def test_columns_info(self):
import liquer.ext.lq_pandas # register pandas commands and state type
from liquer.state_types import encode_state_data, decode_state_data
filename = encode_token(os.path.dirname(
inspect.getfile(self.__class__))+"/test.csv")
assert evaluate(f"df_from-{filename}/df_columns").get() == ["a", "b"]
assert evaluate(
f"df_from-{filename}/columns_info").get()["columns"] == ["a", "b"]
assert evaluate(
f"df_from-{filename}/columns_info").get()["has_tags"] == False
assert evaluate(
f"df_from-{filename}/columns_info").get()["types"]["a"].startswith("int")
filename = encode_token(os.path.dirname(
inspect.getfile(self.__class__))+"/test_hxl.csv")
assert evaluate(f"df_from-{filename}/df_columns").get() == ["a", "b"]
assert evaluate(
f"df_from-{filename}/columns_info").get()["columns"] == ["a", "b"]
assert evaluate(
f"df_from-{filename}/columns_info").get()["has_tags"] == True
assert evaluate(
f"df_from-{filename}/columns_info").get()["tags"]["a"] == "#indicator +num +aaa"
assert evaluate(
f"df_from-{filename}/columns_info").get()["tags"]["b"] == "#indicator +num +bbb"
info = evaluate(f"df_from-{filename}/columns_info").get()
b, mime, tid = encode_state_data(info)
assert info == decode_state_data(b, tid)
def test_from_with_cache(self, httpserver):
import liquer.ext.lq_hxl # register HXL commands and state type
test_hxl = open(
os.path.dirname(inspect.getfile(self.__class__)) +
"/test_hxl.csv").read()
httpserver.expect_request("/test_hxl.csv").respond_with_data(test_hxl)
url = encode_token(httpserver.url_for("/test_hxl.csv"))
query = f"hxl_from-{url}"
with tempfile.TemporaryDirectory() as cachepath:
set_cache(FileCache(cachepath))
state = evaluate(query)
data = state.get()
assert data.columns[0].header == "a"
assert data.columns[0].display_tag == "#indicator+num+aaa"
assert data.columns[1].header == "b"
assert data.columns[1].display_tag == "#indicator+num+bbb"
state = evaluate(query)
data = state.get()
assert data.columns[0].header == "a"
assert data.columns[0].display_tag == "#indicator+num+aaa"
assert data.columns[1].header == "b"
assert data.columns[1].display_tag == "#indicator+num+bbb"
set_cache(None)
def test_ridge(self):
import importlib
import liquer.ext.basic
import liquer.ext.lq_pandas
import liquer.ext.lq_sklearn_regression
from liquer.commands import reset_command_registry
reset_command_registry() # prevent double-registration
# Hack to enforce registering of the commands
importlib.reload(liquer.ext.basic)
importlib.reload(liquer.ext.lq_pandas)
importlib.reload(liquer.ext.lq_sklearn_regression)
@first_command
def test1():
return pd.DataFrame(dict(x=[1, 2, 3], Y=[10, 20, 30]))
@first_command
def test2():
return pd.DataFrame(dict(x=[1, 2, 3], Y=[30, 40, 50]))
@first_command
def test3():
return pd.DataFrame(dict(x1=[1, 2, 3], x2=[0, 0, 1], Y=[30, 40, 55]))
df = evaluate("ns-sklearn/test1/ridge").get()
assert list(df.x)[0] == pytest.approx(9.52381)
df = evaluate("ns-sklearn/test2/ridge").get()
assert list(df.x)[0] == pytest.approx(9.52381)
assert list(df.intercept)[0] == pytest.approx(20.952381)
df = evaluate("ns-sklearn/test3/ridge").get()
assert list(df.x1)[0] == pytest.approx(9.562842)
assert list(df.x2)[0] == pytest.approx(4.918033)
assert list(df.intercept)[0] == pytest.approx(20.901639)
def test_vars(self):
import liquer.ext.basic
assert evaluate("state_variable-abc").get() is None
assert evaluate("let-abc-1/state_variable-abc").get() == "1"
assert evaluate("state_variable-abc").get() is None
set_var("abc", "123")
assert evaluate("state_variable-abc").get() == "123"
assert evaluate("let-abc-1/state_variable-abc").get() == "1"
def test_image(self):
store = MemoryStore()
set_store(store)
@first_command
def image():
return Image.new(mode="RGB", size=(200, 300))
assert evaluate("image").get().size == (200,300)
evaluate_and_save("image/ns-pil/resize-400-600-bilinear/test.png",target_resource_directory="x")
image = evaluate("x/test.png/-/dr").get()
assert image.size == (400,600)
def test_set_tags(self):
import liquer.ext.lq_hxl # register HXL commands and state type
import liquer.ext.lq_pandas # register pandas commands and state type
filename = encode_token(os.path.dirname(
inspect.getfile(self.__class__))+"/test.csv")
data = evaluate(f"df_from-{filename}/set_tags-b-indicator+b-a-indicator+a/df2hxl").get()
assert data.columns[0].header == "a"
assert data.columns[0].display_tag == "#indicator+a"
assert data.columns[1].header == "b"
assert data.columns[1].display_tag == "#indicator+b"
df = evaluate(f"df_from-{filename}/set_tags-b-indicator+b").get()
assert list(df.a) == ["",1,3]
assert list(df.b) == ["#indicator+b",2,4]
def test_teq(self):
import liquer.ext.lq_pandas # register pandas commands and state type
filename = encode_token(os.path.dirname(
inspect.getfile(self.__class__))+"/test_hxl.csv")
state = evaluate(f"df_from-{filename}/teq-a-1")
df = state.get()
assert "a" in df.columns
assert "b" in df.columns
assert list(df.a) == ["#indicator +num +aaa", "1"]
assert list(df.b) == ["#indicator +num +bbb", "2"]
df = evaluate(f"df_from-{filename}/teq-a-3-b-4").get()
assert list(df.a) == ["#indicator +num +aaa", "3"]
assert list(df.b) == ["#indicator +num +bbb", "4"]
df = evaluate(f"df_from-{filename}/teq-a-1-b-4").get()
assert list(df.a) == ["#indicator +num +aaa"]
assert list(df.b) == ["#indicator +num +bbb"]
def test_head(self):
import liquer.ext.lq_pandas # register pandas commands and state type
filename = encode_token(os.path.dirname(
inspect.getfile(self.__class__))+"/test.csv")
df = evaluate(f"df_from-{filename}/head_df-1").get()
assert list(df.a) == [1]
assert list(df.b) == [2]
def split_df(state, *columns):
"""Split of dataframe by columns
Creates a dataframe with unique (combinations of) value from supplied columns and queries
to obtain the corresponding filtered dataframes from the original dataframe.
This behaves like qsplit_df, with two important differenced:
- each generated query is evaluated (and thus eventually cached)
- link is generated and put into link column (state variable link_column)
The split_link_type state variable is used to determine the link type; url by default.
"""
from liquer.parser import parse
state = qsplit_df(state, *columns)
df = state.get().copy()
query_column = state.vars.get("query_column")
if query_column is None:
query_column = "query"
link_column = state.vars.get("link_column")
if link_column is None:
link_column = "link"
split_link_type = state.vars.get("split_link_type")
if split_link_type is None:
split_link_type = "url"
# df.loc[:,link_column] = [evaluate(encode(decode(q)+[["link",split_link_type]])).get() for q in df[query_column]]
df.loc[:, link_column] = [
evaluate(parse(q).with_action("link", split_link_type).encode()).get()
for q in df[query_column]
]
return state.with_data(df)
def tsplit_df(state, *columns):
"""Split of dataframe by columns (version of split_df expecting a first row with tags)"""
from liquer.parser import parse
state = qtsplit_df(state, *columns)
df = state.get().copy()
query_column = state.vars.get("query_column")
if query_column is None:
query_column = "query"
link_column = state.vars.get("link_column")
if link_column is None:
link_column = "link"
split_link_type = state.vars.get("split_link_type")
if split_link_type is None:
split_link_type = "url"
# df.loc[:,link_column] = [""]+[evaluate(encode(decode(q)+[["link",split_link_type]])).get() for q in list(df[query_column])[1:]]
df.loc[:, link_column] = [""] + [
evaluate(parse(q).with_action("link", split_link_type).encode()).get()
for q in list(df[query_column])[1:]
]
return state.with_data(df)
def serve(query):
"""Main service for evaluating queries"""
try:
return response(evaluate(query))
except:
traceback.print_exc()
abort(500)
def test_tsplit(self):
import importlib
import liquer.ext.lq_pandas # register pandas commands and state type
import liquer.ext.basic
from liquer.commands import reset_command_registry
reset_command_registry() # prevent double-registration
# Hack to enforce registering of the commands
importlib.reload(liquer.ext.lq_pandas)
importlib.reload(liquer.ext.basic)
set_var("server", "http://localhost")
set_var("api_path", "/q/")
filename = encode_token(
os.path.dirname(inspect.getfile(self.__class__)) + "/test_hxl.csv")
df = evaluate(f"df_from-{filename}/tsplit_df-a").get()
assert "a" in df.columns
assert "b" not in df.columns
assert list(df.a) == ["#indicator +num +aaa", "1", "3"]
assert list(df["query"])[1:] == [
f"df_from-{filename}/teq-a-1",
f"df_from-{filename}/teq-a-3",
]
assert list(df["link"])[1:] == [
f"http://localhost/q/df_from-{filename}/teq-a-1",
f"http://localhost/q/df_from-{filename}/teq-a-3",
]
def test_work_with_parquet(self):
filename = encode_token(
os.path.dirname(inspect.getfile(self.__class__)) + "/test.csv")
with TemporaryDirectory() as tmpdir:
path = Path(tmpdir) / "test.parquet"
evaluate_and_save(f"df_from-{filename}/test.parquet",
target_directory=tmpdir)
@first_command(volatile=True, cache=False)
def execution_context():
ctx = daf.ExecutionContext()
ctx.register_parquet("a", str(path))
return ctx
@command
def process(ctx):
return ctx.sql("SELECT a, b, a+b AS c FROM a")
evaluate_and_save(f"execution_context/process/result.parquet",
target_directory=tmpdir)
df = evaluate(
f"execution_context/process/datafusion_to_pandas").get()
assert "a" in df.columns
assert "b" in df.columns
assert "c" in df.columns
assert list(df.a) == [1, 3]
assert list(df.b) == [2, 4]
assert list(df.c) == [3, 7]
def test_eq(self):
import liquer.ext.lq_pandas # register pandas commands and state type
filename = encode_token(os.path.dirname(
inspect.getfile(self.__class__))+"/test.csv")
state = evaluate(f"df_from-{filename}/eq-a-1")
df = state.get()
assert "a" in df.columns
assert "b" in df.columns
assert list(df.a) == [1]
assert list(df.b) == [2]
df = evaluate(f"df_from-{filename}/eq-a-3-b-4").get()
assert list(df.a) == [3]
assert list(df.b) == [4]
df = evaluate(f"df_from-{filename}/eq-a-1-b-4").get()
assert list(df.a) == []
assert list(df.b) == []
def test_append_with_cache(self):
import liquer.ext.lq_pandas # register pandas commands and state type
with tempfile.TemporaryDirectory() as cachepath:
set_cache(FileCache(cachepath))
filename = encode_token(os.path.dirname(
inspect.getfile(self.__class__))+"/test.csv")
df = evaluate(f"df_from-{filename}/append_df-{filename}").get()
assert "a" in df.columns
assert "b" in df.columns
assert list(df.a) == [1, 3, 1, 3]
assert list(df.b) == [2, 4, 2, 4]
df = evaluate(f"df_from-{filename}/append_df-{filename}").get()
assert "a" in df.columns
assert "b" in df.columns
assert list(df.a) == [1, 3, 1, 3]
assert list(df.b) == [2, 4, 2, 4]
set_cache(None)
def test_qsplit2(self):
import liquer.ext.lq_pandas # register pandas commands and state type
filename = encode_token(os.path.dirname(
inspect.getfile(self.__class__))+"/test.csv")
df = evaluate(f"df_from-{filename}/qsplit_df-a-b").get()
assert list(df.a) == [1, 3]
assert list(df.b) == [2, 4]
assert list(df["query"]) == [
f"df_from-{filename}/eq-a-1-b-2", f"df_from-{filename}/eq-a-3-b-4"]
def test_df2hxl(self):
import liquer.ext.lq_hxl # register HXL commands and state type
import liquer.ext.lq_pandas # register pandas commands and state type
path = encode_token(os.path.dirname(
inspect.getfile(self.__class__))+"/test_hxl.csv")
data = evaluate(f"df_from-{path}/df2hxl").get()
assert data.columns[0].header == "a"
assert data.columns[0].display_tag == "#indicator+num+aaa"
assert data.columns[1].header == "b"
assert data.columns[1].display_tag == "#indicator+num+bbb"
def test_qsplit1(self):
import liquer.ext.lq_pandas # register pandas commands and state type
filename = encode_token(os.path.dirname(
inspect.getfile(self.__class__))+"/test.csv")
df = evaluate(f"df_from-{filename}/qsplit_df-a").get()
assert "a" in df.columns
assert "b" not in df.columns
assert list(df.a) == [1, 3]
assert list(df["query"]) == [
f"df_from-{filename}/eq-a-1", f"df_from-{filename}/eq-a-3"]
def test_append(self):
import liquer.ext.lq_pandas # register pandas commands and state type
filename = encode_token(os.path.dirname(
inspect.getfile(self.__class__))+"/test.csv")
state = evaluate(f"df_from-{filename}/append_df-{filename}")
df = state.get()
assert "a" in df.columns
assert "b" in df.columns
assert list(df.a) == [1, 3, 1, 3]
assert list(df.b) == [2, 4, 2, 4]
def test_hxl2df(self, httpserver):
import liquer.ext.lq_hxl # register HXL commands and state type
test_hxl = open(os.path.dirname(
inspect.getfile(self.__class__))+"/test_hxl.csv").read()
httpserver.expect_request("/test_hxl.csv").respond_with_data(test_hxl)
url = encode_token(httpserver.url_for("/test_hxl.csv"))
df = evaluate(f"hxl_from-{url}/hxl2df").get()
assert list(df.columns) == ["a", "b"]
assert list(df.a[1:]) == ["1", "3"]
assert list(df.b[1:]) == ["2", "4"]
def test_concat_recipe(self):
import pandas as pd
import liquer.ext.basic
from liquer.commands import reset_command_registry
reset_command_registry() # prevent double-registration
importlib.reload(liquer.ext.basic)
importlib.reload(liquer.ext.lq_pandas)
import liquer.store as st
@first_command
def hello(offset=0):
return pd.DataFrame(
dict(a=[1 + offset, 2 + offset], b=[3 + offset, 4 + offset]))
substore = st.MemoryStore()
substore.store(
"recipes.yaml",
"""
RECIPES:
- filename: hello.parquet
type: pandas_concat
concat:
- hello
- query: hello-10
column: test
value: extra
""",
{},
)
store = RecipeSpecStore(substore)
set_store(store)
assert "hello.parquet" in store.keys()
df = evaluate("hello.parquet/-/dr").get()
assert sorted(df.columns) == ["a", "b", "test"]
assert list(df.a) == [1, 2, 11, 12]
assert list(df.b) == [3, 4, 13, 14]
assert list(df.test) == [None, None, "extra", "extra"]
assert store.get_metadata(
"hello.parquet")["status"] == Status.READY.value
assert store.get_metadata(
"hello.parquet")["recipes_key"] == "recipes.yaml"
assert store.get_metadata("hello.parquet")["has_recipe"] == True
assert store.get_metadata("hello.parquet")["recipes_directory"] == ""
assert store.get_metadata("hello.parquet")[
"recipe_name"] == "recipes.yaml/-Ryaml/RECIPES/0#hello.parquet"
assert store.get_metadata("hello.parquet")["data_characteristics"][
"description"] == "Dataframe with 3 columns and 4 rows."
set_store(None)
reset_command_registry()
def test_workbook(self):
store = MemoryStore()
set_store(store)
filename = encode_token(
os.path.dirname(inspect.getfile(self.__class__)) + "/test.csv")
evaluate_and_save(f"df_from-{filename}/test.xlsx",
target_resource_directory="testdir")
df = evaluate("testdir/test.xlsx/-/workbook/workbook_sheet_df").get()
assert "a" in df.columns
assert "b" in df.columns
assert list(df.a) == [1, 3]
assert list(df.b) == [2, 4]
def get(self, query):
"""Main service for evaluating queries"""
try:
b, mimetype, filename = response(evaluate(query))
except:
traceback.print_exc()
self.set_status(500)
self.finish(f"500 - Failed to create a respone to {query}")
header = "Content-Type"
body = mimetype
self.set_header(header, body)
self.write(b)
def test_pptx(self):
store = MemoryStore()
set_store(store)
@first_command
def make_presentation():
prs = Presentation()
title_slide_layout = prs.slide_layouts[0]
slide = prs.slides.add_slide(title_slide_layout)
title = slide.shapes.title
subtitle = slide.placeholders[1]
title.text = "Hello, World!"
subtitle.text = "python-pptx was here!"
return prs
@command
def add_slide(prs, title="Title"):
bullet_slide_layout = prs.slide_layouts[1]
slide = prs.slides.add_slide(bullet_slide_layout)
shapes = slide.shapes
title_shape = shapes.title
body_shape = shapes.placeholders[1]
title_shape.text = title
tf = body_shape.text_frame
tf.text = 'Find the bullet slide layout'
p = tf.add_paragraph()
p.text = 'Use _TextFrame.text for first bullet'
p.level = 1
return prs
evaluate_and_save(f"make_presentation/add_slide/test.pptx",
target_resource_directory="testdir")
assert store.get_metadata(
"testdir/test.pptx")["type_identifier"] == "pptx_presentation"
prs = evaluate("testdir/test.pptx/-/dr/add_slide-Slide2").get()
assert len(prs.slides) == 3
def test_dr(self):
import pandas as pd
import liquer.ext.basic
from liquer.commands import reset_command_registry
reset_command_registry() # prevent double-registration
importlib.reload(liquer.ext.basic)
importlib.reload(liquer.ext.lq_pandas)
store = MemoryStore()
set_store(store)
store.store("data.csv", b"a,b\n1,2\n3,4", {})
df = evaluate("data.csv/-/dr").get()
assert isinstance(df, pd.DataFrame)
assert len(df) == 2
assert len(df.columns) == 2
assert "a" in df.columns
assert "b" in df.columns
assert list(df.a) == [1, 3]
assert list(df.b) == [2, 4]
def test_link(self):
import liquer.ext.basic
assert evaluate("let-hello-world/state_variable-hello/link").get(
) == "let-hello-world/state_variable-hello"
assert evaluate("let-hello-world/state_variable-hello/link-dataurl"
).get() == "data:text/plain;base64,d29ybGQ="
assert evaluate("let-hello-world/state_variable-hello/link-path").get(
) == "/q/let-hello-world/state_variable-hello"
assert evaluate("let-hello-world/state_variable-hello/link-url").get(
) == "http://localhost/q/let-hello-world/state_variable-hello"
set_var("server", "http://localhost:1234")
set_var("api_path", "/liquer/q/")
assert evaluate("let-hello-world/state_variable-hello/link-path").get(
) == "/liquer/q/let-hello-world/state_variable-hello"
assert evaluate("let-hello-world/state_variable-hello/link-url").get(
) == "http://localhost:1234/liquer/q/let-hello-world/state_variable-hello"
set_var("server", "http://localhost")
set_var("api_path", "/q/")
def get(self, query):
state = evaluate(query)
state_json = state.as_dict()
self.write(json.dumps(state_json))
def serve(query):
"""Main service for evaluating queries"""
return response(evaluate(query))
def debug_json(query):
"""Debug query - returns metadata from a state after a query is evaluated"""
state = evaluate(query)
state_json = state.as_dict()
return jsonify(state_json)
def test_state(self):
from liquer.commands import command_registry
state = evaluate("ns-meta/commands/state").get()
assert state["query"] == "ns-meta/commands"
