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InfectionEvent
- Class in
io.github.ph1lou.werewolfapi.events
InfectionEvent(UUID)
- Constructor for class io.github.ph1lou.werewolfapi.events.
InfectionEvent
InvestigateEvent
- Class in
io.github.ph1lou.werewolfapi.events
InvestigateEvent(UUID, List<UUID>, boolean)
- Constructor for class io.github.ph1lou.werewolfapi.events.
InvestigateEvent
InvisibleEvent
- Class in
io.github.ph1lou.werewolfapi.events
InvisibleEvent(UUID, boolean)
- Constructor for class io.github.ph1lou.werewolfapi.events.
InvisibleEvent
InvisibleState
- Interface in
io.github.ph1lou.werewolfapi.rolesattributs
io.github.ph1lou.werewolfapi
- package io.github.ph1lou.werewolfapi
io.github.ph1lou.werewolfapi.enumlg
- package io.github.ph1lou.werewolfapi.enumlg
io.github.ph1lou.werewolfapi.events
- package io.github.ph1lou.werewolfapi.events
io.github.ph1lou.werewolfapi.rolesattributs
- package io.github.ph1lou.werewolfapi.rolesattributs
isCamp(Camp)
- Method in interface io.github.ph1lou.werewolfapi.rolesattributs.
Roles
isCancelled()
- Method in class io.github.ph1lou.werewolfapi.events.
FirstDeathEvent
isCancelled()
- Method in class io.github.ph1lou.werewolfapi.events.
GoldenAppleParticleEvent
isCancelled()
- Method in class io.github.ph1lou.werewolfapi.events.
SecondDeathEvent
isCancelled()
- Method in class io.github.ph1lou.werewolfapi.events.
ThirdDeathEvent
isCancelled()
- Method in class io.github.ph1lou.werewolfapi.events.
WinConditionsCheckEvent
isDay(Day)
- Method in interface io.github.ph1lou.werewolfapi.
WereWolfAPI
return true if the day's game equal the input' game
isDisplayCamp(Camp)
- Method in interface io.github.ph1lou.werewolfapi.rolesattributs.
Display
isInvisible()
- Method in class io.github.ph1lou.werewolfapi.events.
InvisibleEvent
isInvisible()
- Method in interface io.github.ph1lou.werewolfapi.rolesattributs.
InvisibleState
isRole(Roles)
- Method in interface io.github.ph1lou.werewolfapi.
PlayerWW
isSameCamp()
- Method in class io.github.ph1lou.werewolfapi.events.
InvestigateEvent
isState(State)
- Method in interface io.github.ph1lou.werewolfapi.
PlayerWW
isState(StateLG)
- Method in interface io.github.ph1lou.werewolfapi.
WereWolfAPI
return true if the state of the game equal the input state
isThief()
- Method in interface io.github.ph1lou.werewolfapi.
PlayerWW
isTrollSV()
- Method in interface io.github.ph1lou.werewolfapi.
ConfigWereWolfAPI
isWereWolf()
- Method in class io.github.ph1lou.werewolfapi.events.
SniffEvent
isWereWolf()
- Method in interface io.github.ph1lou.werewolfapi.rolesattributs.
Roles
isWhiteList()
- Method in interface io.github.ph1lou.werewolfapi.
WereWolfAPI
return true if the whitelist is on
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{{define "head"}} {{end}} {{define "content"}}
Use this service at your own risk! Sharing a secret via the Internet is never 100% secure (who knows who is watching). Shareit.direct makes sharing these kind of sensitive data a bit easier and more secure since it is not saving anything on any disk at all.


--- layout: post title: BlueDragon patch available tags: [] status: publish type: post published: true meta: {} ---
Patch for BD (place it in your lib folder and re-cycle BD): ftp://ftp.newatlanta.com/public/bluedragon/6_1/server/patches which helps get rid of the following error message in your log files:
java.lang.StringIndexOutOfBoundsException: String index out of range: 26
[Mon Jun 28 09:51:30 CDT 2004] at java.lang.String.substring(Unknown Source)
[Mon Jun 28 09:51:30 CDT 2004] at com.newatlanta.servletexec.HttpServerRequest.ParseHeader(HttpServerRequest.java:309)


{% extends "layout.html" %} {% block body %}
| PubMedID | Publication Title | Publication year | Abstract | |
|---|---|---|---|---|
| 28770533 | Hyperuricemia, Type 2 Diabetes Mellitus, and Hypertension: an Emerging Association. | 2017 | Type 2 diabetes mellitus and hypertension are two important public health challenges, and both are linked to increased risk of cardiovascular events. Hyperuricemia has recently emerged as an independent risk factor in the development of type 2 diabetes mellitus and hypertension through several proposed mechanisms. | |
| 25623915 | Surgery in the treatment of type 2 diabetes mellitus. | 2015 | The prevalence of diabetes is increasing worldwide, and most of the cases are type 2 diabetes mellitus. The relationship between type 2 diabetes mellitus and obesity is well established, and surgical treatment is widely used for obese patients with type 2 diabetes mellitus | |
| 25867358 | Staging of type 2 diabetes mellitus. | 2015 | There is currently no reported staging system for type 2 diabetes mellitus. Here, I attempted to stage type 2 diabetes mellitus in order to help clinicians, patients, and other interested individuals to effectively evaluate patient conditions | |
| 28770321 | Metformin: clinical use in type 2 diabetes. | 2017 | Metformin is one of the most popular oral glucose-lowering medications, widely considered to be the optimal initial therapy for patients with type 2 diabetes mellitus. ...With no long-term studies comparing it against newer glucose-lowering drugs, some of which have more robust evidence for cardioprotection, metformin's established role as 'foundation therapy' in type 2 diabetes may justifiably be challenged | |
| 28380131 | Burden of type 2 diabetes mellitus in Brazil. | 2017 | Type 2 diabetes mellitus currently ranks high among indicators used in Global Burden of Disease Studies. The current study estimated the burden of disease attributable to type 2 diabetes mellitus and its chronic complications in Brazil, 2008. ...Chronic complications represented 80% of YLDs from type 2 diabetes mellitus. Type 2 diabetes mellitus ranked as a leading health problem in Brazil in 2008, accounting for relevant shares of mortality and morbidity. | |
| 29124701 | Vitamin D and Type 2 Diabetes Mellitus. | 2017 | Type 2 diabetes mellitus (T2DM) has become a significant global health care problem and its reported incidence is increasing at an alarming rate. ...Conflicting evidence is associating low levels of vitamin D in the body to T2DM and as such studies have been conducted to test the effect of vitamin D levels on incidence of diabetes, diabetic control as well as diabetic complications.Despite the conflicting evidence, vitamin D replacement seems to have some beneficial effect on the many aspects of diabetes: incidence, control and complications. | |
| 28072911 | Potential protective effect of lactation against incidence of type 2 diabetes mellitus in women with previous gestational diabetes mellitus: A systematic review and meta-analysis. | 2017 | Lactation may protect women with previous gestational diabetes mellitus (GDM) from developing type 2 diabetes mellitus, but the results of existing studies are inconsistent, ranging from null to beneficial. Longer lactation for more than 4 to 12 weeks postpartum had risk reduction of type 2 diabetes mellitus compared with shorter lactation (OR 0.77, 95% CI 0.01-55.86; OR 0.56, 95% CI 0.35-0.89; OR 0.22, 95% CI 0.13-0.36; type 2 diabetes mellitus evaluation time 2 y, 2-5 y, and 5 y, respectively). | |
| 28072911 | Potential protective effect of lactation against incidence of type 2 diabetes mellitus in women with previous gestational diabetes mellitus: A systematic review and meta-analysis. | 2017 | Lactation may protect women with previous gestational diabetes mellitus (GDM) from developing type 2 diabetes mellitus, but the results of existing studies are inconsistent, ranging from null to beneficial. Longer lactation for more than 4 to 12 weeks postpartum had risk reduction of type 2 diabetes mellitus compared with shorter lactation (OR 0.77, 95% CI 0.01-55.86; OR 0.56, 95% CI 0.35-0.89; OR 0.22, 95% CI 0.13-0.36; type 2 diabetes mellitus evaluation time 2 y, 2-5 y, and 5 y, respectively). | |
| 28072911 | Potential protective effect of lactation against incidence of type 2 diabetes mellitus in women with previous gestational diabetes mellitus: A systematic review and meta-analysis. | 2017 | Lactation may protect women with previous gestational diabetes mellitus (GDM) from developing type 2 diabetes mellitus, but the results of existing studies are inconsistent, ranging from null to beneficial. Longer lactation for more than 4 to 12 weeks postpartum had risk reduction of type 2 diabetes mellitus compared with shorter lactation (OR 0.77, 95% CI 0.01-55.86; OR 0.56, 95% CI 0.35-0.89; OR 0.22, 95% CI 0.13-0.36; type 2 diabetes mellitus evaluation time 2 y, 2-5 y, and 5 y, respectively). | |
| 28072911 | Potential protective effect of lactation against incidence of type 2 diabetes mellitus in women with previous gestational diabetes mellitus: A systematic review and meta-analysis. | 2017 | Lactation may protect women with previous gestational diabetes mellitus (GDM) from developing type 2 diabetes mellitus, but the results of existing studies are inconsistent, ranging from null to beneficial. Longer lactation for more than 4 to 12 weeks postpartum had risk reduction of type 2 diabetes mellitus compared with shorter lactation (OR 0.77, 95% CI 0.01-55.86; OR 0.56, 95% CI 0.35-0.89; OR 0.22, 95% CI 0.13-0.36; type 2 diabetes mellitus evaluation time 2 y, 2-5 y, and 5 y, respectively). | |
| 28072911 | Potential protective effect of lactation against incidence of type 2 diabetes mellitus in women with previous gestational diabetes mellitus: A systematic review and meta-analysis. | 2017 | Lactation may protect women with previous gestational diabetes mellitus (GDM) from developing type 2 diabetes mellitus, but the results of existing studies are inconsistent, ranging from null to beneficial. Longer lactation for more than 4 to 12 weeks postpartum had risk reduction of type 2 diabetes mellitus compared with shorter lactation (OR 0.77, 95% CI 0.01-55.86; OR 0.56, 95% CI 0.35-0.89; OR 0.22, 95% CI 0.13-0.36; type 2 diabetes mellitus evaluation time 2 y, 2-5 y, and 5 y, respectively). | |
| 28072911 | Potential protective effect of lactation against incidence of type 2 diabetes mellitus in women with previous gestational diabetes mellitus: A systematic review and meta-analysis. | 2017 | Lactation may protect women with previous gestational diabetes mellitus (GDM) from developing type 2 diabetes mellitus, but the results of existing studies are inconsistent, ranging from null to beneficial. Longer lactation for more than 4 to 12 weeks postpartum had risk reduction of type 2 diabetes mellitus compared with shorter lactation (OR 0.77, 95% CI 0.01-55.86; OR 0.56, 95% CI 0.35-0.89; OR 0.22, 95% CI 0.13-0.36; type 2 diabetes mellitus evaluation time 2 y, 2-5 y, and 5 y, respectively). | |
| 28072911 | Potential protective effect of lactation against incidence of type 2 diabetes mellitus in women with previous gestational diabetes mellitus: A systematic review and meta-analysis. | 2017 | Lactation may protect women with previous gestational diabetes mellitus (GDM) from developing type 2 diabetes mellitus, but the results of existing studies are inconsistent, ranging from null to beneficial. Longer lactation for more than 4 to 12 weeks postpartum had risk reduction of type 2 diabetes mellitus compared with shorter lactation (OR 0.77, 95% CI 0.01-55.86; OR 0.56, 95% CI 0.35-0.89; OR 0.22, 95% CI 0.13-0.36; type 2 diabetes mellitus evaluation time 2 y, 2-5 y, and 5 y, respectively). | |
| 28072911 | Potential protective effect of lactation against incidence of type 2 diabetes mellitus in women with previous gestational diabetes mellitus: A systematic review and meta-analysis. | 2017 | Lactation may protect women with previous gestational diabetes mellitus (GDM) from developing type 2 diabetes mellitus, but the results of existing studies are inconsistent, ranging from null to beneficial. Longer lactation for more than 4 to 12 weeks postpartum had risk reduction of type 2 diabetes mellitus compared with shorter lactation (OR 0.77, 95% CI 0.01-55.86; OR 0.56, 95% CI 0.35-0.89; OR 0.22, 95% CI 0.13-0.36; type 2 diabetes mellitus evaluation time 2 y, 2-5 y, and 5 y, respectively). | |
| 28072911 | Potential protective effect of lactation against incidence of type 2 diabetes mellitus in women with previous gestational diabetes mellitus: A systematic review and meta-analysis. | 2017 | Lactation may protect women with previous gestational diabetes mellitus (GDM) from developing type 2 diabetes mellitus, but the results of existing studies are inconsistent, ranging from null to beneficial. Longer lactation for more than 4 to 12 weeks postpartum had risk reduction of type 2 diabetes mellitus compared with shorter lactation (OR 0.77, 95% CI 0.01-55.86; OR 0.56, 95% CI 0.35-0.89; OR 0.22, 95% CI 0.13-0.36; type 2 diabetes mellitus evaluation time 2 y, 2-5 y, and 5 y, respectively). | |
| 28072911 | Potential protective effect of lactation against incidence of type 2 diabetes mellitus in women with previous gestational diabetes mellitus: A systematic review and meta-analysis. | 2017 | Lactation may protect women with previous gestational diabetes mellitus (GDM) from developing type 2 diabetes mellitus, but the results of existing studies are inconsistent, ranging from null to beneficial. Longer lactation for more than 4 to 12 weeks postpartum had risk reduction of type 2 diabetes mellitus compared with shorter lactation (OR 0.77, 95% CI 0.01-55.86; OR 0.56, 95% CI 0.35-0.89; OR 0.22, 95% CI 0.13-0.36; type 2 diabetes mellitus evaluation time 2 y, 2-5 y, and 5 y, respectively). | |
| 28072911 | Potential protective effect of lactation against incidence of type 2 diabetes mellitus in women with previous gestational diabetes mellitus: A systematic review and meta-analysis. | 2017 | Lactation may protect women with previous gestational diabetes mellitus (GDM) from developing type 2 diabetes mellitus, but the results of existing studies are inconsistent, ranging from null to beneficial. Longer lactation for more than 4 to 12 weeks postpartum had risk reduction of type 2 diabetes mellitus compared with shorter lactation (OR 0.77, 95% CI 0.01-55.86; OR 0.56, 95% CI 0.35-0.89; OR 0.22, 95% CI 0.13-0.36; type 2 diabetes mellitus evaluation time 2 y, 2-5 y, and 5 y, respectively). | |
| 28072911 | Potential protective effect of lactation against incidence of type 2 diabetes mellitus in women with previous gestational diabetes mellitus: A systematic review and meta-analysis. | 2017 | Lactation may protect women with previous gestational diabetes mellitus (GDM) from developing type 2 diabetes mellitus, but the results of existing studies are inconsistent, ranging from null to beneficial. Longer lactation for more than 4 to 12 weeks postpartum had risk reduction of type 2 diabetes mellitus compared with shorter lactation (OR 0.77, 95% CI 0.01-55.86; OR 0.56, 95% CI 0.35-0.89; OR 0.22, 95% CI 0.13-0.36; type 2 diabetes mellitus evaluation time 2 y, 2-5 y, and 5 y, respectively). | |
| 28072911 | Potential protective effect of lactation against incidence of type 2 diabetes mellitus in women with previous gestational diabetes mellitus: A systematic review and meta-analysis. | 2017 | Lactation may protect women with previous gestational diabetes mellitus (GDM) from developing type 2 diabetes mellitus, but the results of existing studies are inconsistent, ranging from null to beneficial. Longer lactation for more than 4 to 12 weeks postpartum had risk reduction of type 2 diabetes mellitus compared with shorter lactation (OR 0.77, 95% CI 0.01-55.86; OR 0.56, 95% CI 0.35-0.89; OR 0.22, 95% CI 0.13-0.36; type 2 diabetes mellitus evaluation time 2 y, 2-5 y, and 5 y, respectively). | |
| 28072911 | Potential protective effect of lactation against incidence of type 2 diabetes mellitus in women with previous gestational diabetes mellitus: A systematic review and meta-analysis. | 2017 | Lactation may protect women with previous gestational diabetes mellitus (GDM) from developing type 2 diabetes mellitus, but the results of existing studies are inconsistent, ranging from null to beneficial. Longer lactation for more than 4 to 12 weeks postpartum had risk reduction of type 2 diabetes mellitus compared with shorter lactation (OR 0.77, 95% CI 0.01-55.86; OR 0.56, 95% CI 0.35-0.89; OR 0.22, 95% CI 0.13-0.36; type 2 diabetes mellitus evaluation time 2 y, 2-5 y, and 5 y, respectively). | |
| 28072911 | Potential protective effect of lactation against incidence of type 2 diabetes mellitus in women with previous gestational diabetes mellitus: A systematic review and meta-analysis. | 2017 | Lactation may protect women with previous gestational diabetes mellitus (GDM) from developing type 2 diabetes mellitus, but the results of existing studies are inconsistent, ranging from null to beneficial. Longer lactation for more than 4 to 12 weeks postpartum had risk reduction of type 2 diabetes mellitus compared with shorter lactation (OR 0.77, 95% CI 0.01-55.86; OR 0.56, 95% CI 0.35-0.89; OR 0.22, 95% CI 0.13-0.36; type 2 diabetes mellitus evaluation time 2 y, 2-5 y, and 5 y, respectively). |


API¶
Helper Functions¶
csv (txt[, comment]) |
Read the string in txt as csv file and return the content as DataFrame. |
deg2slope (degree) |
convert degrees to a slope (\(\Delta x / \Delta y\)) |
linear (slope, p0) |
get function/expression of a straight line with a given point which it intercepts |
circle (r[, x_m, y_m, clockwise]) |
get function/expression of a circle with a given mid point |
channel_end (r, end_degree) |
get vertical end of the channel based on the radius of the channel and an end angle |
combine_input_files (shape_path[, …]) |
combine all generated shape text files to a single inp-like text file |
-
shape_generator.helpers.
csv
(txt, comment=None)[source]¶ Read the string in txt as csv file and return the content as DataFrame.
Parameters: Returns: csv table as pandas DataFrame
Return type:
-
shape_generator.helpers.
deg2slope
(degree)[source]¶ convert degrees to a slope (\(\Delta x / \Delta y\))
Parameters: degree (float) – angle in degree Returns: slope Return type: float
-
shape_generator.helpers.
linear
(slope, p0)[source]¶ get function/expression of a straight line with a given point which it intercepts
Parameters: Returns: linear function
Return type:
-
shape_generator.helpers.
circle
(r, x_m=0, y_m=0, clockwise=False)[source]¶ get function/expression of a circle with a given mid point
Parameters: Returns: function of the circle
Return type:
-
shape_generator.helpers.
channel_end
(r, end_degree)[source]¶ get vertical end of the channel based on the radius of the channel and an end angle
Parameters: Returns: height of the channel when the circle reaches a certain angle
Return type:
-
shape_generator.helpers.
combine_input_files
(shape_path, delete_original=False)[source]¶ combine all generated shape text files to a single inp-like text file
When running the
shape_generator.shape_generator.Profile.input_file()
function, a .txt file will be created. Those txt files will be combines to a single file with this function. This makes it easier to import all shapes to the .inp file.Parameters:
Main Function¶
Class
shape_generator.shape_generator.CrossSection (label) |
main class A Class that should help to generate custom cross section shapes for the SWMM software. |
Methods
__init__ (label[, long_label, height, width, …]) |
Initialise the cross section class |
add (x_or_expr[, y]) |
add part of cross section |
check_for_slopes ([debug]) |
convert slopes to points |
create_point_cloud () |
create absolute point coordinates and write it into df_abs |
check_point_cloud () |
remove errors from the point cloud, ie.: |
df_rel |
relative point coordinates |
cross_section_area () |
calculate the cross section area |
Macros
generator ([show]) |
check_for_slopes + create_point_cloud + check_point_cloud |
make ([show, plot]) |
generator + profile_abs_plot + input_file |
add_and_show (*args, **kwargs) |
add + generator + profile_abs_figure |
Text-files / Figures
profile_rel_plot ([auto_open, file_format]) |
create a plot graphic into the working_directory with relative dimensions |
profile_abs_plot ([auto_open, file_format]) |
create a plot graphic into the working_directory with absolute dimensions. |
profile_abs_figure () |
create a plot of the absolute dimensions |
dat_file () |
create the EPA-SWMM Curve Data .dat -file, which can be imported into SWMM The file is save into the working_directory . |
inp_string () |
create the curve data for cross section shapes in the .inp -file (SWMM-Input) format, which can be pasted into the input file. |
input_file () |
create the curve data for cross section shapes in the .inp -file (SWMM-Input) format and save it as a separate txt-file. |
Pre defined Cross Sections
standard (label, long_label, height[, width, …]) |
standard cross section |
box (label, height, width[, channel, bench, …]) |
pre defined box (=Kasten) cross section |
box_from_string (label, height, width[, …]) |
create pre defined box (=Kasten) cross section with the string label. |
from_point_cloud (relative_coordinates, …) |
get the cross sections from a point cloud where every point is relative to the lowers point in the cross section |
-
class
shape_generator.shape_generator.
CrossSection
(label, long_label=None, height=None, width=None, add_dim=False, add_dn=None, working_directory='', unit=None)[source]¶ main class A Class that should help to generate custom cross section shapes for the SWMM software.
-
df_abs
¶ maximum 100 points to describe the cross section in absolute values
Type: pandas.DataFrame
-
__init__
(label, long_label=None, height=None, width=None, add_dim=False, add_dn=None, working_directory='', unit=None)[source]¶ Initialise the cross section class
Parameters: - label (str) – main name/label/number of the cross section
- long_label (Optional[str]) – optional longer name of the cross section
- height (float) – absolute height of the CS
- width (Optional[float]) – absolute width of the CS (optional) can be calculated
- add_dim (bool) – if the dimensions should be added to
out_filename
used for the export - add_dn (Optional[float]) – if the channel dimension should be added to
out_filename
used for the export enter the diameter as float - working_directory (str) – directory where the files get saved
- unit (Optional[str]) – enter unit to add the unit in the plots
-
add
(x_or_expr, y=None)[source]¶ add part of cross section
can be a:
- function/expression
- point (x,y) coordinates
- boundary condition (x or y) of a surrounding function = only x or y is given and the other is
None
- slope (x=slope, y=unit of slope)
Parameters:
-
add_and_show
(*args, **kwargs)[source]¶ add
+generator
+profile_abs_figure
and print the raw shape (description of the cross section)
macro function for jupyter example
Parameters:
-
static
box
(label, height, width, channel=None, bench=None, roof=None, rounding=0.0, add_dim=True, long_label=None, unit=None)[source]¶ pre defined box (=Kasten) cross section
see Examples for box shaped profiles
Parameters: - label (str) – see
__init__
- height (float) – see
__init__
- width (float) – see
__init__
- channel (Optional[float]) – diameter of the dry weather channel
- bench (Optional[float]) –
bench (=Berme)
''
: flache Berme'R'
: V-förmiges Profil'H'
: Schräge Verschneidung
- roof (Optional[float]) –
roof (=Decke)
''
: gerade'B'
: Bogen'K'
: Kreis
- rounding (Optional[float]) – rounding of the edges
- add_dim (bool) – see
__init__
- long_label (Optional[str]) – see
__init__
- unit (Optional[str]) – see
__init__
Returns: pre defined box (=Kasten) cross section
Return type: - label (str) – see
-
static
box_from_string
(label, height, width, custom_label=None, unit=None)[source]¶ create pre defined box (=Kasten) cross section with the string label. This function takes the information from the label and pass them to the
box
- function.see Examples for box shaped profiles
Parameters: Returns: pre defined box (=Kasten) cross section
Return type: Examples
-
check_for_slopes
(debug=False)[source]¶ convert slopes to points
Use this function after adding all the necessary descriptions of the cross section with
add
. This function converts slopes into point coordinates and specify boundary condition to (x,y) coordinates for Expressions and slopes.Parameters: debug (bool) – to print debug messages during the runtime
-
check_point_cloud
()[source]¶ remove errors from the point cloud, ie.:
- remove duplicates,
- (if specified) remove points which overlap the overall cross section width and
- other errors…
-
create_point_cloud
()[source]¶ create absolute point coordinates and write it into
df_abs
To create a
pandas.DataFrame
of all the points to describe the cross section. This function replaces the Expressions given inadd
to points with x and y coordinates and writes them into thedf_abs
attribute.
-
cross_section_area
()[source]¶ calculate the cross section area
Returns: area, unit depend on unit of the entered values. Return type: float
-
dat_file
()[source]¶ create the EPA-SWMM Curve Data
.dat
-file, which can be imported into SWMM The file is save into theworking_directory
.
-
df_rel
¶ relative point coordinates
convert the absolute values in the point coordinates to values relative to the cross section height
Returns: point coordinate values relative to the cross section height Return type: pandas.DataFrame
-
static
from_point_cloud
(relative_coordinates, *args, **kwargs)[source]¶ get the cross sections from a point cloud where every point is relative to the lowers point in the cross section
Parameters: - relative_coordinates (pandas.Series) – height-variable as index and width as values with the origin in the lowest point of the cross section
- *args – arguments, see
__init__
- **kwargs – keyword arguments, see
__init__
Returns: of the point cloud
Return type:
-
generator
(show=False)[source]¶ check_for_slopes
+create_point_cloud
+check_point_cloud
macro function
Parameters: show (bool) – see check_for_slopes
debug
- argument and print the created point cloud
-
get_width
()[source]¶ get absolute width of cross section
Returns: width of cross section Return type: float
-
inp_string
()[source]¶ create the curve data for cross section shapes in the
.inp
-file (SWMM-Input) format, which can be pasted into the input file.Returns: formatted text of the data Return type: str
-
input_file
()[source]¶ create the curve data for cross section shapes in the
.inp
-file (SWMM-Input) format and save it as a separate txt-file.This function uses the
inp_string
-function to get the string and saves the string in a file. The file is save into theworking_directory
.
-
make
(show=False, plot=True)[source]¶ generator
+profile_abs_plot
+input_file
macro function
Parameters: - show (bool) – see
generator
arguments - plot (bool) – if
profile_abs_plot
should be executed
- show (bool) – see
-
profile_abs_figure
()[source]¶ create a plot of the absolute dimensions
Returns: plot of the absolute dimensions Return type: matplotlib.figure.Figure
-
profile_abs_plot
(auto_open=False, file_format='png')[source]¶ create a plot graphic into the
working_directory
with absolute dimensions.This function uses the
profile_abs_figure
-function to get a figure and saves the figure in a file.Parameters: - auto_open (bool) – whether the plot should be opened after its creation
- file_format (str) – file format, ie:
png
,pandasf
, … (seematplotlib.figure.Figure.savefig()
)
-
profile_rel_plot
(auto_open=False, file_format='png')[source]¶ create a plot graphic into the
working_directory
with relative dimensionsParameters: - auto_open (bool) – whether the plot should be opened after its creation
- file_format (str) – file format, ie:
png
,pandasf
, … (seematplotlib.figure.Figure.savefig()
)
-
static
standard
(label, long_label, height, width=nan, r_channel=nan, r_roof=nan, r_wall=nan, slope_bench=nan, r_round=nan, r_wall_bottom=nan, h_bench=nan, pre_bench=nan, w_channel=nan, add_dim=False, add_dn=False, unit=None)[source]¶ standard cross section
Parameters: - label (str) – see
__init__
- long_label (str) – see
__init__
- height (float) – see
__init__
- width (float) – see
__init__
- r_channel (float) – radius of the dry-weather channel (=Trockenwetter Rinne)
- w_channel (float) – half width of the channel, only in combination with
r_channel
active - pre_bench (float) – slope of the upper end of the channel in degree, only in combination with
r_channel
active - r_round (float) – radius of the rounding of the edges, only in combination with
r_channel
active - h_bench (float) – height where the bench begins, only in combination with
r_channel
active - slope_bench (float) – slope of the bench (=Berme) in degree, or slope of the rainwater-floor (=Regenwetterrinne)
- r_roof (float) – radius of the roof (=Decke)
- r_wall (float) – radius of the sidewall (=Seitenwand), only in combination with
r_roof
active - r_wall_bottom (float) – radius of the bottom sidewall (=untere Seitenwand), only in combination with
r_wall
active - add_dim (bool) – see
__init__
- add_dn (Optional[float]) – see
__init__
Returns: standard cross section
Return type: Examples
english deutsch channel Trockenwetter-Rinne roof Firste/Decke wall Seitenwand bench Berme - label (str) – see
-


logger - System to control logging of events.
The logger package provides a flexible system for logging messages from different services, at priority levels, with different commands.
To begin using the logger package, we do the following:
package require logger set log [logger::init myservice] ${log}::notice "Initialized myservice logging" ... code ... ${log}::notice "Ending myservice logging" ${log}::delete
In the above code, after the package is loaded, the following things happen:
Initializes the service service for logging. The service names are actually Tcl namespace names, so they are separated with '::'. The service name may not be the empty string or only ':'s. When a logger service is initialized, it "inherits" properties from its parents. For instance, if there were a service foo, and we did a logger::init foo::bar (to create a bar service underneath foo), bar would copy the current configuration of the foo service, although it would of course, also be possible to then separately configure bar. If a logger service is initialized and the parent does not yet exist, the parent is also created. The new logger service is initialized with the default loglevel set with logger::setlevel.
Import the logger service commands into the current namespace. Without the -all option only the commands corresponding to the log levels are imported. If -all is given, all the ${log}::cmd style commands are imported. If the import would overwrite a command an error is returned and no command is imported. Use the -force option to force the import and overwrite existing commands without complaining. If the -prefix option is given, the commands are imported with the given prefix prepended to their names. If the -namespace option is given, the commands are imported into the given namespace. If the namespace does not exist, it is created. If a namespace without a leading :: is given, it is interpreted as a child namespace to the current namespace.
Convenience command for setting up a namespace for logging. Creates a logger service named after the namespace ns (a :: prefix is stripped), imports all the log commands into the namespace, and sets the default logging level, either as specified by level, or inherited from a service in the parent namespace, or a hardwired default, warn.
Returns a list of all the available services.
Globally enables logging at and "above" the given level. Levels are debug, info, notice, warn, error, critical, alert, emergency.
Globally disables logging at and "below" the given level. Levels are those listed above.
Globally enable logging at and "above" the given level. Levels are those listed above. This command changes the default loglevel for new loggers created with logger::init.
Returns a list of the available log levels (also listed above under enable).
Returns the ${log} token created by logger::init for this service.
These are the commands called to actually log a message about an event. ${log} is the variable obtained from logger::init.
Enable logging, in the service referenced by ${log}, and its children, at and above the level specified, and disable logging below it.
Enable logging, in the service referenced by ${log}, and its children, at and above the level specified. Note that this does not disable logging below this level, so you should probably use setlevel instead.
Disable logging, in the service referenced by ${log}, and its children, at and below the level specified. Note that this does not enable logging above this level, so you should probably use setlevel instead. Disabling the loglevel emergency switches logging off for the service and its children.
Set the script to call when the log instance in question changes its log level. If called without a command it returns the currently registered command. The command gets two arguments appended, the old and the new loglevel. The callback is invoked after all changes have been done. If child loggers are affected, their callbacks are called before their parents callback.
proc lvlcallback {old new} { puts "Loglevel changed from $old to $new" } ${log}::lvlchangeproc lvlcallback
This command comes in three forms - the third, older one is deprecated and may be removed from future versions of the logger package. The current set version takes one argument, a command to be executed when the level is called. The callback command takes on argument, the text to be logged. If called only with a valid level logproc returns the name of the command currently registered as callback command. logproc specifies which command will perform the actual logging for a given level. The logger package ships with default commands for all log levels, but with logproc it is possible to replace them with custom code. This would let you send your logs over the network, to a database, or anything else. For example:
proc logtoserver {txt} { variable socket puts $socket "Notice: $txt" } ${log}::logproc notice logtoserver
Trace logs are slightly different: instead of a plain text argument, the argument provided to the logproc is a dictionary consisting of the enter or leave keyword along with another dictionary of details about the trace. These include:
proc - Name of the procedure being traced.
level - The stack level for the procedure invocation (from info level).
script - The name of the file in which the procedure is defined, or an empty string if defined in interactive mode.
caller - The name of the procedure calling the procedure being traced, or an empty string if the procedure was called from the global scope (stack level 0).
procargs - A dictionary consisting of the names of arguments to the procedure paired with values given for those arguments (enter traces only).
status - The Tcl return code (e.g. ok, continue, etc.) (leave traces only).
result - The value returned by the procedure (leave traces only).
Returns a list of the registered logging services which are children of this service.
Returns the name of this service.
Returns the currently enabled log level for this service. If no logging is enabled returns none.
Set the script to call when the log instance in question is deleted. If called without a command it returns the currently registered command. For example:
${log}::delproc [list closesock $logsock]
This command deletes a particular logging service, and its children. You must call this to clean up the resources used by a service.
This command controls logging of enter/leave traces for specified procedures. It is used to enable and disable tracing, query tracing status, and specify procedures are to be traced. Trace handlers are unregistered when tracing is disabled. As a result, there is not performance impact to a library when tracing is disabled, just as with other log level commands.
proc tracecmd { dict } { puts $dict } set log [::logger::init example] ${log}::logproc trace tracecmd proc foo { args } { puts "In foo" bar 1 return "foo_result" } proc bar { x } { puts "In bar" return "bar_result" } ${log}::trace add foo bar ${log}::trace on foo # Output: enter {proc ::foo level 1 script {} caller {} procargs {args {}}} In foo enter {proc ::bar level 2 script {} caller ::foo procargs {x 1}} In bar leave {proc ::bar level 2 script {} caller ::foo status ok result bar_result} leave {proc ::foo level 1 script {} caller {} status ok result foo_result}
Turns on trace logging for procedures registered through the trace add command. This is similar to the enable command for other logging levels, but allows trace logging to take place at any level. The trace logging mechanism takes advantage of the execution trace feature of Tcl 8.4 and later. The trace on command will return an error if called from earlier versions of Tcl.
Turns off trace logging for procedures registered for trace logging through the trace add command. This is similar to the disable command for other logging levels, but allows trace logging to take place at any level. Procedures are not unregistered, so logging for them can be turned back on with the trace on command. There is no overhead imposed by trace registration when trace logging is disabled.
This command returns a list of the procedures currently registered for trace logging, or a flag indicating whether or not a trace is registered for one or more specified procedures.
This command registers one or more procedures for logging of entry/exit traces. Procedures can be specified via a list of procedure names or namespace names (in which case all procedure within the namespace are targeted by the operation). By default, each name is first interpreted as a procedure name or glob-style search pattern, and if not found its interpreted as a namespace name. The -ns option can be used to force interpretation of all provided arguments as namespace names. Procedures must be defined prior to registering them for tracing through the trace add command. Any procedure or namespace names/patterns that don't match any existing procedures will be silently ignored.
This command unregisters one or more procedures so that they will no longer have trace logging performed, with the same matching rules as that of the trace add command.
The logger package is implemented in such a way as to optimize (for Tcl 8.4 and newer) log procedures which are disabled. They are aliased to a proc which has no body, which is compiled to a no op in bytecode. This should make the peformance hit minimal. If you really want to pull out all the stops, you can replace the ${log} token in your code with the actual namespace and command (${log}::warn becomes ::logger::tree::myservice::warn), so that no variable lookup is done. This puts the performance of disabled logger commands very close to no logging at all.
The "object orientation" is done through a hierarchy of namespaces. Using an actual object oriented system would probably be a better way of doing things, or at least provide for a cleaner implementation.
The service "object orientation" is done with namespaces.
The logger package takes extra care to keep the logproc out of the call stack. This enables logprocs to execute code in the callers scope by using uplevel or linking to local variables by using upvar. This may fire traces with all usual side effects.
# Print caller and current vars in the calling proc proc log_local_var {txt} { set caller [info level -1] set vars [uplevel 1 info vars] foreach var [lsort $vars] { if {[uplevel 1 [list array exists $var]] == 1} { lappend val $var <Array> } else { lappend val $var [uplevel 1 [list set $var]] } } puts "$txt" puts "Caller: $caller" puts "Variables in callers scope:" foreach {var value} $val { puts "$var = $value" } } # install as logproc ${log}::logproc debug log_local_var
This document, and the package it describes, will undoubtedly contain bugs and other problems. Please report such in the category logger of the Tcllib Trackers. Please also report any ideas for enhancements you may have for either package and/or documentation.
When proposing code changes, please provide unified diffs, i.e the output of diff -u.
Note further that attachments are strongly preferred over inlined patches. Attachments can be made by going to the Edit form of the ticket immediately after its creation, and then using the left-most button in the secondary navigation bar.
Programming tools


| Class and Description |
|---|
| RBACManager
RBACManager
|
| Class and Description |
|---|
| Permission
Permission
A permission is the wrapper for an operation and a resource effectively saying
that the operation is authorized for that resource.
|
| Class and Description |
|---|
| Role
Role
A role is assignable to a user and effectively grants that user all of the
permissions that are present in that role.
|
| Class and Description |
|---|
| Operation
Operation
In RBAC the operation is an action or functionality that can be linked with a
particular resource into an assignable Permission.
|
| Permission
Permission
A permission is the wrapper for an operation and a resource effectively saying
that the operation is authorized for that resource.
|
| Resource
Resource
Resources are things that can be paired up with operations inside of a
permission.
|
| Role
Role
A role is assignable to a user and effectively grants that user all of the
permissions that are present in that role.
|
| TemplatedRole
TemplatedRole
|
| Class and Description |
|---|
| RBACManager
RBACManager
|
| RbacManagerException
RbacManagerException used by
RBACManager methods to indicate
a fundamental persistence or store issue. |
| Class and Description |
|---|
| Operation
Operation
In RBAC the operation is an action or functionality that can be linked with a
particular resource into an assignable Permission.
|
| Permission
Permission
A permission is the wrapper for an operation and a resource effectively saying
that the operation is authorized for that resource.
|
| RBACManager
RBACManager
|
| RbacManagerException
RbacManagerException used by
RBACManager methods to indicate
a fundamental persistence or store issue. |
| RBACManagerListener
RBACManagerListener
|
| RbacObjectInvalidException
RbacObjectInvalidException
|
| RbacObjectNotFoundException
RbacObjectNotFoundException used by
RBACManager methods to identify
when a RBAC Object Was Not Found. |
| Resource
Resource
Resources are things that can be paired up with operations inside of a
permission.
|
| Role
Role
A role is assignable to a user and effectively grants that user all of the
permissions that are present in that role.
|
| UserAssignment
UserAssignment - This the mapping object that takes the principal for a user and associates it with a
set of Roles.
|
| Class and Description |
|---|
| Operation
Operation
In RBAC the operation is an action or functionality that can be linked with a
particular resource into an assignable Permission.
|
| Permission
Permission
A permission is the wrapper for an operation and a resource effectively saying
that the operation is authorized for that resource.
|
| RBACManager
RBACManager
|
| RbacManagerException
RbacManagerException used by
RBACManager methods to indicate
a fundamental persistence or store issue. |
| RBACManagerListener
RBACManagerListener
|
| RbacObjectInvalidException
RbacObjectInvalidException
|
| RbacObjectNotFoundException
RbacObjectNotFoundException used by
RBACManager methods to identify
when a RBAC Object Was Not Found. |
| Resource
Resource
Resources are things that can be paired up with operations inside of a
permission.
|
| Role
Role
A role is assignable to a user and effectively grants that user all of the
permissions that are present in that role.
|
| UserAssignment
UserAssignment - This the mapping object that takes the principal for a user and associates it with a
set of Roles.
|
| Class and Description |
|---|
| AbstractRBACManager
AbstractRBACManager
|
| Operation
Operation
In RBAC the operation is an action or functionality that can be linked with a
particular resource into an assignable Permission.
|
| Permission
Permission
A permission is the wrapper for an operation and a resource effectively saying
that the operation is authorized for that resource.
|
| RBACManager
RBACManager
|
| RbacManagerException
RbacManagerException used by
RBACManager methods to indicate
a fundamental persistence or store issue. |
| RbacObjectInvalidException
RbacObjectInvalidException
|
| RbacObjectNotFoundException
RbacObjectNotFoundException used by
RBACManager methods to identify
when a RBAC Object Was Not Found. |
| Resource
Resource
Resources are things that can be paired up with operations inside of a
permission.
|
| Role
Role
A role is assignable to a user and effectively grants that user all of the
permissions that are present in that role.
|
| UserAssignment
UserAssignment - This the mapping object that takes the principal for a user and associates it with a
set of Roles.
|
| Class and Description |
|---|
| AbstractRole
AbstractRole useful for common logic that implementors can use.
|
| AbstractUserAssignment
AbstractUserAssignment useful for common logic that implementors can use.
|
| Operation
Operation
In RBAC the operation is an action or functionality that can be linked with a
particular resource into an assignable Permission.
|
| Permission
Permission
A permission is the wrapper for an operation and a resource effectively saying
that the operation is authorized for that resource.
|
| Resource
Resource
Resources are things that can be paired up with operations inside of a
permission.
|
| Role
Role
A role is assignable to a user and effectively grants that user all of the
permissions that are present in that role.
|
| UserAssignment
UserAssignment - This the mapping object that takes the principal for a user and associates it with a
set of Roles.
|
| Class and Description |
|---|
| AbstractRBACManager
AbstractRBACManager
|
| Operation
Operation
In RBAC the operation is an action or functionality that can be linked with a
particular resource into an assignable Permission.
|
| Permission
Permission
A permission is the wrapper for an operation and a resource effectively saying
that the operation is authorized for that resource.
|
| RBACManager
RBACManager
|
| RbacManagerException
RbacManagerException used by
RBACManager methods to indicate
a fundamental persistence or store issue. |
| RBACManagerListener
RBACManagerListener
|
| RbacObjectInvalidException
RbacObjectInvalidException
|
| Resource
Resource
Resources are things that can be paired up with operations inside of a
permission.
|
| Role
Role
A role is assignable to a user and effectively grants that user all of the
permissions that are present in that role.
|
| UserAssignment
UserAssignment - This the mapping object that takes the principal for a user and associates it with a
set of Roles.
|
| Class and Description |
|---|
| AbstractRBACManager
AbstractRBACManager
|
| AbstractRole
AbstractRole useful for common logic that implementors can use.
|
| AbstractUserAssignment
AbstractUserAssignment useful for common logic that implementors can use.
|
| Operation
Operation
In RBAC the operation is an action or functionality that can be linked with a
particular resource into an assignable Permission.
|
| Permission
Permission
A permission is the wrapper for an operation and a resource effectively saying
that the operation is authorized for that resource.
|
| RBACManager
RBACManager
|
| RbacManagerException
RbacManagerException used by
RBACManager methods to indicate
a fundamental persistence or store issue. |
| RbacObjectInvalidException
RbacObjectInvalidException
|
| RbacObjectNotFoundException
RbacObjectNotFoundException used by
RBACManager methods to identify
when a RBAC Object Was Not Found. |
| Resource
Resource
Resources are things that can be paired up with operations inside of a
permission.
|
| Role
Role
A role is assignable to a user and effectively grants that user all of the
permissions that are present in that role.
|
| UserAssignment
UserAssignment - This the mapping object that takes the principal for a user and associates it with a
set of Roles.
|
| Class and Description |
|---|
| Operation
Operation
In RBAC the operation is an action or functionality that can be linked with a
particular resource into an assignable Permission.
|
| Permission
Permission
A permission is the wrapper for an operation and a resource effectively saying
that the operation is authorized for that resource.
|
| Resource
Resource
Resources are things that can be paired up with operations inside of a
permission.
|
| Role
Role
A role is assignable to a user and effectively grants that user all of the
permissions that are present in that role.
|
| Class and Description |
|---|
| RBACManager
RBACManager
|
| Role
Role
A role is assignable to a user and effectively grants that user all of the
permissions that are present in that role.
|
| Class and Description |
|---|
| RBACManager
RBACManager
|
| Class and Description |
|---|
| RBACManager
RBACManager
|
| Class and Description |
|---|
| RBACManager
RBACManager
|
| Class and Description |
|---|
| Permission
Permission
A permission is the wrapper for an operation and a resource effectively saying
that the operation is authorized for that resource.
|
| RBACManager
RBACManager
|
| RbacManagerException
RbacManagerException used by
RBACManager methods to indicate
a fundamental persistence or store issue. |
| RBACManagerListener
RBACManagerListener
|
| Role
Role
A role is assignable to a user and effectively grants that user all of the
permissions that are present in that role.
|
| UserAssignment
UserAssignment - This the mapping object that takes the principal for a user and associates it with a
set of Roles.
|
| Class and Description |
|---|
| RBACManager
RBACManager
|
| RbacManagerException
RbacManagerException used by
RBACManager methods to indicate
a fundamental persistence or store issue. |
Copyright © 2006–2019 The Apache Software Foundation. All rights reserved.


--- subject: Email Template --- {{>header}} Hi [NAME], Title Email content goes here {{>footer}}
Hi [NAME],
Email content goes here


Epan.in Website
Documentation
[
Website
|
ERP
|
Template
|
Developer
]
{$Content}
Visit Forum
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Report Bugs
|
Join The Community
|
Contribute To Epan


dukeslides is an R Markdown document template for xaringan slides that integrates the Duke University color palette
dukeslides
has not been submitted to CRAN (, yet…)
Install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("libjohn/dukeslides")
Create a new R Markdown document from the File menu
In RStudio…
New File -> R Markdown -> From Template -> Slide template for Duke University {dukeslides}
Knit
button to compile the slide into rendered HTML


--- layout: post authors: ["Greg Wilson"] title: "Thanks to RStudio" date: 2015-01-13 time: "09:00:00" tags: ["Sponsorship", "RStudio"] ---
Our thanks to RStudio for their generous donation to help us run Software Carpentry workshops. Inspired by the innovations of R users in science, education, and industry, RStudio develops free and open tools for the R community. These include the RStudio development environment as well as the shiny, ggvis, and dplyr packages (among many others). RStudio also offers enterprise-ready professional products to make it easier for teams to scale and share work.


readObject()
method.
The inspection doesn't report transient fields.
Note: This inspection uses a very conservative control flow algorithm, and may incorrectly report fields as uninitialized.
Example:
class DataObject implements Serializable {
String s; // s is not initialized in readObject
int i;
private void readObject(ObjectInputStream stream) throws IOException {
i = stream.readInt();
}
}


Mongitor
Waiting for connection to Mongitor
Home
Credentials
Start monitoring
Cluster
Servers overview
Databases
User manual
Exit
Mongo router
-
Config servers
-
Mongo router credentials
Other mongo server credentials
Statistics for collection
-
Indexes on collection
-
Shards of collection
-
Dangerous actions
Allow potentially dangerous actions from Mongitor
Balancer


Material Icons are the icon set from Google that are designed under the material design guidelines.
Material Icons fonts by default uses a typographic feature called ligatures, which allows rendering icons by using its textual name.
Material Design Icons by @mervick/mdi-icons implements the ability to use the icons by CSS classes instead of ligatures.
There are 5 themes (fonts) available: Filled (Regular)
, Outlined
, Rounded
, Two-Tone
and Sharp


The Table component is a common component used to display data that can be compared. Usually a set of item of
the same type which data is divided on columns to facilitate the comparison between items.
<fd-table>
There are two different types of table cells you can use, the 'simple' cells and the 'rich' cells. Simple cells can only
display text. Rich cells can contain images, plain text or links. The table component requires only two root arguments, a
'headers' array and a 'tableData' array.
In a table with simple cells, the tableData property is an array of objects containing a 'rowData' property. This
property should contain an array of strings, each string being a cell in the table.
Simple Table
This is an example of a table with simple cells, where the rowData is an array of strings.
Rich Table
This is an example of a table with rich cells, where the rowData is an array of objects, where each object can have any of
the following properties:


| Exposure |
Disaster events |
River bank erosion |
15.507 |
0.000 |
| |
|
Cyclone hazard |
5.167 |
0.009 |
| |
|
Drought intensity |
8.804 |
0.001 |
| Sensitivity |
Health |
Infant mortality rate |
2.548 |
0.086 |
| |
|
Distance to a water source |
2.943 |
0.059 |
| |
|
Unhygienic sanitation condition |
2.951 |
0.058 |
| |
Land resources |
Land degradation |
3.366 |
0.040 |
| |
|
Soil phosphorus |
6.736 |
0.003 |
| |
Agricultural practices |
Rainfed agricultural land |
2.940 |
0.059 |
| |
|
Productivity of rice |
3.387 |
0.039 |
| Adaptive capacity |
Physical capital |
Structurally sound housing |
4.050 |
0.022 |
| |
|
Emergency shelter |
4.726 |
0.013 |
| |
Natural capital |
Open waterbody |
5.316 |
0.008 |
| |
Use of agro-technology |
Improved crop variety |
2.578 |
0.082 |
| |
|
Use of pesticide |
4.219 |
0.019 |
| |
|
Irrigation pump use |
2.940 |
0.059 |


Popular Areas in green mountain college energy festival
Green Mountain College Replaces Lawn with Garden - The Green ...
Jun 13, 2012 . Students at Green Mountain College in Vermont recently ripped up the lawn in front of a farmhouse owned by . Erin Ailworth covers energy and the business of the environment for the Globe. . South by Southwest Festival .
http://www.boston.com/lifestyle/green/greenblog/2012/06/green_mountain_college_replace.html
Pliny Fisk III at Green Mountain College
Green Mountain College Receives Constellation Energy E2 Energy to Educate Grant for Plug-In . GMC Theater Department to Present One Act Play Festival .
http://www.greenmtn.edu/news_events/new_releases.aspx
Rural Vermont responds to Common Cause request from Green ...
Nov 14, 2012 . Rural Vermont fervently supports Green Mountain College's right and . Green Mountain College set a strong example for all of us by having an .
http://vtdigger.org/2012/11/14/rural-vermont-responds-to-common-cause-request-from-green-mountain-college/
News Archive: Green Mountain College
How will the state of Vermont supply energy to its residents in the years ahead? . Their work has also been featured at the Smithsonian Folklife Festival.
http://www.greenmtn.edu/environmental_studies/news_events/news-archive.aspx
2012 Presenter List - SolarFest - New England energy education ...
SolarFest - New England energy education and renewable energy festival. . His day job is as an Engineer for Green Mountain Coffee Roasters. . Fesmire is a professor of philosophy and environmental studies at Green Mountain College.
http://www.solarfest.org/solarfest_presenters.php
Green Mountain Bulletin - Green Mountain College
Green Mountain College excels in most categories, and it's the MVP when it comes to . for the Dorset Theater Festival at the Dorset Playhouse for the past 25 years. . They all bring so much energy and enthusiasm, and they each have their .
http://www.greenmtn.edu/bulletin_winter11/campus_news.aspx


Stock and investments analysis is a theme that can be deeply explored in programming. This includes R language, which already has a big literature, packages and functions developed in this matter. In this post, we’ll do a brief introduction to the subject using the packages quantmod
and ggplot2
.
For those who are beginning, R is a programming language and integrated enviroment focused in statistics, but with a lot of applications in different areas. The software download can be done here using your preferred mirror. I also recommend installing RStudio, which is an interface with a lot of additional resources for R.
The analyzed stock here will be PBR, from the brazilian company Petrobras, with data extracted from Yahoo Finance using the package quantmod. quantmod
is a well known package used to quantitave financial modelling. Also, we’ll use the package ggplot2 for data visualization.
Preparing the environment
The environment preparation is quite simple, even for those who are beginning or those who aren’t used to code. Firstly, we install and load the necessary packages.
rm(list=ls())
install.packages("quantmod")
install.packages("ggplot2")
library(quantmod)
library(ggplot2)
I think it’s important to highlight that we only install the packages once. In the next times we run the code, it’ll be necessary only to load them using the library
command.
After installing and loading the packages, now we’ll download the stock prices series and treat the data in order to get them in the best possible format for the analysis. We download the data using the function getSymbols
.
pbr <- getSymbols("PBR", src = "yahoo", from = "2013-01-01", to = "2017-06-01", auto.assign = FALSE)
Let’s do it step by step!
The first argument PBR
is the symbol in Yahoo Finance of the stock that we’re going to analyze. If you want to look for other stock or asset, you can search its symbol here.
The argument src= "yahoo"
indicates the source of the data. We can also use other sources like Google Finance, FRED, Oanda, local databases, CSVs and many others.
The third and fourth arguments indicate the time period in which the data is going to be extracted, with data in the format “yyyy-mm-dd”.
Lastly, the argument auto.assign = FALSE
allows us to name the dataset with the name we want. In case it’s TRUE
, the name automatically will be the symbol we’re looking for, that is, the first argument.
Addendum
If Yahoo Finance API is not working, it’s possible to download the prices straight from the website. You just have to search for the stock tick or company name. In the asset page, click on “Historical Data”, just like the image below:
Then, select the desired period and click on “Download Data”. The download file will be in the format csv
. Now, just move it for your working directory, that can be discovered using the command getwd()
.
With the file in the folder, use the following commands to read the base:
pbr <- read.csv("PBR.csv")
pbr[,1] <- as.Date(pbr[,1])
pbr <- xts(pbr)
pbr <- pbr[,-1]
The command read.csv()
reads the file and assigns it to an object. Then, we transform the first column of the base to date format. Next, we use the command xts()
to transform the base from dataframe
type to xts
. Lastly, we remove the first column (date), since now the price lines are already indexed by day.
Prices Visualization
Now let’s take a brief look at the data, just to know them better.
## Indexed by objects of class: [Date] TZ: UTC
## xts Attributes:
## List of 2
## $ src : chr "yahoo"
## $ updated: POSIXct[1:1], format: "2017-07-16 20:52:31"
With the commands head()
and tail()
we can see the first and last 6 lines of the base. There are 6 columns with: opening price, maximum and minimum prices, closing price, volume of transactions and adjusted price. Using the command summary()
we verify the descriptive statistics of each price series and volume. The command str()
returns the object structure. In this case, it’s a xts object, a time series.
Now let’s plot daily prices, using the Adjusted Price column, since it incorporates events like splits and dividends distribution, which can affect the series.
ggplot(pbr, aes(x = index(pbr), y = pbr[,6])) + geom_line(color = "darkblue") + ggtitle("Petrobras prices series") + xlab("Date") + ylab("Price") + theme(plot.title = element_text(hjust = 0.5)) + scale_x_date(date_labels = "%b %y", date_breaks = "6 months")
We created this graphic using the command ggplot
. First, we use the object pbr
as the series to be ploted. Then we indicate which elements will be in the axes: index(pbr)
, the date in x-axis, and the adjusted price column, pbr[,6]
, in y-axis. Next, we add the element to be ploted, in this case, a blue line: geom_line(color = "darkblue")
.
Afterwards, we include the title and names of the axes, with the commands ggtitle("Petrobras prices series")
, xlab("Date")
, ylab("Price")
. By standard, the graph title is aligned to the left. To centralize it, the command theme(plot.title = element_text(hjust = 0.5))
is used.
Lastly, to make the temporal axis more informative, we put the date tick at every 6 months in the format mmm aa
using scale_x_date(date_labels = "%b %y", date_breaks = "6 months")
.
In stocks Technical Analysis, a very used technique is the plot of Moving Averages in the prices graphs. A simple moving average is an arithmetic average from the last \(q\) days from a \(x_{t}\) series in the \(t\) time period. So, the moving average \(MA^{q}_{t}\) is given by:
This indicator is interesting because it helps to identify trends and smooths noises from prices. That is, the bigger the days window for the MA calculation, smaller is the MA responsiveness to price variation. The smaller the window, the faster MA adapts itself to changes. Now let’s calculate two moving averages for the stock prices series, one with 10 days window and the other with 30 days:
pbr_mm <- subset(pbr, index(pbr) >= "2016-01-01")
pbr_mm10 <- rollmean(pbr_mm[,6], 10, fill = list(NA, NULL, NA), align = "right")
pbr_mm30 <- rollmean(pbr_mm[,6], 30, fill = list(NA, NULL, NA), align = "right")
pbr_mm$mm10 <- coredata(pbr_mm10)
pbr_mm$mm30 <- coredata(pbr_mm30)
First we subset the base for data since 2016 using the function subset()
. Then, we use the function rollmean()
, which takes as argument: the series \((x_t)\), in this case the adjusted price; the window of periods \((q)\); an optional fill argument, that is used to complete the days where it’s not possible to calculate the moving average, since the enough quantity of days hasn’t passed; the argument align
indicates if the moving average should be calculated using the periods in the left, in the center or in the right of the \(t\) day of the series. Lastly, we add the MA to two new columns in the initial database.
We calculated the two MA using 10 and 30 days of windows, filling the values with NA and using the periods in the left. Afterwards, we can plot both series in the same graphic of prices to identify trends. An existing theory in Technical Analysis is the one that when two MA of short and long term cross each other, there is an indication of buying or selling the stock. When the short term MA cross the long term upwards, there’s a buy a signal. When the opposite happens, there’s a sell signal.
Ploting the prices series and the moving averages for all days since 2016:
ggplot(pbr_mm, aes(x = index(pbr_mm)))
+ geom_line(aes(y = pbr_mm[,6], color = "PBR")) + ggtitle("Petrobras prices series")
+ geom_line(aes(y = pbr_mm$mm10, color = "MM10"))
+ geom_line(aes(y = pbr_mm$mm30, color = "MM30")) + xlab("Date") + ylab("Price")
+ theme(plot.title = element_text(hjust = 0.5), panel.border = element_blank())
+ scale_x_date(date_labels = "%b %y", date_breaks = "3 months")
+ scale_colour_manual("Series", values=c("PBR"="gray40", "MM10"="firebrick4", "MM30"="darkcyan"))
To create the graph, we plot the line of prices and the lines of moving averages. In this case, we plot each line differently, creating a kind of nickname for the color of each one. Then, we add the line scale_colour_manual
, indicating the color of each nickname in order to make the color visible in the legend of the graph.
Verifying the plot, it’s possible to notice that there were 14 points in which the series crossed themselves and 1 point where they overlapped. Following the buy signal, we’d have been successful in 4 of 7 times. Following the sell indication, we’d have done right 5 in 7 times. In total, we’d be 9/14, that is 64% of success with a quite simple indicator. Of course this metric shouldn’d be used alone, many other informations should be considerated.
Returns!
We have seen how the stock price has changed over time. Now we’ll verify how the stock return has behaved in the same period. To do this, we first need to create a new object with the calculated returns, using the adjusted prices column:
pbr_ret <- diff(log(pbr[,6]))
pbr_ret <- pbr_ret[-1,]
What we’ve done here was using logarithm properties to calculate the log-return of the stock. We did:
The diff
command calculates the difference of all values in any vector or element. With this, we only apply the difference to natural logarithms of stock prices.
In addition to this way, it’s possible to calculate returns differently. The package quantmod
has some interesting functions to do this. Firstly, it’s really simple to select only one column of prices of each stock. For example:
Op(pbr)
Cl(pbr)
Ad(pbr)
Each line will have as output the opening, closing and adjusted prices, respectively. The same accounts for other columns: Hi()
, Lo()
and Vo()
, for maximum and minimum prices and volume of transactions.
For the returns, we simply adapt and define which columns will be used, for example: ClCl()
will give us the returns using the closing prices from two periods; OpCl()
will result in the return from the closing price over the opening price from the same day.
Another interesting possibility given by quantmod
is the calculation of returns for different periods. For example, it’s possible to calculate the returns by day, week, month, quarter and year, just by using the following commands:
dailyReturn(pbr)
weeklyReturn(pbr)
monthlyReturn(pbr)
quarterlyReturn(pbr)
yearlyReturn(pbr)
All these commands make the analysis and the calculation much faster and simpler to do.
Now let’s verify some basic statistics from Petrobras returns.
summary(pbr_ret)
## Index PBR.Adjusted
## Min. :2013-01-03 Min. :-0.1852413
## 1st Qu.:2014-02-10 1st Qu.:-0.0209915
## Median :2015-03-18 Median :-0.0005999
## Mean :2015-03-18 Mean :-0.0007548
## 3rd Qu.:2016-04-24 3rd Qu.: 0.0177905
## Max. :2017-05-31 Max. : 0.1555103
sd(pbr_ret)
## [1] 0.03713089
This indicates that in average the stock hasn’t performed well. Now we can plot the returns and see how they’ve done over time:
ggplot(pbr_ret, aes(x = index(pbr_ret), y = pbr_ret))
+ geom_line(color = "deepskyblue4")
+ ggtitle("Petrobras returns series")
+ xlab("Date") + ylab("Return")
+ theme(plot.title = element_text(hjust = 0.5)) + scale_x_date(date_labels = "%b %y", date_breaks = "6 months")
To plot this last graph, we used the same parameters of the prices graph, changing only the line color.
Making a brief analysis of the graphic, it’s possible to see that the smallest return of the series happened around may. More specifically, in may 18, one day after the news release with recordings of brazilian president Michel Temer. In the audios, he agreed to give payments in exchange of silence from arrested politicians. This fact strongly impacted the stocks market, specially brazilian and public companies, like Petrobras.
Now let’s take a small look at the stock returns in 2017:
pbr_ret17 <- subset(pbr_ret, index(pbr_ret) > "2017-01-01")
ggplot(pbr_ret17, aes(x = index(pbr_ret17), y = pbr_ret17))
+ geom_line(color = "deepskyblue4")
+ ggtitle("Petrobras returns series in 2017") + xlab("Date") + ylab("Return")
+ theme(plot.title = element_text(hjust = 0.5)) + scale_x_date(date_labels = "%b %y", date_breaks = "1 months")
summary(pbr_ret17)
sd(pbr_ret17)
## Index PBR.Adjusted
## Min. :2017-01-03 Min. :-0.185241
## 1st Qu.:2017-02-08 1st Qu.:-0.014350
## Median :2017-03-17 Median :-0.002689
## Mean :2017-03-17 Mean :-0.001707
## 3rd Qu.:2017-04-24 3rd Qu.: 0.014977
## Max. :2017-05-31 Max. : 0.068795
## [1] 0.03089105
We separated in an object all of the returns from 2017, using the function subset()
. With this, the descriptive statistics still suggest a negative average return. In other hand, the standard deviation is smaller, which indicates smaller risk. Furthermore, this negative return is given by, in majority, by the big fall in the day 18.
Conclusion
That’s all for this tutorial, folks. In future posts I pretend to do deeper analysis and also approach portfolio and risk theory. I hope you’ve liked!


This was my second actual experience with Morning Glory Seeds. I am under 16 and VERY interested in the 60’s and psychedelia. My first experience with Morning Glory Seeds was spent with a friend of mine, and the entire time I felt very happy, everything seemed great. My friend threw-up in the beginning, and I felt sick towards the end. I also felt a bit depressed the day after, but felt normal after a few days. All in all that experience was one to remember, not to mention it was my first time ever doing any kind of drug in my entire life.
With this experience I was not so fortunate. While I am very glad that I experienced this trip, it was not what I had been looking for the night I had done these seeds for the second time. For preparation I simply ground up the seeds with a mortar and pestle and put the ground up material in gel capsules to swallow. This saved me from the disgusting taste of those seeds, however I later found myself in regret for swallowing the large amount of capsules turned them into a large amount of gel in my stomach, which I was spitting up most of the next day.
The day I took these seeds was a great day. School was not as boring as usual and today was the day a most annoying character was moving, which lifted my mood a lot. This took place on a Friday night, I had the whole next week off from school as well, seemed like a great time to try these seeds again. I spent the whole trip inside of my house.
I swallowed the pills and shortly after I felt nauseous, which I had not experienced in the beginning of my last trip. I spent this time of nausea on a bed, and it eventually passed. By the time it had passed I felt very excited and began to walk around the house. I stopped in the bathroom to look in the mirror at my dilated pupils and well I was there I enjoyed looking at myself in the mirror. I did not see too much of anything, however if I stared at my face it would look as though my mouth was opening, however when I blinked my mouth would still be shut.
I sat down and began to listen to Jimi Hendrix (One of my favorites) and when “Third Stone From The Sun” came around things spun out of control. For those of you that have heard this song you will know how weird it sounds sober, but tripping made this song take on a completely different dimension. I felt like Jimi was talking to me through metaphors in his song.
I began to get tired and lay down, however I could not sleep. Time seemed to slow to a halt. At first I thought it was rather groovy looking at my clock at what seemed like every 30 or so minutes, which actually ended up being about 3 minutes. Later into the night my trip turned sour. I remember I got up and went to the bathroom to splash some water on my face, and when I came back to my bedroom I had no idea if I had just gotten up and gone to the bathroom.
Luckily I had asked my brother earlier to watch over me, and he assured me that I had actually done what I thought I did. This repeated many times, I would get up and do something and when I came back I wouldn’t know if I did what I thought I just did or not. This made me feel INSANE. I began to become scared, and I felt my heart and it was beating extremely fast. I kept thinking I was hearing birds all around, and at one point I thought that I might wake up as somebody else on some beach.
I was worried that I might never come down, for it had seemed like many, many hours had passed since I took the seeds.
I was afraid to do anything for fear that I might suddenly wake up and be doing something different. At one point I think my cat wondered into my room, and when I went to go pet it felt as though it were made out of wool, like a stuffed animal. I was afraid to do anything more that entire night, and I spent the rest of the night lying on my couch reassuring myself that eventually everything would be fine.
I was sweating bullets and everything in my room seemed wrong. I remember I would feel my face and with one had I would feel sweaty skin, and with the other I would feel dry skin, this was a bit weird I thought. I felt like I had been plundered into Wonderland, my Peace flags covering my windows looked as though they were huge and bent inwards, and my chair in front of my computer looked bent at strange angles. I was very scared and I thought that I might have even been dead.
After what seemed like 5 hours I went in the bathroom to check my pupils, and they were still dilated, and it had barely been an hour. I went back to my couch and tried to get to sleep.  I spent the next day sleeping, the entire day, and by that night I felt much better.
Overall this is not an experience I would go looking for again, however I am glad that I have now experienced both ends of tripping. Many things that I felt during this bad trip I cannot put into words, the only thing I can say about it was that at the time it was terrifying to say the least.
I will be trying Morning Glory seeds one last time. I have already started to prepare them by putting them in a pepper mill and grinding them up and soaking the powder in water for a little over a day. I then filtered the liquid through a coffee filter and threw away the seed matter and filter. I am currently waiting for the water to evaporate, which will leave me with a gum that I will soak in alcohol and then filter once more and finally drink. This will probably be my last experience with Morning Glory and I will soon be trying Salvia Divinorum.
I am not sure what triggered my bad trip, but I feel as a result of it that I no longer feel the dreaded “bad trip” and my experimenting with drugs could go much farther than before. I have learned that the old proverb “Time solves all problems” is very true in our world. I have also now experienced one more thing, and experience is everything for many things.
I am not one to try a drug to be “fucked up”. I try drugs in hope of expanding my mind, gaining experiences, and reaching a higher level of conciseness. Thank-you for reading this, and happy tripping to all!
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.


KKKCorrelation: Kappa-kappa-kappa correlations¶
-
class
treecorr.
KKKCorrelation
(config=None, logger=None, **kwargs)[source]¶ Bases:
treecorr.binnedcorr3.BinnedCorr3
This class handles the calculation and storage of a 3-point kappa-kappa-kappa correlation function.
Note
While we use the term kappa (\(\kappa\)) here and the letter K in various places, in fact any scalar field will work here. For example, you can use this to compute correlations of the CMB temperature fluctuations, where “kappa” would really be \(\Delta T\).
See the doc string of
BinnedCorr3
for a description of how the triangles are binned.Ojects of this class holds the following attributes:
- Attributes
nbins – The number of bins in logr where r = d2
bin_size – The size of the bins in logr
min_sep – The minimum separation being considered
max_sep – The maximum separation being considered
nubins – The number of bins in u where u = d3/d2
ubin_size – The size of the bins in u
min_u – The minimum u being considered
max_u – The maximum u being considered
nvbins – The number of bins in v where v = +-(d1-d2)/d3
vbin_size – The size of the bins in v
min_v – The minimum v being considered
max_v – The maximum v being considered
logr1d – The nominal centers of the nbins bins in log(r).
u1d – The nominal centers of the nubins bins in u.
v1d – The nominal centers of the nvbins bins in v.
In addition, the following attributes are numpy arrays whose shape is (nbins, nubins, nvbins):
- Attributes
logr – The nominal center of the bin in log(r).
rnom – The nominal center of the bin converted to regular distance. i.e. r = exp(logr).
u – The nominal center of the bin in u.
v – The nominal center of the bin in v.
meand1 – The (weighted) mean value of d1 for the triangles in each bin.
meanlogd1 – The mean value of log(d1) for the triangles in each bin.
meand2 – The (weighted) mean value of d2 (aka r) for the triangles in each bin.
meanlogd2 – The mean value of log(d2) for the triangles in each bin.
meand2 – The (weighted) mean value of d3 for the triangles in each bin.
meanlogd2 – The mean value of log(d3) for the triangles in each bin.
meanu – The mean value of u for the triangles in each bin.
meanv – The mean value of v for the triangles in each bin.
zeta – The correlation function, \(\zeta(r,u,v)\).
varzeta – The variance of \(\zeta\), only including the shot noise propagated into the final correlation. This does not include sample variance, so it is always an underestimate of the actual variance.
weight – The total weight in each bin.
ntri – The number of triangles going into each bin (including those where one or more objects have w=0).
If
sep_units
are given (either in the config dict or as a named kwarg) then the distances will all be in these units.Note
If you separate out the steps of the
process
command and useprocess_auto
and/orprocess_cross
, then the units will not be applied tomeanr
ormeanlogr
until thefinalize
function is called.The typical usage pattern is as follows:
>>> kkk = treecorr.KKKCorrelation(config) >>> kkk.process(cat) # For auto-correlation. >>> kkk.process(cat1,cat2,cat3) # For cross-correlation. >>> kkk.write(file_name) # Write out to a file. >>> zeta = kkk.zeta # To access zeta directly.
- Parameters
config (dict) – A configuration dict that can be used to pass in kwargs if desired. This dict is allowed to have addition entries besides those listed in
BinnedCorr3
, which are ignored here. (default: None)logger – If desired, a logger object for logging. (default: None, in which case one will be built according to the config dict’s verbose level.)
- Keyword Arguments
**kwargs – See the documentation for
BinnedCorr3
for the list of allowed keyword arguments, which may be passed either directly or in the config dict.
-
__eq__
(other)[source]¶ Return whether two
KKKCorrelation
instances are equal
-
__iadd__
(other)[source]¶ Add a second
KKKCorrelation
’s data to this one.Note
For this to make sense, both
KKKCorrelation
objects should have been usingprocess_auto
and/orprocess_cross
, and they should not have hadfinalize
called yet. Then, after adding them together, you should callfinalize
on the sum.
-
__init__
(config=None, logger=None, **kwargs)[source]¶ Initialize
KKKCorrelation
. See class doc for details.
-
finalize
(vark1, vark2, vark3)[source]¶ Finalize the calculation of the correlation function.
The
process_auto
andprocess_cross
commands accumulate values in each bin, so they can be called multiple times if appropriate. Afterwards, this command finishes the calculation by dividing by the total weight.- Parameters
vark1 (float) – The kappa variance for the first field.
vark2 (float) – The kappa variance for the second field.
vark3 (float) – The kappa variance for the third field.
-
process
(cat1, cat2=None, cat3=None, metric=None, num_threads=None)[source]¶ Accumulate the number of triangles of points between cat1, cat2, and cat3.
If only 1 argument is given, then compute an auto-correlation function.
If 2 arguments are given, then compute a cross-correlation function with the first catalog taking two corners of the triangles. (Not implemented yet.)
If 3 arguments are given, then compute a cross-correlation function.
All arguments may be lists, in which case all items in the list are used for that element of the correlation.
Note
For a correlation of multiple catalogs, it matters which corner of the triangle comes from which catalog. The final accumulation will have d1 > d2 > d3 where d1 is between two points in cat2,cat3; d2 is between points in cat1,cat3; and d3 is between points in cat1,cat2. To accumulate all the possible triangles between three catalogs, you should call this multiple times with the different catalogs in different positions.
- Parameters
cat1 (Catalog) – A catalog or list of catalogs for the first N field.
cat2 (Catalog) – A catalog or list of catalogs for the second N field, if any. (default: None)
cat3 (Catalog) – A catalog or list of catalogs for the third N field, if any. (default: None)
metric (str) – Which metric to use. See Metrics for details. (default: ‘Euclidean’; this value can also be given in the constructor in the config dict.)
num_threads (int) – How many OpenMP threads to use during the calculation. (default: use the number of cpu cores; this value can also be given in the constructor in the config dict.)
-
process_auto
(cat, metric=None, num_threads=None)[source]¶ Process a single catalog, accumulating the auto-correlation.
This accumulates the auto-correlation for the given catalog. After calling this function as often as desired, the
finalize
command will finish the calculation of meand1, meanlogd1, etc.- Parameters
cat (Catalog) – The catalog to process
metric (str) – Which metric to use. See Metrics for details. (default: ‘Euclidean’; this value can also be given in the constructor in the config dict.)
num_threads (int) – How many OpenMP threads to use during the calculation. (default: use the number of cpu cores; this value can also be given in the constructor in the config dict.)
-
process_cross
(cat1, cat2, cat3, metric=None, num_threads=None)[source]¶ Process a set of three catalogs, accumulating the 3pt cross-correlation.
This accumulates the cross-correlation for the given catalogs. After calling this function as often as desired, the
finalize
command will finish the calculation of meand1, meanlogd1, etc.- Parameters
cat1 (Catalog) – The first catalog to process
cat2 (Catalog) – The second catalog to process
cat3 (Catalog) – The third catalog to process
metric (str) – Which metric to use. See Metrics for details. (default: ‘Euclidean’; this value can also be given in the constructor in the config dict.)
num_threads (int) – How many OpenMP threads to use during the calculation. (default: use the number of cpu cores; this value can also be given in the constructor in the config dict.)
-
process_cross21
(cat1, cat2, metric=None, num_threads=None)[source]¶ Process two catalogs, accumulating the 3pt cross-correlation, where two of the points in each triangle come from the first catalog, and one from the second.
This accumulates the cross-correlation for the given catalogs. After calling this function as often as desired, the
finalize
command will finish the calculation of meand1, meanlogd1, etc.Warning
This is not implemented yet.
- Parameters
cat1 (Catalog) – The first catalog to process
cat2 (Catalog) – The second catalog to process
metric (str) – Which metric to use. See Metrics for details. (default: ‘Euclidean’; this value can also be given in the constructor in the config dict.)
num_threads (int) – How many OpenMP threads to use during the calculation. (default: use the number of cpu cores; this value can also be given in the constructor in the config dict.)
-
read
(file_name, file_type=None)[source]¶ Read in values from a file.
This should be a file that was written by TreeCorr, preferably a FITS file, so there is no loss of information.
Warning
The
KKKCorrelation
object should be constructed with the same configuration parameters as the one being read. e.g. the same min_sep, max_sep, etc. This is not checked by the read function.- Parameters
file_name (str) – The name of the file to read in.
file_type (str) – The type of file (‘ASCII’ or ‘FITS’). (default: determine the type automatically from the extension of file_name.)
-
write
(file_name, file_type=None, precision=None)[source]¶ Write the correlation function to the file, file_name.
The output file will include the following columns:
Column
Description
r_nom
The nominal center of the bin in r = d2 where d1 > d2 > d3
u_nom
The nominal center of the bin in u = d3/d2
v_nom
The nominal center of the bin in v = +-(d1-d2)/d3
meand1
The mean value \(\langle d1\rangle\) of triangles that fell into each bin
meanlogd1
The mean value \(\langle \log(d1)\rangle\) of triangles that fell into each bin
meand2
The mean value \(\langle d2\rangle\) of triangles that fell into each bin
meanlogd2
The mean value \(\langle \log(d2)\rangle\) of triangles that fell into each bin
meand3
The mean value \(\langle d3\rangle\) of triangles that fell into each bin
meanlogd3
The mean value \(\langle \log(d3)\rangle\) of triangles that fell into each bin
meanu
The mean value \(\langle u\rangle\) of triangles that fell into each bin
meanv
The mean value \(\langle v\rangle\) of triangles that fell into each bin
zeta
The estimator of \(\zeta(r,u,v)\)
sigma_zeta
The sqrt of the variance estimate of \(\zeta\)
weight
The total weight of triangles contributing to each bin
ntri
The number of triangles contributing to each bin
If
sep_units
was given at construction, then the distances will all be in these units. Otherwise, they will be in either the same units as x,y,z (for flat or 3d coordinates) or radians (for spherical coordinates).- Parameters
file_name (str) – The name of the file to write to.
file_type (str) – The type of file to write (‘ASCII’ or ‘FITS’). (default: determine the type automatically from the extension of file_name.)
precision (int) – For ASCII output catalogs, the desired precision. (default: 4; this value can also be given in the constructor in the config dict.)


This revision introduces much improved 3D resolution of the topology for zoom levels 1 and 2. The resolution is approximately one elevation data point per degree. Elevation is ridiculously exaggerated in the default. 3D topology is currently only supported at zoom levels 1, 2 and 3. Please ignore the extraneous debugging bits.
Sea level is indicated by the blue-green-purple sphere.
This Jaanga cookbook section contains the demo for building a 3D Globe using Three.js and Open Street Map (OSM) data. The app supports the full 18 levels of zoom provided by OSM. The tooltips for the '+' and '-" buttons display the current zoom level.
The source code for the demo is available at: github.com/jaanga/cookbook/
Derivation of the name oSome Globe: oSome = OSM + awesome + "That's some Globe!"
See also post on jaanga.com
General controls:
Coming up next: more detail for level 3 zoom.
It may take twenty seconds or so for the map to appear. The background will turn black until the data starts coming in.
Coming soon:
Sample 3D objects placed on selected levels. For example: Eiffel tower in Paris, Golden Gate Bridge in San Francisco and Opera House in Sydney.
Select a different jQuery UI theme.
jQuery Theme:
jQuery is a multi-browser JavaScript library designed to simplify the client-side scripting of HTML. jQuery UI is a JavaScript library that provides abstractions for low-level interaction, animation, advanced effects, high-level themeable widgets, is built on top of the jQuery JavaScript library and is used to build interactive web applications. Both libraries are free, open source software, licensed under the MIT License.
Jaanga oSome Globe uses both these libraries to create the user experience components of the app.
In particular, Jaanga oSome Globe provides access to 25 sample themes hosted on the jQuery UI web site.
Each time the theme is updated, a fresh URL is created which can be used as a permalink to save the theme selection for future use. Further revisions of Jaanga oSome Globe will provide further features that may be saved as part of the permalink feature.
A nice benefit of the permalink concept is that your choices may be saved and shared without cookies being saved to your computer.
Copyright © 2013 Jaanga contributors
All software and documentation in this repository is available under the MIT license.
The MIT License
Open Street Map
copyright © OpenStreetMap contributors
jQuery
copyright © The jQuery Foundation
jQuery UI
Copyright © 2013 jQuery Foundation and other contributors
Three.js
Copyright © 2010-2013 three.js authors
In the course of preparing this app, data has been obtained via several servers including:
And many thanks to:
The JavaScript DOM
Jaume Sánchez and his "Blocky Earth" for giving me the idea that elevations are obtainable and you can do reverse Mercator projections.
See also and in particular FGx on GitHub which has been the cause and the impetus for this globe.
All coding and testing is on currently carried out on Google Chrome only. On other browsers, this app may have even more issues.
This app is at an early and volatile stage. Not all licensing requirements may not been fully met lat alone identified. It is the intension of the authors to play fair and all such requirements will either be met or the feature in question will turned off.
Correct for reverse Mercator projection. * 2013-06-02
Fix quadrant issues with southern hemisphere level changes. * 2013-05-31
add exaggeration scale drop down menu. 2013-06-09
Zoom level 3 to 32 vertices per tile (now 16)
Download elevation data for individual tiles at run-time.
Improve/refine lights and color schemes.
Adjust smoothness of globe automatically.
Adjust the color correction of the globe.
Add location and level navigation. 2013-06-03
Add 3D topology. 2013-06-08
Add permalinks.
Fix issues where displayed date does not cross the date line.
Ability to add data and code to particular locations and levels.
Add windows - each with its own view of the globe, camera location and settings.
Add camera movement after 30 seconds no mouse movement.
Add home button that resets zoom, camera, level, etc to defaults. 2013-05-31
Don't remove then recall currently loaded bitmaps when panning
Preload adjacent bitmaps
Add level slider widget??
Keep on tweaking camera zoom, pan and rotate settings.
Better way of displaying current level.
Access to Google Maps and others map data.
* priority item


|
Rails Sunday, September 28, 2014 |
We've covered a number of topics in Phase 0, but we haven't touched on Rails yet. To get my feet wet, I went through a tutorial on Rails this week. I thought I'd give a brief overview of what it Rails is and what it lets you do.
What is it?
Rails is a "web application framework." It is a utility that lets you create the common components of your web app -- e.g. all the structures (models, views, controllers), files, and directories -- using terminal commands. As it creates these entities, it adds to them all the boilerplate code they'll use and defines implicit relationships between them.
Why is it useful?
When you're developing, you want to use your time defining how your app works -- i.e. building the unique parts of your application that let it do what it should. You don't want to spend your time writing the scaffolding of the app or the redundant boilerplate that's necessary for each entity you add to it. In fact these developer preferences are reflected in Rail's mission statement: (1) convention over configuration and (2) don't repeat yourself. This means: sacrifice some ability to customize your app infrastructure -- instead adhering to conventional setup -- and profit from not having to write code many times over.
How do you use it?
After installing the rails gem (from terminal: gem install rails
) you can create your web app's scaffold using just one terminal command: rails new your_app_name
. This creates a directory structure for your app and fills it with all the files/templates necessary to start serving your app locally with a simple command: rails server
.
Rails applies the 'model view controller' framework for web apps. It lets you quickly "generate" new entites: models that will often have entries in your app's database, controllers that will let users interact with models, and views that will render your app to users via elegant templates. You use the rails generate your_entity its_name its_instantiation_arguments to do this. Significantly, as each of these entities is generated, all of the interfaces to it are defined according to convention.  Where does Ruby factor in?
You'll use the Ruby language in all the templates Rails created for you in order build out the classes that constitute your models and their methods that will be called as controllers. You can also use a templating language called eRuby to write code for templates that will render your app's interfaces as views.
Summary
From what I can tell, Rails looks like a powerful development framework. It seems like it will make building web apps significantly less redundant by abstracting some of the low-level configuration work. Can't wait to get started with it in about a week at DBC!


Hi, I'm Alessio 👋🏻
I'm a Web Designer & Developer based in Trento, Italy
I create web experiences since 2017 helping people to realize their needs by combining my knowledge and passion with my desire for constant growth through a minimalist approach, believing in quality rather than quantity.


---
bg: "rail_warm.jpg"
layout: default
crawlertitle: "Tâm Le Minh - Doctoral Student in Applied Mathematics"
title: "Tâm Le Minh"
summary: "Doctoral Student in Applied Mathematics"
---
Welcome to my website!
Hi, my name is Tâm Le Minh. I am currently a doctoral student in applied mathematics at the University
of Paris-Saclay.
{% for post in site.posts %}
{{ post.excerpt }}
{% endfor %}


Welcome to Nick's Kentucky Sports Fan Page
I'll be using this site to share opinions, updates, and just geek out about Kentucky Sports, especially Basketball. Join in. Go Cats.
"Apparently the University of Kentucky basketball dynasty is to continue forever"
-Philadelphia Inquirer, Dec 23 1954
Check our History page to learn more details about the Greatest Tradition in College Basketball including:
- Season Results
- Top Scorers
- More coming soon
Similar Sites
Big Blue HistoryThis site is obviously pretty bare bones, but who am I to talk :). It actually has a ton of great content, but looks very amateur (which it is) and is really hard to navigate. Looking at the code, it seems to do its formatting and styling with HTML as opposed to CSS. There's so much content on the home page alone and even more on other pages, so nav
sections would be really helpful.
The content on this site is what makes it great. It's probably the most extensive resource on its subject and comes up high in relevant search results. A little polish would go a long way, though.


Posted on Sept. 27, 2017
The post shows two ways to create a virtual environment in Python; using the virtualenv package and using conda package.
virtualenv
Install the virtualenv package using the following;
pip install virtualenv
to create an environment run the following command:
virtualenv <env_name>
to activate the environment just run
source <env_name>/bin/activate
If you want to install a special version of Python for the environment run the following instead:
virtualenv --python=<path_of_python_2_or_3> <env_name>
Use which python
To know the path to your version
To close your environment type:
deactivate
Conda
virtual environmentYou need to have Anaconda installed to use Conda to create an environment. Download and install Anaconda from here. After Installing Anaconda use the following to create the environment:
conda create --name <env_name> python=3.6
to activate the environment just run
source activate <env_name>
To close your environment type:
source deactivate
To delete your environment type:
conda remove --name <env_name> --all


Visitor arking is available at the Michael Stree parking deck. The street address is
550 Houston Mill Rd NE
Atlanta, GA 30329
For a more accurate location for the entrance of the visitor parking, please click the following link to find your driving direction on Google map


Understand how the shape of the Dart SDK is shifting.
Shapeshift executes against two versions of Dart docgen-generated APIs to expose how your API has changed. Shapeshift generates a set of markdown files that list API differences in your Dart package.