diff --git a/core/postprocessor.py b/core/postprocessor.py
index c941835a831a14a369a79fd28a4c54f0978f09dd..9a04b66f24d2f01259d6b2f1a407b8bf8769ee12 100644
--- a/core/postprocessor.py
+++ b/core/postprocessor.py
@@ -64,11 +64,23 @@ class PostProcessor:
self.cells = self.cells.from_source(
self.expt.current_position.annotation)
+ def get_exp_mo_bud(self):
+ d = {}
+ for pos in self.expt.positions:
+ self.expt.current_position = pos
+ d = {**d, **self.get_pos_mo_bud()}
+
+ self.expt.current_position = self.expt.positions[0]
+
+ return d
def load_extraction(self, folder=None)-> None:
if folder is None:
folder = Path(self.expt.name + '/extraction')
self.extraction = {}
for pos in self.expt.positions:
- self.extraction[pos] = load(folder / Path(pos + '.gz'))
-
+ try:
+ self.extraction[pos] = load(folder / Path(pos + '.gz'))
+
+ except:
+ print(pos, ' not found')
diff --git a/examples/testing.py b/examples/testing.py
index 885e34f62d8f86335dd08c01c0f9e8faf7d14c28..d6b96f61d4d0d918ca366ea9cbd6fcc69c462b30 100644
--- a/examples/testing.py
+++ b/examples/testing.py
@@ -1,10 +1,13 @@
-#!/usr/bin/env python3
-
+from typing import Dict, List, Union
+import numpy as np
+import pandas as pd
+from pandas import Series, DataFrame
from matplotlib import pyplot as plt
-from pandas import Series
+import seaborn as sns
from postprocessor.core.postprocessor import PostProcessor
+<<<<<<< HEAD
from postprocessor.core.tracks import non_uniform_savgol
pp = PostProcessor(source=19916) # 19916
@@ -61,6 +64,9 @@ import numpy as np
diff_kernel = np.array([1, -1])
gr = clean.apply(growth_rate, axis=1)
+=======
+from postprocessor.core.tracks import non_uniform_savgol, clean_tracks
+>>>>>>> 96f513af38080e6ebb6d301159ca973b5d90ce81
def sort_df(df, by="first", rev=True):
@@ -75,6 +81,7 @@ def sort_df(df, by="first", rev=True):
idx = idx[::-1]
return df.loc[idx]
+<<<<<<< HEAD
test = tmp[("GFPFast", np.maximum, "median")]
@@ -84,6 +91,8 @@ ph = ph.stack().reset_index(1)
ph.columns = ["tp", "fl"]
+=======
+>>>>>>> 96f513af38080e6ebb6d301159ca973b5d90ce81
def m2p5_med(ext, ch, red=np.maximum):
m2p5pc = ext[(ch, red, "max2p5pc")]
med = ext[(ch, red, "median")]
@@ -99,3 +108,100 @@ def plot_avg(df):
sns.relplot(x=df["tp"], y=df["val"], kind="line")
plt.show()
+
+def split_data(df:DataFrame, splits:List[int]):
+ dfs = [df.iloc[:,i:j] for i,j in zip( (0,) + splits,
+ splits + (df.shape[1],))]
+ return dfs
+
+def growth_rate(data:Series, alg=None, filt = {'kind':'savgol','window':7, 'degree':3}):
+ if alg is None:
+ alg='standard'
+
+ if filt: #TODO add support for multiple algorithms
+ window = filt['window']
+ degree = filt['degree']
+ data = Series(non_uniform_savgol(data.dropna().index, data.dropna().values,
+ window, degree), index = data.dropna().index)
+
+ diff_kernel = np.array([1,-1])
+
+
+ return Series(np.convolve(data,diff_kernel ,'same'), index=data.dropna().index)
+
+pp = PostProcessor(source=19831)
+pp.load_tiler_cells()
+f = '/home/alan/Documents/sync_docs/libs/postproc/gluStarv_2_0_x2_dual_phl_ura8_00/extraction'
+pp.load_extraction('/home/alan/Documents/sync_docs/libs/postproc/postprocessor/' + pp.expt.name + '/extraction/')
+tmp=pp.extraction['phl_ura8_002']
+
+def _check_bg(data):
+ for k in list(pp.extraction.values())[0].keys():
+ for p in pp.expt.positions:
+ if k not in pp.extraction[p]:
+ print(p, k)
+data = {k:pd.concat([pp.extraction[pos][k] for pos in \
+ pp.expt.positions[:-3]]) for k in list(pp.extraction.values())[0].keys()}
+
+hmap = lambda df: sns.heatmap(sort_df(df), robust=True);
+# from random import randint
+# x = randint(0, len(smooth))
+# plt.plot(clean.iloc[x], 'b')
+# plt.plot(smooth.iloc[x], 'r')
+# plt.show()
+
+
+# data = tmp
+df= data[('general',None,'area')]
+clean = clean_tracks(df, min_len=160)
+clean = clean.loc[clean.notna().sum(axis=1) > 9]
+gr = clean.apply(growth_rate, axis=1)
+splits = (72,108,180)
+gr_sp = split_data(gr, splits)
+
+idx = gr.index
+
+bg = get_bg(data)
+test = data[('GFPFast', np.maximum, 'median')]
+test2 = data[('pHluorin405', np.maximum, 'median')]
+ph = (test/test2).loc[idx]
+c=pd.concat((ph.mean(1), gr.max(1)), axis=1); c.columns = ['ph', 'gr_max']
+# ph = ph.stack().reset_index(1)
+# ph.columns = ['tp', 'fl']
+
+ph_sp=split_data(gr, splits)
+
+def get_bg(data):
+ bg = {}
+ fl_subkeys = [x for x in data.keys() if x[0] in \
+ ['GFP', 'GFPFast', 'mCherry', 'pHluorin405'] and x[-1]!='imBackground']
+ for k in fl_subkeys:
+ nk = list(k)
+ bk = tuple(nk[:-1] + ['imBackground'])
+ nk = tuple(nk[:-1] + [nk[-1] + '_BgSub'])
+ tmp = []
+ for i,v in data[bk].iterrows():
+ if i in data[k].index:
+ newdf = data[k].loc[i] / v
+ newdf.index = pd.MultiIndex.from_tuples([(*i, c) for c in \
+ newdf.index])
+ tmp.append(newdf)
+ bg[nk] = pd.concat(tmp)
+
+ return bg
+
+def calc_ph(bg):
+ fl_subkeys = [x for x in bg.keys() if x[0] in \
+ ['GFP', 'GFPFast', 'pHluorin405']]
+ chs = list(set([x[0] for x in fl_subkeys]))
+ assert len(chs)==2, 'Too many channels'
+ ch1 = [x[1:] for x in fl_subkeys if x[0]==chs[0]]
+ ch2 = [x[1:] for x in fl_subkeys if x[0]==chs[1]]
+ inter = list(set(ch1).intersection(ch2))
+ ph = {}
+ for red_fld in inter:
+ ph[tuple(('ph',) + red_fld)] = bg[tuple((chs[0],) + red_fld)] / bg[tuple((chs[1],) + red_fld)]
+
+# sns.heatmap(sort_df(data[('mCherry', np.maximum, 'max2p5pc_BgSub')] / data[('mCherry', np.maximum, 'median_BgSub')]), robust=True)
+
+# from postprocessor.core.tracks import clean_tracks