt.title("Occurence of succesive module order along time for Ciflorette")
plt.ylabel("cdf")

[22]:

Text(0, 0.5, 'cdf')
../_images/user_3.Architecture_analysis_at_module_scale_45_1.png

  • For Clery variety

[23]:

Clery_frequency = occurence_module_order_along_time(data= Clery_extraction_at_module_scale,frequency_type= "cdf")
Clery_frequency

[23]:
date 2014/12/10 2015/01/07 2015/02/15 2015/03/02 2015/04/03 2015/05/27
order
0 0.818182 0.473684 0.310345 0.236842 0.138462 0.089109
1 1.000000 1.000000 1.000000 0.789474 0.507692 0.297030
2 1.000000 1.000000 1.000000 0.973684 0.923077 0.584158
3 1.000000 1.000000 1.000000 1.000000 1.000000 0.861386
4 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 
[24]:

Clery_frequency.plot.line(marker = "o")
plt.axhline(y = 0.9, color = "r",linestyle="--") # threshold at 0.9 quartile,
plt.axhline(y = 0.75, color = "b",linestyle="--") # threshold at 0.75 quartile
plt.title("Occurence of succesive module order along time for Clery")
plt.ylabel("cdf")

[24]:

Text(0, 0.5, 'cdf')
../_images/user_3.Architecture_analysis_at_module_scale_48_1.png

3.4.2. Pointwise mean no. Leaves, Flowers, Stolon by module order

The first step is calcul mean and standard deviation for each variable by genotype and order

[25]:

Mean= All_varieties_extraction_at_module_scale.groupby(["Genotype", "order"]).mean()
sd= All_varieties_extraction_at_module_scale.groupby(["Genotype", "order"]).std()

3.4.2.1. Pointwise mean no. leaves by module order and génotype

[26]:

pointwisemean_plot(data_mean=Mean,
                   data_sd=sd,
                   varieties=["Gariguette","Capriss","Cir107","Darselect","Ciflorette", "Clery"],
                   variable='nb_total_leaves',
                   title= "Pointwisemean of no.leaves",
                   ylab="Mean no.leaves",
                   expand= 0.1)
../_images/user_3.Architecture_analysis_at_module_scale_53_0.png

3.4.2.2. Pointwise mean of no. total flowers by genotype and orders

[27]:

pointwisemean_plot(data_mean=Mean,
                   data_sd=sd,
                   varieties=["Gariguette","Capriss","Cir107","Darselect","Ciflorette", "Clery"],
                   variable='nb_total_flowers',
                   title= "Pointwisemean of no. flowers",
                   ylab="Mean no. flowers",
                   expand= 0.1)
../_images/user_3.Architecture_analysis_at_module_scale_55_0.png

3.4.3. Number of stolons by module orders

[28]:

pointwisemean_plot(data_mean=Mean,
                   data_sd=sd,
                   varieties=["Gariguette","Capriss","Cir107","Darselect","Ciflorette", "Clery"],
                   variable='nb_stolons',
                   title= "Pointwisemean of no. stolons",
                   ylab="Mean no. stolons", expand=0.1)
../_images/user_3.Architecture_analysis_at_module_scale_57_0.png

3.4.4. Type of Crowns (Extension crown vs Branch crowns)

[29]:

crowntype_distribution(data= All_varieties_extraction_at_module_scale, varieties=["Gariguette","Capriss","Cir107","Darselect","Ciflorette","Clery"],crown_type="branch_crown",expand=0.1)

<matplotlib.colors.LinearSegmentedColormap object at 0x7f5a5d00b550>

[29]:
Main extension_crown branch_crown
Genotype order
Capriss 1 0.0 0.189474 0.810526
Capriss 2 0.0 0.970588 0.029412
Capriss 3 0.0 0.967742 0.032258
Capriss 4 0.0 1.000000 0.000000
Ciflorette 1 0.0 0.347826 0.652174
Ciflorette 2 0.0 0.833333 0.166667
Ciflorette 3 0.0 0.935484 0.064516
Ciflorette 4 0.0 1.000000 0.000000
Ciflorette 5 0.0 1.000000 0.000000
Cir107 1 0.0 0.253247 0.746753
Cir107 2 0.0 0.881818 0.118182
Cir107 3 0.0 0.902439 0.097561
Cir107 4 0.0 1.000000 0.000000
Clery 1 0.0 0.408163 0.591837
Clery 2 0.0 0.793651 0.206349
Clery 3 0.0 0.823529 0.176471
Clery 4 0.0 0.785714 0.214286
Darselect 1 0.0 0.551724 0.448276
Darselect 2 0.0 0.859649 0.140351
Darselect 3 0.0 0.871795 0.128205
Darselect 4 0.0 0.875000 0.125000
Darselect 5 0.0 1.000000 0.000000
Gariguette 1 0.0 0.393617 0.606383
Gariguette 2 0.0 0.806452 0.193548
Gariguette 3 0.0 0.904762 0.095238
Gariguette 4 0.0 1.000000 0.000000
Gariguette 5 0.0 1.000000 0.000000
../_images/user_3.Architecture_analysis_at_module_scale_59_2.png 
[30]:

crowntype_distribution(data= All_varieties_extraction_at_module_scale, varieties=["Gariguette","Capriss","Cir107","Darselect","Ciflorette","Clery"],crown_type="extension_crown",expand=0.1,plot=True)

<matplotlib.colors.LinearSegmentedColormap object at 0x7f5a5cfbe130>

[30]:
Main extension_crown branch_crown
Genotype order
Capriss 1 0.0 0.189474 0.810526
Capriss 2 0.0 0.970588 0.029412
Capriss 3 0.0 0.967742 0.032258
Capriss 4 0.0 1.000000 0.000000
Ciflorette 1 0.0 0.347826 0.652174
Ciflorette 2 0.0 0.833333 0.166667
Ciflorette 3 0.0 0.935484 0.064516
Ciflorette 4 0.0 1.000000 0.000000
Ciflorette 5 0.0 1.000000 0.000000
Cir107 1 0.0 0.253247 0.746753
Cir107 2 0.0 0.881818 0.118182
Cir107 3 0.0 0.902439 0.097561
Cir107 4 0.0 1.000000 0.000000
Clery 1 0.0 0.408163 0.591837
Clery 2 0.0 0.793651 0.206349
Clery 3 0.0 0.823529 0.176471
Clery 4 0.0 0.785714 0.214286
Darselect 1 0.0 0.551724 0.448276
Darselect 2 0.0 0.859649 0.140351
Darselect 3 0.0 0.871795 0.128205
Darselect 4 0.0 0.875000 0.125000
Darselect 5 0.0 1.000000 0.000000
Gariguette 1 0.0 0.393617 0.606383
Gariguette 2 0.0 0.806452 0.193548
Gariguette 3 0.0 0.904762 0.095238
Gariguette 4 0.0 1.000000 0.000000
Gariguette 5 0.0 1.000000 0.000000
../_images/user_3.Architecture_analysis_at_module_scale_60_2.png

3.4.5. Distribution of complete vs complete module as function of module order

3.4.6. As fonction of module orders

  • ** For Gariguette variety**

[31]:

res=pd.crosstab(index= Gariguette_extraction_at_module_scale["order"], columns= Gariguette_extraction_at_module_scale["complete_module"],normalize="index")
res.columns = ["incomplete","complete"]
res

[31]:
incomplete complete
order
0 0.203704 0.796296
1 0.138298 0.861702
2 0.338710 0.661290
3 0.238095 0.761905
4 0.000000 1.000000
5 0.000000 1.000000 
[32]:

res.plot.line(marker="p", color=["red","blue"])
plt.axis([0,5,0,1])
plt.ylabel("Probability")
plt.title("Relative distrivution of complete (True) and incomplete (False) module as function of module order")

[32]:

Text(0.5, 1.0, 'Relative distrivution of complete (True) and incomplete (False) module as function of module order')
../_images/user_3.Architecture_analysis_at_module_scale_65_1.png

  • For Capriss variety

[33]:

res=pd.crosstab(index= Capriss_extraction_at_module_scale["order"], columns= Capriss_extraction_at_module_scale["complete_module"],normalize="index")
res.columns = ["incomplete","complete"]
res

[33]:
incomplete complete
order
0 0.185185 0.814815
1 0.305263 0.694737
2 0.539216 0.460784
3 0.322581 0.677419
4 0.500000 0.500000 
[34]:

res.plot.line(marker="p",color= ["red", "blue"])
plt.axis([0,5,0,1])
plt.ylabel("Probability")
plt.title("Relative distribution of complete (True) and incomplete (False) module as function of module order")

[34]:

Text(0.5, 1.0, 'Relative distribution of complete (True) and incomplete (False) module as function of module order')
../_images/user_3.Architecture_analysis_at_module_scale_68_1.png

  • For Darselect variety

[35]:

res=pd.crosstab(index= Darselect_extraction_at_module_scale["order"], columns= Darselect_extraction_at_module_scale["complete_module"],normalize="index")
res.columns = ["incomplete","complete"]
res

[35]:
incomplete complete
order
0 0.111111 0.888889
1 0.275862 0.724138
2 0.122807 0.877193
3 0.435897 0.564103
4 0.250000 0.750000
5 0.000000 1.000000 
[36]:

res.plot.line(marker="p",color= ["red", "blue"])
plt.axis([0,5,0,1])
plt.ylabel("Probability")
plt.title("Relative distribution of complete (True) and incomplete (False) module as function of module order")

[36]:

Text(0.5, 1.0, 'Relative distribution of complete (True) and incomplete (False) module as function of module order')
../_images/user_3.Architecture_analysis_at_module_scale_71_1.png

  • For Cir107 variety

[37]:

res=pd.crosstab(index=Cir107_extraction_at_module_scale["order"], columns= Cir107_extraction_at_module_scale["complete_module"],normalize="columns")
res.columns = ["incomplete","complete"]
res

[37]:
incomplete complete
order
0 0.088235 0.169811
1 0.343137 0.449057
2 0.431373 0.249057
3 0.117647 0.109434
4 0.019608 0.022642 
[38]:

res.plot.line(marker="p",color= ["red", "blue"])
plt.axis([0,5,0,1])
plt.ylabel("Probability")
plt.title("Relative distribution of complete (True) and incomplete (False) module as function of module order")

[38]:

Text(0.5, 1.0, 'Relative distribution of complete (True) and incomplete (False) module as function of module order')
../_images/user_3.Architecture_analysis_at_module_scale_74_1.png

  • For Ciflorette variety

[39]:

res=pd.crosstab(index=Ciflorette_extraction_at_module_scale["order"], columns= Ciflorette_extraction_at_module_scale["complete_module"],normalize="columns")
res.columns = ["incomplete","complete"]
res

[39]:
incomplete complete
order
0 0.109375 0.200855
1 0.250000 0.423077
2 0.500000 0.196581
3 0.046875 0.119658
4 0.062500 0.055556
5 0.031250 0.004274 
[40]:

res.plot.line(marker="p",color= ["red", "blue"])
plt.axis([0,5,0,1])
plt.ylabel("Probability")
plt.title("Relative distribution of complete (True) and incomplete (False) module as function of module order")

[40]:

Text(0.5, 1.0, 'Relative distribution of complete (True) and incomplete (False) module as function of module order')
../_images/user_3.Architecture_analysis_at_module_scale_77_1.png

  • For Clery variety

[41]:

res=pd.crosstab(index=Clery_extraction_at_module_scale["order"], columns= Clery_extraction_at_module_scale["complete_module"],normalize="columns")
res.columns = ["incomplete","complete"]
res

[41]:
incomplete complete
order
0 0.145161 0.223881
1 0.274194 0.402985
2 0.370968 0.199005
3 0.129032 0.129353
4 0.080645 0.044776 
[42]:

res.plot.line(marker="p",color= ["red", "blue"])
plt.axis([0,5,0,1])
plt.ylabel("Probability")
plt.title("Relative distribution of complete (True) and incomplete (False) module as function of module order")

[42]:

Text(0.5, 1.0, 'Relative distribution of complete (True) and incomplete (False) module as function of module order')
../_images/user_3.Architecture_analysis_at_module_scale_80_1.png

3.4.7. As fonction of date

  • ** Gariguette**

[43]:

res=pd.crosstab(index=Gariguette_extraction_at_module_scale["date"], columns= Gariguette_extraction_at_module_scale["complete_module"],normalize="index")
res.columns = ["incomplete","complete"]
res

[43]:
incomplete complete
date
2014/12/10 1.000000 0.000000
2015/01/08 0.562500 0.437500
2015/02/12 0.121212 0.878788
2015/03/06 0.210526 0.789474
2015/04/02 0.257576 0.742424
2015/05/19 0.037975 0.962025 
[44]:

res.plot.line(marker="p",color= ["red", "blue"])
plt.axis([0,5,0,1])
plt.ylabel("Probability")
plt.title("Relative distribution of complete (True) and incomplete (False) module as function of date")

[44]:

Text(0.5, 1.0, 'Relative distribution of complete (True) and incomplete (False) module as function of date')
../_images/user_3.Architecture_analysis_at_module_scale_84_1.png

  • Capriss

[45]:

res=pd.crosstab(index=Capriss_extraction_at_module_scale["date"], columns= Capriss_extraction_at_module_scale["complete_module"],normalize="index")
res.columns = ["incomplete","complete"]
res

[45]:
incomplete complete
date
2014/12/10 1.000000 0.000000
2015/01/07 0.735294 0.264706
2015/02/15 0.388889 0.611111
2015/03/02 0.476923 0.523077
2015/04/03 0.362637 0.637363
2015/05/27 0.111111 0.888889 
[46]:

res.plot.line(marker="p",color= ["red", "blue"])
plt.axis([0,5,0,1])
plt.ylabel("Probability")
plt.title("Relative distribution of complete (True) and incomplete (False) module as function of date")

[46]:

Text(0.5, 1.0, 'Relative distribution of complete (True) and incomplete (False) module as function of date')
../_images/user_3.Architecture_analysis_at_module_scale_87_1.png

  • Darselect

[47]:

res=pd.crosstab(index=Capriss_extraction_at_module_scale["date"], columns= Capriss_extraction_at_module_scale["complete_module"],normalize="index")
res.columns = ["incomplete","complete"]
res

[47]:
incomplete complete
date
2014/12/10 1.000000 0.000000
2015/01/07 0.735294 0.264706
2015/02/15 0.388889 0.611111
2015/03/02 0.476923 0.523077
2015/04/03 0.362637 0.637363
2015/05/27 0.111111 0.888889 
[48]:

res.plot.line(marker="p",color= ["red", "blue"])
plt.axis([0,5,0,1])
plt.ylabel("Probability")
plt.title("Relative distribution of complete (True) and incomplete (False) module as function of date")

[48]:

Text(0.5, 1.0, 'Relative distribution of complete (True) and incomplete (False) module as function of date')
../_images/user_3.Architecture_analysis_at_module_scale_90_1.png

  • Cir107

[49]:

res=pd.crosstab(index=Cir107_extraction_at_module_scale["date"], columns= Cir107_extraction_at_module_scale["complete_module"],normalize="index")
res.columns = ["incomplete","complete"]
res

[49]:
incomplete complete
date
2014/12/10 0.928571 0.071429
2015/01/08 0.482759 0.517241
2015/02/11 0.263158 0.736842
2015/03/04 0.397260 0.602740
2015/04/02 0.272727 0.727273
2015/05/20 0.085470 0.914530 
[50]:

res.plot.line(marker="p",color= ["red", "blue"])
plt.axis([0,5,0,1])
plt.ylabel("Probability")
plt.title("Relative distribution of complete (True) and incomplete (False) module as function of date")

[50]:

Text(0.5, 1.0, 'Relative distribution of complete (True) and incomplete (False) module as function of date')
../_images/user_3.Architecture_analysis_at_module_scale_93_1.png

  • Ciflorette

[51]:

res=pd.crosstab(index=Ciflorette_extraction_at_module_scale["date"], columns= Ciflorette_extraction_at_module_scale["complete_module"],normalize="index")
res.columns = ["incomplete","complete"]
res

[51]:
incomplete complete
date
2014/12/04 0.846154 0.153846
2015/01/07 0.285714 0.714286
2015/02/13 0.152174 0.847826
2015/03/02 0.320000 0.680000
2015/03/30 0.279412 0.720588
2015/05/27 0.065421 0.934579 
[52]:

res.plot.line(marker="p",color= ["red", "blue"])
plt.axis([0,5,0,1])
plt.ylabel("Probability")
plt.title("Relative distribution of complete (True) and incomplete (False) module as function of date")

[52]:

Text(0.5, 1.0, 'Relative distribution of complete (True) and incomplete (False) module as function of date')
../_images/user_3.Architecture_analysis_at_module_scale_96_1.png

  • Clery

[53]:

res=pd.crosstab(index=Clery_extraction_at_module_scale["date"], columns= Clery_extraction_at_module_scale["complete_module"],normalize="index")
res.columns = ["incomplete","complete"]
res

[53]:
incomplete complete
date
2014/12/10 0.818182 0.181818
2015/01/07 0.526316 0.473684
2015/02/15 0.137931 0.862069
2015/03/02 0.210526 0.789474
2015/04/03 0.276923 0.723077
2015/05/27 0.128713 0.871287 
[54]:

res.plot.line(marker="p",color= ["red", "blue"])
plt.axis([0,5,0,1])
plt.ylabel("Probability")
plt.title("Relative distribution of complete (True) and incomplete (False) module as function of date")

[54]:

Text(0.5, 1.0, 'Relative distribution of complete (True) and incomplete (False) module as function of date')
../_images/user_3.Architecture_analysis_at_module_scale_99_1.png