#ntype

[redwolf]

NTYPE designed X House Alentejo in Portugal -- via ArchDaily

[heatsensors]

Here’s a Quick Guide to Use a Thermocouple!

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[ofgentleresolve]

me, thinking about myungdae and his feralness and comes to the conclusion on the bus: oh my god he's a f*cking cat

evidence:

a. cats require consistent stimulation to get excess energy out ie. play w/ them and give them toys = myungdae has his rubik cubes b. if they like you they will give you prey they hunted = he will deliver you wanted criminals/bad ppl who need to be punished as presents c. may scratch or hiss if petted/touched too much = he can get stabby :/ d. feral cats are not socialized and therefore will avoid humans = myungdae hates crowded places and will do everything in his power to escape said places e. they both like birds and watching birds f. they can be territorial, both of spaces and of people; once they know you're their human, they don't like to share = there are select spots that myungdae will only hang out, everywhere else; also if he's close to u, good luck getting rid of him u won't

[dlyarchitecture]

[ladycamdens]

people said step by step is the worst bl drama they’ve ever seen as if th*ntype isn’t a show that was forced upon all of us

[charnel-doll]

i think it’s fun to include little nods to my other k|ntypes when im writing fic or doing fanart…. the fantasm cinematic universe

[r0b-tk1n]

i cba to sort this out now plus i cant find our pfp image on our pc . shitty placeholder pinned post im redacted from tr/n upr/sing she/her 16y/o robotk/n etc . i seem to be most like prev host but idk which k/ntypes i got other than this one . ill add my name later when im less embarrassed about being a fict/ve (letters replaced are :o, i, i, i, i)

[zkorpionzsting]

🍄 What are your OCs favourite snacks? Their favourite comfort food which always cheers them up when they’re down? Favourite meal to make? Do they enjoy baking and cooking and are they any good in the kitchen?

fuck thekitche do i look like a woman to you bitcy you want. sandwich make it yourslef or marry someone who can cook

whats good is anything spicy btw. and im notfuckin sharing with whoever you are. thr chreeetos flaming hot the xxxxxxxxl orv whatevermtheyr called and the dpritos funnyunds takisv are good too um spicy chicken wkngs from fuvking popeyes andvuh mcds is good but not spoicybenough so i add hot sauce to thstb you know like without hot sauce or spice you dont have a mealok

vini thinks im stipid but hes stupid for beung too fuckjing pussy to fuckuibgf grow up and be. man and add fucjmiung flavor to his eakeass slat is my favrit soicen white ngirl fuckin lkifesteyle fuck

uhate ntyping who invented langwelanglanguage\

umnthe more it hurts te better it tastes

[codehunter]

How do I Make a PDF searchable for a flask search application?

I have been doing research for a very important personal project. I would like to create a Flask Search Application that allows me to search for content across 100 Plus PDF files. I have found Some information around A ElasticSearch Lib that works well with flask.

#!/usr/bin/env python3#-*- coding: utf-8 -*-# import libraries to help read and create PDFimport PyPDF2from fpdf import FPDFimport base64import jsonfrom flask import Flask, jsonify, request, render_template, jsonfrom datetime import datetimeimport pandas as pd# import the Elasticsearch low-level client libraryfrom elasticsearch import Elasticsearch# create a new client instance of Elasticsearchelastic_client = Elasticsearch(hosts=["localhost"])es = Elasticsearch("http://localhost:9200/")app = Flask(__name__)# create a new PDF object with FPDFpdf = FPDF()# use an iterator to create 10 pagesfor page in range(10): pdf.add_page() pdf.set_font("Arial", size=14) pdf.cell(150, 12, txt="Object Rocket ROCKS!!", ln=1, align="C")# output all of the data to a new PDF filepdf.output("object_rocket.pdf")'''read_pdf = PyPDF2.PdfFileReader("object_rocket.pdf")page = read_pdf.getPage(0)page_mode = read_pdf.getPageMode()page_text = page.extractText()print (type(page_text))'''#with open(path, 'rb') as file:# get the PDF path and read the filefile = "Sheet3.pdf"read_pdf = PyPDF2.PdfFileReader(file, strict=False)#print (read_pdf)# get the read object's meta infopdf_meta = read_pdf.getDocumentInfo()# get the page numbersnum = read_pdf.getNumPages()print ("PDF pages:", num)# create a dictionary object for page dataall_pages = {}# put meta data into a dict keyall_pages["meta"] = {}# Use 'iteritems()` instead of 'items()' for Python 2for meta, value in pdf_meta.items(): print (meta, value) all_pages["meta"][meta] = value# iterate the page numbersfor page in range(num): data = read_pdf.getPage(page) #page_mode = read_pdf.getPageMode() # extract the page's text page_text = data.extractText() # put the text data into the dict all_pages[page] = page_text# create a JSON string from the dictionaryjson_data = json.dumps(all_pages)#print ("\nJSON:", json_data)# convert JSON string to bytes-like objbytes_string = bytes(json_data, 'utf-8')#print ("\nbytes_string:", bytes_string)# convert bytes to base64 encoded stringencoded_pdf = base64.b64encode(bytes_string)encoded_pdf = str(encoded_pdf)#print ("\nbase64:", encoded_pdf)# put the PDF data into a dictionary body to pass to the API requestbody_doc = {"data": encoded_pdf}# call the index() method to index the dataresult = elastic_client.index(index="pdf", doc_type="_doc", id="42", body=body_doc)# print the returned sresults#print ("\nindex result:", result['result'])# make another Elasticsearch API request to get the indexed PDFresult = elastic_client.get(index="pdf", doc_type='_doc', id=42)# print the data to terminalresult_data = result["_source"]["data"]#print ("\nresult_data:", result_data, '-- type:', type(result_data))# decode the base64 data (use to [:] to slice off# the 'b and ' in the string)decoded_pdf = base64.b64decode(result_data[2:-1]).decode("utf-8")#print ("\ndecoded_pdf:", decoded_pdf)# take decoded string and make into JSON objectjson_dict = json.loads(decoded_pdf)#print ("\njson_str:", json_dict, "\n\ntype:", type(json_dict))result2 = elastic_client.index(index="pdftext", doc_type="_doc", id="42", body=json_dict)# create new FPDF objectpdf = FPDF()# build the new PDF from the Elasticsearch dictionary# Use 'iteritems()` instead of 'items()' for Python 2""" for page, value in json_data: if page != "meta": # create new page pdf.add_page() pdf.set_font("Arial", size=14) # add content to page output = value + " -- Page: " + str(int(page)+1) pdf.cell(150, 12, txt=output, ln=1, align="C") else: # create the meta data for the new PDF for meta, meta_val in json_dict["meta"].items(): if "title" in meta.lower(): pdf.set_title(meta_val) elif "producer" in meta.lower() or "creator" in meta.lower(): pdf.set_creator(meta_val) """# output the PDF object's data to a PDF file#pdf.output("object_rocket_from_elaticsearch.pdf" )@app.route('/', methods=['GET'])def index(): return jsonify(json_dict)@app.route('/<id>', methods=['GET'])def index_by_id(id): return jsonify(json_dict[id])""" @app.route('/insert_data', methods=['PUT'])def insert_data(): slug = request.form['slug'] title = request.form['title'] content = request.form['content'] body = { 'slug': slug, 'title': title, 'content': content, 'timestamp': datetime.now() } result = es.index(index='contents', doc_type='title', id=slug, body=body) return jsonify(result) """app.run(port=5003, debug=True)

------Progress------ I now have a working solution with no front-end search capability:

# Load_single_PDF_BY_PAGE_TO_index.py #!/usr/bin/env python3#-*- coding: utf-8 -*-# import libraries to help read and create PDFimport PyPDF2from fpdf import FPDFimport base64from flask import Flask, jsonify, request, render_template, jsonfrom datetime import datetimeimport pandas as pd# import the Elasticsearch low-level client libraryfrom elasticsearch import Elasticsearch# create a new client instance of Elasticsearchelastic_client = Elasticsearch(hosts=["localhost"])es = Elasticsearch("http://localhost:9200/")app = Flask(__name__)#with open(path, 'rb') as file:# get the PDF path and read the filefile = "Sheet3.pdf"read_pdf = PyPDF2.PdfFileReader(file, strict=False)#print (read_pdf)# get the read object's meta infopdf_meta = read_pdf.getDocumentInfo()# get the page numbersnum = read_pdf.getNumPages()print ("PDF pages:", num)# create a dictionary object for page dataall_pages = {}# put meta data into a dict keyall_pages["meta"] = {}# Use 'iteritems()` instead of 'items()' for Python 2for meta, value in pdf_meta.items(): print (meta, value) all_pages["meta"][meta] = valuex = 44# iterate the page numbersfor page in range(num): data = read_pdf.getPage(page) #page_mode = read_pdf.getPageMode() # extract the page's text page_text = data.extractText() # put the text data into the dict all_pages[page] = page_text body_doc2 = {"data": page_text} result3 = elastic_client.index(index="pdfclearn", doc_type="_doc", id=x, body=body_doc2) x += 1

The above code loads a single pdf into elasticsearch by page.

from flask import Flask, jsonify, request,render_templatefrom elasticsearch import Elasticsearchfrom datetime import datetimees = Elasticsearch("http://localhost:9200/")app = Flask(__name__)@app.route('/pdf', methods=['GET'])def index(): results = es.get(index='pdfclearn', doc_type='_doc', id='44') return jsonify(results['_source'])@app.route('/pdf/<id>', methods=['GET'])def index_by_id(id): results = es.get(index='pdfclearn', doc_type='_doc', id=id) return jsonify(results['_source'])@app.route('/search/<keyword>', methods=['POST','GET'])def search(keyword): keyword = keyword body = { "query": { "multi_match": { "query": keyword, "fields": ["data"] } } } res = es.search(index="pdfclearn", doc_type="_doc", body=body) return jsonify(res['hits']['hits'])@app.route("/searhbar")def searhbar(): return render_template("index.html")@app.route("/searhbar/<string:box>")def process(box): query = request.args.get('query') if box == 'names': keyword = box body = { "query": { "multi_match": { "query": keyword, "fields": ["data"] } } } res = es.search(index="pdfclearn", doc_type="_doc", body=body) return jsonify(res['hits']['hits'])app.run(port=5003, debug=True)

In the above code we can search across all Pages for a keyword or phrase.

curl http://127.0.0.1:5003/search/test //it works!!

I Found a blog about how to dave PDF files as a Base64 index in ElasticSearch. I have seen DocuSign's API do this for document templating. However, I dont understand How to Jsonify the Base64 PDF in a way thats searchable for ElasticSearch.

curl "http://localhost:9200/pdftext/_doc/42"curl -X POST "http://localhost:9200/pdf/_search?q=*"

I can retrieve the Base64 of a 700 Page document. But I think what I need is to Index and retrieve Each Page of the Document.

Blogs I Have Studied that got me part the way:

https://kb.objectrocket.com/elasticsearch/how-to-index-a-pdf-file-as-an-elasticsearch-index-267

https://blog.miguelgrinberg.com/post/the-flask-mega-tutorial-part-xvi-full-text-search

endgame:

https://towardsdatascience.com/create-a-full-search-engine-via-flask-elasticsearch-javascript-d3js-and-bootstrap-275f9dc6efe1

I will continue to study Elastic Search and Base64 Encoding and decoding. But I would like some help getting to my goal. Any Detailed example would be much appreciated.

https://codehunter.cc/a/flask/how-do-i-make-a-pdf-searchable-for-a-flask-search-application

[shintsukinamiask]

I want two babies!!

“Well shit, get in line, we’re forming a queue I guess.  Make sure to wait to the side and I’ll call you when the rest are done, okay?”

[fumpkins]

Organic Electronics: Scientists Develop a High-Performance Unipolar N-Type Thin-Film Transistor

Scientist making thin-film transistors Left: Tsuyoshi Michinobu, right: Yang Wang.

Scientists at Tokyo Tech report a unipolar n-type transistor with a world-leading electron movement efficiency of as much as 7.16 cm2 V−1 s−1. This accomplishment declares an amazing future for organic electronic devices, consisting of the advancement of ingenious versatile screens and wearable innovations.

Scientists worldwide are on the hunt for unique products that can enhance the efficiency of fundamental parts needed to develop organic electronic devices.

Now, a research study group at Tokyo Tech’s Department of Products Science and Engineering consisting of Tsuyoshi Michinobu and Yang Wang report a method of increasing the electron movement of semiconducting polymers, which have actually formerly shown challenging to enhance. Their high-performance product attains an electron movement of 7.16 cm2 V−1 s−1, representing more than a 40 percent boost over previous similar outcomes.

In their research study released in the Journal of the American Chemical Society, they concentrated on boosting the efficiency of products called n-type semiconducting polymers. These n-type (unfavorable) products are electron dominant, in contrast to p-type (favorable) products that are hole dominant. “As negatively-charged radicals are intrinsically unstable compared to those that are positively charged, producing stable n-type semiconducting polymers has been a major challenge in organic electronics,” Michinobu discusses.

The research study for that reason attends to both a essential obstacle and a useful requirement. Wang keeps in mind that lots of organic solar batteries, for instance, are made from p-type semiconducting polymers and n-type fullerene derivatives. The disadvantage is that the latter are pricey, challenging to manufacture and incompatible with versatile gadgets. “To overcome these disadvantages,” he states, “high-performance n-type semiconducting polymers are highly desired to advance research on all-polymer solar cells.”

The group’s technique included utilizing a series of brand-new poly(benzothiadiazole-naphthalenediimide) derivatives and tweak the product’s foundation conformation. This was enabled by the intro of vinylene bridges1capable of forming hydrogen bonds with surrounding fluorine and oxygen atoms. Presenting these vinylene bridges needed a technical task so regarding enhance the response conditions.

General, the resultant product had actually an enhanced molecular product packaging order and higher strength, which added to the increased electron movement.

Utilizing strategies such as grazing-incidence wide-angle X-ray scattering (GIWAXS), the scientists validated that they accomplished an exceptionally brief π−π stacking distance2 of just 3.40 Å. “This value is among the shortest for high mobility organic semiconducting polymers,” states Michinobu.

There are a number of staying obstacles. “We need to further optimize the backbone structure,” he continues. “At the same time, side chain groups also play a significant role in determining the crystallinity and packing orientation of semiconducting polymers. We still have room for improvement.”

Wang mentions that the most affordable empty molecular orbital (LUMO) levels lay at −3.8 to −3.9 eV for the reported polymers. “As deeper LUMO levels lead to faster and more stable electron transport, further designs that introduce sp2-N, fluorine and chlorine atoms, for example, could help achieve even deeper LUMO levels,” he states.

In future, the scientists will likewise intend to enhance the air stability of n-channel transistors — a essential problem for recognizing useful applications that would consist of complementary metal-oxide-semiconductor (CMOS)-like reasoning circuits, all-polymer solar batteries, organic photodetectors and organic thermoelectrics.

Figure 1.Reasonable style of electron-transporting organic semiconducting polymers and their thin movie analysis and transistor efficiencies.

New post published on: https://livescience.tech/2019/03/01/organic-electronics-scientists-develop-a-high-performance-unipolar-n-type-thin-film-transistor/

[hcadbutt]

i should make more kin ambient noises

[unearthedsounds]

Tomorrow! 🎈5 Years of Unearthed Sounds 🎈We’ll be streaming live across multiple platforms from 2pm Wednesday 3rd April. You should probably take the afternoon off work 😉 —————— #unearthedsounds #newmusic #dubstep #celebration #event #onlineevent #dubstepmusic #ntype #joenice #kromestar #duku #bluesy #sgtpokes (at Unearthed Sounds) https://www.instagram.com/unearthedsounds/p/BvwJO4KFS14/?utm_source=ig_tumblr_share&igshid=2fwjbbb2iw3u

[adbhut-vigyan-blog]

P type and N type semiconductor. For more information about this you can go through our website Adbhutvigyan.com https://www.adbhutvigyan.com/ #semiconductor #ptype #ntype https://www.instagram.com/p/Bs0eAbUBAHC/?utm_source=ig_tumblr_share&igshid=1ii1h40jsqbzy

[rated-r-for-grantaire]

i remember when i was about 8-10 years old, every morning i would wake up before the rest of my family and put on a black dress, turn the shower on, and stand under it and sing Should’ve Said No and record myself, in an effort to recreate Ms Taylor Swift’s most iconic performance ever. sometimes i’d even take my dad’s old guitar too and “play” it.

[vampyretaemin]

literally no one asked for this but i made a bl drama rec list from the dramas i watched this past week :]