# HG changeset patch # User Oleksandr Gavenko # Date 1457551403 -7200 # Node ID 328995b5b8fd0a23295723b4eb577b31ff54350a init stat project diff -r 000000000000 -r 328995b5b8fd .dir-locals.el --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/.dir-locals.el Wed Mar 09 21:23:23 2016 +0200 @@ -0,0 +1,5 @@ +;;; Directory Local Variables +;;; For more information see (info "(emacs) Directory Variables") + +((rst-mode + (fill-column . 80))) diff -r 000000000000 -r 328995b5b8fd Makefile --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Makefile Wed Mar 09 21:23:23 2016 +0200 @@ -0,0 +1,150 @@ + +################################################################ +# Standard GNU Makefile settings. + +SHELL = /bin/sh +export PATH := /bin:/usr/bin:${PATH} + +# Disable built in pattern rules. +MAKEFLAGS += -r +# Disable built in variables. +MAKEFLAGS += -R +# Disable built in suffix rules. +.SUFFIXES: +# Delete target file if command fails. +.DELETE_ON_ERROR: +# Default target. +.DEFAULT_GOAL = all + +################################################################ +# Platform definition. + +host_os := linux +ifneq '' '$(WINDIR)' + host_os := cygwin +endif +target_os := $(host_os) + +################################################################ +# Build tool definition/switches. + +RST2HTML := rst2html +ifeq '$(host_os)' 'cygwin' + RST2HTML := rst2html.py +endif + +RST_WARNING_FLAGS := --halt warning +RST_RENDER_FLAGS := --strip-comments --embed-stylesheet --no-xml-declaration --math-output=HTML --initial-header-level=2 +RST_FLAGS := $(RST_WARNING_FLAGS) $(RST_RENDER_FLAGS) + +################################################################ +# Proj dirs/files. + +RST_FILES := $(wildcard *.rst) + +TMPL_DIR := tmpl + +HTML_DIR := dist/multi-html +RST_HTML_FILES := $(patsubst %.rst,$(HTML_DIR)/%.html,$(RST_FILES)) +HTML_FILES := $(RST_HTML_FILES) $(HTML_DIR)/iframe.html + +################################################################ +# Deploy targets. + +WWW_SRV_NAME := defun.work +WWW_SRV_USER := user +HG_SRV_NAME := hg.defun.work +HG_SRV_USER := user +LOCAL_DIR := /srv/www/stat + +ifneq '' '$(filter deploy%,$(MAKECMDGOALS))' + $(shell rm -f $(HTML_DIR)/rst.tmpl) +endif + +.PHONY: deploy +deploy: deploy2defun-web deploy2defun-hg + +# Will be accessible via: http://stat.defun.work/ +.PHONY: deploy2defun-web +deploy2defun-web: html + rsync --delete -avP -e ssh $(HTML_DIR)/ $(WWW_SRV_USER)@$(WWW_SRV_NAME):/srv/www/stat/ + +.PHONY: deploy2defun-hg +deploy2defun-hg: + hg push ssh://$(HG_SRV_USER)@$(HG_SRV_NAME)//srv/hg/tips || [ $$? = 1 ] + +.PHONY: deploy2local +deploy2local: html + rsync --delete -avP $(HTML_DIR)/ $(LOCAL_DIR) + +################################################################ +# Build targets. + +.PHONY: all +all: + +.PHONY: html +html: $(HTML_FILES) + +$(HTML_DIR)/%.html: %.rst $(TMPL_DIR)/rst.css $(TMPL_DIR)/rst.tmpl $(MAKEFILE_LIST) | $(HTML_DIR) + $(RST2HTML) $(RST_FLAGS) --stylesheet=$(TMPL_DIR)/rst.css --template=$(TMPL_DIR)/rst.tmpl $*.rst $@ + +$(HTML_DIR)/iframe.html: $(RST_FILES) $(MAKEFILE_LIST) | $(HTML_DIR) + { \ +echo ''; \ +echo ''; \ +echo ''; \ +echo '

'; \ +echo ''; \ +} >$@ + +$(TMPL_DIR)/rst.tmpl: $(TMPL_DIR)/rst.tmpl.in $(MAKEFILE_LIST) + sed -e "s|{date}|$$(date +%F)|" -e "s|{rev}|$$(hg id -i)|" <$< >$@ + +################################################################ +# Init targets. + +$(HTML_DIR): + mkdir -p $@ + +################################################################ +# Clean targets. + +.PHONY: distclean +distclean: clean + +.PHONY: clean +clean: + rm -r -f $(HTML_DIR) + +################################################################ +# Helper target. + +.PHONY: help +help: + @echo Supported targets: + @sed -n -e '/^[[:alnum:]_-]*:/{s=^$.*$:.*= \1=;p;}' $(MAKEFILE_LIST) + +.PHONY: check-format-policy +check-format-policy: + \ +for f in $(RST_FILES); do \ + if grep '^.. -\*- coding: utf-8; -\*-' $$f >/dev/null; then :; else \ + echo $$f:1:" Has no 'coding: utf-8' directive."; \ + fi; \ + if grep '^.. contents::' $$f >/dev/null; then :; else \ + echo $$f:7:" Has no 'contents::' directive."; \ + fi; \ + if grep '^ :local:' $$f >/dev/null; then :; else \ + echo $$f:7:" Has no ':local:' directive."; \ + fi; \ +done + diff -r 000000000000 -r 328995b5b8fd combinatoric.rst --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/combinatoric.rst Wed Mar 09 21:23:23 2016 +0200 @@ -0,0 +1,97 @@ + +============== + Combinatoric +============== +.. contents:: + :local: + +Permutation +=========== + +.. math:: perm(n) = n! + +Number of selecting m position from n places +============================================ + +It like a number of binary numbers length :math:`n` with :math:`m` ones. + +So by definition: + +.. math:: + + choose(n, 0) = 1 + choose(n, n) = 1 + + choose(n, <0) = 0 + choose(n, >n) = 0 + + choose(n, m) = choose(n-1, m-1) + choose(n-1, m) + +From this properties by induction: + +.. math:: choose(n, m) = n! / (n-m)! / m! + +Proof: + +.. math:: choose(1, m) = 1 = 1!/(1-m)!/m!, for m∈{0,1} + +Assume that we prove for :math:`n-1`, so: + +.. math:: + + choose(n, m) = choose(n-1, m-1) + choose(n-1, m) + + = (n-1)!/(n-m)!/(m-1)! + (n-1)!/(n-1-m)!/m! + + = (n-1)!/(n-1-m)!/(m-1)!·(1/(n-m) + 1/m) + + = (n-1)!/(n-1-m)!/(m-1)!·(m+(n-m))/(n-m)/m = n!/(n-m)!/m! + +Partitions +========== + +It like a number of numbers in n-base with 0 in m_1 positions, 1 in m_2 +positions, ..., (n-1) in m_n positions. + +.. math:: part(m_1, ..., m_n) = (∑_{i∈1..n} m_i)! / ∏_{i∈1..n} m_i! + +For :math:`n=2` it is a number of combinations: + +.. math:: part(m_1, m_2) = choose(m_1+m_2, m_2) = (m_1+m_2)! / (m_1!·m_2!) + +So: + +.. math:: + + part(m_1, m_2) = choose(m_1+m_2, m_2) + + = choose(m_1+m_2 - 1, m_2) + choose(m_1+m_2 - 1, m_2 - 1) + + = part(m_1 - 1, m_2) + part(m_1, m_2 - 1) + +Generalise thinking: + +.. math:: + + part(m_1, ..., m_n) = part(m_1 - 1, m_2, ..., m_n) + part(m_1, m_2 - 1, m_3, ..., m_n) + ... + part(m_1, ..., m_{n-1}, m_n - 1) + +Now we can prove partition formula by induction: + +.. math:: + + part(m_1, ..., m_n) = ∑_{i=1..n} part(..., m_{i-1}, m_i - 1, m_{i+1}, ...) + + = ∑_{i=1..n} (m_i - 1 + ∑_{j=1..n, j≠i} m_j)! / (m_i - 1)! / ∏_{j=1..n, j≠i} m_j! + + = ∑_{i=1..n} ((∑_{j=1..n} m_j) - 1)! / ∏_{j=1..n} (m_j - 1)! / ∏_{j=1..n,j≠i} m_j + + = ((∑_{j=1..n} m_j) - 1)! / ∏_{j=1..n} (m_j - 1)! · ∑_{i=1..n} 1 / ∏_{j=1..n,j≠i} m_j + + = ((∑_{j=1..n} m_j) - 1)! / ∏_{j=1..n} (m_j - 1)! · ∑_{i=1..n} m_i / ∏_{j=1..n} m_j + + = ((∑_{j=1..n} m_j) - 1)! / ∏_{j=1..n} (m_j - 1)! / ∏_{j=1..n} m_j · ∑_{i=1..n} m_i + + = ((∑_{j=1..n} m_j) - 1)! / ∏_{j=1..n} m_j! · ∑_{i=1..n} m_i + + = (∑_{j=1..n} m_j)! / ∏_{j=1..n} m_j! + diff -r 000000000000 -r 328995b5b8fd index.rst --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/index.rst Wed Mar 09 21:23:23 2016 +0200 @@ -0,0 +1,16 @@ + +==================================== + Notes on probability and statistic +==================================== +.. contents:: + +About +===== + +This notes made by Oleksandr Gavenko during study of probability and statistic. + +License +======= + +Public domain. Do whatever you want. + diff -r 000000000000 -r 328995b5b8fd probability-discrete.rst --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/probability-discrete.rst Wed Mar 09 21:23:23 2016 +0200 @@ -0,0 +1,352 @@ + +============= + Probability +============= +.. contents:: + :local: + +.. role:: def + :class: def + +PMF +=== + +:def:`PMF` or :def:`probability mass function` or :def:`probability law` or +:def:`probability discribuion` of discrete random variable is a function that +for given number give probability of that value. + +To denote PMF used notations: + +.. math:: + + PMF(X = x) = P(X = x) = p_X(x) = P({ω ∈ Ω: X(ω) = x}) + +where :math:`X` is a random variable on space :math:`Ω` of outcomes which mapped +to real number via :math:`X(ω)`. + +Expected value +============== + +:def:`Expected value` of PMF is: + +.. math:: + + E[X] = Σ_{ω∈Ω} Χ(x) * p(ω) = Σ_{x} x * p_X(x) + +We write :math:`a ≤ X ≤ b` for :math:`∀ ω∈Ω a ≤ X(ω) ≤ b`. + +If :math:`X ≥ 0` then :math:`E[X] ≥ 0`. + +if :math:`a ≤ X ≤ b` then :math:`a ≤ E[X] ≤ b`. + +If :math:`Y = g(X)` (:math:`∀ ω∈Ω Y(ω) = g(X(ω))`) then: + +.. math:: + + E[Y] = Σ_{x} g(x) * p_X(x) + +**Proof** TODO: + +.. math:: + + E[Y] = Σ_{y} y * p_Y(y) + + = Σ_{y∈ℝ} y * Σ_{ω∈Ω: Y(ω)=y} p(ω) + + = Σ_{y∈ℝ} y * Σ_{ω∈Ω: g(X(ω))=y} p(ω) + + = Σ_{y∈ℝ} y * Σ_{x∈ℝ: g(x)=y} Σ_{ω∈Ω: X(ω) = x} p(ω) + + = Σ_{y∈ℝ} y * Σ_{x∈ℝ: g(x)=y} p_X(x) + + = Σ_{y∈ℝ} Σ_{x∈ℝ: g(x)=y} y * p_X(x) + + = Σ_{x∈ℝ} Σ_{y∈ℝ: g(x)=y} y * p_X(x) + + = Σ_{x} g(x) * p_X(x) + +.. math:: + + E[a*X + b] = a*E[X] + b + +Variance +======== + +:def:`Variance` is a: + +.. math:: + + var[X] = E[(X - E[X])^2] = E[X^2] - E^2[X] + +:def:`Standard deviation` is a: + +.. math:: + + σ_Χ = sqrt(var[X]) + +Property: + +.. math:: + + var(a*X + b) = a²̇ · var[X] + + +Total probability theorem +========================= + +Let :math:`A_i ∩ A_j = ∅` for :math:`i ≠ j` and :math:`∑_i A_i = Ω`: + +.. math:: + + p_X(x) = Σ_i P(A_i)·p_{X|A_i}(x) + +Conditional PMF +=============== + +:def:`Conditional PMF` is: + +.. math:: + + p_{X|A}(x) = P(X=x | A) + + E[X|A] = ∑_x x·p_{X|A}(x) + +Total expectation theorem +========================= + +.. math:: + + E[X] = Σ_i P(A_i)·E[X|A_i] + +To prove theorem just multiply total probability theorem by :math:`x`. + +Joint PMF +========= + +:def:`Joint PMF` of random variables :math:`X_1,...,X_n` is: + +.. math:: + + p_{X_1,...,X_n}(x_1,...,x_n) = P(AND_{x_1,...,x_n}: X_i = x_i) + +Properties: + +.. math:: + + E[X+Y] = E[X] + E[Y] + +Conditional PMF +=============== + +:def:`Conditional PMF` is: + +.. math:: + + p_{X|Y}(x|y) = P(X=x | Y=y) = P(X=x \& Y=y) / P(Y=y) + +So: + +.. math:: + + p_{X,Y}(x,y) = p_Y(y)·p_{X|Y}(x|y) = p_X(x)·p_{Y|X}(y|x) + + p_{X,Y,Z}(x,y,z) = p_Y(y)·p_{Z|Y}(z|y)·p_{X|Y,Z}(x|y,z) + + ∑_{x,y} p_{X,Y|Z}(x,y|z) = 1 + +Conditional expectation of joint PMF +==================================== + +:def:`Conditional expectation of joint PMF` is: + +.. math:: + + E[X|Y=y] = ∑_x x·p_{X|Y}(x|y) + + E[g(X)|Y=y] = ∑_x g(x)·p_{X|Y}(x|y) + +Total probability theorem for joint PMF +======================================= +.. math:: + + p_X(x) = ∑_y p_Y(y)·p_{X|Y}(x|y) + +Total expectation theorem for joint PMF +======================================= +.. math:: + + E[X] = ∑_y p_Y(y)·E[X|Y=y] + +Independence of r.v. +==================== + +r.v. :math:`X` and :math:`Y` is :def:`independent` if: + +.. math:: + + ∀_{x,y}: p_{X,Y}(x,y) = p_X(x)·p_Y(y) + +So if two r.v. are independent: + +.. math:: + + E[X·Y] = E[X]·E[Y] + + var(X+Y) = var(X) + var(Y) + +Well known discrete r.v. +======================== + +Bernoulli random variable +------------------------- + +:def:`Bernoulli random variable` with parameter :math:`p` is a random variable +that have 2 outcomes denoted as :math:`0` and :math:`1` with probabilities: + +.. math:: + + p_X(0) = 1 - p + + p_X(1) = p + +This random variable models a trial of experiment that result in success or +failure. + +:def:`Indicator` of r.v. event :math:`A` is function:: + + I_A = 1 iff A occurs, else 0 + +.. math:: + + P_{I_A} = p(I_A = 1) = p(A) + + I_A*I_B = I_{A∩B} + +.. math:: + + E[bernoulli(p)] = 0*(1-p) + 1*p = p + + var[bernoulli(p)] = E[bernoulli(p) - E[bernoulli(p)]] + + = (0-p)²·(1-p) + (1-p)²·p = p²·(1-p) + (1 - 2p + p²)·p + + = p² - p³ + p - 2·p² + p³ = p·(1-p) + +Discret uniform random variable +------------------------------- + +:def:`Discret uniform random variable` is a variable with parameters :math:`a` +and :math:`b` in sample space :math:`{x: a ≤ x ≤ b & x ∈ ℕ}` with equal +probability of each possible outcome: + +.. math:: + + p_{unif(a,b)}(x) = 1 / (b-a+1) + +.. math:: + + E[unif(a,b)] = Σ_{a ≤ x ≤ b} x * 1/(b-a+1) + = 1/(b-a+1) * Σ_{a ≤ x ≤ b} x + + = 1/(b-a+1) * (Σ_{a ≤ x ≤ b} a + Σ_{0 ≤ x ≤ b-a} x) + + = 1/(b-a+1) * ((b-a+1)*a + (b-a)*(b-a+1)/2) + + = a + (b-a)/2 + = (b+a)/2 + + +.. math:: + + var[unif(a,b)] = E[unif²(a,b)] - E²[unif(a,b)] + + = ∑_{a≤x≤b} x²/(b-a+1) - (b+a)²/4 + + = 1/(b-a+1)·(∑_{0≤x≤b} x² - ∑_{0≤x≤a-1} x²) - (b+a)²/4 + + = 1/(b-a+1)·(b+3·b²+2·b³ - (a-1)+3·(a-1)²+2·(a-1)³)/6 - (b+a)²/4 + + = (2·b² + 2·a·b + b + 2·a² - a)/6 - (b+a)²/4 + + = (b - a)·(b - a + 2) / 12 + +.. NOTE:: + + From Maxima:: + + sum(i^2,i,0,n), simpsum=true; + + 2 3 + n + 3 n + 2 n + --------------- + 6 + + factor(b+3*b^2+2*b^3 - (a-1)-3*(a-1)^2-2*(a-1)^3); + + 2 2 + (b - a + 1) (2 b + 2 a b + b + 2 a - a) + + factor((2*b^2 + 2*a*b + b + 2*a^2 - a)/6 - (b+a)^2/4), simp=true; + + (b - a) (2 - a + b) + ------------------- + 12 + +Binomial random variable +------------------------ + +:math:`Binomial random variable` is a r.v. with parameters :math:`n` (positive +integer) and p from interval :math:`(0,1)` and sample space of positive integers +from inclusive region :math:`[0, n]`: + +.. math:: + + p_{binom(n,p)}(x) = n!/(x!*(n-x)!) p^x p^{n-x} + +Binomial random variable models a number of success of :math:`n` independent +trails of Bernoulli experimants. + +.. math:: + + E[binom(n,p)] = E[∑_{1≤x≤n} bernoulli(p)] = ∑_{1≤x≤n} E[bernoulli(p)] = n·p + + var[binom(n,p)] = var[∑_{1≤x≤n} bernoulli(p)] = ∑_{1≤x≤n} var[bernoulli(p)] = n·p·(1-p) + +Geometric random variable +------------------------- + +:def:`Geometric random variable` is a r.v. with parameter :math:`p` from +half open interval :math:`(0,1]`, sample space is all positive numbers: + +.. math:: + + p_{geom(p)}(x) = p (1-p)^(x-1) + +This random variable models number of tosses of biased coin until first success. + +.. math:: + + E[geom(p)] = ∑_{x=1..∞} x·p·(1-p)^(x-1) + + = p·∑_{x=1..∞} x·(1-p)^(x-1) + + = p/(1-p)·∑_{x=0..∞} x·(1-p)^x + + = p/(1-p)·(1-p)/(1-p - 1)² = p/p² = 1/p + +.. NOTE:: + + Maxima calculation:: + + load("simplify_sum"); + simplify_sum(sum(k * x^k, k, 0, inf)); + Is abs(x) - 1 positive, negative or zero? + negative; + Is x positive, negative or zero? + positive; + Is x - 1 positive, negative or zero? + negative; + x + ------------ + 2 + x - 2 x + 1 diff -r 000000000000 -r 328995b5b8fd statistics.rst --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/statistics.rst Wed Mar 09 21:23:23 2016 +0200 @@ -0,0 +1,160 @@ + +============ + Statistics +============ +.. contents:: + :local: + +.. role:: def + :class: def + +Markov inequality +================= + +:def:`Markov inequality`: :math:`P(X ≥ a) ≤ E[P]/a` for all :math:`a > 0`. + +Chebyshev inequality +==================== + +:def:`Chebyshev inequality`: if :math:`X` is a random variable with mean +:math:`μ` and variance :math:`σ²` then + +.. math:: + + P(|X-μ| ≥ c) ≤ σ²/c² + +for all :math:`c > 0`. + +Central limit theorem +===================== + +:def:`Central limit theorem`: let :math:`X_1, ..., X_n, ...` be a sequence of +independent identically distributed random variables with common mean :math:`μ` +and variance :math:`σ²` and let: + +.. math:: + + Z_n = ((∑_{1≤i≤n} X_i) - n·μ) / (σ·sqrt(n)) + +Then CDF of :math:`Z_n` converge to standard normal CDF: + +.. math:: + + Φ(z) = 1/(2·π)·∫_{(-∞;z]} exp(-x²/2) 𝑑x + + lim_{n → ∞} P(Z_n ≤ z) = Φ(z) + +Null hypothesis +=============== + +:def:`Null hypothesis` a statement that the phenomenon being studied produces no +effect or makes no difference, assumption that effect actually due to chance. + +p-value +======= + +:def:`p-value` is the probability of the apparent effect under the null +hypothesis. + + https://en.wikipedia.org/wiki/P-value + Wikipedia page + +Significance level +================== + +If the p-value is less than or equal to the chosen :def:`significance level` +(:math:`α`), the test suggests that the observed data are inconsistent with the +null hypothesis, so the null hypothesis should be rejected. + +Hypothesis testing +================== + +:def:`Hypothesis testing` is process of interpretation of statistical +significance of given null hypothesis based on observed p-value from sample with +choosen significance level. + +After finishing hypothesis testing we should reject null hypothesis or fail to +reject due to lack of enough evidence or ... + +Hypothesis testing only take into account: + +* that effect might be due to chance; that is, the difference might appear in a + random sample, but not in the general population + +But it doesn't cover cases: + +* The effect might be real; that is, a similar difference would be seen in the + general population. +* The apparent effect might be due to a biased sampling process, so it would not + appear in the general population. +* The apparent effect might be due to measurement errors. + +Asymptotic approximation +======================== + +CLT say that sample mean distribution is approximated by normal distribution. + +With fair enough number of samples approximation is quite good. + +So during hypothesis testing usually researcher makes assumption that is is safe +to replace unknown distribution of means for independent and identicaly +distributed individual samples with approximation. + +For really small number of samples Student distribution is used instead of +normal distribution. But again it means that researcher made assumption and you +may not agree with it, so it is your right to reject any subsequent decision +based on "wrong" assumption. + +Type I error +============ + +:def:`Type I error` is the incorrect rejection of a true null hypothesis (a +*false positive*). + +Type I error rate is at most :math:`α` (significant level). + +The p-value of a test is the maximum false positive risk you would take by +rejecting the null hypothesis. + +Type II error +============= + +:def:`Type II error` is failing to reject a false null hypothesis (a *false +negative*). + +Probability of type II error usually called :math:`β`. + +Power +===== + +:def:`Power` is a probability to reject null hypothesis when it's false. So +power probability is :math:`1-β`. + +Confidence interval +=================== + +:def:`Confidence interval` + + https://en.wikipedia.org/wiki/Confidence_interval + Wikipedia page + + +Question +======== + +What to do with null hypothesis in classical inference? + +I successfully shirked stat classes 10 years ago (last night reading help me +actually to pass exam) and now when I take several Coursera stat classes I have +difficulties with understanding **null hypothesis**. Somehow with unclear +intuition I passed quizzes but want to understand subject. + +Suppose we have population and sample some data from population. Reasonable +question: is some property of sample make evidence to be true on population? + +Statistic is a real number that can be derived from population or sample. +Classical example is a mean value. + +We ask is it statistically significant that statistic of population is near to +statistic of sample. + diff -r 000000000000 -r 328995b5b8fd tmpl/rst.css --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tmpl/rst.css Wed Mar 09 21:23:23 2016 +0200 @@ -0,0 +1,151 @@ +html, body, iframe { + margin: 0px; + padding: 0px; +} +iframe { + overflow: hidden; +} +.document { + max-width: 800px; + margin: .5em auto; + padding-left: 190px; + padding-right: 10px; + overflow: hidden; +} +#sidebar { position: fixed; } +iframe { border: none; } +#sidebar, iframe { + top: 0; + left: 0; + width: 180px; + height: 100%; +} +@media (max-width: 780px) { + .document { + margin: 5px 10px 1em 10px; + padding: 0; + } + #sidebar { position: inherit; } + #sidebar, iframe { + width: 100%; + height: 7em; + } +} + +.header { + font-size: 1.2em; + text-align: center; +} +.header.small { + font-size: 0.8em; +} + +div.contents ul { + max-width: 600px; + margin: 0 auto; +} +div.contents ul ul { + margin-left: 1em; +} + +a { + text-decoration:none; + color: hsl(240, 100%, 50%); +} +a:hover { opacity: .5; } +h1 a { + color: hsla(0, 0%, 0%, .8); +} +h2 a, h3 a, h4 a, .contents a { + color: hsl(0, 100%, 25%); +} +h1 { text-align: center; } +div > p:first-child:first-letter, +h2 + p:first-letter, +h3 + p:first-letter, +h4 + p:first-letter, +h5 + p:first-letter { + font-weight: bold; +} + +blockquote { + margin: 1em 0; +} +ul, ol { + margin-left: 1em; + padding-left: 1em; +} +dl { + margin-left: 1em; +} +.formula { + padding: 0 .5em; + background-color: hsl(0, 10%, 95%); +} +div.formula { + border: 1px; + border-left-style: solid; + border-right-style: solid; + border-color: hsl(0, 100%, 25%); +} +.literal-block { + margin: 0 0 0 1em; + padding: 5px; + border: 1px; + border-style: solid; + border-color: hsl(0, 100%, 25%); + background-color: hsl(0, 10%, 95%); + overflow: auto; +} +tt.literal { + outline: 1px dotted orange; + padding: 1px; +} +table { border-collapse:collapse; margin-left:auto; margin-right:auto; } +table, tr, td { padding: 3px; border: 1px dotted maroon; background-color: cornsilk; } + +pre.code.keyword, pre.code.tag { + color: blue; +} +pre.code.string, pre.code.preproc { + color: green; +} +pre.code.name.decorator, pre.code.literal { + color: brown; +} +pre.code.comment { + color: magenta; +} +pre.code span.generic.prompt { + color: magenta; +} +pre.code span.generic.output { + color: gray; +} + +.attention, .caution, .danger, .error, .warning, .important, .hint, .note, .tip { + padding: 0px; + margin: 5px 0px; + text-indent: 2em; +} +.hint, .note, .tip { border: green 1px solid; } +.warning, .important { border: gold 1px solid; } +.attention, .caution, .danger, .error { border: red 1px solid; } +.attention p.first, .caution p.first, .danger p.first, .error p.first, .warning p.first, .important p.first, .hint p.first, .note p.first, .tip p.first { + margin: 0px; + padding: 5px 0px; +} +.hint p.first, .note p.first, .tip p.first { background: rgb(176, 255, 176); } +.warning p.first, .important p.first { background: rgb(255, 233, 176); } +.attention p.first, .caution p.first, .danger p.first, .error p.first { background: rgb(255, 196, 180); } +.attention p, .caution p, .danger p, .error p, .warning p, .important p, .hint p, .note p, .tip p { + margin: 5px; +} +.attention pre, .caution pre, .danger pre, .error pre, .warning pre, .important pre, .hint pre, .note pre, .tip pre { + margin: 0 5px 5px 4em; +} + +.def { + font-family: monospace; + color: hsl(120, 100%, 25%); +} diff -r 000000000000 -r 328995b5b8fd tmpl/rst.tmpl.in --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tmpl/rst.tmpl.in Wed Mar 09 21:23:23 2016 +0200 @@ -0,0 +1,23 @@ + + + + + +%(head)s +%(stylesheet)s +%(body_prefix)s + + + +%(body_pre_docinfo)s +%(docinfo)s +%(body)s +%(body_suffix)s