computing len of points directly from parquet#141

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Merged
LucaMarconato merged 1 commit into from
Feb 18, 2023
Merged

computing len of points directly from parquet #141

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34 changes: 24 additions & 10 deletions spatialdata/_core/_spatialdata.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -10,11 +10,11 @@
import zarr
from anndata import AnnData
from dask.dataframe.core import DataFrame as DaskDataFrame
from dask.delayed import Delayed
from geopandas import GeoDataFrame
from multiscale_spatial_image.multiscale_spatial_image import MultiscaleSpatialImage
from ome_zarr.io import parse_url
from ome_zarr.types import JSONDict
from pyarrow.parquet import read_table
from spatial_image import SpatialImage

from spatialdata._core._spatial_query import (
Expand DownExpand Up@@ -817,19 +817,33 @@ def h(s: str) -> str:
elif attr == "polygons":
descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class} " f"shape: {v.shape} (2D polygons)"
elif attr == "points":
if len(v) > 0:
if len(v.dask.layers) == 1:
name, layer = v.dask.layers.items().__iter__().__next__()
if "read-parquet" in name:
t = layer.creation_info["args"]
assert isinstance(t, tuple)
assert len(t) == 1
parquet_file = t[0]
table = read_table(parquet_file)
length = len(table)
else:
length = len(v)
else:
length = len(v)
if length > 0:
n = len(get_dims(v))
dim_string = f"({n}D points)"
else:
dim_string = ""
if descr_class == "Table":
descr_class = "pyarrow.Table"
shape_str = (
"("
+ ", ".join([str(dim) if not isinstance(dim, Delayed) else "<Delayed>" for dim in v.shape])
+ ")"
)
descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class} " f"shape: {shape_str} {dim_string}"
assert len(v.shape) == 2
shape_str = f"({length}, {v.shape[1]})"
# if the above is slow, use this (this actually doesn't show the length of the dataframe)
# shape_str = (
# "("
# + ", ".join([str(dim) if not isinstance(dim, Delayed) else "<Delayed>" for dim in v.shape])
# + ")"
# )
descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class} " f"with shape: {shape_str} {dim_string}"
else:
if isinstance(v, SpatialImage):
descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class}[{''.join(v.dims)}] {v.shape}"
Expand Down
21 changes: 11 additions & 10 deletions spatialdata/_core/models.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -473,16 +473,17 @@ def validate(cls, data: DaskDataFrame) -> None:
logger.info(
f"Instance key `{instance_key}` could be of type `pd.Categorical`. Consider casting it."
)
for c in data.columns:
# this is not strictly a validation since we are explicitly importing the categories
# but it is a convenient way to ensure that the categories are known. It also just changes the state of the
# series, so it is not a big deal.
if is_categorical_dtype(data[c]):
if not data[c].cat.known:
try:
data[c] = data[c].cat.set_categories(data[c].head(1).cat.categories)
except ValueError:
logger.info(f"Column `{c}` contains unknown categories. Consider casting it.")
# commented out to address this issue: https://github.com/scverse/spatialdata/issues/140
# for c in data.columns:
# # this is not strictly a validation since we are explicitly importing the categories
# # but it is a convenient way to ensure that the categories are known. It also just changes the state of the
# # series, so it is not a big deal.
# if is_categorical_dtype(data[c]):
# if not data[c].cat.known:
# try:
# data[c] = data[c].cat.set_categories(data[c].head(1).cat.categories)
# except ValueError:
# logger.info(f"Column `{c}` contains unknown categories. Consider casting it.")

@singledispatchmethod
@classmethod
Expand Down
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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computing len of points directly from parquet#141

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 Merged

LucaMarconato
merged 1 commit into

from

Feb 18, 2023
 Merged


computing len of points directly from parquet
#141
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34 changes: 24 additions & 10 deletions
spatialdata/_core/_spatialdata.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -10,11 +10,11 @@
import zarr
from anndata import AnnData
from dask.dataframe.core import DataFrame as DaskDataFrame
from dask.delayed import Delayed
from geopandas import GeoDataFrame
from multiscale_spatial_image.multiscale_spatial_image import MultiscaleSpatialImage
from ome_zarr.io import parse_url
from ome_zarr.types import JSONDict
from pyarrow.parquet import read_table
from spatial_image import SpatialImage

from spatialdata._core._spatial_query import (
Expand DownExpand Up@@ -817,19 +817,33 @@ def h(s: str) -> str:
 elif attr == "polygons":
 descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class} " f"shape: {v.shape} (2D polygons)"
 elif attr == "points":
 if len(v) > 0:
 if len(v.dask.layers) == 1:
 name, layer = v.dask.layers.items().__iter__().__next__()
 if "read-parquet" in name:
 t = layer.creation_info["args"]
 assert isinstance(t, tuple)
 assert len(t) == 1
 parquet_file = t[0]
 table = read_table(parquet_file)
 length = len(table)
 else:
 length = len(v)
 else:
 length = len(v)
 if length > 0:
 n = len(get_dims(v))
 dim_string = f"({n}D points)"
 else:
 dim_string = ""
 if descr_class == "Table":
 descr_class = "pyarrow.Table"
 shape_str = (
 "("
 + ", ".join([str(dim) if not isinstance(dim, Delayed) else "<Delayed>" for dim in v.shape])
 + ")"
 )
 descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class} " f"shape: {shape_str} {dim_string}"
 assert len(v.shape) == 2
 shape_str = f"({length}, {v.shape[1]})"
 # if the above is slow, use this (this actually doesn't show the length of the dataframe)
 # shape_str = (
 # "("
 # + ", ".join([str(dim) if not isinstance(dim, Delayed) else "<Delayed>" for dim in v.shape])
 # + ")"
 # )
 descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class} " f"with shape: {shape_str} {dim_string}"
 else:
 if isinstance(v, SpatialImage):
 descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class}[{''.join(v.dims)}] {v.shape}"
Expand Down

21 changes: 11 additions & 10 deletions
spatialdata/_core/models.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -473,16 +473,17 @@ def validate(cls, data: DaskDataFrame) -> None:
 logger.info(
 f"Instance key `{instance_key}` could be of type `pd.Categorical`. Consider casting it."
 )
 for c in data.columns:
 # this is not strictly a validation since we are explicitly importing the categories
 # but it is a convenient way to ensure that the categories are known. It also just changes the state of the
 # series, so it is not a big deal.
 if is_categorical_dtype(data[c]):
 if not data[c].cat.known:
 try:
 data[c] = data[c].cat.set_categories(data[c].head(1).cat.categories)
 except ValueError:
 logger.info(f"Column `{c}` contains unknown categories. Consider casting it.")
 # commented out to address this issue: https://github.com/scverse/spatialdata/issues/140
 # for c in data.columns:
 # # this is not strictly a validation since we are explicitly importing the categories
 # # but it is a convenient way to ensure that the categories are known. It also just changes the state of the
 # # series, so it is not a big deal.
 # if is_categorical_dtype(data[c]):
 # if not data[c].cat.known:
 # try:
 # data[c] = data[c].cat.set_categories(data[c].head(1).cat.categories)
 # except ValueError:
 # logger.info(f"Column `{c}` contains unknown categories. Consider casting it.")

 @singledispatchmethod
 @classmethod
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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computing len of points directly from parquet#141

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privacy statement. We’ll occasionally send you account related emails.

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Sign in
to your account

 Merged

LucaMarconato
merged 1 commit into

from

Feb 18, 2023
 Merged


computing len of points directly from parquet
#141
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34 changes: 24 additions & 10 deletions
spatialdata/_core/_spatialdata.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -10,11 +10,11 @@
import zarr
from anndata import AnnData
from dask.dataframe.core import DataFrame as DaskDataFrame
from dask.delayed import Delayed
from geopandas import GeoDataFrame
from multiscale_spatial_image.multiscale_spatial_image import MultiscaleSpatialImage
from ome_zarr.io import parse_url
from ome_zarr.types import JSONDict
from pyarrow.parquet import read_table
from spatial_image import SpatialImage

from spatialdata._core._spatial_query import (
Expand DownExpand Up@@ -817,19 +817,33 @@ def h(s: str) -> str:
 elif attr == "polygons":
 descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class} " f"shape: {v.shape} (2D polygons)"
 elif attr == "points":
 if len(v) > 0:
 if len(v.dask.layers) == 1:
 name, layer = v.dask.layers.items().__iter__().__next__()
 if "read-parquet" in name:
 t = layer.creation_info["args"]
 assert isinstance(t, tuple)
 assert len(t) == 1
 parquet_file = t[0]
 table = read_table(parquet_file)
 length = len(table)
 else:
 length = len(v)
 else:
 length = len(v)
 if length > 0:
 n = len(get_dims(v))
 dim_string = f"({n}D points)"
 else:
 dim_string = ""
 if descr_class == "Table":
 descr_class = "pyarrow.Table"
 shape_str = (
 "("
 + ", ".join([str(dim) if not isinstance(dim, Delayed) else "<Delayed>" for dim in v.shape])
 + ")"
 )
 descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class} " f"shape: {shape_str} {dim_string}"
 assert len(v.shape) == 2
 shape_str = f"({length}, {v.shape[1]})"
 # if the above is slow, use this (this actually doesn't show the length of the dataframe)
 # shape_str = (
 # "("
 # + ", ".join([str(dim) if not isinstance(dim, Delayed) else "<Delayed>" for dim in v.shape])
 # + ")"
 # )
 descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class} " f"with shape: {shape_str} {dim_string}"
 else:
 if isinstance(v, SpatialImage):
 descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class}[{''.join(v.dims)}] {v.shape}"
Expand Down

21 changes: 11 additions & 10 deletions
spatialdata/_core/models.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -473,16 +473,17 @@ def validate(cls, data: DaskDataFrame) -> None:
 logger.info(
 f"Instance key `{instance_key}` could be of type `pd.Categorical`. Consider casting it."
 )
 for c in data.columns:
 # this is not strictly a validation since we are explicitly importing the categories
 # but it is a convenient way to ensure that the categories are known. It also just changes the state of the
 # series, so it is not a big deal.
 if is_categorical_dtype(data[c]):
 if not data[c].cat.known:
 try:
 data[c] = data[c].cat.set_categories(data[c].head(1).cat.categories)
 except ValueError:
 logger.info(f"Column `{c}` contains unknown categories. Consider casting it.")
 # commented out to address this issue: https://github.com/scverse/spatialdata/issues/140
 # for c in data.columns:
 # # this is not strictly a validation since we are explicitly importing the categories
 # # but it is a convenient way to ensure that the categories are known. It also just changes the state of the
 # # series, so it is not a big deal.
 # if is_categorical_dtype(data[c]):
 # if not data[c].cat.known:
 # try:
 # data[c] = data[c].cat.set_categories(data[c].head(1).cat.categories)
 # except ValueError:
 # logger.info(f"Column `{c}` contains unknown categories. Consider casting it.")

 @singledispatchmethod
 @classmethod
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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computing len of points directly from parquet#141

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Already on GitHub?
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to your account

 Merged

LucaMarconato
merged 1 commit into

from

Feb 18, 2023
 Merged


computing len of points directly from parquet
#141
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34 changes: 24 additions & 10 deletions
spatialdata/_core/_spatialdata.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -10,11 +10,11 @@
import zarr
from anndata import AnnData
from dask.dataframe.core import DataFrame as DaskDataFrame
from dask.delayed import Delayed
from geopandas import GeoDataFrame
from multiscale_spatial_image.multiscale_spatial_image import MultiscaleSpatialImage
from ome_zarr.io import parse_url
from ome_zarr.types import JSONDict
from pyarrow.parquet import read_table
from spatial_image import SpatialImage

from spatialdata._core._spatial_query import (
Expand DownExpand Up@@ -817,19 +817,33 @@ def h(s: str) -> str:
 elif attr == "polygons":
 descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class} " f"shape: {v.shape} (2D polygons)"
 elif attr == "points":
 if len(v) > 0:
 if len(v.dask.layers) == 1:
 name, layer = v.dask.layers.items().__iter__().__next__()
 if "read-parquet" in name:
 t = layer.creation_info["args"]
 assert isinstance(t, tuple)
 assert len(t) == 1
 parquet_file = t[0]
 table = read_table(parquet_file)
 length = len(table)
 else:
 length = len(v)
 else:
 length = len(v)
 if length > 0:
 n = len(get_dims(v))
 dim_string = f"({n}D points)"
 else:
 dim_string = ""
 if descr_class == "Table":
 descr_class = "pyarrow.Table"
 shape_str = (
 "("
 + ", ".join([str(dim) if not isinstance(dim, Delayed) else "<Delayed>" for dim in v.shape])
 + ")"
 )
 descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class} " f"shape: {shape_str} {dim_string}"
 assert len(v.shape) == 2
 shape_str = f"({length}, {v.shape[1]})"
 # if the above is slow, use this (this actually doesn't show the length of the dataframe)
 # shape_str = (
 # "("
 # + ", ".join([str(dim) if not isinstance(dim, Delayed) else "<Delayed>" for dim in v.shape])
 # + ")"
 # )
 descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class} " f"with shape: {shape_str} {dim_string}"
 else:
 if isinstance(v, SpatialImage):
 descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class}[{''.join(v.dims)}] {v.shape}"
Expand Down

21 changes: 11 additions & 10 deletions
spatialdata/_core/models.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -473,16 +473,17 @@ def validate(cls, data: DaskDataFrame) -> None:
 logger.info(
 f"Instance key `{instance_key}` could be of type `pd.Categorical`. Consider casting it."
 )
 for c in data.columns:
 # this is not strictly a validation since we are explicitly importing the categories
 # but it is a convenient way to ensure that the categories are known. It also just changes the state of the
 # series, so it is not a big deal.
 if is_categorical_dtype(data[c]):
 if not data[c].cat.known:
 try:
 data[c] = data[c].cat.set_categories(data[c].head(1).cat.categories)
 except ValueError:
 logger.info(f"Column `{c}` contains unknown categories. Consider casting it.")
 # commented out to address this issue: https://github.com/scverse/spatialdata/issues/140
 # for c in data.columns:
 # # this is not strictly a validation since we are explicitly importing the categories
 # # but it is a convenient way to ensure that the categories are known. It also just changes the state of the
 # # series, so it is not a big deal.
 # if is_categorical_dtype(data[c]):
 # if not data[c].cat.known:
 # try:
 # data[c] = data[c].cat.set_categories(data[c].head(1).cat.categories)
 # except ValueError:
 # logger.info(f"Column `{c}` contains unknown categories. Consider casting it.")

 @singledispatchmethod
 @classmethod
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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computing len of points directly from parquet#141

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LucaMarconato
merged 1 commit into

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Feb 18, 2023
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computing len of points directly from parquet
#141
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34 changes: 24 additions & 10 deletions
spatialdata/_core/_spatialdata.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -10,11 +10,11 @@
import zarr
from anndata import AnnData
from dask.dataframe.core import DataFrame as DaskDataFrame
from dask.delayed import Delayed
from geopandas import GeoDataFrame
from multiscale_spatial_image.multiscale_spatial_image import MultiscaleSpatialImage
from ome_zarr.io import parse_url
from ome_zarr.types import JSONDict
from pyarrow.parquet import read_table
from spatial_image import SpatialImage

from spatialdata._core._spatial_query import (
Expand DownExpand Up@@ -817,19 +817,33 @@ def h(s: str) -> str:
 elif attr == "polygons":
 descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class} " f"shape: {v.shape} (2D polygons)"
 elif attr == "points":
 if len(v) > 0:
 if len(v.dask.layers) == 1:
 name, layer = v.dask.layers.items().__iter__().__next__()
 if "read-parquet" in name:
 t = layer.creation_info["args"]
 assert isinstance(t, tuple)
 assert len(t) == 1
 parquet_file = t[0]
 table = read_table(parquet_file)
 length = len(table)
 else:
 length = len(v)
 else:
 length = len(v)
 if length > 0:
 n = len(get_dims(v))
 dim_string = f"({n}D points)"
 else:
 dim_string = ""
 if descr_class == "Table":
 descr_class = "pyarrow.Table"
 shape_str = (
 "("
 + ", ".join([str(dim) if not isinstance(dim, Delayed) else "<Delayed>" for dim in v.shape])
 + ")"
 )
 descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class} " f"shape: {shape_str} {dim_string}"
 assert len(v.shape) == 2
 shape_str = f"({length}, {v.shape[1]})"
 # if the above is slow, use this (this actually doesn't show the length of the dataframe)
 # shape_str = (
 # "("
 # + ", ".join([str(dim) if not isinstance(dim, Delayed) else "<Delayed>" for dim in v.shape])
 # + ")"
 # )
 descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class} " f"with shape: {shape_str} {dim_string}"
 else:
 if isinstance(v, SpatialImage):
 descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class}[{''.join(v.dims)}] {v.shape}"
Expand Down

21 changes: 11 additions & 10 deletions
spatialdata/_core/models.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -473,16 +473,17 @@ def validate(cls, data: DaskDataFrame) -> None:
 logger.info(
 f"Instance key `{instance_key}` could be of type `pd.Categorical`. Consider casting it."
 )
 for c in data.columns:
 # this is not strictly a validation since we are explicitly importing the categories
 # but it is a convenient way to ensure that the categories are known. It also just changes the state of the
 # series, so it is not a big deal.
 if is_categorical_dtype(data[c]):
 if not data[c].cat.known:
 try:
 data[c] = data[c].cat.set_categories(data[c].head(1).cat.categories)
 except ValueError:
 logger.info(f"Column `{c}` contains unknown categories. Consider casting it.")
 # commented out to address this issue: https://github.com/scverse/spatialdata/issues/140
 # for c in data.columns:
 # # this is not strictly a validation since we are explicitly importing the categories
 # # but it is a convenient way to ensure that the categories are known. It also just changes the state of the
 # # series, so it is not a big deal.
 # if is_categorical_dtype(data[c]):
 # if not data[c].cat.known:
 # try:
 # data[c] = data[c].cat.set_categories(data[c].head(1).cat.categories)
 # except ValueError:
 # logger.info(f"Column `{c}` contains unknown categories. Consider casting it.")

 @singledispatchmethod
 @classmethod
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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computing len of points directly from parquet#141

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 Merged

LucaMarconato
merged 1 commit into

from

Feb 18, 2023
 Merged


computing len of points directly from parquet
#141
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34 changes: 24 additions & 10 deletions
spatialdata/_core/_spatialdata.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -10,11 +10,11 @@
import zarr
from anndata import AnnData
from dask.dataframe.core import DataFrame as DaskDataFrame
from dask.delayed import Delayed
from geopandas import GeoDataFrame
from multiscale_spatial_image.multiscale_spatial_image import MultiscaleSpatialImage
from ome_zarr.io import parse_url
from ome_zarr.types import JSONDict
from pyarrow.parquet import read_table
from spatial_image import SpatialImage

from spatialdata._core._spatial_query import (
Expand DownExpand Up@@ -817,19 +817,33 @@ def h(s: str) -> str:
 elif attr == "polygons":
 descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class} " f"shape: {v.shape} (2D polygons)"
 elif attr == "points":
 if len(v) > 0:
 if len(v.dask.layers) == 1:
 name, layer = v.dask.layers.items().__iter__().__next__()
 if "read-parquet" in name:
 t = layer.creation_info["args"]
 assert isinstance(t, tuple)
 assert len(t) == 1
 parquet_file = t[0]
 table = read_table(parquet_file)
 length = len(table)
 else:
 length = len(v)
 else:
 length = len(v)
 if length > 0:
 n = len(get_dims(v))
 dim_string = f"({n}D points)"
 else:
 dim_string = ""
 if descr_class == "Table":
 descr_class = "pyarrow.Table"
 shape_str = (
 "("
 + ", ".join([str(dim) if not isinstance(dim, Delayed) else "<Delayed>" for dim in v.shape])
 + ")"
 )
 descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class} " f"shape: {shape_str} {dim_string}"
 assert len(v.shape) == 2
 shape_str = f"({length}, {v.shape[1]})"
 # if the above is slow, use this (this actually doesn't show the length of the dataframe)
 # shape_str = (
 # "("
 # + ", ".join([str(dim) if not isinstance(dim, Delayed) else "<Delayed>" for dim in v.shape])
 # + ")"
 # )
 descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class} " f"with shape: {shape_str} {dim_string}"
 else:
 if isinstance(v, SpatialImage):
 descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class}[{''.join(v.dims)}] {v.shape}"
Expand Down

21 changes: 11 additions & 10 deletions
spatialdata/_core/models.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -473,16 +473,17 @@ def validate(cls, data: DaskDataFrame) -> None:
 logger.info(
 f"Instance key `{instance_key}` could be of type `pd.Categorical`. Consider casting it."
 )
 for c in data.columns:
 # this is not strictly a validation since we are explicitly importing the categories
 # but it is a convenient way to ensure that the categories are known. It also just changes the state of the
 # series, so it is not a big deal.
 if is_categorical_dtype(data[c]):
 if not data[c].cat.known:
 try:
 data[c] = data[c].cat.set_categories(data[c].head(1).cat.categories)
 except ValueError:
 logger.info(f"Column `{c}` contains unknown categories. Consider casting it.")
 # commented out to address this issue: https://github.com/scverse/spatialdata/issues/140
 # for c in data.columns:
 # # this is not strictly a validation since we are explicitly importing the categories
 # # but it is a convenient way to ensure that the categories are known. It also just changes the state of the
 # # series, so it is not a big deal.
 # if is_categorical_dtype(data[c]):
 # if not data[c].cat.known:
 # try:
 # data[c] = data[c].cat.set_categories(data[c].head(1).cat.categories)
 # except ValueError:
 # logger.info(f"Column `{c}` contains unknown categories. Consider casting it.")

 @singledispatchmethod
 @classmethod
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
computing len of points directly from parquet#141

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Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

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privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Merged

LucaMarconato
merged 1 commit into

from

Feb 18, 2023
 Merged


computing len of points directly from parquet
#141
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34 changes: 24 additions & 10 deletions
spatialdata/_core/_spatialdata.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -10,11 +10,11 @@
import zarr
from anndata import AnnData
from dask.dataframe.core import DataFrame as DaskDataFrame
from dask.delayed import Delayed
from geopandas import GeoDataFrame
from multiscale_spatial_image.multiscale_spatial_image import MultiscaleSpatialImage
from ome_zarr.io import parse_url
from ome_zarr.types import JSONDict
from pyarrow.parquet import read_table
from spatial_image import SpatialImage

from spatialdata._core._spatial_query import (
Expand DownExpand Up@@ -817,19 +817,33 @@ def h(s: str) -> str:
 elif attr == "polygons":
 descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class} " f"shape: {v.shape} (2D polygons)"
 elif attr == "points":
 if len(v) > 0:
 if len(v.dask.layers) == 1:
 name, layer = v.dask.layers.items().__iter__().__next__()
 if "read-parquet" in name:
 t = layer.creation_info["args"]
 assert isinstance(t, tuple)
 assert len(t) == 1
 parquet_file = t[0]
 table = read_table(parquet_file)
 length = len(table)
 else:
 length = len(v)
 else:
 length = len(v)
 if length > 0:
 n = len(get_dims(v))
 dim_string = f"({n}D points)"
 else:
 dim_string = ""
 if descr_class == "Table":
 descr_class = "pyarrow.Table"
 shape_str = (
 "("
 + ", ".join([str(dim) if not isinstance(dim, Delayed) else "<Delayed>" for dim in v.shape])
 + ")"
 )
 descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class} " f"shape: {shape_str} {dim_string}"
 assert len(v.shape) == 2
 shape_str = f"({length}, {v.shape[1]})"
 # if the above is slow, use this (this actually doesn't show the length of the dataframe)
 # shape_str = (
 # "("
 # + ", ".join([str(dim) if not isinstance(dim, Delayed) else "<Delayed>" for dim in v.shape])
 # + ")"
 # )
 descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class} " f"with shape: {shape_str} {dim_string}"
 else:
 if isinstance(v, SpatialImage):
 descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class}[{''.join(v.dims)}] {v.shape}"
Expand Down

21 changes: 11 additions & 10 deletions
spatialdata/_core/models.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -473,16 +473,17 @@ def validate(cls, data: DaskDataFrame) -> None:
 logger.info(
 f"Instance key `{instance_key}` could be of type `pd.Categorical`. Consider casting it."
 )
 for c in data.columns:
 # this is not strictly a validation since we are explicitly importing the categories
 # but it is a convenient way to ensure that the categories are known. It also just changes the state of the
 # series, so it is not a big deal.
 if is_categorical_dtype(data[c]):
 if not data[c].cat.known:
 try:
 data[c] = data[c].cat.set_categories(data[c].head(1).cat.categories)
 except ValueError:
 logger.info(f"Column `{c}` contains unknown categories. Consider casting it.")
 # commented out to address this issue: https://github.com/scverse/spatialdata/issues/140
 # for c in data.columns:
 # # this is not strictly a validation since we are explicitly importing the categories
 # # but it is a convenient way to ensure that the categories are known. It also just changes the state of the
 # # series, so it is not a big deal.
 # if is_categorical_dtype(data[c]):
 # if not data[c].cat.known:
 # try:
 # data[c] = data[c].cat.set_categories(data[c].head(1).cat.categories)
 # except ValueError:
 # logger.info(f"Column `{c}` contains unknown categories. Consider casting it.")

 @singledispatchmethod
 @classmethod
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
computing len of points directly from parquet#141

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Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

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privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Merged

LucaMarconato
merged 1 commit into

from

Feb 18, 2023
 Merged


computing len of points directly from parquet
#141
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34 changes: 24 additions & 10 deletions
spatialdata/_core/_spatialdata.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -10,11 +10,11 @@
import zarr
from anndata import AnnData
from dask.dataframe.core import DataFrame as DaskDataFrame
from dask.delayed import Delayed
from geopandas import GeoDataFrame
from multiscale_spatial_image.multiscale_spatial_image import MultiscaleSpatialImage
from ome_zarr.io import parse_url
from ome_zarr.types import JSONDict
from pyarrow.parquet import read_table
from spatial_image import SpatialImage

from spatialdata._core._spatial_query import (
Expand DownExpand Up@@ -817,19 +817,33 @@ def h(s: str) -> str:
 elif attr == "polygons":
 descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class} " f"shape: {v.shape} (2D polygons)"
 elif attr == "points":
 if len(v) > 0:
 if len(v.dask.layers) == 1:
 name, layer = v.dask.layers.items().__iter__().__next__()
 if "read-parquet" in name:
 t = layer.creation_info["args"]
 assert isinstance(t, tuple)
 assert len(t) == 1
 parquet_file = t[0]
 table = read_table(parquet_file)
 length = len(table)
 else:
 length = len(v)
 else:
 length = len(v)
 if length > 0:
 n = len(get_dims(v))
 dim_string = f"({n}D points)"
 else:
 dim_string = ""
 if descr_class == "Table":
 descr_class = "pyarrow.Table"
 shape_str = (
 "("
 + ", ".join([str(dim) if not isinstance(dim, Delayed) else "<Delayed>" for dim in v.shape])
 + ")"
 )
 descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class} " f"shape: {shape_str} {dim_string}"
 assert len(v.shape) == 2
 shape_str = f"({length}, {v.shape[1]})"
 # if the above is slow, use this (this actually doesn't show the length of the dataframe)
 # shape_str = (
 # "("
 # + ", ".join([str(dim) if not isinstance(dim, Delayed) else "<Delayed>" for dim in v.shape])
 # + ")"
 # )
 descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class} " f"with shape: {shape_str} {dim_string}"
 else:
 if isinstance(v, SpatialImage):
 descr += f"{h(attr + 'level1.1')}{k!r}: {descr_class}[{''.join(v.dims)}] {v.shape}"
Expand Down

21 changes: 11 additions & 10 deletions
spatialdata/_core/models.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -473,16 +473,17 @@ def validate(cls, data: DaskDataFrame) -> None:
 logger.info(
 f"Instance key `{instance_key}` could be of type `pd.Categorical`. Consider casting it."
 )
 for c in data.columns:
 # this is not strictly a validation since we are explicitly importing the categories
 # but it is a convenient way to ensure that the categories are known. It also just changes the state of the
 # series, so it is not a big deal.
 if is_categorical_dtype(data[c]):
 if not data[c].cat.known:
 try:
 data[c] = data[c].cat.set_categories(data[c].head(1).cat.categories)
 except ValueError:
 logger.info(f"Column `{c}` contains unknown categories. Consider casting it.")
 # commented out to address this issue: https://github.com/scverse/spatialdata/issues/140
 # for c in data.columns:
 # # this is not strictly a validation since we are explicitly importing the categories
 # # but it is a convenient way to ensure that the categories are known. It also just changes the state of the
 # # series, so it is not a big deal.
 # if is_categorical_dtype(data[c]):
 # if not data[c].cat.known:
 # try:
 # data[c] = data[c].cat.set_categories(data[c].head(1).cat.categories)
 # except ValueError:
 # logger.info(f"Column `{c}` contains unknown categories. Consider casting it.")

 @singledispatchmethod
 @classmethod
Expand Down