def n_run(self):
for _element in self.elements:
local_matrix(_element)
self.asm_v()
self.full_structure_matrix()
self.set_nodes_loads()
for _element in self.elements:
get_data(self, _element)
def run(self):
for _element in self.elements:
local_matrix(_element)
self.asm_v()
self.full_structure_matrix()
self.set_nodes_loads()
for _element in self.elements:
get_data(self, _element)
for _element in self.elements:
eval_deact_nodes(_element)
deact_nodes(_element)
deact_press_y(_element)
def main():
start = time.time()
# Read parameters from input file.
in_file, in_file_cols, coords_flag, vor_flag, cr_file_cols, mag_range,\
area_frac_range, m_m_n, intens_frac, dens_frac = get_params_in()
# Each sub-list in 'in_file' is a row of the file.
f_name = in_file[:-4]
# Create log file.
save_to_log(f_name, 'Processing file: {}'.format(f_name), 'w')
save_to_log(f_name, 'Mag range: {}'.format(mag_range), 'a')
if vor_flag == 'voronoi':
save_to_log(f_name, 'Area range: {}'.format(area_frac_range), 'a')
save_to_log(f_name, 'Min/max neighbors: {}'.format(m_m_n), 'a')
save_to_log(f_name, "Frame's density fraction: {}".format(
intens_frac), 'a')
save_to_log(f_name, "Frame's I/A fraction: {}\n".format(dens_frac), 'a')
# Get points coordinates and magnitudes.
x_mr, y_mr, mag_mr, x, y, mag, ra_cent, dec_cent = get_data(
in_file, in_file_cols, mag_range, coords_flag, vor_flag)
save_to_log(f_name, 'Photometric data obtained', 'a')
save_to_log(f_name, 'Total number of stars: {}'.format(len(x)), 'a')
if coords_flag == 'deg':
save_to_log(f_name, 'Center of frame: RA={}, DEC={}'.format(
ra_cent, dec_cent), 'a')
else:
save_to_log(f_name, 'Center of frame: x={}, y={}'.format(
ra_cent, dec_cent), 'a')
save_to_log(f_name, '\nStars filtered by mag range: {}'.format(len(x_mr)),
'a')
# Obtain Voronoi diagram using the *magnitude filtered coordinates*.
points = zip(x_mr, y_mr)
vor = Voronoi(points)
save_to_log(f_name, 'Voronoi diagram obtained', 'a')
# Get data on Voronoi diagram.
save_to_log(f_name, 'Processing Voronoi diagram', 'a')
acp_pts, acp_mags, rej_pts, pts_area, pts_vert = get_vor_data(points,
mag_mr, vor)
# Obtain average area *using magnitude filtered stars*.
fr_area = ((max(x_mr) - min(x_mr)) * (max(y_mr) - min(y_mr)))
avr_area = fr_area / len(x_mr)
# Filter out large area values for border polygons.
pts_area_filt, mean_filt_area = large_area_filt(pts_area, avr_area)
save_to_log(f_name, ("Mean Voronoi cells area (stars in mag range): "
"{:g} {}^2".format(mean_filt_area, coords_flag)), 'a')
# Obtain center coordinates and radii either automatically by grouping
# neighbor stars, or by reading those values from a file.
# This function applies the area and neighbors filters.
cent_rad, pts_area_thres, mag_area_thres, pts_neighbors,\
mags_neighbors = get_cent_rad(
f_name, coords_flag, cr_file_cols, m_m_n, vor_flag, ra_cent,
dec_cent, acp_pts, acp_mags, pts_area, pts_vert, mean_filt_area,
area_frac_range)
# Filter/organize groups found by their density, using stars filtered
# by magnitude.
dens_accp_groups, dens_rej_groups, area_frac_range = filt_density(
f_name, fr_area, x_mr, y_mr, mag_mr, cent_rad, vor_flag,
area_frac_range, dens_frac, mean_filt_area)
save_to_log(
f_name, "\nGroups filtered by density (stars/area): {}".format(
len(dens_accp_groups)), 'a')
# Filter/organize groups found by their I/A, using stars filtered
# by magnitude.
intens_acc_dens_acc, intens_acc_dens_rej, intens_rej_dens_acc,\
intens_rej_dens_rej = filt_integ_mag(
x_mr, y_mr, mag_mr, dens_accp_groups, dens_rej_groups,
intens_frac)
save_to_log(
f_name, "\nGroups filtered by intensity/area: {}".format(
len(intens_acc_dens_acc[0])), 'a')
# Write data to file.
save_cent_rad(f_name, cent_rad, intens_acc_dens_acc,
intens_acc_dens_rej, intens_rej_dens_acc,
intens_rej_dens_rej)
save_to_log(f_name, "\nData saved to {}.out file".format(f_name), 'a')
# Make plots.
save_to_log(f_name, '\nPlotting', 'a')
all_plots(f_name, mag_range, area_frac_range, x, y, mag,
coords_flag, vor_flag, x_mr, y_mr, mag_mr, pts_area_filt,
pts_area_thres, mag_area_thres, pts_neighbors, mags_neighbors,
intens_frac, dens_frac, cent_rad, intens_acc_dens_acc,
intens_acc_dens_rej, intens_rej_dens_acc, intens_rej_dens_rej)
# Done.
elapsed = time.time() - start
m, s = divmod(elapsed, 60)
save_to_log(f_name, 'Full run completed in {:.0f}m {:.0f}s.'.format(
m, s), 'a')
# Loop through all clusters processed.
for indx, sub_dir in enumerate(sub_dirs):
cluster = cl_names[indx]
# Check if cluster is in list.
if cluster in clust_list:
print sub_dir, cluster
# Location of the photometric data file for each cluster.
data_phot = '/media/rest/Dropbox/GABRIEL/CARRERA/3-POS-DOC/trabajo/\
data_all/cumulos-datos-fotometricos/'
# Get photometric data for cluster.
filename = glob.glob(join(data_phot, sub_dir, cluster + '.*'))[0]
id_star, x_data, y_data, mag_data, e_mag, col1_data, e_col1 = \
gd.get_data(data_phot, sub_dir, filename)
# Accept and reject stars based on their errors.
bright_end, popt_mag, popt_umag, pol_mag, popt_col1, popt_ucol1, \
pol_col1, mag_val_left, mag_val_right, col1_val_left, col1_val_right, \
acpt_stars, rjct_stars = ear.err_accpt_rejct(id_star, x_data, y_data,
mag_data, e_mag, col1_data,
e_col1)
clust_rad = [radius[indx], 0.]
center_cl = [centers[0][indx], centers[1][indx]]
# Get stars in and out of cluster's radius.
stars_in, stars_out, stars_in_rjct, stars_out_rjct = \
gio.get_in_out(center_cl, clust_rad[0], acpt_stars, rjct_stars)
# Path where the code is running
def run_page2():
load_global_vars()
st.sidebar.title("User settings")
st.title("Correlation overview")
# dd/mm/YY H:M:S
dt_string = TODAY.strftime("%d/%m/%Y %H:%M:%S")
st.write("Last updated at", dt_string)
ex_tickers = "CL=F, DX=F, GC=F, ES=F, NQ=F, DBC"
tickers = st.text_input("Provide ticker symbols, split by comma",
ex_tickers)
df = get_data(tickers=tickers, period="20y")
df_perc = df["Close"].pct_change(periods=1).dropna()
ticker = st.sidebar.selectbox("Select ticker", list(df_perc.columns))
ex_periods = "10, 20, 30, 60, 90, 120, 150, 180, 210"
periods = st.text_input(
"Choose correlation periods, split by comma (expressed in days)",
ex_periods)
periods = periods.split(",")
# convert to integer
periods = [int(p) for p in periods]
# correlation for table
store_corelations = {}
for c in df_perc.columns:
corr_tick = {}
if c != ticker:
for p in periods:
df_perc_period = df_perc.tail(p)
corr_tick[p] = pd.DataFrame(df_perc_period[ticker]).corrwith(
df_perc_period[c], axis=0, drop=False,
method='spearman').values.tolist()[0]
store_corelations[c] = corr_tick
# precision
precision = st.sidebar.number_input("Number of digits for precision",
min_value=1,
max_value=10,
value=3)
st.dataframe(
pd.DataFrame(store_corelations).T.style.set_precision(precision))
# correlation for figure
corr_period = st.sidebar.slider(
"Choose correlation period figure (expressed in days)",
min_value=5,
max_value=200,
value=30)
period_figure = st.sidebar.slider(
"Choose maximum period figure (expressed in days)",
min_value=100,
max_value=2000,
value=1000)
corr_tick_rolling = {}
for c in df_perc.columns:
if c != ticker:
df_perc_period = df_perc.tail(period_figure)
corr_tick_rolling[c] = rolling_spearman(
df_perc_period[ticker].values, df_perc_period[c].values,
corr_period)
# plot fiure
out = pd.DataFrame(corr_tick_rolling,
index=df_perc_period.index).dropna().reset_index()
out_long = pd.melt(out, id_vars='Date', value_vars=out.columns[1:])
out_long = out_long.rename(columns={
"variable": "Ticker",
"value": "Correlation"
})
fig = px.line(out_long,
x="Date",
y="Correlation",
title=f'Rolling correlations with {ticker}',
color="Ticker")
st.plotly_chart(fig, use_container_width=True)
# Also store the parameters associated with this cluster.
final_zams_params = []
for indx, sub_dir in enumerate(sub_dirs):
cluster = cl_names[indx]
# if cluster in gabriel_accept:
if cluster in ruben_accept:
# if cluster == 'L115':
# use_all_clusters = True
# if use_all_clusters:
print sub_dir, cluster
filename = glob.glob(join(data_isos_file, sub_dir, cluster + '.*'))[0]
id_star, x_data, y_data, mag_data, e_mag, col1_data, e_col1 = \
gd.get_data(data_isos_file, sub_dir, filename)
# Accept and reject stars based on their errors.
bright_end, popt_mag, popt_umag, pol_mag, popt_col1, popt_ucol1, \
pol_col1, mag_val_left, mag_val_right, col1_val_left, col1_val_right, \
acpt_stars, rjct_stars = ear.err_accpt_rejct(id_star, x_data, y_data,
mag_data, e_mag, col1_data,
e_col1)
clust_rad = [radius[indx], 0.]
center_cl = [centers[0][indx], centers[1][indx]]
# Get stars in and out of cluster's radius.
stars_in, stars_out, stars_in_rjct, stars_out_rjct = \
gio.get_in_out(center_cl, clust_rad[0], acpt_stars, rjct_stars)
# Path where the code is running
def run_page1():
# load global variables
load_global_vars()
st.title('Market dashboard')
# dd/mm/YY H:M:S
dt_string = TODAY.strftime("%d/%m/%Y %H:%M:%S")
st.write("Last updated at", dt_string)
# import tickers
# with st.spinner("Updating tickers..."):
tickers = get_tickers(dir=DIR_TICKERS, time=dt_string)
st.sidebar.title("User settings")
if st.sidebar.button("Update tickers"):
tickers = pd.read_excel('data/tickers.xlsx')
comput = st.sidebar.selectbox("Choose computation", ["Close", "High", "Low", "Open", "Volume"])
# convert to string
tickers_input = ' '.join(map(str, tickers['Ticker'].values.tolist()))
tickers_list = tickers['Ticker'].values.tolist()
tickers_list.sort()
# choose benchmark
benchmark = st.sidebar.selectbox("Select benchmark", [1] + tickers_list)
# periods
expander_periods = st.sidebar.beta_expander("Define periods")
with expander_periods:
# periods input
period1 = expander_periods.number_input("Period 1", min_value=1, max_value=MAX_PERIOD, value=1, step=1)
period2 = expander_periods.number_input("Period 2", min_value=1, max_value=MAX_PERIOD, value=5, step=1)
period3 = expander_periods.number_input("Period 3", min_value=1, max_value=MAX_PERIOD, value=30, step=1)
period4 = expander_periods.number_input("Period 4", min_value=1, max_value=MAX_PERIOD, value=DAYS_YTD, step=1)
period5 = expander_periods.number_input("Period 5", min_value=1, max_value=MAX_PERIOD, value=365, step=1)
# add periods in list
periods = [period1, period2, period3, period4, period5]
sorts = expander_periods.selectbox("Sort by period", periods)
within_group = expander_periods.checkbox("Sort within group", value=False)
expander_figureOpt = st.sidebar.beta_expander("Figure options")
# figure options
with expander_figureOpt:
ascending = expander_figureOpt.checkbox("Descending", value=True)
fig_width = expander_figureOpt.slider("Figure width", min_value=100, max_value=2000, value=1150, step=1)
fig_height = expander_figureOpt.slider("Figure height", min_value=100, max_value=2000, value=950, step=1)
font_size = expander_figureOpt.number_input("Font size", min_value=4, max_value=30, value=9, step=1)
color_cyc = expander_figureOpt.color_picker('Color for cyclical', value='#CC241D')
color_def = expander_figureOpt.color_picker('Color for defensive', value='#458588')
color_gro = expander_figureOpt.color_picker('Color for growth', value='#689D6A')
color_oth = expander_figureOpt.color_picker('Color for other', value='#928374')
color_discrete_map={
"Cyclical":color_cyc,
"Defensive":color_def,
"Growth":color_gro,
"Other": color_oth
}
if ascending:
asc_desc = "total ascending"
else:
asc_desc = "total descending"
# compute returns
#with st.spinner("Loading data..."):
data = get_data(tickers=tickers_input, period="20y", time=dt_string)
rets = [compute_returns(data=data, column=comput, period=p, benchmark=benchmark) for p in periods]
rets_conc = round(pd.concat(rets, axis=1)*100, ndigits=2)
rets_conc.columns = periods
rets_conc['Ticker'] = rets_conc.index
# expander tickers
expander_ticker = st.beta_expander("Show tickers")
with expander_ticker:
t = expander_ticker.multiselect("Select tickers", options=tickers_list, default=tickers_list)
select_tick = rets_conc[rets_conc['Ticker'].isin(t)]
select_tick = pd.concat([select_tick.set_index('Ticker'), tickers.set_index('Ticker')], axis=1, join='inner').reset_index()
select_tick = select_tick.sort_values(['Group',sorts], ascending=ascending)
select_tick.dropna(inplace=True)
df_plot_long = pd.melt(select_tick, id_vars=['Ticker','Name', 'Group'], var_name='Period', value_name='Return (%)')
#with st.spinner("Updating figure..."):
fig = make_figure(
data=df_plot_long,
fig_width=fig_width,
fig_height=fig_height,
font_size=font_size,
sorting=sorts,
periods=periods,
asc_desc=asc_desc,
within_group=within_group,
color_discrete_map=color_discrete_map
)
# show figure
st.plotly_chart(fig, use_container_width=False)
else:
if cluster in zams_manual_accept or cluster in iso_manual_accept:
run_cluster = True
else:
run_cluster = False
if run_cluster:
print sub_dir, cluster
# Location of the photometric data file for each cluster.
data_phot = '/media/rest/Dropbox/GABRIEL/CARRERA/3-POS-DOC/trabajo/\
data_all/cumulos-datos-fotometricos/'
# Get photometric data for cluster.
filename = glob.glob(join(data_phot, sub_dir, cluster + '.*'))[0]
id_star, x_data, y_data, mag_data, e_mag, col1_data, e_col1 = \
gd.get_data(data_phot, sub_dir, filename)
# Accept and reject stars based on their errors.
bright_end, popt_mag, popt_umag, pol_mag, popt_col1, popt_ucol1, \
pol_col1, mag_val_left, mag_val_right, col1_val_left, col1_val_right, \
acpt_stars, rjct_stars = ear.err_accpt_rejct(id_star, x_data, y_data,
mag_data, e_mag, col1_data,
e_col1)
clust_rad = [radius[indx], 0.]
center_cl = [centers[0][indx], centers[1][indx]]
# Get stars in and out of cluster's radius.
stars_in, stars_out, stars_in_rjct, stars_out_rjct = \
gio.get_in_out(center_cl, clust_rad[0], acpt_stars, rjct_stars)
# Path where the code is running
