import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
from IPython.display import display, HTML
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import statsmodels 
import plotly.io as pio 
pio.renderers.default = "notebook"

# import the custom functions for this project
import cytox.core as ctx

pd.set_option('display.max_rows', None)
pd.set_option('display.max_Columns', None)

df_30min = pd.read_csv(r'..\data\cleaned_data\df_30min.csv')
df_16h = pd.read_csv(r'..\data\cleaned_data\df_16h.csv')

df_30min["Sample"].unique()

array(['DMSO_10%_30min', 'DMSO_30%_30min', 'DMSO_100%_30min',
       'Staurosporine_0.1uM_6h', 'Staurosporine_0.3uM_6h',
       'Staurosporine_1uM_6h', 'Saponin_1uM_6h', 'Saponin_3uM_6h',
       'Saponin_9uM_6h', 'Saponin_1uM_30min', 'Saponin_3uM_30min',
       'Saponin_9uM_30min', 'Verteporfin_0.6uM_30min',
       'Verteporfin_2uM_30min', 'Verteporfin_6uM_30min',
       'Digoxin_0.6uM_30min', 'Digoxin_2uM_30min', 'Digoxin_6uM_30min'],
      dtype=object)

"""
Use the DMSO control for determining the threshold as average of signal in 
each channel + 2 standard devisations
"""
threshold_control_30min = df_30min.loc[
    (df_30min["Sample"] == "DMSO_100%_30min")
]  

casp_thresh_gen_30min = ctx.Categorizer(
    normalized_column="Nuclei - Intensity Nucleus Alexa 488 Mean",
    df_untreated = threshold_control_30min, sd=2
)
_, _, casp_thresh_30min = casp_thresh_gen_30min.threshold_generator()

PI_thresh_gen_30min = ctx.Categorizer(
    normalized_column="Nuclei - Intensity Nucleus Alexa 568 Mean",
    df_untreated = threshold_control_30min, sd=2
)

_, _, PI_thresh_30min = PI_thresh_gen_30min.threshold_generator()

The values for control mean, mean-2SD and mean+2SD, respectively:  903.6842772795218, -8311.604669365695, 10118.97322392474
The values for control mean, mean-2SD and mean+2SD, respectively:  774.8943901345291, -8223.045914084627, 9772.834694353684

"""Use the DMSO control for determining the threshold as average of signal in 
each channel + 2 standard devisations"""
threshold_control_16h = df_16h.loc[(df_16h["Sample"] == "DMSO_100%_16h")]  

casp_thresh_gen_16h = ctx.Categorizer(
    normalized_column="Nuclei - Intensity Nucleus Alexa 488 Mean",
    df_untreated = threshold_control_16h, 
    sd=2
)
_, _, casp_thresh_16h = casp_thresh_gen_16h.threshold_generator()

PI_thresh_gen_16h = ctx.Categorizer(
    normalized_column="Nuclei - Intensity Nucleus Alexa 568 Mean",
    df_untreated = threshold_control_16h, 
    sd=2
)

_, _, PI_thresh_16h = PI_thresh_gen_16h.threshold_generator()

The values for control mean, mean-2SD and mean+2SD, respectively:  965.2248549824467, -8370.34334693707, 10300.793056901965
The values for control mean, mean-2SD and mean+2SD, respectively:  628.1948914393736, -5123.921933518551, 6380.3117163972975

# Separate wells: each replicate in its own subplot
fig = ctx.cytotox_raw_plot(
    df_30min,
    title="30 min incubation",
    casp_thresh=casp_thresh_30min,
    pi_thresh=PI_thresh_30min,
    merge_replicates=False,     
)

# Combined replicates: all wells of a sample merged into one subplot
fig = ctx.cytotox_raw_plot(
    df_30min,
    title="30 min incubation",
    facet_col_spacing=0.04,
    casp_thresh=casp_thresh_30min,
    pi_thresh=PI_thresh_30min,
    merge_replicates=True,
    fig_show=False
)

# Change the distance of x and y axes labels from axes.
fig.for_each_annotation(
    lambda a: (
        a.update(x=-0.12) if a.text == "PI mean signal intensity"
        else a.update(y=-0.045) 
        if a.text == "Caspase 3/7 average signal intensity"
        else None
    )
)

# Separate wells: each replicate in its own subplot
fig = ctx.cytotox_raw_plot(
    df_16h,
    facet_row_spacing=0.08,
    title="16 h incubation",
    casp_thresh=casp_thresh_16h,
    pi_thresh=PI_thresh_16h,
    merge_replicates=False,
    fig_show=False     
)

# Change the distance of x axes labels from axes.
fig.for_each_annotation(
    lambda a: (
        a.update(y=-0.12) if a.text == "Caspase 3/7 average signal intensity"
        else None
    )
)

# Combined replicates: all wells of a sample merged into one subplot
fig = ctx.cytotox_raw_plot(
    df_16h,
    title="16 h incubation",
    casp_thresh=casp_thresh_16h,
    pi_thresh=PI_thresh_16h,
    merge_replicates=True,
    fig_show=False
)

# Change the distance of x and y axes labels from axes.
fig.for_each_annotation(
    lambda a: (
        a.update(x=-0.12) if a.text == "PI mean signal intensity"
        else a.update(y=-0.1) 
        if a.text == "Caspase 3/7 average signal intensity"
        else None
    )
)

cytotox_table_30min = ctx.compute_cytotox_table(
    df_30min,
    casp_thresh=casp_thresh_30min,
    pi_thresh=PI_thresh_30min,
)

cytotox_table_30min.head()

cytotox_table_16h = ctx.compute_cytotox_table(
    df_16h,
    casp_thresh=casp_thresh_16h,
    pi_thresh=PI_thresh_16h,
)

cytotox_table_16h.head()

	Sample	Replicate_num	Total_cell_count	live_cell_count	live_cell_percent	Caspase_positive_count	Caspase_positive_percent	PI_positive_count	PI_positive_percent
0	DMSO_10%_30min	1	949	918	96.733404	15	1.580611	26	2.739726
1	DMSO_10%_30min	2	874	850	97.254005	8	0.915332	20	2.288330
2	DMSO_30%_30min	1	924	909	98.376623	8	0.865801	12	1.298701
3	DMSO_30%_30min	2	1268	1257	99.132492	4	0.315457	10	0.788644
4	DMSO_100%_30min	1	1402	1386	98.858773	12	0.855920	11	0.784593

	Sample	Replicate_num	Total_cell_count	live_cell_count	live_cell_percent	Caspase_positive_count	Caspase_positive_percent	PI_positive_count	PI_positive_percent
0	DMSO_10%_16h	1	1	1	100.000000	0	0.000000	0	0.000000
1	DMSO_10%_16h	2	1961	1951	99.490056	5	0.254972	10	0.509944
2	DMSO_30%_16h	1	1899	1892	99.631385	4	0.210637	5	0.263296
3	DMSO_30%_16h	2	1728	1723	99.710648	4	0.231481	4	0.231481
4	DMSO_100%_16h	1	2107	2078	98.623636	23	1.091599	17	0.806834

Import the cleaned data¶

Define a threshold¶

30 min data¶

16 h¶

Graphs¶

30 min¶

16 h¶

Sample overview compiled from single cell data¶

30 min¶

16 h¶