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ctf_simulation.py
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"""
MIT License
Copyright (c) 2020-2023 Wen Jiang
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
def import_with_auto_install(packages, scope=locals()):
if isinstance(packages, str): packages=[packages]
for package in packages:
if package.find(":")!=-1:
package_import_name, package_pip_name = package.split(":")
else:
package_import_name, package_pip_name = package, package
try:
scope[package_import_name] = __import__(package_import_name)
except ImportError:
import subprocess
subprocess.call(f'pip install {package_pip_name}', shell=True)
scope[package_import_name] = __import__(package_import_name)
required_packages = "streamlit numpy scipy bokeh".split()
import_with_auto_install(required_packages)
import streamlit as st
import numpy as np
np.bool8 = bool # fix for bokeh 2.4.3
#from memory_profiler import profile
#@profile(precision=4)
def main():
title = "CTF Simulation"
st.set_page_config(page_title=title, layout="wide")
hosted, host = is_hosted(return_host=True)
if hosted and host in ['heroku']:
st.error(f"This app hosted on Heroku will be unavailable starting November 28, 2022 [when Heroku discontinues free hosting service](https://blog.heroku.com/next-chapter). Please switch to [the same app hosted elsewhere](https://jianglab-ctfsimulation-streamlit-app-of60di.streamlitapp.com)")
session_state = st.session_state
if "defocus_0" not in session_state: # only run once at the start of the session
st.elements.lib.policies._shown_default_value_warning = True
ctfs = parse_query_parameters()
set_session_state_from_ctfs(ctfs)
embedded = session_state.embedded
ctfs = get_ctfs_from_session_state()
st.title(session_state.title)
if embedded:
col_params, col_1d = st.columns((1, 5))
else:
col_params = st.sidebar
st.info(ctf_latex())
with col_params:
if embedded:
n = 1
else:
n = int(st.number_input('Number of CTFs', value=max(1, len(ctfs)), min_value=1, step=1))
if len(ctfs)==0:
ctfs = [ CTF() for i in range(n) ]
elif n>len(ctfs):
ctfs += [ ctfs[-1].copy() for i in range(n-len(ctfs)) ]
else:
ctfs = ctfs[:n]
set_session_state_from_ctfs(ctfs)
assert(n == len(ctfs))
show_marker_empties = []
apix_wrong_empties = []
defocus_wrong_empties = {} # only for |CTF|
ctf_intact_first_peak_empties = []
for i in range(n):
if n>1:
expander = st.expander(label=f"CTF {i+1}", expanded=True if i==n-1 else False)
else:
import contextlib
expander = contextlib.nullcontext()
with expander:
if not embedded:
options = ('CTF', '|CTF|', 'CTF^2')
st.radio(label='CTF type', options=options, index=0, horizontal=True, key=f"ctf_type_{i}")
st.number_input('defocus (µm)', value=st.session_state[f"defocus_{i}"], step=0.1, format="%.5g", help=f"{ctf_latex(colored_attrs=['defocus'])} \nPositive number for under-focus and negative number for over-focus. Scherzer defocus = {ctfs[i].scherzer_defocus(extended=False):.4f} µm. extended Scherzer defocus = {ctfs[i].scherzer_defocus():.4f} µm", key=f"defocus_{i}")
if embedded:
rotavg = False
else:
if ctfs[i].ctf_type=='|CTF|':
defocus_wrong_empties[i] = st.empty()
st.number_input('astigmatism mag (µm)', value=st.session_state[f"dfdiff_{i}"], min_value=0.0, step=0.01, format="%g", help=f"{ctf_latex(colored_attrs=['dfdiff'])} \nMagnitude of astimgatism (=maximal defocus - minimal defocus)", key=f"dfdiff_{i}")
if n==1 and ctfs[i].dfdiff:
value = ctfs[i].ctf_type == 2
rotavg = st.checkbox(label='plot rotational average', value=value, key="rotavg")
else:
rotavg = False
st.number_input('astigmatism angle (°)', value=st.session_state[f"dfang_{i}"], min_value=0.0, max_value=360., step=1.0, format="%g", help=f"{ctf_latex(colored_attrs=['dfang'])} \nMInplane angle of the maximal dofocus direction", key=f"dfang_{i}")
st.number_input('phase shift (°)', value=st.session_state[f"phaseshift_{i}"], min_value=0.0, max_value=360., step=1.0, format="%g", help=f"{ctf_latex(colored_attrs=['phaseshift'])} \nPhase shift from phase plate", key=f"phaseshift_{i}")
st.number_input('pixel size (Å/pixel)', value=st.session_state[f"apix_{i}"], min_value=0.1, step=0.01, format="%g", help=f"{ctf_latex(colored_attrs=['sampling'])} \nPixel size of the image. It determines the spatial frequency in the Fourier space. The spatial frequency at the edge of the Fourier transform (e.g. Nyquiest) is 1/(2*pixel size)", key=f"apix_{i}")
apix_wrong_empties.append(st.empty())
st.number_input('voltage (kV)', value=st.session_state[f"voltage_{i}"], min_value=10., step=100., format="%g", help=f"{ctf_latex(colored_attrs=['voltage'])} \n" + r"${\textcolor{red}{\lambda}}=\frac{h}{\sqrt{2m_0e{\textcolor{red}{V}}(1+\frac{e{\textcolor{red}{V}}}{2m_0c^2)})}}$" + " \nAccelerating voltage of the gun. It determines the electron wave length", key=f"voltage_{i}")
st.number_input('cs (mm)', value=st.session_state[f"cs_{i}"], min_value=-3.0, step=0.1, format="%g", help=f"{ctf_latex(colored_attrs=['cs'])} \nSpherical aberration coefficient", key=f"cs_{i}")
st.number_input('amplitude contrast (percent)', value=st.session_state[f"ampcontrast_{i}"], min_value=0.0, max_value=100., step=10.0, format="%g", help=f"{ctf_latex(colored_attrs=['ampcontrast'])} \nAmplitude contrast (0-100)", key=f"ampcontrast_{i}")
if not embedded:
st.number_input('image size (pixel)', value=int(st.session_state[f"imagesize_{i}"]), min_value=16, max_value=4096, step=4, key=f"imagesize_{i}")
st.slider('over-sample (1x, 2x, 3x, etc)', value=int(st.session_state[f"over_sample_{i}"]), min_value=1, max_value=6, step=1, help=f"{ctf_latex(colored_attrs=['sampling'])} \nIncrease the image size to n times of the original size. It will make the Fourier space pixel size n time finer to better sample the CTF oscillations", key=f"over_sample_{i}")
#with st.expander("envelope functions", expanded=False):
st.number_input('b-factor (Å^2)', value=st.session_state[f"bfactor_{i}"], min_value=0.0, step=10.0, format="%g", help=f"{ctf_latex(colored_attrs=['bfactor'])} \nEnvelope function", key=f"bfactor_{i}")
st.number_input('beam convergence semi-angle (mrad)', value=st.session_state[f"alpha_{i}"], min_value=0.0, step=0.05, format="%g", key=f"alpha_{i}")
dE = st.number_input('energy spread (eV)', value=st.session_state[f"dE_{i}"], min_value=0.0, step=0.2, format="%g", key=f"dE_{i}")
dI = st.number_input('objective lens current spread (ppm)', value=st.session_state[f"dI_{i}"], min_value=0.0, step=0.2, format="%g", key=f"dI_{i}")
if dE or dI:
st.number_input('cc (mm)', value=2.7, min_value=0.0, step=0.1, format="%g", key=f"cc_{i}")
st.number_input('sample vertical motion (Å)', value=st.session_state[f"dZ_{i}"], min_value=0.0, step=20.0, format="%g", key=f"dZ_{i}")
st.number_input('sample horizontal motion (Å)', value=st.session_state[f"dXY_{i}"], min_value=0.0, step=0.2, format="%g", key=f"dXY_{i}")
show_marker_empties.append(st.empty())
ctf_intact_first_peak_empties.append(st.empty())
if embedded:
show_1d = True
show_2d = False
show_psf = False
show_avg = False
plot_s2 = False
show_data = False
share_url = False
show_qr = False
env_only = False
simulate_wrong_apix = False
simulate_wrong_defocus = False
else:
value = int(st.session_state.get("show_1d", 1))
show_1d = st.checkbox('Show 1D CTF', value=value, key="show_1d")
value = int(st.session_state.get("show_2d", 0))
show_2d = st.checkbox('Show 2D CTF', value=value, key="show_2d")
if show_1d:
value = int(st.session_state.get("show_psf", 0))
show_psf = st.checkbox('Show point spread function', value=value, key="show_psf")
for i in range(n):
show_marker_empties[i].checkbox(label='Show markers on CTF line plots', key=f"show_marker_{i}")
ctf_intact_first_peak_empties[i].checkbox(label='Ignore CTFs until first peak?', help="Illustrate the meaning of Relion option 'Ignore CTFs until first peak?'", key=f"ctf_intact_first_peak_{i}")
simulate_wrong_apix = st.checkbox('Simulate effect of wrong pixel size', value=0, key="simulate_wrong_apix", help="while TEM magnification is highly reproducible, the absolute magnification is often insufficiently calibrated and it is not uncommon to have 1-2% errors. This option will allow you to simulate the effect of inaccurate magnification (and the pixel size based on the magnification) on CTF fitting: small pixel size errors can be sufficiently compensated by defocus but perfect compensation can only be achieved by changing both defocus and cs")
if simulate_wrong_apix:
for i in range(n):
apix_wrong_empties[i].number_input('wrong pixel size (Å/pixel)', min_value=0.0, step=0.01, format="%g", key=f"apix_wrong_{i}", help="if a wrong pixel size is used, the CTF curve can still be perfectedly fitted with another set of defocus and cs values: df*(apix_correct/apix_wrong)^2, cs*(apix_correct/apix_wrong)^4")
simulate_wrong_defocus = st.checkbox('Simulate effect of wrong defocus', value=0, key="simulate_wrong_defocus", help="Only used for |CTF| mode. The defocus could be inaccurate due to many reasons, for example, fitting error, astigmatism, sample tilt, thick ice, large particle, etc. Turn on this option to simulate the effect of wrong defocus on CTF phase correction")
if simulate_wrong_defocus:
abs_mode = False
for i in range(n):
if ctfs[i].ctf_type == '|CTF|':
abs_mode = True
break
if abs_mode:
for i in range(n):
if i not in defocus_wrong_empties: continue
defocus_wrong_empties[i].number_input('wrong defocus (µm)', step=0.1, format="%.5g", help=f"Positive number for under-focus and negative number for over-focus", key=f"defocus_wrong_{i}")
else:
st.warning(f'"Simulate effect of wrong defocus" only works for |CTF| mode')
show_data = st.checkbox('Show CTF raw data', value=False, key="show_data")
else:
show_psf = False
show_data = False
if show_2d:
value = int(st.session_state.get("simulate_ctf_effect", 0))
simulate_ctf_effect = st.checkbox('Simulate CTF effect on images', value=value, key="simulate_ctf_effect")
if simulate_ctf_effect:
simulate_ctf_effect_container = st.container()
if show_1d or show_2d:
value = int(st.session_state.get("plot_s2", 0))
plot_s2 = st.checkbox(label='Plot s^2 as x-axis/radius', value=value, key="plot_s2")
value = int(st.session_state.get("env_only", 0))
env_only = st.checkbox(label='Plot only envelope functions', value=value, key="env_only")
else:
plot_s2 = False
env_only = False
if n>1:
value = int(st.session_state.get("show_avg", 0))
show_avg = st.checkbox('Plot average CTF', value=value, key="show_avg")
else:
show_avg = 0
share_url = st.checkbox('Show sharable URL', value=False, help="Include relevant parameters in the browser URL to allow you to share the URL and reproduce the plots", key="share_url")
if share_url:
show_qr = st.checkbox('Show QR code of the URL', value=False, help="Display the QR code of the sharable URL", key="show_qr")
else:
show_qr = False
ctfs = get_ctfs_from_session_state()
ctf_labels = ctf_varying_parameter_labels(ctfs)
if not embedded:
if show_1d and show_2d:
col_1d, col_2d = st.tabs(["1-D", "2-D"])
else:
col_1d, _ = st.columns((1, 0.01))
col_2d = col_1d
if show_1d:
with col_1d:
from bokeh.plotting import figure
from bokeh.models import LegendItem
if plot_s2:
x_label = "s^2 (1/Å^2)"
hover_x_var = "s^2"
hover_x_val = "$x 1/Å^2"
else:
x_label = "s (1/Å)"
hover_x_var = "s"
hover_x_val = "$x 1/Å"
y_label = f"{' / '.join([ctf.ctf_type for ctf in ctfs])}"
tools = 'box_zoom,crosshair,hover,pan,reset,save,wheel_zoom'
hover_tips = [("Res", "@res Å"), (hover_x_var, hover_x_val), ("fval", "$y")]
if n>1 or (n==1 and ctfs[0].dfdiff):
hover_tips = [("CTF type", "@ctf_type"), ("Defocus", "@defocus µm")] + hover_tips
fig = figure(title="", x_axis_label=x_label, y_axis_label=y_label, tools=tools, tooltips=hover_tips)
fig.title.align = "center"
fig.title.text_font_size = "32px"
fig.xaxis.axis_label_text_font_size = "24pt"
fig.yaxis.axis_label_text_font_size = "24pt"
fig.xaxis.major_label_text_font_size = "16pt"
fig.yaxis.major_label_text_font_size = "16pt"
from bokeh.palettes import Category10
colors = Category10[10]
line_dashes = 'dashed solid dotted dotdash dashdot'.split()
legends = []
raw_data = []
mins = []
maxs = []
for i in range(n):
label0 = ctf_labels[i]
color = colors[ i % len(colors) ]
if n==1 and ctfs[i].dfdiff:
defocuses = [ctfs[i].defocus - ctfs[i].dfdiff/2, ctfs[i].defocus, ctfs[i].defocus + ctfs[i].dfdiff/2]
else:
defocuses = [ctfs[i].defocus]
for di, defocus in enumerate(defocuses):
if simulate_wrong_defocus and ctfs[i].ctf_type=="|CTF|":
ctf_tmp = ctfs[i].copy()
ctf_tmp.ctf_type = "CTF"
s, s2, ctf_correct = ctf_tmp.ctf1d(plot_s2, defocus_override=ctf_tmp.defocus, use_apix_wrong=simulate_wrong_apix, env_only=env_only)
_, _, ctf_wrong = ctf_tmp.ctf1d(plot_s2, defocus_override=ctf_tmp.defocus_wrong, use_apix_wrong=simulate_wrong_apix, env_only=False)
sign = - np.sign(ctf_wrong)
ctf = - ctf_correct * sign
else:
s, s2, ctf = ctfs[i].ctf1d(plot_s2, defocus_override=defocus, use_apix_wrong=simulate_wrong_apix, env_only=env_only)
mins.append(np.min(ctf))
maxs.append(np.max(ctf))
x = s2 if plot_s2 else s
res = np.hstack(([1e6], 1/s[1:]))
source = dict(x=x, res=res, y=ctf)
if n>1 or (n==1 and ctfs[0].dfdiff):
source["ctf_type"] = [ctfs[i].ctf_type] * len(x)
source["defocus"] = [defocus] * len(x)
line_dash = line_dashes[di] if len(defocuses)>1 else "solid"
line_width = 2 if len(defocuses)==1 or di==1 else 1
line = fig.line(x='x', y='y', color=color, source=source, line_dash=line_dash, line_width=line_width)
if ctfs[i].show_marker:
fig.circle(x='x', y='y', color=color, source=source)
if len(defocuses)>1:
label = f"defocus={round(defocus, 4):g} µm"
else:
label = label0
legends.append(LegendItem(label=label, renderers=[line]))
if show_data or show_avg:
raw_data.append((label, s, x, ctf))
if show_data:
if len(ctfs)>1 or len(defocuses)>1:
label = f"{y_label} ({label})"
else:
label = f"{y_label}"
if n==1 and rotavg:
_, _, ctf_2d = ctfs[i].ctf2d(plot_s2, env_only=env_only)
rad_profile = compute_radial_profile(ctf_2d)
source = dict(x=x, res=res, y=rad_profile)
source["ctf_type"] = ['rotavg'] * len(x)
source["defocus"] = [f'mean={ctfs[0].defocus:g}'] * len(x)
line = fig.line(x='x', y='y', source=source, color='red', line_dash="solid", line_width=line_width*2)
label = f"defocus={ctfs[i].defocus}/dfdiff={ctfs[i].dfdiff}-rotavg"
legends.append(LegendItem(label=label, renderers=[line]))
if show_data:
label = f"{y_label} ({label})"
raw_data.append((label, s, x, rad_profile))
if n>1 and show_avg:
bad_attrs_mapping = dict(imagesize="image size", over_sample="over-sample", ctf_type="CTF type")
attrs_diff = ctf_varying_parameters(ctfs)
bad_attrs_diff = [f"'{bad_attrs_mapping[attr]}'" for attr in attrs_diff if attr in bad_attrs_mapping]
if bad_attrs_diff:
st.warning(f"Cannot show the average CTF. Make sure all CTF curves have the same {', '.join(bad_attrs_diff)} values")
else:
ctf_curves = np.vstack([raw_data[i][-1] for i in range(len(raw_data))])
ctf_avg = np.mean(ctf_curves, axis=0)
source = dict(x=x, res=res, y=ctf_avg)
source["ctf_type"] = ['average'] * len(x)
source["defocus"] = [f'mean={np.mean([ctf.defocus for ctf in ctfs]):g}'] * len(x)
line = fig.line(x='x', y='y', source=source, color='red', line_dash="solid", line_width=line_width*2)
label = f"average"
legends.append(LegendItem(label=label, renderers=[line]))
if show_data:
label = f"{y_label} ({label})"
raw_data.append((label, s, x, ctf_avg))
fig.x_range.start = 0
fig.x_range.end = source['x'][-1]
if not env_only and len([True for ctf in ctfs if ctf.ctf_intact_first_peak]):
fig.y_range.start = -1.05 if np.min(mins)<0 else 0
fig.y_range.end = 1.05
else:
fig.y_range.start = -1.0 if np.min(mins)<0 else 0
fig.y_range.end = 1.0
if len(legends)>1:
from bokeh.models import Legend
legend = Legend(items=legends, location="top_center", spacing=10, orientation="horizontal")
legend.label_text_font_size = '24pt'
fig.add_layout(legend, "above")
fig.legend.click_policy= "hide"
from bokeh.models import CustomJS
from bokeh.events import MouseMove, DoubleTap
toggle_legend_js = CustomJS(args=dict(leg=fig.legend[0]), code="""
if (leg.visible) {
leg.visible = false
}
else {
leg.visible = true
}
""")
fig.js_on_event(DoubleTap, toggle_legend_js)
st.bokeh_chart(fig, use_container_width=True)
del fig
if len([True for ctf in ctfs if ctf.ctf_intact_first_peak]):
st.write("[Relion source code implementing \"Ignore CTFs until first peak\"](https://github.com/3dem/relion/blob/dcab7933398a8b728e56a08ea1bb2539a5ba71d4/src/ctf.h#L204)")
if not embedded:
if show_psf:
tools = 'box_zoom,crosshair,hover,pan,reset,save,wheel_zoom'
hover_tips = [("x", "$x Å"), (f"PSF", "$y")]
n = len(ctfs)
if n>1:
hover_tips = [("Defocus", "@defocus µm")] + hover_tips
fig = figure(title=f"Point Spread Function", x_axis_label="x (Å)", y_axis_label="PSF", tools=tools, tooltips=hover_tips)
fig.title.align = "center"
fig.title.text_font_size = "32px"
fig.xaxis.axis_label_text_font_size = "24pt"
fig.yaxis.axis_label_text_font_size = "24pt"
fig.xaxis.major_label_text_font_size = "16pt"
fig.yaxis.major_label_text_font_size = "16pt"
legends = []
for i in range(n):
x_psf, psf = ctfs[i].psf1d(env_only=env_only)
source = dict(x=x_psf, y=psf)
if n>1: source["defocus"] = [ctfs[i].defocus] * len(x_psf)
line = fig.line(x='x', y='y', source=source, line_width=2, color=colors[i%len(colors)])
if ctfs[i].show_marker:
fig.circle(x='x', y='y', color=color, source=source)
legends.append(LegendItem(label=ctf_labels[i], renderers=[line]))
if len(legends)>1:
from bokeh.models import Legend
legend = Legend(items=legends, location="top_center", spacing=10, orientation="horizontal")
legend.label_text_font_size = '24pt'
fig.add_layout(legend, "above")
fig.legend.click_policy= "hide"
from bokeh.models import CustomJS
from bokeh.events import MouseMove, DoubleTap
toggle_legend_js = CustomJS(args=dict(leg=fig.legend[0]), code="""
if (leg.visible) {
leg.visible = false
}
else {
leg.visible = true
}
""")
fig.js_on_event(DoubleTap, toggle_legend_js)
st.text("") # workaround for a layout bug in streamlit
st.bokeh_chart(fig, use_container_width=True)
del fig
if show_2d and not embedded:
with col_2d:
st.text("") # workaround for a layout bug in streamlit
show_color = False
ctf_2d_avg = None
fig2ds = []
for i in range(n):
ds, ds2, ctf_2d = ctfs[i].ctf2d(plot_s2, env_only=env_only)
if show_avg:
if ctf_2d_avg is None: ctf_2d_avg = ctf_2d * 1.0
else: ctf_2d_avg += ctf_2d
dxy = ds2 if plot_s2 else ds
title = ctf_labels[i]
fig2d = generate_image_figure(ctf_2d, dxy, ctfs[i].ctf_type, title, plot_s2, show_color)
fig2ds.append(fig2d)
del ctf_2d
if ctf_2d_avg is not None:
title = "average"
fig2d = generate_image_figure(ctf_2d_avg, dxy, ctfs[i].ctf_type, title, plot_s2, show_color)
fig2ds.append(fig2d)
if len(fig2ds)>1:
from bokeh.models import CrosshairTool
crosshair = CrosshairTool(dimensions="both")
crosshair.line_color = 'red'
for fig in fig2ds: fig.add_tools(crosshair)
from bokeh.layouts import gridplot
figs_grid = gridplot(children=[fig2ds], toolbar_location=None)
st.bokeh_chart(figs_grid, use_container_width=True)
del figs_grid
else:
st.bokeh_chart(fig2d, use_container_width=True)
del fig2d
if simulate_ctf_effect:
with simulate_ctf_effect_container:
with st.expander("Specify a simulation image", expanded=False):
input_modes = ["Pattern"]
emdb_ids = get_emdb_ids()
input_modes += ["EMDB ID"]
if "emd_id" not in session_state:
import random
session_state.emd_id = random.choice(emdb_ids)
input_modes += ["URL"]
input_mode = st.radio(label="Choose an input mode:", options=input_modes, index=2, horizontal=True, key="input_mode")
if input_mode == "Pattern":
mapping = \
{ "Lens Focus Test Chart" : "https://i.ebayimg.com/images/g/~goAAOSw-o9cXayp/s-l1600.jpg", \
"TV Test Signal" : "https://cdn4.vectorstock.com/images/1000x1000/67/43/1946743.jpg?download=1", \
"Spiral Rainbow Sqaures" : "http://www.ulrichmutze.de/cpmgraphics/testpattern.jpg"
}
pattern_option = st.selectbox('Select a geometric pattern', options=["Delta Function"] + list(mapping.keys()), key="pattern")
if pattern_option not in ["Delta Function"]:
input_txt = mapping[pattern_option]
elif input_mode == "EMDB ID":
do_random_embid = st.checkbox("Choose a random EMDB ID", value=False)
if do_random_embid:
help = "Randomly select another EMDB ID"
button_clicked = st.button(label="Change EMDB ID", help=help)
if button_clicked:
import random
session_state.emd_id = random.choice(emdb_ids)
input_txt = f"EMD-{session_state.emd_id}"
else:
label = "Input an EMDB ID"
value = f"EMD-{session_state.emd_id}"
input_txt = st.text_input(label=label, value=value).strip()
session_state.emd_id = input_txt.lower().split("emd_")[-1]
elif input_mode == "URL":
label = "Input an image url:"
value = "https://upload.wikimedia.org/wikipedia/commons/d/d3/Albert_Einstein_Head.jpg"
input_txt = st.text_input(label=label, value=value, key="url").strip()
image = None
link = None
if input_mode == "Pattern" and pattern_option == "Delta Function":
nx = ctfs[0].imagesize * ctfs[0].over_sample
ny = nx
image = np.zeros((ny, nx), dtype=np.float32)
image[ny//2, nx//2] = 255
elif emdb_ids and input_txt.startswith("EMD-"):
emd_id = input_txt[4:]
if emd_id in emdb_ids:
session_state.emd_id = emd_id
image = get_emdb_image(emd_id, invert_contrast=-1, rgb2gray=True, output_shape=(ctfs[0].imagesize*ctfs[0].over_sample, ctfs[0].imagesize*ctfs[0].over_sample))
link = f'[EMD-{emd_id}](https://www.emdataresource.org/EMD-{emd_id})'
else:
emd_id_bad = emd_id
from random import choice
emd_id = choice(emdb_ids)
st.warning(f"EMD-{emd_id_bad} does not exist. Please input a valid id (for example, a randomly selected valid id {emd_id})")
elif input_txt.startswith("http") or input_txt.startswith("ftp"): # input is a url
url = input_txt
image = get_image(url, invert_contrast=0, rgb2gray=True, output_shape=(ctfs[0].imagesize*ctfs[0].over_sample, ctfs[0].imagesize*ctfs[0].over_sample))
if image is not None:
image = image[::-1, :]
link = f'[Image Link]({url})'
else:
st.warning(f"{url} is not a valid image link")
elif len(input_txt):
st.warning(f"{input_txt} is not a valid image link")
if image is not None:
st.subheader("Simulated CTF effects on images")
import_with_auto_install(["skimage:scikit_image"])
image = normalize(image)
fig2d = generate_image_figure(image, dxy=1.0, ctf_type=None, title="Original Image", plot_s2=False, show_color=show_color)
st.bokeh_chart(fig2d, use_container_width=True)
del fig2d
if link: st.markdown(link, unsafe_allow_html=True)
image_avg = None
fig2ds = []
for i in range(n):
_, _, ctf_2d = ctfs[i].ctf2d(plot_s2=False, env_only=env_only)
from skimage.transform import resize
image_work = resize(image, (ctfs[i].imagesize*ctfs[i].over_sample, ctfs[i].imagesize*ctfs[i].over_sample), anti_aliasing=True)
image2 = np.abs(np.fft.ifft2(np.fft.fft2(image_work)*np.fft.fftshift(ctf_2d)))
if show_avg:
if image_avg is None: image_avg = image2 * 1.0
else: image_avg += image2
title = ctf_labels[i]
fig2d = generate_image_figure(image2, dxy=1.0, ctf_type=None, title=title, plot_s2=False, show_color=show_color)
fig2ds.append(fig2d)
del ctf_2d, image2, image_work
del image
if image_avg is not None:
title = "average"
fig2d = generate_image_figure(image_avg, dxy=1.0, ctf_type=None, title=title, plot_s2=False, show_color=show_color)
fig2ds.append(fig2d)
if len(fig2ds)>1:
from bokeh.models import CrosshairTool
crosshair = CrosshairTool(dimensions="both")
crosshair.line_color = 'red'
for fig in fig2ds: fig.add_tools(crosshair)
from bokeh.layouts import gridplot
figs_grid = gridplot(children=[fig2ds], toolbar_location=None)
st.bokeh_chart(figs_grid, use_container_width=True)
del figs_grid
else:
st.bokeh_chart(fig2d, use_container_width=True)
del fig2d
if not embedded and show_1d and show_data:
with col_1d:
import pandas as pd
for i, (col3_label, s, x, ctf) in enumerate(raw_data):
columns = [x_label, "Res (Å)", col3_label]
maxlen = max(map(len, columns))
columns = [col.rjust(maxlen+10) for col in columns]
data = np.zeros((len(x), 3))
data[:,0] = x
s[0] = 1e-6 # avoid divsion by zero warning
data[:,1] = 1./s
data[:,2] = ctf
df = pd.DataFrame(data, columns=columns)
st.dataframe(df, width=None)
label = f"Download the data - {col3_label}"
st.markdown(get_table_download_link(df, label=label), unsafe_allow_html=True)
if share_url:
set_query_parameters(ctfs)
if show_qr:
with col_1d:
qr_image = qr_code()
st.image(qr_image)
else:
st.query_params.clear()
with col_1d:
if not embedded:
st.markdown("**Learn more about [Contrast Transfer Function (CTF)](https://en.wikipedia.org/wiki/Contrast_transfer_function):**\n* [CTF Tutorial, Wen Jiang](https://docs.google.com/presentation/d/e/2PACX-1vTB-nZBdKVjEdDqV4DNxm7znY_dH4biyHieLNzi-i1I1kNJYgvjT72INbFpK9cUFTO95l8gKDynzGFx/pub?start=true&loop=true&delayms=3000)\n* [The contrast transfer function, Grant Jensen](https://www.youtube.com/watch?v=mPynoF2j6zc&t=2s)\n* [Defocus phase contrast, Fred Sigworth](https://www.youtube.com/watch?v=Y8wivQTJEHQ&list=PLRqNpJmSRfar_z87-oa5W421_HP1ScB25&index=5)\n")
st.markdown("*Developed by the [Jiang Lab@Purdue University](https://jiang.bio.purdue.edu/ctfsimulation). Report problems to [CTFSimulation@GitHub](https://github.com/jianglab/ctfsimulation/issues)*")
hide_streamlit_style = """
<style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style>
"""
st.markdown(hide_streamlit_style, unsafe_allow_html=True)
def generate_image_figure(image, dxy, ctf_type, title, plot_s2=False, show_color=False):
w, h = image.shape
tools = 'box_zoom,crosshair,pan,reset,save,wheel_zoom'
from bokeh.plotting import figure
fig2d = figure(frame_width=w, frame_height=h,
x_range=(-w//2*dxy, (w//2-1)*dxy), y_range=(-h//2*dxy, (h//2-1)*dxy),
tools=tools)
fig2d.grid.visible = False
fig2d.axis.visible = False
fig2d.toolbar_location = None
if title:
fig2d.title.text = title
fig2d.title.align = "center"
fig2d.title.text_font_size = "18px"
if ctf_type is not None:
if plot_s2:
source_data = dict(image=[image], x=[-w//2*dxy], y=[-h//2*dxy], dw=[w*dxy], dh=[h*dxy])
tooltips = [
("Res", "@res Å"),
("s", "@s 1/Å"),
("s2", "@s2 1/Å^2"),
('angle', '@ang °'),
(ctf_type, '@image')
]
else:
source_data = dict(image=[image], x=[-w//2*dxy], y=[-h//2*dxy], dw=[w*dxy], dh=[h*dxy])
tooltips = [
("Res", "@res Å"),
("s", "@s 1/Å"),
('angle', '@ang °'),
(ctf_type, '@image')
]
else:
source_data = dict(image=[image], x=[-w//2*dxy], y=[-h//2*dxy], dw=[w*dxy], dh=[h*dxy])
tooltips = [
("x", "$x Å"),
("y", "$y Å"),
("val", '@image')
]
palette = "Spectral11" if show_color else "Greys256" # "Viridis256"
fig2d_image = fig2d.image(source=source_data, image='image', palette=palette, x='x', y='y', dw='dw', dh='dh')
from bokeh.models.tools import HoverTool
image_hover = HoverTool(renderers=[fig2d_image], tooltips=tooltips)
fig2d.add_tools(image_hover)
if ctf_type is not None:
# avoid the need for embeddedding res/s/s2 image -> smaller fig object and less data to transfer
from bokeh.models import CustomJS
from bokeh.events import MouseMove
mousemove_callback_code = """
var x = cb_obj.x
var y = cb_obj.y
var angle = Math.round(Math.atan2(y, x)*180./Math.PI * 100)/100
console.log(x, y, angle)
var s, res, s2
if(s2) {
s2 = Math.round(Math.hypot(x, y) * 1e3)/1e3
s = Math.round(Math.sqrt(s2) * 1e3)/1e3
res = Math.round(1./s * 100)/100
hover.tooltips[0][1] = res.toString() + " Å"
hover.tooltips[1][1] = s.toString() + " Å"
hover.tooltips[2][1] = s2.toString() + " 1/Å^2"
hover.tooltips[3][1] = angle.toString() + " °"
}
else {
s = Math.round(Math.hypot(x, y) * 1e3)/1e3
res = Math.round(1./s * 100)/100
hover.tooltips[0][1] = res.toString() + " Å"
hover.tooltips[1][1] = s.toString() + " 1/Å"
hover.tooltips[2][1] = angle.toString() + " °"
}
"""
mousemove_callback = CustomJS(args={"hover":fig2d.hover[0], "s2":plot_s2}, code=mousemove_callback_code)
fig2d.js_on_event(MouseMove, mousemove_callback)
return fig2d
def set_session_state_from_ctfs(ctfs, remove_extra_keys=True):
if len(ctfs)==0: return
n = len(ctfs)
for i in range(n):
d = ctfs[i].get_dict()
for attr in d.keys():
attr_i = f"{attr}_{i}"
if attr_i not in st.session_state:
st.session_state[attr_i] = d[attr]
if remove_extra_keys:
try:
for k in st.session_state:
if k.rfind("_")==-1: continue
attr, i = k.rsplit("_", maxsplit=1)
if attr in d and int(i)>=n:
del st.session_state[k]
except:
pass
def get_ctfs_from_session_state():
d = CTF().get_dict()
attrs = []
for k in st.session_state:
if k.rfind("_")==-1: continue
attr, i = k.rsplit("_", maxsplit=1)
if attr in d:
i = int(i)
if attr in ["imagesize", "over_sample", "show_marker", "ctf_intact_first_peak"]:
attrs.append( (i, attr, int(st.session_state[k])) )
else:
attrs.append( (i, attr, st.session_state[k]) )
if len(attrs)<1:
return []
attrs.sort()
n = attrs[-1][0]+1
ctfs = [CTF() for i in range(n)]
for i, attr, val in attrs:
setattr(ctfs[i], attr, val)
return ctfs
def set_query_parameters(ctfs):
state = st.session_state
d = {}
default_vals = CTF().get_dict()
for attr in default_vals.keys():
if attr == "ctf_type":
vals = [getattr(ctfs[i], attr) for i in range(len(ctfs))]
vals_set = set(vals)
if len(vals_set)>1 or list(vals_set)[0] != "CTF":
d[attr] = vals
else:
vals = np.array([getattr(ctfs[i], attr) for i in range(len(ctfs))])
if np.any(vals - default_vals[attr]):
d[attr] = vals
if state.show_1d:
if "rotavg" in state and state.rotavg:
d["rotavg"] = 1
if state.show_psf:
d["show_psf"] = 1
if state.show_data:
d["show_data"] = 1
if state.simulate_wrong_apix:
d["simulate_wrong_apix"] = 1
if state.simulate_wrong_defocus:
d["simulate_wrong_defocus"] = 1
else:
d["show_1d"] = 0
if state.show_2d:
d["show_2d"] = 1
if state.simulate_ctf_effect:
d["simulate_ctf_effect"] = 1
if state.input_mode == "URL":
d["url"] = state.url
else:
d["input_mode"] = state.input_mode
if state.input_mode == "Pattern":
if state.pattern != "Delta Function":
d["pattern"] = state.pattern
elif state.input_mode == "EMDB ID":
d["emd_id"] = state.emd_id
if "plot_s2" in state and state.plot_s2: d["plot_s2"] = 1
if "show_avg" in state and state.show_avg: d["show_avg"] = 1
if "env_only" in state and state.env_only: d["env_only"] = 1
if "embedded" in state and state.embedded: d["embedded"] = 1
if "share_url" in state and state.share_url: d["share_url"] = 1
if "show_qr" in state and state.show_qr: d["show_qr"] = 1
if "title" in state and state.title != "CTF Simulation": d["title"] = state.title
st.query_params.update(d)
def parse_query_parameters():
query_params = st.query_params
ctf_attrs = CTF().get_dict().keys()
ns = [len(query_params.get_all(attr)) for attr in ctf_attrs if attr in query_params]
if not ns:
ctfs = []
else:
n = int(max(ns))
ctfs = [CTF() for i in range(n)]
str_types = ["ctf_type"]
int_types = ["imagesize", "over_sample", "show_marker"]
for attr in ctf_attrs:
if attr in query_params:
for i in range(len(query_params.get_all(attr))):
if attr in str_types:
setattr(ctfs[i], attr, query_params.get_all(attr)[i])
elif attr in int_types:
setattr(ctfs[i], attr, int(query_params.get_all(attr)[i]))
else:
setattr(ctfs[i], attr, float(query_params.get_all(attr)[i]))
int_types = "show_1d show_2d show_psf show_data plot_s2 show_avg share_url show_qr rotavg simulate_ctf_effect simulate_wrong_apix simulate_wrong_defocus env_only".split()
float_types = []
other_attrs = [ attr for attr in query_params if attr not in ctf_attrs ]
for attr in other_attrs:
if attr == "embedded":
st.session_state.embedded = "embedded" in query_params and query_params["embedded"]!='0'
elif attr == "title":
st.session_state.title = query_params[attr]
elif attr in int_types:
st.session_state[attr] = int(query_params[attr])
elif attr in float_types:
st.session_state[attr] = float(query_params[attr])
else:
st.session_state[attr] = query_params[attr]
if "embedded" not in st.session_state:
st.session_state.embedded = 0
if st.session_state.embedded or "title" not in st.session_state:
st.session_state.title = "CTF Simulation"
return ctfs
def ctf_varying_parameter_labels(ctfs):
str_types = ["ctf_type"]
int_types = ["imagesize", "over_sample", "show_marker", "ctf_intact_first_peak"]
ret = []
attrs = ctf_varying_parameters(ctfs)
if attrs:
for ctf in ctfs:
tokens = []
for attr in attrs:
if attr in str_types:
tokens += [f'{attr}={getattr(ctf, attr)}']
elif attr in int_types:
tokens += [f'{attr}={int(getattr(ctf, attr))}']
else:
tokens += [f'{attr}={getattr(ctf, attr):.6g}']
s = '/'.join(tokens)
ret.append(s)
else:
if len(ctfs)>1:
ret = [f'{i+1}' for i in range(len(ctfs))]
else:
ret = [""]
return ret
def ctf_varying_parameters(ctfs):
if len(ctfs)<2: return []
attrs = "voltage cs ampcontrast defocus defocus_wrong dfdiff dfang phaseshift bfactor alpha cc dE dI dZ dXY apix apix_wrong imagesize over_sample ctf_type show_marker ctf_intact_first_peak".split()
ret = []
for attr in attrs:
vals = [getattr(ctfs[i], attr) for i in range(len(ctfs))]
if attr in ["ctf_type"]:
if len(set(vals))>1:
ret.append(attr)
else:
vals = np.array(vals)
if np.std(vals)>1e-6:
ret.append(attr)
return ret
class CTF:
def __init__(self, voltage=300.0, cs=2.7, ampcontrast=7.0, defocus=0.5, dfdiff=0.0, dfang=0.0, phaseshift=0.0, bfactor=0.0, alpha=0.0, cc=2.7, dE=0.0, dI=0.0, dZ=0.0, dXY=0.0, apix=1.0, imagesize=256, over_sample=1, ctf_type='CTF', show_marker=0, ctf_intact_first_peak=0, apix_wrong=0.0, defocus_wrong=None):
self.voltage = voltage
self.cs = cs
self.ampcontrast = ampcontrast
self.defocus = defocus
self.defocus_wrong = defocus_wrong if defocus_wrong is not None else defocus
self.dfdiff = dfdiff
self.dfang = dfang
self.phaseshift = phaseshift
self.bfactor = bfactor
self.alpha = alpha
self.cc = cc
self.dE = dE
self.dI = dI
self.dZ = dZ
self.dXY = dXY
self.apix = apix
self.apix_wrong = apix_wrong if apix_wrong>0 else apix
self.imagesize = int(imagesize)
self.over_sample = int(over_sample)
self.ctf_type = ctf_type # CTF, |CTF|, CTF^2
self.show_marker = int(show_marker)
self.ctf_intact_first_peak = int(ctf_intact_first_peak)
def __str__(self):
return str(self.get_dict())
def __repr__(self):
return self.__str__()
def copy(self):
import copy
return copy.copy(self)
def get_dict(self):
ret = {}
for attr in sorted(self.__dict__):
ret[attr] = self.__dict__[attr]
return ret
def wave_length(self):
wl = 12.2639 / np.sqrt(self.voltage * 1000.0 + 0.97845 * self.voltage * self.voltage) # Angstrom
return wl
def s_at_1st_peak(self, defocus_final=None):
wl = self.wave_length() # Angstrom
phaseshift = self.phaseshift * np.pi / 180.0 + np.arcsin(self.ampcontrast/100.)
defocus = self.defocus if defocus_final is None else defocus_final
# a*x^2 + b*x + c = 0
a = 2*np.pi*.25*self.cs*1e7*wl**3
b = 2*np.pi*(-0.5*defocus*1e4*wl)
c = - phaseshift + np.pi/2
s2 = (-b - np.sqrt(b*b-4*a*c))/(2*a)
s = np.sqrt(s2)
return s
def scherzer_defocus(self, extended=True):
f = np.sqrt(self.cs*1e3 * self.wave_length()*1e-4 ) # micrometer
if extended: f *= np.sqrt(4./3.)
return f
#@st.cache_data(persist=True, show_spinner=False)
def ctf1d(self, plot_s2=False, defocus_override=None, use_apix_wrong=False, env_only=False):
defocus_final = defocus_override if defocus_override is not None else self.defocus
s_nyquist = 1./(2*self.apix)
if plot_s2:
ds2 = s_nyquist*s_nyquist/(self.imagesize//2*self.over_sample)
s2 = np.arange(self.imagesize//2*self.over_sample+1, dtype=np.float32)*ds2
s = np.sqrt(s2)
else:
ds = s_nyquist/(self.imagesize//2*self.over_sample)
s = np.arange(self.imagesize//2*self.over_sample+1, dtype=np.float32)*ds
s2 = s*s
wl = self.wave_length() # Angstrom
phaseshift = self.phaseshift * np.pi / 180.0 + np.arcsin(self.ampcontrast/100.)
gamma =2*np.pi*(-0.5*defocus_final*1e4*wl*s2 + .25*self.cs*1e7*wl**3*s2**2) - phaseshift
from scipy.special import j0, sinc
env = np.ones_like(gamma)
if self.bfactor: env *= np.exp(-self.bfactor*s2/4.0)
if self.alpha: env *= np.exp(-np.power(np.pi*self.alpha*(1.0e7*self.cs*wl*wl*s*s*s-1e4*defocus_final*s), 2.0)*1e-6)
if self.dE: env *= np.exp(-np.power(np.pi*self.cc*wl*s*s* self.dE/self.voltage, 2.0)/(16*np.log(2))*1e8)
if self.dI: env *= np.exp(-np.power(np.pi*self.cc*wl*s*s* self.dI, 2.0)/(4*np.log(2))*1e2)
if self.dZ: env *= j0(np.pi*self.dZ*wl*s*s)
if self.dXY: env *= sinc(np.pi*self.dXY*s)
if env_only:
return s, s2, env
ctf = np.sin(gamma)
if self.ctf_intact_first_peak:
s_peak = self.s_at_1st_peak(defocus_final=defocus_final)
mask = np.where(s<=s_peak)
ctf[mask] = -1
ctf *= env
if self.ctf_type == "CTF^2": ctf = ctf*ctf
elif self.ctf_type == "|CTF|": ctf = np.abs(ctf)
if use_apix_wrong and self.apix_wrong > 0 and self.apix_wrong != self.apix:
s *= self.apix/self.apix_wrong
s2*= (self.apix/self.apix_wrong)**2
return s, s2, ctf
#@st.cache_data(persist=True, show_spinner=False)
def psf1d(self, defocus_override=None, env_only=False):
defocus_final = defocus_override if defocus_override is not None else self.defocus
s_nyquist = 1./(2*self.apix)
ds = s_nyquist/(self.imagesize//2)
s = (np.arange(self.imagesize, dtype=np.float32) - self.imagesize//2)*ds
s2 = s*s
wl = self.wave_length() # Angstrom
phaseshift = self.phaseshift * np.pi / 180.0 + np.arcsin(self.ampcontrast/100.)
gamma =2*np.pi*(-0.5*defocus_final*1e4*wl*s2 + .25*self.cs*1e7*wl**3*s2**2) - phaseshift
from scipy.special import j0, sinc
env = np.ones_like(gamma)
if self.bfactor: env *= np.exp(-self.bfactor*s2/4.0)
if self.alpha: env *= np.exp(-np.power(np.pi*self.alpha*(1.0e7*self.cs*wl*wl*s*s*s-1e4*defocus_final*s), 2.0)*1e-6)
if self.dE: env *= np.exp(-np.power(np.pi*self.cc*wl*s*s* self.dE/self.voltage, 2.0)/(16*np.log(2))*1e8)
if self.dI: env *= np.exp(-np.power(np.pi*self.cc*wl*s*s* self.dI, 2.0)/(4*np.log(2))*1e2)
if self.dZ: env *= j0(np.pi*self.dZ*wl*s*s)
if self.dXY: env *= sinc(np.pi*self.dXY*s)
if env_only:
ctf = env
else:
ctf = np.sin(gamma) * env
if self.ctf_type == "CTF^2": ctf = ctf*ctf
elif self.ctf_type== "|CTF|": ctf = np.abs(ctf)
unity = np.ones((self.imagesize,), dtype=np.complex64)
psf = np.real( np.fft.ifft( unity * np.fft.ifftshift(ctf) ) )
psf = np.fft.fftshift(psf)
psf /= np.linalg.norm(psf, ord=2)
x = (np.arange(self.imagesize)-self.imagesize//2) * self.apix
return x, psf
#@st.cache_data(persist=True, show_spinner=False)
def ctf2d(self, plot_s2=False, env_only=False):
s_nyquist = 1./(2*self.apix)
if plot_s2:
ds = None
ds2 = s_nyquist*s_nyquist/(self.imagesize//2*self.over_sample)
sx2 = np.arange(-self.imagesize*self.over_sample//2, self.imagesize*self.over_sample//2) * ds2
sy2 = np.arange(-self.imagesize*self.over_sample//2, self.imagesize*self.over_sample//2) * ds2
sx2, sy2 = np.meshgrid(sx2, sy2, indexing='ij')
theta = -np.arctan2(sy2, sx2)
s2 = np.hypot(sx2, sy2)
s = np.sqrt(s2)
del sx2, sy2
else:
ds2 = None
ds = s_nyquist/(self.imagesize//2*self.over_sample)
sx = np.arange(-self.imagesize*self.over_sample//2, self.imagesize*self.over_sample//2) * ds
sy = np.arange(-self.imagesize*self.over_sample//2, self.imagesize*self.over_sample//2) * ds
sx, sy = np.meshgrid(sx, sy, indexing='ij')
theta = -np.arctan2(sy, sx)
s2 = sx*sx + sy*sy
s = np.sqrt(s2)
del sx, sy