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from itertools import permutations, product, repeat, takewhile
from functools import partial, reduce
from operator import add, itemgetter
from collections import defaultdict
from subprocess import call
import ast
import astor
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import matplotlib as mpl
from scipy.signal import argrelmax
from scipy.stats import gaussian_kde
from scipy.stats import ttest_ind
from more_itertools import interleave_longest
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import common.statesN as f
import common.seg as seg
import common.edict as edict
import common.lin as lin
from common.seg import STissue
import grates as grs
import patternTransitions as p
geneNms: List[str] = ['"AG"',
'"AHP6"',
'"ANT"',
'"AP1"',
'"AP2"',
'"AP3"',
'"AS1"',
'"ATML1"',
'"CLV3"',
'"CUC1_2_3"',
'"ETTIN"',
'"FIL"',
'"LFY"',
'"MP"',
'"PHB_PHV"',
'"PI"',
'"PUCHI"',
'"REV"',
'"SEP1"',
'"SEP2"',
'"SEP3"',
'"STM"',
'"SUP"',
'"SVP"',
'"WUS"']
mpl.rcParams.update(mpl.rcParamsDefault)
sts_ts = f.states_per_t()
fst = itemgetter(0)
snd = itemgetter(1)
plt.rc('xtick', labelsize=fontsize)
plt.rc('ytick', labelsize=fontsize)
plt.rc('axes', labelsize=fontsize)
params = {'legend.fontsize': fontsize}
plt.rcParams.update(params)
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class BExpr2():
def __init__(self, e_str):
self.e = ge(e_str)
def __hash__(self):
gns = sorted([hash(g) for g in getGenes(getTopExpr(self.e))])
op = hash(ast.dump(getTopExpr(self.e).op))
return sum(gns) + op
def __eq__(self, other):
return hash(self) == hash(other)
def __repr__(self):
import astor
return astor.to_source(self.e).replace('"', '').strip()
class BExpr2Region():
def __init__(self, t, ts, e_str):
self.t = t
self.e = ge(e_str)
self.ts = ts
def __hash__(self):
return sum(get_sts_expr(self.ts, self.e, sts_ts[self.t]))
def __eq__(self, other):
return hash(self) == hash(other)
class GPairRegions():
def __init__(self, t, ts, g1, g2):
self.t = t
self.g1 = g1
self.g2 = g2
self.e1 = ge("'{g1}' and '{g2}'".format(g1=g1, g2=g2))
self.e2 = ge("'{g1}' and not '{g2}'".format(g1=g1, g2=g2))
self.ts = ts
def __hash__(self):
sts = sts_ts[self.t]
sts_e1 = get_sts_expr(self.ts, self.e1, sts)
sts_e2 = get_sts_expr(self.ts, self.e2, sts)
return (hash(frozenset(sts_e1)) +
hash(frozenset(sts_e2)))
def __eq__(self, other):
return hash(self) == hash(other)
def plot(self, txt=""):
d = dict()
grs_state = get_grs_state()
sts = sts_ts[self.t]
sts_e1 = get_sts_expr(self.ts, self.e1, sts)
sts_e2 = get_sts_expr(self.ts, self.e2, sts)
gr_e1 = meanor0(reduce(add, [grs_state[st] for st in sts_e1], []))
gr_e2 = meanor0(reduce(add, [grs_state[st] for st in sts_e2], []))
he, le = [e for e, gr in sorted([(self.e1, gr_e1), (self.e2, gr_e2)],
key=snd, reverse=True)]
for c in self.ts:
if seg.evalB(getTopExpr(he), c.exprs):
else:
d[c.cid] = 0.5
for c in self.ts:
if seg.evalB(getTopExpr(le), c.exprs):
d[c.cid] = 0.0
f = plot_ts_q_(self.ts, d,
lb="{g1}-{g2}".format(g1=self.g1, g2=self.g2),
bounds=(0.1, 0.9), txt=txt)
return f, d
def get_regions(self) -> Tuple[Set[int], Set[int]]:
grs_state = get_grs_state()
sts = sts_ts[self.t]
sts_e1 = get_sts_expr(self.ts, self.e1, sts)
sts_e2 = get_sts_expr(self.ts, self.e2, sts)
gr_e1 = meanor0(reduce(add, [grs_state[st] for st in sts_e1], []))
gr_e2 = meanor0(reduce(add, [grs_state[st] for st in sts_e2], []))
he, le = [e for e, gr in sorted([(self.e1, gr_e1), (self.e2, gr_e2)],
key=snd, reverse=True)]
r1 = list()
r2 = list()
for c in self.ts:
if seg.evalB(getTopExpr(he), c.exprs):
r1.append(c.cid)
elif seg.evalB(getTopExpr(le), c.exprs):
r2.append(c.cid)
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def plot_distr(self):
set_plot_params(fontsize=12)
sns.set_style("white")
cls = sns.color_palette("coolwarm")
grs_state = get_grs_state()
sts = sts_ts[self.t]
sts_e1 = get_sts_expr(self.ts, self.e1, sts)
sts_e2 = get_sts_expr(self.ts, self.e2, sts)
grs_e1 = reduce(add, [grs_state[st] for st in sts_e1])
grs_e2 = reduce(add, [grs_state[st] for st in sts_e2])
gr_e1 = np.mean(reduce(add, [grs_state[st] for st in sts_e1]))
gr_e2 = np.mean(reduce(add, [grs_state[st] for st in sts_e2]))
h_grs, l_grs = [e for e, gr in sorted([(grs_e1, gr_e1), (grs_e2, gr_e2)],
key=snd, reverse=True)]
he, le = [e for e, gr in sorted([(self.e1, gr_e1), (self.e2, gr_e2)],
key=snd, reverse=True)]
fig = plt.figure(figsize=(3.5, 3.7))
ax = fig.add_subplot('111')
ax.set_xlim(-0.05, 0.18)
sns.distplot(h_grs, bins=10, color=cls[-1], kde=True, ax=ax,
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