From 0bb1854ed7a4a8dc83a669e207cd9722d9366b22 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Al=C3=A1n=20Mu=C3=B1oz?= <amuoz@ed.ac.uk>
Date: Sat, 24 Apr 2021 22:33:51 +0100
Subject: [PATCH] update setup.py
Former-commit-id: 11d322c1681aeecfc8a2b9b086fb73374b5720f2
---
core/cell.py | 1 +
examples/testing.py | 79 +++++++++++++++++++++++++++------------------
2 files changed, 49 insertions(+), 31 deletions(-)
diff --git a/core/cell.py b/core/cell.py
index 0d1c48ea..f96f30d5 100644
--- a/core/cell.py
+++ b/core/cell.py
@@ -18,5 +18,6 @@ def growth_rate(data:Series, alg=None, filt = 'savgol'):
if alg is None:
alg='standard'
+
diff --git a/examples/testing.py b/examples/testing.py
index 70150066..885e34f6 100644
--- a/examples/testing.py
+++ b/examples/testing.py
@@ -7,53 +7,67 @@ from pandas import Series
from postprocessor.core.postprocessor import PostProcessor
from postprocessor.core.tracks import non_uniform_savgol
-pp = PostProcessor(source=19831)
+pp = PostProcessor(source=19916) # 19916
pp.load_tiler_cells()
-f = '/home/alan/Documents/libs/extraction/extraction/examples/gluStarv_2_0_x2_dual_phl_ura8_00/extraction'
-pp.load_extraction('/home/alan/Documents/libs/extraction/extraction/examples/' + pp.expt.name + '/extraction/')
+# f = '/home/alan/Documents/libs/extraction/extraction/examples/gluStarv_2_0_x2_dual_phl_ura8_00/extraction'
+f = "/home/alan/Documents/libs/extraction/extraction/examples/pH_calibration_dual_phl__ura8__by4741__01"
+pp.load_extraction(
+ "/home/alan/Documents/libs/extraction/extraction/examples/"
+ + pp.expt.name
+ + "/extraction/"
+)
-tmp=pp.extraction['phl_ura8_002']
+tmp = pp.extraction[pp.expt.positions[0]]
# prepare data
-test = tmp[('GFPFast', np.maximum, 'mean')]
+test = tmp[("GFPFast", np.maximum, "mean")]
clean = test.loc[test.notna().sum(axis=1) > 30]
window = 9
degree = 3
-savgol_on_srs = lambda x: Series(non_uniform_savgol(x.dropna().index, x.dropna().values,
- window, degree), index=x.dropna().index)
+savgol_on_srs = lambda x: Series(
+ non_uniform_savgol(x.dropna().index, x.dropna().values, window, degree),
+ index=x.dropna().index,
+)
smooth = clean.apply(savgol_on_srs, axis=1)
from random import randint
x = randint(0, len(smooth))
-plt.plot(clean.iloc[x], 'b')
-plt.plot(smooth.iloc[x], 'r')
+plt.plot(clean.iloc[x], "b")
+plt.plot(smooth.iloc[x], "r")
plt.show()
-def growth_rate(data:Series, alg=None, filt = {'kind':'savgol','window':9, 'degree':3}):
+
+def growth_rate(
+ data: Series, alg=None, filt={"kind": "savgol", "window": 9, "degree": 3}
+):
if alg is None:
- alg='standard'
+ alg = "standard"
+
+ if filt: # TODO add support for multiple algorithms
+ data = Series(
+ non_uniform_savgol(
+ data.dropna().index, data.dropna().values, window, degree
+ ),
+ index=data.dropna().index,
+ )
- if filt: #TODO add support for multiple algorithms
- data = Series(non_uniform_savgol(data.dropna().index, data.dropna().values,
- window, degree), index = data.dropna().index)
+ return Series(np.convolve(data, diff_kernel, "same"), index=data.dropna().index)
-
-
- return Series(np.convolve(data,diff_kernel ,'same'), index=data.dropna().index)
import numpy as np
-diff_kernel = np.array([1,-1])
+
+diff_kernel = np.array([1, -1])
gr = clean.apply(growth_rate, axis=1)
-
-def sort_df(df, by='first', rev=True):
+
+def sort_df(df, by="first", rev=True):
nona = df.notna()
- if by=='len':
+ if by == "len":
idx = nona.sum(axis=1)
- elif by=='first':
+ elif by == "first":
idx = nona.idxmax(axis=1)
idx = idx.sort_values().index
@@ -62,23 +76,26 @@ def sort_df(df, by='first', rev=True):
return df.loc[idx]
-test = tmp[('GFPFast', np.maximum, 'median')]
-test2 = tmp[('pHluorin405', np.maximum, 'median')]
-ph = test/test2
+
+test = tmp[("GFPFast", np.maximum, "median")]
+test2 = tmp[("pHluorin405", np.maximum, "median")]
+ph = test / test2
ph = ph.stack().reset_index(1)
-ph.columns = ['tp', 'fl']
+ph.columns = ["tp", "fl"]
+
def m2p5_med(ext, ch, red=np.maximum):
- m2p5pc = ext[(ch, red, 'max2p5pc')]
- med = ext[(ch, red, 'median')]
+ m2p5pc = ext[(ch, red, "max2p5pc")]
+ med = ext[(ch, red, "median")]
- result = m2p5pc/med
+ result = m2p5pc / med
return result
+
def plot_avg(df):
df = df.stack().reset_index(1)
- df.columns = ['tp', 'val']
+ df.columns = ["tp", "val"]
- sns.relplot(x=df['tp'],y=df['val'], kind='line')
+ sns.relplot(x=df["tp"], y=df["val"], kind="line")
plt.show()
--
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