def get_figure(ax, file_df, gradient_df, strain_des):
time = 48.0
location = "center"
strain_map, des_strain_map = strainmap.load()
print(des_strain_map)
# stp, width = 5, 1
# wt_sigby jlb021
# delru_sigby jlb088
# delqp_sigby jlb039
# 2xqp_sigby jlb095
# delsigb_sigby jlb098
# fig.figimage(skimage.io.imread("10x_delqp_48_image_crop.jpg"))
for c, (strain) in enumerate(strain_des): # , ,"delsigb_sigby"]
fids = file_df[(file_df["time"] == time)
& (file_df["location"] == location)
& (file_df["strain"] == des_strain_map[strain])].index
print(strain, " has ", len(fids))
df = gradient_df[gradient_df["file_id"].isin(fids)]
print(file_df.loc[fids, "name"])
df = df[df["cdist"] > 2.0] # ignore top 2um for consistency
df[["cdist", "ratio",
"file_id"]].to_csv(f"source_data/figure8_b_{strain}.tsv", sep="\t")
df_mean = df.groupby("cdist").mean()
df_sem = df.groupby("cdist").sem()
# df_mean[df]
print(len(df))
color = figure_util.strain_color[des_strain_map[strain].upper()]
label = figure_util.strain_label[des_strain_map[strain].upper()]
ax.plot(df_mean.index, df_mean["ratio"], color=color,
label=label) # /df["mean_red"])
ax.fill_between(
df_mean.index,
df_mean["ratio"] - df_sem["ratio"],
df_mean["ratio"] + df_sem["ratio"],
color=color,
alpha=0.4,
) # /df["mean_red"])
# ax[c].plot(df_mean.index, df_mean["ratio"], color="purple")#/df["mean_red"])
# ax[c].fill_between(df_mean.index, df_mean["ratio"]-df_sem["ratio"],df_mean["ratio"]+df_sem["ratio"],color="purple", alpha=0.4 )#/df["mean_red"])
# ax[c].set_xlim(left=0, right=200)
# leg = ax.legend(loc="upper right")
# leg.get_frame().set_alpha(1.0)
# ax.set_ylim(0, 0.2700)
ax.set_ylim(0, 0.4500)
ax.set_xlim(left=0, right=150)
# ax.set_xlabel("Distance from air interface (μm)")
ax.set_ylabel("Ratio of P$_{sigB}$-YFP to P$_{sigA}$-RFP")
# ax.text(-0.05, letter_lab[1], "D", transform=ax.transAxes, fontsize=8)
return ax
import matplotlib.pyplot as plt
import matplotlib.gridspec as gs
import pandas as pd
import skimage.io
import sys
print(sys.path)
import lib.strainmap as strainmap
#from figure_util import dpi
import lib.figure_util as figure_util
figure_util.apply_style()
strain_map, des_strain_map = strainmap.load()
letter_lab = (-0.08, 1.0)
fig = plt.figure()
grid = gs.GridSpec(2, 3, height_ratios=[3, 1])
grid.update(left=0.1, right=0.98, bottom=0.1, top=0.99, hspace=0.04)
#grid.update(hspace=0.05)
this_dir = os.path.dirname(os.path.realpath(__file__))
strains = [figure_util.strain_label[s] for s in ["JLB021", "JLB088", "JLB039"]]
biofilm_images = [
"SigB_48hrs_center_1_1_100615_sect.jpg", "delRU_48hrs_3_6_100615sect.jpg",
"delQP_48hrs_2_5_100615_sect.jpg"
]
def main():
basedir = os.path.join(this_dir, "../../datasets/LSM700_63x_sigb")
if not USE_CACHE_PLOTS:
cell_df = pd.read_hdf(os.path.join(basedir, "single_cell_data.h5"),
"cells")
file_df = filedb.get_filedb(os.path.join(basedir, "file_list.tsv"))
else:
file_df = None
cell_df = None
time = 48
location = "center"
slice_srt, slice_end = 5, 7 #10, 15
#slice_srt, slice_end = 5, 6 #10, 15 #
# There is no major difference between 5-6 and 7-8, just the QP skew is bigger in 5-6
#slice_srt, slice_end = 7, 8 #10, 15
# Moving to 2um because it makes the plots look nicer.
# fig, ax = plt.subplots(4, 2)
# axhisto = ax[:, 1]
# aximage = ax[:, 0]
fig, ax = plt.subplots(2, 4)
axhisto = ax[1, :]
aximage = ax[0, :]
for i, (name, path, roi, chans) in enumerate(image_list):
impath = os.path.join(image_base_dir, path)
aximage[i] = subfig_draw_bin.get_figure(aximage[i],
name,
impath,
roi,
chans,
FP_max_min,
(slice_srt, slice_end),
add_scale_bar=i == 0)
aximage[i].set_title("")
aximage[i].text(imgletter_lab[0],
imgletter_lab[1],
topletters[i],
transform=aximage[i].transAxes,
**letter_settings,
color="white")
text_x = 0.40
text_top = 0.85
line_sep = 0.15
title_loc, cv_loc, samp_loc, cell_loc = [
(text_x, text_top - (line_sep * i)) for i in range(4)
]
strain_map, des_strain_map = strainmap.load()
gchan = "green_raw_bg_meannorm"
rchan = "red_raw_bg_meannorm"
if not USE_CACHE_PLOTS:
cell_df = cell_df[cell_df[rchan] > 0].copy()
#max_val = 30000
#max_val = 1.0 #6.0 #20000
#gmax_val = 1.0 #7.5
max_val = 6.5 #2.5
gmax_val = 6.5 #0.75
nbins = 150
rbins = (0, max_val, nbins)
gbins = (0, gmax_val, nbins)
percentile = 0 #99
list_of_histos = [("wt_sigar_sigby", rchan, "WT P$_{sigA}$-RFP",
figure_util.strain_color["JLB021"]),
("wt_sigar_sigby", gchan, "WT P$_{sigB}$-YFP",
figure_util.strain_color["JLB021"]),
("delqp_sigar_sigby", gchan, "ΔrsbQP P$_{sigB}$-YFP",
figure_util.strain_color["JLB039"]),
("delru_sigar_sigby", gchan, "ΔrsbRU P$_{sigB}$-YFP",
figure_util.strain_color["JLB088"])]
print("-----------")
for i, (strain, chan, label, color) in enumerate(list_of_histos):
print(label)
strain_df = None
if not USE_CACHE_PLOTS:
fids = file_df[(file_df["time"] == time)
& (file_df["location"] == location) &
(file_df["strain"] == des_strain_map[strain])].index
strain_df = cell_df[cell_df["global_file_id"].isin(fids)]
dset = time, location, strain
plot_args = {"color": color, "max_min": "std", "mode_mean": False}
tbins = gbins
if "red" in chan:
tbins = rbins
args = (axhisto[i], strain_df, chan, tbins, (slice_srt, slice_end),
dset, percentile, USE_CACHE_PLOTS, this_dir, plot_args)
axhisto[i], _, meandmed = subfig_indivfile_histo.get_figure(*args)
axhisto[i].text(1.0,
hisletter_lab[1],
label,
horizontalalignment='right',
verticalalignment='top',
color="black",
fontsize=plt.rcParams["axes.titlesize"],
transform=axhisto[i].transAxes)
axhisto[i].text(hisletter_lab[0],
hisletter_lab[1],
letters[i],
transform=axhisto[i].transAxes,
**letter_settings)
#leg = axhisto[0].legend(loc="center right")
#axhisto[-1].set_xlabel("Mean normalised cell fluorecence")
axhisto[0].set_ylabel("Percentage of cells")
axhisto[0].set_xlim(0, max_val)
for a in np.ravel(axhisto):
#a.set_ylabel("Percentage of cells")
a.set_xlabel("Mean normalised cell fluorecence")
a.set_ylim(0, 5)
a.set_xlim(0, gmax_val)
a.tick_params(axis='x', which='both',
direction='out') #, length=2, pad=0)
a.tick_params(axis='y', which='both',
direction='out') #, length=2, pad=0)
# for a in axhisto[:-1]:
# a.set_xticklabels([])
for a in axhisto[1:]:
a.set_yticklabels([])
filename = "demo_longtail"
#fig.subplots_adjust(left=000, ri0ht=0.98, top = 1.0, bottom=0.06, hspace=0.08, wspace=0.2)
#width, height = figure_util.get_figsize(figure_util.fig_width_small_pt, wf=1.0, hf=1.7)
fig.subplots_adjust(left=0.05,
right=0.99,
top=1.0,
bottom=0.10,
hspace=0.08,
wspace=0.15)
width, height = figure_util.get_figsize(figure_util.fig_width_big_pt,
wf=1.0,
hf=0.5)
fig.set_size_inches(width, height)
figure_util.save_figures(fig, filename, ["png", "pdf"], this_dir)
def main():
curve_score_methods = {
"std": ("Standard Deviation", 2.5, lambda d, h, b: np.std(d)),
"mean": ("Mean", 4.0, lambda d, h, b: np.mean(d)),
"cv": ("Coefficient of variation", 1.2,
lambda d, h, b: scipy.stats.variation(d)),
"skew": ("modern skew", 3.5, lambda d, h, b: scipy.stats.skew(d)),
"skew_normed":
("Skew", 4.0, lambda d, h, b: scipy.stats.skew(d, bias=False)),
"mode": ("Mode", 3.5, lambda d, h, b: b[h.argmax()]),
"num": ("# cells", 2000, lambda d, h, b: len(d)),
"pearson_mode_mean":
("pearson Mode mean", 1.2, pearson_mode_mean_skew),
"non_parameteric_skew": ("Non parameteric", 0.4, non_parametric_skew),
"kurtosis": ("Kurtosis", 8.0, lambda d, h, b: scipy.stats.kurtosis(d))
}
plot_colors = [ #"mean",
"std",
#"cv",
#"skew",
#"num",
"skew_normed",
#"pearson_mode_mean",
#"non_parameteric_skew",
#"kurtosis",
]
basedir = "../../datasets/biofilm_cryoslice/LSM700_63x_sigb"
#cell_df = pd.read_hdf(os.path.join(basedir, "new_edge_bgsubv2_maxnorm_lh1segment.h5"), "cells")
#cell_df = pd.read_hdf(os.path.join(basedir, "new_edge_bgsubv2_maxnorm_lh1segment.h5"), "cells")
#cell_df = pd.read_hdf(os.path.join(basedir, "mini_bgsubv2_maxnorm_comp5.h5"), "cells")
cell_df = pd.read_hdf(os.path.join(basedir, "bgsubv2_maxnorm_comp5.h5"),
"cells")
cell_df = cell_df[cell_df["red_bg_maxnorm"] > 0]
cell_df = cell_df[cell_df["distance"] > 2]
time = 48 #.0
location = "center"
file_df = filedb.get_filedb(os.path.join(basedir, "file_list.tsv"))
strain_map, des_strain_map = strainmap.load()
percentile = 0 #99#
gmax = None
rmax = None
gmax = 0.7
rmax = 3.0
strains = [("wt_sigar_sigby", "red_bg_maxnorm", rmax),
("wt_sigar_sigby", "green_bg_maxnorm", gmax),
("delqp_sigar_sigby", "green_bg_maxnorm", gmax),
("delru_sigar_sigby", "green_bg_maxnorm", gmax),
("2xqp_sigar_sigby", "green_bg_maxnorm", gmax)]
fig, ax = plt.subplots(len(plot_colors),
len(strains),
sharex=True,
sharey=True)
for c, (strain, chan, maxv) in enumerate(strains):
strain_num = des_strain_map[strain]
distances, sbins, histograms, stats = get_strain_result(
file_df, cell_df, time, location, strain_num, chan, maxv,
percentile, curve_score_methods)
for r, k in enumerate(plot_colors):
color = figure_util.strain_color[strain_num.upper()]
ax[r, c], mx, mv = plot_curves(ax[r, c], color, distances, sbins,
histograms, stats, k)
if c == len(strains) - 1:
posn = ax[r, c].get_position()
cbax = fig.add_axes(
[posn.x0 + posn.width + 0.01, posn.y0, 0.02, posn.height])
max_val = curve_score_methods[k][1]
label = curve_score_methods[k][0]
sm = plt.cm.ScalarMappable(cmap=plt.cm.plasma,
norm=plt.Normalize(vmin=0,
vmax=max_val))
sm._A = []
plt.colorbar(sm, cax=cbax) #, fig=fig)
cbax.set_ylabel(label, rotation=-90, labelpad=8)
#max_val = m
#metric_name = n
# ax[0].set_title("WT P$_{sigA}$-RFP")
# ax[1].set_title("WT P$_{sigB}$-YFP")
# ax[2].set_title("ΔrsbQP P$_{sigB}$-YFP")
# ax[3].set_title("ΔrsbRU P$_{sigB}$-YFP")
# for a in ax[1:]:
# a.set_ylabel("")
# ax[0].set_ylabel("Distance from air interface (μm)")
plt.show()
#dataset_dir = "datasets/LSM780_10x_sigb/"
dataset_dir = "/media/nmurphy/BF_Data_Orange/datasets/lsm700_live20x_newstrain1"
#gradient_df = pd.read_hdf(dataset_dir + "gradient_data.h5", "data")
gradient_df = pd.read_hdf(
os.path.join(dataset_dir, "gradient_data_distmap.h5"), "data")
#gradient_df["ratio"] = gradient_df["green_bg_mean"]/gradient_df["red_bg_mean"]
gradient_df[
"ratio"] = gradient_df["green_raw_mean"] / gradient_df["red_raw_mean"]
output_dir = os.path.join(dataset_dir, "gradient_summary")
file_df = filedb.get_filedb(os.path.join(dataset_dir, "file_list.tsv"))
time = 48.0
#strain_map, des_strain_map = strainmap.load()
strain_to_type, type_to_strain = strainmap.load()
cell_types = np.unique([t[0] for t in strain_to_type.values()])
strain_to_type = {s: t[0] for s, t in strain_to_type.items()}
type_to_strain = dict(zip(cell_types, [[]] * len(cell_types)))
for strain, typel in strain_to_type.items():
type_to_strain[typel] = type_to_strain[typel] + [strain]
def get_strain(name):
fdf = file_df[(file_df["time"] == time)
& (file_df["strain"].isin(type_to_strain[name]))]
fids = fdf.index
#print(fdf.columns)
e = len((fdf["strain"] + fdf["dirname"]).unique())
print(name, " has ", len(fids), "images from ", e, "experiments")
print(fdf[["dirname", "name"]])
def main():
curve_score_methods = {
"std":
("Standard deviation", (0.0, 1.0), lambda d, h, b: np.std(d)), # 1.5,
"mean": ("Mean", (0.0, 4.0), lambda d, h, b: np.mean(d)),
"cv": (
"Coefficient of variation",
(0.3, 0.8),
lambda d, h, b: scipy.stats.variation(d),
),
# "skew": ("modern skew",
# 0.0, 3.0,
# lambda d, h, b: scipy.stats.skew(d)),
"skew_normed": (
"Skew",
(0.0, 2.9),
lambda d, h, b: scipy.stats.skew(d, bias=False),
),
# "mode": ("Mode",
# 0.0, 3.5,
# lambda d, h, b: b[h.argmax()]),
# "num": ("# cells",
# 0.0, 2000,
# lambda d, h, b: len(d)),
# "pearson_mode_mean": ("pearson Mode mean",
# 0.0, 1.2,
# joy_plots_of_gradients.pearson_mode_mean_skew),
# "non_parameteric_skew": ("Non parameteric",
# 0.0, 0.4,
# joy_plots_of_gradients.non_parametric_skew),
"kurtosis":
("Kurtosis", (0.0, 8.0), lambda d, h, b: scipy.stats.kurtosis(d)),
}
plot_colors = [ # "mean",
# "std",
"cv",
# "skew",
# "num",
# "skew_normed",
"skew_normed", # same as pandas
# "pearson_mode_mean",
# "non_parameteric_skew",
# "kurtosis",
]
# basedir = "../../data/bio_film_data/63xdatasets"
#this_dir = os.path.dirname(__file__)
this_dir = "/media/nmurphy/BF_Data_Orange/"
#basedir = os.path.join(this_dir, "../../datasets/LSM700_63x_sigb")
basedir = os.path.join(this_dir, "datasets/LSM700_63x_sigb")
# cell_df = pd.read_hdf(os.path.join(basedir, "edge_redo_lh1segment_data_bg_back_bleed.h5"), "cells")
# cell_df = pd.read_hdf(os.path.join(basedir, "new_edge_bgsubv2_maxnorm_lh1segment.h5"), "cells")
cell_df = pd.read_hdf(os.path.join(basedir, "single_cell_data.h5"),
"cells")
# cell_df = pd.read_hdf(os.path.join(basedir, "edge_redo_lh1segment_data.h5"), "cells")
# cell_df = pd.read_hdf(os.path.join(basedir, "lh1segment_bgsub_data.h5"), "cells")
# cell_df = cell_df[cell_df["area"] > 140]
cell_df = cell_df[cell_df["distance"] > 2]
time = 48 # .0
location = "center"
file_df = filedb.get_filedb(os.path.join(basedir, "file_list.tsv"))
strain_map, des_strain_map = strainmap.load()
# cbar_mins = {0: 0.5, 1:0.0}
percentile = 0 # 99#
# green_chan = "meannorm_green"
# red_chan = "meannorm_red"
rmax = 6.5
gmax = 6.5 # 0.4
green_chan = "green_raw_bg_mean"
red_chan = "red_raw_bg_mean"
rmax = 50000
gmax = 10000
strains = [
("wt_sigar_sigby", red_chan, rmax, "WT\n P$_{sigA}$-RFP"),
("wt_sigar_sigby", green_chan, gmax, "WT\n P$_{\mathit{sigB}}$-YFP"),
(
"delru_sigar_sigby",
green_chan,
gmax,
"Δ$\mathit{rsbRU}$\n P$_{\mathit{sigB}}$-YFP",
),
(
"delqp_sigar_sigby",
green_chan,
gmax,
"Δ$\mathit{rsbQP}$\n P$_{\mathit{sigB}}$-YFP",
),
]
# ("2xqp_sigar_sigby", green_chan, gmax, "2$\\times$rsbQP\n P$_{sigB}$-YFP")]
fig, ax = plt.subplots(len(plot_colors), len(strains), sharey=True)
for c, (strain, chan, max_val, name) in enumerate(strains):
strain_num = des_strain_map[strain]
distances, sbins, histograms, stats = joy_plots_of_gradients.get_strain_result(
file_df,
cell_df,
time,
location,
strain_num,
chan,
max_val,
percentile,
curve_score_methods,
)
for r, k in enumerate(plot_colors):
color = figure_util.strain_color[strain_num.upper()]
ax[r, c], mv, leglist = joy_plots_of_gradients.plot_curves(
ax[r, c], color, distances, sbins, histograms, stats, k)
if c == len(strains) - 1:
posn = ax[r, c].get_position()
cbax = fig.add_axes([
posn.x0 + posn.width + 0.0005, posn.y0, 0.015, posn.height
])
label = curve_score_methods[k][0]
min_zval = curve_score_methods[k][1][0]
max_zval = curve_score_methods[k][1][1]
sm = plt.cm.ScalarMappable(
cmap=plt.get_cmap("viridis"),
norm=plt.Normalize(vmin=min_zval, vmax=max_zval),
)
sm._A = []
_ = plt.colorbar(sm, cax=cbax) # , fig=fig)
cbax.set_ylabel(label, rotation=-90, labelpad=8)
cbax.tick_params(direction="out")
if r == 0:
ax[r, c].set_title(name, fontsize=6)
ax[r, c].get_xaxis().set_ticklabels([])
ax[r, c].set_xlim(0, max_val)
# this didnt return the right mode for some reason
# leg = ax[0, -1].legend(leglist)
leg = ax[0, -1].legend(leglist, ["Mode", "Mean"],
loc="lower left",
bbox_to_anchor=(0.84, 0.97))
leg.set_zorder(400)
for a in ax.flatten():
a.tick_params(direction="out")
ax[0, 0].annotate(
"Distance from top of biofilm (μm)",
xy=(0, 0),
xytext=(0.02, 0.5),
textcoords="figure fraction",
# arrowprops=dict(facecolor='black', shrink=0.05),
horizontalalignment="center",
verticalalignment="center",
fontsize="medium",
color=mpl.rcParams["axes.labelcolor"],
rotation=90,
)
ax[1, 2].annotate(
"Normalized fluoresence",
xy=(0, 0),
xytext=(0.5, 0.04),
textcoords="figure fraction",
# arrowprops=dict(facecolor='black', shrink=0.05),
horizontalalignment="center",
verticalalignment="center",
fontsize="medium",
color=mpl.rcParams["axes.labelcolor"],
)
# for a in ax[:, 0].flatten():
# ticklabs = a.yaxis.get_ticklabels()
# ticklabs = a.get_yticks()#.tolist()
# ticklabs[-1] = ''
letters = ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J"]
# letter_lab = (-0.13, 0.98)
for a, l in zip(ax.flatten(), letters):
a.annotate(
l,
xy=(0, 0),
xytext=(-0.13, 0.95),
textcoords="axes fraction",
# arrowprops=dict(facecolor='black', shrink=0.05),
horizontalalignment="center",
verticalalignment="center",
fontsize=figure_util.letter_font_size,
color="black",
)
# a.text(letter_lab[0], letter_lab[1], l, transform=a.transAxes, fontsize=8)
filename = "sup_meta_histo"
width, height = figure_util.get_figsize(figure_util.fig_width_medium_pt,
wf=1.0,
hf=0.6)
fig.subplots_adjust(left=0.085,
right=0.89,
top=0.89,
bottom=0.13,
hspace=0.20,
wspace=0.25)
fig.set_size_inches(width, height) # common.cm2inch(width, height))
figure_util.save_figures(fig, filename, ["png", "pdf"], this_dir)
def main():
basedir = os.path.join(this_dir, "../../datasets/LSM700_63x_sigb")
time = 48
location = "center"
# slice_srt, slice_end = 5, 7 #10, 15
slice_srt_end = (5, 7)
fig, ax = plt.subplots(2, 2)
axhisto = ax[1, 1]
aximage = [ax[0, 0], ax[0, 1], ax[1, 0]]
for i, (name, path, roi, chans) in enumerate(image_list):
impath = os.path.join(image_base_dir, path)
aximage[i] = subfig_draw_bin.get_figure(
aximage[i],
name,
impath,
roi,
chans,
FP_max_min,
slice_srt_end,
add_scale_bar=i == 0,
)
aximage[i].set_title("")
aximage[i].text(imgletter_lab[0],
imgletter_lab[1],
topletters[i],
transform=aximage[i].transAxes,
**letter_settings) # , color="white")
aximage[i].text(0.05,
0.05,
name,
transform=aximage[i].transAxes,
**label_settings,
color="white")
#####################
## Histograms
generate_data_subset = False
strain_map, des_strain_map = strainmap.load()
file_df = filedb.get_filedb(os.path.join(basedir, "file_list.tsv"))
cachedpath = os.path.join(basedir, "histo_tops_normed.h5")
gchan = "green_raw_bg_mean"
rchan = "red_raw_bg_mean"
nbins = 150
gmax = 1
gbins = np.linspace(0, gmax, nbins)
list_of_histos = [
# ("2xqp_sigar_sigby", gchan, rchan, gbins, slice_srt_end, "2xQP", strain_color["JLB095"]),
(
"wt_sigar_sigby",
gchan,
rchan,
gbins,
slice_srt_end,
r"WT P$_{\mathit{sigB}}$-YFP",
strain_color["JLB021"],
),
(
"delru_sigar_sigby",
gchan,
rchan,
gbins,
slice_srt_end,
r"Δ$\mathit{rsbRU}$ P$_{\mathit{sigB}}$-YFP",
strain_color["JLB088"],
),
(
"delqp_sigar_sigby",
gchan,
rchan,
gbins,
slice_srt_end,
r"Δ$\mathit{rsbQP}$ P$_{\mathit{sigB}}$-YFP",
strain_color["JLB039"],
),
]
axes = [axhisto] * len(list_of_histos)
if generate_data_subset:
df = pd.read_hdf(os.path.join(basedir, "single_cell_data.h5"), "cells")
cellsdf = subfig_normalised_histos.get_data_subset(
df, file_df, list_of_histos, time, location, cachedpath)
else:
cellsdf = pd.read_hdf(cachedpath, "cells")
axes = subfig_normalised_histos.get_figure(cellsdf, file_df, axes, time,
location, list_of_histos)
axes[0].legend()
axhisto.text(hisletter_lab[0],
hisletter_lab[1],
letters[0],
transform=axhisto.transAxes,
**letter_settings)
axhisto.set_ylabel("Percentage of cells")
axhisto.set_xlim(0, gmax)
axhisto.set_ylim(0, 8.5)
axhisto.tick_params(axis="x", which="both",
direction="out") # , length=2, pad=0)
axhisto.tick_params(axis="y", which="both",
direction="out") # , length=2, pad=0)
axhisto.yaxis.set_major_locator(mticker.MaxNLocator(nbins=3, integer=True))
axhisto.set_xlabel("Normalised cell fluorecence")
filename = "demo_longtail"
fig.subplots_adjust(left=0.05,
right=0.95,
top=0.99,
bottom=0.1,
hspace=0.08,
wspace=0.20)
width, height = figure_util.get_figsize(figure_util.fig_width_small_pt,
wf=1.0,
hf=0.9)
fig.set_size_inches(width, height)
figure_util.save_figures(fig, filename, ["png", "pdf"], this_dir)
def main():
this_dir = os.path.dirname(__file__)
basedir = os.path.join(this_dir, "../../datasets/LSM700_63x_sigb")
#cell_df = pd.read_hdf(os.path.join(basedir, "edge_redo_lh1segment_data_bg_back_bleed.h5"), "cells")
cell_df = pd.read_hdf(os.path.join(basedir, "single_cell_data.h5"), "cells")
print(cell_df.columns)
file_df = filedb.get_filedb(os.path.join(basedir, "file_list.tsv"))
file_df.loc[file_df["time"] == 26.0, ['time']] = 24.0
file_df.loc[file_df["time"] == 38.0, ['time']] = 36.0
USE_CACHE_PLOTS = False
time = 48
location = "center"
slice_srt, slice_end = 5, 7
fig, axhisto = plt.subplots(1, 1)
strain_map, des_strain_map = strainmap.load()
gchan = "green_raw_bg_autofluor_bleedthrough_meannorm"
gmax_val = 20
nbins=150
gbins = (0, gmax_val, nbins)
percentile = 0
list_of_histos = [
("wt_sigar_sigby", gchan, "WT P$_{sigB}$-YFP", strain_color["JLB021"]),
("delqp_sigar_sigby", gchan, "ΔrsbQP P$_{sigB}$-YFP", strain_color["JLB039"]),
("delru_sigar_sigby", gchan, "ΔrsbRU P$_{sigB}$-YFP", strain_color["JLB088"]),
("2xqp_sigar_sigby", gchan, "2$\\times$rsbQP P$_{sigB}$-YFP", strain_color["JLB095"]),
]
print("-----------")
lelines = []
lelabs = []
for i, (strain, chan, label, color) in enumerate(list_of_histos):
print(label)
fids = file_df[(file_df["time"] == time) &
(file_df["location"] == location) &
(file_df["strain"] == des_strain_map[strain])].index
strain_df = cell_df[cell_df["global_file_id"].isin(fids)]
#strain_df = get_strain(file_df, cell_df, strain)
plot_args = {"color":color, "max_min":"none", "mode_mean":False}
tbins = gbins
dset = time, location, strain
args = (axhisto, strain_df, chan, tbins, (slice_srt, slice_end), dset, percentile, USE_CACHE_PLOTS, this_dir, plot_args)
axhisto, line, _ = subfig_indivfile_histo.get_figure(*args)
lelines += [line]
lelabs += [label]
axhisto.legend(lelines, lelabs)
axhisto.set_xlabel("Normalised cell fluorecence (bleed through subtracted)")
axhisto.set_ylabel("Percentage of cells")
axhisto.set_ylim(0, 7)
axhisto.set_xlim(0, gmax_val)
filename = "sup_bleed_histo"
fig.subplots_adjust(left=0.1, right=0.9, top = 0.98, bottom=0.2)#, hspace=0.35, wspace=0.2)
width, height = figure_util.get_figsize(figure_util.fig_width_small_pt, wf=1.0, hf=0.5)
fig.set_size_inches(width, height)# common.cm2inch(width, height))
figure_util.save_figures(fig, filename, ["png", "pdf"], this_dir)