Once, while talking with a friend with firsthand experience of many drugs, the topic of lucid dreams came up. My friend recommended 5-HTP as a substance with some positive effects on the lucidity and clarity of dreams. After doing research online, I decided to experiment upon myself to see what effects 5-HTP would have. I purchased the 5-HTP in capsules, completely legally, from a nutrition/bodybuilding store. Here are the notes from my 'experiment':
Day 0 - no drugs
Increased dreams over the last few days probably brought on by talking about dreams with my friend. Dreams generally indistinct and hard to remember. Stressed out from some social problems. Nervous about taking 5-HTP though the literature says it's quite safe.
Day 1 - 100mg 5-HTP (two capsules) at 11:08 PM
Nervousness and trepidation. I'm not sure what effect the drug will have on me. I'm folding socks, waiting for something to happen.
T+0:15
A definite ringing in my ears. Something is happening.
T+0:30
One of the effects of 5-HTP is to feel tired, but I don't feel tired at all. In fact I can't sleep. It might be just nerves... or it might be the 5-HTP. I try and go to bed but I feel stressed out.
T+1:30
Thoughts are racing through my mind. I feel as if I'm half-asleep, half-awake... like I'm getting rest but still conscious. Overall the feeling is not pleasant.
T+7:00 (next morning)
My dreams from the night were very brief and not lucid, but they did have a certain clarity about them. (Reading these notes two years later I remember the imagery quite clearly). In one I was in a great city of light blue marble and stone with a friend of mine, and thousands of people were walking through the streets. Hundreds of yellow cabs drove by, but I couldn't hail them. I worried I would be late for work.
Day 2 - 100mg 5-HTP
Going to bed I still felt very jittery and nervous, and I had a hard time getting to sleep. Before I nodded off I looked about my room and had the distinct impression my vision had become 'high-def'. It felt as if my eyes were somehow more open, that they could see more clearly, though what exactly I was experiencing more clearly I couldn't say.
The dream was fairly drawn out and highly detailed. I went to a movie theatre and watched a popular film (my consciousness dipping in and out of the movie at different points) but the projectionist had mixed up the reels making the movie's plot totally nonsensical. The theatre was huge and featured many balconies and concession stands. It was made of white stucco. When I finally left the theatre I found the building felt very familiar. I felt certain I had been here before in other dreams. The dream was not lucid, unfortunately. Still, the level of detail was intense enough that I decided to continue to take the 5-HTP.
Day 3 - 100mg 5-HTP
Once again I had a hard time falling asleep: I noticed a stiffness in my jaw, and otherwise tossed and turned like the previous nights.
The dream was once again very detailed. It centred around an angry university professor I once had. In the dream the professor was bossing around a kid whose phone had rung in class. The prof ordered the kid out of the room and on the way out the kid pulled the fire alarm. The class was forced to evacuate through a number of twisting hallways behind the lecture theatre.
The morning after I experienced a strange effect: while waiting for a bus to arrive I experienced anti-deja-vu. Deja-vu is, of course, the feeling you've already experienced something that you haven't experienced before. Thus, the anti-deja-vu was the feeling that I had never experienced something which I had experienced many times: namely waiting for the bus. My eyes fixed upon a streetlight that was different than the others on the street and I was utterly convinced that the streetlight was never there before-- that I was still in a dream and the light was a key telling me that this was just an illusion. However, I was not dreaming at all. The feeling eventually passed.
My friend had warned me that 5-HTP sometimes makes one confuse dream and reality, but I didn't expect the effect to be so pronounced.
Day 4 - 100mg 5-HTP
Still had a hard time sleeping, but not as bad as before.
The dream was cinematic nonsense involving a conspiracy involving the Church of Later-day Saints and zombies. My brother and I were operatives told to disable some kind of zombie-making machine in a Mormon temple.  Again, the dream world felt extremely detailed, and I still can recall images from this dream to this day. I have never been in a Mormon temple.
Day 5+
I continued on with the 'experiment' for a few more days, but I didn't take notes of the dreams, though I seem to recall they were unremarkable.
In the end I found that 5-HTP did have a pronounced effect on the detail of my dreams and added a realness I had never felt before. I also came to find the half-asleep half-awake feeling almost pleasurable. That said, insomnia continued to plague me while I took the drug, and it never made my dreams lucid as I had hoped. A higher dosage might have helped, but I was wary of making the insomnia worse, and triggering more bouts of anti-deja-vu. It did not seem worthwhile to continue.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Initial design and results based on contextual inquiry with potential users. Analysis on what tasks the target audience desires most.
View Project PDF View Presentation Download Presentation PPTA video prototype demonstrating three main use cases. Also includes sketches of initial UI ideas.
View Project PDF View Video on Youtube Download SourceResults of usability testing on a paper prototype to determine revision areas in the overall design.
View Project PDF View Presentation PDFInteractive prototype using Proto.io implementing the feedback from the low-fi prototype. This demonstrates how a user would interact with the Merge application
View Project PDF View Prototype View Presentation PDF Download Presentation PPTFeedback given by another group evaluating our medium-fidelity prototype. It is based on the ten different heuristics for interfaces. We incorporated this feedback into our high-fidelity prototype.
View Project PDFPrototype that takes into account all of the previous feedback and implements the Merge application so that it feels like a finished product. Running on Android.
View Project PDF View Prototype View Presentation PDF Download Presentation PPTVisual representation of the purpose of Merge and what it provides for users.
View Project PDF View Final Presentation Slide


Extrema-weighted feature extraction for varying length functional data. Functional data analysis method that performs dimensionality reduction based on predefined features and allows for quantile weighting. Method implemented as presented in Van den Boom et al. (2018) <doi:10.1093/bioinformatics/bty120>.
| Version: | 0.2-2 |
| Imports: | mgcv |
| Published: | 2018-07-12 |
| Author: | Willem van den Boom [aut, cre] |
| Maintainer: | Willem van den Boom <willem at wvdboom.nl> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | xwf results |
| Reference manual: | xwf.pdf |
| Package source: | xwf_0.2-2.tar.gz |
| Windows binaries: | r-devel: xwf_0.2-2.zip, r-release: xwf_0.2-2.zip, r-oldrel: xwf_0.2-2.zip |
| OS X binaries: | r-release: xwf_0.2-2.tgz, r-oldrel: xwf_0.2-2.tgz |
| Old sources: | xwf archive |
Please use the canonical form https://CRAN.R-project.org/package=xwf to link to this page.


A brief class on form validation and ng-messages.
New to Angular 1.3+. Updates to simplify the validation pipeline, as well as options for handling ng-model.
Brand new module used for displaying of validation messages. Handles logic of which message to display at what time.


Issues
| # | Summary | Product | Status | Kind | When |
|---|---|---|---|---|---|
| 1133 |
Add "events" model |
wincent.com | open | feature request | |
| 1131 |
Add in-browser (Selenium) tests |
wincent.com | closed | feature request | Created , updated |
| 1130 |
log out/log in link-toggling broken in latest deployment (8 September 2008) |
wincent.com | closed | bug | Created , updated |
| 1126 |
Update to PCRE 8.02 |
REnamer | open | feature request | Created , updated |
| 1124 |
Update server to Git <IP_ADDRESS> |
wincent.com | closed | feature request | Created , updated |
| 1123 |
Update to nginx 0.6.32 |
wincent.com | closed | feature request | Created , updated |
| 1118 |
Spam comments modify issue dates, bumping spammed issues up the list |
wincent.com | closed | bug | Created , updated |
| 1114 |
Upgrade to Ragel 6.3 |
wikitext | closed | feature request | Created , updated |
| 1113 |
Synergy should detect and be able to control RealPlayer |
Synergy | open | feature request | Created , updated |
| 1112 |
Program closes immediately when the splash screen come up. |
Synergy Advance | closed | support ticket | Created , updated |


-

  Python 3.7.4 (default, Sep 7 2019, 18:27:02)  >>> import requests  >>> r = requests.get(‘https://api.github.com/repos/psf/requests’)  >>> r.json()[“description”]  ‘A simple, yet elegant HTTP library.’

-

This software has been designed for you, with much joy, by Kenneth Reitz & is protected by The Python Software Foundation.

Requests is an elegant and simple HTTP library for Python, built with ♥.

`python
>>> import requests
>>> r = requests.get('https://api.github.com/user', auth=('user', 'pass'))
>>> r.status_code
200
>>> r.headers['content-type']
'application/json; charset=utf8'
>>> r.encoding
'utf-8'
>>> r.text
'{"type":"User"...'
>>> r.json()
{'disk_usage': 368627, 'private_gists': 484, ...}
`

Requests allows you to send HTTP/1.1 requests extremely easily. There’s no need to manually add query strings to your URLs, or to form-encode your PUT & POST data — but nowadays, just use the json method!
Requests is one of the most downloaded Python package today, pulling in around 14M downloads / week— according to GitHub, Requests is currently [depended upon](https://github.com/psf/requests/network/dependents?package_id=UGFja2FnZS01NzA4OTExNg%3D%3D) by 500,000+ repositories. You may certainly put your trust in this code.

Supported Features & Best–Practices

Requests is ready for the demands of building robust and reliable HTTP–speak applications, for the needs of today.
-

International Domains and URLs + Keep-Alive & Connection Pooling
Sessions with Cookie Persistence + Browser-style SSL Verification
Basic & Digest Authentication + Familiar dict–like Cookies
Automatic Decompression of Content + Automatic Content Decoding
Automatic Connection Pooling + Unicode Response Bodies<super>*</super>
Multi-part File Uploads + SOCKS Proxy Support
Connection Timeouts + Streaming Downloads
Automatic honoring of .netrc + Chunked HTTP Requests
&, of course, rock–solid stability!

-

✨ 🍰 ✨

Requests Module Installation¶
The recommended way to install the requests module is to simply use [pipenv](https://pipenv.kennethreitz.org) (or pip, of course):
`console
$ pipenv install requests
Adding requests to Pipfile's [packages]…
✔ Installation Succeeded
…
`
Requests officially supports Python 2.7 & 3.5+.
## P.S. — Documentation is available at [requests.readthedocs.io](https://requests.readthedocs.io/en/latest/).
-

-

-


A garden is a friend you can visit anytime. Mother nature has the best box of crayons. To plant a garden is to believe in tomorrow. Nature never goes out of style. Anyone who has time for drama is not gardening enough. Gardeners are alchemists.
What did the bra say to the hat?  Why do crabs never give to charity? Because they're shellfish. I'd like to make a shout out to sidewalks, for keeping me off the streets. How do you make hold water?  Due to quarantine, I'll only be telling inside jokes.


Frame 1
Frame 2
Frame 3
Frame 4
Frame 5
Frame 6
Frame 7
Frame 8
Frame 9
Frame 10
Total
How many pins did you knock over?
10
8
9
5
6
7
1
2
3
4
Game Over
Play Again


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#Tutorial: Use machine learning to model protein-ligand affinity.
Written by Evan Feinberg and Bharath Ramsundar
Copyright 2016, Stanford University
This DeepChem tutorial demonstrates how to use mach.ine learning for modeling protein-ligand binding affinity
Overview:
In this tutorial, you will trace an arc from loading a raw dataset to fitting a cutting edge ML technique for predicting binding affinities. This will be accomplished by writing simple commands to access the deepchem Python API, encompassing the following broad steps:
First, let’s point to a “dataset” file. This can come in the format of a
CSV file or Pandas DataFrame. Regardless of file format, it must be
columnar data, where each row is a molecular system, and each column
represents a different piece of information about that system. For
instance, in this example, every row reflects a protein-ligand complex,
and the following columns are present: a unique complex identifier; the
SMILES string of the ligand; the binding affinity (Ki) of the ligand to
the protein in the complex; a Python list
of all lines in a PDB file
for the protein alone; and a Python list
of all lines in a ligand
file for the ligand alone.
This should become clearer with the example. (Make sure to set
DISPLAY = True
)
%load_ext autoreload
%autoreload 2
%pdb off
# set DISPLAY = True when running tutorial
DISPLAY = False
# set PARALLELIZE to true if you want to use ipyparallel
PARALLELIZE = False
import warnings
warnings.filterwarnings('ignore')
Automatic pdb calling has been turned OFF
import deepchem as dc
from deepchem.utils import download_url
import os
download_url("https://s3-us-west-1.amazonaws.com/deepchem.io/datasets/pdbbind_core_df.csv.gz")
data_dir = os.path.join(dc.utils.get_data_dir())
dataset_file= os.path.join(dc.utils.get_data_dir(), "pdbbind_core_df.csv.gz")
raw_dataset = dc.utils.save.load_from_disk(dataset_file)
Let’s see what dataset
looks like:
print("Type of dataset is: %s" % str(type(raw_dataset)))
print(raw_dataset[:5])
print("Shape of dataset is: %s" % str(raw_dataset.shape))
Type of dataset is: <class 'pandas.core.frame.DataFrame'> pdb_id smiles 0 2d3u CC1CCCCC1S(O)(O)NC1CC(C2CCC(CN)CC2)SC1C(O)O 1 3cyx CC(C)(C)NC(O)C1CC2CCCCC2C[NH+]1CC(O)C(CC1CCCCC... 2 3uo4 OC(O)C1CCC(NC2NCCC(NC3CCCCC3C3CCCCC3)N2)CC1 3 1p1q CC1ONC(O)C1CC([NH3+])C(O)O 4 3ag9 NC(O)C(CCC[NH2+]C([NH3+])[NH3+])NC(O)C(CCC[NH2... complex_id 0 2d3uCC1CCCCC1S(O)(O)NC1CC(C2CCC(CN)CC2)SC1C(O)O 1 3cyxCC(C)(C)NC(O)C1CC2CCCCC2C[NH+]1CC(O)C(CC1C... 2 3uo4OC(O)C1CCC(NC2NCCC(NC3CCCCC3C3CCCCC3)N2)CC1 3 1p1qCC1ONC(O)C1CC([NH3+])C(O)O 4 3ag9NC(O)C(CCC[NH2+]C([NH3+])[NH3+])NC(O)C(CCC... protein_pdb 0 ['HEADER 2D3U PROTEINn', 'COMPND 2D3U P... 1 ['HEADER 3CYX PROTEINn', 'COMPND 3CYX P... 2 ['HEADER 3UO4 PROTEINn', 'COMPND 3UO4 P... 3 ['HEADER 1P1Q PROTEINn', 'COMPND 1P1Q P... 4 ['HEADER 3AG9 PROTEINn', 'COMPND 3AG9 P... ligand_pdb 0 ['COMPND 2d3u ligand n', 'AUTHOR GENERA... 1 ['COMPND 3cyx ligand n', 'AUTHOR GENERA... 2 ['COMPND 3uo4 ligand n', 'AUTHOR GENERA... 3 ['COMPND 1p1q ligand n', 'AUTHOR GENERA... 4 ['COMPND 3ag9 ligand n', 'AUTHOR GENERA... ligand_mol2 label 0 ['### n', '### Created by X-TOOL on Thu Aug 2... 6.92 1 ['### n', '### Created by X-TOOL on Thu Aug 2... 8.00 2 ['### n', '### Created by X-TOOL on Fri Aug 2... 6.52 3 ['### n', '### Created by X-TOOL on Thu Aug 2... 4.89 4 ['### n', '### Created by X-TOOL on Thu Aug 2... 8.05 Shape of dataset is: (193, 7)
One of the missions of deepchem
is to form a synapse between the
chemical and the algorithmic worlds: to be able to leverage the powerful
and diverse array of tools available in Python to analyze molecules.
This ethos applies to visual as much as quantitative examination:
import nglview
import tempfile
import os
import mdtraj as md
import numpy as np
import deepchem.utils.visualization
#from deepchem.utils.visualization import combine_mdtraj, visualize_complex, convert_lines_to_mdtraj
def combine_mdtraj(protein, ligand):
chain = protein.topology.add_chain()
residue = protein.topology.add_residue("LIG", chain, resSeq=1)
for atom in ligand.topology.atoms:
protein.topology.add_atom(atom.name, atom.element, residue)
protein.xyz = np.hstack([protein.xyz, ligand.xyz])
protein.topology.create_standard_bonds()
return protein
def visualize_complex(complex_mdtraj):
ligand_atoms = [a.index for a in complex_mdtraj.topology.atoms if "LIG" in str(a.residue)]
binding_pocket_atoms = md.compute_neighbors(complex_mdtraj, 0.5, ligand_atoms)[0]
binding_pocket_residues = list(set([complex_mdtraj.topology.atom(a).residue.resSeq for a in binding_pocket_atoms]))
binding_pocket_residues = [str(r) for r in binding_pocket_residues]
binding_pocket_residues = " or ".join(binding_pocket_residues)
traj = nglview.MDTrajTrajectory( complex_mdtraj ) # load file from RCSB PDB
ngltraj = nglview.NGLWidget( traj )
ngltraj.representations = [
{ "type": "cartoon", "params": {
"sele": "protein", "color": "residueindex"
} },
{ "type": "licorice", "params": {
"sele": "(not hydrogen) and (%s)" % binding_pocket_residues
} },
{ "type": "ball+stick", "params": {
"sele": "LIG"
} }
]
return ngltraj
def visualize_ligand(ligand_mdtraj):
traj = nglview.MDTrajTrajectory( ligand_mdtraj ) # load file from RCSB PDB
ngltraj = nglview.NGLWidget( traj )
ngltraj.representations = [
{ "type": "ball+stick", "params": {"sele": "all" } } ]
return ngltraj
def convert_lines_to_mdtraj(molecule_lines):
molecule_lines = molecule_lines.strip('[').strip(']').replace("'","").replace("\\n", "").split(", ")
tempdir = tempfile.mkdtemp()
molecule_file = os.path.join(tempdir, "molecule.pdb")
with open(molecule_file, "w") as f:
for line in molecule_lines:
f.write("%s\n" % line)
molecule_mdtraj = md.load(molecule_file)
return molecule_mdtraj
first_protein, first_ligand = raw_dataset.iloc[0]["protein_pdb"], raw_dataset.iloc[0]["ligand_pdb"]
protein_mdtraj = convert_lines_to_mdtraj(first_protein)
ligand_mdtraj = convert_lines_to_mdtraj(first_ligand)
complex_mdtraj = combine_mdtraj(protein_mdtraj, ligand_mdtraj)
ngltraj = visualize_complex(complex_mdtraj)
ngltraj
A Jupyter Widget
Now that we’re oriented, let’s use ML to do some chemistry.
So, step (2) will entail featurizing the dataset.
The available featurizations that come standard with deepchem are ECFP4
fingerprints, RDKit descriptors, NNScore-style bdescriptors, and hybrid
binding pocket descriptors. Details can be found on deepchem.io
.
grid_featurizer = dc.feat.RdkitGridFeaturizer(
voxel_width=16.0, feature_types="voxel_combined",
voxel_feature_types=["ecfp", "splif", "hbond", "pi_stack", "cation_pi", "salt_bridge"],
ecfp_power=5, splif_power=5, parallel=True, flatten=True)
compound_featurizer = dc.feat.CircularFingerprint(size=128)
Note how we separate our featurizers into those that featurize individual chemical compounds, compound_featurizers, and those that featurize molecular complexes, complex_featurizers.
Now, let’s perform the actual featurization. Calling
loader.featurize()
will return an instance of class Dataset
.
Internally, loader.featurize()
(a) computes the specified features
on the data, (b) transforms the inputs into X
and y
NumPy arrays
suitable for ML algorithms, and (c) constructs a Dataset()
instance
that has useful methods, such as an iterator, over the featurized data.
This is a little complicated, so we will use MoleculeNet to featurize
the PDBBind core set for us.
PDBBIND_tasks, (train_dataset, valid_dataset, test_dataset), transformers = dc.molnet.load_pdbbind_grid()
Loading dataset from disk.
TIMING: dataset construction took 0.020 s
Loading dataset from disk.
TIMING: dataset construction took 0.008 s
Loading dataset from disk.
TIMING: dataset construction took 0.008 s
Loading dataset from disk.
Now, we conduct a train-test split. If you’d like, you can choose
splittype="scaffold"
instead to perform a train-test split based on
Bemis-Murcko scaffolds.
We generate separate instances of the Dataset() object to hermetically seal the train dataset from the test dataset. This style lends itself easily to validation-set type hyperparameter searches, which we will illustate in a separate section of this tutorial.
The performance of many ML algorithms hinges greatly on careful data preprocessing. Deepchem comes standard with a few options for such preprocessing.
Now, we’re ready to do some learning!
To fit a deepchem model, first we instantiate one of the provided (or
user-written) model classes. In this case, we have a created a
convenience class to wrap around any ML model available in Sci-Kit Learn
that can in turn be used to interoperate with deepchem. To instantiate
an SklearnModel
, you will need (a) task_types, (b) model_params,
another dict
as illustrated below, and (c) a model_instance
defining the type of model you would like to fit, in this case a
RandomForestRegressor
.
from sklearn.ensemble import RandomForestRegressor
sklearn_model = RandomForestRegressor(n_estimators=100)
model = dc.models.SklearnModel(sklearn_model)
model.fit(train_dataset)
from deepchem.utils.evaluate import Evaluator
import pandas as pd
metric = dc.metrics.Metric(dc.metrics.r2_score)
evaluator = Evaluator(model, train_dataset, transformers)
train_r2score = evaluator.compute_model_performance([metric])
print("RF Train set R^2 %f" % (train_r2score["r2_score"]))
evaluator = Evaluator(model, valid_dataset, transformers)
valid_r2score = evaluator.compute_model_performance([metric])
print("RF Valid set R^2 %f" % (valid_r2score["r2_score"]))
computed_metrics: [0.88553937155927021]
RF Train set R^2 0.885539
computed_metrics: [0.31742980243495422]
RF Valid set R^2 0.317430
In this simple example, in few yet intuitive lines of code, we traced the machine learning arc from featurizing a raw dataset to fitting and evaluating a model.
Here, we featurized only the ligand. The signal we observed in R^2 reflects the ability of circular fingerprints and random forests to learn general features that make ligands “drug-like.”
predictions = model.predict(test_dataset)
print(predictions)
[ 5.2497 5.0708 6.2361 7.1606 5.1387 6.6723 6.7259 6.2996 6.5986
5.5831 6.7202 6.6823 6.4065 6.1988 6.8872 6.0689 6.7044 7.2988
6.6718 6.7882]
# TODO(rbharath): This cell visualizes the ligand with highest predicted activity. Commenting it out for now. Fix this later
#from deepchem.utils.visualization import visualize_ligand
#top_ligand = predictions.iloc[0]['ids']
#ligand1 = convert_lines_to_mdtraj(dataset.loc[dataset['complex_id']==top_ligand]['ligand_pdb'].values[0])
#if DISPLAY:
# ngltraj = visualize_ligand(ligand1)
# ngltraj
# TODO(rbharath): This cell visualizes the ligand with lowest predicted activity. Commenting it out for now. Fix this later
#worst_ligand = predictions.iloc[predictions.shape[0]-2]['ids']
#ligand1 = convert_lines_to_mdtraj(dataset.loc[dataset['complex_id']==worst_ligand]['ligand_pdb'].values[0])
#if DISPLAY:
# ngltraj = visualize_ligand(ligand1)
# ngltraj
The preceding simple example, in few yet intuitive lines of code, traces the machine learning arc from featurizing a raw dataset to fitting and evaluating a model.
In this next section, we illustrate deepchem
’s modularity, and
thereby the ease with which one can explore different featurization
schemes, different models, and combinations thereof, to achieve the best
performance on a given dataset. We will demonstrate this by examining
protein-ligand interactions.
In the previous section, we featurized only the ligand. The signal we observed in R^2 reflects the ability of grid fingerprints and random forests to learn general features that make ligands “drug-like.” In this section, we demonstrate how to use hyperparameter searching to find a higher scoring ligands.
def rf_model_builder(model_params, model_dir):
sklearn_model = RandomForestRegressor(**model_params)
return dc.models.SklearnModel(sklearn_model, model_dir)
params_dict = {
"n_estimators": [10, 50, 100],
"max_features": ["auto", "sqrt", "log2", None],
}
metric = dc.metrics.Metric(dc.metrics.r2_score)
optimizer = dc.hyper.HyperparamOpt(rf_model_builder)
best_rf, best_rf_hyperparams, all_rf_results = optimizer.hyperparam_search(
params_dict, train_dataset, valid_dataset, transformers,
metric=metric)
Fitting model 1/12
hyperparameters: {'n_estimators': 10, 'max_features': 'auto'}
computed_metrics: [0.25804004860413576]
Model 1/12, Metric r2_score, Validation set 0: 0.258040
best_validation_score so far: 0.258040
Fitting model 2/12
hyperparameters: {'n_estimators': 10, 'max_features': 'sqrt'}
computed_metrics: [0.31894454238841463]
Model 2/12, Metric r2_score, Validation set 1: 0.318945
best_validation_score so far: 0.318945
Fitting model 3/12
hyperparameters: {'n_estimators': 10, 'max_features': 'log2'}
computed_metrics: [0.40151233479373227]
Model 3/12, Metric r2_score, Validation set 2: 0.401512
best_validation_score so far: 0.401512
Fitting model 4/12
hyperparameters: {'n_estimators': 10, 'max_features': None}
computed_metrics: [0.2379004574282495]
Model 4/12, Metric r2_score, Validation set 3: 0.237900
best_validation_score so far: 0.401512
Fitting model 5/12
hyperparameters: {'n_estimators': 50, 'max_features': 'auto'}
computed_metrics: [0.31407341153260904]
Model 5/12, Metric r2_score, Validation set 4: 0.314073
best_validation_score so far: 0.401512
Fitting model 6/12
hyperparameters: {'n_estimators': 50, 'max_features': 'sqrt'}
computed_metrics: [0.30561076245035912]
Model 6/12, Metric r2_score, Validation set 5: 0.305611
best_validation_score so far: 0.401512
Fitting model 7/12
hyperparameters: {'n_estimators': 50, 'max_features': 'log2'}
computed_metrics: [0.15265818454692826]
Model 7/12, Metric r2_score, Validation set 6: 0.152658
best_validation_score so far: 0.401512
Fitting model 8/12
hyperparameters: {'n_estimators': 50, 'max_features': None}
computed_metrics: [0.3023042218537606]
Model 8/12, Metric r2_score, Validation set 7: 0.302304
best_validation_score so far: 0.401512
Fitting model 9/12
hyperparameters: {'n_estimators': 100, 'max_features': 'auto'}
computed_metrics: [0.33459631598792483]
Model 9/12, Metric r2_score, Validation set 8: 0.334596
best_validation_score so far: 0.401512
Fitting model 10/12
hyperparameters: {'n_estimators': 100, 'max_features': 'sqrt'}
computed_metrics: [0.31318915098968536]
Model 10/12, Metric r2_score, Validation set 9: 0.313189
best_validation_score so far: 0.401512
Fitting model 11/12
hyperparameters: {'n_estimators': 100, 'max_features': 'log2'}
computed_metrics: [0.21173765911803388]
Model 11/12, Metric r2_score, Validation set 10: 0.211738
best_validation_score so far: 0.401512
Fitting model 12/12
hyperparameters: {'n_estimators': 100, 'max_features': None}
computed_metrics: [0.35420481043072938]
Model 12/12, Metric r2_score, Validation set 11: 0.354205
best_validation_score so far: 0.401512
computed_metrics: [0.83300249084895006]
Best hyperparameters: (10, 'log2')
train_score: 0.833002
validation_score: 0.401512
%matplotlib inline
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
rf_predicted_test = best_rf.predict(test_dataset)
rf_true_test = test_dataset.y
plt.scatter(rf_predicted_test, rf_true_test)
plt.xlabel('Predicted pIC50s')
plt.ylabel('True IC50')
plt.title(r'RF predicted IC50 vs. True pIC50')
plt.xlim([2, 11])
plt.ylim([2, 11])
plt.plot([2, 11], [2, 11], color='k')
plt.show()


--- layout: post title: M101 MongoDB for Java Developers' final exam Answer(M101J) date: 2013-09-20 17:57:00.000000000 +05:30 categories: [] tags: - Final Exam - M101J - m101j final exam answers - MongoDB - MongoDB Final Exam Answers - MongoDB for Java Developers - technical status: publish type: post published: true meta: blogger_bid: '2279754227642197645' blogger_blog: ankurthetechie.blogspot.com blogger_id: '8391140581667576206' blogger_author: g116451164261752827176 blogger_comments: '42' blogger_permalink: /2013/09/final-exam-answers-with-explanation-for.html blogger_thumbnail: http://1.bp.blogspot.com/-vmO3TOXBLnk/Ujxtp5oIYYI/AAAAAAAAALg/zBKBOSRabYc/s400/Untitled.png _thumbnail_id: '93' author: login: admin@reportlingo email<EMAIL_ADDRESS>display_name: admin@reportlingo first_name: Ankur last_name: Gupta excerpt: !ruby/object:Hpricot::Doc options: {} ---
Question 1 :
Here we need to query the enron dataset calculate the number of messages sent by Andrew Fastow, CFO, to Jeff Skilling, the president. Andrew Fastow's email addess was<EMAIL_ADDRESS>Jeff Skilling's email was<EMAIL_ADDRESS>So for this first we need to download the enron zip/tar and then import in the mongoDB database name enron and collection name messages . Command for import
mongoimport -d enron -c messages > enron.json
Now switch to mongo Shell commands:
use enron
db.messages.find({"headers.To":"andrew.fastow@enron.com","headers.From":"jeff.skilling@enron.com"}).count()
This will produce the answer as 3
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Question 2:
Please use the Enron dataset you imported for the previous problem. For this question you will use the aggregation framework to figure out pairs of people that tend to communicate a lot. To do this, you will need to unwind the To list for each message.
The mongo shell command which will retrieve the desired answer would be
db.messages.aggregate([
{
$project: {
from: "$headers.From",
to: "$headers.To"
}
},
{
$unwind: "$to"
},
{
$group : { _id : { _id: "$_id", from: "$from", to: "$to" }
}
},
{
$group : { _id : { from: "$_id.from", to: "$_id.to" }, count: {$sum :1}
}
},
{
$sort : {count:-1}
},
{
$limit: 2
}
])
This would give you the top 2 communication , and check the top most which would turn out to be :
"result" : [
{
"_id" : {
"from"<EMAIL_ADDRESS>"to"<EMAIL_ADDRESS>},
"count" : 750
},
{
"_id" : {
"from"<EMAIL_ADDRESS>"to"<EMAIL_ADDRESS>},
"count" : 679
}
],
"ok" : 1
So, it clearly shows the answer is<EMAIL_ADDRESS>to<EMAIL_ADDRESS>(adsbygoogle = window.adsbygoogle || []).push({});
Question 3:
In this problem you will update a document in the Enron dataset to illustrate your mastery of updating documents from the shell. Please add the email address<EMAIL_ADDRESS>to the list of addresses in the "headers.To" array for the document with "headers.Message-ID" of "<8147308.1075851042335.JavaMail.evans@thyme>"
For this there would be a simple update expression using mongo shell as :
db.messages.update({"headers.Message-ID":"<8147308.1075851042335.JavaMail.evans<EMAIL_ADDRESS>Then run the validation code and get the validation code as : 897h6723ghf25gd87gh28
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Question 4:
Enhancing the Blog to support viewers liking certain comments.
Here you need to work on the BlogPostDAO.java at the area marked as XXXXXX
postsCollection.update(new BasicDBObject("permalink", permalink), new BasicDBObject("$inc", new BasicDBObject("comments." + ordinal + ".num_likes", 1)));
Here in the above command we search the posts collection with the permanent link and increment the like counter by one for the comment which is clicked for like or in other words the ordinal or the order of the comment in the comments array, this ensures that the like is incremented for the comment clicked for like.
Doing this you could see that the like button starts working.
Now run the validator , and you will get the code as : 983nf93ncafjn20fn10f
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Question 5 :
In this question a set of indexes are given and we have to select the indexes which might have been used , in execution of
db.fubar.find({'a':{'$lt':10000}, 'b':{'$gt': 5000}}, {'a':1, 'c':1}).sort({'c':-1})
As the Find portion searches on a,b and a,c and sorting is carries on c reverse order.
_id_ -- This index is not used either in sort or find clause of the operation
a_1_b_1 -- This index is used in the find operation as find is on a,b
a_1_c_1 -- This index is used in the find operation as find is on a,c
c_1 -- This index is also used, because there is a provision that a index is not utilized for the find operation but for the sort it is used as sort({'c':-1})
a_1_b_1_c_1 - This involves all the three a,b,c and this is also used as it can also be used as a valid index
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Question 6
Suppose you have a collection of students of the following form:
{ "_id" : ObjectId("50c598f582094fb5f92efb96"), "first_name" : "John", "last_name" : "Doe", "date_of_admission" : ISODate("2010-02-21T05:00:00Z"), "residence_hall" : "Fairweather", "has_car" : true, "student_id" : "2348023902", "current_classes" : [ "His343", "Math234", "Phy123", "Art232" ] }
Now suppose that basic inserts into the collection, which only include the last name, first name and student_id, are too slow. What could potentially improve the speed of inserts. Check all that apply.
Add an index on last_name, first_name if one does not already exist.
Set w=0, j=0 on writes
Remove all indexes from the collection
Provide a hint to MongoDB that it should not use an index for the inserts
Build a replica set and insert data into the secondary nodes to free up the primary nodes.
option 1 - As a fact adding index affects reading not writing so it would be indifferent with the indexing so not this option
Option 2 seems to be valid as when w=0 and j=0 is done for the writes no waiting is done at all are no wait is required to obtain as the write confirmations , simply the data is dumped without verification therefore speeding the writes
Option 3 removing indexes would actually help as it would reduce the load and speed up the writing process
Option 4 This seems absurd
Option 5 This is not actually possible as writes are not possible on the secondary nodes so not valid option
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Question 7
You have been tasked to cleanup a photosharing database. The database consists of two collections, albums, and images. Every image is supposed to be in an album, but there are orphan images that appear in no album. Here are some example documents (not from the collections you will be downloading).
When you are done removing the orphan images from the collection, there should be 90,017 documents in the images collection.
In order to remove the Orphans talked I wrote a Java Program :
/**
*
* @author Ankur Gupta
*/
public class Test {
public static void main(String[] args) throws IOException {
MongoClient c = new MongoClient(new MongoClientURI("mongodb://localhost"));
DB db = c.getDB("finaltask");
int i =0;
DBCollection album = db.getCollection("albums");
DBCollection image = db.getCollection("images");
DBCursor cur = image.find();
cur.next();
while (cur.hasNext()){
Object id = cur.curr().get("_id");
DBCursor curalbum = album.find(new BasicDBObject("images", id));
if(!curalbum.hasNext()){
image.remove(new BasicDBObject("_id", id));
}
cur.next();
}
}
}
In order to verify above statement after removing orphans :
db.albums.aggregate({$unwind:"$images"},{$group:{_id:null,sum:{$sum:"$images"},count:{$sum:1}}})
The result looks like:
"result" : [
{
"_id" : null,
"sum" : NumberLong("4501039268"),
"count" : 90017
}
],
"ok" : 1
To prove you did it correctly, what are the total number of images with the tag 'sunrises" after the removal of orphans?
db.images.find({"tags":"sunrises"}).count()
This will fetch the final answer as 45044
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Question 8:
Supposed we executed the following Java code. How many animals will be inserted into the "animals" collection?
public class Question8 { public static void main(String[] args) throws IOException { MongoClient c = new MongoClient(new MongoClientURI("mongodb://localhost")); DB db = c.getDB("test"); DBCollection animals = db.getCollection("animals"); BasicDBObject animal = new BasicDBObject("animal", "monkey"); animals.insert(animal); animal.removeField("animal"); animal.append("animal", "cat"); animals.insert(animal); animal.removeField("animal"); animal.append("animal", "lion"); animals.insert(animal); } }
When you run the above , then you will see an error is thrown that there is a duplicate ID , as we are trying to add , documents again and again on the same Id as we are modifying the same document . So the only one document will be inserted in the collection which will be the first insert as {_id::xxx,"animal","monkey"}
then when again ("animal","cat") is tried to push then the id is same so , it throws duplicate key . So answer is that only one document gets inserted.
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Question 9:
Imagine an electronic medical record database designed to hold the medical records of every individual in the United States. Because each person has more than 16MB of medical history and records, it's not feasible to have a single document for every patient. Instead, there is a patientcollection that contains basic information on each person and maps the person to a patient_id, and arecord collection that contains one document for each test or procedure. One patient may have dozens or even hundreds of documents in the record collection.
We need to decide on a shard key to shard the record collection. What's the best shard key for therecord collection, provided that we are willing to run scatter gather operations to do research and run studies on various diseases and cohorts? That is, think mostly about the operational aspects of such a system.
patient_id
_id
primary care physican (your principal doctor)
date and time when medical record was created
patient first name
patient last name
Here among the options given for the shard key most favourable is patient_id , as there are large number of patient_id and they have been distributed in different diseases, and when a scatter gather operation is carried out then the data is far more expanded on the basis of patient_id.
Other options are not suitable for the scatter and gather operation.
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Question 10:
Understanding the output of explain We perform the following query on the enron dataset:
db.messages.find({'headers.Date':{'$gt': new Date(2001,3,1)}},{'headers.From':1, _id:0}).sort({'headers.From':1}).explain()
and get the following explain output.
{ "cursor" : "BtreeCursor headers.From_1", "isMultiKey" : false, "n" : 83057, "nscannedObjects" : 120477, "nscanned" : 120477, "nscannedObjectsAllPlans" : 120581, "nscannedAllPlans" : 120581, "scanAndOrder" : false, "indexOnly" : false, "nYields" : 0, "nChunkSkips" : 0, "millis" : 250, "indexBounds" : { "headers.From" : [ [ { "$minElement" : 1 }, { "$maxElement" : 1 } ] ] }, "server" : "Andrews-iMac.local:27017" }
Here the correct options will be :
Option 1 seems to be correct as if you could notice that "cursor" : "BtreeCursor headers.From_1" that means that headers.From_1 is used which is not in the find clause but is in the sorting
Option 2 also seems to be correct as "cursor" : "BtreeCursor headers.From_1" the cursor is used in the sorting phase
Option 3 This option is wrong as 83057 records as n=83057
Option 4 This option is correct as if we see nscanned objects is 120477 so it has scanned all
Hope that above explanation prove helpful, please put your precious comments and suggestions on better method to do any question.
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◄ 23rd IPHO 1992 ►
Country results • Individual results
| Contestant | Country | Rank | Award |
|---|---|---|---|
| Chen Han | People's Republic of China | 1 | Gold Medal |
| Andray Frolov | Russia | 2 | Gold Medal |
| Li Yi | People's Republic of China | 3 | Gold Medal |
| Shi Changchun | People's Republic of China | 4 | Gold Medal |
| Eric David Miller | United States of America | 5 | Gold Medal |
| Motrunich Alexei | Ukraine | 6 | Gold Medal |
| Zhang Lintao | People's Republic of China | 7 | Gold Medal |
| Florea Horatiu Stoica | Romania | 8 | Gold Medal |
| Volker Moritz | Germany | 9 | Gold Medal |
| Guliaev Nicolay | Russia | 10 | Gold Medal |
| Rusinkiewicz Szymon | United States of America | 11 | Gold Medal |
| Luo Weidong | People's Republic of China | 12 | Gold Medal |
| Bezrukov Fedor | Russia | 13 | Gold Medal |
| Rix Antony | United Kingdom | 14 | Silver Medal |
| Pliszka Jacek | Poland | 15 | Silver Medal |
| Anatoli Olkhovets | Ukraine | 16 | Silver Medal |
| Banaszek Konrad | Poland | 17 | Silver Medal |
| Smye Graham | United Kingdom | 18 | Silver Medal |
| Fagan Matthew | Australia | 19 | Silver Medal |
| Chris Stolk | Netherlands | 20 | Silver Medal |
| Filip Münz | Czechoslovakia | 21 | Silver Medal |
| Wagenaar Daniel | Netherlands | 22 | Silver Medal |
| Ivchenko Nickolay | Ukraine | 23 | Silver Medal |
| Turnbull Alistair P. | United Kingdom | 24 | Silver Medal |
| Pankov Sergey | Russia | 25 | Silver Medal |
| Dénes Molnár | Hungary | 26 | Silver Medal |
| Martin Kassabov | Bulgaria | 27 | Silver Medal |
| Calin Ciocarlie | Romania | 28 | Silver Medal |
| Schulz Michael | United States of America | 29 | Silver Medal |
| Shutenko Timur | Ukraine | 30 | Silver Medal |
| Haynes Peter D. | United Kingdom | 31 | Silver Medal |
| Kupčo Alexander | Czechoslovakia | 32 | Silver Medal |
| Yongjik Kim | Republic of Korea | 33 | Silver Medal |
| Rodwell Steven | Australia | 34 | Bronze Medal |
| Heiss Peter | Germany | 35 | Bronze Medal |
| Tomáš Kočka | Czechoslovakia | 36 | Bronze Medal |
| Rascius Algirdas | Lithuania | 37 | Bronze Medal |
| Egorov Juri | Ukraine | 38 | Bronze Medal |
| Zaharia Sorin | Romania | 39 | Bronze Medal |
| Paul Tupper | Canada | 40 | Bronze Medal |
| Mehmet Özgür Oktel | Turkey | 41 | Bronze Medal |
| Odagiri Kosuke | United Kingdom | 42 | Bronze Medal |
| Christoph Gadermaier | Austria | 43 | Bronze Medal |
| Wilms Maikel | Netherlands | 44 | Bronze Medal |
| Lombardi Marco | Italy | 45 | Bronze Medal |
| Desset Claude | Belgium | 46 | Bronze Medal |
| Worlen Stefan | Germany | 47 | Bronze Medal |
| Arbatsky Dmitry | Russia | 48 | Bronze Medal |
| Imre Kondor | Hungary | 49 | Bronze Medal |
| Ralf Seidel | Germany | 50 | Bronze Medal |
| Jiří Vaníček | Czechoslovakia | 51 | Bronze Medal |
| Salih Adem | Turkey | 52 | Bronze Medal |
| Egidijus Anisimovas | Lithuania | 53 | Bronze Medal |
| Hulsebos Edo | Netherlands | 54 | Bronze Medal |
| Premont Patrick | Canada | 55 | Bronze Medal |
| Reimar Finken | Germany | 56 | Bronze Medal |
| Phichet Kittara | Thailand | 57 | Bronze Medal |
| Ozgoly Shahram | Islamic Republic of Iran | 58 | Honourable Mention |
| Cheong Gerald | Australia | 59 | Honourable Mention |
| Bergli Joakim | Norway | 60 | Honourable Mention |
| Ferrari Gabriele | Italy | 61 | Honourable Mention |
| Lim Hway Kiong | Singapore | 62 | Honourable Mention |
| Ioan Mihai Mitrea | Romania | 63 | Honourable Mention |
| Eftekhar A. Asghar | Islamic Republic of Iran | 64 | Honourable Mention |
| Basso Filippo | Italy | 65 | Honourable Mention |
| Leisink Martijn | Netherlands | 66 | Honourable Mention |
| Frykholm Niklas | Sweden | 67 | Honourable Mention |
| Sándor Katz | Hungary | 68 | Honourable Mention |
| Ivan Daikov | Bulgaria | 69 | Honourable Mention |
| Dunn Adrian | Canada | 70 | Honourable Mention |
| Czajkowski Marek | Poland | 71 | Honourable Mention |
| Fung Duncan K. T. | Singapore | 72 | Honourable Mention |
| Chen Fred | Australia | 73 | Honourable Mention |
| Persson Anders | Sweden | 74 | Honourable Mention |
| Ville Voipio | Finland | 75 | Honourable Mention |
| Maltchev Plamen | Bulgaria | 76 | Honourable Mention |
| Ilia Vassilev | Bulgaria | 77 | Honourable Mention |
| Andreasson Fredrik | Sweden | 78 | Honourable Mention |
| Jens Dean | United States of America | 79 | Honourable Mention |
| Strombergsson Andreas | Sweden | 80 | Honourable Mention |
| Huang Kee-Dyi | Singapore | 81 | Honourable Mention |
| Vaccarella Steven | Australia | 82 | Honourable Mention |
| James Carwil | United States of America | 83 | Honourable Mention |
| Béla Kulcsár | Hungary | 84 | Honourable Mention |
| Lumme Aki | Finland | 85 | Honourable Mention |
| Pafka Szilard | Romania | 86 | Honourable Mention |
| Zacharia George | Cyprus | 87 | Honourable Mention |
| Katalin Varju | Hungary | 88 | Honourable Mention |
| Tuleja Slavomír | Czechoslovakia | 89 | Honourable Mention |
| Maricevic Mario | Croatia | 90 | Honourable Mention |
| Gangstad Finn Arne | Norway | 91 | Honourable Mention |
| Kasprzak Jacek | Poland | 92 | Honourable Mention |
Results may be incomplete or incorrect.
Please send the relevant information to the webmaster:
<EMAIL_ADDRESS>If you are a participant you can send a personal page URL to be attached to your name.
If you are a participant you can send a personal page URL to be attached to your name.


Puppy Fam Finder
Julien Homble - Leah Marks - Danielle Potemri - Kathryn Sullivan
Find Your Puppy's Family!
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Name: Anthony Muli
Nickname: ANTO
Age: 20 Years
A highly motivated, articulate
and enthusiastic individual who
has the enthusiasm and
ambition to
apply themselves to
the role of associate project manage.
Thrives on working with people within a
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success.
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Need me! Get me now.
Written and compiled by Anthony Muli.
<EMAIL_ADDRESS>Tel:+254796577998
Copy& All rights reserved.2019


IWO is a minimal Open Source home automation system. Its purpose is to allow the control of arbirary household devices, such as projectors, televisions, radio socket to name a few, with the Alexa voice assistant. Altough it is not just limited to for this purpose and can be easily extended with additional features.
Current features support the following:
- Read and send infrared signals
- Read and send radio (433MHz) signals
- Create groups that combine multiple signals
- Access signals and groups through voice commands
For feature requests and bug report please file an issue at the github project page.
List of known signals
| Id | Name | Code On | Code Off | State | Delete |
Read new signal
| Id | Name | Code On | Code Off | ||
| |
|
+ |
List of known groups
| Id | Name | Signals On | Signals Off | State | Delete |
Create new group
| Id | Name | Signals On | Signals Off | ||
| + |


This is the partial for view 1.
{{title}}
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{{wallet.firstName}}
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{{wallet.email}}
{{wallet.birthDate}}
{{wallet.walletBalance}}


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Class: Sass::Tree::Visitors::Cssize
- Inherits:
-
Base
- Object
- Base
- Sass::Tree::Visitors::Cssize
- Defined in:
- /usr/local/google-old/home/code/sass-site/.sass/lib/sass/tree/visitors/cssize.rb
Overview
A visitor for converting a static Sass tree into a static CSS tree.
Defined Under Namespace
Class Method Summary (collapse)
-
+ ((Tree::Node, Sass::Util::SubsetMap)) visit(root)
The resulting tree of static nodes and the extensions defined for this tree.
Instance Method Summary (collapse)
-
- (Cssize) initialize
constructor
protected
A new instance of Cssize.
-
- (Tree::Node) parent
protected
Returns the immediate parent of the current node.
-
- visit(node)
protected
If an exception is raised, this adds proper metadata to the backtrace.
- - visit_atroot(node) protected
-
- visit_children(parent)
protected
Keeps track of the current parent node.
-
- (Array<Sass::Tree::Node>) visit_children_without_parent(node)
protected
Like , but doesn’t set .
-
- visit_directive(node)
protected
Bubbles a directive up through RuleNodes.
-
- visit_extend(node)
protected
Registers an extension in the
@extends
subset map. -
- visit_import(node)
protected
Modifies exception backtraces to include the imported file.
-
- visit_media(node)
protected
Bubbles the
@media
directive up through RuleNodes and merges it with other@media
directives. -
- visit_prop(node)
protected
Converts nested properties into flat properties and updates the indentation of the prop node based on the nesting level.
-
- ((Tree::Node, Sass::Util::SubsetMap)) visit_root(node)
protected
In Ruby 1.8, ensures that there’s only one
@charset
directive and that it’s at the top of the document. -
- visit_rule(node)
protected
Updates the indentation of the rule node based on the nesting level.
-
- visit_supports(node)
protected
Bubbles the
@supports
directive up through RuleNodes. -
- visit_trace(node)
protected
Asserts that all the traced children are valid in their new location.
-
- (Object) with_parent(parent) { ... }
protected
Runs a block of code with the current parent node replaced with the given node.
Methods inherited from Base
Constructor Details
- (Cssize) initialize (protected)
Returns a new instance of Cssize
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# File '/usr/local/google-old/home/code/sass-site/.sass/lib/sass/tree/visitors/cssize.rb', line 16
def initialize
@parents = []
@extends = Sass::Util::SubsetMap.new
end
|
Class Method Details
+ ((Tree::Node, Sass::Util::SubsetMap)) visit(root)
Returns The resulting tree of static nodes and the extensions defined for this tree
6 |
# File '/usr/local/google-old/home/code/sass-site/.sass/lib/sass/tree/visitors/cssize.rb', line 6
def self.visit(root); super; end
|
Instance Method Details
- (Tree::Node) parent (protected)
Returns the immediate parent of the current node.
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# File '/usr/local/google-old/home/code/sass-site/.sass/lib/sass/tree/visitors/cssize.rb', line 12
def parent
@parents.last
end
|
- visit(node) (protected)
If an exception is raised, this adds proper metadata to the backtrace.
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# File '/usr/local/google-old/home/code/sass-site/.sass/lib/sass/tree/visitors/cssize.rb', line 22
def visit(node)
super(node)
rescue Sass::SyntaxError => e
e.modify_backtrace(:filename => node.filename, :line => node.line)
raise e
end
|
- visit_atroot(node) (protected)
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# File '/usr/local/google-old/home/code/sass-site/.sass/lib/sass/tree/visitors/cssize.rb', line 189
def visit_atroot(node)
# If there aren't any more directives or rules that this @at-root needs to
# exclude, we can get rid of it and just evaluate the children.
if @parents.none? {|n| node.exclude_node?(n)}
results = visit_children_without_parent(node)
results.each {|c| c.tabs += node.tabs if bubblable?(c)}
if !results.empty? && bubblable?(results.last)
results.last.group_end = node.group_end
end
return results
end
# If this @at-root excludes the immediate parent, return it as-is so that it
# can be bubbled up by the parent node.
return Bubble.new(node) if node.exclude_node?(parent)
# Otherwise, duplicate the current parent and move it into the @at-root
# node. As above, returning an @at-root node signals to the parent directive
# that it should be bubbled upwards.
bubble(node)
end
|
- visit_children(parent) (protected)
Keeps track of the current parent node.
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# File '/usr/local/google-old/home/code/sass-site/.sass/lib/sass/tree/visitors/cssize.rb', line 30
def visit_children(parent)
with_parent parent do
parent.children = visit_children_without_parent(parent)
parent
end
end
|
- (Array<Sass::Tree::Node>) visit_children_without_parent(node) (protected)
Like , but doesn’t set .
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# File '/usr/local/google-old/home/code/sass-site/.sass/lib/sass/tree/visitors/cssize.rb', line 42
def visit_children_without_parent(node)
node.children.map {|c| visit(c)}.flatten
end
|
- visit_directive(node) (protected)
Bubbles a directive up through RuleNodes.
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# File '/usr/local/google-old/home/code/sass-site/.sass/lib/sass/tree/visitors/cssize.rb', line 236
def visit_directive(node)
return node unless node.has_children
return bubble(node) if parent.is_a?(Sass::Tree::RuleNode)
yield
# Since we don't know if the mere presence of an unknown directive may be
# important, we should keep an empty version around even if all the contents
# are removed via @at-root. However, if the contents are just bubbled out,
# we don't need to do so.
directive_exists = node.children.any? do |child|
next true unless child.is_a?(Bubble)
next false unless child.node.is_a?(Sass::Tree::DirectiveNode)
child.node.resolved_value == node.resolved_value
end
if directive_exists
[]
else
empty_node = node.dup
empty_node.children = []
[empty_node]
end + debubble(node.children, node)
end
|
- visit_extend(node) (protected)
Registers an extension in the @extends
subset map.
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# File '/usr/local/google-old/home/code/sass-site/.sass/lib/sass/tree/visitors/cssize.rb', line 125
def visit_extend(node)
node.resolved_selector.members.each do |seq|
if seq.members.size > 1
raise Sass::SyntaxError.new("Can't extend #{seq.to_a.join}: can't extend nested selectors")
end
sseq = seq.members.first
if !sseq.is_a?(Sass::Selector::SimpleSequence)
raise Sass::SyntaxError.new("Can't extend #{seq.to_a.join}: invalid selector")
elsif sseq.members.any? {|ss| ss.is_a?(Sass::Selector::Parent)}
raise Sass::SyntaxError.new("Can't extend #{seq.to_a.join}: can't extend parent selectors")
end
sel = sseq.members
parent.resolved_rules.members.each do |member|
unless member.members.last.is_a?(Sass::Selector::SimpleSequence)
raise Sass::SyntaxError.new("#{member} can't extend: invalid selector")
end
parent_directives = @parents.select {|p| p.is_a?(Sass::Tree::DirectiveNode)}
@extends[sel] = Extend.new(member, sel, node, parent_directives, :not_found)
end
end
[]
end
|
- visit_import(node) (protected)
Modifies exception backtraces to include the imported file.
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# File '/usr/local/google-old/home/code/sass-site/.sass/lib/sass/tree/visitors/cssize.rb', line 153
def visit_import(node)
visit_children_without_parent(node)
rescue Sass::SyntaxError => e
e.modify_backtrace(:filename => node.children.first.filename)
e.add_backtrace(:filename => node.filename, :line => node.line)
raise e
end
|
- visit_media(node) (protected)
Bubbles the @media
directive up through RuleNodes and merges it with other @media
directives.
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# File '/usr/local/google-old/home/code/sass-site/.sass/lib/sass/tree/visitors/cssize.rb', line 263
def visit_media(node)
return bubble(node) if parent.is_a?(Sass::Tree::RuleNode)
return Bubble.new(node) if parent.is_a?(Sass::Tree::MediaNode)
yield
debubble(node.children, node) do |child|
next child unless child.is_a?(Sass::Tree::MediaNode)
# Copies of `node` can be bubbled, and we don't want to merge it with its
# own query.
next child if child.resolved_query == node.resolved_query
next child if child.resolved_query = child.resolved_query.merge(node.resolved_query)
end
end
|
- visit_prop(node) (protected)
Converts nested properties into flat properties and updates the indentation of the prop node based on the nesting level.
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# File '/usr/local/google-old/home/code/sass-site/.sass/lib/sass/tree/visitors/cssize.rb', line 172
def visit_prop(node)
if parent.is_a?(Sass::Tree::PropNode)
node.resolved_name = "#{parent.resolved_name}-#{node.resolved_name}"
node.tabs = parent.tabs + (parent.resolved_value.empty? ? 0 : 1) if node.style == :nested
end
yield
result = node.children.dup
if !node.resolved_value.empty? || node.children.empty?
node.send(:check!)
result.unshift(node)
end
result
end
|
- ((Tree::Node, Sass::Util::SubsetMap)) visit_root(node) (protected)
In Ruby 1.8, ensures that there’s only one @charset
directive and that it’s at the top of the document.
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# File '/usr/local/google-old/home/code/sass-site/.sass/lib/sass/tree/visitors/cssize.rb', line 64
def visit_root(node)
yield
if parent.nil?
# In Ruby 1.9 we can make all @charset nodes invisible
# and infer the final @charset from the encoding of the final string.
if Sass::Util.ruby1_8?
charset = node.children.find {|c| c.is_a?(Sass::Tree::CharsetNode)}
node.children.reject! {|c| c.is_a?(Sass::Tree::CharsetNode)}
node.children.unshift charset if charset
end
imports_to_move = []
import_limit = nil
i = -1
node.children.reject! do |n|
i += 1
if import_limit
next false unless n.is_a?(Sass::Tree::CssImportNode)
imports_to_move << n
next true
end
if !n.is_a?(Sass::Tree::CommentNode) &&
!n.is_a?(Sass::Tree::CharsetNode) &&
!n.is_a?(Sass::Tree::CssImportNode)
import_limit = i
end
false
end
if import_limit
node.children = node.children[0...import_limit] + imports_to_move +
node.children[import_limit..-1]
end
end
return node, @extends
rescue Sass::SyntaxError => e
e.sass_template ||= node.template
raise e
end
|
- visit_rule(node) (protected)
Updates the indentation of the rule node based on the nesting level. The selectors were resolved in Perform.
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# File '/usr/local/google-old/home/code/sass-site/.sass/lib/sass/tree/visitors/cssize.rb', line 216
def visit_rule(node)
yield
rules = node.children.select {|c| bubblable?(c)}
props = node.children.reject {|c| bubblable?(c) || c.invisible?}
unless props.empty?
node.children = props
rules.each {|r| r.tabs += 1} if node.style == :nested
rules.unshift(node)
end
rules = debubble(rules)
unless parent.is_a?(Sass::Tree::RuleNode) || rules.empty? || !bubblable?(rules.last)
rules.last.group_end = true
end
rules
end
|
- visit_supports(node) (protected)
Bubbles the @supports
directive up through RuleNodes.
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# File '/usr/local/google-old/home/code/sass-site/.sass/lib/sass/tree/visitors/cssize.rb', line 279
def visit_supports(node)
return node unless node.has_children
return bubble(node) if parent.is_a?(Sass::Tree::RuleNode)
yield
debubble(node.children, node)
end
|
- visit_trace(node) (protected)
Asserts that all the traced children are valid in their new location.
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# File '/usr/local/google-old/home/code/sass-site/.sass/lib/sass/tree/visitors/cssize.rb', line 162
def visit_trace(node)
visit_children_without_parent(node)
rescue Sass::SyntaxError => e
e.modify_backtrace(:mixin => node.name, :filename => node.filename, :line => node.line)
e.add_backtrace(:filename => node.filename, :line => node.line)
raise e
end
|
- (Object) with_parent(parent) { ... } (protected)
Runs a block of code with the current parent node replaced with the given node.
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# File '/usr/local/google-old/home/code/sass-site/.sass/lib/sass/tree/visitors/cssize.rb', line 52
def with_parent(parent)
@parents.push parent
yield
ensure
@parents.pop
end
|


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