diff --git a/Pipfile b/Pipfile new file mode 100644 index 0000000..5e75813 --- /dev/null +++ b/Pipfile @@ -0,0 +1,14 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[dev-packages] + +[packages] +pandas = "*" +jupyter = "*" +matplotlib = "*" + +[requires] +python_version = "3.7" diff --git a/Pipfile b/Pipfile new file mode 100644 index 0000000..5e75813 --- /dev/null +++ b/Pipfile @@ -0,0 +1,14 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[dev-packages] + +[packages] +pandas = "*" +jupyter = "*" +matplotlib = "*" + +[requires] +python_version = "3.7" diff --git a/ccn2019/ccn2019.rev3.Rmd b/ccn2019/ccn2019.rev3.Rmd index e62332e..2e890f6 100644 --- a/ccn2019/ccn2019.rev3.Rmd +++ b/ccn2019/ccn2019.rev3.Rmd @@ -76,11 +76,8 @@ ```{r preprocessing, message=FALSE, warning=FALSE, include=FALSE} -#! =============================================== #! A function to mark lures in a sequence - -#! =============================================== -#! Extract t,tl,l,ll,u,ul,s,sl,a,al +#! Extracts t,tl,l,ll,u,ul,s,sl,a,al #! a and al are respectively accuracy and recent accuracy seqs.raw <- NB %>% group_by(participant) %>% @@ -226,9 +223,9 @@ Correctness refers to the `correct` column of the dataset, which shows if the response was correct or not, for both targets and non-targets. The following code trains two models to predict correctness of a trial without using current stimulus and its type. It only uses predictors that describes the sliding window, and experimental condition (N, and V, which is the number of unique items presented in the sequence). -The goal of this analysis is to find out if statistics that are extracted from previous trials via the sliding window can describe the correctness of choice. +The goal of this analysis is to find out if statistics that are extracted from previous trials via the sliding window can describe the correctness of choice that is accuracy of only one trial. -In addition to the model comparasion plotd, he final output of this section is a single ROC plot that compares new and old models. The same chunk can be used for other categorical models, including RT_CAT and penalized models. +In addition to the model comparasion plots, the final output of this section is a ROC plot that compares new and old models. The same code chunk can be used for other categorical models, including RT_CAT and penalized models of accuracy. Note: uncomment desired `old.f` and `new.f` formulas to run respective analysis. diff --git a/Pipfile b/Pipfile new file mode 100644 index 0000000..5e75813 --- /dev/null +++ b/Pipfile @@ -0,0 +1,14 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[dev-packages] + +[packages] +pandas = "*" +jupyter = "*" +matplotlib = "*" + +[requires] +python_version = "3.7" diff --git a/ccn2019/ccn2019.rev3.Rmd b/ccn2019/ccn2019.rev3.Rmd index e62332e..2e890f6 100644 --- a/ccn2019/ccn2019.rev3.Rmd +++ b/ccn2019/ccn2019.rev3.Rmd @@ -76,11 +76,8 @@ ```{r preprocessing, message=FALSE, warning=FALSE, include=FALSE} -#! =============================================== #! A function to mark lures in a sequence - -#! =============================================== -#! Extract t,tl,l,ll,u,ul,s,sl,a,al +#! Extracts t,tl,l,ll,u,ul,s,sl,a,al #! a and al are respectively accuracy and recent accuracy seqs.raw <- NB %>% group_by(participant) %>% @@ -226,9 +223,9 @@ Correctness refers to the `correct` column of the dataset, which shows if the response was correct or not, for both targets and non-targets. The following code trains two models to predict correctness of a trial without using current stimulus and its type. It only uses predictors that describes the sliding window, and experimental condition (N, and V, which is the number of unique items presented in the sequence). -The goal of this analysis is to find out if statistics that are extracted from previous trials via the sliding window can describe the correctness of choice. +The goal of this analysis is to find out if statistics that are extracted from previous trials via the sliding window can describe the correctness of choice that is accuracy of only one trial. -In addition to the model comparasion plotd, he final output of this section is a single ROC plot that compares new and old models. The same chunk can be used for other categorical models, including RT_CAT and penalized models. +In addition to the model comparasion plots, the final output of this section is a ROC plot that compares new and old models. The same code chunk can be used for other categorical models, including RT_CAT and penalized models of accuracy. Note: uncomment desired `old.f` and `new.f` formulas to run respective analysis. diff --git a/ccn2019/ccn2019_diagrams.R b/ccn2019/ccn2019_diagrams.R index 329d274..f22cf1f 100644 --- a/ccn2019/ccn2019_diagrams.R +++ b/ccn2019/ccn2019_diagrams.R @@ -71,7 +71,7 @@ to_auc_label <- function(model, auc) { paste(model, "\nAUC=", - format(auc, digits=4), + format(auc, digits=3), sep = "" ) } @@ -95,9 +95,12 @@ #scale_y_continuous(labels = scales::percent) + scale_fill_brewer(palette = "Greens") + scale_color_manual(values=c("black", "#808080", "gray")) + - theme(legend.position = "none") + theme(legend.position = "none", text=element_text(size=16,family="Helvetica Neue Light")) +#library(extrafont) +#font_import(pattern = "Helvet.*") ggsave("fig1.png", plot = last_plot(), width = 4, height = 4) +#embed_fonts("fig1.pdf") library(latex2exp) diff --git a/Pipfile b/Pipfile new file mode 100644 index 0000000..5e75813 --- /dev/null +++ b/Pipfile @@ -0,0 +1,14 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[dev-packages] + +[packages] +pandas = "*" +jupyter = "*" +matplotlib = "*" + +[requires] +python_version = "3.7" diff --git a/ccn2019/ccn2019.rev3.Rmd b/ccn2019/ccn2019.rev3.Rmd index e62332e..2e890f6 100644 --- a/ccn2019/ccn2019.rev3.Rmd +++ b/ccn2019/ccn2019.rev3.Rmd @@ -76,11 +76,8 @@ ```{r preprocessing, message=FALSE, warning=FALSE, include=FALSE} -#! =============================================== #! A function to mark lures in a sequence - -#! =============================================== -#! Extract t,tl,l,ll,u,ul,s,sl,a,al +#! Extracts t,tl,l,ll,u,ul,s,sl,a,al #! a and al are respectively accuracy and recent accuracy seqs.raw <- NB %>% group_by(participant) %>% @@ -226,9 +223,9 @@ Correctness refers to the `correct` column of the dataset, which shows if the response was correct or not, for both targets and non-targets. The following code trains two models to predict correctness of a trial without using current stimulus and its type. It only uses predictors that describes the sliding window, and experimental condition (N, and V, which is the number of unique items presented in the sequence). -The goal of this analysis is to find out if statistics that are extracted from previous trials via the sliding window can describe the correctness of choice. +The goal of this analysis is to find out if statistics that are extracted from previous trials via the sliding window can describe the correctness of choice that is accuracy of only one trial. -In addition to the model comparasion plotd, he final output of this section is a single ROC plot that compares new and old models. The same chunk can be used for other categorical models, including RT_CAT and penalized models. +In addition to the model comparasion plots, the final output of this section is a ROC plot that compares new and old models. The same code chunk can be used for other categorical models, including RT_CAT and penalized models of accuracy. Note: uncomment desired `old.f` and `new.f` formulas to run respective analysis. diff --git a/ccn2019/ccn2019_diagrams.R b/ccn2019/ccn2019_diagrams.R index 329d274..f22cf1f 100644 --- a/ccn2019/ccn2019_diagrams.R +++ b/ccn2019/ccn2019_diagrams.R @@ -71,7 +71,7 @@ to_auc_label <- function(model, auc) { paste(model, "\nAUC=", - format(auc, digits=4), + format(auc, digits=3), sep = "" ) } @@ -95,9 +95,12 @@ #scale_y_continuous(labels = scales::percent) + scale_fill_brewer(palette = "Greens") + scale_color_manual(values=c("black", "#808080", "gray")) + - theme(legend.position = "none") + theme(legend.position = "none", text=element_text(size=16,family="Helvetica Neue Light")) +#library(extrafont) +#font_import(pattern = "Helvet.*") ggsave("fig1.png", plot = last_plot(), width = 4, height = 4) +#embed_fonts("fig1.pdf") library(latex2exp) diff --git a/py/.ipynb_checkpoints/pandas-checkpoint.ipynb b/py/.ipynb_checkpoints/pandas-checkpoint.ipynb new file mode 100644 index 0000000..3ff7e55 --- /dev/null +++ b/py/.ipynb_checkpoints/pandas-checkpoint.ipynb @@ -0,0 +1,67 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": false, + "name": "#%% r\n" + } + }, + "outputs": [], + "source": [ + "a <- 3\n", + "print(a)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**a** \n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + }, + "pycharm": { + "stem_cell": { + "cell_type": "raw", + "metadata": { + "collapsed": false + }, + "source": [ + "Simulate \n" + ] + } + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Pipfile b/Pipfile new file mode 100644 index 0000000..5e75813 --- /dev/null +++ b/Pipfile @@ -0,0 +1,14 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[dev-packages] + +[packages] +pandas = "*" +jupyter = "*" +matplotlib = "*" + +[requires] +python_version = "3.7" diff --git a/ccn2019/ccn2019.rev3.Rmd b/ccn2019/ccn2019.rev3.Rmd index e62332e..2e890f6 100644 --- a/ccn2019/ccn2019.rev3.Rmd +++ b/ccn2019/ccn2019.rev3.Rmd @@ -76,11 +76,8 @@ ```{r preprocessing, message=FALSE, warning=FALSE, include=FALSE} -#! =============================================== #! A function to mark lures in a sequence - -#! =============================================== -#! Extract t,tl,l,ll,u,ul,s,sl,a,al +#! Extracts t,tl,l,ll,u,ul,s,sl,a,al #! a and al are respectively accuracy and recent accuracy seqs.raw <- NB %>% group_by(participant) %>% @@ -226,9 +223,9 @@ Correctness refers to the `correct` column of the dataset, which shows if the response was correct or not, for both targets and non-targets. The following code trains two models to predict correctness of a trial without using current stimulus and its type. It only uses predictors that describes the sliding window, and experimental condition (N, and V, which is the number of unique items presented in the sequence). -The goal of this analysis is to find out if statistics that are extracted from previous trials via the sliding window can describe the correctness of choice. +The goal of this analysis is to find out if statistics that are extracted from previous trials via the sliding window can describe the correctness of choice that is accuracy of only one trial. -In addition to the model comparasion plotd, he final output of this section is a single ROC plot that compares new and old models. The same chunk can be used for other categorical models, including RT_CAT and penalized models. +In addition to the model comparasion plots, the final output of this section is a ROC plot that compares new and old models. The same code chunk can be used for other categorical models, including RT_CAT and penalized models of accuracy. Note: uncomment desired `old.f` and `new.f` formulas to run respective analysis. diff --git a/ccn2019/ccn2019_diagrams.R b/ccn2019/ccn2019_diagrams.R index 329d274..f22cf1f 100644 --- a/ccn2019/ccn2019_diagrams.R +++ b/ccn2019/ccn2019_diagrams.R @@ -71,7 +71,7 @@ to_auc_label <- function(model, auc) { paste(model, "\nAUC=", - format(auc, digits=4), + format(auc, digits=3), sep = "" ) } @@ -95,9 +95,12 @@ #scale_y_continuous(labels = scales::percent) + scale_fill_brewer(palette = "Greens") + scale_color_manual(values=c("black", "#808080", "gray")) + - theme(legend.position = "none") + theme(legend.position = "none", text=element_text(size=16,family="Helvetica Neue Light")) +#library(extrafont) +#font_import(pattern = "Helvet.*") ggsave("fig1.png", plot = last_plot(), width = 4, height = 4) +#embed_fonts("fig1.pdf") library(latex2exp) diff --git a/py/.ipynb_checkpoints/pandas-checkpoint.ipynb b/py/.ipynb_checkpoints/pandas-checkpoint.ipynb new file mode 100644 index 0000000..3ff7e55 --- /dev/null +++ b/py/.ipynb_checkpoints/pandas-checkpoint.ipynb @@ -0,0 +1,67 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": false, + "name": "#%% r\n" + } + }, + "outputs": [], + "source": [ + "a <- 3\n", + "print(a)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**a** \n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + }, + "pycharm": { + "stem_cell": { + "cell_type": "raw", + "metadata": { + "collapsed": false + }, + "source": [ + "Simulate \n" + ] + } + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/py/pandas.ipynb b/py/pandas.ipynb index e5206af..ca54328 100644 --- a/py/pandas.ipynb +++ b/py/pandas.ipynb @@ -2,14 +2,97 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { - "collapsed": true + "collapsed": true, + "pycharm": { + "name": "#%%\n", + "is_executing": false + } }, + "outputs": [ + { + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mcoins_random_walk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0mcoins\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"head\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: append() takes exactly one argument (0 given)" + ], + "ename": "TypeError", + "evalue": "append() takes exactly one argument (0 given)", + "output_type": "error" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "np.random.seed(123)\n", + "coins = []\n", + "coins_random_walk = [0]\n", + "for i in range(10):\n", + " if np.random.randint(0,2) == 0:\n", + " coins_random_walk.append()\n", + " coins.append(\"head\")\n", + " else:\n", + " coins.append(\"tail\")\n", + "print(coins)" + ] + }, + { + "cell_type": "code", + "execution_count": null, "outputs": [], "source": [ - "\n" - ] + "A = [1,2,3]\n", + "B = A + [4,5]\n", + "B.append(6)\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n", + "is_executing": false + } + } + }, + { + "cell_type": "code", + "execution_count": 18, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# matplotlib\n", + "import matplotlib.pyplot as plt\n", + "plt.scatter([1,2,3], [7,8,9], s=[3,60,90], c=['red','blue','green'], alpha=0.5)\n", + "\n", + "plt.text(10, 8.5, \"Here is some\")\n", + "\n", + "plt.grid(True)\n", + "\n", + "plt.xscale('log') \n", + "plt.xlabel(\"X Label\")\n", + "plt.ylabel(\"Y Label\")\n", + "plt.title(\"Title\")\n", + "plt.xticks([10,20,30], [\"10x\", \"20x\",\"30x\"])\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% \n", + "is_executing": false + } + } } ], "metadata": { @@ -24,6 +107,22 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" + }, + "kernelspec": { + "name": "python3", + "language": "python", + "display_name": "Python 3" + }, + "pycharm": { + "stem_cell": { + "cell_type": "raw", + "source": [ + "Simulate \n" + ], + "metadata": { + "collapsed": false + } + } } }, "nbformat": 4, diff --git a/Pipfile b/Pipfile new file mode 100644 index 0000000..5e75813 --- /dev/null +++ b/Pipfile @@ -0,0 +1,14 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[dev-packages] + +[packages] +pandas = "*" +jupyter = "*" +matplotlib = "*" + +[requires] +python_version = "3.7" diff --git a/ccn2019/ccn2019.rev3.Rmd b/ccn2019/ccn2019.rev3.Rmd index e62332e..2e890f6 100644 --- a/ccn2019/ccn2019.rev3.Rmd +++ b/ccn2019/ccn2019.rev3.Rmd @@ -76,11 +76,8 @@ ```{r preprocessing, message=FALSE, warning=FALSE, include=FALSE} -#! =============================================== #! A function to mark lures in a sequence - -#! =============================================== -#! Extract t,tl,l,ll,u,ul,s,sl,a,al +#! Extracts t,tl,l,ll,u,ul,s,sl,a,al #! a and al are respectively accuracy and recent accuracy seqs.raw <- NB %>% group_by(participant) %>% @@ -226,9 +223,9 @@ Correctness refers to the `correct` column of the dataset, which shows if the response was correct or not, for both targets and non-targets. The following code trains two models to predict correctness of a trial without using current stimulus and its type. It only uses predictors that describes the sliding window, and experimental condition (N, and V, which is the number of unique items presented in the sequence). -The goal of this analysis is to find out if statistics that are extracted from previous trials via the sliding window can describe the correctness of choice. +The goal of this analysis is to find out if statistics that are extracted from previous trials via the sliding window can describe the correctness of choice that is accuracy of only one trial. -In addition to the model comparasion plotd, he final output of this section is a single ROC plot that compares new and old models. The same chunk can be used for other categorical models, including RT_CAT and penalized models. +In addition to the model comparasion plots, the final output of this section is a ROC plot that compares new and old models. The same code chunk can be used for other categorical models, including RT_CAT and penalized models of accuracy. Note: uncomment desired `old.f` and `new.f` formulas to run respective analysis. diff --git a/ccn2019/ccn2019_diagrams.R b/ccn2019/ccn2019_diagrams.R index 329d274..f22cf1f 100644 --- a/ccn2019/ccn2019_diagrams.R +++ b/ccn2019/ccn2019_diagrams.R @@ -71,7 +71,7 @@ to_auc_label <- function(model, auc) { paste(model, "\nAUC=", - format(auc, digits=4), + format(auc, digits=3), sep = "" ) } @@ -95,9 +95,12 @@ #scale_y_continuous(labels = scales::percent) + scale_fill_brewer(palette = "Greens") + scale_color_manual(values=c("black", "#808080", "gray")) + - theme(legend.position = "none") + theme(legend.position = "none", text=element_text(size=16,family="Helvetica Neue Light")) +#library(extrafont) +#font_import(pattern = "Helvet.*") ggsave("fig1.png", plot = last_plot(), width = 4, height = 4) +#embed_fonts("fig1.pdf") library(latex2exp) diff --git a/py/.ipynb_checkpoints/pandas-checkpoint.ipynb b/py/.ipynb_checkpoints/pandas-checkpoint.ipynb new file mode 100644 index 0000000..3ff7e55 --- /dev/null +++ b/py/.ipynb_checkpoints/pandas-checkpoint.ipynb @@ -0,0 +1,67 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": false, + "name": "#%% r\n" + } + }, + "outputs": [], + "source": [ + "a <- 3\n", + "print(a)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**a** \n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + }, + "pycharm": { + "stem_cell": { + "cell_type": "raw", + "metadata": { + "collapsed": false + }, + "source": [ + "Simulate \n" + ] + } + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/py/pandas.ipynb b/py/pandas.ipynb index e5206af..ca54328 100644 --- a/py/pandas.ipynb +++ b/py/pandas.ipynb @@ -2,14 +2,97 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { - "collapsed": true + "collapsed": true, + "pycharm": { + "name": "#%%\n", + "is_executing": false + } }, + "outputs": [ + { + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mcoins_random_walk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0mcoins\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"head\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: append() takes exactly one argument (0 given)" + ], + "ename": "TypeError", + "evalue": "append() takes exactly one argument (0 given)", + "output_type": "error" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "np.random.seed(123)\n", + "coins = []\n", + "coins_random_walk = [0]\n", + "for i in range(10):\n", + " if np.random.randint(0,2) == 0:\n", + " coins_random_walk.append()\n", + " coins.append(\"head\")\n", + " else:\n", + " coins.append(\"tail\")\n", + "print(coins)" + ] + }, + { + "cell_type": "code", + "execution_count": null, "outputs": [], "source": [ - "\n" - ] + "A = [1,2,3]\n", + "B = A + [4,5]\n", + "B.append(6)\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n", + "is_executing": false + } + } + }, + { + "cell_type": "code", + "execution_count": 18, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# matplotlib\n", + "import matplotlib.pyplot as plt\n", + "plt.scatter([1,2,3], [7,8,9], s=[3,60,90], c=['red','blue','green'], alpha=0.5)\n", + "\n", + "plt.text(10, 8.5, \"Here is some\")\n", + "\n", + "plt.grid(True)\n", + "\n", + "plt.xscale('log') \n", + "plt.xlabel(\"X Label\")\n", + "plt.ylabel(\"Y Label\")\n", + "plt.title(\"Title\")\n", + "plt.xticks([10,20,30], [\"10x\", \"20x\",\"30x\"])\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% \n", + "is_executing": false + } + } } ], "metadata": { @@ -24,6 +107,22 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" + }, + "kernelspec": { + "name": "python3", + "language": "python", + "display_name": "Python 3" + }, + "pycharm": { + "stem_cell": { + "cell_type": "raw", + "source": [ + "Simulate \n" + ], + "metadata": { + "collapsed": false + } + } } }, "nbformat": 4, diff --git a/py/vscode_jupyter_test.py b/py/vscode_jupyter_test.py new file mode 100644 index 0000000..ccecc43 --- /dev/null +++ b/py/vscode_jupyter_test.py @@ -0,0 +1,4 @@ +#%% +print("test") + +#%%