initial commit

.editorconfig

@@ -0,0 +1,28 @@
+root = true
+
+[*]
+charset = utf-8
+end_of_line = lf
+
+# starlark
+[*.star]
+indent_style = tab
+indent_size = 4
+tab_width = 4
+
+# go files (excluding templates)
+[*.{go,mod,sum}]
+indent_style = tab
+indent_size = 4
+tab_width = 4
+
+[*.nix]
+indent_style = space
+indent_size = 2
+
+[*.md]
+trim_trailing_whitespace = false
+
+[*.{yaml,yml}]
+indent_style = space
+indent_size = 2

.envrc

@@ -0,0 +1,7 @@
+use flake
+
+# ref: https://github.com/direnv/direnv/wiki/Vim
+# comment out if not planning to use this
+#add_extra_vimrc
+
+# vim: ff=unix ft=sh

.gitattributes

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+*.md		diff=markdown

.gitignore

@@ -0,0 +1,97 @@
+.direnv
+
+# binaries/symlinks to binaries
+result
+result-*
+
+# generated from starlark
+.drone.yml
+
+.vimrc
+
+# coverage or other binary files
+*.out
+
+### Python ###
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/

default.nix

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+(
+  import
+  (
+    let
+      lock = builtins.fromJSON (builtins.readFile ./flake.lock);
+    in
+      fetchTarball {
+        url = "https://github.com/edolstra/flake-compat/archive/${lock.nodes.flake-compat.locked.rev}.tar.gz";
+        sha256 = lock.nodes.flake-compat.locked.narHash;
+      }
+  )
+  {
+    src = ./.;
+  }
+)
+.defaultNix

flake.lock

@@ -0,0 +1,44 @@
+{
+  "nodes": {
+    "flake-compat": {
+      "flake": false,
+      "locked": {
+        "lastModified": 1650374568,
+        "narHash": "sha256-Z+s0J8/r907g149rllvwhb4pKi8Wam5ij0st8PwAh+E=",
+        "owner": "edolstra",
+        "repo": "flake-compat",
+        "rev": "b4a34015c698c7793d592d66adbab377907a2be8",
+        "type": "github"
+      },
+      "original": {
+        "owner": "edolstra",
+        "repo": "flake-compat",
+        "type": "github"
+      }
+    },
+    "nixpkgs": {
+      "locked": {
+        "lastModified": 1666244578,
+        "narHash": "sha256-OO0F2b83isMVHYtcvxiExig28zPE7Weo7r1fHtQTZzU=",
+        "owner": "NixOS",
+        "repo": "nixpkgs",
+        "rev": "260eb420a2e55e3a0411e731b933c3a8bf6b778e",
+        "type": "github"
+      },
+      "original": {
+        "owner": "NixOS",
+        "ref": "nixpkgs-unstable",
+        "repo": "nixpkgs",
+        "type": "github"
+      }
+    },
+    "root": {
+      "inputs": {
+        "flake-compat": "flake-compat",
+        "nixpkgs": "nixpkgs"
+      }
+    }
+  },
+  "root": "root",
+  "version": 7
+}

flake.nix

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+{
+  description = "Identification and modeling of stochastic signals - protocols";
+  inputs = {
+    nixpkgs.url = "github:NixOS/nixpkgs/nixpkgs-unstable";
+    flake-compat = {
+      url = "github:edolstra/flake-compat";
+      flake = false;
+    };
+  };
+
+  outputs = {
+    self,
+    nixpkgs,
+    ...
+  }: let
+    projname = "ak9im";
+
+    # to work with older version of flakes
+    lastModifiedDate =
+      self.lastModifiedDate or self.lastModified or "19700101";
+
+    # Generate a user-friendly version number.
+    version = "v0.0.0";
+
+    supportedSystems = ["x86_64-linux" "x86_64-darwin" "aarch64-linux" "aarch64-darwin"];
+    forAllSystems = nixpkgs.lib.genAttrs supportedSystems;
+    pkgs = forAllSystems (system: nixpkgs.legacyPackages.${system});
+    # Nixpkgs instantiated for supported system types.
+    nixpkgsFor = forAllSystems (system:
+      import nixpkgs {
+        inherit system;
+        overlays = [
+          # no overlay imports atm
+          # (import ./overlay.nix)
+        ];
+      });
+  in {
+    formatter = forAllSystems (
+      system:
+        nixpkgsFor.${system}.alejandra
+    );
+    packages = forAllSystems (system: let
+      baseurl = "https://git.dotya.ml/wanderer/ak9im/";
+    in rec {
+      p1 = pkgs.${system}.poetry2nix.mkPoetryApplication {
+        name = "p1";
+        projectDir = ./p1;
+        meta = {
+          description = self.description + ": p1";
+          homepage = baseurl + "p1";
+          license = nixpkgs.lib.licenses.gpl3;
+          maintainers = ["wanderer"];
+          platforms = nixpkgs.lib.platforms.linux ++ nixpkgs.lib.platforms.darwin;
+        };
+      };
+      default = p1;
+    });
+
+    apps = forAllSystems (system: rec {
+      p1 = {
+        type = "app";
+        program = "${self.packages.${system}.${projname}}/bin/p1";
+      };
+      default = p1;
+    });
+
+    devShells = forAllSystems (system: let
+      upcache = pkgs.writeShellScriptBin "upcache" ''
+        ## refs:
+        ##   https://fzakaria.com/2020/08/11/caching-your-nix-shell.html
+        ##   https://nixos.wiki/wiki/Caching_nix_shell_build_inputs
+        nix-store --query --references $(nix-instantiate shell.nix) | \
+          xargs nix-store --realise | \
+          xargs nix-store --query --requisites | \
+          cachix push ${projname}
+        nix build --json \
+          | jq -r '.[].outputs | to_entries[].value' \
+          | cachix push ${projname}
+      '';
+      add-license = pkgs.writeShellScriptBin "add-license" ''
+        go run github.com/google/addlicense@v1.0.0 -v \
+          -c "wanderer <a_mirre at utb dot cz>" \
+          -l "GPL-3.0-or-later" -s .
+      '';
+    in {
+      default = pkgs.${system}.mkShellNoCC {
+        name = "${projname}-" + version;
+        shellHook = ''
+          echo " -- in ${projname} dev shell..."
+        '';
+
+        packages = with pkgs.${system}; [
+          (poetry2nix.mkPoetryEnv {projectDir = ./p1;})
+          python3Packages.numpy
+          python3Packages.pandas
+          python3Packages.matplotlib
+          poetry
+        ];
+      };
+    });
+  };
+}

p1/README.md

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+# p1
+
+this subproject contains code for protocol no. 1.

p1/p1/funcs.py

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+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as pyplt
+
+
+def plt_ticks_size() -> int:
+	return 10
+
+
+def load_d(path: str) -> pd.DataFrame():
+	return pd.read_csv(path, float_precision='round_trip', dtype='float64')
+
+
+def plot_d(dat: pd.Series, fname: str = 'plot_input_data', colour: str = ''):
+	if colour == '':
+		colour = 'blue'
+
+	pyplt.plot(dat, color=colour)
+	pyplt.xlim(0, len(dat))
+	pyplt.ylim(min(dat) - 0.3, max(dat) + 0.3)
+	pyplt.xticks(size=plt_ticks_size())
+	pyplt.yticks(size=plt_ticks_size())
+	pyplt.savefig(fname + '.jpg')
+	# pplt.show(block=0)
+	# rework this
+	# ref: https://stackoverflow.com/a/46418284
+	pyplt.close()
+
+
+def mean(dat: pd.Series) -> float:
+	return dat.sum() / len(dat)
+
+
+def variance(dat: pd.Series) -> float:
+	return sum(pow(dat - mean(dat), 2)) / len(dat)
+
+
+def histogram(dat: pd.Series, bins: int = 10, fname: str = 'hist', colour: str = 'blue'):
+	pyplt.hist(dat, color=colour, bins=bins)
+	pyplt.xticks(size=plt_ticks_size())
+	pyplt.yticks(size=plt_ticks_size())
+	pyplt.savefig(fname + '.jpg')
+	pyplt.close()
+
+
+def distr_func(dat: pd.Series, fname: str = 'dist', colour: str = 'blue'):
+	dat.plot.density(color=colour)
+	pyplt.savefig(fname + '.jpg')
+	pyplt.close()
+
+
+def std_dev(dat: pd.Series) -> float:
+	return np.sqrt(variance(dat))
+
+
+def mean_diff(dat: pd.DataFrame) -> pd.Series:
+	return dat - dat.mean()
+
+
+def covar(dat: pd.DataFrame) -> float:
+	dat = dat.apply(mean_diff)
+	return dat['u'].dot(dat['y']) / len(dat)
+
+
+def covar_coeff(cov: float, std_dev_u: float, std_dev_y: float) -> float:
+	return cov / (std_dev_u * std_dev_y)
+
+
+# m is the max permissible shift value.
+def auto_corellation(dat: list, max_shift_n: int = .1) -> float:
+	v = []
+	m = len(dat) * max_shift_n
+	cur_shift = 0
+
+	while m >= cur_shift:
+		r = 0
+
+		for i in range(len(dat) - cur_shift):
+			r += dat[i] * dat[i + cur_shift]
+
+		r = r / len(dat) - cur_shift
+		v.append(r)
+		cur_shift += 1
+
+	return v
+
+
+def mutual_corellation(dx: list, dy: list, max_shift_n: int = .1) -> float:
+	v = []
+	m = len(dx) * max_shift_n
+	cur_shift = 0
+
+	while m >= cur_shift:
+		r = 0
+
+		for i in range(len(dx) - cur_shift):
+			r += dx[i] * dy[i + cur_shift]
+
+		r = r / (len(dx) - cur_shift)
+		v.append(r)
+		cur_shift += 1
+
+	return v
+
+
+def auto_covar(dat: list, max_shift_n: int = .1) -> float:
+	v = []
+	mean = np.mean(dat)
+	m = len(dat) * max_shift_n
+	cur_shift = 0
+
+	while m >= cur_shift:
+		r = 0
+
+		for i in range(len(dat) - cur_shift):
+			r += (dat[i] - mean) * (dat[i + cur_shift] - mean)
+
+		r = r / (len(dat) - cur_shift)
+		v.append(r)
+		cur_shift += 1
+
+	return v
+
+
+def mutual_covar(dx: list, dy: list, max_shift_n: int = .1) -> float:
+	v = []
+	mean_x, mean_y = np.mean(dx), np.mean(dy)
+	m = len(dx) * max_shift_n
+	cur_shift = 0
+
+	while m >= cur_shift:
+		r = 0
+
+		for i in range(len(dx) - cur_shift):
+			r += (dx[i] - mean_x) * (dy[i + cur_shift] - mean_y)
+
+		r = r / (len(dx) - cur_shift)
+		v.append(r)
+		cur_shift += 1
+
+	return v
+
+
+def plot_autocorellation(dat: pd.DataFrame, fname: str = 'autocorellation', colour: str = 'blue'):
+	d = auto_corellation(dat.tolist())
+	pyplt.scatter(range(0, len(d)), d, color=colour)
+	pyplt.savefig(fname + '.jpg')
+	pyplt.close()
+
+
+def plot_mutual_corellation(d1: pd.DataFrame, d2: pd.DataFrame, fname: str = 'mutcorellation', colour: str = 'blue'):
+	d = mutual_corellation(d1.tolist(), d2.tolist())
+	pyplt.scatter(range(0, len(d)), d, color=colour)
+	pyplt.savefig(fname + '.jpg')
+	pyplt.close()
+
+
+def plot_autocovariance(dat: pd.DataFrame, fname: str = 'autocovariance', colour: str = 'blue'):
+	d = auto_covar(dat.tolist())
+	pyplt.scatter(range(0, len(d)), d, color=colour)
+	pyplt.savefig(fname + '.jpg')
+	pyplt.close()
+
+
+def plot_mutual_covariance(d1: pd.DataFrame, d2: pd.DataFrame, fname: str = 'mutcovariance', colour: str = 'blue'):
+	d = mutual_covar(d1.tolist(), d2.tolist())
+	pyplt.scatter(range(0, len(d)), d, color=colour)
+	pyplt.savefig(fname + '.jpg')
+	pyplt.close()

p1/p1/main.py

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+import sys
+import getopt
+import numpy as np
+import funcs as f
+
+
+def main(argv):
+	inputfile = ''
+	try:
+		opts, args = getopt.getopt(argv, "hi:o:", ["ifile="])
+	except getopt.GetoptError:
+		print('main.py -i <inputfile>')
+		sys.exit(2)
+	for opt, arg in opts:
+		if opt == '-h':
+			print('main.py -i <inputfile>')
+			sys.exit()
+		elif opt in ("-i", "--ifile"):
+			inputfile = arg
+	if inputfile != '':
+		print('Input file is', inputfile)
+	else:
+		print('no input file provided, see help (-h)\nexiting...')
+		sys.exit(1)
+
+	#  d = f.load_d("./dat.csv")
+	d = f.load_d(inputfile)
+	# d = d.astype('float64')
+	print(d.head())
+
+	# data plots
+	du = d['u']
+	dy = d['y']
+	f.plot_d(du, fname='u_input_plot')
+	f.plot_d(dy, fname='y_input_plot', colour='green')
+
+	# mean and variance
+	mean_u = f.mean(d['u'])
+	mean_y = f.mean(d['y'])
+
+	variance_u = f.variance(d['u'])
+	variance_y = f.variance(d['y'])
+
+	hist_u = f.histogram(d['u'], fname='u_hist')
+	hist_y = f.histogram(d['y'], fname='y_hist', colour='green')
+
+	dist_u = f.distr_func(d['u'], fname='u_dist')
+	dist_y = f.distr_func(d['y'], fname='y_dist', colour='green')
+
+	cov = f.covar(d)
+	std_dev_u = f.std_dev(d['u'])
+	std_dev_y = f.std_dev(d['y'])
+	cov_c = f.covar_coeff(cov, std_dev_u, std_dev_y)
+
+	print("data covariance:\n", d.cov())
+
+	# print the matrix.
+	print(np.array([[variance_u, cov], [cov, variance_y]]))
+
+	f.plot_autocorellation(dat=d['u'], fname='u_autocorellation')
+	f.plot_autocorellation(dat=d['y'], fname='y_autocorellation', colour='green')
+
+	f.plot_mutual_corellation(d1=d['u'], d2=d['y'], fname='mutual_corellation_uy')
+
+	f.plot_autocovariance(dat=d['u'], fname='u_autocovariance')
+	f.plot_autocovariance(dat=d['y'], fname='y_autocovariance', colour='green')
+
+	f.plot_mutual_covariance(d1=d['u'], d2=d['y'], fname='mutual_covariance_uy')
+
+
+if __name__ == "__main__":
+	main(sys.argv[1:])

p1/poetry.lock

@@ -0,0 +1,8 @@
+package = []
+
+[metadata]
+lock-version = "1.1"
+python-versions = "^3.10"
+content-hash = "53f2eabc9c26446fbcc00d348c47878e118afc2054778c3c803a0a8028af27d9"
+
+[metadata.files]

p1/pyproject.toml

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+[tool.poetry]
+name = "p1"
+version = "0.0.0"
+description = ""
+authors = ["surtur <a_mirre@utb.cz>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.10"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

p1/tox.ini

@@ -0,0 +1,6 @@
+[flake8]
+indent_char='tab'
+extend-ignore = W191, E501
+; ignore =
+; # tabs are ok
+; W191

shell.nix

@@ -0,0 +1,16 @@
+(
+  import
+  (
+    let
+      lock = builtins.fromJSON (builtins.readFile ./flake.lock);
+    in
+      fetchTarball {
+        url = "https://github.com/edolstra/flake-compat/archive/${lock.nodes.flake-compat.locked.rev}.tar.gz";
+        sha256 = lock.nodes.flake-compat.locked.narHash;
+      }
+  )
+  {
+    src = ./.;
+  }
+)
+.shellNix