def plot_WW_distr(infile, outdir, out_formats=["pdf", "png"]):
""" Plot the distribution that is stored in the given file.
"""
base_name = os.path.basename(infile).replace(".csv", "")
log.debug("Reading file: {}.csv".format(base_name))
reader = IOR.Reader(infile)
# Get the pandas dataframe for the cut histograms
df = reader["Data"]
# Get the data
angles = ("costh_Wminus_star", "costh_l_star", "phi_l_star")
x = np.array([df["BinCenters:{}".format(angle)] for angle in angles])
y = np.array(df["Cross sections"])
xmin = np.array(
[np.amin(df["BinLow:{}".format(angle)]) for angle in angles])
xmax = np.array(
[np.amax(df["BinUp:{}".format(angle)]) for angle in angles])
# Bin counting assumes that bins are not binned thinner than 1/1000 and that
# absolute values are of order 0.1-1
nbins = np.array(
[len(np.unique((x[d] * 10000.).astype(int))) for d in range(len(x))])
create_2D_projection_plot(x, y, xmin, xmax, nbins, angles, base_name,
outdir, out_formats)
for i in range(len(angles)):
create_1D_projection_plot(i, x, y, xmin, xmax, nbins, angles,
base_name, outdir, out_formats)
def plot_mumu_distr(infile, outdir, out_formats=["pdf", "png"]):
""" Plot the distribution that is stored in the given file.
"""
base_name = os.path.basename(infile).replace(".csv", "")
log.debug("Reading file: {}.csv".format(base_name))
reader = IOR.Reader(infile)
# Get the pandas dataframe for the cut histograms
df = reader["Data"]
# Get the data
x_name = "costh_f_star"
x = np.array(df["BinCenters:{}".format(x_name)])
y = np.array(df["Cross sections"])
xmin = np.array(np.amin(df["BinLow:{}".format(x_name)]))
xmax = np.array(np.amax(df["BinUp:{}".format(x_name)]))
# Bin counting assumes that bins are not binned thinner than 1/1000 and that
# absolute values are of order 0.1-1
nbins = len(np.unique((x * 10000.).astype(int)))
# Create the figure and plot
fig = plt.figure(figsize=(6.5, 5), tight_layout=True)
ax = plt.gca()
ax.hist(x=x,
weights=y,
bins=nbins,
range=(xmin, xmax),
ls="-",
lw=3,
histtype=u'step')
# Useful limits
ax.set_xlim(xmin, xmax)
# ax.set_ylim(0, 1.1*ax.get_ylim()[1])
# Useful labels
ax.set_xlabel("${}$".format(PN.observable_str(x_name, "mumu")))
ax.set_ylabel("$d\\sigma [$fb$]$")
# Mark which process it is
chirality = IOFH.find_chirality(base_name)
Z_direction = IOFH.find_Z_direction(base_name)
mass_label = IOFH.find_2f_mass_label(base_name)
process_str = "${}$".format(
PN.difermion_process_str("mu", chirality, mass_label, Z_direction))
ax.set_title(process_str)
# Save the plot in files
for out_format in out_formats:
format_dir = "{}/{}".format(outdir, out_format)
IOSH.create_dir(format_dir)
fig.savefig("{}/{}_{}.{}".format(format_dir, base_name, x_name,
out_format),
transparent=True,
bbox_inches='tight')
"swap":[[rates set], NUM]
}
convention = { instruemtns convention }
Day_Counter = function of year fraction
"""
from IO import Reader as reader
from curve import Day_Counter_func as Day_Counter
from curve import Boot_Strapping as B_S
from curve import Boot_Strap_Tools_Func as BS_TF
import pandas as pd
""" Get raw data
"""
f_name = "Raw_Inputs.xlsx"
s_name = "Data"
c_num, f_num, s_num = 5,7,100
reader = reader.excel_reader(f_name)
ccy, date, Cash, Future, Swap = reader.read_instruments(s_name)
instrument = {}
instrument["cash"] = [ Cash, c_num ]
instrument["future"] = [ Future, f_num ]
instrument["swap"] = [ Swap, s_num ]
""" Set up valuation date and currency
then generate the cure by bootstrapping
"""
value_date = date
currency = ccy
convention = convention_book[currency]
Day_Counter = Day_Counter.Day_Counter(convention["LIBOR_day_count"])
curve = B_S.boot_strapping_LIBOR( value_date,
convention,
instrument,
log.basicConfig(level=log.INFO) # Set logging level
PDF.set_default_mpl_format()
MCLumi = 5000 # MC Statistics is 5ab^-1
input_dir = "/home/jakob/DESY/MountPoints/DUST/TGCAnalysis/SampleProduction/NewMCProduction/2f_Z_l/PrEWInput/MuAcc_costheta_0.9925"
# input_dir = "/home/jakob/DESY/MountPoints/DUST/TGCAnalysis/SampleProduction/NewMCProduction/2f_Z_l/PrEWInput/MuAcc_costheta_0.9925/TrueAngle"
output_dir = input_dir + "/shape_checks"
IOSH.create_dir(output_dir)
log.info("Looking in dir: {}".format(input_dir))
for file_path in IOSH.find_files(input_dir, ".csv"):
# Read the input file
base_name = os.path.basename(file_path).replace(".csv","")
log.info("Reading file: {}.csv".format(base_name))
reader = IOR.Reader(file_path)
# Get the pandas dataframe for the cut histograms
angle = "costh_f_star_true" if "TrueAngle" in input_dir else "costh_f_star"
df = reader["Data"]
bin_vals = np.array(df["Cross sections"])
bin_middles = np.array(df["BinCenters:{}".format(angle)])
edges_min = np.array(df["BinLow:{}".format(angle)])
edges_max = np.array(df["BinUp:{}".format(angle)])
bin_width = edges_max[0] - edges_min[0]
# Rescale to MC Lumi
bin_vals *= MCLumi
# Perform fits to the distributions
fit_vals_ha, p_ha, cov_ha = SST.fit_1D(SSF.helicity_amplitudes, bin_vals, edges_min, edges_max, bounds=[[0,0],[np.inf,np.inf]])
Outputs:
fixed swap rate on leg2 (in default)
Can be adjusted and out put all detailed cash flows
@author: Shaolun Du
@contacts: [email protected]
"""
import os
import xlwings as xw
from IO import Reader as reader
from Pricing import FX_Pricer as Pricer
""" Set up enviroments
"""
dir_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
f_name = dir_path + "\\Basic Pricing.xlsm"
reader = reader.excel_reader(f_name)
inputs = reader.read_basic_fx_input("Pricer")
val_date = inputs["cv date"]
npv_date = inputs["value date"]
source = inputs["source"]
""" Choose Pricing Source
"""
fx_name = "FX_Rate"
s_name = "Rate_History"
s_fx_name = "FX_Curve_History"
s_opt_name = "Vol_Surface"
sheet_name = "Pricer"
opt_source = "LIVE"
db_name = "Yield_Curve"
output_dir = input_dir_nocor + "/ISR_shape_checks"
IOSH.create_dir(output_dir)
file_pairs = [
["2f_mu_81to101_BZ_250_eLpR.csv", "2f_mu_81to101_BZ_true_250_eLpR.csv"],
["2f_mu_81to101_BZ_250_eRpL.csv", "2f_mu_81to101_BZ_true_250_eRpL.csv"],
["2f_mu_81to101_FZ_250_eLpR.csv", "2f_mu_81to101_FZ_true_250_eLpR.csv"],
["2f_mu_81to101_FZ_250_eRpL.csv", "2f_mu_81to101_FZ_true_250_eRpL.csv"],
]
for file_pair in file_pairs:
# Read the input file
base_name = file_pair[0].replace(".csv", "")
log.info("Processing: {}".format(base_name))
reader_nocor = IOR.Reader(input_dir_nocor + "/" + file_pair[0])
reader_sicor = IOR.Reader(input_dir_sicor + "/" + file_pair[1])
# Get the pandas dataframe for the cut histograms
df_nocor = reader_nocor["Data"]
df_sicor = reader_sicor["Data"]
bin_vals_nocor = MCLumi * np.array(df_nocor["Cross sections"])
bin_vals_sicor = MCLumi * np.array(df_sicor["Cross sections"])
angle = "costh_f_star"
bin_middles = np.array(df_nocor["BinCenters:{}".format(angle)])
edges_min = np.array(df_nocor["BinLow:{}".format(angle)])
edges_max = np.array(df_nocor["BinUp:{}".format(angle)])
bin_width = edges_max[0] - edges_min[0]
# Perform fits to the distributions
@author: Shaolun Du @contacts: [email protected] """ import os import xlwings as xw from IO import Reader as reader from DB import dbExecute as db """ Set up enviroments """ dir_path = os.path.dirname( os.path.dirname( os.path.realpath(__file__))) f_name = dir_path + "\\Basic Pricing.xlsm" reader = reader.excel_reader(f_name) inputs = reader.read_basic_mkt_input("Pricer") cv_date = inputs["curve date"] val_date = inputs["value date"] source = inputs["source"] schema_name = "Yield_Curve" # Load libor rates curve tb_name = "yield_curve" sqlstring = "Select * from "+tb_name+" where sdate = '"+str(cv_date)+"'" ans_libor = db.dbExecute( schema_name, sqlstring ) # Load fx rates curve tb_name = "fx_curve"
from IO import Reader as reader
from Live_Pricing.SW_Pricer import Pricer as Pricer
from curve import Curve_Keeper as C_K
""" Set up enviroments
"""
dir_path = os.path.dirname(os.path.realpath(__file__))
f_name = dir_path+"\\Live Pricing.xlsm"
fx_name = "FX_Live"
balance_name = "Schedule"
""" Set up objects
"""
reader = reader.excel_reader(f_name)
fx_spot = reader.read_FX_rates(fx_name)
source = reader.read_source("Pricer")
""" Choose Pricing Source
"""
if "H" in source.upper():
s_name = "Rate_History"
s_fx_name = "FX_Curve_History"
elif "L" in source.upper():
s_name = "Rate_Live"
s_fx_name = "FX_Curve_Live"
cv_instrument = reader.read_instruments(s_name, 8)
cv_fx_instrument = reader.read_instruments(s_fx_name, 8)
import IO.Reader as Reader
import portfolio.Portfolio as Portfolio
from IO import Writer as Writer
start = time.time()
""" Setup reader
"""
f_name = "Test.xlsx"
s_name = ["Swap"]
#s_name = [ "FX" ]
report_currency = "LCL"
output_fname = "Answer.xlsx"
output_sname = "Result"
writer = Writer.Writer(output_fname)
writer.add_sheet(output_sname)
reader = Reader.excel_reader(f_name)
for sheet in s_name:
reader.read(sheet)
""" Test on Portfolio Object
"""
curve_date = "2018-11-30"
val_date = "2018-11-30"
Port = Portfolio.Portfolio(curve_date, reader)
Port.analysis(val_date)
fx_usd, sw_usd, bd_usd, sp_usd = Port.translate_to_ccy(report_currency)
usd_bk = {**fx_usd, **sw_usd, **bd_usd, **sp_usd}
future_cf, delta_cf, fx_cf, risks = Port.gen_result_tables(usd_bk)
""" Writting into excel uotput file
"""
@contact: [email protected] """ import DB.dbExecute as dbEx from IO import Reader as Reader from Live_Pricing import Vol_Live as Vol from Live_Pricing import OPT_Pricer as OPT # Step one load current market vol data # also load current market rates data f_name = "Live Pricing.xlsm" s_name = "Vol_Surface" sdate = "2019-03-25" schema_name = "Yield_Curve" tb_opt = "vcub" opt_source = "LIVE" num = 4 Reader = Reader.excel_reader(f_name) data = Reader.read_vol( s_name, num ) vol_s = Vol.Val_Surface( sdate, schema_name, tb_opt ) vol_s.get_raw_data( opt_source, data ) # Step two load option/swaption balance schedule balance_name = "Schedule" Reader.read( balance_name ) name, raw_data = Reader.get_raw_data() cv_instrument = Reader.read_instruments("Rate_Live", 8) disc_cv_details = { "type" : "LIBOR", "spread" : 0 }
@Author: Alan @Contacts: [email protected] """ import os import xlwings as xw from IO import Reader as reader import Live_Pricing.SW_Pricer as Pricer """ Set up enviroments """ dir_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) f_name = dir_path + "\\Live Pricing.xlsm" ccy_list = ["USD", "EUR", "COP", "BRL", "JPY", "GBP", "CHF", "CAD"] """ Set up objects """ reader = reader.excel_reader(f_name) source = reader.read_source("Pricer") """ Choose Pricing Source """ if "H" in source.upper(): s_name = "Rate_History" s_fx_name = "FX_Curve_History" elif "L" in source.upper(): s_name = "Rate_Live" s_fx_name = "FX_Curve_Live" cv_instrument = reader.read_instruments(s_name, 8) cv_fx_instrument = reader.read_instruments(s_fx_name, 8) """ Start Generating Curves """
@Contacts: [email protected] """ import os import xlwings as xw from IO import Reader as reader import Pricing.SWAP_Pricer as Pricer import Pricing.LP_Tools as Tools """ Set up enviroments """ dir_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) f_name = dir_path + "\\Basic Pricing.xlsm" ccy_list = ["USD", "EUR", "COP", "BRL", "JPY", "GBP", "CHF", "CAD"] """ Set up objects """ reader = reader.excel_reader(f_name) data = reader.read_basic_swap_input("Pricer") source = data["source"] freq, isEOM, Margin = reader.read_DFs("Pricer") if "Y" in isEOM.upper(): isEOM = True else: isEOM = False """ Choose Pricing Source """ if "H" in source.upper(): s_name = "Rate_History" s_fx_name = "FX_Curve_History" elif "L" in source.upper():