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Add tutorial 12 13 14 15 in unsolved-exercise folder and update readme
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‎15-tutorial.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This tutorial will help you utilise the module to get details about the data model of various mines. These are additional examples, besides the ones covered [here](http://intermine.org/intermine-ws-python/intermine.html#module-intermine.model).Each class (data type) has information containing references to other objects in the data model, collections of references or attribute details."
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"This tutorial will help you utilise the module to get details about the data model of various mines. These are additional examples, besides the ones covered [here](http://intermine.org/intermine-ws-python/intermine.html#module-intermine.model). Each class (data type) has information containing references to other objects in the data model, collections of references or attribute details."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Let's begin with something basic, say you want to see all the fields a data type contains (including all Attributes, References and Collections listed alphabetically),then-"
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"Let's begin by showing all the fields a data type contains (including all Attributes, References and Collections listed alphabetically),then-"
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]
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},
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{

‎unsolved-exercises/12-tutorial.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Intermine-Python: Tutorial 12: More about Queries"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Welcome to your twelfth intermine-python tutorial. "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This tutorial will cover some more functionalities of a query. Queries are the basis of all research in InterMine and being able to manage them more effectively is always useful. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from intermine.webservice import Service"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"service = Service(\"https://www.flymine.org/query/service\")\n",
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"query=service.new_query()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"query.add_view(\"Gene.organism.name\",\"Gene.symbol\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Suppose, the query is not as simple as a strict cumulative filter and the user wants combinations of constraints. For example, the user wants all genes such that the gene symbol is either ‘eve’ or ‘zen’. This can be incorporated in the following way using set_logic: "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"gene_is_eve = query.add_constraint(\"Gene.symbol\", \"=\", \"eve\")\n",
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"gene_is_zen = query.add_constraint(\"Gene.symbol\", \"=\", \"zen\")\n",
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"query.set_logic(gene_is_eve | gene_is_zen)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"for row in query.rows():\n",
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" print(row)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The query results can be converted into a dictionary in the following way: "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"for row in query.rows():\n",
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" print(row.to_d())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Similarly, row.to_l() can be used for conversion of the results into a list."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"count() can be used to print the total number of rows in a query: "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"query.count()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"to_xml() can be used to return a readable XML serialisation of the query:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"query.to_xml()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"clear_view() can be used to clear the output column list:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"for row in query.rows():\n",
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" print(row.to_l())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"query.clear_view()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"for row in query.rows():\n",
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" print(row)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In these ways, queries can be utilized to a greater extent and produce more fruitful results. "
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]
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}
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],
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"metadata": {
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"anaconda-cloud": {},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python_defaultSpec_1593349094533"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.3-final"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}

‎unsolved-exercises/13-tutorial.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Intermine-Python: Tutorial 13: Query Manager"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This tutorial will tell you about the Query Manager and how to it can be used to make better use of queries. We hope at this stage you have the 'intermine' package installed "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We start by importing the module from InterMine package. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from intermine import query_manager as qm"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The next step will be to access the account whose query options we want to manage"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"qm.save_mine_and_token(\"flymine\",\"41v6bat743zf06jcXek5\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"There are now four functions that we can utilise:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"qm.post_query('<query name=\"test\" model=\"genomic\" view=\"Gene.length\\\n",
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" Gene.symbol\" longDescription=\"\" sortOrder=\"Gene.length asc\">\\\n",
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" </query>')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"'''posts a query with name and other information as above'''"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"qm.get_all_query_names()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"'''returns the names of all the saved queries in user account'''"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"qm.get_query('test')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"'''returns information about the query whose name is 'test'''"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"qm.delete_query('test')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"'''deletes the query whose name is 'test' from user's account'''"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now that we have made dealing with queries easier, the next tutorial will have details of how we can visualise the data using Python client."
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]
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}
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],
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"metadata": {
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"anaconda-cloud": {},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.3-final"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}

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