('GFP-A', 1, 'r'),\
('GFP-B', 2, 'g'),\
('GFP-C', 3, 'c'),\
('GFP-D', 4, 'c:'),\
('BLANK', 5, 'b')]
pp = PdfPages('../res/2010-07-11_lior.pdf')
rows = [('-IPTG', [0,1], 30), ('+IPTG', [3,4], 30)]
for i in range(len(rows)):
fig = figure()
(name, r_list, max_T) = rows[i]
for j in range(len(r_list)):
for k in range(len(columns)):
(t, od) = vp.get_data(0, r_list[j], columns[k][1])
(t, gfp) = vp.get_data(1, r_list[j], columns[k][1])
plot(t, gfp/od, columns[k][2])
legend_text = []
for k in range(len(columns)):
(leg, r, color) = columns[k]
legend_text.append("%s" % (leg))
legend(legend_text, loc='upper right')
title(name)
xlabel('Time (hr)')
ylabel('GFP/OD')
yscale('log')
#axis([0, max_T, 5e3, 1e5])
pp.savefig(fig)
rcParams["legend.fontsize"] = 6
rcParams["font.family"] = "sans-serif"
rcParams["font.size"] = 8
rcParams["lines.linewidth"] = 0.6
rcParams["lines.markersize"] = 2
fit_window_size = 1.5 # hours
fit_start_threshold = 0.01
leg = ["SOC+kana", "M9+CAS+Glu+kana", "M9+AA+Nt+Glu+kana", "M9+Glu+kana", "M9+Acetate+kana"]
color = ["g", "r", "b", "m", "c"]
gr_mat = zeros((5, 10))
subplot(2, 1, 1)
for c in range(0, 5):
for r in range(5):
(t, v) = vp.get_data(0, r, c)
plot(t, v - min(v), color[r])
(gr, err) = vp.fit_growth(t, v, fit_window_size, fit_start_threshold)
gr_mat[r, c] = gr
legend([leg[r] + " (%.0f min)" % (log(2) * 60 / mean(gr_mat[r, 0:5])) for r in range(5)], loc="lower right")
title("PRK")
xlabel("Time (hr)")
ylabel("OD")
yscale("log")
axis([0, t.max(), 0.001, 1])
subplot(2, 1, 2)
for c in range(5, 10):
for r in range(5):
(t, v) = vp.get_data(0, r, c)
rows = [('-IPTG (1)', 0, 'r'),\
('+IPTG (1)', 2, 'g'),\
('-IPTG (2)', 1, 'r--'),\
('+IPTG (2)', 3, 'g--')]
for (plot_index, plot_label, y_min, y_max) in plot_types:
sys.stderr.write("Plotting " + plot_label + " ... \n")
for i in range(len(columns)):
fig = figure()
(name, c_list, max_T) = columns[i]
gr_mat = zeros((len(c_list), len(rows)))
for j in range(len(c_list)):
for k in range(len(rows)):
if (plot_index == "divide"):
(time0, values0) = vp.get_data(0, rows[k][1], c_list[j])
(time1, values1) = vp.get_data(1, rows[k][1], c_list[j])
time = time0
values = values1/values0
else:
(time, values) = vp.get_data(plot_index, rows[k][1], c_list[j])
plot(time, values, rows[k][2])
gr_mat[j, k] = vp.fit_growth(time, values, fit_window_size, fit_start_threshold)
legend_text = []
for k in range(len(rows)):
(leg, r, color) = rows[k]
max_dt = log(2)*60/min(gr_mat[:,r])
min_dt = log(2)*60/max(gr_mat[:,r])
legend_text.append("%s (%.0f-%0.f min)" % (leg, min_dt, max_dt))
legend(legend_text, loc='lower right')
"""
time - a vector of the time of each measurement (in hours)
cell_count - a vector of the cell-count at each time point
window - the size of the window for which to calculate the growth rates (in hours)
start_time - the time point after which the input data can be trusted
Note: the cell_count can be any measure that is proportional to the
number of cells. For example, Optical Density (in OD), or level of
luminescence (in CPS).
"""
from victor_parser import VictorParser
import pylab
if (__name__ == "__main__"):
vp = VictorParser()
fname = "../data/Elad's OD600_20100701_188.xls"
vp.parse_excel(fname)
(time, cell_count) = vp.get_data(0, 3, 0)
pylab.figure()
pylab.subplot(1,2,1)
print "Logistic fit: gr = %.2f" % vp.fit_growth2(time, cell_count, plot_figure=True)
pylab.subplot(1,2,2)
print "linear fit in logscale: gr = %.2f" % vp.fit_growth(time, cell_count, window_size=1.5, start_threshold=0.01, plot_figure=True)
pylab.show()
(row_labels[r], "divide", (0, t_max), (1e2, 1e7), 'Limun/OD', y_label))
for (plot_title, victor_index, t_range, y_range, y_label,
data_series) in plots:
sys.stderr.write("Plotting %s (%s) ... \n" %
(plot_title, str(victor_index)))
fig = figure()
title(plot_title)
xlabel('Time (hr)')
ylabel(y_label)
legend_text = []
for (label, (color, cells)) in data_series.iteritems():
for (row, col) in cells:
if (victor_index == "divide"):
(time0, values0) = vp.get_data(0, row, col)
(time1, values1) = vp.get_data(1, row, col)
time = time0
values = values1 / values0
else:
(time, values) = vp.get_data(victor_index, row, col)
plot(time, values, color)
growth_rate = vp.fit_growth(time, values, fit_window_size,
fit_start_threshold)
legend_text.append("%s (%.0f min)" %
(label, log(2) * 60 / growth_rate))
legend(legend_text, loc='upper left')
yscale('log')
axis([t_range[0], t_range[1], y_range[0], y_range[1]])
pp.savefig(fig)
('AA', 4, 'c:'),\
('acetate', 5, 'b'),\
('glucose', 6, 'm'),\
('glucose stationary', 7, 'm:')]
pp = PdfPages('../res/2010-07-04.pdf')
columns = [('PRK', [0,1,2], 15), ('GFP', [3,4,5], 20), ('control', [6,7], 50)]
for i in range(len(columns)):
fig = figure()
(name, c_list, max_T) = columns[i]
gr_mat = zeros((len(c_list), len(rows)))
for j in range(len(c_list)):
for k in range(len(rows)):
(t, v) = vp.get_data(0, rows[k][1], c_list[j])
plot(t, v-min(v), rows[k][2])
gr_mat[j, k] = vp.fit_growth(t, v, fit_window_size, fit_start_threshold)
#gr_mat[j, k] = vp.fit_growth2(t, v)
legend_text = []
for k in range(len(rows)):
(leg, r, color) = rows[k]
max_dt = log(2)*60/min(gr_mat[:,r])
min_dt = log(2)*60/max(gr_mat[:,r])
legend_text.append("%s (%.0f-%0.f min)" % (leg, min_dt, max_dt))
legend(legend_text, loc='lower right')
title(name)
xlabel('Time (hr)')
ylabel('GFP')
yscale('log')
"""
time - a vector of the time of each measurement (in hours)
cell_count - a vector of the cell-count at each time point
window - the size of the window for which to calculate the growth rates (in hours)
start_time - the time point after which the input data can be trusted
Note: the cell_count can be any measure that is proportional to the
number of cells. For example, Optical Density (in OD), or level of
luminescence (in CPS).
"""
from victor_parser import VictorParser
import pylab
if (__name__ == "__main__"):
vp = VictorParser()
fname = "../data/Elad's OD600_20100701_188.xls"
vp.parse_excel(fname)
(time, cell_count) = vp.get_data(0, 3, 0)
pylab.figure()
pylab.subplot(1, 2, 1)
print "Logistic fit: gr = %.2f" % vp.fit_growth2(
time, cell_count, plot_figure=True)
pylab.subplot(1, 2, 2)
print "linear fit in logscale: gr = %.2f" % vp.fit_growth(
time,
cell_count,
window_size=1.5,
start_threshold=0.01,
plot_figure=True)
pylab.show()
rows = [('SOC', 0, 'y'),\
('casa', 1, 'r'),\
('ALL', 2, 'g'),\
('AA1', 3, 'c'),\
('AA2', 4, 'k'),\
('Gluc', 5, 'b'),\
('M9-', 6, 'm')]
for i in range(len(columns)):
fig = figure()
(name, c_list, max_T) = columns[i]
gr_mat = zeros((len(c_list), len(rows)))
for j in range(len(c_list)):
for k in range(len(rows)):
(t, v) = vp.get_data(0, rows[k][1], c_list[j])
plot(t, v-min(v), rows[k][2])
gr_mat[j, k] = vp.fit_growth(t, v, fit_window_size, fit_start_threshold)
#gr_mat[j, k] = vp.fit_growth2(t, v)
legend_text = []
for k in range(len(rows)):
(leg, r, color) = rows[k]
max_dt = log(2)*60/min(gr_mat[:,r])
min_dt = log(2)*60/max(gr_mat[:,r])
legend_text.append("%s (%.0f-%0.f min)" % (leg, min_dt, max_dt))
legend(legend_text, loc='lower right')
title(name)
xlabel('Time (hr)')
ylabel('GFP')
yscale('log')
rcParams['lines.linewidth'] = 0.6
rcParams['lines.markersize'] = 2
fit_window_size = 1.5 # hours
fit_start_threshold = 0.01
leg = [
'SOC+kana', 'M9+CAS+Glu+kana', 'M9+AA+Nt+Glu+kana', 'M9+Glu+kana',
'M9+Acetate+kana'
]
color = ['g', 'r', 'b', 'm', 'c']
gr_mat = zeros((5, 10))
subplot(2, 1, 1)
for c in range(0, 5):
for r in range(5):
(t, v) = vp.get_data(0, r, c)
plot(t, v - min(v), color[r])
(gr, err) = vp.fit_growth(t, v, fit_window_size, fit_start_threshold)
gr_mat[r, c] = gr
legend([
leg[r] + " (%.0f min)" % (log(2) * 60 / mean(gr_mat[r, 0:5]))
for r in range(5)
],
loc='lower right')
title('PRK')
xlabel('Time (hr)')
ylabel('OD')
yscale('log')
axis([0, t.max(), 0.001, 1])
subplot(2, 1, 2)
plots.append((row_labels[r], 0, (0, t_max), (3e-2, 7e-1), 'OD', y_label))
plots.append((row_labels[r], 1, (0, t_max), (1e1, 1e7), 'Lumin', y_label))
plots.append((row_labels[r], "divide", (0, t_max), (1e2, 1e7), 'Limun/OD', y_label))
for (plot_title, victor_index, t_range, y_range, y_label, data_series) in plots:
sys.stderr.write("Plotting %s (%s) ... \n" % (plot_title, str(victor_index)))
fig = figure()
title(plot_title)
xlabel('Time (hr)')
ylabel(y_label)
legend_text = []
for (label, (color, cells)) in data_series.iteritems():
for (row, col) in cells:
if (victor_index == "divide"):
(time0, values0) = vp.get_data(0, row, col)
(time1, values1) = vp.get_data(1, row, col)
time = time0
values = values1/values0
else:
(time, values) = vp.get_data(victor_index, row, col)
plot(time, values, color)
growth_rate = vp.fit_growth(time, values, fit_window_size, fit_start_threshold)
legend_text.append("%s (%.0f min)" % (label, log(2)*60/growth_rate))
legend(legend_text, loc='upper left')
yscale('log')
axis([t_range[0], t_range[1], y_range[0], y_range[1]])
pp.savefig(fig)
pp.close()
